1 Introduction and Background

At present, prison management is facing unprecedented challenges, and it is urgent to introduce a new generation of artificial intelligence technology to build intelligent management solutions. On the one hand, the structure of prisoners is becoming increasingly complex: the number of criminals involved in crimes committed by underworld forces and endangering national security accounts for a certain proportion, and such criminals are highly organized, have a heavy confrontational mentality, and are difficult to reform, which seriously threatens prison security; They often form gangs and resist reform, which puts tremendous pressure on traditional regulatory methods. On the other hand, the prison police force is gradually aging, and the workload is increasing: the police force is relatively insufficient, and long-term high-intensity monitoring and management make it difficult for manual monitoring to detect all signs of risk in time. Under the dual pressure of limited manpower and increased risks, how to improve the security factor of prisons has become an urgent problem to be solved.

With the development of the Internet of Things, big data, and artificial intelligence, the concept of "Wisdom Prison" came into being. Through the deployment of sensing equipment and intelligent analysis system, Wisdom Prison realizes comprehensive perception, intelligent research and judgment, and active early warning of the prison environment and detainees. Using AI technology to independently analyze massive monitoring data, potential safety hazards can be discovered in advance and timely warnings can be provided, assisting scientific decision-making, reducing risk factors, and improving regulatory efficiency. This is consistent with the direction of China's modernization of prison governance, and it is also an inevitable requirement of the current prison management work. In this context, it is of great significance to introduce an intelligent management scheme based on the mesh DIKWP model and the principle of artificial awareness: it will help build a safe, controllable, intelligent and adaptive prison management system, improve the quality and safety level of rehabilitation, and realize the modernization of prison governance capacity.

2 Construction goals

With the core goal of improving prison safety and the effectiveness of rehabilitation, this plan proposes intelligent functions around the two aspects of "safety prevention" and "psychological correction", so that prison management can shift from passive response to active prevention. The specific construction goals are as follows:

2.1 Real-time intelligent reminder of the danger of criminals: AI is used to analyze the behavior and status of detainees in real time, intelligently judge potential risks, and alert the supervising police in a timely manner. The system will give a dynamic risk assessment for each offender, and automatically send an alert to the police officer on duty when a criminal has an actual or potential security threat (such as violent conflict, escape attempt, self-harm and suicide, etc.). This goal aims to move the traditional post-event handling forward to pre-warning, so that the police can intervene early to prevent problems before they occur, and greatly improve the safety factor of prisons.

2.2 Real-time intelligent reminders for convicts' psychological correction work: Through intelligent analysis of convicts' psychological assessment results, daily performance, and psychological counseling records, psychological abnormalities or correctional needs are discovered in real timeand remind psychological correction personnel to pay attention. The system will track the mental health indicators and reform progress of the convict, and when it is found that a convict has accumulated negative emotions, psychological crises (such as depressive tendencies, increased aggression), or has not reached the goal of the correctional stage, it will promptly notify the psychological counselor or the education and reform police to take measures. This goal ensures that psychological correction work is targeted, prevents safety accidents caused by untimely intervention in psychological problems, and promotes the rehabilitation and rehabilitation of offenders.

The above two goals complement each other: safety precautions focus on the early warning of external behavioral risks, and psychological correction reminders focus on the early warning of internal psychological changes. The two jointly build a dual line of defense of "security + correction" in smart prisons, realize the overall control of people, things and situations, and not only maintain the safety of supervision but also promote education and reform.

3 Functional Requirements Analysis

Focusing on the above goals, the system needs to meet the following functional requirements to ensure that the intelligent reminder mechanism is feasible and integrated into the daily workflow of the police.

3.1 Intelligent reminder function of security risks: The system should comprehensively analyze the multi-dimensional information of each criminal, automatically judge security risks, and remind the police on duty to focus on prevention. These include:

l Sentence factor: Reminders are triggered according to the length of the offender's sentence and progress. For example, when the sentence is about to expire, remind to prevent escape attempts, and pay attention to psychological imbalance or impulsive behavior when the sentence is too long.

l Important life events: monitor the impact of major events in the offender's personal life (such as the death of an immediate family member, marital changes, case progress, etc.) on his emotions and behaviors. Early warning when a negative event occurs that may trigger retaliation, psychosocial or extreme behavior.

l Rehabilitation performance: Evaluate the performance changes of convicts in their daily rehabilitation, including performance in labor reform, compliance with prison regulations, and records of rewards and punishments. If a person has recently violated discipline frequently and his anti-reform sentiment has increased, the police officer should be reminded to strengthen discipline; On the contrary, if you are suddenly too "active", you should also be wary of pretending to deceive and deceive to reduce the sentence.

l Abnormal behavior: real-time analysis of the data of the criminal's daily behavior and physiological state (such as positioning trajectory, contact with personnel, dynamics in the prison, heart rate and blood pressure, etc.). Identify potential hazards in the event of unusual patterns (e.g., frequent movement late at night, contact with suspicious people, emotional agitation, or drastic fluctuations in health indicators).

l Real-time early warning: Based on the above factors, the current risk factor and cause explanation of each criminal are given, and the high-risk person is alerted in real time. The content of the reminder includes the identity of the offender, its location, the type of risk (such as the risk of fighting, the risk of self-harm, etc.), and the recommended measures to deal with it, so as to facilitate the rapid response of the police. Potential risks (which have not yet developed into a definite hazard) should also be brought to the attention by cue or marking.

3.2 Intelligent reminder function for psychological corrections: The system should be connected to prison psychological correction operations, to help psychological cadres understand the changes in the psychological state of criminals in a timely manner and adjust correction strategies. The main needs include:

l Psychological test analysis: Integrate the data of psychological assessment and daily psychological testing of criminals in prison to automatically assess the level of mental health. For example, when the scores of depression scale and anxiety scale are abnormal, an early warning is generated, indicating the need for further intervention.

l Psychological counseling record tracking: Integrate the conversation records and counseling notes between the counselor and the offender, and use natural language processing to identify negative tendencies or red flags (such as despair, suicidal thoughts or hostile emotions in speech). If a serious warning is found after a conversation, immediately alert the person in charge of the psychological center to the offender.

l Progress of correctional goals: The system should track the completion of the individualized psychological correction plan and phased goals formulated for each offender. If a convict fails to meet the correction goals (e.g., attitude change, emotional control without improvement) for many consecutive times, the correctional staff is reminded to adjust the plan or strengthen counseling.

l Key personnel identification: Combined with psychological assessment and daily behavior, list criminals with psychological crisis tendencies or serious disorders as key targets, mark them in the system, and regularly remind them of the frequency of re-evaluation. For those who may injure themselves or others, the warning level should be increased and the safety department should be synchronized.

l Information feedback: After receiving the reminder, psychological correction personnel can record the measures taken and observation results through the system as a reference for follow-up AI evaluation. The system learns the effects of different interventions and continuously optimizes the accuracy of follow-up reminders.

Through the above functions, the system can realize all-round dynamic monitoring and intelligent reminder of the "physiology-psychology" of prison prisoners: it not only captures the signs of overt dangerous behaviors in a timely manner, but also has insight into the hidden psychological change trend, and finally assists the supervision and education personnel to take precise actions.

4. Technical scheme design

In order to achieve the above functional requirements, we propose a system architecture design based on the mesh DIKWP model and the principle of artificial consciousness. The solution integrates the principles of cognitive science into the AI system to achieve perception, decision-making and feedback capabilities similar to human consciousness, and provides reliable intelligent management support in complex prison scenarios.

