Graphene physiotherapy data processing system and method for dysmenorrhea meridian effect comparison

Abstract

This invention belongs to the field of medical data processing technology and provides a graphene physiotherapy data processing system and method for comparing the effects of acupoints on dysmenorrhea. Based on preset physiotherapy parameters, it records physiotherapy execution data for acupoint groups and non-acupoint groups. Multi-dimensional data of subjects are collected according to preset time nodes. Subjects are randomly divided into acupoint groups and non-acupoint groups according to preset rules, and the baseline data of the grouped subjects is used as benchmark data. The benchmark data is classified, and corresponding statistical analysis methods are used to analyze the classification results for different categories. Combining the benchmark data, the physiotherapy execution data, and the multi-dimensional data, the specificity quantification results of the acupoint effects in the acupoint groups and non-acupoint groups are calculated according to the specificity quantification formula. A standardized data report is generated based on the statistical analysis results and the specificity quantification results of the acupoint effects. This achieves objective collection, precise processing, and quantitative analysis of physiotherapy effect data for acupoints and non-acupoints.

Description

Technical Field

[0001] This invention belongs to the field of medical data processing technology, and in particular relates to a graphene physiotherapy data processing system and method for comparing the effects of acupoints on dysmenorrhea. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Graphene far-infrared therapy, with its gentle warming effect and strong penetration, has been gradually applied to auxiliary treatment scenarios for gynecological symptoms such as dysmenorrhea. The specificity of acupoint effects in Traditional Chinese Medicine (TCM) is one of the core research directions in the field of external TCM treatment. By comparing the therapeutic response data of acupoints with those of non-acupoints, the specificity of acupoint effects can be objectively verified, thus providing data support for acupoint adaptation schemes for graphene therapy products.

[0004] In existing technologies, data processing for graphene therapy in the area of ​​comparing the effects of acupoints on dysmenorrhea largely relies on manual recording of subject symptom scores, therapy parameters, and related test data. Key intervention parameters such as application location, therapy temperature, and duration are prone to deviation due to different operators, making it difficult to guarantee data integrity and consistency. At the same time, the existing data classification, screening, and statistical processing are mainly done manually, which is not only time-consuming and labor-intensive, but also lacks standardized statistical analysis methods, resulting in insufficient objectivity and accuracy in data processing. It is difficult to accurately distinguish the differences in therapeutic effects between acupoints and non-acupoints, and it is impossible to form a standardized technical solution that can be applied on a large scale. Summary of the Invention

[0005] To address at least one of the technical problems mentioned above, this invention provides a graphene physiotherapy data processing system and method for comparing the effects of acupoints on dysmenorrhea. This system deeply integrates a standardized graphene physiotherapy terminal with an intelligent data processing system, enabling precise differentiation between the therapeutic effects of acupoints and non-acupoints, thus preventing the formation of a standardized technical solution that can be applied on a large scale.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: The first aspect of the present invention provides a graphene physiotherapy data processing system for comparing the effects of acupoints on dysmenorrhea, including a graphene acupoint patch physiotherapy terminal, a multi-dimensional data acquisition terminal and a data processing server, wherein the data processing server is communicatively connected to the graphene acupoint patch physiotherapy terminal and the multi-dimensional data acquisition terminal respectively. The graphene acupoint patch therapy terminal is used to record the therapy execution data of acupoint groups and non-acupoint groups based on preset therapy parameters. The multi-dimensional data acquisition terminal is used to collect multi-dimensional data of the subjects according to preset time nodes. The data includes dysmenorrhea symptom quantitative scoring data, physical sign data, serological test data, and physiotherapy safety feedback data. The data processing server includes: The subject grouping control submodule is used to randomly divide subjects into acupoint groups and non-acupoint groups according to preset rules, and use the baseline data of the subjects after grouping as the reference data. The statistical analysis submodule is used to classify the baseline data and to analyze the classification results of different classifications using corresponding statistical analysis methods. The specificity quantification submodule is used to combine the physiotherapy execution data and multi-dimensional data to calculate the specificity quantification results of the acupoint effect of the acupoint group and the non-acupoint group according to the specificity quantification calculation formula, and to calibrate abnormal data based on the benchmark data. The automatic results output submodule is used to automatically generate standardized data reports based on statistical analysis results and quantitative results of acupoint effect specificity.

