A human meridian electric conduction data abnormal site identification method and system and moxibustion instrument

Abstract

The application discloses a human meridian electric conductance data abnormal site identification method and system and a moxibustion instrument, and belongs to the technical field of medical data processing. The method takes a specific meridian node as an electric conductance value sampling site, obtains a theoretical reference value and a correlation feature based on big data, and establishes a symptom and initial scheme checking response table. By comparing the actual electric conductance value, the correlation feature and the symptom performance, the abnormal sites are classified and marked, a fusion weight is configured to determine the final abnormal electric conductance data and the corresponding sampling sites. Meanwhile, a verification moxibustion scheme can be generated according to a reference moxibustion scheme to stimulate an individual, and abnormal data can be further identified by analyzing feedback data. Subsequent medical staff can refer to the above abnormal electric conductance data and the corresponding sites to accurately formulate an adaptive moxibustion scheme, which is beneficial to improving the effect of moxibustion treatment.

Description

Technical Field

[0001] This application belongs to the field of medical data processing technology, and relates to a method, system and moxibustion device for identifying abnormal sites in human meridian electrical conductivity data. Background Technology

[0002] Traditional Chinese medicine (TCM) theory holds that meridians are the channels through which "qi" and blood circulate; when qi and blood are abundant, the meridians are unobstructed. Based on this TCM theory, many hospitals and health centers now offer moxibustion treatment tailored to individual needs. Moxibustion uses moxa sticks made from mugwort leaves to generate heat that stimulates acupoints or specific areas of the body. By stimulating the flow of qi, it adjusts disordered physiological and biochemical functions, thereby achieving the purpose of preventing and treating diseases.

[0003] In current practice, many medical staff in hospital departments, due to a weak understanding of traditional Chinese medicine theory, often simply select existing moxibustion plans based on individual symptoms when formulating moxibustion treatment plans. However, as is well known, different individuals have different physical constitutions. Even if the symptoms are the same, the treatment effects of using the same moxibustion plan will not be the same, resulting in poor clinical efficacy or even adverse effects. This phenomenon prevents moxibustion techniques from playing their due role.

[0004] Modern research has found that the electrical resistance along meridian pathways is lower and the conductivity is higher than that of surrounding non-meridian areas. When the function of the body's internal organs or the state of Qi and blood changes, the conductivity of the corresponding meridians also changes. By measuring the conductivity of specific acupoints using a detector, the functional state of the relevant meridians and their associated organs can be indirectly determined. This partially transforms the subjective experience of traditional Chinese medicine's "observation, auscultation, inquiry, and palpation" into quantifiable data, providing an objective reference for diagnostic work.

[0005] In practice, although it is possible to obtain the electrical conductivity data of individual meridians using a detector, due to the differences in the physical functions and health status of each patient, there are no clear abnormal indicators in the electrical conductivity data of various parts of the meridians. That is, medical staff cannot directly determine abnormal electrical conductivity data through the above data, and therefore cannot obtain clues and references for abnormal meridian sites. Relying solely on experience to make abnormal judgments is not conducive to the accurate formulation of subsequent moxibustion plans. Summary of the Invention

[0006] To address the problem that abnormal electrical conductivity data at various sampling points along the human meridians is difficult to identify, leading to medical staff relying solely on experience to formulate moxibustion plans and resulting in poor therapeutic effects, this application aims to provide a method for identifying abnormal electrical conductivity data points along the human meridians. This method, through meridian electrical conductivity detection and intelligent data analysis, combined with traditional Chinese medicine diagnostic theory, accurately identifies abnormal electrical conductivity data at various sampling points along the meridians. This provides strong support for physicians in formulating moxibustion plans, shifting moxibustion from experience-based operations to data-driven approaches, thereby improving efficacy and reliability. To implement the above-mentioned method for identifying abnormal electrical conductivity data points along the human meridians, this application aims to provide a system for identifying abnormal electrical conductivity data points along the human meridians. A third objective is to protect a moxibustion device equipped with the above-mentioned system for identifying abnormal electrical conductivity data points along the human meridians. The specific solutions are as follows:

[0007] A method for identifying abnormal sites in human meridian electrical conductivity data includes:

[0008] Specific meridian nodes are used as sampling points for conductivity values;

[0009] Based on big data analysis, theoretical reference values ​​of conductivity at each sampling point are obtained, as well as reference relationship characteristics that reflect the correlation between conductivity values ​​at each sampling point;

[0010] Based on big data analysis, the symptoms and reference moxibustion schemes corresponding to abnormal conductivity values ​​and / or abnormal reference relationship characteristics at each sampling point are obtained and stored as a symptom verification and response table.

[0011] The actual conductivity values ​​at each sampling site of the current individual are obtained and actual relationship features are generated. The actual conductivity values ​​at each sampling site are compared with the theoretical reference values ​​for that sampling site. Sampling sites with abnormal conductivity values ​​are identified and marked as Class I abnormal sites. The actual relationship features associated with the actual conductivity values ​​at each sampling site are compared with reference relationship features. Sampling sites with abnormal relationship features are identified and marked as Class II abnormal sites. Sampling sites with both abnormal conductivity values ​​and abnormal relationship features are marked as Class III abnormal sites.

[0012] Obtain the current individual's symptom presentation, and generate suspected abnormal sampling sites based on the symptom checklist, marking them as four types of abnormal sites;

[0013] Configure fusion weights for the anomaly types of each sampling site, and determine the anomaly sampling sites of the current individual based on the set fusion algorithm;

[0014] Obtaining current individual symptom presentation includes one or more of the following methods: history taking, visual inspection, palpation, percussion, auscultation, and auxiliary examinations.

[0015] Through the above technical solution, reference conductivity values ​​and reference relationship characteristics of each meridian are generated based on big data. When detecting the meridian patency of an individual, the data of a single sampling point is not used as a reference. Instead, the individual's symptoms and the correlation between the conductivity values ​​of each sampling point are comprehensively considered. By combining symptoms with detection data, detection errors are eliminated, making the determination of abnormal points more accurate.

[0016] Furthermore, the method for identifying abnormal sites in human meridian electrical conductance data also includes:

[0017] Acquire the individual's various symptom manifestations and identify at least one normal symptom manifestation;

[0018] According to the symptom checklist, find the sampling sites corresponding to the above-mentioned normal symptom manifestations, their theoretical reference values ​​of conductivity, and reference relationship characteristics;

[0019] Moxibustion was performed according to the reference moxibustion protocol, and the actual conductivity values ​​of the above-mentioned sampling sites and their associated actual relationship characteristics were collected after moxibustion, and the individual's symptom manifestations were obtained.

