background

Prisons are currently facing increasingly complex criminal structures and serious rehabilitation challenges. On the one hand, the number of criminals involved in organized crime and vice, as well as those who endanger national security, has increased, and their strong organization and will to resist and reform have brought high risks to supervision. On the other hand, many prison police teams are gradually aging, and it is difficult to pay attention to the dynamic changes of each convict in a timely manner under the overload of management tasks. There are lags and omissions in the traditional manual supervision method, and there is an urgent need to improve the timeliness and accuracy of supervision with the help of artificial intelligence technology. Based on this background, the Data-Information-Knowledge-Wisdom - Purpose (DIKWP) artificial intelligence model and related patented technologies are introduced to build an intelligent reminder system, which can assist the police to monitor and warn criminals of dangerous behaviors and psychological states in real time, and escort prison safety and criminal correction.

Construction goals

The aim of this project is to develop and deploy an intelligent alert system that integrates into the existing prison management platform to achieve the following two goals:

 Real-time intelligent reminder of the danger of criminals: AI is used to analyze data such as the progress of the criminal's sentence, recent life events, reform performance, and changes in behavior patterns, and real-time reminders of which criminals currently exist in real danger or potential risks to the prison police, and assist in preventing and warning emergencies.

 Real-time intelligent reminders for psychological correction of convicts: Based on the convict's psychological assessment report, psychological counseling records, and the implementation of personalized correctional treatment plans, etc., intelligently identify changes in the psychological state of convicts, and remind psychological corrections police to promptly pay attention to individuals who need key interventions, to prevent psychological crises and improve the effectiveness of corrections.

Through the realization of the above goals, the burden of daily supervision and psychological counseling of front-line police will be reduced, the initiative and refinement level of prison management will be improved, and a safe, stable and intelligent supervision environment will be built.

Functional requirements analysis

According to the business needs of prison management and the above objectives, the system needs to have the following main functions:

 Intelligent early warning of dangerous behaviors: Comprehensive analysis of multi-dimensional data such as basic information of criminals (length of sentence, name of crime, labels related to crime and vice, etc.), recent major life events (such as receipt of judgments, family changes, imprisonment of accomplices, etc.), reform performance (reward and punishment records, violations of rules and disciplines), daily behavior and social networks (interactions with other prisoners, signs of gangs), and changes in physiological and psychological states (mood swings, abnormal behaviors) and other multi-dimensional data, to assess the dangerous tendencies of each criminal. When a criminal is judged to be at risk of potential violent conflict, escape, self-harm, or inciting disturbances, the system should send an early warning reminder to the police on duty in real time, indicating the type of risk and the factors on which it is based, to help the police take preventive measures.

 Psychological correction attention reminder: For the police in the psychological center, the system needs to track and monitor the mental health and correction progress of each convict, including regular psychological assessment scores, daily psychological counseling records, psychological crisis events (such as impulsiveness, self-harm tendencies), and the completion of their personal correction plans. When it is discovered that the convict's psychological state has deteriorated (such as an increase in depression index), relapsed into a negative psychological pattern, or has not participated in psychological correction activities as planned, the system promptly reminds the psychological police to pay attention to it and recommends possible interventions. For example, if a criminal is depressed recently and has poor sleep, it is recommended to arrange an in-depth psychological counseling.

 Personalized reminder mechanism: The system reminder should be personalized and scenario-based according to the situation of different criminals. The same incident has different degrees of impact on different criminals in different situations, and the reminder strategy should be dynamically adjusted. The system needs to support different reminder methods for different risk levels (such as general concern, key warning, and emergency alarm), and allow continuous optimization of reminder rules based on feedback from the police to avoid excessive disturbance or omission of important information, and realize automatic adjustment and personalized intelligent reminders.

 Information Visualization and Reporting (optional): Provides an intuitive interface for police officers to view a prison-wide overview of the risk landscape and mental health. For example, the current list of high-risk offenders, risk ratings, and statistics of recent early warning events can be displayed through the dashboard. Regularly generate reports to summarize the risk change trend and the progress of psychological correction work in each prison area, etc., to assist in management decision-making.

