Intelligent moxibustion application scheme generation system based on acupoint thermosensitive moxibustion gas yield quantification

Abstract

The invention provides a moxibustion application scheme intelligent generation system based on acupoint thermosensitive moxibustion gas obtaining quantification, and the system comprises a user interaction module which is used for multi-terminal interaction among doctors, patients and family members, and provides image-text, voice and video communication functions; the AI diagnosis and treatment module analyzes symptoms input by a user through a natural language processing technology, generates a preliminary scheme in combination with a thermosensitive moxibustion diagnosis and treatment database, and automatically switches to a doctor end when the matching degree is lower than a threshold value; and the visual guidance module calls a terminal camera to realize two working modes: a two-way video guidance channel is established when a doctor is connected, and the deviation of a moxibustion application part is monitored through a skeleton recognition algorithm when AI autonomously operates, and the like. The AI diagnosis and treatment module and the symptom-acupoint mapping sub-module are combined with the natural language processing and decision tree algorithm, so that the symptom of the patient can be quickly analyzed, the personalized acupoint scheme is recommended, and compared with the traditional mode depending on the experience of a doctor, the accuracy and efficiency of diagnosis and treatment are remarkably improved, and meanwhile, the misdiagnosis risk is reduced.

Description

Technical Field

[0001] The present invention relates to the field of medical assistance technology, and in particular to an intelligent moxibustion scheme generation system based on quantification of acupoint thermal-sensitive moxibustion qi. Background Art

[0002] Thermal moxibustion is a traditional Chinese medicine method that stimulates acupuncture points through moxibustion to achieve the effects of warming the meridians and dispelling cold, strengthening the body and eliminating pathogenic factors. It is widely used in the treatment of chronic diseases and daily health care. However, traditional thermal moxibustion is complicated to operate, and ordinary users, especially the elderly, find it difficult to master the correct operation method. There is a risk of poor effect or burns due to improper operation. At the same time, traditional thermal moxibustion relies on the experience of doctors and lacks standardized diagnosis and treatment procedures, resulting in unstable efficacy; communication between patients and doctors mainly relies on offline consultations, lacks real-time interaction and remote guidance; the feeling of qi during the moxibustion process is difficult to quantify, and it is impossible to form a systematic feedback mechanism to optimize the treatment plan; there is a lack of real-time monitoring and intelligent guidance of the moxibustion process, making it difficult to ensure the accuracy and safety of moxibustion.

[0003] Therefore, in order to solve the above problems, an intelligent moxibustion plan generation system based on the quantification of acupoint thermal-sensitive moxibustion qi is developed. Summary of the Invention

[0004] In order to overcome the shortcomings of the prior art, the present invention provides an intelligent moxibustion plan generation system based on the quantification of Qi obtained by acupoint thermosensitive moxibustion.

[0005] The technical solution of the present invention is: an intelligent moxibustion scheme generation system based on acupoint thermal-sensitive moxibustion and quantification of Qi, comprising:

[0006] A user interaction module, which is used for multi-terminal interaction between doctors, patients and their families, and provides text, voice and video communication functions;

[0007] An AI diagnosis and treatment module, which uses natural language processing technology to analyze user input symptoms, generates a preliminary plan based on the thermosensitive moxibustion diagnosis and treatment database, and automatically transfers the patient to the doctor's end when the matching degree falls below a threshold;

[0008] A visual guidance module that uses the terminal camera to implement two working modes: establishing a two-way video guidance channel when the doctor is connected, and monitoring the deviation of the moxibustion site through a bone recognition algorithm when the AI is running autonomously;

[0009] A feedback analysis module collects voice feedback from patients during moxibustion and generates a Deqi scale containing ten-dimensional indicators such as skin flushing, heat transfer path, and gastrointestinal reaction through semantic analysis;

[0010] The scheme optimization module uses the Bayesian optimization algorithm to perform multi-objective evaluation on the Qi meter data, dynamically updates the moxibustion parameter combination to form an iterative scheme library, and outputs the optimal diagnosis and treatment plan.

[0011] As a preferred embodiment of the present invention, the AI diagnosis and treatment module includes:

[0012] Symptom-Acupoint Mapping Submodule: Build a decision tree model containing clinical cases and associate the symptom-acupoint correspondence. The decision tree splitting criterion is: D is the data set, p i is the probability of category i, and the splitting condition is: Among them, D j For the sub-dataset, based on the symptom input, a list of recommended acupoints is output;

[0013] Risk warning submodule: Through comparison with the contraindication database, warnings are triggered for high-risk moxibustion areas such as the lumbar sacral area of pregnant women and the feet of diabetic patients.

