Palm temperature monitor for TCM diagnosis

Abstract

The present invention provides a palm temperature monitor for Traditional Chinese Medicine (TCM) diagnosis. The monitor comprises an infrared sensor array that collects discrete palm temperature data; a temperature field reconstruction module that generates a continuous temperature field distribution; a TCM zoning module that divides viscera-related regions according to meridian maps; a dynamic coupling analysis module that calculates thermodynamic coupling parameters between regions; an abnormality diagnosis module that generates graded pathological indicators based on the coupling parameters; an environmental compensation module that corrects the discrete temperature data; a syndrome mapping module that matches the pathological indicators with a TCM syndrome database; and an output module that integrates the temperature field distribution with the syndrome matching results to generate a diagnostic report. This device can accurately reflect the functional status of the viscera, providing an objective and quantitative basis for TCM diagnosis, thereby improving diagnostic efficiency and accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of traditional Chinese medicine diagnosis, and more particularly, to a palm temperature monitor for traditional Chinese medicine diagnosis. Background Art

[0002] In Traditional Chinese Medicine (TCM) diagnosis, traditional diagnostic methods rely primarily on the physician's subjective experience, such as inspection, auscultation, questioning, and palpation, lacking objective, quantitative testing indicators and supporting equipment. In recent years, with the advancement of modern technology, some TCM diagnostic devices based on biomedical engineering have emerged, such as those that measure the body's surface temperature distribution to aid diagnosis. These devices typically utilize infrared thermal imaging to obtain local or global temperature information, attempting to reflect physiological or pathological conditions through temperature changes. However, existing temperature monitoring devices still have limitations in TCM diagnosis. Firstly, traditional devices often rely on single-point or localized temperature measurement, making it difficult to fully capture subtle changes in the palm temperature field. Secondly, they lack zonal analysis and dynamic coupling calculations integrated with TCM meridian theory, making it impossible to accurately locate the functional status of internal organs. Furthermore, existing devices lack adequate compensation for environmental factors, which can easily lead to measurement errors and compromise the accuracy and reliability of diagnostic results.

[0003] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the existing technology: First, the existing equipment is unable to organically combine palm temperature data with the theory of traditional Chinese medicine meridians, making it difficult to achieve standardization and quantification of traditional Chinese medicine diagnosis; second, the existing equipment lacks analysis of the dynamic characteristics of the temperature field and cannot reflect the interaction relationship between the internal organs; finally, the existing equipment does not compensate for environmental factors sufficiently, resulting in the measurement results being greatly affected by the environment, making it difficult to meet the accuracy requirements of traditional Chinese medicine diagnosis. Summary of the Invention

[0004] The present invention provides a palm temperature monitor for traditional Chinese medicine diagnosis, comprising:

[0005] (1) an infrared sensor array module configured to collect discrete temperature data from the palm surface;

[0006] (2) a temperature field reconstruction module, which receives the discrete temperature data and generates a continuous temperature field distribution;

[0007] (3) a TCM partitioning module, which divides the continuous temperature field distribution into viscera-related regions based on a preset meridian map;

[0008] (4) A dynamic coupling analysis module receives the continuous temperature field distribution and partition results and calculates the thermodynamic coupling parameters between regions;

[0009] (5) an abnormality diagnosis module, generating a graded pathology index based on the thermodynamic coupling parameters;

[0010] (6) an environmental compensation module, which corrects the discrete temperature data according to real-time environmental parameters;

[0011] (7) Syndrome type mapping module, matching the pathological indicators with the TCM syndrome type database to obtain syndrome type matching results;

[0012] (8) An output module, integrating the continuous temperature field distribution and the syndrome matching result to generate a diagnosis report.

[0013] Furthermore, the temperature field reconstruction module includes:

[0014] (2.1) Data interpolation unit, receiving the discrete temperature data ,implement:

[0015] ①Construct interpolation function:

[0016]

[0017] in, is the weight coefficient of the i-th sensor, is a cubic radial basis function and , is the Euclidean distance between the point to be inserted and the i-th sensor; is the linear compensation coefficient;

[0018] ② Solve the coefficients through the constraint equations:

[0019]

[0020] (2.2) The boundary compensation unit receives the interpolated temperature field and executes:

[0021] ①Extract the temperature gradient modulus to meet The boundary point set of is the spatial temperature gradient;

[0022] ②Use cubic spline interpolation method to fit the boundary curve ,in is the boundary arc length parameter;

[0023] ③Calculate curvature , and correct the temperature value:

[0024]

[0025] (2.3) The noise suppression unit performs the following operations on the compensated temperature field:

[0026] ① Calculate the temperature field difference at adjacent moments ,in is the current temperature value, is the temperature value at the previous moment;

[0027] ②Set the number of variational mode decomposition layers ,in is the floor function.

[0028] Furthermore, the noise suppression unit performs:

[0029] (3.1) Decompose the temperature field into Intrinsic Mode Function ,in ;

[0030] (3.2) Eliminate the highest frequency component ;

[0031] (3.3) Reconstruct the noise-reduced temperature field:

[0032]

[0033] The weight factor Used to suppress high-frequency noise.

[0034] Furthermore, the dynamic coupling analysis module performs:

[0035] (4.1) Construction of space-time tensor: Based on the continuous temperature field distribution and time series data, the space-time tensor is constructed:

[0036]

[0037] in is the time derivative, is the spatial temperature gradient;

[0038] (4.2) Calculation of inter-region coupling parameters: Based on the space-time tensor and TCM partitioning results, the coupling coefficients of adjacent regions are calculated:

[0039]

[0040] in, is the geometric range of adjacent regions m and n, is the temperature difference between regions; is the shortest distance from the region boundary to the measurement point, is the area of ​​the region;

[0041] (4.3) Solution of dynamic equations: Based on the space-time tensor and coupling coefficient , solve the dynamic evolution equation:

[0042]

[0043] in , is the initial space-time tensor.