The above functional requirements will be realized in the technical solution through the combination of DIKWP intelligent model and the patented "intelligent reminder mechanism for scenes, events, people and purpose matching" (CN110969420A). The system design will ensure seamless integration with existing prison information systems and strict adherence to data privacy and security norms.

Overview of technical solutions

Overview of the DIKWP model and the intelligent reminder mechanism

In this scheme, the DIKWP cognitive model proposed by Prof. Yucong Duan is used as the theoretical basis for system intelligence analysis. DIKWP is an extension of the traditional Data-Information-Knowledge-Wisdom (DIKW) model by adding a "Purpose/Purpose" element at the top level. This allows the system to not only distill information, knowledge, and wisdom decisions at a step-by-step basis when processing criminal-related data, but also to introduce a goal-oriented purpose to guide the entire analysis process. For example, the core purpose of the system is to "ensure prison safety and improve the effectiveness of correction", which will run through all aspects of data processing to ensure that the final reminder results are consistent with prison management goals.

Different from the traditional linear hierarchy, the DIKWP model adopts a network interaction structure, and the information between the layers can flow in both directions and be iteratively updated. This means that the system not only obtains knowledge and wisdom from the data layer, but also influences the data collection and analysis at the lower level based on the high-level strategy (Purpose), so as to achieve a dynamic feedback loop. Specifically, when analyzing the risk of criminals, the system will continuously adjust the underlying model parameters according to the actual feedback (such as the results of police handling and the effect of psychological counseling), so that the reminder mechanism becomes more and more accurate. This "DIKWP×DIKWP" interaction model will be embodied in the fact that there is a set of cognitive processing chain from data to purpose within the system, and at the same time, the system and police users also form another set of cognitive interaction chain. The system provides Wisdom decisions (reminders) to users, and users take actions and feedback the results into the system as new data, thus forming a two-way iterative closed-loop intelligent processing.

At the same time, we will integrate the intelligent reminder mechanism of "matching scenes, events, characters and purposes" described in the patent No. CN110969420A to integrate multi-dimensional context information into the reminder logic. This mechanism emphasizes the comprehensive consideration of scene factors (time and place) and participation factors (events, people, and purpose) when reminding decision-making, and realizes the automatic adjustment of reminder intensity through machine learning. This program will draw on the mathematical model and adjustment strategy in the patent, so that the system can generate appropriate reminders at the appropriate time and in an appropriate way according to the behavioral and psychological conditions of different criminals in special prison scenarios. For example, in the middle of the night or during sensitive hours, the system increases the sensitivity of risk alerts; In the daily routine scenario, reduce the frequency of unnecessary reminders. For example, for criminal offenders or those who are emotionally unstable, the warning level triggered by the same event will be higher than that of ordinary offenders. With this scenario-based and personalized matching, false negatives and false positives are minimized.

The overall architecture of the system

The system is designed with a modular hierarchical architecture and operates as an embedded intelligent reminder module for the existing prison management system. The overall architecture can be divided into a data layer, a business logic layer (analysis and decision-making layer) and a presentation layer, and is run through the DIKWP model. The main components are as follows:

1. Data & Information: responsible for the collection, access and preprocessing of various raw data. The data sources include: (1) the basic information database of criminals (sentence, crime type, organizational relationship, etc.); (2) Transform the performance database (reward and punishment records, labor/academic performance, violation of rules and disciplines, etc.); (3) data from psychological assessment and counseling systems (psychometric test scores, counseling notes, mental health records); (4) Monitoring and daily event collection (surveillance video analysis results, prison guard log records, emergency reports); (5) Other relevant data sources (such as meeting records, communication monitoring data, etc.). The data layer cleans, structures, and fuses these heterogeneous data into a standard information format for analysis. For example, the continuous monitoring observation notes are summarized into behavioral event entries, and the psychological counselor's text records are extracted from emotional labels and keyword information. The data layer also establishes a data update interface to realize real-time collection and triggering of new events to ensure the timeliness of analysis.