[0020] If the actual relationship characteristics of the conductivity values ​​at the above sampling sites are similar to the reference relationship characteristics, and the aforementioned normal symptom manifestations remain unchanged, then the measured actual conductivity values ​​and associated actual relationship characteristics are used as a reference to correct the theoretical reference values ​​and reference relationship characteristics of the conductivity values ​​corresponding to the sampling sites.

[0021] By combining the above technical solutions with the symptom checklist, the fluctuation range of conductivity values ​​at each sampling point under the current normal symptom presentation of an individual can be determined. This makes the standard for judging abnormal conductivity data, namely the theoretical reference value and reference relationship characteristics of conductivity, more accurate, and also makes the judgment of the effect of moxibustion more accurate.

[0022] Furthermore, the method for identifying abnormal sites in human meridian electrical conductance data also includes:

[0023] Based on big data analysis, the relationships between various symptom manifestations are established and stored as a symptom association table;

[0024] Acquire an individual's symptom presentation and, based on the symptom association table, locate the potential symptoms of the proposed individual and their associated specific sampling sites;

[0025] Obtain the first conductivity value at the specific sampling site mentioned above for each individual;

[0026] Based on the potential symptoms, a stimulation action is applied to the individual, and the second conductivity value at the specific sampling site of the individual after the stimulation action is applied is obtained.

[0027] By comparing the difference between the first and second conductance values ​​and combining this with the theoretical reference values ​​of the conductance values ​​at the specific sampling points, it is determined whether the change in conductance value caused by the stimulus action is within the theoretical reference value:

[0028] If the change in conductivity is too small or too large, the aforementioned potential symptoms will be included in the abnormal symptoms.

[0029] The stimulation actions include traction and pressing, acupuncture, temperature stimulation, or emotional stimulation.

[0030] The above technical solution can infer an individual's potential symptoms by combining their current symptoms with the conductivity values ​​at specific sampling sites, thereby improving the accuracy and comprehensiveness of symptom diagnosis. This is beneficial for determining whether the conductivity data at relevant sampling sites is abnormal and provides accurate data reference for the formulation of subsequent moxibustion plans.

[0031] Furthermore, fusion weights are configured for the anomaly types of each sampling site, and the anomalous sampling sites of the current individual are determined based on the set fusion algorithm. The formula for calculating the anomaly probability of the sampling site is as follows:

[0032] P i =ω1·x1+ω2·x2+ω3·x3+ω4·x4;

[0033] ω1, ω2, ω3, and ω4 are the fusion weight values ​​corresponding to the first, second, third, and fourth types of abnormal sites, respectively, and their sum is 1.

[0034] x1, x2, x3, and x4 represent the states of the abnormal types, with values ​​of 0 or 1, representing normal and abnormal states respectively.

[0035] The above technical solution allows for flexible setting of fusion weights corresponding to various abnormal sites, making the determination of the final abnormal conductance data more reliable.

[0036] Furthermore, the method for identifying abnormal sites in human meridian electrical conductance data also includes:

[0037] Based on the symmetrical characteristics of the left and right meridians of the human body, a set number of symmetrical sampling points are selected as quick reference points;

[0038] Acquire and store the theoretical reference values ​​of conductivity corresponding to the above-mentioned quick reference sites, and the reference relationship characteristics between them and the conductivity values ​​of symmetrical sampling sites;

[0039] According to the symptom checklist, obtain and store the symptom manifestations corresponding to abnormal conductivity values ​​or reference relationship characteristics of the aforementioned quick reference sites, and store them as quick reference symptoms;

[0040] The above-mentioned quick reference symptoms and their corresponding reference moxibustion plans are stored together;

[0041] If an individual's symptoms are consistent with the quick reference symptoms, the conductivity value and actual relationship characteristics of the quick reference site are obtained. If the conductivity value and / or actual relationship characteristics are abnormal, the reference moxibustion scheme is used as the verification moxibustion scheme.

[0042] Through the above technical solution, several sampling sites are set for rapid diagnosis. When an individual's symptoms are consistent with the quick reference symptoms, the diagnosis of syndromes such as "deficiency, excess, cold, and heat" is accelerated, thereby accurately selecting acupoints and quickly generating a moxibustion plan for verification. The individual is then stimulated with moxibustion using the above-mentioned moxibustion plan to determine the sites and states of abnormal electrical conductance data.

[0043] Furthermore, the method also includes generating a verification moxibustion plan based on the symptom checklist and according to the abnormal sampling sites to verify the abnormal identification results, including:

[0044] Based on the theoretical reference value corresponding to the conductivity value at the abnormal sampling site and the detected actual conductivity value, the degree of abnormality of the conductivity value at the abnormal sampling site is analyzed, and the abnormality is classified according to the degree of abnormality.

[0045] Based on the symptom checklist, a reference moxibustion plan is determined, and the reference moxibustion plan is divided into at least two moxibustion stages according to different grading standards.

[0046] The initial moxibustion stage is a trial moxibustion stage, used to observe the change in conductivity values ​​at the abnormal sampling sites after moxibustion, so as to adjust the moxibustion plan for subsequent stages.

[0047] The degree of abnormality is positively correlated with the number of stages in the reference moxibustion plan.

[0048] Using the above technical solution, the reference moxibustion plan is divided into two stages according to the degree of abnormality of the conductivity value of the sampling site. The moxibustion parameters are flexibly adjusted to determine the response of the conductivity data at each sampling site after stimulation, thereby identifying abnormal data and corresponding sites.

[0049] Furthermore, the method also includes: collecting the conductivity values ​​of each sampling site after moxibustion and the actual relationship characteristics between the conductivity values;

[0050] Based on whether the conductivity value and actual relationship characteristics at the abnormal sampling sites change toward the theoretical reference value and reference relationship characteristics, the aforementioned generated moxibustion scheme for verification is adjusted, and a new moxibustion scheme for verification is output.

[0051] This includes adjusting the generated moxibustion verification plan and outputting a new moxibustion verification plan, including:

[0052] Multiple samplings were conducted to obtain the conductivity values ​​at abnormal sampling sites before and after moxibustion in the initial stage. The numerical change trends of the above conductivity values ​​and their associated actual relationship characteristics were calculated.

[0053] Based on the numerical change trend and the theoretical reference value and reference relationship characteristics of the conductivity value at the above-mentioned abnormal sampling points, the trend direction of the generated conductivity value is analyzed.

[0054] Analyze the trend of the difference between the actual conductivity value and the theoretical reference value before and after moxibustion as the moxibustion parameters change, and adjust the subsequent moxibustion plan accordingly.