[0044] Furthermore, the abnormality diagnosis module performs:

[0045] (5.1) Obtain the maximum temperature gradient of each region from the dynamic coupling analysis module:

[0046]

[0047] (5.2) Calculate the temperature difference threshold between adjacent areas:

[0048]

[0049] in is the current average palm temperature;

[0050] (5.3) When there exists a region satisfying The temperature difference between adjacent areas When , a three-level abnormality indicator is generated.

[0051] Furthermore, the three-level abnormal indicators include:

[0052] (6.1) Level 1 anomaly: A single area continuously meets Up to 120 seconds;

[0053] (6.2) Secondary anomaly: temperature difference between two adjacent areas Lasts 60 seconds;

[0054] (6.3) Level 3 anomaly: Both level 1 and level 2 anomaly conditions are met.

[0055] Furthermore, the certificate type mapping module performs:

[0056] (7.1) Construct the feature vector:

[0057]

[0058] (7.2) Calculation of syndrome matching degree:

[0059]

[0060] in is the historical statistical parameter of the kth syndrome type;

[0061] (7.3) When When the pulse wave data is called, the correction matching degree is calculated .

[0062] Furthermore, the coefficient Replace with:

[0063]

[0064] Furthermore, when When executing:

[0065] (9.1) Calculate the temperature difference between three adjacent frames:

[0066]

[0067] in is the temperature field of the first two frames;

[0068] (9.2) If , it is determined to be a transient interference and the original temperature field is restored.

[0069] Furthermore, the environmental compensation module performs:

[0070] (10.1) Real-time collection of ambient temperature and humidity ;

[0071] (10.2) Calculate the temperature compensation:

[0072]

[0073] (10.3) When When , correct the sensor data:

[0074]

[0075] in is the original temperature value, To compensate for the duration.

[0076] According to the above-mentioned embodiment of the present invention, there are at least the following beneficial effects: the palm temperature monitor for TCM diagnosis of the present invention can realize the automated processing from discrete palm temperature data collection to TCM syndrome diagnosis. Through the infrared sensor array module and the temperature field reconstruction module, the continuous temperature field distribution of the palm surface can be accurately obtained, and the temperature field can be partitioned and analyzed based on the TCM meridian theory, thereby providing an objective basis for the evaluation of the functional status of the internal organs. The dynamic coupling analysis module can calculate the thermodynamic coupling parameters between regions, further reflect the interaction relationship between the internal organs, and provide more comprehensive pathological information for TCM diagnosis. The abnormal diagnosis module can generate graded pathological indicators based on the coupling parameters, which helps to quickly identify potential health problems.

[0077] Furthermore, the environmental compensation module provides real-time correction of discrete temperature data, effectively reducing the impact of environmental factors on measurement results and improving diagnostic accuracy and reliability. The syndrome mapping module matches pathological indicators with the Traditional Chinese Medicine (TCM) syndrome database, providing patients with personalized TCM syndrome-based diagnoses. Overall, this monitor provides an objective, quantitative, and efficient technical approach for TCM diagnosis, promoting the modernization and standardization of TCM diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily apparent by reading the following detailed description with reference to the accompanying drawings, in which several embodiments of the present invention are shown by way of example and not limitation, in which:

[0079] Figure 1 This is a structural schematic diagram of a palm temperature monitor for traditional Chinese medicine diagnosis provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0080] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided solely to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. Rather, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0081] Those skilled in the art will appreciate that the embodiments of the present invention may be implemented as a system, apparatus, device, method, or computer program product. Therefore, the present invention may be implemented in the following forms: entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or in a combination of hardware and software.

[0082] It should be noted that any number of elements in the drawings is for illustration only and not for limitation, and any naming is only for distinction and does not have any limiting meaning.

[0083] Reference below Figure 1 , Figure 1 This is a schematic diagram of the structure of a palm temperature monitor for traditional Chinese medicine diagnosis provided by one embodiment of the present invention. Figure 1 As shown, a palm temperature monitor 100 for traditional Chinese medicine diagnosis includes:

[0084] (1) an infrared sensor array module configured to collect discrete temperature data from the palm surface;

[0085] (2) a temperature field reconstruction module, which receives the discrete temperature data and generates a continuous temperature field distribution;

[0086] (3) a TCM partitioning module, which divides the continuous temperature field distribution into viscera-related regions based on a preset meridian map;

[0087] (4) A dynamic coupling analysis module receives the continuous temperature field distribution and partition results and calculates the thermodynamic coupling parameters between regions;

[0088] (5) an abnormality diagnosis module, generating a graded pathology index based on the thermodynamic coupling parameters;

[0089] (6) an environmental compensation module, which corrects the discrete temperature data according to real-time environmental parameters;

[0090] (7) Syndrome type mapping module, matching the pathological indicators with the TCM syndrome type database to obtain syndrome type matching results;

[0091] (8) An output module, integrating the continuous temperature field distribution and the syndrome matching result to generate a diagnosis report.