[0007] Furthermore, the graphene acupoint patch therapy terminal performs therapy intervention according to the standardized procedures, specifically including: the acupoint group is precisely applied to the designated pain management acupoints, and the non-acupoint group is applied to the corresponding non-acupoint locations on the side; the terminals corresponding to the acupoint group and the non-acupoint group are uniformly preset with therapy temperature, single session duration and intervention course, and automatically record the application location, therapy temperature, duration and execution time parameters.

[0008] Furthermore, the data processing server also includes a data preprocessing submodule, which is used to clean, verify and classify the collected physiotherapy execution data and multi-dimensional data to obtain a standardized database, and to label the data source, collection time and collection terminal information.

[0009] Furthermore, in the statistical analysis submodule, the analysis of the classification results for different categories using corresponding statistical analysis methods includes: For continuous data, the normality is first checked by the Shapiro-Wilk test and the homogeneity of variance is checked by the Levene test. If the set conditions are met, the data is judged to be normally distributed and homogeneous in variance; otherwise, it is non-normally distributed. For categorical data, frequency and proportion statistics are used, and the chi-square test logic is directly applied.

[0010] Furthermore, in the specificity quantification submodule, the formula for calculating the specificity difference of acupoint effects is as follows: , in, This indicates the difference in the specificity of the acupoint effect. This indicates the overall therapeutic effect index of the acupoint group. This indicates the overall therapeutic effect index of the non-meridian, non-acupoint group.

[0011] Furthermore, the specificity quantification submodule also includes a standardization correction for missing or outlier data. The formula for calculating the outlier correction coefficient is as follows: , Where K represents the outlier correction factor (%). This represents the effective sample size that was actually completed in the experiment. This indicates the initial sample size set in the experimental plan.

[0012] A second aspect of the present invention provides a method for processing graphene physiotherapy data based on the graphene physiotherapy data processing system for comparing the effects of acupoints on dysmenorrhea as described in the first aspect, comprising the following steps: Based on preset physiotherapy parameters, record physiotherapy execution data for acupoint groups and non-acupoint groups; Multidimensional data of the subjects were collected according to preset time nodes. The data included quantitative scoring data of dysmenorrhea symptoms, physical signs data, serological test data and physiotherapy safety feedback data. Subjects were randomly divided into acupoint group and non-acupoint group according to preset rules, and the baseline data of the subjects after grouping were used as the reference data. The baseline data is classified, and the classification results of different categories are analyzed using corresponding statistical analysis methods. By combining the physiotherapy execution data and multi-dimensional data, the specific quantitative results of the acupoint effect of the acupoint group and the non-acupoint group are calculated according to the specific quantitative calculation formula, and abnormal data are calibrated based on the benchmark data. Standardized data reports are automatically generated based on statistical analysis results and quantitative results of the specificity of acupoint effects.

[0013] A third aspect of the present invention provides a computer-readable storage medium.

[0014] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the graphene physiotherapy data processing method for comparing the effects of acupoints on dysmenorrhea as described above.

[0015] A fourth aspect of the present invention provides a computer device.

[0016] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the graphene physiotherapy data processing method for comparing the effects of acupoints on dysmenorrhea as described above.

[0017] A fifth aspect of the present invention provides a computer device.

[0018] A program product, which is a computer program product, includes a computer program that, when executed by a processor, implements the steps in the graphene physiotherapy data processing method for comparing the effects of acupoints on dysmenorrhea as described above.

[0019] Compared with the prior art, the beneficial effects of the present invention are: This invention deeply integrates a standardized graphene therapy terminal with an intelligent data processing system, enabling precise differentiation of therapeutic effects between acupoints and non-acupoints. It overcomes the arbitrariness and non-standardization of traditional manual data collection, achieving standardized control over the entire process of graphene acupoint patch therapy intervention and data collection, ensuring data consistency and repeatability. It also solves the problems of low efficiency and large errors in manual data processing by constructing an automated data preprocessing, classification, archiving, and statistical analysis system to accurately quantify the therapeutic effects of acupoints and non-acupoints.

