A data analysis method and device for treating chronic non-specific low back pain

Abstract

The application discloses a kind of chronic non-specific lumbar pain treatment data analysis method and device, it is related to medical data analysis processing technical field.Processing method includes: obtaining patient data to be treated;Acupoint graph is handled by segmentation, and local acupoint graph is obtained;According to individual difference data and the individual difference model constructed, the individual difference influence factor data of patient to be treated is obtained, according to individual difference influence factor data and individual difference factor, corresponding individual difference correction factor is obtained;Local acupoint graph is adjusted by optimizing through individual difference correction factor, and acupoint image data is obtained, acupoint analysis coefficient is obtained based on acupoint image data to generate acupoint visual feedback report, and acupoint analysis coefficient is used to quantitatively analyze the acupoint situation of patient to be treated.The technical effect of more accurately analyzing acupoint related treatment data can be achieved by using the application.

Description

Technical Field

[0001] This invention relates to the field of medical data analysis and processing technology, and in particular to a method and apparatus for analyzing treatment data of chronic nonspecific low back pain. Background Technology

[0002] Chronic non-specific low back pain (CNLBP) has a long course, unsatisfactory treatment outcomes, a high recurrence rate after treatment, and easily leads to a series of psychological and social problems, seriously affecting patients' quality of life and imposing a huge economic burden on society. Both Huangdi Neijing (Yellow Emperor's Inner Classic) and Zhigu acupuncture originate from the *Huangdi Neijing*. Huangdi Neijing treats according to the principles of the classics, effectively regulating Qi and blood and promoting their circulation; Zhigu acupuncture stimulates the periosteum, providing powerful analgesia, and both have shown significant clinical efficacy in treating chronic low back pain.

[0003] Currently, research on the combined use of Huangdi Neizhen (Yellow Emperor's Inner Acupuncture) and Zhigu acupuncture for the treatment of chronic nonspecific low back pain is still in its preliminary stages, but the application of acupuncture in the treatment of chronic low back pain has a certain foundation. However, existing techniques for analyzing treatment data for chronic nonspecific low back pain suffer from the problem of focusing solely on acupoint-related treatment data obtained during the treatment process, leading to inaccurate results. Summary of the Invention

[0004] To address the technical problem of inaccurate treatment data analysis results in existing technologies, this invention provides a method and apparatus for analyzing treatment data of chronic nonspecific low back pain. The technical solution is as follows:

[0005] On the one hand, a treatment data analysis method for chronic nonspecific low back pain is provided, which is implemented by a treatment data analysis device for chronic nonspecific low back pain, and the method includes:

[0006] S1. Obtain patient data to be treated, including acupuncture point chart, individual difference data, and individual difference factors;

[0007] S2. The acupuncture point map is segmented to obtain a local acupuncture point map, which includes a local top view acupuncture point map and a local side view acupuncture point map.

[0008] S3. Based on the individual difference data and the constructed individual difference model, obtain the individual difference influencing factor data of the patient to be treated, and obtain the corresponding individual difference correction factor based on the individual difference influencing factor data and the individual difference factor. The individual difference correction factor is used to reduce the acupuncture point error caused by individual differences in the patient to be treated.

[0009] S4. The local acupuncture point map is optimized and adjusted by the individual difference correction factor to obtain acupuncture point image data. Based on the acupuncture point image data, the acupuncture point analysis coefficient is obtained to generate an acupuncture point visualization feedback report. The acupuncture point analysis coefficient is used to quantitatively analyze the acupuncture situation of the acupuncture points of the patient to be treated.

[0010] On the other hand, a treatment data analysis device for chronic nonspecific low back pain is provided, which is applied to a treatment data analysis method for chronic nonspecific low back pain. The device includes:

[0011] The acquisition unit is used to acquire data of the patient to be treated, including acupuncture point diagrams, individual difference data, and individual difference factors.

[0012] An image segmentation unit is used to segment the acupuncture point map to obtain a local acupuncture point map, which includes a local top-view acupuncture point map and a local side-view acupuncture point map.