[0092] It should be noted that the core of this TCM palm temperature monitor lies in the collaborative operation of a series of modules to collect and analyze palm temperature and ultimately generate a diagnostic report. The infrared sensor array module collects discrete temperature data from the palm surface. Discrete temperature data refers to temperature values ​​measured by multiple sensors at different locations on the palm. The temperature field reconstruction module converts this discrete temperature data into a continuous temperature field distribution, providing a more intuitive representation of temperature variations on the palm surface. The TCM zoning module divides the continuous temperature field into regions associated with internal organs based on a preset meridian map. The meridian map refers to a map of the human meridians drawn according to TCM theory, which serves as a guide for zoning. The dynamic coupling analysis module calculates the thermodynamic coupling parameters between regions. These parameters reflect the temperature correlation between regions. The abnormality diagnosis module generates graded pathological indicators based on the coupling parameters to assess whether the palm temperature distribution is normal. The environmental compensation module considers the impact of environmental factors on measurement results and improves measurement accuracy by correcting the discrete temperature data. The syndrome mapping module matches pathological indicators with a TCM syndrome database, which contains a variety of TCM syndromes and their corresponding pathological features. Finally, the output module integrates the continuous temperature field distribution with the syndrome matching results to generate a diagnostic report, providing a reference for TCM diagnosis.

[0093] Specifically, the infrared sensor array module consists of multiple high-precision infrared sensors evenly distributed across the palm contact surface, collecting real-time temperature data from various locations on the palm surface. The temperature field reconstruction module converts discrete temperature data into a continuous temperature field by constructing an interpolation function. This interpolation function construction involves setting parameters such as weight coefficients and cubic radial basis functions. The weight coefficients adjust the importance of each sensor data in the interpolation process, and the cubic radial basis function is a mathematical function used to smoothly connect discrete data points. The boundary compensation unit addresses boundary effects in the temperature field by extracting boundary point sets and fitting boundary curves to further correct temperature values. The noise suppression unit identifies noise by calculating the difference in the temperature field between adjacent moments and uses variational mode decomposition techniques to reduce the noise in the temperature field. The dynamic coupling analysis module constructs a spatiotemporal tensor based on the continuous temperature field distribution and time series data, reflecting the dynamic changes in the temperature field by calculating time derivatives and spatial gradients. The calculation of inter-region coupling parameters takes into account factors such as the geometric extent, temperature difference, and area of ​​adjacent regions to quantify the thermodynamic correlation between regions. The Abnormality Diagnosis Module calculates the maximum temperature gradient in each region based on the output of the Dynamic Coupling Analysis Module and combines it with the temperature difference thresholds of adjacent regions to determine whether an abnormality exists. The Syndrome Mapping Module constructs feature vectors and calculates the syndrome matching degree, matching pathological indicators with syndromes in the Traditional Chinese Medicine (TCM) syndrome database to obtain the closest syndrome result.

[0094] Preferably, the interpolation function in the temperature field reconstruction module can be optimized using a variety of mathematical methods, such as adjusting the distribution method of weight coefficients to improve the interpolation accuracy. In the boundary compensation unit, a more complex curve fitting algorithm can be introduced to improve the accuracy of boundary processing. In the noise suppression unit, the number of layers of variational mode decomposition can be adjusted according to the actual application scenario to better adapt to temperature field data with different noise levels. In the dynamic coupling analysis module, the construction of the space-time tensor can consider introducing more space-time dimension information to more comprehensively reflect the dynamic characteristics of the temperature field. In the abnormal diagnosis module, the calculation of the temperature difference threshold can be personalized according to the physiological characteristics of different populations to improve the accuracy of diagnosis.

[0095] In some embodiments, the temperature field reconstruction module includes:

[0096] (2.1) Data interpolation unit, receiving the discrete temperature data ,implement:

[0097] ①Construct interpolation function:

[0098]

[0099] in, is the weight coefficient of the i-th sensor, is a cubic radial basis function and , is the Euclidean distance between the point to be inserted and the i-th sensor; is the linear compensation coefficient;

[0100] ② Solve the coefficients through the constraint equations:

[0101]

[0102] (2.2) The boundary compensation unit receives the interpolated temperature field and executes:

[0103] ①Extract the temperature gradient modulus to meet The boundary point set of is the spatial temperature gradient;

[0104] ②Use cubic spline interpolation method to fit the boundary curve ,in is the boundary arc length parameter;

[0105] ③Calculate curvature , and correct the temperature value:

[0106]

[0107] (2.3) The noise suppression unit performs the following operations on the compensated temperature field:

[0108] ① Calculate the temperature field difference at adjacent moments ,in is the current temperature value, is the temperature value at the previous moment;

[0109] ②Set the number of variational mode decomposition layers ,in is the floor function.

[0110] It should be noted that the temperature field reconstruction module is one of the core components of the monitor. Its main function is to generate a continuous temperature field distribution through interpolation and compensation of discrete temperature data. This module includes a data interpolation unit, a boundary compensation unit, and a noise suppression unit. The data interpolation unit connects discrete temperature data points by constructing an interpolation function to form a continuous temperature field. The interpolation function combines weight coefficients, cubic radial basis functions, and linear compensation term coefficients to smoothly transition temperature data. The boundary compensation unit processes the boundary areas of the temperature field, extracting boundary point sets and fitting boundary curves to correct the temperature values ​​at the boundaries to reduce errors caused by boundary effects. The noise suppression unit analyzes the differences in temperature fields between adjacent moments to identify and suppress noise, thereby improving the quality of temperature field data. The synergistic effect of these units ensures the accuracy and reliability of temperature field reconstruction.

