Quantitative analysis and evaluation method and system for acupuncture effect

Abstract

The invention discloses an acupuncture effect quantitative analysis and evaluation method and system, and relates to the technical field of quantitative analysis, and the method comprises the steps: collecting patient data, and carrying out the preprocessing of the collected patient data; feature extraction is carried out based on the preprocessed patient data; performing statistical analysis based on the patient data; constructing a multiple regression model, and generating an acupuncture effect quantitative analysis report in combination with a statistical analysis result; encrypting the patient data and the acupuncture effect quantitative analysis report; according to the method, the extracted feature data is subjected to statistical analysis, the multiple regression model is constructed to predict the quantized value of the acupuncture effect of the patient, key factors related to the acupuncture treatment effect are recognized, the acupuncture effect is quantitatively described, specific numerical values are provided for doctors, the doctors can conveniently evaluate and compare the acupuncture treatment effect, and the accuracy of acupuncture treatment is improved. And the scientificity and effectiveness of acupuncture treatment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of quantitative analysis, and in particular to a method and system for quantitative analysis and evaluation of acupuncture effects. Background Art

[0002] In the context of the development of modern medicine, acupuncture therapy has not only been widely used in the field of traditional Chinese medicine, but has also gradually gained recognition in the Western medical community. The evaluation of the efficacy of acupuncture has always been an important issue of concern to the medical community. Traditionally, the evaluation of acupuncture effects mainly relies on patients' subjective feedback and doctors' clinical experience, which lacks scientificity and objectivity. With the advancement of biomedical engineering and information technology, many studies have been devoted to developing quantitative analysis methods for acupuncture efficacy. For example, bioelectric signals and hemodynamic parameters are used to evaluate acupuncture effects. These methods provide important technical support for the scientificization and standardization of acupuncture therapy. In addition, existing studies have made certain progress in data acquisition and processing, using a variety of sensor technologies to achieve real-time monitoring and analysis of patients' physiological signals. The development of these technologies has promoted the modernization of acupuncture efficacy evaluation methods and improved the credibility and clinical application effect of acupuncture therapy.

[0003] Although the existing technology has made certain progress in the evaluation of acupuncture efficacy, there are still many shortcomings. Existing research mostly focuses on the collection and analysis of a single physiological signal, ignoring the comprehensive application of multimodal signals. This one-sided evaluation method leads to an incomplete and inaccurate understanding of the effect of acupuncture. To address this problem, we have invented a quantitative analysis and evaluation method for acupuncture, which improves the objectivity and accuracy of the quantitative evaluation of acupuncture effects through systematic data collection, preprocessing, feature extraction and statistical analysis. Summary of the invention

[0004] In view of the problems existing in the above-mentioned existing acupuncture effect quantitative analysis and evaluation method and system, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is that the analytical data for quantitative evaluation of acupuncture effects is single and lacks comprehensive application of multimodal signals.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a method for quantitative analysis and evaluation of acupuncture effects, which includes collecting patient data and preprocessing the collected patient data; extracting features based on the preprocessed patient data; performing statistical analysis based on the patient data; constructing a multivariate regression model and generating a quantitative analysis report on acupuncture effects in combination with the statistical analysis results; and encrypting the patient data and the quantitative analysis report on acupuncture effects.

[0007] As a preferred embodiment of the method for quantitative analysis and evaluation of acupuncture effect of the present invention, the collecting of patient data and preprocessing of the collected patient data include:

[0008] Collecting neural electrical signal data and heart rates of multiple patients with the same disease, wherein the neural electrical signal data includes the patients' electroencephalogram (EEG) signals and electromyography (EMG) signals;

[0009] Use a heart rate monitoring device and deploy skin patch sensors at different acupuncture points on the patient's body to record the patient's neural electrical signal data before and after acupuncture treatment, heart rate changes, and the position, strength, and angle of acupuncture. Generate an EEG and an EMG based on the patient's EEG and EMG signals, respectively.

[0010] The patients’ pain intensity scores were obtained by using a visual analog scale;

[0011] The preprocessing of the collected patient data includes denoising, data smoothing and data normalization.