2. Knowledge & Wisdom: This is the core of the system's intelligent analysis and decision-making, using the knowledge and wisdom layer capabilities of the DIKWP model. Firstly, the risk and psychological knowledge graph of criminals is constructed, and the entities such as personal attributes, relationship networks, events and states of criminals are represented in the form of nodes, and the correlation relationships are represented by edges to form a network knowledge structure. The knowledge graph fusion synchronously carries the multi-dimensional factors in the patent mechanism: the scene nodes include time (day and night shifts, special holidays, approaching the end of sentence, etc.) and location (prisons, wind fields, labor workshops, etc.); Event nodes include rewards and punishments, conflicts and disputes, family meetings, etc.; Character nodes include the convict himself, his co-defendants, co-inmates, police officers and other related roles; The Purpose node indicates possible motivational tendencies (such as revenge, suicide, escape attempts, or a desire to rehabilitate). The system transforms the information of the data layer into structured knowledge through the knowledge graph, and uses the graph database to achieve efficient correlation query and reasoning. Secondly, on the basis of knowledge graph and rule model, an intelligent inference engine is developed. The engine adopts the method of rule reasoning combined with machine learning: several built-in risk assessment rules and psychological early warning rules (formulated by senior police officers and psychological experts, such as "if criminal A violates the law twice in the past 1 month and is close to completing his sentence, the risk of escape increases"), etc.), and at the same time, machine learning models are introduced to predict complex patterns (such as dangerous behavior prediction models and psychological crisis prediction models trained based on historical data). When the inference engine obtains new data or an event is triggered, it synthesizes the rule inference and model calculation results to produce the corresponding Wisdom decision—that is, whether to need a reminder, what to remind, and how urgent it is.

3. Purpose Layer and Decision Feedback (Purpose): At the top level of the DIKWP architecture, the system defines a clear decision-making purpose, that is, the core goal of "ensuring regulatory safety and promoting educational transformation". The Purpose layer strategically filters and optimizes the preliminary results output by the inference engine to match the overall prison management purpose. For example, for each item to be reminded, the Purpose layer judges its consistency and priority with the system goal according to the preset policy - if a risk reminder helps prevent major security incidents and is urgent, it will be firmly enforced; If a psychological reminder can be postponed under the current police tension, the reminder can be postponed or combined. For example, if a certain type of reminder is often ignored or marked as invalid by the police, it may indicate that it does not meet the actual needs, and the system will reduce its trigger sensitivity; On the other hand, if an unforewarned emergency occurs, the precursor information is analyzed to update the rule base, reflecting the purpose-oriented optimization of "starting with the end in mind".

4. Presentation: The system presents the reminder results to the prison administrator through a friendly interface. As the programme is integrated with the existing management system, the UI layer can be an extension of the existing civilian police work platform. For example, in the criminal information management interface used by the police on a daily basis, a "smart reminder" column or pop-up window is added to highlight it when there is a new high-risk warning. At the same time, it provides a query and analysis interface, allowing users to view the risk assessment details and psychological state curves (data visualization) of individual criminals, as well as global risk heat maps, statistical reports, etc. (if visualization function is implemented). The UI layer also includes a configuration management interface for system administrators to adjust the parameters of the alert mechanism (such as thresholds for different risk levels), what types of alerts to subscribe to, and data rights management settings.

The above architecture is embedded in the existing system through loose coupling: the data layer is connected to the prison database and business system through interfaces; The inference engine runs in real time as a background service and writes the results to the reminder notification table. The front-end interface invokes the data provided by the new module or microservice in the existing system. The entire architecture ensures that data is not leaked externally, the processing process is safe and controllable, and the scalability is strong to facilitate future function upgrades.