[0111] Specifically, the interpolation function in the data interpolation unit is constructed using mathematical methods. Its core is the cubic radial basis function, which assigns weights based on the distance between sensor measurement points to generate a smooth temperature distribution. The weight coefficients adjust the contribution of each sensor data point in the interpolation process, while the linear compensation term coefficients correct the linear trend of the overall temperature field. The boundary compensation unit identifies boundary points by calculating the temperature gradient modulus. When the gradient modulus exceeds a set threshold (e.g., 1.2), the point is considered to be in the boundary region. The boundary curve is fitted using cubic spline interpolation, a commonly used curve fitting method that can generate a smooth boundary curve based on the distribution of boundary points. Boundary temperature values ​​are corrected by calculating curvature and applying a correction formula. Curvature reflects the degree of curvature of the boundary shape, and the correction formula adjusts the temperature value based on the curvature to reduce the impact of boundary effects. The noise suppression unit identifies noise by calculating the difference between the temperature fields at adjacent moments. A greater difference indicates a higher likelihood of noise. The number of variational mode decomposition layers is adjusted according to the dynamic change frequency of the temperature field. By decomposing the temperature field into multiple intrinsic mode functions, high-frequency noise components are eliminated, thereby achieving noise reduction.

[0112] Preferably, the interpolation function in the data interpolation unit can be further optimized, for example, by adjusting the distribution method of the weight coefficients to make it more consistent with the actual characteristics of the palm temperature distribution. In the boundary compensation unit, more complex curve fitting algorithms, such as high-order polynomial fitting or Bezier curve fitting, can be introduced to improve the accuracy and adaptability of boundary processing. In the noise suppression unit, the number of layers of variational mode decomposition can be dynamically adjusted according to the actual application scenario. For example, in an environment with more high-frequency noise, the number of decomposition layers can be increased to better suppress noise. In addition, adaptive filtering technology can also be introduced to dynamically adjust the filtering parameters according to the real-time changes in the temperature field to further improve the noise reduction effect.

[0113] In some embodiments, the noise suppression unit performs:

[0114] (3.1) Decompose the temperature field into Intrinsic Mode Function ,in ;

[0115] (3.2) Eliminate the highest frequency component ;

[0116] (3.3) Reconstruct the noise-reduced temperature field:

[0117]

[0118] The weight factor Used to suppress high-frequency noise.

[0119] It should be noted that the noise suppression unit is an important component of the temperature field reconstruction module. Its main function is to reduce the noise of the compensated temperature field to improve the quality and reliability of the temperature field data. This unit decomposes the temperature field into multiple intrinsic mode functions (IMFs), removes the high-frequency noise components, and reconstructs the denoised temperature field. The intrinsic mode functions here refer to the components that can reflect the different frequency characteristics of the temperature field obtained through variational mode decomposition (VMD) technology. In this way, high-frequency noise in the temperature field can be effectively removed while retaining the effective low-frequency signals, thereby providing a more accurate data basis for subsequent diagnostic analysis.

[0120] Specifically, the operating steps of the noise suppression unit include three main links. First, the temperature field is decomposed into multiple intrinsic mode functions through variational mode decomposition technology. Variational mode decomposition is an adaptive signal decomposition method that can decompose the signal into several components with different frequency characteristics according to its characteristics. In the present invention, the number of decomposition layers is set according to the dynamic change frequency of the temperature field, specifically 3 plus half of the temperature field change frequency (rounded down). Secondly, the highest frequency component is eliminated because the high-frequency component usually contains more noise information. Finally, the denoised temperature field is generated by weighted reconstruction, where the weight factor is used to further suppress the influence of high-frequency noise. This noise reduction method can effectively balance the noise reduction effect and signal fidelity, ensuring the accuracy and reliability of the temperature field data.

[0121] Preferably, the operating steps of the noise suppression unit can be further optimized. For example, in the setting of the number of decomposition layers, it can be dynamically adjusted according to the specific application scenario and noise level of the temperature field. For environments with more high-frequency noise, the number of decomposition layers can be appropriately increased to remove the noise more thoroughly; while for environments with lower noise levels, the number of decomposition layers can be reduced to improve computational efficiency. In addition, the setting of the weight factor can also be adjusted according to actual needs. For example, weights can be dynamically assigned according to the frequency characteristics of the intrinsic mode function, so that the weight of the high-frequency component is smaller, thereby further suppressing the high-frequency noise. As an alternative, other noise reduction algorithms, such as wavelet transform or adaptive filtering technology, can also be introduced and combined with variational mode decomposition to improve the noise reduction effect.

[0122] In some embodiments, the dynamic coupling analysis module performs:

[0123] (4.1) Construction of space-time tensor: Based on the continuous temperature field distribution and time series data, the space-time tensor is constructed:

[0124]

[0125] in is the time derivative, is the spatial temperature gradient;

[0126] (4.2) Calculation of inter-region coupling parameters: Based on the space-time tensor and TCM partitioning results, the coupling coefficients of adjacent regions are calculated:

[0127]

[0128] in, is the geometric range of adjacent regions m and n, is the temperature difference between regions; is the shortest distance from the region boundary to the measurement point, is the area of ​​the region;

[0129] (4.3) Solution of dynamic equations: Based on the space-time tensor and coupling coefficient , solve the dynamic evolution equation:

[0130]

[0131] in , is the initial space-time tensor.

[0132] It should be noted that the dynamic coupling analysis module is a key component of the monitor, used to analyze the dynamic characteristics of the palm temperature field and the inter-regional relationships. Its core function is to quantitatively analyze the thermodynamic connections between different palm regions by constructing a spatiotemporal tensor and calculating inter-regional coupling parameters. The spatiotemporal tensor is a multidimensional array that describes the temporal and spatial variations of the temperature field, while the coupling parameters reflect the strength of the temperature correlation between adjacent regions. These analyses provide Traditional Chinese Medicine (TCM) diagnoses with dynamic information on the functional status of internal organs and their interactions, enabling more accurate identification of potential pathological changes.