[0020] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0021] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0022] Figure 1 This is a block diagram of a graphene physiotherapy data processing system for comparing the effects of acupoints on dysmenorrhea, provided in an embodiment of the present invention. Figure 2 This is a flowchart of a graphene physiotherapy data processing method for comparing the effects of acupoints on dysmenorrhea, provided in an embodiment of the present invention. Detailed Implementation

[0023] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0024] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0025] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0026] Example 1 like Figure 1 As shown, this embodiment provides a graphene physiotherapy data processing system for comparing the effects of acupoints on dysmenorrhea, including a graphene acupoint patch physiotherapy terminal, a multi-dimensional data acquisition terminal, and a data processing server. The data processing server is communicatively connected to both the physiotherapy terminal and the data processing server. Among them, the graphene acupoint patch therapy terminal is used to record the physiotherapy execution data of the acupoint group and the non-acupoint group based on preset physiotherapy parameters; The multi-dimensional data acquisition terminal is used to collect multi-dimensional data of the subjects according to preset time nodes. The data includes dysmenorrhea symptom quantitative scoring data, physical sign data, serological test data, and physiotherapy safety feedback data. The data processing server includes: The subject grouping control submodule is used to randomly divide subjects into acupoint groups and non-acupoint groups according to preset rules, and use the baseline data of the subjects after grouping as the reference data. The statistical analysis submodule is used to classify the baseline data and to analyze the classification results of different classifications using corresponding statistical analysis methods. The specificity quantification submodule is used to combine the physiotherapy execution data and multi-dimensional data to calculate the specificity quantification results of the acupoint effect of the acupoint group and the non-acupoint group according to the specificity quantification calculation formula, and to calibrate abnormal data based on the benchmark data. The automatic results output submodule is used to automatically generate standardized data reports based on statistical analysis results and quantitative results of acupoint effect specificity.

[0027] The above technical solutions overcome the arbitrariness and non-standardization of traditional manual data collection, and achieve standardized management and control of the entire process of graphene acupoint patch therapy intervention and data collection, ensuring data consistency and repeatability; solve the problems of low efficiency and large error in manual data processing, and build an automated data preprocessing, classification and archiving and statistical analysis system to accurately quantify the differences in therapeutic effects between acupoints and non-acupoints. It should be noted that in this embodiment, the communication connection method between the three core modules is not limited and can be set according to the actual situation in the field. For example, data interaction can be achieved through wireless communication. As a further implementation method, the graphene acupoint patch therapy terminal performs standardized therapy interventions, specifically including: the acupoint group is precisely applied to the designated pain management acupoints, and the non-acupoint group is applied to the corresponding non-acupoint locations on the side; the terminals corresponding to the acupoint group and the non-acupoint group are uniformly preset with therapy temperature, single session duration and intervention course, and automatically record parameters such as application location, therapy temperature, duration and execution time, and synchronize them to the data processing server in real time to form a therapy execution ledger, ensuring that there is no human error in the intervention process.

[0028] In this embodiment, the terminals corresponding to the acupoint group and the non-acupoint group are uniformly preset with physiotherapy temperature, single session duration and intervention course. For example, the two groups of terminals are uniformly preset with physiotherapy temperature of 45°C and single session duration of 30 minutes. The intervention is performed on the first 1-2 days of menstruation, and a complete course of treatment is 3 consecutive menstrual cycles. The graphene acupoint patch therapy terminal incorporates a graphene heating module, a temperature control module, and a positioning indicator module. These modules work together to achieve precise and standardized therapy interventions at acupoints and non-acupoint locations. Simultaneously, it automatically records therapy execution data and synchronizes it in real-time to an intelligent data processing server. The temperature control module outputs temperature and duration control commands to the graphene heating module, while also transmitting temperature control parameters and work records to the positioning indicator module. This achieves the correlation recording of "temperature-duration-application location," fully meeting the standardized and repeatable requirements of the patented technology solution.

[0029] As a further implementation method, the preset time nodes in the multi-dimensional data acquisition terminal can be set according to actual needs, such as before treatment, after each course of intervention, and after all 3 courses of treatment. The quantitative scoring data for dysmenorrhea symptoms can specifically include the Cox Dysmenorrhea Symptom Scale and the McGill Pain Scale scores; physical signs data can include data such as the duration of pain; serological test data can include serum prostaglandin-related test data. As a further implementation, the data processing server also includes a data preprocessing submodule, which is used to clean, verify and classify the collected physiotherapy execution data and multi-dimensional data to obtain a standardized database, and to label the data source, collection time and collection terminal information.