[0013] The correction unit is used to obtain data on individual difference influencing factors of the patient to be treated based on the individual difference data and the constructed individual difference model, and to obtain corresponding individual difference correction factors based on the individual difference influencing factor data and individual difference factors. The individual difference correction factors are used to reduce the acupuncture point errors of the patient to be treated caused by individual differences.

[0014] The generation unit is used to optimize and adjust the local acupuncture point map through individual difference correction factors to obtain acupuncture point image data. Based on the acupuncture point image data, the acupuncture point analysis coefficient is obtained to generate an acupuncture point visualization feedback report. The acupuncture point analysis coefficient is used to quantitatively analyze the acupuncture situation of the acupuncture points of the patient to be treated.

[0015] On the other hand, a treatment data analysis device for chronic nonspecific low back pain is provided, the treatment data analysis device for chronic nonspecific low back pain comprising: a processor; a memory storing computer-readable instructions, wherein when the computer-readable instructions are executed by the processor, any one of the methods described above for treatment data analysis of chronic nonspecific low back pain is implemented.

[0016] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction is stored therein, the at least one instruction being loaded and executed by a processor to implement any of the methods in the above-described methods for analyzing treatment data of chronic nonspecific low back pain.

[0017] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0018] This invention addresses the problem in existing technologies of limited analysis of acupoint-related treatment data obtained during the treatment of chronic nonspecific low back pain. It provides a method and system for analyzing treatment data of chronic nonspecific low back pain. The method involves segmenting the acupuncture point map of the patient to obtain a local acupuncture point map. Individual difference data of the patient is then input into a constructed individual difference model to obtain data on influencing factors of individual differences. Corresponding individual difference correction factors are then obtained. These correction factors are used to optimize and adjust the acupuncture point map, resulting in acupuncture point image data. Finally, acupuncture point analysis coefficients are derived from the acupuncture point image data to generate a visual feedback report on acupuncture treatment. This approach achieves a more accurate analysis of acupoint-related treatment data. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used 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 those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart of a treatment data analysis method for chronic nonspecific low back pain provided in an embodiment of the present invention;

[0021] Figure 2 This is a block diagram of a treatment data analysis device for chronic nonspecific low back pain provided in an embodiment of the present invention;

[0022] Figure 3 This is a schematic diagram of the structure of a data analysis device for the treatment of chronic nonspecific low back pain provided in an embodiment of the present invention. Detailed Implementation

[0023] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0024] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0025] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0026] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0027] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0028] This invention provides a method for analyzing treatment data of chronic nonspecific low back pain. This method can be implemented using a treatment data analysis device for chronic nonspecific low back pain, which can be a terminal or a server. Figure 1 The flowchart shown illustrates a method for analyzing treatment data of chronic nonspecific low back pain. This method's processing flow may include the following steps:

[0029] S1. Obtain patient data, which includes acupuncture point charts, individual difference data, and individual difference factors.

[0030] In one feasible implementation, the acupuncture point map is acquired through a scanning device, which refers to a high-definition camera, including but not limited to infrared imaging devices and ultrasonic scanners. The scanning device scans the patient's body from both a top-down and side-view perspectives to obtain the corresponding acupuncture point map. The acupuncture visualization feedback report is generated using image processing software, including but not limited to 3D Slicer and Agisoft Metashape. The method provided in this embodiment increases the monitoring of the acupuncture treatment and analyzes the acupuncture point-related treatment data in the form of images, improving the efficiency of treatment data analysis, enriching the analysis format, and ensuring the accuracy of the treatment data. Furthermore, by analyzing the local acupuncture images of each acupuncture point, more accurate analysis of the acupuncture point-related treatment data is achieved.

[0031] S2. The acupuncture point map is segmented to obtain a local acupuncture point map, which includes a local top view acupuncture point map and a local side view acupuncture point map.