[0133] Specifically, the operation of the dynamic coupling analysis module includes three main steps. First, the construction of the space-time tensor is based on the continuous temperature field distribution and its time series data. Among them, the time derivative is used to describe the rate of change of temperature over time, and the spatial gradient reflects the trend of temperature change in space. These two parameters together constitute the basic framework of the space-time tensor, which can fully capture the dynamic characteristics of the temperature field. Secondly, the calculation of the inter-regional coupling parameters is based on the results of traditional Chinese medicine zoning. By quantifying information such as the temperature difference, geometric range and boundary distance of adjacent regions, the coupling coefficient is calculated. The size of the coupling coefficient directly reflects the degree of thermodynamic correlation between the two regions. The greater the temperature difference or the smaller the regional area, the higher the coupling coefficient is generally. Finally, the solution of the dynamic evolution equation further simulates the dynamic change process of the temperature field. By introducing the coupling coefficient and the initial space-time tensor, the future change trend of the temperature field can be predicted, providing richer dynamic information for diagnosis.

[0134] Preferably, the operation of the dynamic coupling analysis module can be further refined and optimized. For example, in the construction of the space-time tensor, higher-order time derivatives or spatial gradients can be introduced to more finely capture the dynamic changes of the temperature field. For the calculation of the coupling parameters, more weight factors can be introduced to take into account the physiological characteristics of different regions or the strength of the internal organs in traditional Chinese medicine theory, so as to more accurately reflect the actual coupling relationship between regions. In addition, the solution of the dynamic evolution equation can adopt more advanced numerical methods, such as finite element analysis or adaptive time step algorithm, to improve the solution accuracy and efficiency. As an alternative, a machine learning algorithm can also be introduced to automatically optimize the calculation model of the coupling parameters by learning a large amount of temperature field data, thereby further improving the accuracy and reliability of the diagnosis.

[0135] In some embodiments, the abnormality diagnosis module performs:

[0136] (5.1) Obtain the maximum temperature gradient of each region from the dynamic coupling analysis module:

[0137]

[0138] (5.2) Calculate the temperature difference threshold between adjacent areas:

[0139]

[0140] in is the current average palm temperature;

[0141] (5.3) When there exists a region satisfying The temperature difference between adjacent areas When , a three-level abnormality indicator is generated.

[0142] It should be noted that the abnormality diagnosis module is a key component of the monitor for identifying and evaluating abnormalities in the palm temperature field. Its core function is to generate graded pathology indices by analyzing the coupling parameters output by the dynamic coupling analysis module, combining the maximum temperature gradient and temperature differences between adjacent areas of the palm temperature field. These indices quantify the degree of abnormality, helping the Traditional Chinese Medicine diagnostic system more accurately identify potential pathological conditions. The maximum temperature gradient refers to the maximum rate of temperature change within a region of the temperature field, while the temperature difference threshold is a reference value dynamically calculated based on the current average palm temperature, used to determine whether the temperature difference between regions exceeds the normal range.

[0143] Specifically, the operation process of the abnormal diagnosis module includes three main steps. First, the maximum temperature gradient of each area is obtained from the dynamic coupling analysis module. This parameter reflects the area with the most drastic temperature change in the temperature field. Secondly, the temperature difference threshold of the adjacent areas is calculated based on the average temperature of the current palm. The threshold is determined by a linear equation with a base value of 2.0 and a slope of 0.3, reflecting the linear relationship between the average palm temperature and the temperature difference threshold. Finally, when the maximum temperature gradient of a certain area exceeds 3.5 and the temperature difference of the adjacent areas exceeds the calculated threshold, the system generates a three-level abnormality indicator. This hierarchical diagnosis method can classify according to the severity of the abnormality, providing more detailed reference information for TCM diagnosis.

[0144] The operation of the anomaly diagnosis module can be further refined and optimized. For example, the calculation of the maximum temperature gradient can incorporate a time dimension to analyze the changing trend of the temperature gradient over a period of time, thereby more accurately determining the persistence and stability of the anomaly. The calculation of the temperature difference threshold can be personalized based on the physiological characteristics of different populations (such as age, gender, and physical fitness) to improve diagnostic accuracy and adaptability. Furthermore, machine learning algorithms can be introduced to automatically optimize the thresholds and grading criteria for anomaly diagnosis by learning from large amounts of sample data, further enhancing the intelligent level of diagnosis.

[0145] In some embodiments, the three-level abnormality indicators include:

[0146] (6.1) Level 1 anomaly: A single area continuously meets Up to 120 seconds;

[0147] (6.2) Secondary anomaly: temperature difference between two adjacent areas Lasts 60 seconds;

[0148] (6.3) Level 3 anomaly: Both level 1 and level 2 anomaly conditions are met.

[0149] It should be noted that the three-level abnormality indicator is a further refinement and classification of the output results of the abnormality diagnosis module, aiming to more accurately describe the abnormal conditions of the palm temperature field. Among them, the first-level abnormality indicates that the maximum temperature gradient of a single area exceeds the threshold and persists for a certain period of time, reflecting a significant temperature change in the local area; the second-level abnormality focuses on the temperature difference between adjacent areas exceeding the threshold and persisting for a certain period of time, reflecting the temperature imbalance between regions; the third-level abnormality is the most serious situation that meets both the first-level and second-level abnormality conditions, indicating that the palm temperature field has significant local abnormalities and inter-regional disharmony. This grading method can provide more detailed abnormality information for TCM diagnosis, helping doctors to more accurately judge pathological conditions.