4.1 Theoretical basis: the network DIKWP model and the principle of artificial consciousness

DIKWP Model Cognitive System: The DIKWP model is a new generation of artificial intelligence cognitive framework proposed by Professor Yucong Duan, which adds a "Purpose/Purpose" layer on the basis of the traditional "Data-Information-Knowledge-Wisdom" pyramid model. The five levels are, in order, Data (D), Information (I), Knowledge (K), Wisdom (W), and Purpose. Unlike linear layering, the DIKWP model adopts a network structure, and the layers are not transmitted in one direction, but through two-way associations to form a semantic feedback network. High-level Wisdom and Purpose can have a counter-effect on low-level data and information processing, and low-level new information will also continuously update cognitive results, thus forming a multi-directional feedback and dynamic iterative cognitive closed loop. This structure ensures that a unified cognitive semantic space is formed within the AI system, and every step of the reasoning decision can be traced and explained at the semantic level.

In the DIKWP model, the functional positioning of each layer is as follows:

l Data layer (D): Perception of the acquisition and representation of raw data, including surveillance camera video streams, sensor signals, text records, etc. In this scheme, the data layer is responsible for collecting various raw data in the prison and providing basic materials for subsequent processing.

l Information layer (I): Perform preliminary processing and feature extraction on the data, and transform the chaotic raw data into meaningful information. For example, the "fight" incident is identified through video analysis, and the keywords of the criminal's emotional changes are extracted through text analysis. The information layer outputs structured information entries (events, metrics, etc.) that reflect key details in the data.

l Knowledge layer (K): Integrate information into a knowledge graph or rule base to form a comprehensive understanding of the environment and objects. The knowledge layer establishes the files and relationships about the offender, including personal background, social relationships, historical behavior, psychological characteristics and other knowledge. It is mentioned that the DIKWP model realizes two-way semantic feedback at all levels, which is reflected in the knowledge layer by integrating new information into the existing knowledge graph and modifying prior knowledge (for example, if a criminal's recent behavior deviates from the previous pattern, the knowledge layer adjusts its perception of danger accordingly). The knowledge layer also contains rules of expert experience, such as "if a prisoner's emotions fluctuate violently due to the death of a family member, the risk factor will increase in the next week", etc., which are used to assist decision-making.

l Wisdom Layer (W): The advanced analysis and decision-making capabilities of the corresponding system, i.e., judgment and reasoning. The Wisdom layer is based on the comprehensive semantic understanding provided by the knowledge layer for deep reasoning, prediction, and decision-making. For example, taking into account the offender's current information and past knowledge, assessing his or her recent risk level, or giving a recommendation for psychological treatment. The Wisdom layer also uses machine learning models to make predictions about complex nonlinear relationships (e.g., training a model on historical data to predict the probability of violence). Different from traditional AI, the decision-making of the Wisdom layer under the DIKWP system can be explained, because each decision can be traced back to the basis of the knowledge and information layer, making the AI decision-making process transparent and auditable.

Purpose /Purpose Layer (P): This is the top layer unique to the DIKWP model, which represents the global goal, purpose, and value constraints of the system. The Purpose layer plays a role similar to human will in artificial intelligence, and is the driving force and constraint mechanism of the entire cognitive process. By embedding the purpose inside the model, AI not only becomes smarter, but also ensures that its behavior always serves predetermined value and security needs. The Purpose layer makes the whole DIKWP system form a closed-loop control: when the decision result of the Wisdom layer deviates from the intended purpose, the Purpose layer will feed this deviation back to the lower layer for adjustment (such as re-evaluating the weight of certain information or introducing new data), so as to modify the system behavior to make it consistent with the goal. In this scheme, the Purpose layer clarifies the core purpose of "ensuring prison security and promoting the rehabilitation of criminals", and the modules of the drive system work in coordination around this purpose. At the same time, when a decision is not aligned with the purpose, the Purpose layer can step in to adjust the parameters (e.g., reduce unnecessary reminders to reduce distractions, or increase sensitivity for security purposes).

Closed-loop principle of artificial consciousness: Artificial consciousness is to make AI systems have characteristics similar to human subjective consciousness, including autonomous perception of the environment, formation of internal purpose, making decisions, and learning self-adjustment from results. Drawing on the theory of artificial consciousness, this scheme designs a closed-loop process of perception-purpose-decision-action-feedback, so that the system has a certain degree of autonomy and self-regulation ability:

l Perception: Corresponding to the data layer and information layer, the system continuously obtains sensory inputs from the external environment (camera images, microphone sounds, sensor readings, etc.) and perceives key information. From the perspective of artificial consciousness, this step allows the AI to "perceive" the surrounding events and object states, which is equivalent to sensory functions such as vision and hearing.

l Purpose (Intention): Subjective evaluation of the current situation and goal motivation are formed within the AI. Through the Purpose layer, the system connects pre-set goals (safety and correction) with perceived realities, which in turn generate motivation for the next action. For example, when it senses that "offender A is agitated and approaching the forbidden area of the prison area", the purpose layer generates a "need for further monitoring/intervention A" based on the goal of "preventing escape". Purpose is equivalent to the AI's intrinsic drive and preference, which determines the focus of attention and resource allocation.

l Decision: In the Wisdom layer, under the guidance of the established purpose, the perceptual information is inferentially evaluated, and specific action decisions are made. Continuing with the above example, the Wisdom layer will evaluate various data of offender A to determine whether he is attempting to commit misdeeds, and if so, decide to raise the alarm and advise the nearest police officer to approach and observe. If you are unsure, you may first strengthen covert surveillance of A. Decision-making is the concretization of Purpose, which maps out a course of action to achieve the goal.

l Action: The system transforms decisions into actual output behaviors to exert influence on the environment. This includes sending reminder notifications to users, invoking linkage devices (e.g., broadcasting alarms, automatically locking doors), and other practical actions. The action is equivalent to AI acting on the environment through an actuator, which is mainly information output for human execution in prison scenarios, but can also include direct control of some devices under the conditions of the Internet of Things.

l Feedback: After the action is executed, the system obtains information again through environmental changes or user feedback, evaluates the effect of the action, and updates its own status. This forms a closed loop: for example, after the alarm is issued, the police officer takes measures to stabilize the emotion of offender A, and the system senses that the conflict has subsided, so it records the incident and the results of the disposal in the knowledge base; If the analysis later reveals that the alarm is actually a false positive, the Purpose layer and the Wisdom layer will adjust the model parameters to reduce the false positive rate under similar conditions in the future. This feedback mechanism gives AI the ability to self-learn and self-correct, which is equivalent to a process of "self-reflection" in artificial consciousness. In particular, we introduce the "dual circulation" structure proposed by Professor Yucong Duan to increase the metacognitive cycle outside the basic cognitive loop. The metacognitive cycle enables the system to "observe its own thinking" like a human, monitor and evaluate its own decision-making, and adjust cognitive process parameters (such as reminder thresholds and model weights) when necessary to achieve self-monitoring and regulation. This ensures the stability of the long-term operation of the system, prevents decision-making from getting out of control due to environmental changes or model deviations, and gives the system a preliminary "self-awareness" prototype.

Through the multi-layer semantic network of the DIKWP model and the closed-loop mechanism of artificial consciousness, the AI part of the system will have a processing chain and adaptive ability similar to human cognition: it can not only digest complex information in layers, but also have internal purpose-driven and self-correction functions. This provides a theoretical basis for building a safe, reliable, and intelligent management system in a highly complex prison environment.

4.2 Overall system architecture

Based on the above theories, the overall architecture of the Wisdom Prison Intelligent Management System is designed, as described below. The system as a whole can be divided into three parts: perception acquisition layer, cognitive decision-making layer and interactive execution layer, and each part is composed of several modules, which are integrated through standard interfaces to form a unified intelligent management platform.

l Perception collection layer (data acquisition and pre-processing): responsible for the collection, transmission and preliminary processing of various data in the prison, which is equivalent to the "sensory organ" and "neural network" of the system. These include:

Ø Environmental perception subsystem: IoT sensors and intelligent monitoring equipment deployed throughout the prison, such as high-definition video cameras, infrared night vision cameras, microphones, access control sensors, positioning tags, vital signs monitoring bracelets, etc. This subsystem collects dynamic information in areas such as prisons and activity venues 24 hours a day, and uploads the data through a secure network.