[0030] Specifically, firstly, invalid or missing data is removed, and data from dropped subjects are separately labeled and archived; then, a three-dimensional classification is completed according to group, time node, and data type to generate a standardized database, labeling the data source, collection time, and collection terminal information to achieve full-process data traceability.

[0031] In the statistical analysis submodule, different statistical analysis methods are used to analyze different types of data; For measurement data: first, use the Shapiro-Wilk test to check for normality and the Levene test to check for homogeneity of variance. If the set conditions are met, it is determined that it conforms to normal distribution and homogeneity of variance; otherwise, it is non-normal distribution. Count data uses frequency and proportion statistics, which directly fits the chi-square test logic.

[0032] Specifically, in the analysis of normally distributed continuous data, independent samples t-tests were used for between-group comparisons, and paired samples t-tests were used for within-group comparisons before and after treatment. Results were presented as follows: (Mean ± Standard Deviation) represents the core statistic, and the formula for its calculation is as follows: , in, The mean of the two sample groups. The standard deviations of the two groups of samples are: There are two sample sizes.

[0033] When analyzing non-normally distributed quantitative data, the Wilcoxon rank-sum test and paired rank-sum test are used, and the results are expressed as the median (interquartiles). This indicates that no mean calculation will be performed to avoid data bias.

[0034] When analyzing count data, the Pearson chi-square test was used. This test is applicable to count data such as the distribution of efficacy and the incidence of adverse reactions between two groups. The validation criterion is P < 0.05, which indicates that the difference is statistically significant.

[0035] Through the above statistical analysis, after verifying the normality of the measurement data, the independent samples t-test, paired samples t-test, or nonparametric rank-sum test were performed accordingly; the chi-square test was used for the count data, and P < 0.05 was used as the criterion for statistical significance; the focus was on completing the inter-group comparison between the acupoint group and the non-acupoint group, and the intra-group comparison before and after treatment in each group, automatically calculating the effect difference value, and quantifying the difference in physiotherapy response between acupoint and non-acupoint groups.

[0036] In the specificity quantification submodule, unlike conventional simple efficacy statistics, in order to accurately distinguish the differences in physiotherapy responses between acupoints and non-acupoints, a core calculation formula is used to calculate the specificity quantification results; physiotherapy execution data and multi-dimensional data are used for comprehensive treatment index calculation and acupoint effect specificity difference calculation. The formula for calculating the specificity difference of acupoint effects is as follows: , in, This indicates the difference in the specificity of the acupoint effect. This indicates the overall therapeutic effect index of the acupoint group. This indicates the overall therapeutic effect index of the non-meridian, non-acupoint group; when >20% is considered to indicate that the acupoint effect is specific, 10% ≤ ≤20% indicates weak specificity. <10% indicates no obvious specificity. This difference is the core output quantitative indicator of the system, which directly reflects the targeted value of acupoints.

[0037] in, or The formula for calculating the comprehensive efficacy index of a single group is as follows: , Among them, S b S represents the total score of the subjects in the group before treatment. a This represents the total score of the subjects within the group after treatment. Scoring criteria: E > 90% indicates significant improvement, 70% ≤ E ≤ 89% indicates moderate improvement, 30% ≤ E ≤ 69% indicates slight improvement, and E < 30% indicates no improvement.

[0038] Based on the baseline data, the effective sample size for completing the experiment can be obtained. Then, the outlier correction coefficient is calculated. When the outlier correction coefficient meets the set threshold, it indicates that the data is valid. The formula for calculating the outlier correction factor is: , Where K represents the outlier correction factor (%). This represents the effective sample size that was actually completed in the experiment. This indicates the initial sample size set in the experimental plan; Final analysis is performed only on datasets with K ≥ 85% to ensure data validity.

[0039] The system automatically completes the calculation of the above formulas and simultaneously generates four core data items: statistics, P-value, therapeutic effect index, and acupoint effect difference. No manual calculation is required, thus completely avoiding errors caused by manual calculation.

[0040] The above scheme incorporates a built-in fixed algorithm and core calculation formula, requiring no manual intervention throughout the process. It first verifies the normality and homogeneity of variance of the data, then conducts data analysis across dimensions. The core quantitative analysis step embeds a dedicated calculation formula, accurately achieving inter-group difference comparison, intra-group before-and-after comparison, and quantification of the specificity of acupoint effects.