[0150] Specifically, the criterion for a first-level abnormality is that the maximum temperature gradient of a single area exceeds 3.5 and lasts for 120 seconds. This parameter setting is based on the range of changes in the palm temperature gradient under normal physiological conditions. When the temperature gradient of a certain area significantly exceeds the normal range and lasts for a long time, it can be considered that the area may be abnormal. The criterion for a second-level abnormality is that the temperature difference between adjacent areas exceeds the dynamically calculated temperature difference threshold and lasts for 60 seconds. The calculation of the temperature difference threshold takes into account the current average temperature of the palm and reflects the relativity of temperature changes. When the temperature difference between adjacent areas exceeds the normal range, it may indicate an incoordination between the functions of the internal organs. The third-level abnormality meets both the first-level and second-level abnormality conditions, indicating that there are serious local abnormalities and imbalances between regions in the palm temperature field, which is the most serious case in abnormal diagnosis.

[0151] Preferably, the parameters for abnormality diagnosis can be optimized based on the actual application scenario. For example, for a first-level abnormality, the duration can be adjusted based on the physiological characteristics of different populations. For example, the duration threshold for the elderly can be appropriately extended to accommodate their slower physiological changes. For a second-level abnormality, the formula for calculating the temperature difference threshold can be dynamically adjusted based on seasonal or ambient temperature changes to more accurately reflect the normal temperature range under different conditions. Furthermore, additional physiological parameters (such as heart rate and blood pressure) can be incorporated as auxiliary judgment criteria to further improve the accuracy and reliability of abnormality diagnosis.

[0152] In some embodiments, the certificate type mapping module performs:

[0153] (7.1) Construct the feature vector:

[0154]

[0155] (7.2) Calculation of syndrome matching degree:

[0156]

[0157] in is the historical statistical parameter of the kth syndrome type;

[0158] (7.3) When When the pulse wave data is called, the correction matching degree is calculated .

[0159] It should be noted that the syndrome mapping module is a key component of the monitor, used to match pathological indicators with TCM syndromes. Its core function is to compare the pathological indicators output by the dynamic coupling analysis module with standard syndromes in the TCM syndrome database by constructing eigenvectors and calculating syndrome matching degrees, thereby obtaining the closest syndrome matching result. The eigenvector is a mathematical tool used to integrate the multiple dimensions of pathological indicators into a quantifiable vector form; the syndrome matching degree is obtained by calculating the similarity between the eigenvector and the syndrome parameters in the database, and is used to evaluate the degree of match between the pathological indicator and a specific TCM syndrome.

[0160] Specifically, the operation of the syndrome mapping module includes three main steps. First, when constructing the feature vector, pathological indicators such as the maximum temperature gradient of each region, the temperature difference between adjacent regions, and the coupling parameters between regions are integrated into a vector form for subsequent matching calculations. Second, when calculating the syndrome matching degree, the feature vector is compared with the historical statistical parameters of each syndrome in the traditional Chinese medicine syndrome database, and the similarity between the two is quantified using a mathematical formula. The calculation formula for the syndrome matching degree includes a normalization process for the sum of squares of the differences in each dimension to ensure that the matching degree calculation is comparable. Finally, when the matching degree is lower than the set threshold (such as 0.6), the system will call the pulse wave data as supplementary information to further correct the matching degree, thereby improving the accuracy and reliability of the diagnosis.

[0161] Preferably, the operation of the syndrome mapping module can be further refined and optimized. For example, when constructing feature vectors, different pathological indicators can be assigned different weights based on Traditional Chinese Medicine theory and clinical experience to highlight the importance of certain key indicators. For calculating syndrome matching, more complex similarity calculation methods, such as machine learning-based similarity metrics, can be introduced to improve matching accuracy and adaptability. Furthermore, when using pulse wave data for correction, the correction strategy can be dynamically adjusted based on pulse wave characteristics (such as pulse strength and rhythm) to further optimize matching results. As an alternative, multimodal data (such as tongue image and complexion) can be introduced as supplementary information and combined with pathological indicators to further enhance the accuracy and comprehensiveness of syndrome matching.

[0162] In some embodiments, the coefficient Replace with:

[0163]

[0164] It should be noted that the coefficient adjustment in the dynamic equation is dynamically adjusted based on the coupling coefficient to more accurately reflect the dynamic characteristics of the temperature field. The coupling coefficient here is a quantitative parameter that quantifies the strength of the temperature correlation between adjacent regions. Its magnitude directly affects the accuracy of the temperature simulation in the dynamic equation. By introducing a piecewise function to adjust the coefficient, the evolution of the temperature field can be more flexibly controlled under different coupling strengths, thereby improving the adaptability of the diagnostic model to different pathological conditions.

[0165] Specifically, the coefficient adjustment of the dynamic equation is based on the size of the coupling coefficient, which is divided into three intervals: when the coupling coefficient is greater than 0.7, the coefficient is 0.15; when the coupling coefficient is between 0.4 and 0.7, the coefficient is 0.1; when the coupling coefficient is less than 0.4, the coefficient is 0.05. The purpose of this segmented setting is to adjust the sensitivity of the dynamic equation according to the temperature change characteristics under different coupling strengths. For example, when the coupling coefficient is large, the change in temperature field may be more drastic, so a higher coefficient is required to enhance the dynamic response; when the coupling coefficient is small, the temperature change is relatively gentle, and a lower coefficient helps to avoid excessive amplification of noise. In this way, the dynamic equation can more accurately simulate the dynamic evolution process of the temperature field and provide a more reliable basis for traditional Chinese medicine diagnosis.

[0166] Preferably, the adjustment of the dynamic equation coefficients can be further optimized according to the actual application scenario. For example, more interval divisions of the coupling coefficient can be introduced to achieve more refined dynamic adjustment. In addition, the coefficient values ​​can also be dynamically calibrated based on clinical data to adapt to the temperature variation characteristics of different populations or different pathological conditions. As an alternative, it is possible to consider introducing an adaptive algorithm to dynamically adjust the coefficient value based on the real-time monitoring of the coupling coefficient, thereby further improving the adaptability and accuracy of the dynamic equation.