[0012] As a preferred solution of the acupuncture effect quantitative analysis and evaluation method of the present invention, wherein: the feature extraction based on the pre-processed patient data includes frequency domain feature extraction and time domain feature extraction;

[0013] Cut the continuous neural electrical signal into multiple time windows;

[0014] The frequency domain feature extraction refers to converting the neural electrical signal in each time window from the time domain to the frequency domain using the fast Fourier transform FFT, and calculating the power spectrum density of different frequency domains:

[0015]

[0016] Where P(f) is the power spectral density at frequency f, X(f) is the frequency domain signal after fast Fourier transform, and N is the total number of sampling points in the frequency domain signal;

[0017] The time domain feature extraction refers to calculating the statistical parameters of the neural electrical signal data in each time window, including the mean, variance, peak value, standard deviation of the heart rate variability signal and the average heart rate;

[0018] Generate a feature data vector F, including the power spectral density in different frequency domains, the mean, variance, peak value of the neural electrical signal data, the patient's pain severity score, and the standard deviation and average heart rate of the heart rate variability signal.

[0019] As a preferred embodiment of the method for quantitative analysis and evaluation of acupuncture effect of the present invention, the statistical analysis based on patient data includes:

[0020] Perform data difference analysis on each type of patient data, draw a histogram of the data, and observe whether the shape of the histogram is close to the bell curve. If it is close to the bell curve, it is judged that the data belongs to the normal distribution; if it is not close to the bell curve, it is judged that the data does not belong to the normal distribution;

[0021] Choose different statistical tests based on the distribution characteristics of each type of data;

[0022] For normally distributed data, paired sample t test was used;

[0023] For data that were not normally distributed, the Wilcoxon signed-rank test was used;

[0024] The P values ​​of paired sample t test data and Wilcoxon signed rank test data were calculated using statistical software R and Python, respectively;

[0025] A significance level threshold Pl is set. If P < Pl, acupuncture treatment is determined to be effective; if P ≥ Pl, acupuncture treatment is determined to be ineffective, and the effectiveness of acupuncture treatment is used as the result of statistical analysis.

[0026] As a preferred embodiment of the method for quantitative analysis and evaluation of acupuncture effect of the present invention, the construction of a multivariate regression model includes:

[0027] Use statistical software R and Python to build a multivariate regression model, use the patient's historical acupuncture data, divide the historical acupuncture data into a training set and a test set, extract the characteristic data of the training set to form a characteristic vector Q, and input it into the multivariate regression model for model training;

[0028] The multiple regression model is expressed as:

[0029] Y=β0+β1X1+β2X2+…++β m X m +Φ

[0030] Among them, Y is the quantitative value of acupuncture treatment effect, X1, X2, ..., X n are factors that affect the effect of acupuncture treatment, β0, β1, ..., β m is the regression parameter, Φ is the error term;

[0031] Regression parameters were calculated by the least squares method;

[0032] Use training set data to verify the model's predictive function and optimize model performance;

[0033] The eigenvector F was input into the trained multiple regression model to obtain the quantitative values ​​of the acupuncture treatment effect, including the degree of pain relief and the improvement in functional recovery.

[0034] As a preferred embodiment of the method for quantitative analysis and evaluation of acupuncture effect of the present invention, the method for generating a quantitative analysis report of acupuncture effect by combining statistical analysis results comprises:

[0035] An acupuncture treatment effect analysis report is generated based on the statistical analysis results of patient data and the quantitative value of acupuncture treatment effect of the multivariate regression model. According to the acupuncture treatment effect analysis report, the doctor dynamically adjusts the acupuncture operation including adjusting the acupuncture angle, strength and acupuncture position.

[0036] As a preferred solution of the acupuncture effect quantitative analysis and evaluation method of the present invention, wherein: the encryption of patient data and acupuncture treatment effect analysis report refers to:

[0037] The asymmetric encryption algorithm RSA is used to encrypt patient data and acupuncture effect quantitative analysis reports. The keys generated include public keys and private keys. The public keys are used to encrypt patient data and acupuncture effect quantitative analysis reports, and the secure communication protocol HTTPS is used for data transmission. The private key is used to decrypt and read the data when accessing the data.

