Background and significance of the project

Pain and itching are one of the most common subjective symptoms in humans, and chronic pain (such as neuropathic pain, cancer pain, migraine, etc.) and chronic itching seriously affect the quality of life of patients and also place a heavy burden on the medical system. The incidence of neuropathic pain in the global population is about 7%~10%, and due to the lack of effective radical treatment, the quality of life of patients has declined for a long time. At present, most of the available analgesic treatments are mainly symptomatic, and it is difficult to completely solve the pain problem from the mechanism. Similarly, chronic pruritus is more than 20% prevalent in the elderly population, and the treatment of unexplained pruritus and intractable pruritus presents significant challenges. Traditional analgesic drugs (e.g., opioids) have limited efficacy and significant side effects, and abuse can lead to addiction and other social problems; Although neuromodulation techniques (such as spinal cord stimulation) provide new approaches, most of the existing devices are open loops or simple feedback, lack of intelligent decision-making ability, and efficacy optimization still mainly relies on manual experience. It can be seen that there is an urgent scientific research and industrial need in the analysis of the mechanism of pain/itching and the innovation of intervention strategies.

At present, advances in artificial intelligence and bioelectronics technology bring new hope to solve this problem. For example, the exploration of implantable smart chips for pain management has emerged: the INS2 implantable neurostimulation sensor chip developed in Australia can monitor spinal nerve pain signals in real time and release pulses to block signal transmission when pain is detected, thereby relieving pain. This technology is thought to have the potential to revolutionize chronic pain treatment. Another example is the AI analgesic pump system that has emerged in China, which applies the Internet of Things and artificial intelligence to postoperative analgesia management, which can monitor the medication in real time, intelligently warn of insufficient or excessive analgesia, and realize the objective quantification of subjective pain feelings. These advances show that intelligent and closed-loop pain intervention is the future development trend. However, the current system is still mainly at the signal or data-driven level, and does not make full use of the high-level understanding of "pain semantics" and "consciousness factors", and it is difficult to adapt to individual differences or complex situations in a timely manner. Pain and itching are not only physiological signals, but also involve high-level semantic factors such as cognition, emotion, and purpose—e.g., anxiety exacerbates the experience of pain, and distraction relieves itching. This suggests that a novel strategy that integrates AI semantic understanding and biofeedback modulation is needed to resolve and intervene in pain/pruritus at a higher level.

The significance of this project is to introduce the latest artificial consciousness theory and semantic modeling methods, treat pain/itching as a semantic phenomenon that is inconsistent with the human body's information and purpose, and develop an active closed-loop intelligent intervention system to improve the level of pain/itch management from the root. By combining the DIKWP artificial consciousness model, the concept of active medicine, and the artificial consciousness processing unit (ACPU) architecture proposed by Professor Yucong Duan, we hope to break through the traditional analgesic thinking and achieve the following goals:

• Re-examine pain/pruritus from the mechanism level, model it as a semantic deviation phenomenon, deepen the understanding of the mechanism of pain occurrence, perception and regulation, and provide a new paradigm for medical research.

• At the technical level, an autonomous intelligent analgesia system is built, which integrates chips, algorithms and neural interfaces, which can perceive the semantics of the patient's pain status, intelligently decide on the analgesic plan and implement intervention, forming a closed loop of "perception-cognition-regulation", and improving the accuracy and individualization of analgesic treatment.

• At the industrial level, we will develop domestic analgesic chips and AI dispensing systems with independent intellectual property rights, create an overall solution for pain intervention that can be implemented, seize the opportunity in the emerging field of intelligent medicine, meet major clinical needs and drive the development of related industries.

In summary, this project closely follows the national key R&D direction (mechanism and intervention of pain and pruritus diseases), integrates artificial intelligence and biomedical frontier theories, and has important scientific value, as well as significant clinical translation significance and market prospects.

Theoretical Basis and Model Architecture

This project will take the DIKWP artificial consciousness model as the core theoretical basis, and combine the artificial consciousness processing unit (ACPU) architecture and active medicine concept to establish a semantic cognitive model and intervention framework for pain/itching. The key theories and models are described below:

The DIKWP Model and the Theory of Artificial Consciousness

The DIKWP model is a new generation of artificial consciousness cognitive framework proposed by Professor Yucong Duan's team, which adds a "Purpose" layer on the basis of the classic DIKW (Pyramid) model (Data-Information-Knowledge-Wisdom). Different from linear hierarchies, DIKWP realizes bidirectional feedback and iterative update of the semantics of each layer through a mesh structure. This model aims to simulate the complete process of human consciousness from low-level perception to high-level decision-making: starting from the raw data (D), distilling information (I), knowledge (K) and wisdom (W) layer by layer, and finally rising to the Purpose (P) layer; In turn, the purpose and wisdom of the higher levels guide the selection and interpretation of low-level data and information, thus forming a closed-loop cognitive cycle.

The DIKWP model gives AI systems a "common cognitive language" that allows every step of the decision to be tracked and explained. In particular, the introduction of the "Purpose/Purpose" layer allows AI decision-making to embed human value goals to ensure that AI behavior is aligned with human Purpose. In the study of artificial consciousness, the DIKWP model is regarded as an important way to realize machine self-awareness and explainable decision-making: through mathematical modeling of each layer of semantic process, the machine can simulate the cognitive and conscious functions of humans, and disclose the purpose and value evolution behind each step of reasoning. Prof. Yucong Duan noted, "The DIKWP model builds a common cognitive language between humans and machines, making every step of the AI decision traceable, interpreted, and understood by humans. By embedding the key layer of 'purpose' into the model, we can not only make AI smarter, but also ensure that it remains in service of human values and safety needs." “

Mesh DIKWP and the "Conscious Bug" Theory: Yucong Duan and his team further developed the DIKWP model, proposing to incorporate both human subconscious and conscious processes into the model architecture. They divide the DIKWP hierarchy into parallel subconscious and conscious streams to more realistically simulate human cognitive mechanisms. At the same time, the "consciousness bug theory" is proposed, which believes that the emergence and evolution of consciousness are related to the process of constantly discovering and correcting one's own cognitive biases (bugs). When there is a "purpose shift" or deviation in information processing, it can trigger awareness and adjustment of consciousness. Drawing on this idea, this project argues that pain**/itching can be seen as a "deviation" or "error" signal between the body's message and purpose**, which needs to be corrected by introducing higher cognitive feedback (artificial awareness). This is discussed in more detail in the semantic model of pain below.

ACPU architecture: Artificial awareness processing unit

The Artificial Consciousness Processing Unit (ACPU) is a dedicated intelligent architecture designed based on the DIKWP model, which can be understood as a chip or modular system embedded in the artificial consciousness model. ACPU aims to realize the processing flow of data-information-knowledge-wisdom-purpose at the hardware level, and realize cognitive functions similar to human consciousness. The core idea is to add cognitive processing units to the traditional computing architecture, so that the machine has the "self-knowledge" of its own computing process and a high-level semantic understanding of external information.