[0055] The moxibustion parameters include moxibustion duration and moxibustion temperature.

[0056] The above technical solution can be used to adjust the parameters of the reference moxibustion scheme according to the changing trend of the conductivity value at the abnormal sampling site, and generate a corresponding verification moxibustion scheme, which is conducive to improving the accuracy of identifying abnormal meridian conductivity data.

[0057] A system for identifying abnormal sites in human meridian electrical conductivity data includes:

[0058] The meridian conductivity detection unit is used to obtain the actual conductivity value of the current individual at a preset specific meridian sampling point, and to generate the actual relationship characteristics between the conductivity values ​​at each sampling point;

[0059] A reference data storage unit is used to acquire and store the theoretical reference value of the conductivity value at each of the sampling sites, as well as the reference relationship feature used to reflect the correlation between the conductivity values ​​at each of the sampling sites; acquire the symptom manifestations and reference moxibustion plans corresponding to abnormal conductivity values ​​at each of the sampling sites and / or abnormal reference relationship features, and store them as a symptom check and response table;

[0060] An abnormal site identification unit, connected to the meridian conductivity detection unit and the reference data storage unit, is used to compare the actual conductivity value at each sampling site with the theoretical reference value, analyze and confirm sampling sites with abnormal conductivity values, and mark them as Class I abnormal sites; compare the actual relationship characteristics associated with the actual conductivity values ​​at each sampling site with the reference relationship characteristics, analyze and confirm sampling sites with abnormal relationship characteristics, and mark them as Class II abnormal sites; and mark sampling sites with both abnormal conductivity values ​​and abnormal relationship characteristics as Class III abnormal sites.

[0061] The symptom collection and analysis unit is used to acquire the current individual's symptom presentation and identify suspected abnormal sampling sites according to the symptom checklist, marking them as four types of abnormal sites;

[0062] An abnormal site determination unit, connected to the symptom collection and analysis unit, is used to configure fusion weights for the abnormal types of each sampling site, determine the abnormal sampling sites of the current individual based on a set fusion algorithm, and output them.

[0063] The symptom collection includes one or more of the following: history taking, visual inspection, palpation, percussion, auscultation, and auxiliary examinations.

[0064] The formula for calculating the probability of anomalies at sampling sites is:

[0065] P i =ω1·x1+ω2·x2+ω3·x3+ω4·x4;

[0066] ω1, ω2, ω3, and ω4 are the fusion weight values ​​corresponding to the first, second, third, and fourth types of abnormal sites, respectively, and their sum is 1.

[0067] x1, x2, x3, and x4 represent the states of the abnormal types, with values ​​of 0 or 1, representing normal and abnormal states respectively.

[0068] Furthermore, the human body meridian conductance data abnormality site identification system also includes:

[0069] The normal symptom identification unit is connected to the reference data storage unit, the symptom acquisition and analysis unit, and the meridian conductance detection unit. It is used to acquire various symptom manifestations of an individual and determine at least one normal symptom manifestation. According to the symptom checklist, it finds the sampling site corresponding to the above-mentioned normal symptom manifestation and its theoretical reference value and reference relationship characteristics of conductance value.

[0070] The reference value correction unit is configured to collect the actual conductivity value and its associated actual relationship characteristics at the individual sampling site after moxibustion, and to obtain the individual's symptom manifestations after moxibustion: if the actual relationship characteristics of the conductivity value at the sampling site are similar to the reference relationship characteristics, and the normal symptom manifestations remain unchanged, then the measured actual conductivity value and associated actual relationship characteristics are used as a reference to correct the theoretical reference value of the conductivity value corresponding to the sampling site and the reference relationship characteristics stored in the reference data storage unit.

[0071] A potential symptom identification and verification unit, connected to the reference data storage unit, the symptom acquisition and analysis unit, and the meridian conductance detection unit, is configured to establish the correlation between various symptom manifestations based on big data analysis and store it as a symptom correlation table; acquire the symptom manifestations of an individual, and search for the potential symptoms of the individual and their associated specific sampling sites according to the symptom correlation table; acquire the first conductance value at the specific sampling site of the individual, apply a stimulation action to the individual according to the potential symptoms, acquire the second conductance value at the specific sampling site of the individual after applying the stimulation action, compare the difference between the first conductance value and the second conductance value, and combine it with the theoretical reference value of the conductance value at the specific sampling site to determine whether the change in conductance value caused by the stimulation action is within the normal fluctuation range: if the change in conductance value is too small or too large, then the potential symptom is included in the abnormal symptoms;

[0072] The stimulation actions include traction and pressing, acupuncture, temperature stimulation, or emotional stimulation.

[0073] A moxibustion device includes a moxibustion frame with adjustable position and angle, and a moxibustion box set on the frame, and also includes the human meridian electrical conduction data abnormal site identification system as described above.

[0074] The meridian conductivity detection unit includes a first electrode disposed at a set position on the individual's body and a second electrode disposed at a sampling point. The first electrode, the second electrode, and the individual's body form a conductive path.

[0075] The symptom collection and analysis unit includes a specific physiological parameter collection component and a human-computer interaction component, configured to directly collect physiological parameter characteristic data that reflects an individual's symptoms, or to allow manual input and confirmation of an individual's symptom presentation.

[0076] Through the above technical solution, the moxibustion device can collect the electrical conductivity value at a set location on an individual's body and determine abnormal sampling sites. Through meridian electrical conductivity detection and intelligent data analysis, it can accurately identify abnormal electrical conductivity data and their corresponding sampling sites, providing a reference for medical staff to formulate moxibustion plans.

[0077] This application includes at least one of the following beneficial effects:

[0078] Based on big data analysis, reference conductivity values ​​and reference relationship characteristics of each meridian are generated. When detecting the meridian patency of an individual, the data of a single sampling point is not used as a reference. Instead, the individual's symptoms and the correlation between the conductivity values ​​of each sampling point are considered. By combining the symptoms with the detection data, detection errors are eliminated, and the identification of abnormal points is more accurate. This provides a precise reference for medical staff to formulate moxibustion plans and is also conducive to improving the effect of subsequent moxibustion treatment. Attached Figure Description

[0079] Figure 1 A schematic diagram illustrating a method for identifying abnormal sites in human meridian electrical conductivity data;

[0080] Figure 2 A diagram illustrating methods for improving symptom presentation;

[0081] Figure 3 This is a schematic diagram showing the connection of each functional module in the human body meridian electrical conductivity data abnormality site identification system.

[0082] Attached reference numerals: 1. Meridian conductivity detection unit; 2. Reference data storage unit; 3. Abnormal site identification unit; 4. Symptom collection and analysis unit; 5. Abnormal site determination unit. Detailed Implementation

[0083] The embodiments of this application are described in detail below, and examples of the embodiments are shown in the accompanying drawings.