[0032] Optionally, S2 segments the acupuncture point map to obtain a local acupuncture point map, including:

[0033] S21. Obtain the acupuncture point map and filter the corresponding acupuncture points. Match the needle insertion points in the acupuncture point map with the filtered acupuncture points to obtain the acupuncture points.

[0034] S22. Number the acupuncture points in the acupuncture point map and establish a coordinate system with the preset needle insertion point of each acupuncture point as the origin.

[0035] S23. Based on the number of each acupuncture point, the acupuncture point map is segmented to obtain the corresponding local acupuncture point map.

[0036] Among them, the acupuncture treatments include Huangdi Neizhen (Yellow Emperor's Inner Acupuncture) and Zhiguzhen (Bone-Penetrating Acupuncture).

[0037] In one feasible implementation, the insertion point represents the point where the needle used for treatment contacts the patient's skin, extracted by image processing software; the preset insertion point represents the center point of the area corresponding to the acupoints to be needled on a standard human acupoint map; the acupoints to be needled for the treatment are marked on the standard human acupoint map using image processing software to obtain the selected acupoints; matching the insertion points on the acupoint map with the selected acupoints means comparing the acupoints to be needled with the currently selected acupoints that have already been needled to determine whether all the acupoints to be needled have been needled, thus not only determining the location of each acupoint but also ensuring... This verifies that all acupoints have been needled without omission. If any omissions are found, the doctor is given a voice prompt to inform them of the missed acupoints. The specific matching method involves using image processing software to detect the presence of needle insertion points within the corresponding body part area of ​​the selected acupoints based on a human acupoint chart. Only after ensuring that needle insertion points exist at each acupoint is segmented, thus guaranteeing specific analysis of each acupoint. This increases the data sample of acupoint-related treatment data, improves the usability and accuracy of acupoint-related treatment data, lays the foundation for subsequent data analysis, and provides doctors with data samples of needle application at each acupoint.

[0038] S3. Based on individual difference data and the constructed individual difference model, obtain data on individual difference influencing factors of the patients to be treated. Based on the data on individual difference influencing factors and individual difference factors, obtain the corresponding individual difference correction factors. The individual difference correction factors are used to reduce the acupuncture point errors caused by individual differences in the patients to be treated.

[0039] Optionally, S3 obtains data on individual difference influencing factors of the patient to be treated based on individual difference data and the constructed individual difference model, and obtains corresponding individual difference correction factors based on the individual difference influencing factor data and individual difference factors, including:

[0040] S31. Perform data preprocessing on individual difference data, which includes body shape data and skin data. Body shape data includes individual weight, individual height, and individual body fat percentage. Skin data includes skin thickness, skin elasticity coefficient, and skin surface fluctuation index. Data preprocessing is used to de-unitize and normalize the individual difference data.

[0041] S32. Input the individual difference data and individual difference weights into the individual difference model to obtain the corresponding individual difference influencing factor data. The individual difference influencing factor data includes body shape difference factor and skin layer difference factor. The individual difference model is used to establish a unique mapping relationship between individual difference data and individual difference influencing factor data. The individual difference weights include body shape difference weight and skin difference weight. The body shape difference weights include weight weight, height weight and body fat percentage weight. The skin difference weights include skin thickness weight, skin elasticity weight and skin surface weight.

[0042] In one feasible implementation, the specific constraint expressions for the individual difference model are as follows: (1) and (2):

[0043] (1)

[0044] (2)

[0045] In the formula, f represents the patient's ID number. F represents the total number of patients to be treated. This represents the individual weight of the f-th patient awaiting treatment. This represents the individual height of the f-th patient awaiting treatment. This represents the individual body fat percentage of the f-th patient to be treated. This represents the skin thickness of the f-th patient to be treated. This represents the skin elasticity coefficient of the f-th patient to be treated. This represents the skin surface fluctuation index of the f-th patient to be treated. Indicates body weight. Indicates height weighting. Indicates body fat percentage weighting. Indicates skin thickness weight. Indicates skin elasticity weight. Indicates the weight of the skin surface. This represents the body size difference factor for the f-th patient to be treated. This represents the skin layer difference factor of the f-th patient to be treated.