According to the literature, ACPU as a whole consists of three core modules: one is the subliminal computing unit (SCU), which corresponds to the parallel processing of the lower levels (data and information) in the DIKWP model, which is responsible for extracting features and semantic patterns from sensor inputs, emphasizing high throughput and heterogeneous computing (such as using GPU to accelerate parallel computing); The second is the Consciousness Decision Unit (CDU), which corresponds to the sequential logical reasoning of the high-level (knowledge, wisdom, and purpose), runs on the CPU and other general computing units, and is responsible for comprehensively analyzing semantic information and forming interpretable decisions and purpose outputs. The third is the Semantic Fusion Unit (SCFU), which connects the subconscious mind and the conscious unit to realize the two-way interaction and integration of information between the two. This architecture is similar to the coordination between fast unconscious reactions and slow conscious thinking in the human brain: the SCU provides rapid perception and initial response to the environment, the CDU conducts in-depth evaluation and purpose generation, and the SCFU continuously compares the two to ensure overall consistency and efficiency.

It is worth mentioning that the ACPU architecture embodies the idea of "perception-cognition-regulation" integrated semantic engine: the external input is semantic processed by the perception module, enters the cognitive decision-making module for high-level understanding and planning, and then outputs to the control execution module to complete specific intervention actions to achieve closed-loop control. This is a good fit for the process we need in our pain intervention (which will be discussed later in the context of the specific protocol). In general, ACPU provides a technical blueprint for this project: we can design software and hardware systems based on the ACPU concept, so that they have the ability to complete the whole process from pain signal acquisition, semantic understanding, to intervention execution.

The concept of active medicine and the DIKWP active semantic framework

Active medicine is a new medical concept proposed by Professor Yucong Duan in recent years, the core of which is to make full use of AI and artificial consciousness technology in the diagnosis and treatment process to realize the transformation from passive treatment to active health management. Compared with traditional medicine, which passively responds to patients' symptoms, proactive medicine emphasizes prediction, prevention, and personalized intervention, enabling the medical system to actively sense the patient's status and adjust the plan in time. The DIKWP model plays a key role in active medicine and is used to construct multi-level semantic portraits of patients and decision support systems.

Yucong Duan et al. proposed that the DIKWP cognitive map could be constructed for each patient, and the objective data of the patient (D), the subjective symptoms and descriptions (I), the relevant medical knowledge (K), the doctor's diagnosis and treatment ideas (W) and the patient's health goals (P) could be integrated to form a complete semantic description of the patient's health status. Then, the individual graph was semantically compared with the standard knowledge graph in the medical field ("standard medical knowledge DIKWP graph") to find the difference between the two. These differences reflect deviations from the patient's health or desired state, i.e., issues that require attention and intervention. For example, by comparing a patient's symptoms and test results to a standard diagnostic knowledge base, overlooked diagnostic clues or inconsistencies can be identified to aid in a more comprehensive treatment plan. This process ensures that the patient's subjective feelings and individual differences are not overlooked, but are integrated into the AI's decision-making basis.

Under the framework of active medicine, AI (especially AI systems with initial human awareness) not only participates in data analysis, but can also simulate doctor-patient interactions and internal cognitive processes. By introducing an artificial consciousness model into the cognitive space, AI can map the external doctor-patient communication and internal thought process into computable reasoning steps in the DIKWP model. To put it simply, AI is trained to be a "virtual expert" who understands both modern medicine and the individual needs of patients, and forms a decision-making team with human doctors. This human-machine convergence of wisdom can bridge the limitations of a single subject's knowledge or perspective, and provide more comprehensive and accurate decision support. More importantly, no matter how intelligent AI is, the final decision must be guided by the ultimate interests of patients (the purpose layer), and both humans and AI should follow medical ethics and humanistic concepts. This ensures that active medicine still aims to "treat diseases and save lives", with the help of AI but without deviating from humanistic care.

Summary: Based on the above theories, this project will take the semantic stratification and feedback mechanism provided by the DIKWP model as the ideological basis, use the sensory-cognitive-regulatory closed loop provided by the ACPU architecture as the technical blueprint, and implement the patient-centered principle of active medicine to design our pain/itch intervention system. In the following, we will describe in detail how to integrate the pain/itch problem into the above theoretical framework, and establish the corresponding semantic model and feedback control strategy.

Semantic modeling of pain/pruritus with active intervention mechanisms

This project innovatively proposes to model pain (including itching) as the phenomenon of "information-purpose deviation" in the semantic space of DIKWP, and model the intervention treatment process as "semantic-driven regulation".Feedback closed loop. To put it simply, we see pain/itching as an important "semantic message" sent by the body to the brain/consciousness layer, and when this information is inconsistent with the normal purpose or target state of the body, it manifests itself as a painful experience; Our intervention strategy is to correct this deviation at the semantic level and realign the information with the purpose. In this part, we construct a semantic model of pain/pruritus, and design an intelligent mechanism for active intervention based on the three-layer structure of "somatic semantic self-perception-external semantic interpretation-interactive consensus".

DIKWP semantic space model of pain/pruritus

In the DIKWP semantic space, we can describe the process of producing pain or itching experiences with a five-layer structure:

• Data Layer (D): Raw signal and physiological data corresponding to pain/itching. For example, the electrical signals generated when nociceptors are stimulated, the frequency of impulses that cause C-fiber conduction caused by skin stimulation, and changes in the level of inflammatory factors. This is the lowest level of objective data, the material basis for the production of pain/itching.

• Information layer (I): corresponds to the preliminary processing and characterization of these raw signals, i.e., pain**/itch information**. For example, after the signal is screened and encoded by the posterior horn of the spinal cord and the thalamus, the ascending pathway sends sensory information about "pain" or "itching" to the brain, including intensity, location, nature, etc. There are already certain semantics here, such as "thumb tingling" and "back itching", which are the manifestations after the data has given preliminary meaning.

• Knowledge layer (K): corresponds to knowledge, memory, and pattern recognition related to pain/itching. This includes the individual's past experience (what it means to have similar pain in the past), medical knowledge (what may be the cause of the pain), and cultural and psychological factors (such as knowing whether the itching and scratching will relieve or worsen). At this level, the brain compares the current pain and itch information with the existing knowledge base. For example, "sharp stabbing pain + redness" matches "may be pain due to infectious inflammation", or "paroxysmal shock-like pain" matches "may be neuropathic pain". The knowledge layer gives a deeper semantic explanation to pain/itching – pain is no longer just a signal, but is associated with a meaning or cause.

• Wisdom Layer (W): Comprehensive assessment and decision-making in response to pain/itch situations. The Wisdom layer reflects the organism/individual's ability to solve problems. For example, after integrating the information at the knowledge level, the brain generates countermeasures: "This pain may be appendicitis, you need to seek medical attention as soon as possible" or "This itch may be a mosquito bite, just apply medicine to relieve the itch". At this level, the individual also considers the situation: Is there something more urgent at the moment? Is the pain/itching severe enough to interrupt your current activity? The output of the Wisdom layer is a treatment plan or response strategy to a pain/itch stimulus, which already includes value trade-offs and purpose choices.