[0084] In the description of this specification, the references to "certain embodiments," "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples" refer to specific features, structures, materials, or characteristics described in connection with the described embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0085] This embodiment first provides a method for identifying abnormal sites in human meridian electrical conductivity data based on multi-source data fusion and dynamic feedback, such as... Figure 1 As shown, the method is executed by a cloud server and / or a local high-performance computing terminal, and the specific steps are as follows:

[0086] S100 uses specific meridian nodes as conductivity sampling points;

[0087] S210, based on big data analysis, obtains theoretical reference values ​​of conductivity at each sampling point, as well as reference relationship characteristics to reflect the correlation between conductivity values ​​at each sampling point;

[0088] S220, Based on big data analysis, obtain the symptoms and reference moxibustion schemes corresponding to abnormal conductivity values ​​and / or abnormal reference relationship characteristics at each sampling point, and store them as a symptom verification and response table;

[0089] S310: Obtain the actual conductivity values ​​at each sampling site of the current individual and generate actual relationship features. Compare the actual conductivity values ​​at each sampling site with the theoretical reference values ​​for that sampling site. Analyze and confirm sampling sites with abnormal conductivity values ​​and mark them as Class I abnormal sites. Compare the actual relationship features associated with the actual conductivity values ​​at each sampling site with the reference relationship features. Analyze and confirm sampling sites with abnormal relationship features and mark them as Class II abnormal sites. Sampling sites with both abnormal conductivity values ​​and abnormal relationship features are marked as Class III abnormal sites.

[0090] S320, Obtain the current individual's symptom presentation, and generate suspected abnormal sampling sites based on the symptom checklist, marking them as four types of abnormal sites;

[0091] S400 configures fusion weights for the anomaly types of each sampling site, determines the anomaly sampling sites of the current individual based on the set fusion algorithm, and outputs them.

[0092] Step S200 above is a preparation step, used to build the big data basic database and theoretical model.

[0093] In detail, in step S200 above, a basic database is first constructed based on clinical data of moxibustion. The data sources of the database include confirmed case data, covering individual patients of different ages, genders, constitutions, and disease types. The data includes: skin conductivity values ​​at preset sampling points on specific meridians, such as the twelve regular meridians and the eight extraordinary meridians, individual symptom manifestations, and corresponding effective moxibustion plans. The sampling points are preferably acupoints at the intersection of meridians, such as original acupoints and back-shu points. Individual symptom manifestations are obtained through consultation or testing. The reference moxibustion plan includes the name of the acupoint to be moxibusted, the moxibustion temperature, and the moxibustion duration.

[0094] In step S210, the method for obtaining the theoretical reference value includes: using a cluster analysis algorithm to statistically analyze the conductivity values ​​of each sampling site in a healthy state for people of the same age, gender, and physical condition, calculating the mean and standard deviation, and generating the theoretical reference value of the conductivity value of each sampling site.

[0095] The reference relationship features include the range of numerical ratios and slope thresholds between the conductivity values ​​of each sampling point. The extraction methods include: using graph neural networks or Pearson correlation coefficient matrices to analyze the correlation patterns of conductivity values ​​between sampling points in healthy individuals. For example, analyzing the synchronous change patterns of conductivity values ​​at "Hegu" and "Taichong" acupoints to extract reference relationship features that reflect the flow patterns of Qi and blood in the meridians.

[0096] In step S220, the method for establishing the symptom check and response table includes: analyzing the mapping relationship between abnormal conductivity values ​​or abnormal relationship characteristics and specific symptoms, such as headache, insomnia, and bloating, through association rule analysis algorithm, and generating a reference moxibustion program library to form a symptom check and response table.

[0097] Steps S310 and S320 are the data collection and feature generation steps for the current individual, specifically including:

[0098] Conductivity detection: Using a high-precision bioelectrical impedance analysis device, the actual conductivity values ​​of the current individual at preset specific meridian sampling points are obtained. Actual relationship feature generation: Based on the actual conductivity values ​​acquired in step S310, actual relationship features between each sampling point are calculated and generated.

[0099] Symptom data collection: By inputting doctor's consultation results through a human-computer interaction interface, or by combining image recognition, pressure sensors, audio analysis, or by accessing auxiliary examination reports from the hospital's HIS system, the current individual's symptoms can be comprehensively obtained.

[0100] In detail, in the embodiments of this application, obtaining the current individual's symptom manifestations includes one or more combinations of medical history taking, visual inspection, palpation, percussion, auscultation, and auxiliary examinations.

[0101] Step S300 also includes multi-category abnormal site identification and classification labeling:

[0102] The actual collected individual data is compared with the model established in S210-S220, and a four-level anomaly labeling is performed, specifically including:

[0103] Type I anomaly labeling: The actual conductivity value of each sampling point is compared with the theoretical reference value. If the deviation exceeds the preset threshold, such as ±5%, the above sampling point is labeled as a "Type I anomaly point", that is, a single point value is abnormal.

[0104] Type II anomaly labeling: The actual relationship characteristics calculated from the actual conductivity value are compared with the reference relationship characteristics. If the actual relationship characteristics are inconsistent with the reference relationship characteristics, such as excessive left-right imbalance or vertical conduction blockage, the above sampling points are labeled as "Type II anomaly points", that is, numerical relationship logic anomalies.

[0105] Three types of anomaly labeling: If a site simultaneously meets the conditions for both type one and type two anomaly labeling, i.e., both the numerical value and the relationship are abnormal, then the above sampling site is labeled as a "type three anomaly site", i.e., a severe composite anomaly.

[0106] Four types of abnormality markers: Based on the individual's current symptom presentation, the symptom checklist is consulted to infer the sampling sites where abnormalities should theoretically occur. If a site is detected with slight fluctuations but does not reach the aforementioned threshold, it is marked as a "Type IV abnormal site," i.e., a symptom-driven suspected abnormality.

[0107] In step S400 above, fusion weights are configured for the anomaly types of each sampling site, and the anomalous sampling sites of the current individual are determined based on the set fusion algorithm. The formula for calculating the anomalous probability of a sampling site is:

[0108] P i =ω1·x1+ω2·x2+ω3·x3+ω4·x4;

[0109] ω1, ω2, ω3, and ω4 are the fusion weight values ​​corresponding to the first, second, third, and fourth types of abnormal sites, respectively, and their sum is 1; x1, x2, x3, and x4 represent the state of the abnormal type, and their values ​​are 0 or 1, representing the normal state and the abnormal state, respectively.