[0046] S33. By performing a comprehensive correction operation on the data of factors influencing individual differences through individual difference factors, an individual difference correction factor is obtained. The comprehensive correction operation represents the mapping method of the degree of influence of body shape difference factors and skin layer difference factors on individual differences.

[0047] In one feasible implementation, the specific limiting expression of the individual difference correction factor is as follows (3):

[0048] (3)

[0049] In the formula, f represents the patient's ID number. F represents the total number of patients to be treated. This represents the body size difference factor for the f-th patient to be treated. This represents the skin layer difference factor of the f-th patient to be treated. This represents the body size weighting factor. Indicates skin weighting factor, This represents the individual difference correction factor for the f-th patient to be treated.

[0050] In this embodiment, individual weight is obtained using an electronic scale, individual height is obtained using a height measuring device, and individual body fat percentage is obtained using a body fat scale; skin thickness is obtained using an ultrasound scanner, skin elasticity coefficient is obtained using a skin elasticity measuring device (such as a cutometer), and skin surface fluctuation index is obtained using a skin surface roughness scanner; individual difference data for each patient to be treated are comprehensively quantified through an individual difference model to obtain more accurate data on individual difference influencing factors. Based on the obtained data on individual difference influencing factors, a more accurate individual difference correction factor is obtained. The individual difference correction factor helps to dynamically adjust the acupuncture point images according to the individual characteristics of each patient to be treated, which not only ensures the rigor of the acupuncture point image analysis, but also reduces the impact of individual differences on the imaging of acupuncture point images, thereby improving the accuracy of acupuncture point-related treatment data.

[0051] In the individual difference model, the larger the body size data, the greater the individual differences caused by body size, and the greater the individual differences caused by body size that need to be corrected, and thus the larger the corresponding body size difference factor. Similarly, the larger the skin data, the greater the individual differences caused by skin, and the greater the individual differences caused by skin that need to be corrected, and thus the larger the corresponding skin difference factor. The individual difference correction factor combines the body size difference factor and the skin difference factor for comprehensive analysis, thereby obtaining a more accurate individual difference correction factor to reduce the impact of individual differences on the acupuncture point map.

[0052] Specifically, individual difference weights are obtained from a preset database. Each individual difference weight represents the degree of influence of individual difference data on individual difference influencing factor data. There is a unique mapping relationship between each individual difference weight and the individual difference influencing factor data, and the value ranges from 0 to 1. In a specific embodiment, weight, height, and body fat percentage weights are obtained in real-time from the preset database. These weights represent the degree of influence of individual weight, height, and body fat percentage on body shape difference factors, and the sum of these weights is 1. Similarly, skin thickness, skin elasticity, and skin surface weights are obtained in real-time from the preset database. These weights represent the degree of influence of skin thickness, skin elasticity coefficient, and skin surface fluctuation index on skin difference factors, and the sum of these weights is 1.

[0053] Specifically, individual difference factors are obtained from a preset database, and these factors represent the degree of influence of individual difference influencing factors on individual difference correction factors. Each individual difference factor has a unique mapping relationship with the individual difference correction factor, and its value ranges from 0 to 1. In one specific embodiment, body shape weight factors and skin weight factors are obtained in real-time from the preset database. These factors represent the degree of influence of body shape difference factors and skin layer difference factors on individual difference correction factors, respectively, and the sum of the body shape weight factors and skin weight factors is 1.

[0054] S4. Optimize and adjust the local acupuncture point map through individual difference correction factors to obtain acupuncture point image data. Based on the acupuncture point image data, obtain the acupuncture point analysis coefficient to generate a visual feedback report on acupuncture. The acupuncture point analysis coefficient is used to quantitatively analyze the acupuncture situation of the acupuncture points of the patients to be treated.

[0055] Optionally, S4 optimizes and adjusts the local acupuncture point map using an individual difference correction factor to obtain acupuncture point image data, including:

[0056] S41. Optimize the height of the remaining needles on the local side of the acupuncture point map according to the individual difference correction factor to obtain the optimized height of the remaining needles on the local side. The optimization process represents the mapping method between the individual difference correction factor and the height of the remaining needles on the local side of the acupuncture point map.