Key technology implementation details

1. Scene, event, and person are integrated with the reminder mechanism that matches the purpose

The intelligent reminder logic at the core of this system will be strictly combined with the multi-dimensional matching mechanism proposed by CN110969420A patent. These include:

1. Multi-dimensional factor modeling: According to the patented method, the factors influencing the reminder decision are divided into two categories: scenario factors and participation factors. Scene factors include both temporal and spatial dimensions. In this system, the time dimension is refined into daily work and rest time period (such as day/night, weekday/weekend), major time nodes (approaching the end of sentence, early stage of detention, anniversary of the crime, etc.), and the spatial dimension refers to the current environment of the convict (prison, release area, interview room, labor post, etc.). Participation factors include the event dimension (the type of event the offender is currently experiencing, such as being punished, being granted a commutation, and having a dispute), the person dimension (the person involved, such as a cell member, a member of a hostile gang, family visits, etc.), and the purpose dimension (a presumed subjective purpose, such as whether there is a tendency to violence, self-harm, escape attempts, or other psychological motives). The data for these dimensions is underpinned by the aforementioned data layers and knowledge graphs.

2. Reminder Significance Calculation: Drawing on the formula model in patents, design an algorithm to calculate the "Reminder Significance" or risk score. First, the scene saliency SIG_SC = α * SC_T + β * SC_P. Among them, SC_T and SC_P represent the importance scores of the current time and place factors, respectively, and α and β are weighted (which can be adjusted and optimized according to historical data). For example, late at night and in a prison cell, both SC_T and SC_P may be given high values, as violent clashes are more harmful at night. Then, the significance SIG_PT of the participating factors is calculated, and the three factors of event, person, and purpose are combined with the scene factor, and the formula is similar to SIG_PT = m * PT_E * SIG_SC + n * PT_R * SIG_SC + k * PT_I * SIG_SC (where PT_E, PT_R, and PT_I represent the parameter values extracted from the event, person, and purpose factors, respectively, and m, n, and k are the corresponding weights). With this formula, the system dynamically adjusts the final reminder intensity. For example, if the current scene SIG_SC is very high (indicating that time and space are sensitive), and the incident is a fight (PT_E high), the person involved is the backbone of the underworld (PT_R high), and the purpose is judged to have a tendency to retaliate (PT_I high), then the calculated reminder will have a very high significance, and the system will trigger a high-level alarm to notify the on-duty leader and police reinforcements. On the other hand, if the scene is general and the event/person has little impact, it may only produce a low-level prompt for the police in the prison district to know.

3. Purpose Identification and Privacy Protection: For the acquisition of "Purpose" factors, the system uses natural language processing and behavior analysis technology to extract emotional tendencies and potential Purpose from the words and deeds of criminals. However, in order to comply with the principles of data privacy, we use the same strategy as the patent mechanism: only the propensity is judged, and the specific content is not touched. For example, when analyzing the letters or phone calls of criminals to their families, only the emotional polarity (positive/negative) and the frequency of stress words are extracted, but the specific conversation content is not spied on. Psychological counseling records also mainly identify changes in psychological status (such as being more anxious or calmer) and respect the privacy of communication. These extracted Purpose clues are quantified and calculated as PT_I input reminders. While protecting privacy, it can still allow the system to grasp the psychological dynamics of criminals and use them to assist in judgment.

4. Machine Learning Adaptive Tuning: The patent mentions that alert parameters are continuously modified by introducing big data interfaces and user behavior learning. The system will achieve this: the initial parameters of α, β, m, n, k, etc., can be trained on a large amount of historical prison event data to obtain default values. After the system is put into operation, it will continuously monitor the effect of reminders (such as the feedback of the police on the reminders and the actual unwarned events), and use online learning or regular offline training to update the model. For example, if the risk is found to be underestimated in certain scenarios, the system will automatically increase the SIG_SC or relevant weights under similar conditions. If a certain type of alert is statistically proven to be a false positive, the weight of its parameters is reduced accordingly. In this way, the reminder mechanism will become more and more relevant to the actual situation of the prison, forming a closed-loop Wisdom optimization chain.

2. Intelligent reminder module of criminal danger

The module focuses on monitoring the unsafe behavior and signs of events of criminals, assessing the danger level in real time and issuing early warnings. Its implementations include:

1. Risk Feature Extraction: The system builds a risk profile for each offender, including static and dynamic features. Static characteristics such as the length of the sentence (the closer the sentence expires, there may be a risk of all-or-nothing), the nature of the case (the basic risk of violent offenders is higher), organizational relationship (the potential risk of collusion among gang members), past violation history, etc. Dynamic characteristics such as: recent behavioral events (fighting, indiscipline, incitement to others, etc.), emotional state (derived from daily observation and NLP analysis of chat content), social network changes (whether there is frequent contact with certain sensitive people), etc. The system continuously fetches and updates these characteristics from the data layer.