[0110] During the output determination process, if P i If the value is greater than a set threshold, such as 0.8, then the above sampling site will be identified as an abnormal sampling site for the current individual.

[0111] Once the above-mentioned abnormal sampling sites are obtained, they can be output to medical staff through human-computer interaction devices, such as touch screens, to provide reference for medical staff to formulate relevant moxibustion plans.

[0112] To verify the accuracy of identifying the aforementioned abnormal sampling sites, this application also includes:

[0113] S500, based on the symptom checklist and according to the above abnormal sampling sites, generate a moxibustion plan for verification, and collect the conductivity values ​​of each sampling site after moxibustion and the actual relationship characteristics between each conductivity value;

[0114] S600, based on whether the conductivity value and actual relationship characteristics at the abnormal sampling site change toward the theoretical reference value and reference relationship characteristics, adjust the generated moxibustion scheme for verification and output a new moxibustion scheme for verification.

[0115] Step S500 is the step of generating and dynamically adjusting the graded moxibustion plan, which specifically includes:

[0116] S510, based on the confirmed abnormal sampling sites and their degree of abnormality, i.e., P i The value is used to match a reference moxibustion plan from the symptom checklist, including acupoint selection, moxibustion temperature, and moxibustion duration.

[0117] S520, based on the severity of the abnormality, breaks down the reference moxibustion plan into multiple stages.

[0118] The initial stage is an exploratory period, during which the moxibustion program is short-duration and low-temperature, used to observe individual bodily responses; the subsequent stage is the treatment period, used to adjust moxibustion parameters based on feedback from the initial stage.

[0119] In practical applications, the degree of abnormality is positively correlated with the number of stages in the reference moxibustion plan. That is, the greater the degree of abnormality, the more stages the reference moxibustion plan is divided into. In practice, when the conductivity value of a sampling site on an individual's body is significantly lower than the theoretical reference value, the optimal moxibustion plan is gradually determined through multi-stage trial moxibustion. Finally, it is implemented as a verification moxibustion plan, and the individual's physiological response after moxibustion is observed.

[0120] S530, Real-time Feedback Adjustment: Before and after moxibustion in the initial stage, the conductivity values ​​of abnormal sites are collected multiple times, and the numerical change trend of the above conductivity values ​​and relationship characteristics over time is calculated.

[0121] Determine the trend direction: If the data converges toward the theoretical reference value, that is, the anomaly decreases, then increase the moxibustion time in the subsequent stages as planned; if the data diverges or does not change, then automatically adjust the moxibustion parameters, such as changing acupoints, adjusting the moxibustion temperature, or terminating the current plan and re-evaluating.

[0122] In practical applications, due to individual differences in physical condition, the conductivity value of a certain sampling site under normal conditions will differ from the theoretical reference value. To correct the relationship between the theoretical reference value and the reference value, making it more suitable for the actual situation of the individual, preferably, the method described in this application further includes:

[0123] A200 involves obtaining various symptom manifestations of an individual and identifying at least one normal symptom manifestation, such as the individual experiencing normal pain when a certain part of their body is pressed.

[0124] A210, according to the symptom checklist, find the sampling sites corresponding to the above-mentioned normal symptom manifestations, their theoretical reference values ​​of conductivity, and reference relationship characteristics.

[0125] A220, moxibustion is performed according to the reference moxibustion plan, and the actual conductivity values ​​of the above-mentioned sampling sites and their associated actual relationship characteristics are collected after moxibustion.

[0126] A230. After moxibustion, the individual's symptom presentation is obtained. If the actual relationship characteristics of the conductivity values ​​at the above sampling sites are similar to the reference relationship characteristics, and the aforementioned normal symptom presentation remains unchanged, then the measured actual conductivity values ​​and associated actual relationship characteristics are used as a reference to correct the theoretical reference values ​​and reference relationship characteristics of the conductivity values ​​corresponding to the sampling sites.

[0127] In practice, the accuracy and reliability of medical history taking vary from person to person. To avoid missing relevant symptoms, optimization is needed, such as... Figure 2 As shown, the method for identifying abnormal sites in human meridian electrical conductivity data described in this application further includes:

[0128] B100 establishes the correlation between various symptom manifestations based on big data analysis and stores them as a symptom association table.

[0129] B200: Obtain the individual's symptom presentation and, based on the symptom association table, find the potential symptoms of the proposed individual and their associated specific sampling sites. For example, based on the symptom association table, infer the potential symptoms that the individual with lower back pain may have, such as kidney deficiency.

[0130] B300, obtains the first conductivity value at the specific sampling site mentioned above for an individual.

[0131] B400, based on the potential symptoms, applies a stimulation action to the individual and obtains the second conductivity value at the specific sampling site of the individual after the stimulation action is applied.

[0132] B500 compares the difference between the first and second conductance values, and combines them with the theoretical reference value of the conductance value at the specific sampling point to determine whether the change in conductance value caused by the stimulus action is within the theoretical reference value: if the change in conductance value is too small or too large, the potential symptoms are included in the abnormal symptoms.

[0133] The stimulation actions include, but are not limited to: traction and pressing, acupuncture, temperature stimulation, or emotional stimulation.

[0134] The above technical solution can infer an individual's potential symptoms by combining their current symptoms with the conductivity values ​​at specific sampling sites, thereby improving the accuracy and comprehensiveness of symptom diagnosis and making the moxibustion plan generated later more suitable, thus improving the treatment effect.

[0135] To expedite the diagnosis of syndromes such as "deficiency, excess, cold, and heat," thereby enabling precise acupoint selection and rapid generation of reference moxibustion plans to improve diagnostic and treatment efficiency, the method for identifying abnormal sites in human meridian electrical conductivity data in this application embodiment further includes:

[0136] C100 selects a set number of symmetrical sampling points as quick reference points based on the symmetrical characteristics of the left and right meridians of the human body.

[0137] C200, acquires and stores the theoretical reference value of the conductivity corresponding to the above-mentioned quick reference site, and the reference relationship characteristics between it and the conductivity value of the symmetrical sampling site;

[0138] C300, according to the symptom checklist, obtains and stores the symptom manifestations corresponding to abnormal conductivity values ​​or reference relationship characteristics of the aforementioned quick reference sites, and stores them as quick reference symptoms;

[0139] C400, associated with the above quick reference symptoms and their corresponding reference moxibustion plans.