• Purpose layer (P): corresponds to the ultimate purpose, will, and value orientation of the individual. In a healthy state, people's purpose is usually not to want to have pain, and to pursue disease-free comfort ("seeking advantages and avoiding disadvantages" is the basic biological purpose). In the context of the illness, there are also individual goals for treatment (e.g., "want to relieve pain and regain ability to work" or "stay on task even if there is pain"). The Purpose layer represents the state that an individual wishes to achieve and the choices that are driven by values. At the unconscious level, purpose is also embodied in the body's survival instinct (instinctively avoiding damage and alleviating pain).

According to the above model, we consider pain**/itching as** an alarming semantics generated at the higher level when the perception at the data/information level deviates from the goal of the Purpose layer. For example, when tissue damage (D-layer data) triggers intense pain signals (Layer I) and the "pain-free normal" state pursued by the Purpose layer is broken, this deviation is identified as "harmful/abnormal" by the Knowledge/Wisdom layer, thus presenting itself as a painful experience in consciousness. The purpose of this experience is to prompt the individual to take action to eliminate deviations and restore balance (e.g., to distance themselves from the source of harm or seek medical treatment). Similarly, pruritus, as a secondary warning signal, is usually less semantic than painful (pruritus indicates minor irritation or skin abnormalities, but less urgency than severe pain), but the Purpose layer is still a comfortable state that wants to be itch-free, so pruritus drives the individual to scratch or process to restore the balance of Purpose.

Message-Purpose Deviation refers to the degree to which the signal information perceived by the body does not match the individual's expectation/purpose. For example, if a person has the purpose of maintaining good health (no pain or itching), but receives pain information at the moment, it indicates that there has been a significant deviation; Itching also represents some degree of deviation, but may not be as severe as pain. Professor Yucong Duan's "BUG Theory" of consciousness believes that the awakening of consciousness often stems from the detection and correction of this bias, and pain/itching is precisely the "deviation warning mechanism" evolved by biological consciousness. Therefore, from the perspective of artificial consciousness, we can equate pain/itching with a type of "bug report" or "abnormal semantic markup" generated within the system, indicating that there is information that is not fit for purpose and needs to be paid attention to and addressed.

Semantic feedback intervention structure for active analgesia

After understanding the semantic nature of pain/pruritus, we devise corresponding active intervention mechanisms to regulate and eliminate this information-purpose deviation in a semantic-driven manner. We propose a three-layer interaction structure, corresponding to somatic semantic self-perception, external semantic interpretation and interactive consensus regulation, to construct a closed-loop feedback intelligent system from pain perception to intervention execution.

• (1) Somatic semantic self-perception: This layer emphasizes the patient's own real-time perception and internal feedback of pain/itching. These include the patient's physiological sensations (nerve signaling to the center) and subjective sensations (self-assessment of the degree of distress). In our system, "somatic self-sensing" is realized by in-vivo sensors and algorithms: implantable or wearable sensor chips continuously collect physiological data related to pain/itching (such as nerve discharge, electromyography, skin conductance, inflammatory indicators, etc.), which are locally processed by the SCU module of ACPU to extract the semantic features of somatic states—for example, "the current pain signal intensity is 8/10, derived from the L5 nerve segment; with a stressful increase in heart rate". This information forms the content of layers D and I in the patient's own DIKWP semantic map, and can be partially elevated to layer K (e.g., in the context of the patient's medical history, it is known that this is "postoperative wound pain"). At the same time, the patient's subjective pain score, facial expressions, body movements, etc. are also captured by sensors (cameras, pressure sensing, etc.), enriching the Level I information. Through this layer, the system has a self-perception of the current pain state: it contains both objective physiological signals and subjective semantic descriptions.

• (2) External semantic interpretation: This layer introduces the participation of medical knowledge and external intelligence to semantic interpretation and evaluation of the patient's pain/itching. That is, to construct a "standard medical knowledge DIKWP map" and compare it with the current state of the patient. Our system will be connected to a medical knowledge base (provided by the AI Doctor module or a cloud-based database), which covers multi-level knowledge of pain mechanisms, physiology, treatment methods, etc. (K-layer), as well as expert-trained decision-making rules and ethical constraints (W and P layers). When the patient's semantic state is uploaded from the somatic self-sensing layer, the cognitive decision-making unit (CDU) of the ACPU will call the knowledge base for matching and reasoning: judging the pain/Possible causes of pruritus, severity, and recommended interventions. For example, the detection of "high-frequency discharge of L5 nerve segment and severe pain in the patient's subjectivity" combined with the background knowledge of "the third postoperative day" can be interpreted by the system as "neuralgia caused by inflammation of the surgical incision". Combined with the knowledge base, this pain mechanism may involve inflammatory mediators and nerve sensitization, and the recommended intervention is a combination of analgesics + anti-inflammatory drugs. For example, for intractable pruritus, the knowledge base may suggest "consider central pruritus, antihistamine is ineffective, neuromodulation should be tried", etc. The external semantic interpretation layer is equivalent to the role of the doctor, which places the patient's information in the medical semantic space for understanding, and outputs the semantic decision of diagnosis and protocol。 In this process, the DIKWP model ensures the integration of multi-source knowledge: whether it is modern medicine or traditional medical knowledge, it participates in inference through a unified semantic framework. For example, traditional Chinese medicine believes that "liver fire can cause headaches", and modern medicine believes that "trigeminal neuralgia is related to vascular compression", both of which are expressed as corresponding semantic rules in our knowledge base. When it comes to specific pain, AI can refer to modern knowledge and ignore traditional experience. This makes the explanation of the system more comprehensive.

• (3) Interactive consensus control: With the information of the patient's own perception and external AI interpretation, the system enters the third layer - human-computer interaction to reach consensus and implement regulation。 First of all, after the AI gives the initial intervention plan, it needs to interact with the patient (and the human doctor) to confirm it, which is out of safety and ethical considerations, and is also the embodiment of the "patient-centered" nature of active medicine. Specifically, the AI may present the recommendation in natural language or visualization: "Detecting that you have severe postoperative pain, and recommending an increase in the dose of analgesics and the use of neurostimulation devices to assist, do you agree to do so?" Patients can respond to their wishes through a human-machine interface. If the patient finds the pain tolerable and does not want to add the drug, the system needs to adjust the regimen (respecting the patient's purpose is an important part of the P layer). With the participation of human doctors, doctors can also review the AI plan and modify it. This consensus interaction process is equivalent to integrating the AI wisdom decision-making with the patient/doctor's subjective wisdom to ensure that the intervention decision is in line with medical ethics and the ultimate benefit of the patient. Once a consensus is reached, the system moves on to the execution stage: mobilizing the semantic-neural coupling interface to implement precise intervention for the patient. The semantic-neural coupling interface is one of the key technologies we designed, acting as a translator from semantic instructions to neural signals. For example, if the final decision is to "increase the degree of analgesia and focus on relieving pain in the L5 segment", then the interface will adjust the parameters of the implantable analgesic chip accordingly, release pulses of specific intensity and frequency at the corresponding position of L5 to suppress signal transduction, and drive the AI dispensing pump to accurately infuse the appropriate amount of painkiller drugs. The entire implementation process is coordinated by the ACPU's control module (which may correspond to part of the SCFU/CDU), and the various intervention methods work in synergy. After execution, the system continues to monitor pain indicator changes through sensors to move on to the next cycle.