[0057] S42. Modify the height of the remaining needles on the local side of the acupuncture point diagram to the optimized height of the remaining needles on the local side.

[0058] In one feasible implementation, the acupuncture point diagrams are adjusted based on the individual difference correction factors obtained from the patients to be treated. This ensures the accuracy and dynamism of the treatment data analysis for each patient, and also ensures that the acupuncture point diagrams for each patient have individual characteristics. After optimizing and adjusting the acupuncture point diagrams through the individual difference correction factors, the obtained acupuncture point image data will be more accurate, improving the efficiency of subsequent acupuncture point image data analysis and ensuring the usability and accuracy of the acupuncture point image data. Furthermore, the individual difference correction factors are mainly used to optimize and adjust the relevant height data (lateral residual needle height and residual needle height) of the treatment data related to acupuncture points, making the use of individual difference correction factors selective and ensuring the rigor of their use. This results in more accurate acupuncture point image data, thereby ensuring the accuracy of subsequent image analysis.

[0059] Optionally, the acupoint image data includes acupoint accuracy selection data, acupoint needling angle data, and acupoint needling depth data; the acupoint analysis coefficients include acupoint accuracy analysis coefficients, acupoint needling angle analysis coefficients, and acupoint needling depth analysis coefficients.

[0060] In this embodiment, "residual needle" refers to a needle that is not fully inserted into the skin; "acupoint number" represents the number of acupoints with needle insertion points; "needle-acupoint concentric distance" represents the distance between the needle insertion point and the origin in the local acupuncture point map, within a coordinate system with the needle insertion point as the origin. The needle insertion point is automatically extracted using Python and OpenCV; "acupoint deviation number" represents the number of acupoints outside the area corresponding to the needle insertion point; "residual needle length" represents the length of the line imaged on the acupuncture point map representing the distance between the needle insertion point and the needle that is not fully inserted into the skin, which is detected using Hough Transform and its length calculated; "residual needle angle" represents the angle between the line imaged by the residual needle and the positive direction of the horizontal axis in the coordinate system of the local acupuncture point map; "lateral residual needle height" represents the distance between the end point of the line imaged by the residual needle and the horizontal axis in the coordinate system of the local lateral acupuncture point map; and "residual needle overlap" represents the distance between the residual needle imaged by the needle and the horizontal axis.

[0061] The length of the overlap between the line and the preset needle line is the ratio of the preset needle line to the image line. The preset needle line represents the image line corresponding to the operation technique based on the standard treatment needling method. The lateral residual needle angle represents the angle between the residual needle image line and the horizontal axis in the coordinate system of the local lateral needling acupoint map. By acquiring the above acupoint image data, a data foundation is laid for subsequent image analysis, and labeled data samples are also provided for the acupuncture visualization feedback report.

[0062] Optionally, S4 obtains acupuncture point analysis coefficients based on acupuncture point image data, including:

[0063] S43. Based on the accuracy of acupoint selection data, determine the accuracy analysis coefficient of acupoints.

[0064] Optionally, S43 determines the acupoint accuracy analysis coefficient based on the acupoint accuracy selection data, including:

[0065] S431. Based on the acupoint accuracy selection data, obtain the corresponding reference acupoint selection data. The acupoint accuracy selection data includes the number of acupoints, the concentric distance between acupoints, and the number of acupoint deviations. The reference acupoint selection data includes the number of preset acupoints and the acupoint selection weight. The acupoint selection weight includes the weight of the number of acupoints, the weight of the concentric distance between acupoints, and the weight of the number of acupoint deviations.

[0066] S432. The deviation between the number of acupoints and the preset number of acupoints is processed to obtain the corresponding deviation of the number of acupoints.

[0067] In one feasible implementation, the deviation between the number of acupoints and the preset number of acupoints is processed to obtain the corresponding acupoint number deviation. .