[0014] As a preferred embodiment of the present invention, the visual guidance module includes:

[0015] Infrared thermal imaging unit: detects the temperature gradient distribution in the moxibustion area through a mobile phone connected to an external thermal imager;

[0016] Posture correction unit: Based on the OpenPose algorithm, it identifies the angle deviation of moxibustion and detects the key points of the human body. The coordinates of the key points are:

[0017] Calculation of moxibustion angle:

[0018] Among them, (x1, y1) and (x2, y2) are the coordinates of the key points. When the angle deviation exceeds 15°, a vibration or voice prompt is triggered, and when the offset exceeds 15°, a vibration prompt is triggered.

[0019] As a preferred embodiment of the present invention, the feedback analysis module implements:

[0020] Multimodal input interface: supports converting the voice description of "heat sensation spreading to the waist" into structured data. The multimodal input interface supports multiple input methods such as voice, text, and images, and converts multimodal data into a structured gas volume scale;

[0021] Dynamic scale generation: A three-dimensional thermal sensation model is established based on the indicators of "thermal penetration depth" and "heat expansion range". The Qi scoring model is: Among them, w i is the weight coefficient, f i It is the frequency of Qi indicator.

[0022] As a preferred embodiment of the present invention, the solution optimization module includes:

[0023] Multi-objective optimization engine: Pareto frontier calculation to balance moxibustion duration and onset speed;

[0024] Case matching algorithm: Use cosine similarity to retrieve historical successful cases and generate 3 recommended solutions.

[0025] As a preferred embodiment of the present invention, the gas meter data collection includes:

[0026] Biosensor group: integrated skin temperature and microcirculation blood flow velocity detection module;

[0027] Image analysis unit: Analyzes the skin flushing diffusion area using the HSV color space.

[0028] As a preferred embodiment of the present invention, the solution optimization module adopts:

[0029] Deep reinforcement learning model: Using the Qi indicator as the reward function, the moxibustion parameter optimization strategy network is trained;

[0030] Knowledge graph engine: Construct a triple relationship network of symptoms-acupoints-efficacy.

[0031] As a preferred embodiment of the present invention, it also includes a wearable moxibustion device that provides real-time feedback on moxibustion pressure and temperature parameters.

[0032] By adopting the above technical solution, the present invention has the following advantages:

[0033] 1. This invention uses an AI diagnosis and treatment module and a symptom-acupoint mapping submodule, combined with natural language processing and decision tree algorithms, to quickly analyze patient symptoms and recommend personalized acupoint plans. Compared with the traditional approach that relies on physician experience, it significantly improves the accuracy and efficiency of diagnosis and treatment, while reducing the risk of misdiagnosis.

[0034] 2. Through the visual guidance module and feedback analysis module, the system of the present invention can monitor the moxibustion site and the Qi feeling in real time, and dynamically generate a Qi meter. Combined with the multi-objective optimization engine and deep reinforcement learning model, the system can continuously optimize the moxibustion plan to maximize the therapeutic effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a structural schematic diagram of the present invention.

[0036] Figure 2 This is a structural diagram of the AI diagnosis and treatment module of the present invention.

[0037] Figure 3 Schematic diagram of the structure of the visual guidance module of the present invention.

[0038] Figure 4This is a schematic diagram of the thermosensitive moxibustion qi moxibustion sensation scale A of the present invention.

[0039] Figure 5 This is a schematic diagram of the thermosensitive moxibustion qi moxibustion sensation scale B of the present invention.

[0040] Figure 6 This is a flowchart of the steps for intelligent diagnosis and solution generation of the present invention.

[0041] Figure 7 This is a flowchart of the steps of thermosensitive moxibustion diagnosis and treatment and visual guidance of the present invention.

[0042] Figure 8 This is a flow chart of the steps of gas feedback and scale generation of the present invention.

[0043] Figure 9 Flowchart of the steps for optimizing and iterating the solution of the present invention.