2. Risk assessment model: The above characteristics are used to input a comprehensive risk assessment model for scoring. The model is composed of a rule engine + machine learning: the rules section defines some high-risk models based on expert knowledge, such as "short sentence + recent frustration = > high risk of self-harm" or "terrorists + contact with sensitive areas = > possibility of premeditated violence", and the risk score is increased if the conditions are met. The machine learning part can use classification or predictive models (such as random forests, graph neural networks, etc.) to make risk predictions on complex feature combinations based on large-scale historical data training. This is where the knowledge graph comes into play, and indirect risk factors can be deduced (e.g., A and B often conspire and B has planned a riot, then A is also at high risk). The model outputs the current risk level (e.g., 1-5 or low, medium, high) and the main risk factors for each offender.

3. Real-time trigger and notification: When the risk level reaches the preset threshold, the module generates a danger reminder event. Different reminder methods are adopted according to the severity of the risk: the general risk can be marked with a yellow alert on the system interface; If the risk is high, the red will be highlighted and an audible/SMS reminder will be triggered to notify the police on duty; In the case of an extreme emergency (e.g., when imminent violence is anticipated), the police are linked to the prison emergency response plan (e.g., broadcasting an alarm and notifying the command center). The content of the reminder includes the name of the convict, the prison area where he is located, a description of the risk type, and a description of relevant indicators (for example, "I have had frequent contact with X recently, and I am suspected of planning a crowd fight"), so that the police can quickly understand the situation. All reminder events will also be logged for later analysis and verification.

4. Linkage disposal and feedback: After receiving the reminder, the police can confirm and fill in the disposition situation through the system interface (such as conducting conversation education, strengthening inspections, or reporting to leaders, etc.). These results are fed back to the system and stored in the data layer. On the one hand, the system will record which reminders have successfully prevented accidents and which have been confirmed to be false positives, so as to use them as annotated data in subsequent model optimization. On the other hand, the knowledge graph is also updated with the disposal results, such as "offender X was found to have signs of violation at time Y and has been warned in time" will reduce his short-term risk score. This realizes closed-loop risk management - the system discovers risks, prompts intervention, human-machine collaborative disposal, and then corrects the system judgment according to the results.

3. Intelligent reminder module for criminal psychological correction

This module aims to assist psychological correction staff to grasp the changes in the mental health of offenders in a timely manner and ensure that correctional measures are targeted. Key implementation details include:

 Psychological state monitoring: The system establishes a psychological file for each offender, and collects information such as psychological assessment scale scores (such as depression, self-rated stress, irritable tendency, etc.), regular comments from psychological counselors, psychiatric diagnosis and treatment records (if any), and daily behavior (sleep, diet, social participation). In particular, some objective quantitative methods are introduced into the system: for example, the analysis of the emotional tendency of criminals to use words when writing letters to their families or talking to people over a period of time (NLP sentiment analysis); Monitor changes in daily routines (e.g., recently being left alone and not out of the cell, which may indicate social withdrawal). These data are processed and mapped into psychological state characteristics, such as "increased depression potential", "moderate anxiety index", "low willingness to obey", etc.

 Judgment of correction needs: Combined with each offender's individualized correction plan (which may include reform goals, frequency of psychological counseling, courses to be completed, etc.), the system determines whether the current psychological state deviates from the normal track and requires additional intervention. The implementation method is also a rule + model: at the rule level, several situations worth reminding are listed, such as: "depression score > 80 in two consecutive psychological assessments and is on the rise" or "missed the agreed psychological counseling and recently experienced family changes", etc., and the match triggers the reminder. At the model level, a psychological crisis prediction model can be trained, and the above psychological characteristics can be input to output the probability of serious psychological problems. For high-risk individuals (if there is a suggestion of suicide with self-harm), the model gives a high alarm value.