[0068] S433. The accuracy of acupoints is processed by the deviation of acupoint number, the concentric distance between acupoints and needles, and the number of acupoint deviations, along with the corresponding acupoint selection weights, to obtain the acupoint accuracy analysis coefficient. The acupoint accuracy processing represents a comprehensive quantitative processing method that integrates the influence of acupoint selection accuracy on the degree of influence of the relationship between acupoint number deviation, concentric distance between acupoints and needles, and the number of acupoint deviations on the degree of influence of acupoint selection accuracy.

[0069] In one feasible implementation, the specific limiting expression for the acupoint accuracy analysis coefficient is as follows (4):

[0070] (4)

[0071] In the formula, This indicates the number of acupuncture points on the top-down view of the acupuncture point diagram. This indicates the preset number of acupoints. This indicates the concentric distance between acupuncture points when viewed from above in a diagram showing the acupuncture points. This indicates the number of acupuncture points deviating from the top view of the acupuncture point diagram. Indicates the weight of acupoint number. Indicates the weight of the concentric distance between acupoints. This indicates that the acupoints deviate from the weighted quantity. The coefficient representing the accuracy analysis of acupuncture points in a top-down view of the acupuncture point diagram, where e represents the natural constant.

[0072] In this embodiment, the preset number of acupoints represents the number of acupoints that need to be needled according to the treatment acupuncture method. The acupoint accuracy analysis coefficient combines the number of acupoints, the concentric distance between acupoints and needles, and the number of acupoint deviations for comprehensive analysis. In the formula, the acupoint deviation, the concentric distance between acupoints and needles, and the number of acupoint deviations obtained based on the preset number of acupoints are all negatively correlated with the acupoint accuracy analysis coefficient. The larger the acupoint deviation, the concentric distance between acupoints and needles, and the number of acupoint deviations, the lower the degree of conformity between the doctor's selection of acupoints and the standard operation method of the treatment acupuncture method. By analyzing the acupoint accuracy analysis coefficient, the accuracy of the doctor's selection of acupoints is analyzed more accurately. At the same time, it helps the doctor understand the doctor's acupuncture operation method based on the marked acupoint accuracy selection data, thereby improving the accuracy of the doctor's acupuncture operation method.

[0073] Specifically, the acupoint selection weights are obtained from a preset database, and the acupoint selection weights represent the degree of influence of the acupoint selection accuracy data on the acupoint accuracy analysis coefficient. Each acupoint selection accuracy data has a unique mapping relationship with the acupoint accuracy analysis coefficient, and the value ranges from 0 to 1. In a specific embodiment, the acupoint number weight, the acupoint concentric distance weight, and the acupoint deviation weight are obtained in real time from the preset database. These weights represent the degree of influence of the acupoint number, acupoint concentric distance, and acupoint deviation weight on the acupoint accuracy analysis coefficient, respectively, and the sum of these weights is 1.

[0074] S44. Based on the acupuncture angle data, determine the acupuncture angle analysis coefficient.

[0075] Optionally, S44 determines the acupuncture point angle analysis coefficient based on the acupuncture point angle data, including:

[0076] S441. Obtain corresponding reference acupoint angle data based on acupoint needling angle data. The acupoint needling angle data includes the length of the remaining needle, the angle of the remaining needle, the height of the remaining needle on the side, and the overlap of the remaining needles. The reference acupoint angle data includes reference angle data and acupoint angle weights. The reference acupoint angle data includes the reference remaining needle length, the reference remaining needle angle, the reference height of the remaining needle on the side, and the reference overlap of the remaining needles. The acupoint angle weights include the weight of the remaining needle length, the weight of the remaining needle angle, the weight of the remaining needle height on the side, and the weight of the remaining needle overlap.