[0044] Figure 10 This is a flowchart of the steps of the local joint thermosensitive moxibustion treatment guidance operation method of the present invention. DETAILED DESCRIPTION

[0045] Reference herein to an embodiment means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present invention. The appearance of such a phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0046] Example 1

[0047] Intelligent generation system of moxibustion scheme based on acupoint thermal moxibustion and quantification of Qi, such as Figure 1 As shown, it includes a user interaction module, an AI diagnosis and treatment module, a visual guidance module, a feedback analysis module, a solution optimization module and a wearable moxibustion device. The user interaction module is used for multi-terminal interaction between doctors, patients and their families, and provides text, voice and video communication functions. The AI diagnosis and treatment module uses natural language processing technology to analyze user input symptoms, and generates a preliminary solution in combination with the thermosensitive moxibustion diagnosis and treatment database. When the matching degree is lower than the threshold, it automatically transfers to the doctor side. The visual guidance module calls the terminal camera to realize two working modes: a two-way video guidance channel is established when the doctor is connected, and the displacement of the moxibustion site is monitored by the bone recognition algorithm when the AI runs autonomously. The feedback analysis module collects the patient's voice feedback during the moxibustion process, and generates a qi scale containing ten-dimensional indicators such as skin flushing, heat transfer path, and gastrointestinal reaction through semantic analysis. The solution optimization module uses the Bayesian optimization algorithm to perform multi-objective evaluation on the qi scale data, dynamically updates the moxibustion parameter combination to form an iterative solution library, and outputs the optimal diagnosis and treatment plan.

[0048] like Figure 2 As shown in the figure, the AI diagnosis and treatment module includes a symptom-acupoint mapping submodule and a risk warning submodule. The symptom-acupoint mapping submodule automatically matches recommended thermosensitive moxibustion acupoints based on the symptoms described by the patient, establishes a mapping relationship between symptoms and acupoints, and provides personalized acupoint recommendations. Based on classical Chinese medicine literature and clinical cases, a symptom-acupoint mapping database is constructed, and a decision tree algorithm is used to establish the mapping relationship between symptoms and acupoints. The decision tree splitting criterion is: D is the data set, p i is the probability of category i, and the splitting condition is: Among them, D j For the sub-dataset, based on the symptom input, a list of recommended acupoints (such as "Guan Yuan" and "Zusanli") is output;

[0049] The risk warning submodule uses an interactive question-and-answer method to detect whether the patient has contraindications to thermosensitive moxibustion (such as pregnant women and diabetic patients), and issues warnings for high-risk moxibustion areas (such as the lumbar sacral area and feet). When a high-risk rule is matched, the system will issue a warning through a pop-up window or voice prompt, and at the same time generate a question-and-answer form for the patient to fill in accordingly, thereby supplementing and improving the data.

[0050] like Figure 3 As shown, the visual guidance module includes an infrared thermal imaging unit and a posture correction unit. The infrared thermal imaging unit uses infrared thermal imaging technology to detect the temperature distribution of the moxibustion area and monitor the temperature changes during the moxibustion process in real time to ensure safety. During use, the mobile phone is connected to an external infrared thermal imager to generate a temperature matrix T based on the thermal imaging data. The regional temperature data is obtained by the matrix algorithm to monitor abnormalities. The posture correction unit uses bone recognition technology to detect whether the moxibustion posture is correct and provides real-time correction prompts to ensure the moxibustion effect. The OpenPose model is used to detect key points of the human body. The coordinates of the key points are as follows:

[0051] Calculation of moxibustion angle:

[0052] Among them, (x1, y1) and (x2, y2) are the coordinates of the key points. When the angle deviation exceeds 15°, vibration or voice prompts are triggered.

[0053] The feedback analysis module realizes the multimodal input interface and dynamic scale generation. The multimodal input interface supports multiple input methods such as voice, text, and image, and converts multimodal data into a structured gas scale (such as Figure 4 and Figure 5 The dynamic scale is generated based on patient feedback to quantify Deqi indicators (such as thermal penetration depth and thermal expansion range). The Deqi scoring model is: Among them, w iis the weight coefficient, f i It is the frequency of Qi indicator.

[0054] The solution optimization module includes a multi-objective optimization engine and a case matching algorithm. The multi-objective optimization engine uses To calculate the objective function, where x is the moxibustion parameter, s i (x) is the Qi index. The case matching algorithm uses collaborative filtering algorithm to calculate and match:

[0055]

[0056] Where u is the user, i is the solution, and N(u) is the adjacent user.

[0057] The data acquisition of the Deqi meter includes a biosensor group and an image analysis unit. The biosensor group collects physiological data such as epidermal temperature and microcirculatory blood flow rate, and integrates a temperature sensor and a photoelectric sensor. The image analysis unit analyzes the skin flushing diffusion area through the HSV color space.