Ø Business data integration subsystem: Integrate the existing prison business information system data, including basic criminal files (crimes, sentences, criminal backgrounds, etc.), prison management systems (such as prisoner transfers, reward and punishment records), psychological correction systems (psychological assessment results, consultation records), and police duty logs. This structured data is periodically synchronized to the intelligent management system through interfaces to provide background information support for AI analysis.

Ø Data preprocessing module: filter and convert the perceived raw data to make it suitable for subsequent analysis. On the one hand, multimedia data such as video frames and audio clips are compressed, object detection and other preprocessing (such as identifying the location coordinates of people and objects, and extracting sound features); On the other hand, multi-source data is aligned by time label and stored in a time series database or message queue. The pre-processing module ensures that the upper-layer AI obtains high-quality, standardized data streams, improving analysis efficiency and accuracy.

l Cognitive decision-making layer (DIKWP intelligent core): This is the brain of the system, which adopts the DIKWP mesh model architecture, including five layers of semantic processing units of data, information, knowledge, Wisdom and Purpose, and several AI algorithm models to realize intelligent analysis and decision-making of the prison situation. The internal modules of the cognitive decision-making layer interact with each other through the semantic bus to share the extracted semantic information and context, so as to form an organically linked agent

Ø Data layer module: connect to the perception layer and accept the preprocessed data stream. The module continuously listens to various sensor data, and packages and submits data fragments to the information layer for processing when there are trigger conditions. For example, when a vigorous movement or fighting posture is detected in the surveillance video screen, the relevant video frame data is intercepted; Collect physiological data such as heart rate per minute at regular intervals; When a new violation record is entered in the duty log, the text is immediately transmitted. The Data Layer module manages both the data cache and the histogram database, which is used to store short-term snapshots of raw data so that the information layer can request contextual data for further analysis when needed (e.g., video backtracking for a certain time period).

Ø Information layer module: pattern recognition and event extraction for input from the data layer. A variety of AI algorithm models are applied here:

ü Computer vision analysis: use the image data in the video stream to detect and identify human behaviors and events. For example, the action recognition model is used to identify actions such as fighting, falling, and self-harm; Face recognition confirms the identity and location of the person; Object detection, locating offending items (e.g., knives appearing), etc.

ü Speech and text processing: NLP/speech recognition of voice and text collected within the monitoring range. For example, analyze the content or tone of conversation/shouting in the prison cell, and identify abusive quarrels and abnormal shouting; Textual sentiment analysis is carried out on the content of criminals' letters and telegrams to extract the emotional tendencies and potential purposes in them.

ü Behavior pattern analysis: Based on the data of wearable devices and positioning systems, analyze the behavior trajectory and physiological indicators of criminals. For example, abnormal daily activity paths (walking around in the middle of the night) and sudden increases in heart rate and blood pressure. Anomaly detection models combined with machine learning identify behaviors that deviate from normal patterns.

ü Event generation: The information layer converts the above analysis results into structured events and information output. For example, an event object is generated: "Offender A and offender B had a physical altercation in the playground for 10 seconds"; or status information: "Offender C's heart rate reached the warning value three times in 5 minutes, accompanied by emotional shouting". Each message has elements such as time, place, and whom. These events/information are published to the semantic bus for processing by the knowledge layer subscription.

l Knowledge layer module: responsible for knowledge fusion and scenario modeling. It takes a structured stream of events from the information layer and fuses historical data and background knowledge to form a global semantic understanding of the current situation. Implementation mechanisms include:

ü Knowledge graph construction: A prison semantic knowledge graph covering people, things, time, places and things is established within the system. The nodes include criminals, police officers, places, items, event types, etc., and the edges represent relationships (such as "participated in a certain event", "belongs to a certain group", "recent psychological state" is... ）。 When the information layer extracts a new event, the knowledge layer updates the graph, for example, adding a "brawl incident-time" record to the offender A node, and connecting A with a "conflict" relationship edge with the object B. For example, if A and B both belong to the same gang and have multiple conflicts, it may imply gang infighting or management loopholes, which need to be paid attention to by senior management.

ü Situational semantic understanding: the knowledge layer gives meaning to events in combination with the context of the environment. For example, determine what scene a brawl was in (canteen?). Factory floor? ), is it related to a special day (close to a holiday?). Just finished meeting with the family? ）。 These situational factors are helpful for the subsequent fine judgment of the Wisdom layer. The knowledge layer also calculates some key indicators, such as each criminal's "risk score", "psychological stability index", etc., which are automatically updated based on the attributes in the knowledge graph and the frequency of recent events.

ü Rules and reasoning: Built-in expert rule base and logical reasoning machine to perform deductive reasoning on the knowledge graph. The rule base is abstracted from prison management experience and policies and regulations, such as: "If the offender is depressed for three consecutive days and refuses to let the offender out, it is judged that there is a depression warning"; "A criminal who has frequent contact with other gang members and has secret conversations is suspected of planning violations." When the knowledge layer detects the trigger condition of the rule, it generates the corresponding inference conclusion information and pushes it to the Wisdom layer (for example, "Inference conclusion: Offender X may be planning a group event").

ü Two-way feedback: The knowledge layer not only accepts the input of the information layer from the bottom up, but also accepts the adjustment instructions from the Wisdom layer and the Purpose layer from the top down. For example, the Wisdom layer may find that the evaluation of certain events is biased, and will require the knowledge layer to readjust the weight of certain knowledge (such as increasing the risk weight of the offender node for recent serious disciplinary violations). This feedback allows knowledge representations to be dynamically updated to more accurately reflect reality.

l Wisdom Layer Module: The Wisdom Layer is the decision-making and prediction center, which synthesizes the extensive semantic information of the knowledge layer and uses advanced AI models and optimization algorithms to make judgment decisions

ü Security risk assessment model: One of the core functions of the Wisdom layer is risk assessment. The system rates each offender's current and future danger based on the state provided by the knowledge layer. The model integrates a variety of algorithms: first, the statistical learning model, a classification/regression model trained based on historical accident data, is input as a vector of offender attributes and recent behavior features, and outputs the probability of violations or violent incidents in a short period of time; The second is the knowledge reasoning model, which integrates the regular reasoning conclusions with the results of the statistical model to form a more explanatory risk judgment. For example, the model might output "Offender A violence risk score = 85/100, high, reason: recent conflict with others + emotional instability after family changes". Based on this, the Wisdom layer decides whether an alert is required.

ü Psychological state analysis model: corresponding to the need for correction reminders, the Wisdom layer contains a mental health assessment algorithm. It combines the data of psychological assessment scales, the results of sentiment analysis of the text of counseling conversations, and the indicators of daily behavior, and uses the method of combining expert system and machine learning to evaluate the psychological state of criminals. For example, rating depression and suicidal tendencies, and rating motivation for reform. When a person's psychological risk exceeds the threshold, the Wisdom layer will prepare a corresponding reminder message to the psychological treatment staff, including a description of the problem and a suggested plan (for example, "Offender B has a high risk of depression recently, it is recommended to arrange an in-depth psychological counseling").

ü Multi-dimensional Purpose Understanding: The Wisdom layer also pays attention to the purpose judgment of the offender himself, especially for safety-related behaviors. With the help of the semantic graph and NLP analysis provided by the knowledge layer, the system tries to infer the purpose behind certain behaviors. For example, by analyzing the keywords and emotional polarity of the criminal's conversation, it is judged that his current purpose is to seek help, provoke confrontation, or escape reform. This kind of content purpose analysis uses natural language processing techniques to extract sentiment and purpose propensity, which then affects the urgency of the reminder. It is worth noting that in order to protect privacy, the system does not involve specific conversation details when extracting the Purpose tendency, and only focuses on the semantics and the overall tendency of emotions. This guarantees that the privacy of the prison's internal communications is respected to a certain extent, and that alarms are only triggered when a clear danger is detected.