[0038] As a preferred solution of the acupuncture effect quantitative analysis and evaluation system of the present invention, it includes:

[0039] Data collection and preprocessing module, where users collect patient data and preprocess it;

[0040] A feature extraction module, used to extract the time domain features and frequency domain features of the preprocessed patient data and generate a feature data vector in combination with the patient's pain level score;

[0041] Data analysis module, used for statistical analysis of patient data;

[0042] Model building module, used to build a multivariate regression model, generate acupuncture effect prediction results and generate acupuncture effect quantitative analysis report in combination with statistical analysis results;

[0043] The data encryption module is used to encrypt patient data and acupuncture effect quantitative analysis reports.

[0044] A computer device comprises: a memory and a processor; the memory stores a computer program, and the processor implements the steps of a method for quantitative analysis and evaluation of acupuncture effects when executing the computer program.

[0045] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for quantitative analysis and evaluation of acupuncture effects.

[0046] The beneficial effects of the present invention are as follows: the present invention predicts the quantitative value of the patient's acupuncture effect by statistically analyzing the extracted feature data and constructing a multivariate regression model, identifies the key factors related to the acupuncture treatment effect, and quantitatively describes the acupuncture effect, providing doctors with specific numerical values, which is convenient for doctors to evaluate and compare the acupuncture treatment effects, thereby improving the scientificity and effectiveness of acupuncture treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0048] Figure 1 The figure is a flowchart of a method for quantitative analysis and evaluation of acupuncture effects.

[0049] Figure 2 Schematic diagram of the statistical analysis process.

[0050] Figure 3 This is a structural diagram of a quantitative analysis and evaluation system for acupuncture effects. DETAILED DESCRIPTION

[0051] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0052] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0053] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0054] Example 1

[0055] Reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a method for quantitative analysis and evaluation of acupuncture effects, the method for quantitative analysis and evaluation of acupuncture effects includes:

[0056] S1. Collect patient data and pre-process the collected patient data;

[0057] Furthermore, collecting patient data and preprocessing the collected patient data include:

[0058] Collecting neural electrical signal data and heart rates of multiple patients with the same disease, wherein the neural electrical signal data includes the patients' electroencephalogram (EEG) signals and electromyography (EMG) signals;

[0059] Use a heart rate monitoring device and deploy skin patch sensors at different acupuncture points on the patient's body to record the patient's neural electrical signal data before and after acupuncture treatment, heart rate changes, and the position, strength, and angle of acupuncture. Generate an EEG and an EMG based on the patient's EEG and EMG signals, respectively.

[0060] The patients’ pain intensity scores were obtained by using a visual analog scale;

[0061] The preprocessing of the collected patient data includes denoising, data smoothing and data normalization.

[0062] By collecting patient data and preprocessing the data, noise and outliers in the original data are eliminated, data quality is improved, and the data is normalized to make the physiological data of different patients comparable, enhance data consistency, provide a basis for subsequent feature extraction steps, and ensure the accuracy and effectiveness of subsequent analysis.

[0063] S2, feature extraction based on preprocessed patient data;

[0064] Furthermore, feature extraction based on the preprocessed patient data includes frequency domain feature extraction and time domain feature extraction;

[0065] Cut the continuous neural electrical signal into multiple time windows;

[0066] The frequency domain feature extraction refers to converting the neural electrical signal in each time window from the time domain to the frequency domain using the fast Fourier transform FFT, and calculating the power spectrum density of different frequency domains:

[0067]

[0068] Where P(f) is the power spectral density at frequency f, X(f) is the frequency domain signal after fast Fourier transform, and N is the total number of sampling points in the frequency domain signal;

[0069] The time domain feature extraction refers to calculating the statistical parameters of the neural electrical signal data in each time window, including the mean, variance, peak value, standard deviation of the heart rate variability signal and the average heart rate;

[0070] Generate a feature data vector F, including the power spectral density in different frequency domains, the mean, variance, peak value of the neural electrical signal data, the patient's pain severity score, and the standard deviation and average heart rate of the heart rate variability signal.