[0058] like Figure 6-Figure 9 As shown in the figure, in the specific use process, the steps of using the system are as follows:

[0059] Step 1: Intelligent diagnosis and solution generation

[0060] Step 1.1: The patient or family member logs in through the app, selects their identity (patient / family member), and fills in basic information (age, gender, medical history, etc.). The system assigns permissions based on the user's identity. Family members can describe symptoms on the patient's behalf, but the diagnosis and treatment plan is only visible to the patient.

[0061] Step 1.2: The patient or family member describes their symptoms (e.g., "low back pain" or "stomach discomfort") using text, voice, or pictures. The AI assistant analyzes the symptom keywords through natural language processing and matches them with the thermosensitive moxibustion diagnosis and treatment database. If a match is successful, a preliminary diagnosis and treatment plan is generated.

[0062] Step 1.3: If the AI fails to match the symptoms or the matching confidence level falls below a preset threshold (i.e., 80%), the system automatically transfers the patient to an online doctor, who communicates with the patient via video, provides additional information, and manually generates a treatment plan.

[0063] Step 1.4: The system pushes the plan generated by AI or doctors to the patient, and the patient proceeds to the next step after confirmation.

[0064] Step 2: Thermal moxibustion diagnosis and treatment with visual guidance

[0065] Step 2.1: The system prompts the patient to prepare moxibustion tools (such as moxa sticks, moxibustion box, etc.) according to the plan, and uses the mobile phone or tablet camera to enter the visual guidance mode;

[0066] Step 2.2: Select the visual guidance mode. In the AI autonomous mode, the system uses a bone recognition algorithm to locate the moxibustion site, such as the Guanyuan acupoint, and monitors the angle and position of the moxibustion in real time. If the deviation exceeds a threshold (such as 15°), it will be corrected through vibration or voice prompts. In the doctor connection mode, the doctor observes the patient's moxibustion process through video and provides real-time guidance on adjusting the position and angle.

[0067] Step 2.3: The system uses infrared thermal imaging technology to monitor the temperature changes in the moxibustion area to ensure that the temperature is within a safe range (such as 40-50°C). If the temperature is abnormal or the moxibustion time is too long, the system will automatically issue a warning.

[0068] Step 3: Deqi Feedback and Scale Generation

[0069] Step 3.1: After the moxibustion treatment, the system collects the patient's experience through interactive Q&A (e.g., "Is there a spreading heat sensation?", "Are there bowel movements?"). The patient can describe their feelings through voice, and the system converts them into text using voice recognition technology.

[0070] Step 3.2: Based on patient feedback, the system generates a scale combining the following De Qi types (including diffuse flushing, extremity heat, body heat, non-thermal sensation, heat transfer, gastrointestinal motility reaction, facial flushing, heat preference, heat expansion, and heat penetration). Each indicator is graded by intensity (e.g., 0-5 points) to generate a structured De Qi scale.

[0071] Step 3.3: The Qi meter is associated with the patient's basic information and moxibustion plan and stored to form a personal diagnosis and treatment file. The system uses the data analysis module to preliminarily evaluate the effect of this moxibustion.

[0072] Step 4: Solution optimization and iteration

[0073] Step 4.1: The system inputs the Deqi scale data into the optimization model and uses Bayesian optimization or multi-objective optimization algorithms to evaluate the effectiveness of the moxibustion plan. The model comprehensively considers factors such as Deqi intensity, patient comfort, and moxibustion duration to generate optimization recommendations.

[0074] Step 4.2: Based on the model calculation results, the system selects three optimized solutions from the solution library. If the optimized solution differs significantly from the current solution (e.g., the Qi intensity is increased by more than 20%), the system prompts the patient to try the new solution.

[0075] Step 4.3: The system regularly reminds patients to undergo thermosensitive moxibustion treatment and dynamically adjusts the plan based on historical data. Through machine learning algorithms, a personalized thermosensitive moxibustion diagnosis and treatment model for patients is gradually established.

[0076] Example 2

[0077] It should be noted that, specifically for the diagnosis and treatment guidance application of local joints, there is also a wearable moxibustion device, including a flexible pressure sensing layer and a temperature control module. The flexible pressure sensing layer is distributed around the joint and provides real-time feedback on the uniformity of the moxibustion pressure. The temperature control module maintains the moxibustion temperature within the range of 40-48°C through a thermocouple sensor, and automatically cuts off the heat source and issues an alarm when the temperature exceeds the limit. Figure 10 As shown, the following steps are included:

[0078] Step S1: The patient selects the target joint and describes the symptoms through the user interaction module. The AI diagnosis and treatment module generates preliminary moxibustion points and parameters. If the AI cannot match the symptoms or the matching confidence level is below the threshold, the system automatically transfers the call to the doctor, who conducts additional consultations and generates a plan.