 Reminder content and interaction: When it is determined that a criminal needs psychological attention, the system generates a psychological correction reminder. The target of the reminder is the police officer of the psychological center or the relevant counselor. The reminder can be a system message or email, and it will be highlighted on the interface of the psychological correction work platform. Include the offender's name, ward/number, an overview of the concerns (e.g., "significant depressive mood" or "suspected recurrence of PTSD problems"), and recommended initial measures (e.g., "Recommend an individual counseling session within the week" or "Consider adjusting correctional goals"). Suggested measures can be automatically generated according to the system's built-in knowledge base, which brings together corrective strategies and intervention methods formulated by psychologists to correspond to different psychological problems. After the police intervene, they can record the actual measures taken and feedback on the effects in the system.

 Tracking and evaluation: The system continuously tracks the subsequent changes in the mental state of the reminded subject. If the intervention is effective (e.g., the next time the depression index decreases), the system marks the case as successful and reinforces the strategy of using the same reminder for similar situations; If there is no improvement or even deterioration, the system flags the method in the knowledge base as potentially ineffective, prompting a psychologist to intervene to re-evaluate the orthodontic treatment plan. This continuous evaluation mechanism ensures that the quality of psychological correction reminders is continuously improved. In addition, the system can count the number and type of psychological warnings and the success rate of corrections on a monthly basis, providing a quantitative basis for management to improve resource allocation (e.g., investing more psychological counseling efforts in prisons with frequent warnings).

4. Security and privacy technology

In view of the sensitivity of prison data and strict security requirements, the system is designed with multiple layers of data security and privacy protection measures:

 Data access control: All modules of the system are deployed on the prison's internal network and follow the principle of least privilege to access data. Users with different roles (prison district police, psychological police, administrators, etc.) can only view and process information within the scope of their authority. For example, psychological correction data can only be accessed by authorized personnel of the psychological center, and ordinary supervision police do not have the right to view specific psychological reports, and can only receive prompts that need attention but do not have detailed privacy content. The system integrates with existing privilege systems through single sign-on to ensure that unauthorized data flow is not increased.

 Privacy-preserving design: As mentioned in the Purpose analysis section above, the system uses anonymization and generalization methods when processing data as much as possible. When it comes to the content analysis of calls and letters, the "information" layer data such as sentiment and keyword frequency is extracted, and the original full text is not stored, and the analysis process is completed in memory and the results are only saved in the form of labels or scores. In the psychological counseling record, if there is highly sensitive privacy (family background, criminal details, etc.), these fields are skipped during the system analysis and only focus on the content related to the psychological state. These practices are in line with the spirit of the patent's "smart reminder with privacy in mind". In addition, all personally sensitive fields are encrypted or hashed when stored in the database, so even if the underlying administrator directly reads the database table, the plaintext information cannot be easily obtained.

 Security protection and auditing: Necessary security protection measures are deployed in the system operating environment, including firewall isolation, intrusion detection, anti-virus, and measures to prevent adversarial sample attacks on AI models (to prevent criminals from deliberately disguising data to deceive the system). Important reminder operations and parameter changes are logged and regularly audited by the prison supervision department to ensure that the system behavior is compliant and transparent. If abnormal access or data leakage is suspected, the system can automatically alarm and block relevant functions in time. At the same time, the system is strengthened in accordance with the requirements of the national information security level protection to ensure the safety and reliability of the whole solution.

How to integrate with existing systems

This solution emphasizes the function expansion without destroying the existing system architecture, and the specific integration ideas are as follows:

 Data layer integration: Obtain the required data from the existing prison management system through standard database connections, middleware message queues, or API interfaces. For example, the basic information and daily records of the offender are provided by the database of the original supervision system. Psychological assessment data are imported from the psychological counseling system; Monitoring alarms are received through the message push mechanism. The new system does not duplicate data sources, but uses existing data as a data consumer to achieve loose coupling. The implementation of the data interface will be developed in cooperation with the original system vendor to ensure that the data is synchronized in real time without affecting the performance of the original system.