[0140] If an individual's symptoms match the aforementioned quick reference symptoms, the conductivity value and actual relationship characteristics of the quick reference site are obtained. If the conductivity value and / or actual relationship characteristics are abnormal, the aforementioned reference moxibustion plan is used as the verification moxibustion plan. For example, if the test reveals an imbalance in the left and right sides of the liver meridian and a low value, combined with symptoms such as poor mood, it can be directly diagnosed as "liver qi stagnation," and a corresponding moxibustion plan can be generated. The above plan can directly generate a reference moxibustion plan based on typical characteristics, significantly shortening the diagnosis time.

[0141] To implement the above-mentioned method for identifying abnormal sites in human meridian electrical conductivity data, this application also discloses a system for identifying abnormal sites in human meridian electrical conductivity data, such as... Figure 3 As shown, it mainly includes the following functional modules: Meridian Conductivity Detection Unit 1, Reference Data Storage Unit 2, Abnormal Site Identification Unit 3, Symptom Collection and Analysis Unit 4, and Abnormal Site Determination Unit 5.

[0142] The meridian conductivity detection unit 1 is used to acquire the actual conductivity value of the current individual at a preset specific meridian sampling point, and to generate the actual relationship characteristics between the conductivity values ​​at each sampling point. Specifically, the meridian conductivity detection unit 1 includes a detection end and a data analysis end. The detection end includes a patch-type or handheld detection electrode, and the data analysis end includes a local processor or a cloud processor, used to process the collected conductivity value data from each sampling point.

[0143] Reference data storage unit 2 is used to acquire and store the theoretical reference values ​​of conductivity at each sampling point, as well as reference relationship characteristics reflecting the correlation between conductivity values ​​at each sampling point. It also acquires the symptom manifestations and reference moxibustion plans corresponding to abnormal conductivity values ​​and / or abnormal reference relationship characteristics at each sampling point, and stores them as a symptom check and response table. In this embodiment, the reference data storage unit 2 is configured in a cloud server, facilitating data updates and improvements for large datasets. During use, relevant data can be obtained directly from the cloud server through the data interface of the local device.

[0144] The abnormal site identification unit 3 is connected to the meridian conductivity detection unit 1 and the reference data storage unit 2. It compares the actual conductivity values ​​at each sampling site with the theoretical reference values, analyzes and confirms sampling sites with abnormal conductivity values, and marks them as Class I abnormal sites. It also compares the actual relationship characteristics associated with the actual conductivity values ​​at each sampling site with the reference relationship characteristics, analyzes and confirms sampling sites with abnormal relationship characteristics, and marks them as Class II abnormal sites. Sampling sites with both abnormal conductivity values ​​and abnormal relationship characteristics are marked as Class III abnormal sites. The symptom collection and analysis unit 4 is used to acquire the current individual's symptom presentation and, based on the symptom checklist, confirms suspected abnormal sampling sites, marking them as Class IV abnormal sites. Symptom collection includes one or more of the following: history taking, visual inspection, palpation, percussion, auscultation, and auxiliary examinations.

[0145] The abnormal site determination unit 5 is connected to the symptom collection and analysis unit 4. It is used to configure fusion weights for the abnormal types of each sampling site, determine the abnormal sampling sites of the current individual based on the set fusion algorithm, and output them through the human-computer interaction device.

[0146] In this embodiment, a verification moxibustion plan generation unit is also included, connected to the abnormal site determination unit 5, the meridian conductivity detection unit 1, and the reference data storage unit 2. This unit is configured to generate a verification moxibustion plan based on a symptom checklist and the pre-determined abnormal sampling sites. The verification plan optimization unit is configured to collect the conductivity values ​​of each sampling site after moxibustion and the actual relationship characteristics between these values. Based on whether the conductivity values ​​and actual relationship characteristics at the abnormal sampling sites change towards the theoretical reference values ​​and reference relationship characteristics, the previously generated verification moxibustion plan is adjusted to obtain a new verification moxibustion plan. This facilitates subsequent precise stimulation of individuals, thereby identifying abnormal conductivity data.

[0147] It should be noted that the above-mentioned moxibustion protocol for verification is not the final moxibustion protocol for therapeutic purposes. The above-mentioned moxibustion protocol for verification is only used as a stimulation method to obtain individual physiological feedback, thereby identifying individual abnormal electrical conductance data and corresponding sites, and providing accurate and reliable data reference for the formulation of subsequent treatment moxibustion protocols.

[0148] The optimized human meridian electrical conductance data abnormal site identification system described in this application further includes: a normal symptom manifestation identification unit, a reference value correction unit, and a potential symptom identification and verification unit.

[0149] The normal symptom identification unit is connected to the reference data storage unit 2, the symptom acquisition and analysis unit 4, and the meridian conductivity detection unit 1. It is used to acquire various symptom manifestations of an individual and identify at least one normal symptom manifestation. Based on the symptom checklist, it finds the sampling site corresponding to the above-mentioned normal symptom manifestation and its theoretical reference value and reference relationship characteristics of conductivity. The reference value correction unit is configured to acquire the actual conductivity value at the individual's sampling site and its associated actual relationship characteristics after moxibustion. After moxibustion, the unit acquires the individual's symptom manifestations. If the actual relationship characteristics of the conductivity value at the sampling site are similar to the reference relationship characteristics, and the normal symptom manifestation remains unchanged, then the measured actual conductivity value and associated actual relationship characteristics are used as a reference to correct the theoretical reference value of conductivity and reference relationship characteristics corresponding to the sampling site stored in the reference data storage unit 2.

[0150] The potential symptom identification and verification unit is connected to the reference data storage unit 2, the symptom acquisition and analysis unit 4, and the meridian conductance detection unit 1. It is configured to establish the correlation between various symptom manifestations based on big data analysis and store them as a symptom correlation table. It acquires the symptom manifestations of an individual and finds the potential symptoms of the individual and their associated specific sampling sites according to the symptom correlation table. It acquires the first conductance value at the specific sampling site of the individual, applies a stimulation action to the individual according to the potential symptoms, acquires the second conductance value at the specific sampling site of the individual after applying the stimulation action, compares the difference between the first and second conductance values, and combines the theoretical reference value of the conductance value at the specific sampling site to determine whether the change in conductance value caused by the stimulation action is within the normal fluctuation range. If the change in conductance value is too small or too large, the potential symptoms are included in the abnormal symptoms. The stimulation actions include traction and pressing, acupuncture, temperature stimulation, or emotional stimulation.

[0151] Finally, this application proposes to protect a moxibustion device, which includes a moxibustion frame with adjustable position and angle and a moxibustion box set on the frame. The moxibustion box is equipped with a smoke exhaust pipe. During moxibustion, the position and angle of the moxibustion frame are adjusted so that the moxibustion box is close to the acupoints on the individual's body to be treated.