[0167] In some embodiments, when When executing:

[0168] (9.1) Calculate the temperature difference between three adjacent frames:

[0169]

[0170] in is the temperature field of the first two frames;

[0171] (9.2) If , it is determined to be a transient interference and the original temperature field is restored.

[0172] It should be noted that the noise suppression unit introduces the calculation of the temperature difference between three adjacent frames when processing high-frequency noise to further distinguish between transient interference and actual temperature changes. The temperature difference between three adjacent frames here refers to the degree of difference between the temperature field of the current frame and the temperature field of the previous two frames. By calculating this temperature difference, it can be determined whether the temperature change is a transient interference. When the temperature difference is less than the set threshold, the system considers the change to be a transient interference and restores the original temperature field, thus avoiding misjudgments caused by noise. This method can effectively suppress high-frequency noise during dynamic monitoring and improve the accuracy and stability of temperature field data.

[0173] Specifically, the noise suppression unit identifies instantaneous interference by calculating the temperature difference between three adjacent frames. In actual operation, the system records the temperature field of the current frame and the temperature field data of the previous two frames in real time, and then calculates the absolute value of the temperature difference between the current frame and the previous two frames. If the temperature difference is less than the set threshold (such as 2°C), it is judged as instantaneous interference. At this time, the system will restore the original temperature field, that is, ignore the instantaneous change to maintain the continuity and stability of the temperature field data. The core of this method is to utilize the redundancy of time series data and identify noise by comparing multiple frames of data, thereby improving the accuracy and reliability of noise suppression. In addition, the threshold setting can be adjusted according to the actual application scenario and noise level to adapt to different monitoring environments.

[0174] Preferably, the operation of the noise suppression unit can be further refined. For example, an adaptive threshold adjustment mechanism could be introduced to dynamically adjust the temperature difference threshold based on real-time noise levels, thereby more flexibly responding to noise interference in different environments. Furthermore, other noise detection methods (such as wavelet-based noise detection) could be combined with the three-frame temperature difference method to improve the accuracy and robustness of noise identification. As an alternative, the introduction of deep learning algorithms could be considered to automatically identify and suppress noise through training models, further enhancing the intelligence level of the system.

[0175] In some embodiments, the environmental compensation module performs:

[0176] (10.1) Real-time collection of ambient temperature and humidity ;

[0177] (10.2) Calculate the temperature compensation:

[0178]

[0179] (10.3) When When , correct the sensor data:

[0180]

[0181] in is the original temperature value, To compensate for the duration.

[0182] It's important to note that the core function of the environmental compensation module is to collect real-time ambient temperature and humidity data and dynamically adjust the sensor's measured temperature based on these parameters to compensate for the effects of these environmental factors on measurement results. Environmental compensation here refers to improving measurement accuracy by quantifying the impact of environmental factors (such as temperature and humidity) on the measured data and correcting for them. The compensation is calculated based on the difference between these environmental parameters and standard conditions (such as 25°C and 50% humidity). Specific compensation logic is used to adjust the sensor's measured temperature to ensure accurate and reliable measurement results.

[0183] Specifically, the operation of the environmental compensation module includes three main steps. First, the real-time acquisition of ambient temperature ( ) and humidity ( ), these parameters reflect the actual environmental conditions at the time of measurement. Secondly, a temperature compensation is calculated based on the difference between the environmental parameters and the standard conditions. This compensation is calculated by multiplying the difference between the ambient temperature and the standard temperature (25°C) by 0.5, and adding the difference between the ambient humidity and the standard humidity (50%) by 0.2. This calculation method reflects the degree to which changes in ambient temperature and humidity affect the measurement results. Finally, if the absolute value of the compensation exceeds 1°C, the raw sensor data is corrected. This correction process is implemented using an exponential decay function to ensure a smooth transition and avoid measurement errors caused by excessive compensation.

[0184] The operation of the environmental compensation module can be further refined and optimized. For example, the compensation coefficients (0.5 and 0.2) can be adjusted based on the actual application scenario to accommodate measurement requirements under different environmental conditions. The compensation duration can be dynamically adjusted based on the stability of the actual measurement environment. For example, in scenarios with rapidly changing environments, the compensation time can be shortened to quickly respond to environmental changes. Furthermore, more complex environmental models can be introduced to account for the impact of other environmental factors (such as airflow velocity and light intensity) on measurement results, and compensation accuracy can be further improved through multi-parameter compensation logic.

[0185] The aforementioned embodiments of the present invention have the following beneficial effects: The TCM diagnostic palm temperature monitor of the present invention, through the collaborative operation of multiple modules, can automate the entire process from palm temperature data collection to TCM syndrome diagnosis. The infrared sensor array module accurately collects discrete temperature data from the palm surface, providing reliable basic information for subsequent analysis. The temperature field reconstruction module utilizes interpolation functions and boundary compensation techniques to generate a continuous temperature field distribution, addressing the limitation of traditional devices that can only obtain discrete data. The TCM zoning module divides viscera-related regions based on preset meridian maps, integrating the temperature field with TCM theory to provide a basis for assessing visceral function. The dynamic coupling analysis module calculates thermodynamic coupling parameters between regions, further reflecting the interactions between viscera and facilitating comprehensive analysis of pathological conditions. The abnormality diagnosis module generates graded pathological indicators based on the coupling parameters, enabling rapid identification of potential health issues and providing graded early warnings. The syndrome mapping module matches pathological indicators with the TCM syndrome database, providing patients with personalized TCM syndrome diagnosis results and improving the targetedness and accuracy of diagnoses.