The above three-layer structure corresponds to the architecture of the dual-loop interaction under the DIKWP × DIKWP model: the patient has a set of DIKWP semantic processing processes (sensation-cognition-response in vivo), and the external AI also has a set of DIKWP cognitive cycles (perceiving the patient→ knowledge reasoning→ decision-making purpose). When the two sets of cognitive cycles interact with each other, a closed-loop system of dual cycles is formed. Among them, the patient side is similar to the first cycle (object system), and the AI side is the second cycle (metacognitive system), and the two are constantly exchanging information and adjusting strategies through semantic interfaces. This architecture allows AI to self-monitor, self-reflect, and self-regulate the patient's condition, similar to the ability to have initial self-awareness, which is suitable for pain management. Because the analgesic process needs to constantly adjust the plan according to the effect, it is equivalent to the AI reflecting on the results of its own intervention behavior (for example, if it finds that a plan is not effective, it will adjust itself to avoid using ineffective solutions all the time).

Through the three-layer closed-loop of "somatic semantic self-perception, external semantic interpretation, and interactive consensus", we realize the semantic feedback control of active analgesia: the pain generation → is detected as an abnormal deviation by semantics→ AI understands the semantics and proposes countermeasures→ interacts with people, confirms with people→, implements interventions, reduces pain, → feedback rebalance. Throughout the process, each step is driven at the semantic level, rather than simply signaling. For example, instead of mechanically venting stimulation or administering drugs, AI understands "what kind of pain the patient is having, why it hurts, and what pain-relieving effect is needed", and then purposefully regulates the relevant neural pathways and drug doses. This kind of semantic-driven regulation is more intelligent and personalized than traditional methods, which reflects the advantages of "intelligent self-knowledge" in active medicine. In addition, the system also considers the combination of energy field and information field when intervening: on the one hand, it acts on the body through physical energy means such as electrical stimulation and drugs (energy field regulation), and on the other hand, it ensures that these physical effects meet the requirements of the information layer and the purpose layer (coupling control of the information field to the energy field) through semantic operation. The research of Yucong Duan's team in other fields has shown that the internal cognitive state of AI can be effectively correlated with the external energy/information environment using DIKWP semantic graph and coupling tensor technology. Here, we apply it to the biomedical field to establish the coupling of semantics and neural signals, so that the intervention of machines in pain has both physical effects and semantic guidance, so as to achieve more refined, effective, safe and controllable therapeutic effects.

Project Objectives

Based on the above-mentioned theoretical framework and mechanism design, the project formulates the following overall goals and specific objectives:

• The overall goal is to elucidate the semantic mechanism of somatic receptive diseases (pain, itching, etc.), develop a set of active analgesic/antipruritic closed-loop system based on artificial consciousness semantic engine, integrate domestic analgesic chips, AI dispensing and semantic neural interface technologies, realize intelligent perception, semantic understanding and precise intervention of pain/itching, and provide a new generation of efficient, safe and personalized analgesic solutions for clinical practice.

• Objective 1 (Mechanism Analysis): To establish a semantic model of pain/itch based on the DIKWP model, and to reveal the mechanism of pain and itch in the deviation between the information layer and the purpose layer; To study the key neural circuits of pain sensing and cognition, and the correspondence between them and the semantic level, and to form a full-chain cognitive map of pain/itching from physiology to semantics.

• Target 2 (Models and Algorithms): Design and implement an application architecture for **the Artificial Consciousness Processing Unit (**ACPU) in the field of pain intervention. A semantic-driven analgesic decision-making algorithm is developed, including pain semantic recognition, pain-causing pattern diagnosis, intervention strategy planning, human-machine consensus decision-making and other modules, so that the system has adaptive learning and reasoning capabilities.

• Specific Goal 3 (Core Technology R&D): Develop a prototype of a domestic implantable analgesic chip, which can monitor and regulate pain-related nerve signals in real time; Develop a prototype of an AI intelligent dispensing system to intelligently adjust the dosage and combination based on pain assessment; A virtual neuronal semantic simulation platform was built to simulate the neurosemantic process of pain generation and intervention, and to verify the algorithm and hardware scheme. A semantic-neural coupling interface device was developed to realize the closed-loop connection between ACPU semantic instructions and bioelectrical stimulation/drug delivery devices.

• Target 4 (System Integration and Validation): Integrate the above components to build a closed-loop prototype system for active analgesia and complete validation in animal models and preliminary human trials. To evaluate the efficacy, safety, and stability of the system in typical pain (e.g., neuralgia, inflammatory pain, cancer pain) and pruritus. Continuously optimize the performance of the system, including the improvement of analgesic effect, intervention response delay, human-computer interaction experience and other indicators.

• Specific Objective 5 (Achievement Output and Transformation): At the completion of the project, a complete technical intellectual property portfolio (patents, software copyrights, etc.) will be formed, high-level academic papers will be published to explain the model and test results, and preliminary industry standards/specifications for intelligent analgesic systems will be formulated. Promote the industrialization of project results, cooperate with medical device manufacturers, start clinical trials and approval processes, and strive to achieve product application within 3-5 years after the end of the project to serve patients with chronic pain and itching.

Research content and task breakdown

Focusing on the above objectives, the research content of this project is subdivided into several task modules, each of which corresponds to a set of specific research tasks and technical research points. The main task breakdowns are listed below in logical order:

1. Semantic analysis of pain/pruritus mechanisms

• Task 1.1: Physiological signal acquisition and feature extraction. Multimodal pain/pruritus related data were collected, including neuroelectrical activity (e.g., peripheral nerve discharge, EEG/brain function imaging), autonomic response (heart rate, blood pressure, electrodermal imaging, etc.), biochemical markers (inflammatory mediators, stress hormones), and patient subjective reports. Establish standardized data sets. A signal processing algorithm was developed to extract key features from the raw data that can characterize the degree and nature of pain, such as spectral components, discharge frequency patterns, etc., corresponding to the data layer and information layer of the DIKWP model.