 Business logic embedding: The smart alert engine can be deployed as a standalone microservice or module, integrated with the main system through service calls. When a new data event (such as a violation record) occurs, the main system calls the API of the reminder service to analyze and obtain whether the alert is generated and its level. Or the analysis is passively triggered by the reminder module subscribing to the main system message. The analysis results are stored in a shared alert result database, and the main system can query the alert information periodically or display the alert information on the original interface through a joint table query. For old systems that do not have a service-oriented architecture, they can also be integrated by embedding the analysis SDK library on their servers. In short, make sure that the new features are seamless for users: still use the familiar system, just with more smart alerts.

 Front-end interface integration: Utilize the UI framework of the original system to add necessary pages or controls. For example, add a column of "Risk Level" icons to the offender list page. Display the "Smart Reminders" module card on the personal details page to list the current reminders and historical alerts; Highlight the people you focus on in your counseling calendar. These front-end changes are implemented through front-end and back-end joint debugging, which is consistent with the style of the original system. Some of the new and complex interfaces, such as the Prison-wide Risk Posture Dashboard, are available as stand-alone menus for users with permissions. Through good UI/UX design, it is ensured that the police can get started without additional training and improve acceptance.

 System performance and scaling: Due to the inline integration, the new module needs to be optimized for performance and not slow down the main system. We can take advantage of mechanisms such as asynchronous processing, caching, and incremental updates. For example, most of the analysis is done asynchronously in the background, and the results are cached for front-end query. High-frequency and small data (such as daily behavior logs) are accumulated to a certain threshold and then processed in batches. The module adopts an extensible architecture, so if you need to upgrade AI algorithms or add new data sources in the future, you only need to replace or add corresponding services without affecting the whole. This protects existing investments and leaves room for long-term evolution.

Implement the plan in phases

In order to build this intelligent reminder system smoothly and efficiently, the project will be carried out in the following stages:

The first stage: demand research and system design (about 2 months)
in-depth investigation of the current situation of prison management, collect the needs of front-line police and psychological staff, and refine the functional list and indicator system. Analyze the data structure of the existing information system and formulate the data interface scheme. Set up a R&D team to design the system architecture and technical route, including the DIKWP model application scheme and the details of the integration of the patent mechanism. Produce documents such as "Project Requirements Specification" and "System Architecture Design Scheme", which will be reviewed and confirmed by the prison management department.

Phase 2: Core module development and pilot verification (about 4 months)
Develop the back-end logic of the two core modules, danger reminder and psychological reminder, including data interface adaptation, knowledge graph construction, rule engine and preliminary machine learning model training, reminder calculation algorithm (to implement patent formula), basic feedback mechanism, etc. At the same time, a simple test interface was developed for internal debugging. 1-2 prisons were selected as pilots, and the test version of the system was deployed to verify the real data. Through the pilot operation, the accuracy of the model and the effectiveness of the reminder are evaluated, user feedback is collected, and problems are found and corrected in time. The goal of this stage is to achieve the availability of core functions and the preliminary and reasonable effects of the main algorithm parameters.

The third stage: complete system development and integration (about 3 months)
on the basis of the pilot, improve all functional modules of the system. Develop front-end interfaces and integrate with legacy systems; Implement security and access control modules; Optimize algorithm performance and stability. Write data visualization and report generation components, if needed. Complete the deployment of privacy protection measures (data encryption, log auditing). Cooperate with the original system supplier to complete the interface joint debugging test to ensure that the new and old systems work together correctly. Conduct full-process functional testing, performance testing, and security penetration testing to fix all issues found.

Stage 4: Step-by-step deployment and personnel training (about 1 month)
Develop a step-by-step go-live plan and gradually deploy the system to the province/national prison environment. It can be put into operation in several key pilot units first, and then implemented to other units after summarizing experience. Training is also carried out for different user groups: training for prison police on how to view and respond to intelligent reminders, training for psychological staff on how to use system insight to adjust correction methods, and training for system administrators on configuration management and troubleshooting. Collect suggestions from users for subsequent optimization.