[0071] By extracting feature data related to the therapeutic effects of acupuncture and integrating data from different neural electrical signals, we provide a comprehensive understanding of the therapeutic effects of acupuncture, improve the depth of analysis and evaluation of the therapeutic effects of acupuncture, provide a scientific basis for subsequent statistical analysis and model building, and ensure the rigor and scientific nature of the acupuncture therapeutic effect analysis process. Feature extraction reveals the different responses of individual patients to acupuncture therapy, providing data support for the formulation of personalized acupuncture treatment plans.

[0072] S3, statistical analysis based on patient data;

[0073] Furthermore, statistical analysis based on patient data includes:

[0074] Perform data difference analysis on each type of patient data, draw a histogram of the data, and observe whether the shape of the histogram is close to the bell curve. If it is close to the bell curve, it is judged that the data belongs to the normal distribution; if it is not close to the bell curve, it is judged that the data does not belong to the normal distribution;

[0075] Choose different statistical tests based on the distribution characteristics of each type of data;

[0076] For normally distributed data, paired sample t test was used;

[0077] For data that were not normally distributed, the Wilcoxon signed-rank test was used;

[0078] The P values ​​of paired sample t test data and Wilcoxon signed rank test data were calculated using statistical software R and Python, respectively;

[0079] Set the significance level threshold Pl. If P < Pl, it is concluded that acupuncture treatment is effective; if P ≥ Pl, it is judged that acupuncture treatment is ineffective, and the effectiveness of acupuncture treatment is used as the result of statistical analysis.

[0080] By conducting statistical analysis on patient data, we can identify key factors related to the effect of acupuncture treatment, provide a quantitative description of the effect of acupuncture treatment, and enhance the scientificity and credibility of the results of the analysis of the effect of acupuncture treatment. The results of the analysis of the effect of acupuncture treatment can provide data support and decision-making support for clinicians, and help formulate scientific and effective treatment plans. In addition, through statistical analysis, the transparency of the data processing process is improved, which is helpful to formulate standards and specifications for the evaluation of acupuncture treatment effects and promote the development of industry standardization.

[0081] S4. Construct a multivariate regression model and generate a quantitative analysis report on acupuncture effects based on statistical analysis results;

[0082] Furthermore, constructing a multiple regression model includes,

[0083] Use statistical software R and Python to build a multivariate regression model, use the patient's historical acupuncture data, divide the historical acupuncture data into a training set and a test set, extract the characteristic data of the training set to form a characteristic vector Q, and input it into the multivariate regression model for model training;

[0084] The multiple regression model is expressed as:

[0085] Y=β0+β1X1+β2X2+…++β m X m +Φ

[0086] Among them, Y is the quantitative value of acupuncture treatment effect, X1, X2, ..., X n are factors that affect the effect of acupuncture treatment, including acupuncture angle, position and strength, β0, β1, ..., β m is the regression parameter, Φ is the error term;

[0087] Regression parameters were calculated by the least squares method;

[0088] Use training set data to verify the model's predictive function and optimize model performance;

[0089] The eigenvector F was input into the trained multiple regression model to obtain the quantitative values ​​of the acupuncture treatment effect, including the degree of pain relief and the improvement in functional recovery.

[0090] By using statistical software R and Python to construct a multivariate regression model, patient data can be quickly processed, multiple variables can be comprehensively considered, and the impact of different data characteristics on the effect of acupuncture treatment can be predicted. This improves the efficiency of processing patient data and enhances the adaptability of the model. The multivariate regression model can provide doctors with scientific prediction results of acupuncture treatment effects, help doctors optimize acupuncture treatment strategies, and improve treatment effects, thereby significantly improving the scientificity, accuracy, and practicality of acupuncture treatment effect evaluation.

[0091] Furthermore, the quantitative analysis report of acupuncture effect is generated by combining the statistical analysis results, including:

[0092] An acupuncture treatment effect analysis report is generated based on the statistical analysis results of patient data and the quantitative value of acupuncture treatment effect of the multivariate regression model. According to the acupuncture treatment effect analysis report, the doctor dynamically adjusts the acupuncture operation including adjusting the acupuncture angle, strength and acupuncture position.