• Task 1.2: Pain/Itch Semantic Model Building. Based on the data of task 1.1 and combined with medical knowledge and literature, the semantic elements of pain/pruritus were refined, including the sensory dimension (intensity, location, nature), emotional dimension (degree of unhappiness, emotional response), and cognitive dimension (controllability, expected harm). These elements are mapped to the semantic nodes of each layer of DIKWP to form a formal model. For example, ontology or ontology can be used to describe the correlation between the definition of "burning pain due to nerve injury" at the knowledge level and the purpose layer of "avoiding re-injury of the limb". Output pain/pruritus semantic ontology and cognitive graphs.

• Task 1.3: Information-Purpose Deviation Mechanism Study. The model of task 1.2 was used to analyze the differences in the causes and deviation characteristics of different types of pain/pruritus. For example, neuralgia is an abnormal deviation caused by oversensitivity to information, and cancer pain is a conflict between the signal of continuous tissue damage and the desire to live. This paper introduces Professor Yucong Duan's consciousness bug theory to explore the relationship between pain perception and prediction error (bias) signals, and reveals how pain arouses conscious attention as a deviation signal. This task will generate new understanding of the mechanism of pain/pruritus and inform subsequent intervention strategies.

 Artificial conscious analgesia models and algorithms

• Task 2.1: ACPU architecture design is customized for analgesia. Referring to the ACPU white paper architecture and ACPU applications in other fields, the ACPU module division and data flow suitable for pain intervention are designed. Specifically, it includes: the design of the Subliminal Computing Unit (SCU) to determine which sensory inputs (neural signals, sensing data) the SCU acquires and what parallel algorithms are used to extract pain semantic features; Design of Conscious Decision Unit (CDU) – to determine which inference algorithms (e.g., knowledge graph-based reasoning, reinforcement learning decision-making) are built into the CDU for pain diagnosis and analgesic program planning; The design of the Semantic Fusion Unit (SCFU) – to determine how the SCU interacts with the CDU, especially in the case of acute and intense pain, the SCU can directly trigger a rapid reflex intervention, and in the general case, the CDU will make a co-ordinated decision, and the results of the two will be output through the SCFU fusion. Draw an architecture diagram to indicate the functions and interfaces of each module.

• Task 2.2: Pain semantic understanding and diagnostic algorithms. A semantic parsing algorithm was developed to run on the ACPU, enabling the system to automatically identify the current pain/itch semantic state. Includes: feature-based pain estimation (mapping sensing features to pain intensity scores); Pain classification (distinguish between semantic categories such as neuropathic, inflammatory, ischemic pain, etc.); Inference of the cause of pain (inference of possible causes, such as "nerve compression" or "tissue hypoxia" in combination with the knowledge base). A combination of machine learning and knowledge reasoning is adopted: for example, a deep learning model is trained to classify pain, and then the model output is interpreted with knowledge graph rules to ensure interpretability. Outputs an instance of the DIKWP semantic graph of the current patient, including the association of symptoms-> diagnosis.

• Task 2.3: Semantic-driven analgesic decision algorithms. Based on the semantic state of the patient obtained in task 2.2, a decision-making algorithm was developed to generate an intervention plan. This algorithm takes the difference between the patient's DIKWP profile and the standard medical DIKWP profile as input, with the goal of minimizing the difference (i.e., eliminating pain deviations). Planning search or reinforcement learning methods can be used to search for the optimal path in the semantic space. The combination of multiple interventions (medications, electrical stimulation, psychological guidance, etc.) and their expected effects at the semantic level need to be considered. Purpose-level constraints (e.g., patients who want to be awake and do not want drugs with large side effects) are introduced to screen the protocol, so that the decision-making is in line with the patient's wishes and medical ethics. The final output includes: intervention type, dose/intensity, target of action, duration and other parameters (with semantic annotations, such as "XX drug Y mg is administered, expected to reduce the inflammatory response and relieve pain score 2 points").

• Task 2.4: Human-Computer Semantic Interaction and Learning. Design human-computer interfaces so that AI decisions can be understood and fed back by patients and doctors. This includes presenting decision-making options in natural language and visualization for patient confirmation, as well as receiving verbal or keystroke input from the patient (e.g., feedback on pain relief). At the same time, a learning mechanism is established: the model and decision-making strategy are adjusted according to the actual changes in pain after implementation (closed-loop learning). Reinforcement learning or Bayesian adaptive methods are used to update the internal parameters of the ACPU to make the system "smarter" the more it is used. Pay special attention to the correction of the purpose deviation problem: if there is a discrepancy between the AI decision and the patient's expectation many times, the cause should be analyzed and the purpose understanding module of the AI should be corrected. The goal is to continuously optimize value alignment to ensure that AI always serves the real needs of patients.

 R&D of key hardware and devices

• Task 3.1: Development of domestic implantable analgesic chips. Based on the existing research and development results of neurostimulator in China, an implantable microchip was designed to realize the dual functions of pain signal monitoring and electrical stimulation (similar to INS2 chip). The main modules of the chip include: a miniature neural signal harvester (multi-channel microelectrode that collects the discharge signal of the pain fibers of the spinal cord or peripheral nerves); Signal processing and communication unit (initial filtering, amplification, and wireless transmission of data to an external ACPU or reception of external commands); Low-frequency/high-frequency electrical stimulation generators (when receiving analgesic instructions, release specific parameters of electrical stimulation to the target nerve structure, such as blocking nerve conduction with high-frequency pulses); Power & Packaging (Seamless Wireless Rechargeable Battery, Biocompatible Package). Focus on the miniaturization and safety of the chip: the target size is < coin, which is convenient for implantation close to the spinal nerve; Ensure that the stimulation precision is adjustable (0-10V pulse, the intensity can be fine-tuned) and that the tissue is not damaged. The R&D process includes simulation design, FPGA validation, tape-out manufacturing, and in vitro testing.

• Task 3.2: AI intelligent dispensing system development. A computer-controlled analgesic drug dispensing device (intelligent infusion pump) was developed, which combined with AI algorithm to accurately control drug delivery. In terms of hardware, it includes a high-precision multi-channel syringe pump that can store a variety of analgesic-related drugs (such as opioids, local anesthetics, anti-inflammatory drugs, etc.); The sensing module monitors the infusion pressure and the remaining amount of the drug; The wireless communication interface accepts the ACPU command. In terms of software, we have developed a medication decision-making engine that adjusts the dose and infusion speed of each drug according to the AI decision-making plan, and responds to changes in the patient's status in real time (e.g., automatic bolus injection of analgesics based on sudden increases in pain). Introduce safety policies to prevent overdose (built-in dose cap, automatic discontinuation and alarm for abnormal conditions). The system is similar to the clinical PCA (Patient Controlled Analgesia Pump) but smarter: with the assistance of AI, it is able to objectively monitor pain and automatically adjust the dose. We will work with the Department of Clinical Anesthesiology to test the reliability and effectiveness of the AI dispensing system in a simulated ward environment.