Stage 5: Operation Evaluation and Continuous Optimization (Long-term Continuity)
After the system is launched, it will enter into daily operation and maintenance. Establish a regular evaluation mechanism, such as quarterly analysis of key indicators such as the accuracy rate of system early warnings, the proportion of incidents that cannot be detected in advance, and the success rate of psychological crisis intervention, and compare the evaluation benefits with the baseline data before the launch. Based on the results of the evaluation, the model and rule base are continuously improved, such as updating the algorithmic model to reduce false positives and false negatives. At the same time, we will pay close attention to emerging needs (such as adapting to new regulatory methods, introducing physiological data of wearable devices, etc.), and plan to include subsequent version upgrades to continuously improve the intelligence level of the system.

Through the steady implementation of the above stages, the system functions are gradually implemented and effective, and the impact on the daily work of the prison is reduced. Agile and iterative methods are used in project management, and users are continuously communicated during the development process to ensure that the output meets actual needs.

Expected results

After the completion and use of this technical solution, it will produce significant benefits in the field of prison management and convict correction:

 Improve safety prevention capabilities: The system realizes real-time early warning of high-risk behaviors, greatly reducing the probability of sudden violent incidents, prison escape attempts, and self-harm accidents. Once there are signs, the police can intervene and deal with them as soon as possible, and eliminate potential safety hazards in the bud. This will improve the overall security posture of the prison and reduce the number of vicious incidents and casualties caused by untimely early warning.

 Strengthen the effectiveness of reform and psychological correction: For criminals in need of psychological intervention, ensure timely discovery and guidance, so as to avoid the accumulation of psychological problems and even behavioral problems. Accurate reminders allow psychological resources to be optimally allocated, and the limited teachers' energy is used on those who need the most attention, so as to improve the success rate of correctional treatment and the quality of criminal reform. In the long run, the improvement of the mental health and rehabilitation attitude of inmates will help reduce the recidivism rate and maintain social security.

 Reduce the work pressure of the police: The intelligent reminder system acts as an "auxiliary supervisor" and "psychological counselor" for 7x24 hours, automatically analyzing massive information and prompting key points, greatly reducing the burden of repeated inspections and manual judgment of the police. The police can devote more energy to humanized law enforcement and individual assistance and education, rather than being tired of dealing with daily complicated information. Especially for the elderly police and the inexperienced, the system provides reliable decision-making support and improves work confidence and efficiency.

 Scientific decision-making and system upgrading: The large amount of data and analysis results accumulated by the system provide a basis for scientific decision-making in prison management. Managers can adjust their monitoring strategies based on the data reports, such as strengthening management measures for high-risk groups and improving correction plans for common psychological problems. This has led to a shift in prison management from experience-driven to data-driven. In addition, the introduction of the DIKWP artificial intelligence model and a number of patented technologies in this solution is an important practice for the upgrading of prison informatization from digital to intelligent, laying the foundation for the construction of a more comprehensive Wisdom prison system in the future.

In short, the implementation of the intelligent reminder system will significantly improve the safety factor of prisons and the effectiveness of rehabilitation, and achieve the goals of "prevention before they occur" and "precise correction". At the same time, this solution strictly adheres to data security and privacy requirements to ensure that the application of technology does not generate new management risks. Through human-machine collaboration and intelligent empowerment, prison management will reach a new level.

conclusion

This report proposes a complete and detailed technical solution, which combines **Professor Yucong Duan's DIKWP mesh cognitive model with his authorized patent "Intelligent Reminder Mechanism for Matching Scenes, Events, Characters and Purposes".**Organically combined, the solution ideas are given for the two major needs of offender risk control and psychological correction in prison management. Through the modular architecture design, the solution realizes the full mining and utilization of data, the correlation reasoning of knowledge and the generation of Wisdom decisions, which can be embedded in existing systems and form a closed-loop intelligent processing chain. On the implementation path, step-by-step phased steps are planned to reduce project risk and ensure final results. It is expected that after the system is put into use, it will effectively improve the proactive prevention ability of prison safety management, optimize the pertinence and timeliness of psychological correction, and set a benchmark for the modernization and wisdom of prison management. In the next step, we will promote the implementation of the plan under the guidance of relevant departments, continue to optimize and improve, and make it truly become the right-hand man of the prison police and an important technical support for maintaining the safety of supervision and the quality of rehabilitation.