[0152] Furthermore, the aforementioned moxibustion device also includes the previously mentioned system for identifying abnormal sites in human meridian electrical conductivity data. The meridian electrical conductivity detection unit 1 includes a first electrode positioned at a predetermined location on the individual's body and a second electrode positioned at a sampling site. The first electrode, the second electrode, and the individual's body form a conductive path. Specifically, the first electrode is configured as a large-area patch, fixed to a reference potential point such as the individual's wrist or ankle. The second electrode is configured as an array of probes or a movable probe, integrated into the end of a robotic arm or a separate handheld pen, precisely contacting specific meridian sampling sites. The conductive path, through a constant current source circuit, forms a loop with the first electrode, human tissue, and the second electrode, measuring the voltage drop to calculate the conductivity value.

[0153] The symptom acquisition and analysis unit 4 includes a specific physiological parameter acquisition component and a human-computer interaction component. It is configured to directly acquire physiological parameter characteristic data reflecting an individual's symptoms, or to allow manual input and confirmation of an individual's symptom presentation. The human-computer interaction component includes a high-definition touchscreen display. Running the human-computer interaction interface of the symptom acquisition and analysis unit 4 guides the doctor or individual to complete symptom entry. The specific physiological parameter acquisition component includes a heart rate monitor, a skin temperature detector, etc.

[0154] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for identifying abnormal sites in human meridian electrical conductivity data, characterized in that, include: Specific meridian nodes are used as sampling points for conductivity values; Based on big data analysis, theoretical reference values ​​of conductivity at each sampling point are obtained, as well as reference relationship characteristics that reflect the correlation between conductivity values ​​at each sampling point; Based on big data analysis, the symptoms and reference moxibustion schemes corresponding to abnormal conductivity values ​​and / or abnormal reference relationship characteristics at each sampling point are obtained and stored as a symptom verification and response table. The actual conductivity values ​​at each sampling site of the current individual are obtained and actual relationship features are generated. The actual conductivity values ​​at each sampling site are compared with the theoretical reference values ​​of that sampling site. Sampling sites with abnormal conductivity values ​​are analyzed and confirmed, and marked as a type of abnormal site. The actual relationship characteristics associated with the actual conductivity values ​​at each sampling point are compared with the reference relationship characteristics. Sampling points with abnormal relationship characteristics are identified and marked as Class II abnormal points. Sampling points with both abnormal conductivity values ​​and abnormal relationship characteristics are marked as Class III abnormal points. Obtain the current individual's symptom presentation, and generate suspected abnormal sampling sites based on the symptom checklist, marking them as four types of abnormal sites; Configure fusion weights for the anomaly types of each sampling site, determine the anomaly sampling sites of the current individual based on the set fusion algorithm, and output them; Obtaining current individual symptom presentation includes one or more of the following methods: history taking, visual inspection, palpation, percussion, auscultation, and auxiliary examinations. The formula for calculating the probability of anomalies at sampling sites is: P i =ω1·x1+ω2·x2+ω3·x3+ω4·x4; ω1, ω2, ω3, and ω4 are the fusion weight values ​​corresponding to the first, second, third, and fourth types of abnormal sites, respectively, and their sum is 1. x1, x2, x3, and x4 represent the states of the abnormal types, with values ​​of 0 or 1, representing normal and abnormal states respectively.

2. The method for identifying abnormal sites in human meridian electrical conductivity data according to claim 1, characterized in that, The method for identifying abnormal sites in human meridian electrical conductivity data further includes: Obtain the current individual's various symptom manifestations and identify at least one normal symptom manifestation; According to the symptom checklist, find the sampling sites corresponding to the above-mentioned normal symptom manifestations, their theoretical reference values ​​of conductivity, and reference relationship characteristics; Moxibustion is performed according to the reference moxibustion plan. After moxibustion, the actual conductivity values ​​of the above sampling sites of the current individual and their associated actual relationship characteristics are collected, and the symptom manifestations of the current individual are obtained. If the actual relationship characteristics of the conductivity values ​​at the above sampling sites are similar to the reference relationship characteristics, and the aforementioned normal symptom manifestations remain unchanged, then the measured actual conductivity values ​​and associated actual relationship characteristics are used as a reference to correct the theoretical reference values ​​and reference relationship characteristics of the conductivity values ​​corresponding to the sampling sites.

3. The method for identifying abnormal sites in human meridian electrical conductivity data according to claim 2, characterized in that, The method for identifying abnormal sites in human meridian electrical conductivity data further includes: Based on big data analysis, the relationships between various symptom manifestations are established and stored as a symptom association table; The system acquires the current individual's symptom presentation and uses the symptom association table to identify the potential symptoms of the current individual and their associated specific sampling sites. Obtain the first conductivity value at the specific sampling site of the current individual; Based on the potential symptoms, a stimulation action is applied to the current individual, and the second conductivity value at the specific sampling site of the current individual after the stimulation action is applied is obtained. By comparing the difference between the first and second conductance values ​​and combining this with the theoretical reference values ​​of the conductance values ​​at the specific sampling points, it is determined whether the change in conductance value caused by the stimulus action is within the theoretical reference value: If the change in conductivity is too small or too large, the aforementioned potential symptoms will be included in the abnormal symptoms. The stimulation actions include traction and pressing, acupuncture, temperature stimulation, or emotional stimulation.

4. The method for identifying abnormal sites in human meridian electrical conductivity data according to claim 2, characterized in that, The method for identifying abnormal sites in human meridian electrical conductivity data also includes: Based on the symmetrical characteristics of the left and right meridians of the human body, a set number of symmetrical sampling points are selected as quick reference points; Acquire and store the theoretical reference values ​​of conductivity corresponding to the above-mentioned quick reference sites, and the reference relationship characteristics between them and the conductivity values ​​of symmetrical sampling sites; According to the symptom checklist, obtain and store the symptom manifestations corresponding to abnormal conductivity values ​​or reference relationship characteristics of the aforementioned quick reference sites, and store them as quick reference symptoms; The above-mentioned quick reference symptoms and their corresponding reference moxibustion plans are stored together; If the current individual's symptoms are consistent with the quick reference symptoms, the conductivity value and actual relationship characteristics of the quick reference site are obtained; if the above conductivity value and / or actual relationship characteristics are abnormal, the reference moxibustion scheme is used as the moxibustion scheme for subsequent verification.