ü Comprehensive decision-making and resource optimization: Finally, the Wisdom layer comprehensively considers the results of security risks, psychological states, Purpose judgments, etc., and makes decisions and plans according to the overall goals of the system. Objective constraints provided by the Purpose layer, such as the principle of "safety above all else" and the principle of "renovation must take into account humane care", are introduced as the overall optimization direction of decision-making. The Wisdom layer implements a multi-objective decision optimization: on the premise of ensuring that the safety purpose is not violated, the interference with the normal order is minimized, and the transformation effect is taken into account. For example, if a convict is at medium risk but is participating in important rehabilitation activities, the system may choose to give a soft reminder (such as recommending enhanced observation in secret) instead of immediate mandatory quarantine, so as to balance the contradiction between safety and rehabilitation. This reflects the human-friendly and purpose-oriented nature of AI decision-making.

ü Interpretation and credibility assessment:D The added advantage of the IKWP model is that every decision has a trace, and the Wisdom layer generates an interpretable report for the important decision, indicating what key data, knowledge, and rules are cited for the decision. At the same time, the system evaluates the credibility of its own decision-making (for example, the credibility index is given based on model confidence and data completeness), and provides it to human decision-makers for reference. This design increases the transparency and credibility of AI-assisted decision-making.

● Purpose layer module: The purpose layer is the global control unit, which runs through and supervises the entire cognitive process. Its key features include:

ü Objective management: store and maintain the core objectives and policy parameters of the system. For example, "ensuring prison safety" and "promoting the rehabilitation of criminals" are refined into several quantifiable objective functions or KPI indicators for the Wisdom layer to refer to when making decisions. Define the priority strategy for risk alerts and psychological alerts (security alerts are never missed, psychological alerts can be as accurate as possible but can be delayed, etc.).

ü Behavioral constraints: Monitor the decision-making of the Wisdom layer, and constrain or correct behaviors that may violate the overall purpose. For example, if AI decision-making tends to trigger alarms frequently for absolute security, resulting in a strain on management resources and even affecting the transformation atmosphere, the Purpose layer will slow down the frequency of reminders by adjusting thresholds and other methods to maintain a balance between system operation and management goals. Conversely, if the system is found to have underreported or underestimated a certain type of risk (deviating from the "security first" goal), the Purpose layer will increase the weight of that type of event, forcing the Wisdom layer to be more sensitive to similar situations.

ü Metacognitive self-regulation: The Purpose layer realizes the metacognitive cycle, that is, the system examines and regulates its own cognition and behavior. It aggregates feedback (such as which alerts turned out to be false positives/false negatives), evaluates the deviation of the current decision-making model from the goal, and adjusts internal model parameters, knowledge weights, and even information collection strategies. This self-regulation ensures continuous improvement in system performance and goal alignment, evolving to a higher level of intelligence.

ü Policy interface: Provide manual intervention interface, allowing system administrators to configure or fine-tune system policies at the Purpose layer. For example, during temporary major events, adjust the weight of security purpose to the highest (strengthen all security warnings); Or manually set the "Focus on" tag for a specific offender, and the Purpose layer will apply stricter standards and push special alerts to such objects. This design combines automation and manual control to ensure that the system is flexible and controllable.

l Interactive execution layer (user interface and execution unit): responsible for transforming the output of the cognitive decision-making layer into actual actions for people or devices, and receiving feedback and instructions from users. It is the window through which the system interacts with the outside world, including:

ü Intelligent reminder interface: an interactive interface provided to prison police and psychological correction cadres, which is used to receive system reminders, view analysis details and give feedback. The interface can be in the form of multi-terminal adaptation: in the prison command center, a large-screen monitoring interface is set up to display the whole prison risk situation map and high-risk personnel ranking in real time; For the front-line police on duty, provide mobile terminal APP or wearable devices (smart watches, etc.) to receive vibration/pop-up reminders to quickly understand the police situation at the scene; For psychological counselors, there is a special workbench interface to display the list of key focus objects and psychological dynamic curves. The alert interface focuses on information visualization and ease of use: colors and icons are used to identify the risk level, and click on the alert to view the analysis basis and recommended actions of AI, so that users can efficiently obtain key information.

ü Alarm and linkage system: For serious emergencies (such as brawls, large-scale riot attempts, etc.), the system can directly trigger the linkage of physical alarm devices. For example, a siren is sounded, a warning light is lit, or a command is broadcast through an intercom system. These interconnected device interfaces are integrated with traditional prison alarm systems to ensure automatic and rapid response when AI detects a major danger. In addition, it connects to the interface of prison access control, lighting and other IoT devices, and performs predefined security actions under specific circumstances (such as automatically locking access control in specific areas, turning on emergency lighting, etc.), so as to gain disposal time for the police. Of course, these actions are controlled by Purpose-layer policies to avoid false triggers (e.g., automatic door locking only when the highest alarm level is reached).

ü User feedback and control: The interaction layer receives user input at the same time, such as the feedback of the police on the processing results of each reminder (false positive/true, measures taken, etc.), and the psychological counselor fills in the intervention record. These feedbacks are transmitted back to the knowledge base of the cognitive decision-making layer through the interface, enriching the data learned by the subsequent model. In addition, the system provides a management console for administrators to configure system parameters (implemented through the Purpose layer interface, such as adjusting the sensitivity of reminders), audit logs, and model update management. Through the two-way interaction between humans and systems, a closed-loop management model of "human-machine collaboration" is formed: AI provides decision-making suggestions, humans implement and supervise, and human feedback feeds AI improvement.

To sum up, all levels of the system architecture are closely coordinated: the perception layer provides all-round data senses, the cognitive layer uses the DIKWP model as the core for intelligent analysis and decision-making, and the interactive execution layer transforms intelligence into action and absorbs feedback. The whole architecture not only has a clear division of labor, but also realizes information sharing and decision-making closed-loop through a network feedback mechanism, reflecting the adaptive and autonomous collaboration capabilities of artificial consciousness, which is very suitable for the complex and dynamic application scenarios of Wisdom Prison.

4.3 Workflows and Data Flows

In order to illustrate the operation mechanism of the system, the following is a typical real-time risk reminder process as an example to describe the whole process of data flow of the system from data perception to reminder feedback:

Step 1: Multi-source data perception – Late one night, the sensor and system database in the prison area generated the following data: surveillance cameras captured two convicts in the corridor of the prison hall unusually approaching, and their limbs and behaviors were tense; At the same time, a wristband worn by a convict showed a sudden increase in his heart rate; In the prison information system, the convict had just received a letter from his wife filing for divorce during the day. All of this abnormal data is preprocessed and fed into the data layer of the AI system in real time.

Step 2: Event and Information Extraction – The visual analysis model at the information layer identifies that two people in the corridor appear to be arguing violently (judged to be a "conflict" incident), and the speech analysis captures the abusive words mixed in it; Analysis of physiological data pointed out that the heart rate of offender X had reached 120 beats per minute, which was significantly higher than the benchmark and was in a state of "emotional agitation". The business data interface indicates that criminal X experienced a family change on the same day (divorce letter record). The information layer distills these points into structured information: Event 1: "Offender X and Offender Y had an argument at 22:30 in the corridor of Cell 2"; State 1: "Offender X has abnormal physiological indicators and is suspected of being emotionally out of control"; Message 1: "Offender X receives a divorce letter during the day". This information is published to the knowledge layer with metadata such as time, place, and people involved.