[0041] The final results automatic output submodule automatically generates a standardized data report based on the statistical analysis results. This report includes a statistical table comparing the two sets of data, an effect difference curve, and a safety data summary table. It clearly marks the differences in core data and their statistical significance, and directly outputs objective data conclusions that can be used for scientific research analysis and product optimization, without the need for manual secondary processing.

[0042] Experimental verification Based on the comparative data processing of acupoint and non-acupoint effects in this system Subject screening and grouping: Sixty-six eligible subjects with primary dysmenorrhea were recruited and automatically randomly divided into an acupoint group (Qihai acupoint) and a non-acupoint group (1.0 cun lateral to Qihai acupoint), with 33 subjects in each group. The grouping information was encrypted and blinded. Baseline CMSS scores, McGill pain scores, serum PGF2α and PGE2 indices of all subjects were collected and entered into the system baseline database. Physiotherapy intervention implementation: Both groups used the graphene acupoint patch physiotherapy terminal of this invention, uniformly set to 45℃, 30 minutes / session, intervention on the 1st-2nd day of menstruation, for 3 consecutive menstrual cycles, and the terminal automatically synchronized all intervention parameters to the server; Data collection: Before intervention, after the first course of treatment, and after the third course of treatment, various scores and test data were entered through the data collection terminal. The system automatically verified the completeness and no invalid data was entered. Data Processing and Analysis: The system automatically cleans and verifies the normality of the data, and uses built-in algorithms and formulas for analysis. The average efficacy index of the acupoint group was 82.3%, and the average efficacy index of the non-acupoint group was 57.1%. The system automatically calculated the difference in efficacy between the acupoint and meridian groups, which was 25.2%. A difference greater than 20% was considered a significant specificity for the acupoint effect. Paired t-tests within the groups showed that the indicators in both groups were better after treatment than before treatment. The independent t-test between groups showed that the acupoint group had significantly better improvement. The data results are automatically calculated throughout the process, with no manual calculation required.

[0043] Results output: The system automatically generates a complete data report, clearly indicating the differences in effects between the two groups, providing objective data support for the study of acupoint specificity.

[0044] Example 2 like Figure 2 As shown, this embodiment provides a graphene therapy data processing method for comparing the effects of acupoints on dysmenorrhea, including the following steps: Step 1: Based on preset physiotherapy parameters, record the physiotherapy execution data for the acupoint group and the non-acupoint group; Step 2: Collect multi-dimensional data of the subjects according to the preset time nodes. The data includes quantitative scoring data of dysmenorrhea symptoms, physical signs data, serological test data and physiotherapy safety feedback data. Step 3: Randomly divide the subjects into acupoint group and non-acupoint group according to the preset rules, and use the baseline data of the subjects after grouping as the reference data; Step 4: Classify the baseline data, and analyze the classification results using corresponding statistical analysis methods for different classifications; Step 5: Combining the physiotherapy execution data and multi-dimensional data, calculate the specific quantitative results of the acupoint effect of the acupoint group and the non-acupoint group according to the specific quantitative calculation formula, and calibrate the abnormal data based on the benchmark data; Step 6: Automatically generate a standardized data report based on the statistical analysis results and the quantitative results of the specificity of acupoint effects.

[0045] As a further embodiment, in step 1, a standardized physiotherapy intervention is performed, specifically including: the acupoint group is precisely applied to the designated pain management acupoints, and the non-acupoint group is applied to the corresponding non-acupoint locations on the side; the terminals corresponding to the acupoint group and the non-acupoint group are uniformly preset with physiotherapy temperature, single session duration and intervention course, and the parameters such as application location, physiotherapy temperature, duration and execution time are automatically recorded and synchronized to the data processing server in real time to form a physiotherapy execution ledger, ensuring that there is no human error in the intervention process.