5. The method for identifying abnormal sites in human meridian electrical conductivity data according to claim 1, characterized in that, The method also includes a verification moxibustion plan based on a symptom checklist and generated according to abnormal sampling sites to verify the abnormal identification results, including: Based on the theoretical reference value corresponding to the conductivity value at the abnormal sampling point and the detected actual conductivity value, the degree of abnormality of the conductivity value at the abnormal sampling point is analyzed, and the abnormality is classified according to the degree of abnormality. Based on the symptom checklist, a reference moxibustion plan is determined, and the reference moxibustion plan is divided into at least two moxibustion stages according to different grading standards. The initial moxibustion stage is a trial moxibustion stage, used to observe the change in conductivity values ​​at the abnormal sampling sites after moxibustion, so as to adjust the moxibustion plan for subsequent stages. The degree of abnormality is positively correlated with the number of stages in the reference moxibustion plan.

6. The method for identifying abnormal sites in human meridian electrical conductivity data according to claim 5, characterized in that, The method also includes: collecting the conductivity values ​​of each sampling site after moxibustion and the actual relationship characteristics between the conductivity values; Based on whether the conductivity value and actual relationship characteristics at the abnormal sampling sites change toward the theoretical reference value and reference relationship characteristics, the generated moxibustion scheme for verification is adjusted and a new moxibustion scheme for verification is output. This includes adjusting the generated moxibustion verification plan and outputting a new moxibustion verification plan, including: Multiple samplings were conducted to obtain the conductivity values ​​at abnormal sampling sites before and after moxibustion in the initial stage. The numerical change trends of the above conductivity values ​​and their associated actual relationship characteristics were calculated. Based on the numerical change trend and the theoretical reference value and reference relationship characteristics of the conductivity value at the above-mentioned abnormal sampling points, the trend direction of the generated conductivity value is analyzed. Analyze the trend of the difference between the actual conductivity value and the theoretical reference value before and after moxibustion as the moxibustion parameters change, and adjust the subsequent moxibustion plan accordingly. The moxibustion parameters include moxibustion duration and moxibustion temperature.

7. A system for identifying abnormal sites in human meridian electrical conductivity data, characterized in that, include: The meridian conductivity detection unit (1) is used to obtain the actual conductivity value of the current individual at a preset specific meridian sampling point and generate the actual relationship characteristics between the conductivity values ​​at each sampling point; Reference data storage unit (2) is used to acquire and store the theoretical reference value of the conductivity value at each of the sampling points, and the reference relationship features used to reflect the correlation between the conductivity values ​​at each of the sampling points; Obtain the symptoms and reference moxibustion plans corresponding to abnormal conductivity values ​​and / or abnormal reference relationship characteristics at each sampling site, and store them as a symptom check and response table; The abnormal site identification unit (3) is connected to the meridian conductivity detection unit (1) and the reference data storage unit (2) for comparing the actual conductivity value at each sampling site with the theoretical reference value, analyzing and confirming the sampling sites with abnormal conductivity values, and marking them as a type of abnormal site; The actual relationship characteristics associated with the actual conductivity values ​​at each sampling point are compared with the reference relationship characteristics. Sampling points with abnormal relationship characteristics are analyzed and identified, and marked as Class II abnormal points. Sampling points with abnormal conductivity values ​​and abnormal relationship characteristics are marked as Class III abnormal points. The symptom collection and analysis unit (4) is used to obtain the current individual's symptom manifestations and to confirm the sampling sites of suspected abnormalities according to the symptom checklist, and mark them as four types of abnormal sites; The abnormal site determination unit (5) is connected to the symptom collection and analysis unit (4) and is used to configure fusion weights for the abnormal types of each sampling site and determine the abnormal sampling sites of the current individual based on the set fusion algorithm. Symptom collection includes one or more of the following: history taking, visual inspection, palpation, percussion, auscultation, and auxiliary examinations. The formula for calculating the probability of anomalies at sampling sites is: P i =ω1·x1+ω2·x2+ω3·x3+ω4·x4; ω1, ω2, ω3, and ω4 are the fusion weight values ​​corresponding to the first, second, third, and fourth types of abnormal sites, respectively, and their sum is 1. x1, x2, x3, and x4 represent the states of the abnormal types, with values ​​of 0 or 1, representing normal and abnormal states respectively.

8. The human meridian electrical conductance data abnormality site identification system according to claim 7, characterized in that, The human body meridian conductivity data abnormality site identification system also includes: The normal symptom identification unit is connected to the reference data storage unit (2), the symptom acquisition and analysis unit (4), and the meridian conductance detection unit (1). It is used to acquire various symptom manifestations of the current individual and determine at least one normal symptom manifestation. According to the symptom check table, it finds the sampling site corresponding to the above normal symptom manifestation and its theoretical reference value and reference relationship characteristics of its conductance value. The reference value correction unit is configured to collect the actual conductivity value at the current individual's sampling site and its associated actual relationship characteristics after moxibustion, and to obtain the current individual's symptom manifestations after moxibustion. If the actual relationship characteristics of the conductivity value at the sampling site are similar to the reference relationship characteristics, and the normal symptom remains unchanged, then the measured actual conductivity value and the associated actual relationship characteristics are used as a reference to correct the theoretical reference value of the conductivity value and the reference relationship characteristics of the sampling site stored in the reference data storage unit (2). The potential symptom identification and verification unit is connected to the reference data storage unit (2), the symptom acquisition and analysis unit (4), and the meridian conductance detection unit (1). It is configured to establish the correlation between various symptom manifestations based on big data analysis and store it as a symptom association table. It acquires the symptom manifestations of the current individual and finds the potential symptoms of the current individual and their associated specific sampling sites according to the symptom association table. It acquires the first conductance value at the specific sampling site of the current individual, applies a stimulation action to the current individual according to the potential symptoms, acquires the second conductance value at the specific sampling site of the current individual after applying the stimulation action, compares the difference between the first conductance value and the second conductance value, and determines whether the change in conductance value caused by the stimulation action is within the normal fluctuation range by combining the theoretical reference value of the conductance value at the specific sampling site. If the change in conductance value is too small or too large, the potential symptoms are included in the abnormal symptoms. The stimulation actions include traction and pressing, acupuncture, temperature stimulation, or emotional stimulation.

9. A moxibustion device, characterized in that, It includes a moxibustion frame with adjustable position and angle and a moxibustion box set on the frame, and also includes the human meridian electrical conduction data abnormal site identification system as described in claim 7 or 8. Among them, the meridian conduction detection unit (1) includes a first electrode set at a set position on the current individual's body and a second electrode set at the sampling point. The first electrode, the second electrode and the current patient's body form a conductive path. The symptom collection and analysis unit includes a specific physiological parameter collection component and a human-computer interaction component, configured to directly collect physiological parameter characteristic data that reflects the current individual's symptoms, or to allow manual input and confirmation of the current individual's symptom presentation.

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