• Task 3.3: Virtual Neuron Semantic Simulation Platform. Development of a software simulation platform for simulating pain generation and intervention mechanisms in a computer environment. The platform consists of two parts of the model: one is neural network simulation, which builds a multi-layer neuronal network model based on the known pain conduction pathway (peripheral-spinal cord-brain) to reproduce the transmission and amplification process of pain signals (such as simulating central sensitization involving C fibers and microglia); The second is semantic cognitive simulation, which builds a high-level agent based on the DIKWP model, corresponding to a virtual "artificial patient", with parameters such as pain threshold and emotion, and can produce "subjective responses" to input from neural networks. This virtual patient is embedded with the ACPU algorithm, allowing them to experience pain and be affected by the intervention just like a real person. Using this platform, we can verify the logic of our algorithm without turning on human/animal experiments: for example, by inputting a continuous pain stimulus, we can observe whether the ACPU can correctly diagnose and output analgesic instructions, and whether the discharge of the virtual neural network is reduced. You can also test the effect of different parameter settings to provide a basis for actual system parameter tuning. The platform visualizes neural activity and semantic state over time, providing researchers with visual feedback.

• Task 3.4: Semantic-neural coupling interface. This is the bridge that connects "brain semantic decision-making" and "peripheral neuromodulation". In hardware, a brain-computer interface or signal converter is included to translate the high-level semantic instructions output by the ACPU into specific low-level control signals and distribute them to the executors in tasks 3.1 and 3.2. For example, when the ACPU decides that "pain in the L5 area needs to be reduced at level 3", the interface will decode it as: "Send stimulation command to the implanted chip (channel x, frequency y, for z seconds) + command syringe pump to administer w mg", and execute it synchronously. In turn, the interface aggregates the state of the actuator and the patient's physiological feedback, converting the underlying data into semantic information and sending it back to the ACPU (e.g., "Stimulation completed, patient's heart rate drops, pain relief is possible"). Technically, there is a need to develop reliable communication protocols (to ensure low-latency transmission between multimodal devices inside and outside the human body), standard semantic instruction formats (semantic tags that define various intervention actions), and exception handling mechanisms (e.g., response when intervention fails). This interface technology is also of general significance and can be generalized to other applications of semantic control biological systems.

 System integration and validation

• Task 4.1: Active analgesia system integration. Integrate all of the aforementioned components – ACPU algorithm platform, analgesic chip, dispensing system, semantic interface, etc. – into a unified working system. Set up a laboratory testbed, including an industrial computer/embedded board with ACPU software (or burn the ACPU algorithm on an FPGA/ASIC to simulate a dedicated chip), and work in tandem with the implantable analgesic chip and the intelligent infusion pump through wired/wireless connection. Ensure that the communication protocol of each module is consistent, the data format is matched, and the whole system is smooth from sensing input to intervention output. Optimize system latency so that the total delay from detecting escalated pain to performing the intervention is within seconds to ensure timeliness.

• Task 4.2: Animal Experiment Validation. Under strict ethical approval, appropriate animal models (e.g., rat chronic sciatica model, monkey pruritus model, etc.) were selected for systematic testing. First, the safety of the implanted chip and the range of stimulation parameters were tested on anesthetized animals. The closed-loop analgesic effect was then validated on free-roaming pain model animals: for example, the control group used traditional constant stimulation or constant administration, and our experimental group used AI closed-loop adjustment to compare pain behavior scores, neuronal firing inhibition, and other indicators. It is expected to prove that the AI semantic system can more effectively reduce painful behaviors (such as shortening the time of licking the feet, increasing the amount of activity), and the dosage is more economical and the side effects are fewer. For the pruritus model, to assess the effect of AI intervention in reducing scratching behavior. Physiological data were collected to verify the accuracy of the system's identification of different types of pain and the rationality of decision-making. If problems are found, improve the algorithm or parameters in time.

• Task 4.3: Preliminary clinical trial (exploratory) Under the premise of ensuring safety, it is planned to select a small number of patients with severe chronic pain (such as cancer pain and amputated neuralgia) for exploratory clinical experience. After obtaining the patient's informed consent, we install our system (implanted chip or patch, electrical stimulation device combined with drug delivery pump, ACPU decision module running on a portable device). The effect of systematic automatic analgesia over a period of time was observed, including changes in pain self-rating, changes in the amount of analgesic drugs, and improvement in daily function. It also focuses on collecting feedback from patients and physicians: for example, are the system recommendations reasonable? Is the human-computer interaction friendly? Further refinement of the system based on feedback. At this stage, we also evaluate the robustness of the system (failure rate over long periods of operation) and safety (e.g., preventing over-stimulation by mistake). This will lay the groundwork for the next step of large-scale clinical testing.

 Outputs and translations of results

• Task 5.1: Collation and publication of theoretical results. Summarize the theoretical innovations of the project in the mechanism of pain/itching and artificial consciousness model, and write high-level academic papers and monographs. For example, a series of papers have been submitted to internationally renowned journals and conferences in the direction of pain semantic model, active medicine DIKWP framework, and artificial consciousness analgesia algorithm to enhance academic influence. At the same time, the key theories developed in the course of the research (such as the "semantic deviation hypothesis of pain") are considered for patent application for protection.

• Task 5.2: Patent and Intellectual Property Layout: At least 10 domestic and foreign invention patents have been applied for around the core technology of this project, including but not limited to: the application method of DIKWP in pain management, the artificial consciousness analgesia decision-making system, the implantable semantic analgesia chip circuit, the communication protocol to realize the semantic-neural interface, the AI drug delivery control method, etc. Obtain access to PCT international applications for some patents in order to protect the global market. Build a complete IP portfolio to provide technical barriers for future industrialization. (Professor Yucong Duan's DIKWP technology system has 114 patents, and we will also learn from his experience to improve the patent layout).

• Task 5.3: Standards and norm development. Combined with the results of the project, participate in the formulation of industry standards or guidelines for intelligent analgesic systems. For example, in conjunction with the Medical Device Standards Committee, it put forward recommendations such as safety specifications, data interface standards, and evaluation indicators for implantable AI analgesic devices. Promote the inclusion of the concept of "active analgesia" in the diagnosis and treatment guidelines for chronic pain, and improve clinical acceptance.

• Task 5.4: Industrial Transformation and Cooperation. Actively cooperate with medical device companies and pharmaceutical companies to explore the path of project productization. It is planned to set up an entrepreneurial team or set up a joint laboratory with existing enterprises to accelerate the transformation of prototypes into products. Conduct market research to evaluate the capacity and segmentation needs of the chronic pain management market, and provide a basis for product positioning and functional improvement. Seek investment and government support to complete clinical trials, regulatory approvals, and more required for product registration. The goal is to launch a commercial intelligent analgesic system, including implanted chips and supporting AI equipment, within 3-5 years after the end of the project, and take the lead in pilot application in several pain diagnosis and treatment centers in China, and gradually promote it. In the long run, it can be expanded to a series of active medical products in the fields of itching, rehabilitation, and mental stress, forming a new industrial direction of "artificial consciousness medicine".

The above tasks will be carried out in parallel by the research group according to the plan, and dynamically adjusted according to the results at each stage. The tasks are closely related to each other, forming a complete chain from basic research to application implementation.