Step 3: Contextual Knowledge Fusion – After receiving the above information, the knowledge layer first locates the relevant nodes in the prison knowledge graph: find the offender X and Y nodes, associate event 1 with the two and mark the location = corridor; Update the "Family Situation" attribute of the offender's X node to "marriage breakdown (day)", and record the minor incident that he has had two disputes with others on that day; According to the rule base, a relevant rule was matched: "If a prisoner is exposed to a major negative event in a short period of time and has abnormal physiological/behavioral abnormalities, he is at risk of aggressive behavior". The knowledge layer enforces the rule and generates an inference conclusion: "Offender X may be currently violent". In addition, the knowledge layer calculates the "risk scenario" parameter: because the time is late at night (the force on duty is weak) and the location is in the core area of the prison, once the brawl may affect many people around, the scenario risk factor is taken as a high value.

Step 4: Risk Wisdom Assessment – The Wisdom layer summarizes all the current information of criminal X from the knowledge layer: his personal attributes (member of the underworld gang, less than 15 years in prison, recent emotional instability), event 1, reasoning conclusion (suspected violent tendencies), scenario risk factor (high), etc. The security risk assessment model calculates this and comprehensively scores it with reference to the machine learning prediction model and knowledge rules. At this time**, under the guidance of the Purpose layer, the system takes "absolute prevention of major security incidents" as the priority** Purpose, so the Wisdom layer improves the sensitivity to conflict events, and the weighted scenarios and Purpose factors affect - the formula is equivalent to increasing the correlation values in the m, n, and k coefficients, so that the reminder significance Sig reaches above the warning line. Eventually, the Wisdom layer concluded that Offender X has a risk factor of = 92 (extremely high), which can lead to a brawl or violent behavior, requiring immediate intervention. At the same time, the risk of offender Y was also assessed (due to the possibility of being assaulted due to the involvement in the dispute), a medium risk value was given, and isolation was recommended.

In terms of psychology, the psychological model of the Wisdom layer noticed that offender X experienced divorce during the day, which was a major stress event according to the psychological assessment rules, and X had a tendency to have an impulsive personality on weekdays, so the model judged that he had the psychological risk of self-abandonment and aggression against others. The Mental State Index rated X as "extremely unstable".

Step 5: Decision and Purpose Verification – Based on the above analysis, the Wisdom layer forms a specific disposition decision: a red alert is issued to the officer on duty with the content "Offender X is suspected of being emotionally out of control and may commit murder." It is recommended that police forces be dispatched to the scene immediately to control the situation; At the same time, a command was sent to the remote loudspeaker system in the prison area, warning X and Y to stop the conflict in deterrent language; Offender X is also placed on a "high-risk list" for subsequent focused monitoring. This decision is submitted to the Purpose layer for review, and the Purpose layer approves execution as it is fully compliant with the highest Purpose of putting security first. In addition, the Purpose layer has updated the system's short-term management strategy for X: the monitoring sensitivity of X in the coming week has been increased, such as early warning whenever X is close to others or mood swings to prevent retaliation/self-harm. The Purpose layer also notes the case at the same time, which is used to train the system to be more sensitive to the pattern of "risk induced by major life changes".

Step 6: Alarm execution and linkage – The interactive execution layer receives the decision of the Wisdom layer and takes immediate action: the mobile app of the police on duty pops up an emergency alarm, vibrates and highlights the head picture of the offender X, the prison where he is located and the description of the police situation, accompanied by a piercing prompt sound; The X icon on the large screen of the command center flashes red, marking "high risk"; Sirens sounded in the prison area to deter and warn all inmates. Almost at the same time, a pre-recorded dignified password from the police on duty was transmitted over the radio: "Warning! All personnel are not to fight, and they are to go to bed immediately...". After receiving the App's instructions, the two nearby police officers rushed to Prison No. 2, controlled criminals X and Y within tens of seconds, and separated the two from each other. The whole set of linkage mechanisms quickly contained the possibility of greater violence.

Step 7: Result feedback and learning – After the situation subsided, the police on duty recorded the incident in the system (X beat Y because of family changes, but fortunately stopped it in time and did not cause serious injuries). The system senses that the conflict has stopped, the scene is quiet, and continues to monitor through microphones and cameras to confirm that there is no spread, and then marks the alarm status as "dismissed". Subsequently, the knowledge layer updated the map: criminal X added a new record of "attempted brawl"; Added a "Attacked" record. According to the statistics of the Wisdom layer, the alarm accurately prevented a violent accident, and confirmed that the AI judgment was basically correct according to the feedback from the police. The Purpose layer thus reinforces the confidence in the rules of a similar pattern ("major negative events + night conflicts"). At the same time, the system learning module incorporates the data of this case into the training set to improve the parameters of the risk model.

It is worth noting that if the feedback indicates that the judgment is wrong (for example, the police report that X has no attack attempt, this time it is a false alarm), the Purpose layer will intervene to adjust: reduce the weight of relevant pattern features to avoid repeated false positives in the future; At the same time, the case is used to train to improve the accuracy of the model. Through continuous feedback learning, the system gradually optimizes the accuracy and adaptability of reminders.

The above process demonstrates a complete closed loop of security risk alerts. In the same way, in terms of psychological correction reminders, the system process is similar: the perception layer collects data such as psychological tests and conversation texts, the information layer extracts psychological abnormal signals (such as the increase of negative words in the text), the knowledge layer combines the background of the offender (such as first-time offenders/recidivists, personality traits), the Wisdom layer evaluates the level of psychological crisis and gives intervention decisions (such as suggesting increasing the frequency of psychological counseling or referring to professional treatment), notifies the psychologist through the interactive layer, and feeds back the correction effect in the follow-up. Regardless of security or psychological scenarios, DIKWP's hierarchical processing and closed-loop adjustment mechanism ensure that the system can operate autonomously in a complex and changeable prison environment, and continue to evolve and improve performance.

4.4 Model and algorithm design

In the core of system intelligence, we comprehensively use AI technologies such as knowledge graph, rule reasoning, and machine learning to achieve a high-accuracy reminder function. The following describes the key models and algorithms:

1. Multi-factor intelligent reminder model: For the decision-making of risk reminders, we introduce the "intelligent reminder mechanism of scene-event-person-purpose matching" proposed by patent CN110969420A, which takes the five dimensions of time, space, event, person, and purpose as important factors affecting the triggering of reminders. In this plan, these dimensions are customized based on the characteristics of prison business:

l Time dimension: distinguish between daytime and nighttime, weekdays and holidays, and special sensitive periods (such as the eve of major events). The security risks are different at different times, and the reminder mechanism will automatically increase the sensitivity of the reminder during the period when the police force on duty is relatively weak (such as late at night).

l Spatial dimension: pay attention to the security level of the offender's location. Using the location parameters such as prison cells, labor workshops, and forbidden areas, the model determines whether the convict enters a special area (such as near the prison wall and cordon). If suspicious behavior occurs in high-risk areas, the alert priority is escalated.

● Event dimension: Analyze the types and characteristics of current events. For example, incidents such as brawls, self-harm, and illegal assemblies vary in severity; Critical events (e.g., planning signs) are not yet illegal, but they need to be vigilant. The model calculates the event parameter PT_E by comparing the event feature quantity with the preset mode. Serious events directly trigger high-level alarms, while general events need to be judged by superimposing other factors.

l Character dimension: Consider the identity category and danger level of the person involved. For example, the principal offenders and habitual offenders of underworld forces are high-risk persons (PT_R high); Low risk (PT_R low) for first-time offenders or rehabilitated activists. In addition, if the identity of the people involved in the incident is special (e.g., contact with people related to the case, meeting with gang enemies), the weight of reminders will also be increased.

l Purpose dimension: Infer the potential purpose tendency of offenders through NLP and behavior analysis. For example, the speech shows provocative aggression, negative misanthropy, etc. Only tendencies are judged and no specific privacy content is involved. Once a dangerous purpose is perceived (e.g., flight tendencies, suicidal thoughts), the alert mechanism greatly increases the risk score PT_I the person, even if there is no obvious behavior yet.