[0079] Step S2: The visual guidance module locates acupuncture points through bone recognition and monitors the moxibustion angle and temperature distribution in real time. If the infrared thermal imaging detects a temperature exceeding 50°C or bone recognition fails three times in a row, the system automatically switches to the doctor's video guidance mode.

[0080] Step S3: During the moxibustion process, the wearable device synchronizes pressure and temperature data, and the feedback analysis module generates a dynamic evaluation scale that includes indicators of heat diffusion range, pain relief, and joint range of motion improvement;

[0081] Step S4: The scheme optimization module combines the scale data with historical cases to output the adjusted moxibustion scheme, including acupoint offset correction or temperature optimization suggestions. The system regularly reminds patients to undergo thermosensitive moxibustion treatment and dynamically adjusts the scheme based on historical data.

[0082] Specifically, in step S2, if the infrared thermal imaging detects that the temperature exceeds 50°C or the bone recognition fails three times in a row, the system automatically switches to the doctor video guidance mode, and the doctor remotely adjusts the moxibustion parameters.

[0083] Example 3

[0084] Basic moxibustion sensation feedback questionnaire

[0085] Skin reactions:

[0086] Question: Is there any redness at the moxibustion site?

[0087] ○ None ○ Mild (local) ○ Significant (diameter > 3 cm) ○ Widespread

[0088] Upload pictures: Take pictures of the skin condition at the moxibustion site.

[0089] Thermal Type:

[0090] Question: Which of the following types of heat sensation do you feel? (Multiple choice)

[0091] □Surface warmth □Deep penetrating heat □Conduction along the meridians □No heat sensation (only burning pain) Scoring method:

[0092] The flushing area was scored as follows: none = 0 points; mild = 1 point; marked = 3 points; extensive = 5 points.

[0093] Thermal Diversity Score: +1 point for each selected item (maximum 4 points).

[0094] Result classification:

[0095] Low response (0-3 points): It is recommended to adjust the acupoints or moxibustion distance.

[0096] Typical response (4-6 points): Maintain the current plan.

[0097] Strong reaction (7-9 points): Reduce the amount of moxibustion by 20% next time.

[0098] Multidimensional Qi Quantification Questionnaire

[0099] Heat transfer path:

[0100] Question: Does the heat transfer to other parts of the body? Please indicate the direction (e.g., "from waist to soles of feet"). Interactive diagram: Draw the heat transfer path on a human body diagram.

[0101] Physiological reactions:

[0102] Question: Do the following reactions occur after moxibustion? (Multiple choice)

[0103] □Increased bowel sounds □Sweating □Local muscle twitching □Drowsiness

[0104] Scoring Methodology:

[0105] Heat transfer path scoring: no conduction = 0 points; unidirectional = 2 points; multidirectional = 4 points.

[0106] Physiological response score: +1 point for each item (additional points: bowel sounds +2 points).

[0107] Result classification:

[0108] Meridian-sensitive type (≥5 points): give priority to meridian-related acupoints.

[0109] Organ reaction type (bowel sounds / prominent sweating): Strengthen the abdominal acupoints.

[0110] Insensitive type (≤2 points): Use scar moxibustion or increase the intensity of stimulation.

[0111] Long-term efficacy follow-up questionnaire

[0112] Symptom relief:

[0113] Question: How much improvement have the original symptoms (such as pain) had? (VAS 0-10 points).

[0114] Sustained reaction:

[0115] Question: Do the following persistent effects occur within 6 hours after moxibustion?

[0116] ○ Residual warmth ○ Relaxation ○ Thirst ○ Fatigue

[0117] Scoring Methodology:

[0118] Symptom relief rate: (previous score - current score) / previous score × 100%.

[0119] Continuous reaction weight: positive reaction (feeling of ease) +2 points; negative reaction (fatigue) -1 point.

[0120] Result classification:

[0121] Markedly effective (remission rate ≥ 50%): Extend the treatment interval to 3 days.

[0122] Optimization required (relief rate <30% and negative reaction): Change acupoints or combine with cupping.

[0123] The above embodiments are provided to persons familiar with the art for implementing or using the present invention. Personnel familiar with the art may make various modifications or changes to the above embodiments without departing from the spirit of the present invention. Therefore, the scope of protection of the present invention is not limited to the above embodiments, but should be the maximum scope of the innovative features mentioned in the claims.