Technical route and implementation plan

In order to achieve the above research content and objectives, the project has developed a systematic technical route. The overall technical solution is shown in the figure (a project technical roadmap can be attached here): we take the DIKWP*DIKWP dual-cycle architecture as the framework, and closely integrate the three levels of biological signal processing, artificial intelligence semantic decision-making, and biofeedback execution, and gradually realize the closed-loop system in stages.

1. Overall architecture: The overall architecture of the project is divided into three layers, from bottom to top: data perception layer, semantic decision-making layer and execution feedback layer。 The data perception layer consists of various biosensors and implanted chips, which collect multimodal information of the patient's body and perform preliminary processing (corresponding to the D/I layer and self-sensing layer of the DIKWP model). The semantic decision-making layer is composed of the Artificial Consciousness Engine (ACPU), which combines with the external knowledge base to carry out high-level semantic understanding and intervention decision-making on the perceptual data (corresponding to the K/W/P layer and the external interpretation layer). The executive feedback layer includes effectors such as electrical stimulators and smart drug pumps, which intervene on patients under semantic instructions and feed back the results through sensors (corresponding to the interactive consensus layer). Layer 3 is connected by high-speed communication and a unified data/semantic interface to form a closed loop.

2. Key technical path: The signals obtained by the data perception layer are first extracted from the pain characteristics (such as signal amplitude and frequency, which are converted into indicators of pain intensity and type) through parallel calculation of the SCU module. These primary semantic features are passed to the CDU module, which invokes the knowledge graph for inference (such as the aforementioned diagnosis and scenario planning algorithms). After the CDU derives a decision (including intervention semantics, such as pain reduction X%, use of Y means), it is handed over to the executive layer for concretization through the SCFU module. Semantic-neural interface parsing decisions at the execution layer: parameterization of the implanted chip on the one hand (e.g., adjusting the stimulus intensity frequency) and control of the drug delivery pump on the other hand (e.g., adjusting the dose), and possibly signaling the patient (e.g., APP notification). During the execution process, the data perception layer continuously monitors the effect and feeds back new data to the SCU to form a closed loop. Throughout the pathway, we use hierarchical control: minor pain may be mainly adapted by the SCU (e.g., automatically fine-tuning the stimulation intensity without reporting to the CDU); Severe or complex pain is escalated to the global management of the CDU, which determines the cross-modal approach as a whole. This layered control ensures that timeliness is as important as optimizing the overall effect.

3. Phased advancement: The technical route is implemented according to the strategy of simulation first and then physical object, local first and then integration. In the initial stage, the algorithm was verified by a virtual simulation platform, and the closed-loop was run in a pure software environment (Phase 1). In the medium term, the hardware unit is developed and gradually connected to the closed loop: the analgesic chip and the dispensing pump are first connected separately to test their respective controlled effects, and then connected in parallel to form a complete system test (Phase 2). Later validation in a real biological environment: fine-tuning animal experiments and finally applying to human experiments (Phase 3). Each stage is connected and promoted, and the hardware parameters are designed on the basis of simulation results, and the clinical protocol is optimized on the basis of animal results to reduce the risk of R&D.

4. Safety & Redundancy Design: System safety is explicitly considered in the route. All automatic decision-making has a human-computer interaction confirmation mechanism, and a one-key emergency stop function prevents malfunction. Dual-logic verification: Each key decision of the AI is checked by independent rules (such as whether the drug dose is exceeded), and only when it is passed, it is executed to avoid black-box errors. Encryption and handshake mechanisms are used in communication to prevent external interference. The implanted chip is designed with fail-safe mode: once the AI command is lost, it can be reduced to the regular SCS mode to maintain the basic analgesia. There is also redundancy in the data layer and decision-making layer: multi-mode acquisition and verification of important physiological signals, and multiple model voting of decision-making algorithms to ensure reliability.

5. Project path: In the implementation of the project, the parallel team collaboration mode will be adopted. The biomedical team is responsible for sensors, chips, and animal testing; The AI team is responsible for ACPU software and algorithm development; The system integration team is responsible for interfaces, communications, embedded implementations, and more. Through multidisciplinary cooperation, the system is gradually built and improved according to a unified roadmap. In terms of project management, milestone control is adopted, and the review is carried out at the end of each stage, and the quality is checked before moving to the next stage to ensure that the effectiveness and safety of the final system meet medical requirements.

In summary, the technical roadmap strives to modularize and hierarchical complex cross-domain problems to reduce the difficulty and ensure the implementation of the final results. From semantic theory innovation to engineering implementation, there is a clear route guarantee for each step. This route also embodies the guiding ideology of comprehensive theory + engineering framework + industry landing: it not only gives full play to the advantages of high-level semantic intelligence, but also pays attention to the feasibility of the underlying hardware, and finally produces an applicable system instead of staying in theory. Next, we detail the project's phase plan and milestones.

Phase goals and timelines

The project is planned to be implemented in three phases over a five-year period, each with clear milestones and target outcomes:

• Year 1-2 (Principle Model and Key Technology Verification):
  Milestone 1: Completion of the construction of the pain/itch semantic model and the initial implementation of the artificial consciousness analgesia algorithm. Within the first 18 months, complete most of the work on Tasks 1 and 2. Specific objectives include: publishing at least one theoretical paper to elaborate on the semantic model of DIKWP pain and the information-purpose deviation mechanism; Build a basic virtual simulation platform, run through the closed-loop logic of the ACPU algorithm, and verify the effectiveness of the algorithm in the software environment (for example, the pain reduction index in the simulation meets the expectations); Apply for 2-3 basic invention patents (such as pain semantic analysis methods, artificial consciousness decision-making methods). Evaluation indicators: The accuracy rate of the pain recognition algorithm reaches more than 80%, and the consistency rate between the scheme given by semantic decision-making and the expert plan > 70%. The stage risk lies in model complexity control and algorithm convergence, which needs to be solved by constantly testing and adjusting parameters.

• Phase 2 (Year 3-4, System Development and Integration Testing):
  Milestone 2: Completion of small-scale verification of core hardware development and system integration. At 36 months, the goals include: developing a prototype of an implantable analgesic chip and verifying stimulation accuracy (error <5%) and monitoring sensitivity through bench testing; Developed a prototype of AI dispensing and tested the dose control error of <10% in a simulated patient environment; A closed-loop system was initially integrated to verify that the analgesic effect was at least 30% higher than that of the control in small animals (such as rat pain models). He has published 1-2 engineering application papers and applied for more than 5 patents (chips, interfaces, and systems). By the end of Year 4, Milestone 3: Systematic trials on large animals (e.g., pigs or monkeys) were completed to prove that implanted devices are safe and feasible, and that AI decisions are effective in 90% of scenarios. Evaluation index: The real-time performance of the system met the requirements (the total delay of perception-decision-execution<1 second), and the animal behavioral pain score was significantly improved (P<0.05). The key difficulties in the stage are the multi-module collaboration and the uncertainty of animal testing, which require repeated debugging.