The model takes these five-dimensional parameters as input and uses a linear-weighted or machine learning model to calculate the Alert Significance SIG. The available formula for simplified representation is as follows: Sig = m*PT_E*Sig_SC + n*PT_R*Sig_SC + k*PT_I*Sig_SC (Sig_SC represents the scene factor, m, n, and k are the adjustment coefficients), which means that participating factors such as events, people, and purpose jointly affect the reminder intensity in a specific scenario. The adjustment coefficient is initially obtained by big data training, and then continuously corrected and optimized according to user feedback in actual use. Through this multi-factor model, the system can realize situational adaptive intelligent reminder adjustment: it will not hesitate to give strong alarms when it is really needed, and automatically reduce the sensitivity when there is a lot of interference information, so as to achieve both timely and personalized.

2. Machine learning and deep learning applications: In order to enhance the system's ability to recognize complex patterns, we have introduced machine learning models in several aspects:

l Risk prediction model: Supervised learning is used to train based on historical prison event data. For example, using conflicts, suicides and other events that occurred in the past few years, extracting a series of characteristics (human characteristics, event precursors, psychometric values, etc.) as training samples, and establishing classification models (such as random forests and XGBoost) to predict the probability of someone having an accident in the future. The model is trained and deployed in the Wisdom layer, where a daily rolling risk score is calculated for each offender. This data-driven model captures implicit associations and improves prediction accuracy compared to fixed rules. However, the model results are combined with knowledge rules to avoid over-reliance on statistical escape interpretiveness.

● Anomaly detection model: For new risks that lack labeled data, we use unsupervised/semi-supervised learning algorithms for anomaly pattern recognition. For example, the clustering algorithm learns the normal behavior pattern of criminals, and once someone's behavior deviates too much from the group mean, it is recorded as an anomaly; Alternatively, autoencoder neural networks can be used to screen for anomalies in high-dimensional sensing data (e.g., abnormal fluctuations in physiological signals). These algorithms provide exploratory clues to unknown risks, prompting human experts to pay attention and flag new rules.

l Deep learning perception model: In the information layer, especially in computer vision and speech recognition, we use deep neural network models such as CNN and RNN to improve perception accuracy. For example, face recognition uses deep CNN to improve the recognition rate, action recognition uses 3D convolutional neural network to capture complex body movements, and speech recognition uses Transformer structure to recognize keywords in monitored audio. These models are pre-trained on public datasets and synthetic simulation data, and fine-tuned with a portion of actual prison scenario data to ensure their effectiveness in this scenario.

l Sentiment Analysis and Purpose Recognition: Sentiment Purpose Analysis of Text and Speech Content uses NLP deep models, such as pre-trained language models such as BERT, to do transfer learning on prison conversation records to adapt to domain language (e.g., including foul language). The model outputs speaker sentiment (positive, neutral, negative) and potential purpose tags (help, threat, self-harm, etc.), and whenever there is a Purpose label with a high degree of risk correlation, the system will mark the increased PT_I and participate in the decision as an important factor. The whole NLP process also follows the privacy protection design: the model can run in a local closed environment, does not upload the call content to the cloud, and only feeds back the classification tag of the result, and does not save the complete dialogue text to prevent the leakage of sensitive information.

3. Knowledge Reasoning and Rules Engine: Despite the predictive power brought by machine learning, we still need to be rule-driven to ensure reliability and policy compliance in a highly security-sensitive environment such as prisons. The system has a built-in efficient rules engine:

l Rules in the form of rules: Rules are written in the form of IFTTT or first-order logic, and the prison management regulations and expert experience are solidified. For example: "IF a criminal contacts the forbidden area and THEN calls the police at night"; Another example: "IF low mental health score AND three consecutive refusals of counseling THEN psychological crisis reminder". The rule base is constantly expanding, including safety rules, psychological rules, and compliance rules (e.g., AI must not discriminately increase the risk level due to a certain factor, etc.).

l Inference execution: The knowledge layer continuously monitors the knowledge graph, and immediately deduces the conclusion when the trigger conditions are met, so as to speed up the response. This is especially critical in emergency events: for example, when an overt violation such as "someone climbing a fence" is directly detected, an alert can be triggered in seconds via the rules engine without having to wait for complex model calculations.

● Conflict resolution: When the rule inference is inconsistent with the machine learning conclusion, the system follows the "security first" principle to take the maximum value, and the Purpose layer records this conflict for subsequent adjustment of the model. For example, if the rule judges high risk but the model score is low, the model should be temporarily treated as high risk and labeled with data for the model to learn, and the model should be improved to the corresponding score next time.

l Knowledge learning: For new situations that are not covered, the rules are gradually supplemented through manual feedback. The system interface provides experts to enter the rules function, and after the occurrence of important accidents, the cause can be analyzed and the preventive measures can be transformed into new rules and injected into the system, so that it can draw inferences from one another. This ensures that the system logic is up-to-date and highly interpretable.

4. Online learning and adaptive adjustment: The system has the ability to continuously learn during operation:

l Parameter tuning: reminds the threshold and weight of the model, which will be automatically adjusted according to the user's response behavior. If a certain type of reminder is marked as "false alarm and ignored" by the police many times, the system reduces the frequency and weight of such reminders; On the contrary, if some accidents actually occur that are not notified, the system will automatically tighten the threshold to increase sensitivity. This learning mechanism based on user operation ensures that the reminder mechanism gradually fits the actual management habits.

l Personalized settings: The system allows administrators to make presets for special scenarios or personnel, for example, if a key criminal is set to "first-level strict control", it will be reported immediately no matter how subtle the behavior is; Some low-risk personnel can temporarily reduce the level of reminders so as not to interfere with the normal modification. The above configurations can be learned and incorporated by the system, "remembering" the manager's choice preferences, and automatically applying them in similar situations in the future, so that it can truly vary from person to person and from situation to situation.

l Model updates: AI models are trained incrementally on a regular basis. For example, the newly accumulated data is added to the training set every month to retrain the risk prediction model to continuously improve its accuracy. The NLP model can also be fine-tuned with the latest consulting records to master new jargon and slang. The updated model will be deployed online after passing the test and verification, while the old model will be retained for future reference, in line with the principle of continuous evaluation and gradual progress in AI governance.

Through the design and integration of the above-mentioned multi-level models and algorithms, the system can work accurately, efficiently and flexibly in the complex and ever-changing prison environment. The DIKWP framework ensures that each algorithm module is coordinated and purpose-oriented, while machine learning gives it the ability to grow, and the rules ensure a safe bottom line and transparent interpretation, and the combination of the two enables an innovative AI system with "intelligent self-knowledge".

4.5 System Interfaces and Integration

In order to ensure that the scheme can be implemented in practical engineering, the system pays attention to standard interfaces and compatible integration in the architecture design. Key interfaces and integration points include:

l Data interface integration: The system connects with various prison information systems and equipment through standardized data interfaces. RESTful APIs, Web services, and other methods are used to obtain the required data (such as basic information of criminals and duty records) from the criminal management database and security supervision system, and provide SDKs for the surveillance video platform to push the analysis results. For hardware-aware devices (such as cameras and sensors), the IoT MQTT protocol or dedicated drivers are used to decouple message queues to achieve plug-and-play access to devices. In the interface design, the existing prison informatization specifications are fully considered to ensure seamless connection with the original system and reduce the difficulty of reform.

l Module communication interface: Each module communicates through semantic bus and event-driven mechanism, adopts a unified protocol format (such as JSON message to describe events/knowledge objects), and publishes/subscribes to various topics. In this way, new modules or algorithm plug-ins can be easily accessed or replaced without affecting the whole. In particular, the interaction between the five layers of DIKWP modules is carried out through well-defined semantic objects (such as Data objects, Info events, Knowledge graph fragments, etc.) to maintain inter-layer decoupling and flexible expansion.

l User interaction interface: provide application interfaces adapted to different terminals. For example, mobile APP interfaces, web interfaces, and large-screen visualization interfaces all obtain data by calling unified back-end services to ensure a consistent experience. The front-end calls the back-end REST API through HTTPS to obtain the required reminder list, chart data, etc., which realizes the decoupling of the interface and logic, and facilitates the upgrading of front-end functions or the development of new terminal access in the future (for example, AR glasses interfaces may be added for patrol officers to view risk information in real time in the future).