[0046] In this embodiment, the terminals corresponding to the acupoint group and the non-acupoint group are uniformly preset with physiotherapy temperature, single session duration and intervention course. For example, the two groups of terminals are uniformly preset with physiotherapy temperature of 45°C and single session duration of 30 minutes. The intervention is performed on the first 1-2 days of menstruation, and a complete course of treatment is 3 consecutive menstrual cycles. In step 2, the preset time points can be set according to actual needs, such as before treatment, after each course of intervention, and after all 3 courses of treatment are completed; The quantitative scoring data for dysmenorrhea symptoms can specifically include the Cox Dysmenorrhea Symptom Scale and the McGill Pain Scale scores; physical signs data can include data such as the duration of pain; serological test data can include serum prostaglandin-related test data. In step 4, appropriate statistical analysis methods are used to analyze different types of data; For measurement data: first, use the Shapiro-Wilk test to check for normality and the Levene test to check for homogeneity of variance. If the set conditions are met, it is determined that it conforms to normal distribution and homogeneity of variance; otherwise, it is non-normal distribution. Count data uses frequency and proportion statistics, which directly fits the chi-square test logic.

[0047] Specifically, in the analysis of normally distributed continuous data, independent samples t-tests were used for between-group comparisons, and paired samples t-tests were used for within-group comparisons before and after treatment. Results were presented as follows: (Mean ± Standard Deviation) represents the core statistic, and the formula for its calculation is as follows: , in, The mean of the two sample groups. The standard deviations of the two groups of samples are: There are two sample sizes.

[0048] When analyzing non-normally distributed quantitative data, the Wilcoxon rank-sum test and paired rank-sum test are used, and the results are expressed as the median (interquartiles). This indicates that no mean calculation will be performed to avoid data bias.

[0049] When analyzing count data, the Pearson chi-square test was used. This test is applicable to count data such as the distribution of efficacy and the incidence of adverse reactions between two groups. The validation criterion is P < 0.05, which indicates that the difference is statistically significant.

[0050] In step 5, the system automatically completes the calculation of the above formula and simultaneously generates four core data items: statistics, P-value, therapeutic effect index, and acupoint effect difference. No manual calculation is required, thus completely avoiding errors caused by manual calculation.

[0051] The above scheme incorporates a built-in fixed algorithm and core calculation formula, requiring no manual intervention throughout the process. It first verifies the normality and homogeneity of variance of the data, then conducts data analysis across dimensions. The core quantitative analysis step embeds a dedicated calculation formula, accurately achieving inter-group difference comparison, intra-group before-and-after comparison, and quantification of the specificity of acupoint effects.

[0052] The final results automatic output submodule automatically generates a standardized data report based on the statistical analysis results. This report includes a statistical table comparing the two sets of data, an effect difference curve, and a safety data summary table. It clearly marks the differences in core data and their statistical significance, and directly outputs objective data conclusions that can be used for scientific research analysis and product optimization, without the need for manual secondary processing.

[0053] It should be noted that the specific implementation of the graphene physiotherapy data processing system for comparing the effects of acupoints on dysmenorrhea in this embodiment of the invention is similar to the specific implementation of the graphene physiotherapy data processing method for comparing the effects of acupoints on dysmenorrhea in this embodiment of the invention. For details, please refer to the description in the method section. To reduce redundancy, it will not be repeated here.

[0054] Example 3 This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the graphene physiotherapy data processing method for comparing the effects of acupoints on dysmenorrhea as described above.

[0055] Example 4 This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the graphene physiotherapy data processing method for comparing the effects of acupoints on dysmenorrhea as described above.

[0056] Example 5 This embodiment provides a program product, which is a computer program product, including a computer program. When the computer program is executed by a processor, it implements the steps in the graphene physiotherapy data processing method for comparing the effects of acupoints on dysmenorrhea as described above.

[0057] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0058] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0059] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0060] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0061] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0062] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A graphene physiotherapy data processing system for comparing the effects of acupoints on dysmenorrhea, characterized in that, It includes a graphene acupoint patch therapy terminal, a multi-dimensional data acquisition terminal, and a data processing server. The data processing server is communicatively connected to both the graphene acupoint patch therapy terminal and the multi-dimensional data acquisition terminal. The graphene acupoint patch therapy terminal is used to record the therapy execution data of acupoint groups and non-acupoint groups based on preset therapy parameters. The multi-dimensional data acquisition terminal is used to collect multi-dimensional data of the subjects according to preset time nodes. The data includes dysmenorrhea symptom quantitative scoring data, physical sign data, serological test data, and physiotherapy safety feedback data. The data processing server includes: The subject grouping control submodule is used to randomly divide subjects into acupoint groups and non-acupoint groups according to preset rules, and use the baseline data of the subjects after grouping as the reference data. The statistical analysis submodule is used to classify the baseline data and to analyze the classification results of different classifications using corresponding statistical analysis methods. The specificity quantification submodule is used to combine the physiotherapy execution data and multi-dimensional data to calculate the specificity quantification results of the acupoint effect of the acupoint group and the non-acupoint group according to the specificity quantification calculation formula, and to calibrate abnormal data based on the benchmark data. The automatic results output submodule is used to automatically generate standardized data reports based on statistical analysis results and quantitative results of acupoint effect specificity.