[0093] By generating acupuncture effect quantitative analysis reports, a comprehensive evaluation of the treatment effect of acupuncture is provided to ensure the comprehensiveness and objectivity of the results of acupuncture treatment. The data is presented in charts to improve the visualization of the data, making it easier for doctors, patients and their families to understand and increase patients' confidence in treatment. The acupuncture effect quantitative analysis report provides quantitative treatment effect data, provides a scientific basis for doctors, supports doctors to dynamically adjust treatment plans, provides a scientific basis for communication between doctors and patients, and promotes harmonious doctor-patient relationships. The acupuncture effect quantitative analysis report can also be used to track the patient's treatment effect over a long period of time and provide continuous evaluation and feedback.

[0094] S5. Encrypt patient data and acupuncture effect quantitative analysis report;

[0095] Furthermore, the patient data and acupuncture treatment effect analysis report are encrypted.

[0096] The asymmetric encryption algorithm RSA is used to encrypt patient data and acupuncture effect quantitative analysis reports. The keys generated include public keys and private keys. The public keys are used to encrypt patient data and acupuncture effect quantitative analysis reports, and the secure communication protocol HTTPS is used for data transmission. The private key is used to decrypt and read the data when accessing the data.

[0097] By using the asymmetric encryption algorithm RSA to encrypt patient data and acupuncture effect quantitative analysis reports, unauthorized personnel are prevented from accessing patient data and acupuncture effect quantitative analysis reports, ensuring that the patient's personal privacy data is not leaked during data transmission and storage, effectively preventing data leakage and tampering, ensuring the security and integrity of patient data, improving patients' trust in the hospital, and reducing the risks and responsibilities caused by data leakage.

[0098] Example 2

[0099] Reference Figure 3 , which is the second embodiment of the present invention, is different from the previous embodiment and provides a quantitative analysis and evaluation system for acupuncture effects, including:

[0100] Data collection and preprocessing module, where users collect patient data and preprocess it;

[0101] A feature extraction module, used to extract the time domain features and frequency domain features of the preprocessed patient data and generate a feature data vector in combination with the patient's pain level score;

[0102] Data analysis module, used for statistical analysis of patient data;

[0103] Model building module, used to build a multivariate regression model, generate acupuncture effect prediction results and generate acupuncture effect quantitative analysis report in combination with statistical analysis results;

[0104] The data encryption module is used to encrypt patient data and acupuncture effect quantitative analysis reports.

[0105] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0106] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0107] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0108] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

Claims

1. A method for quantitative analysis and evaluation of acupuncture effects, characterized in that: include, Collect patient data and pre-process the collected patient data; Perform feature extraction based on preprocessed patient data; Perform statistical analysis based on patient data; Construct a multivariate regression model and generate a quantitative analysis report on acupuncture effects based on statistical analysis results; Patient data and acupuncture effect quantitative analysis reports are encrypted.

2. The method for quantitative analysis and evaluation of acupuncture effect according to claim 1, characterized in that: The collecting of patient data and preprocessing of the collected patient data include: Collecting neural electrical signal data and heart rates of multiple patients with the same disease, wherein the neural electrical signal data includes the patients' electroencephalogram (EEG) signals and electromyography (EMG) signals; Use a heart rate monitoring device and deploy skin patch sensors at different acupuncture points on the patient's body to record the patient's neural electrical signal data before and after acupuncture treatment, heart rate changes, and the position, strength, and angle of acupuncture. Generate an EEG and an EMG based on the patient's EEG and EMG signals, respectively. The patients’ pain intensity scores were obtained by using a visual analog scale; The preprocessing of the collected patient data includes denoising, data smoothing and data normalization.

3. The method for quantitative analysis and evaluation of acupuncture effect as claimed in claim 2, characterized in that: The feature extraction based on the pre-processed patient data includes frequency domain feature extraction and time domain feature extraction; Cut the continuous neural electrical signal into multiple time windows; The frequency domain feature extraction refers to converting the neural electrical signal in each time window from the time domain to the frequency domain using the fast Fourier transform FFT, and calculating the power spectrum density of different frequency domains: Where P(f) is the power spectral density at frequency f, X(f) is the frequency domain signal after fast Fourier transform, and N is the total number of sampling points in the frequency domain signal; The time domain feature extraction refers to calculating the statistical parameters of the neural electrical signal data in each time window, including the mean, variance, peak value, standard deviation of the heart rate variability signal and the average heart rate; Generate a feature data vector F, including the power spectral density in different frequency domains, the mean, variance, peak value of the neural electrical signal data, the patient's pain severity score, and the standard deviation and average heart rate of the heart rate variability signal.