● System management interface: In order to configure and monitor the system, this solution provides a management console, and after passing the security authentication, you can access some management APIs, including: rule base management (adding and deleting rules), model management (publishing new models, rolling back old versions), parameter settings (adjusting sensitivity and reminder methods), audit log query, etc. The console interface implements hierarchical authorization, and different levels of administrators can access different functions to protect system security.

l Integration with higher-level platforms: Considering that Wisdom Prison is usually an overall project, the system also reserves interfaces to connect with higher-level supervision platforms (such as provincial/national prison management platforms). Through regular reporting of key indicators and safety status data, and acceptance of superior instructions (such as the province's unified safety warning notice), vertical integration is realized. The interface complies with relevant industry standards and the requirements of judicial departments (such as GA public security industry standards) to ensure the standardization and scalability of the solution.

l Cross-system collaboration: Within the prison, the intelligent management system can also be coordinated with other subsystems, such as integrating with the prison emergency command system, and automatically generating a plan process in the command system when AI judges that the risk reaches a certain level; Linked with the broadcast intercom system to achieve AI audio warning; Integrate with access control to realize the dynamic control of people-to-> lock doors; In the future, it can also be combined with the interface of the robot patrol system, and the AI will command the security robot to go to the scene to provide support. All of this collaboration is achieved via standard interface calls or message event-driven, with a high degree of flexibility.

The interface design highlights the "open and closed principle": it is open and compatible with the existing system (it does not replace the original functions but provides a value-added intelligence layer), and it is closed and stable for future expansion (the core interface remains unchanged, and the new functions are extended through the existing interface). This ensures that the solution can be quickly deployed in the existing prison environment during the implementation of the project, and is easy to maintain and upgrade, reducing the overall construction and operation and maintenance costs.

5 Design for Safety, Ethics and Compliance

In a highly sensitive law enforcement environment such as prisons, the introduction of AI systems must not only focus on technical performance, but also strictly adhere to safety, ethics, and legal guidelines. The design of this solution integrates security and compliance requirements throughout, and measures are taken from data use to decision-making mechanism to ensure that the behavior of the system is controllable, credible, and legal.

5.1 Data Privacy Protection

Prison management involves a large amount of personal information and sensitive data (bioindicators, psychological counseling records, etc.) of prisoners, and we attach great importance to data privacy protection:

l Principle of minimized collection: The data collected by the system is strictly limited to what is necessary for the performance of safety and correction functions. For example, audio and video surveillance is only deployed in public and regulatory necessary areas, and the content of private meetings or communications is not monitored without judicial permission; The content of psychological counseling is only used for psychological model analysis, and the results only output the degree of risk, and the complete conversation will not be recorded to avoid excessive exposure of privacy. The use of all data fields is clearly defined for purpose, avoiding unnecessary data collection and retention.

l Data storage encryption: The database that centrally stores the personal data and sensor data of criminals is protected by strong encryption algorithms, and sensitive fields (names, ID numbers, etc.) are desensitized or hashed. Highly sensitive information such as psychological assessment and counseling records are stored in isolation and are only authorized to be accessed by psychological corrections personnel. The system transmission channel fully uses SSL/TLS encryption to prevent network eavesdropping.

● Access control and audit: Implement strict data access classification policies, and different user roles can only obtain data related to their work. For example, ordinary police officers on duty can only see the key information of real-time police information on the terminal, and cannot browse the complete psychological file; Psychologists have access to psychological records, but do not have access to other security data. Every access and operating system is logged for easy audit trail and prevention of internal misuse. Regularly inspect data access logs by prison discipline inspection or superiors, and once illegal access to personal information is discovered, strict accountability is formed, and institutionalized supervision is formed.

l Privacy-preserving computing and anonymization: In order to use data to train AI models under the premise of protecting privacy, we use privacy-preserving computing technology. For example, when it is necessary to aggregate the data of multiple criminals for modeling, the personal identification information is removed or replaced by anonymous IDs, and the sensitive attributes are added with noise or homomorphic encryption methods are used to make it difficult for the model to deduce the individual identity. The trained model only needs to input real-time metrics of the current offender when inferring without revealing historical data. Especially in the psychological text analysis part, the system only extracts the emotional tendency parameters for decision-making, and does not save the original dialogue text, so as to minimize the privacy exposure.

l Comply with legal requirements: Strictly abide by the Personal Information Protection Law of the People's Republic of China, the Data Security Law and other laws and regulations to ensure that the information processing of criminals and related personnel is legal and compliant. All data will be used to demonstrate and inform before use, such as some of the analysis of the content of the offender's communication, and the rules will inform the offender that the communication will be monitored by AI security at the time of prison education, and the necessary informed consent basis will be obtained. At the same time, we regularly carry out data security impact assessments, find risks and rectify them in a timely manner, and ensure that the data processing activities of the system are always within the legal red line.

5.2 System Security and Fault Tolerance

As the core security system, this solution ensures system security from the physical, network and application levels:

l Physical deployment security: The system server is deployed in the prison intranet computer room, which is on duty and monitored 24 hours a day, and the fireproof, moisture-proof and anti-sabotage measures are complete. Dual-channel power supply and UPS are set up at key nodes to prevent system paralysis caused by power failure. For data center rooms, access control, video surveillance and other measures are adopted, and unauthorized personnel cannot access the equipment.

l Network security: The prison intranet where the system is located is physically isolated from the external Internet or strictly controlled through the network gate, and only specific data is allowed to be reported to the superior platform and desensitized to prevent external network attacks. The internal network is divided into subnets and security domains, such as camera equipment and sensors connected to the isolated network segment, the access range is restricted through firewalls, and important servers are placed in the core network domain for key protection. The intrusion detection system is enabled to monitor abnormal traffic in real time, and firewall policies are deployed to block unauthorized access and malicious scanning. All communication interfaces require strong identity authentication and permission verification to prevent insiders from escalating or unauthorized operations through interfaces.

● Application security: The code development follows the security coding specification, and the key modules go through threat modeling and penetration testing to find vulnerabilities. Verify and filter AI model inputs to defend against adversarial sample attacks (e.g., someone deliberately creates abnormal data in an attempt to deceive the system); The AI permission is strictly restricted on the output, and the AI is not allowed to directly perform dangerous actions involving personal safety, and all key actions can only take effect after human confirmation or secondary signals (for example, the AI can suggest opening the door but must be confirmed by the administrator before it can be executed). In addition, the system provides an emergency manual takeover switch: once the AI system is abnormal (frequent false alarms or abnormal decision-making), the duty officer can switch the system to manual monitoring mode with one click, suspend the AI decision-making output, and ensure that people can intervene and control the situation at any time. This design guarantees that the AI is "forever on a chain" and does not get out of control.

l Redundancy and fault tolerance: In order to pursue high availability, redundant deployment is adopted for key components: server dual-machine hot standby, network link redundancy, and multi-point monitoring sensors cross-covering key areas to prevent single point of failure. At the software level, fault detection and automatic switchover are implemented, such as when an analysis module crashes, the backup module is automatically put on top. Regular snapshot backup of databases, remote backup of important logs, to prevent data loss. In terms of fault-tolerant design, each module has a timeout and exception handling mechanism for downstream dependencies, and once a certain link data is missing or delayed, the system will be reasonably degraded instead of stuck. For example, if a temporary camera fails, decisions are made based only on other sensing information, and maintenance is reported at the same time, so that the entire set of reminders does not fail.