[0186] In addition, the environmental compensation module can collect ambient temperature and humidity in real time and correct discrete temperature data, effectively reducing the impact of environmental factors on measurement results and improving the reliability and stability of diagnosis. The noise suppression unit removes high-frequency noise through variational mode decomposition technology, which can further optimize the quality of temperature field data and ensure the accuracy of analysis results. The dynamic equation solving module can solve dynamic evolution equations based on space-time tensors and coupling coefficients, providing theoretical support for the dynamic monitoring of organ functional status. When the environmental compensation module detects significant environmental changes, it can automatically adjust the compensation strategy to further enhance the adaptability and stability of the device.

[0187] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or a terminal device such as a computer, server, mobile phone, or tablet.

[0188] The above descriptions merely illustrate some preferred embodiments of the present invention and the underlying technical principles. Those skilled in the art should understand that the scope of the invention encompassed by the embodiments of the present invention is not limited to technical solutions formed by specific combinations of the aforementioned technical features. It also encompasses other technical solutions formed by any combination of the aforementioned technical features or their equivalents, without departing from the aforementioned inventive concept. For example, a technical solution formed by replacing the aforementioned features with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention.

Claims

1. A palm temperature monitor for traditional Chinese medicine diagnosis, characterized in that: Includes the following modules connected in sequence: an infrared sensor array module configured to collect discrete temperature data from a palm surface; a temperature field reconstruction module, receiving the discrete temperature data and generating a continuous temperature field distribution; A traditional Chinese medicine partitioning module divides the continuous temperature field distribution into viscera-related areas based on a preset meridian map; A dynamic coupling analysis module receives the continuous temperature field distribution and partition results and calculates the thermodynamic coupling parameters between regions; an abnormality diagnosis module, generating a graded pathology index according to the thermodynamic coupling parameter; An environmental compensation module, which corrects the discrete temperature data according to real-time environmental parameters; A syndrome type mapping module matches the pathological indicators with the TCM syndrome type database to obtain a syndrome type matching result; An output module integrates the continuous temperature field distribution and the syndrome matching result to generate a diagnosis report; wherein the dynamic coupling analysis module performs the following steps: A space-time tensor is constructed based on the continuous temperature field distribution and time series data. The space-time tensor is: in, is the time derivative, is the spatial temperature gradient; Based on the space-time tensor and TCM partitioning results, the coupling coefficient of adjacent regions is calculated: in, is the geometric range of adjacent regions m and n, is the temperature difference between regions; is the shortest distance from the region boundary to the measurement point, is the area of ​​the region; Based on the space-time tensor and coupling coefficient , solve the dynamic evolution equation: in, , is the initial space-time tensor; Among them, the coefficients in the dynamic evolution equation are Replace with: is the coupling coefficient between adjacent regions.

2. The monitoring device according to claim 1, wherein: The temperature field reconstruction module includes: A data interpolation unit receives the discrete temperature data ,implement: Construct the interpolation function, the interpolation function is: in, is the weight coefficient of the i-th sensor, is a cubic radial basis function and , is the Euclidean distance between the point to be inserted and the i-th sensor; is the linear compensation coefficient; The boundary compensation unit is used to receive the interpolated temperature field and perform: Extract the temperature gradient modulus to meet The boundary point set of is the spatial temperature gradient; Using cubic spline interpolation to fit the boundary curve ,in is the boundary arc length parameter; Calculate the curvature and correct the temperature value: Where K' is the curvature of the boundary curve; The noise suppression unit performs the following steps on the compensated temperature field: Calculate the temperature field difference at adjacent moments ,in is the current temperature value, is the temperature value at the previous moment; Set the number of variational mode decomposition levels ,in is the floor function.

3. The monitoring instrument according to claim 2, characterized in that The noise suppression unit performs the following steps: Decompose the temperature field into Intrinsic Mode Function ,in ; Eliminate the highest frequency components ; Reconstruct the denoised temperature field: Among them, the weight factor Used to suppress high-frequency noise.

4. The monitoring instrument according to claim 1, wherein: The abnormality diagnosis module performs the following steps: Obtain the maximum temperature gradient in each region from the dynamic coupling analysis module: Calculate the temperature difference threshold between adjacent areas: in, is the current average palm temperature; When there is a region that satisfies The temperature difference between adjacent areas When , a three-level abnormality indicator is generated.

5. The monitoring instrument according to claim 4, characterized in that: The three-level abnormal indicators include: Level 1 anomaly: A single area continuously satisfies Up to 120 seconds; Secondary anomaly: temperature difference between two adjacent areas Lasts 60 seconds; Level 3 abnormality: Both level 1 and level 2 abnormality conditions are met.

6. The monitoring instrument according to claim 1, wherein: The certificate type mapping module performs the following steps: Construct the feature vector: in, is the maximum temperature gradient, is the temperature difference between two adjacent areas, is the coupling coefficient of adjacent regions; Calculate the syndrome matching degree: in, is the historical statistical parameter of the kth syndrome type, is the i-th component of the eigenvector v; when When the pulse wave data is called, the correction matching degree is calculated .

7. The monitoring instrument according to claim 2, characterized in that when When , the noise suppression unit also performs the following steps: Calculate the temperature difference between three adjacent frames: in, is the temperature field of the first two frames; like , it is determined to be a transient interference and the original temperature field is restored.

8. The monitoring instrument according to claim 1, wherein: The environmental compensation module performs the following steps: Real-time collection of ambient temperature and humidity ; Calculate the temperature compensation amount: when When , correct the sensor data: in, is the original temperature value, To compensate for the duration.

Images