Claims

1. An intelligent moxibustion scheme generation system based on the quantification of acupoint thermal moxibustion qi, characterized by: It includes: a user interaction module, which is used for multi-terminal interaction between doctors, patients and their families, and provides graphic, text, voice and video communication functions; An AI diagnosis and treatment module, which uses natural language processing technology to analyze user input symptoms, generates a preliminary plan based on the thermosensitive moxibustion diagnosis and treatment database, and automatically transfers the patient to the doctor's end when the matching degree falls below a threshold; A visual guidance module that uses the terminal camera to implement two working modes: establishing a two-way video guidance channel when the doctor is connected, and monitoring the deviation of the moxibustion site through a bone recognition algorithm when the AI is running autonomously; A feedback analysis module collects voice feedback from patients during moxibustion and generates a Deqi scale containing ten-dimensional indicators such as skin flushing, heat transfer path, and gastrointestinal reaction through semantic analysis; The scheme optimization module uses the Bayesian optimization algorithm to perform multi-objective evaluation on the Qi meter data, dynamically updates the moxibustion parameter combination to form an iterative scheme library, and outputs the optimal diagnosis and treatment plan.

2. The intelligent moxibustion scheme generation system based on the acupoint thermal moxibustion qi quantification according to claim 1 is characterized in that: The AI diagnosis and treatment module includes: Symptom-Acupoint Mapping Submodule: Build a decision tree model containing clinical cases and associate the symptom-acupoint correspondence. The decision tree splitting criterion is: D is the data set, p i is the probability of category i, and the splitting condition is: Among them, D j For the sub-dataset, based on the symptom input, a list of recommended acupoints is output; Risk warning submodule: Through comparison with the contraindication database, warnings are triggered for high-risk moxibustion areas such as the lumbar sacral area of pregnant women and the feet of diabetic patients.

3. The intelligent moxibustion scheme generation system based on the acupoint thermal moxibustion qi quantification according to claim 1 is characterized in that: The visual guidance module includes: Infrared thermal imaging unit: detects the temperature gradient distribution in the moxibustion area through a mobile phone connected to an external thermal imager; Posture correction unit: Based on the OpenPose algorithm, it identifies the angle deviation of moxibustion and detects the key points of the human body. The coordinates of the key points are: Calculation of moxibustion angle: Among them, (x1, y1) and (x2, y2) are the coordinates of the key points. When the angle deviation exceeds 15°, vibration or voice prompts are triggered.

4. The intelligent moxibustion scheme generation system based on the acupoint thermal moxibustion qi quantification according to claim 1 is characterized in that: The feedback analysis module implements: Multimodal input interface: supports converting the voice description of "heat sensation spreading to the waist" into structured data. The multimodal input interface supports multiple input methods such as voice, text, and images, and converts multimodal data into a structured gas volume scale; Dynamic scale generation: A three-dimensional thermal sensation model is established based on the relevant indicators of "thermal penetration depth" and "heat expansion range". The Qi scoring model is: Among them, w i is the weight coefficient, f i It is the frequency of Qi indicator.

5. The intelligent moxibustion scheme generation system based on the acupoint thermal moxibustion qi quantification according to claim 1 is characterized in that: The solution optimization module includes: Multi-objective optimization engine: Pareto frontier calculation to balance moxibustion duration and onset speed; Case matching algorithm: Use cosine similarity to retrieve historical successful cases and generate 3 recommended solutions.

6. The intelligent moxibustion scheme generation system based on the acupoint thermal moxibustion qi quantification according to claim 1 is characterized in that: The data collection of the gas meter includes: Biosensor group: integrated skin temperature and microcirculation blood flow velocity detection module; Image analysis unit: Analyzes the skin flushing diffusion area using the HSV color space.

7. The intelligent moxibustion scheme generation system based on the acupoint thermal moxibustion qi quantification according to claim 1 is characterized in that: The solution optimization module adopts: Deep reinforcement learning model: Using the Qi indicator as the reward function, the moxibustion parameter optimization strategy network is trained; Knowledge graph engine: Construct a triple relationship network of symptoms-acupoints-efficacy.

8. The intelligent moxibustion scheme generation system based on the acupoint thermal moxibustion qi quantification according to claim 1 is characterized in that: It also includes a wearable moxibustion device that provides real-time feedback on moxibustion pressure and temperature parameters.

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