5.3 AI Ethics and Policy Compliance

When introducing AI-assisted prison management, we are deeply aware of the ethical and legal challenges it may bring, so we have set up multiple safeguards at the level of system decision-making and use:

l Transparent and explainable decision-making: Prison management is directly related to the freedom and safety of personnel, and AI decision-making must be open and transparent. The DIKWP structure makes every step of the system's reasoning traceable, and this solution provides managers with detailed decision-making basis reports through interfaces, recording why AI determines that someone is at high risk (what data and rules are cited). When the system recommends drastic measures, such as segregating an inmate, the set of explanatory reports can be considered by the prison board to ensure that the decision-making process is subject to human oversight and accountability. In the event of a misjudgment that causes adverse consequences, the AI logic can be traced back to the records to improve the algorithm or adjust the rules to prevent the recurrence of the same mistakes.

l Avoid bias and discrimination: AI models may be biased due to unbalanced training data, and we take measures to ensure that the system output is fair and just. The model training set tries to cover different types of criminals to prevent a certain type of person from being biased to predict its high risk because of a large sample size. In addition, manual verification of ethical rules is introduced: for example, risk assessment is not affected by non-behavioral factors such as race and religion; Give the same early warning standards to the same behavior, and do not favor one over the other. If it is found that the model has a high false positive rate for a certain group, the threshold will be manually adjusted or the rule correction will be added, so as to ensure that the system does not have subjective discrimination. In terms of psychological treatment, it is also ensured that AI does not label and insult specific psychological disorders, but neutrally describes the state for professional reference.

l Human-machine collaboration and manual adjudication: Emphasizing the status of AI-assisted decision-making, rather than replacing humans to make final decisions. All system alerts are recommendations in nature, and the specific disposition is still at the discretion of the civilian police or prison superintendent. This plan clearly requires that AI alerts must be confirmed by the police officer on duty, and the psychological assessment given by AI must be interviewed and verified by a psychologist. In particular, decisions involving serious consequences (such as increased punishment and compulsory treatment) can only be based on the AI report, and the final conclusion will be ruled through established legal procedures, so as to prevent the occurrence of "AI convictions" violations. Human beings maintain the right to know and veto AI, and AI must not interfere with human final discretion.

Respect for the basic rights of criminals: Although prisoners are restricted in their personal freedom, they still enjoy basic rights such as human dignity in accordance with the law. The system is designed to avoid violating these bottom lines: not using shaming language or publicly publicizing someone's psychological problems, not arbitrarily expanding surveillance into areas that are not allowed by law, and not directly punishing or discriminating against criminals based on AI assessments. When implementing psychological interventions, humanized counseling is carried out by professionals, rather than simply forcing medication based on AI conclusions. In short, technology has always served the purpose of helping criminals rehabilitate, not to become a new tool of coercive control.

l Policy and regulatory compliance: This plan conforms to the guiding ideology and normative requirements of the Chinese judicial administrative department for the construction of Wisdom Prison. For example, the Ministry of Justice emphasizes the principle of "early warning and situation analysis", and this system achieves this through AI intelligent research and judgment. At the same time, it follows the national policies on the safety, controllability and ethics of artificial intelligence, such as the "Principles for the Governance of the New Generation of Artificial Intelligence" and the "Measures for the Review of Science and Technology Ethics", etc., to implement explainable, controllable, and responsible AI in prison scenarios. Before the implementation, we will organize ethical arguments and legal compliance assessments, and invite legal experts, ethics experts and regulatory representatives to review the plan to ensure that it is not contrary to law and social morality before it can be deployed. Establish a regular algorithm assessment and reporting system during the operation process, continuously report the effectiveness of the system and existing problems to the higher-level organs, accept supervision, and make timely adjustments according to policies. If necessary, you can apply for safety assessment and industry standard certification from relevant national departments to ensure that the technology and products used are within the scope of the license.

Combining the above measures, the system has established a solid bottom line of safety, ethics and law from both technical and management aspects. While ensuring that AI empowers prison management to improve efficiency, it also ensures that AI is controlled by laws and regulations and human values, so as to truly achieve "technology for good, technology for the rule of law".

6 Implementation Prospects

Based on the cutting-edge DIKWP artificial consciousness theory and rich engineering considerations, this technical solution proposes a new framework for intelligent prison management of Wisdom. In order to put the plan into practice, we finally put forward the prospect and suggestions for the implementation of the project:

Phased implementation route: Considering the differences in the current status of prison informatization and the complexity of AI systems, it is recommended to implement it in stages. In the first stage, pilot prisons with better conditions can be selected to deploy basic functions (such as video intelligent analysis and early warning, basic risk model) to verify the technical feasibility. In the second stage, the whole prison was expanded and the psychological correction module was launched, which was deeply integrated with the original management system. In the third stage, the algorithm will be optimized and the rule base will be improved based on the pilot experience, and gradually rolled out to more prison units. Reduce implementation risk by taking a step-by-step approach and smoothing the transition.

Personnel training and process optimization: The introduction of intelligent systems requires supporting personnel training. Prison police and psychological corrections personnel should be trained in the use of AI systems, so that they can master interface operations, alarm handling processes, feedback labeling methods, and so forth. At the same time, the existing workflow is optimized, AI reminders are incorporated into the duty code, alarm response SOPs are formulated, and the legal effect and disposal requirements of AI prompts are clarified to ensure smooth human-machine cooperation. For example, a daily risk briefing system is established, and reports are generated by AI for the leadership team to discuss and judge. Only when both the human and machine aspects are in place can the system be truly effective.

Performance and safety assessment: After implementation, the performance indicators of the system (such as effective alarm rate, accident reduction rate, improvement of correction success rate, etc.) and safety indicators (false positives and false negatives, data security incidents, etc.) should be continuously evaluated. Adjust the problems found in a timely manner, and suspend relevant functions for rectification if necessary. Pay special attention to the hidden dangers caused by algorithm bias, and establish a regular review mechanism to eliminate undesirable biases. Feedback from frontline prison staff should also be collected to assess the impact of AI on workload and psychological stress, so as to ensure that the system effectively helps police reduce the burden and increase efficiency, rather than increasing the burden.

Patented technology integration: The patented achievements of Professor Yucong Duan's team (such as intelligent reminder mechanism CN110969420A, etc.) have been partially referred to in the design of this scheme, and the subsequent implementation can be carried out in cooperation with relevant teams or enterprises to modularly integrate the patented technology into the system. For example, the semantic analysis engine in the patent, the Purpose recognition module, etc., are introduced to speed up the development progress and take advantage of their advancement. At the same time, pay attention to obtaining legal authorization, abide by intellectual property norms, and create an example for the combination of production, education and research.

Application prospects: Once the Wisdom prison management system is successfully implemented, it will significantly improve the level of prison safety management and the quality of rehabilitation, and realize the goal of "prevention before it happens, correction and treatment in the invisible." ". AI real-time risk analysis can minimize the occurrence of violence, escape, suicide and other accidents in prison, and ensure the safety of people's lives and property. Intelligent psychological counseling helps offenders rebuild their personality and smoothly return to society. In addition, the concept and architecture of the system (DIKWP artificial awareness model) have a wide range of reference significance in the field of law enforcement security, and can be popularized and applied to detention centers, community corrections supervision, and other places that need comprehensive safety warning. Through continuous practice and improvement, it will also accumulate valuable experience for the application of China's sovereign artificial intelligence technology in public safety scenarios.

Conclusion: The Wisdom prison intelligent management solution proposed in this report integrates cutting-edge AI cognitive models with the actual needs of prison management, and strives to build a safe, efficient and people-oriented new prison management ecosystem. Through systematic architecture design and comprehensive security compliance considerations, the solution has good implementability and promotion value. We believe that under the guidance of the competent authorities and the collaboration of the industry, with the help of innovative technologies such as DIKWP artificial awareness, the blueprint of Wisdom Prison will surely move from vision to reality, and provide strong scientific and technological support for the modernization of prison governance in the new era. We will continue to refine and refine the details of the plan to ensure that this intelligent system can be implemented as soon as possible and make a positive contribution to prison security and social harmony.