• Year 5:
  Milestone 4: Completion of initial clinical trials and project closure. Within the fifth year, with regulatory clearance, the system was trialed for a small number of volunteer patients (e.g., 2-3 patients with severe chronic pain). Objective: The accuracy of the verification system in identifying human pain was >85%, and the subjective report of analgesic effect was improved compared with the previous protocol. No serious adverse events occurred. Collect clinical data to optimize human-computer interaction design. At the end of the project, submit the project summary report and the next clinical trial application materials. Complete all expected paper patent indicators, and form technical specifications and business plans that can be promoted. Evaluation indicators: the patient's pain VAS score decreased by >=50%, and the quality of life score was improved; Physician and patient satisfaction is high. The difficulties in the stage are individual clinical differences and safety supervision, which need to be closely coordinated with hospitals and regulatory authorities.

Convergence between phases: The first phase provides algorithms and models for the second phase to materialize, and the results of the second phase lay the foundation for the third phase of clinical trials. We set up regular checkpoints, such as internal review progress every six months, and timely find lags or problems for resource adjustment. Leave a buffer on the timeline to deal with unforeseen challenges to ensure that key milestones are completed on time. The whole plan strives to make steady progress and accelerate the R&D process while ensuring safety and reliability, so that innovation can benefit patients as soon as possible.

Expected outcomes and conversion paths

The successful completion of this project will bring significant scientific research results and application value. Key expected accomplishments include:

• Scientific innovation: This project will propose a semantic space model of pain/itch for the first time, combining biomedical and artificial intelligence semantic theories, and is expected to lead a new pain research paradigm in the world. The theoretical papers are expected to be published in top journals in the fields of artificial intelligence, cognitive neuroscience, and pain medicine. In particular, the application of the DIKWP active medicine framework in pain management will enrich the practical examples of artificial consciousness theory and promote the evolution of artificial intelligence from perceptual intelligence to cognitive intelligence. The project will also form an interdisciplinary talent training effect, cultivating interdisciplinary research talents who understand both AI and medicine.

• Core technology and patents: Focusing on the artificial conscious analgesic system, we will build a comprehensive portfolio of independent intellectual property rights. It is expected to produce no less than 10 invention patents (including international patents), covering all key links from algorithms to devices, such as: "pain recognition method based on DIKWP model", "analgesic decision-making system driven by artificial consciousness and its implementation", "implantable pain relief chip device", "method and device for semantic and neural signal conversion", etc. These patents will provide support for China to establish a leading position in technology in the fields of intelligent medical care and artificial intelligence chips. In addition, intellectual property rights such as software copyrights and new methods for medical use will also be deployed simultaneously.

• Prototype and demonstration system: The goal of this project is to develop a prototype of an active analgesic system. Expected outcomes include: a prototype of an implantable analgesic chip (stable operation in an experimental environment, with monitoring + stimulation function); A set of AI analgesic decision-making software (running in real time on an industrial PC or embedded platform, which can be connected to medical devices); An integrated closed-loop analgesic demonstration system (already proven effective in animal trials) These prototype systems will serve as the basis for subsequent productization, which can be displayed in the results exhibition or clinical pilot, intuitively reflect the project innovation, and attract clinical and industrial interest.

• Clinical benefits: If the system is put into clinical practice, it will greatly improve the management level of chronic pain and itching. Personalized pain control is expected: the intensity of analgesia is adjusted in real time according to the patient's status, so that the pain is tolerated and does not affect life; Reduce drug side effects: reduce the dosage of opioids and other drugs through multimodal analgesia and precision drug delivery, and reduce the risk of addiction and adverse reactions; Improve treatment adherence: Patients participate in human-machine decision-making, giving a sense of control and improving confidence in treatment. For the healthcare system, it can reduce the rate of repeat visits and the length of hospital stay, and reduce the cost of medical care. Especially in the field of cancer pain, intelligent analgesic systems are expected to become an important tool for palliative care and benefit the majority of patients.

• Economic and industrial transformation: Chronic pain is a huge market, with tens of millions of chronic pain patients in China alone, and the market is in urgent need of effective management solutions. The results of this project can form a series of product lines: such as intelligent analgesic implants, AI pain management software, home version of pain monitoring equipment, etc. It is conservatively estimated that the related products can cover hundreds of hospitals and pain diagnosis and treatment centers within 5 years after they are launched, forming an output value of billions of yuan. The project team or cooperative enterprises can expand the domestic and foreign markets with patented technology and occupy a leading position in the field of intelligent medical segmentation. In terms of industry drive, the project will promote the development and integration of domestic chips, medical AI, biosensing and other industries, which is in line with the national strategic direction of "new generation artificial intelligence" and "digital health" industries.

• Social impact: The improvement of pain and itch management will significantly improve the quality of life of patients, which has good social benefits. The results of the project are in line with the requirements of the "Healthy China 2030" plan for chronic disease prevention and treatment and health science and technology innovation. Through popular science publicity and training for clinicians, our concept of active analgesia will gradually be accepted by the society, change the traditional concept of the public that "can only tolerate or abuse painkillers" for pain, and advocate a more scientific and humanized management method. In addition, this project demonstrates the innovation ability of China's scientific research in the intersection of artificial intelligence and biomedicine, and the successful transformation will boost the confidence of academia and industry in original technology, which is of exemplary significance.

Translation pathway: In order to ensure the smooth progress of the above results to the clinic and market, we have developed a clear translation plan. After the completion of the project, the data of all aspects will be summarized, the product design will be improved, and the medical device registration application will be initiated as soon as possible (the implantable analgesic system is a Class III medical device and requires regulatory approval). It is planned to cooperate with large tertiary hospitals to carry out larger-scale clinical trials to obtain sufficient evidence of safety and efficacy. And cooperate with medical device companies to carry out small batch trial production to optimize the process and reduce costs. Make full use of national and local policies for the transformation of scientific and technological achievements, and apply for special funds or industrial fund support. In terms of market promotion, we first set benchmark cases in high-demand areas such as tumor pain and nerve injury pain, and then gradually expand to general chronic pain clinics and even community families. In terms of operation mode, the product can adopt a combination of equipment + service, and while the hospital purchases equipment, we provide AI cloud services to continuously upgrade the algorithm to maintain the leading efficacy. In the long run, medical insurance payment and commercial insurance support can also be explored, and intelligent analgesia can be incorporated into pain management norms to expand patient accessibility.

In short, this project will be based on solid theory and mature engineering implementation, and produce a series of high-level, in-depth and warm-hearted results, realize the leap from papers to products, laboratories to clinics, and promote the in-depth integration and development of pain medicine and artificial intelligence. We are confident that through the implementation of this project, we will bring good news to the majority of pain and itching patients and add new growth points to China's intelligent medical industry. This is not only a scientific research topic, but also an innovative practice that benefits people's livelihood and leads the future.