2. The graphene physiotherapy data processing system for comparing the effects of acupoints on dysmenorrhea as described in claim 1, characterized in that, The graphene acupoint patch therapy terminal performs therapy intervention according to the standardized procedures, which include: the acupoint group is precisely applied to the designated pain management acupoints, and the non-acupoint group is applied to the corresponding non-acupoint locations on the side; the terminals corresponding to the acupoint group and the non-acupoint group are uniformly preset with therapy temperature, single session duration and intervention course, and automatically record the application location, therapy temperature, duration and execution time parameters.

3. The graphene physiotherapy data processing system for comparing the effects of acupoints on dysmenorrhea as described in claim 1, characterized in that, The data processing server also includes a data preprocessing submodule, which is used to clean, verify and classify the collected physiotherapy execution data and multi-dimensional data to obtain a standardized database, and to label the data source, collection time and collection terminal information.

4. The graphene physiotherapy data processing system for comparing the effects of acupoints on dysmenorrhea as described in claim 1, characterized in that, In the statistical analysis submodule, the analysis of the classification results for different categories using corresponding statistical analysis methods includes: For continuous data, the normality is first checked by the Shapiro-Wilk test and the homogeneity of variance is checked by the Levene test. If the set conditions are met, the data is judged to be normally distributed and homogeneous in variance; otherwise, it is non-normally distributed. For categorical data, frequency and proportion statistics are used, and the chi-square test logic is directly applied.

5. The graphene physiotherapy data processing system for comparing the effects of acupoints on dysmenorrhea as described in claim 1, characterized in that, In the specificity quantification submodule, the formula for calculating the specificity difference of acupoint effect is: ， in, This indicates the difference in the specificity of the acupoint effect. This indicates the overall therapeutic effect index of the acupoint group. This indicates the overall therapeutic effect index of the non-meridian, non-acupoint group.

6. The graphene physiotherapy data processing system for comparing the effects of acupoints on dysmenorrhea as described in claim 1, characterized in that, The specificity quantification submodule also includes a standardization correction for missing or outlier data. The formula for calculating the outlier correction coefficient is as follows: ， Where K represents the outlier correction factor (%). This represents the effective sample size that was actually completed in the experiment. This indicates the initial sample size set in the experimental plan.

7. A method for processing graphene physiotherapy data based on the graphene physiotherapy data processing system for comparing the effects of acupoints on dysmenorrhea as described in any one of claims 1-6, characterized in that, Includes the following steps: Based on preset physiotherapy parameters, record physiotherapy execution data for acupoint groups and non-acupoint groups; Multidimensional data of the subjects were collected according to preset time nodes. The data included quantitative scoring data of dysmenorrhea symptoms, physical signs data, serological test data and physiotherapy safety feedback data. Subjects were randomly divided into acupoint group and non-acupoint group according to preset rules, and the baseline data of the subjects after grouping were used as the reference data. The baseline data is classified, and the classification results of different categories are analyzed using corresponding statistical analysis methods. By combining the physiotherapy execution data and multi-dimensional data, the specific quantitative results of the acupoint effect of the acupoint group and the non-acupoint group are calculated according to the specific quantitative calculation formula, and abnormal data are calibrated based on the benchmark data. Standardized data reports are automatically generated based on statistical analysis results and quantitative results of the specificity of acupoint effects.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the graphene physiotherapy data processing method for comparing the effects of acupoints on dysmenorrhea as described in claim 7.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the graphene physiotherapy data processing method for comparing the effects of acupoints on dysmenorrhea as described in claim 7.

10. A program product, said program product being a computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps in the graphene physiotherapy data processing method for comparing the effects of acupoints on dysmenorrhea as described in claim 7.

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