4. The method for quantitative analysis and evaluation of acupuncture effect as claimed in claim 3, characterized in that: The statistical analysis based on patient data includes: Perform data difference analysis on each type of patient data, draw a histogram of the data, and observe whether the shape of the histogram is close to the bell curve. If it is close to the bell curve, it is judged that the data belongs to the normal distribution; if it is not close to the bell curve, it is judged that the data does not belong to the normal distribution; Choose different statistical tests based on the distribution characteristics of each type of data; For normally distributed data, paired sample t test was used; For data that were not normally distributed, the Wilcoxon signed-rank test was used; The P values ​​of paired sample t test data and Wilcoxon signed rank test data were calculated using statistical software R and Python, respectively; Set the significance level threshold Pl. If P < Pl, it is concluded that acupuncture treatment is effective. If P ≥ Pl, acupuncture treatment is judged to be ineffective, and the effectiveness of acupuncture treatment is used as the result of statistical analysis.

5. The method for quantitative analysis and evaluation of acupuncture effect according to claim 4, characterized in that: The construction of the multivariate regression model includes: Use statistical software R and Python to build a multivariate regression model, use the patient's historical acupuncture data, divide the historical acupuncture data into a training set and a test set, extract the characteristic data of the training set to form a characteristic vector Q, and input it into the multivariate regression model for model training; The multiple regression model is expressed as: Y=β0+β1X1+β2X2+…++β m X m +F Among them, Y is the quantitative value of acupuncture treatment effect, X1, X2, ..., X n are factors that affect the effect of acupuncture treatment, β0, β1, ..., β m is the regression parameter, Φ is the error term; Regression parameters were calculated by the least squares method; Use training set data to verify the model's predictive function and optimize model performance; The eigenvector F was input into the trained multiple regression model to obtain the quantitative values ​​of the acupuncture treatment effect, including the degree of pain relief and the improvement in functional recovery.

6. The method for quantitative analysis and evaluation of acupuncture effect according to claim 5, characterized in that: The acupuncture effect quantitative analysis report generated by combining the statistical analysis results includes: An acupuncture treatment effect analysis report is generated based on the statistical analysis results of patient data and the quantitative value of acupuncture treatment effect of the multivariate regression model. According to the acupuncture treatment effect analysis report, the doctor dynamically adjusts the acupuncture operation including adjusting the acupuncture angle, strength and acupuncture position.

7. The method for quantitative analysis and evaluation of acupuncture effect according to claim 6, characterized in that: The encryption of patient data and acupuncture treatment effect analysis report refers to: The asymmetric encryption algorithm RSA is used to encrypt patient data and acupuncture effect quantitative analysis reports. The keys generated include public keys and private keys. The public keys are used to encrypt patient data and acupuncture effect quantitative analysis reports, and the secure communication protocol HTTPS is used for data transmission. The private key is used to decrypt and read the data when accessing the data.

8. A system for quantitative analysis and evaluation of acupuncture effects according to any one of claims 1 to 7, characterized in that: include, Data collection and preprocessing module, where users collect patient data and preprocess it; A feature extraction module, used to extract the time domain features and frequency domain features of the pre-processed patient data and generate a feature data vector in combination with the patient's pain level score; Data analysis module, used for statistical analysis of patient data; Model building module, used to build a multivariate regression model, generate acupuncture effect prediction results and generate acupuncture effect quantitative analysis report in combination with statistical analysis results; The data encryption module is used to encrypt patient data and acupuncture effect quantitative analysis reports.

9. A computer device comprising: A memory and a processor; the memory stores a computer program, wherein the processor implements the steps of the acupuncture effect quantitative analysis and evaluation method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the acupuncture effect quantitative analysis and evaluation method according to any one of claims 1 to 7 are implemented.

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