[0077] S442. An angle analysis coefficient for acupuncture point application is obtained by performing angle analysis processing based on the remaining needle length, remaining needle angle, lateral remaining needle height, and remaining needle overlap, along with the corresponding acupoint angle weights and reference acupoint angle data. Angle analysis processing represents a comprehensive quantitative processing method that quantifies the influence of remaining needle length, remaining needle angle, lateral remaining needle height, and remaining needle overlap on the accuracy of acupoint application angle, as well as the influence of the interrelationship among remaining needle length, remaining needle angle, lateral remaining needle height, and remaining needle overlap on the accuracy of acupoint application angle.

[0078] In one feasible implementation, the specific limiting expression for the acupuncture angle analysis coefficient is as follows (5):

[0079] (5)

[0080] In the formula, n represents the acupoint number. N represents the total number of acupoints. This represents the remaining needle length in the local acupuncture point diagram for the nth acupuncture point. This represents the remaining needle angle in the local acupuncture point diagram for the nth acupuncture point. This represents the lateral residual needle height in the local acupuncture point diagram for the nth acupuncture point. This represents the degree of overlap of remaining needles in the local acupuncture point diagram for the nth acupuncture point. Indicates the reference remaining needle length. Indicates the reference balance angle. Indicates the reference side residual needle height. Indicates the reference overlap of the needles. Indicates the weight of the remaining needle length. Indicates the weight of the remaining needle angle. Indicates the weight of the remaining needle height on the side. Indicates the weight of the overlap of the remaining needles. This represents the individual difference correction factor for the f-th patient to be treated. This represents the acupuncture angle analysis coefficient of the local acupuncture point diagram for the nth acupuncture point.

[0081] In this embodiment, the acupuncture point angle analysis coefficient is specifically analyzed in conjunction with the remaining needle length, remaining needle angle, lateral remaining needle height, remaining needle overlap, and corresponding reference angle data. In the formula, the smaller the relative deviation of each acupuncture point angle data, the larger the corresponding acupuncture point angle analysis coefficient, indicating that the acupuncture point angle conforms to the standard operating technique of the treatment acupuncture method. By analyzing the acupuncture point angle analysis coefficient, it is helpful to more objectively reflect the degree of conformity between the doctor's acupuncture angle and the standard treatment acupuncture angle, thereby helping the doctor to more clearly understand the accuracy of the doctor's acupuncture operation technique, and thus adjust in time to improve the accuracy of the acupuncture point angle.

[0082] Specifically, the acupoint angle weights are obtained from a preset database, and the acupoint angle weights represent the degree of influence of the acupoint needling angle data on the acupoint needling angle analysis coefficient. There is a unique mapping relationship between the acupoint needling angle data and the acupoint needling angle analysis coefficient, and the values ​​range from 0 to 1. In a specific embodiment, the remaining needle length weight, remaining needle angle weight, lateral remaining needle height weight, and remaining needle overlap weight are obtained in real time from the preset database. The remaining needle length weight, remaining needle angle weight, lateral remaining needle height weight, and remaining needle overlap weight represent the degree of influence of the remaining needle length, remaining needle angle, lateral remaining needle height, and remaining needle overlap on the acupoint needling angle analysis coefficient, respectively, and the sum of the remaining needle length weight, remaining needle angle weight, lateral remaining needle height weight, and remaining needle overlap weight is 1.

[0083] Specifically, the reference acupoint angle data is obtained from a preset database. In one specific embodiment, the reference angle data is preset and entered into the preset database by a professional doctor according to the standard operating procedures of the treatment acupuncture method (i.e., Huangdi Neizhen and Zhiguzhen).

[0084] S45. Based on the acupuncture depth data, determine the acupuncture depth analysis coefficient.

[0085] Optionally, S45 determines the acupuncture depth analysis coefficient based on acupuncture depth data, including:

[0086] S451. Based on the acupuncture depth data, obtain the corresponding reference acupuncture depth data. The acupuncture depth data includes the length of the remaining needle, the height of the lateral remaining needle, and the angle of the lateral remaining needle. The reference acupuncture depth data includes the reference acupuncture depth data and the acupuncture depth weight. The reference acupuncture depth data includes the reference remaining needle length, the reference lateral remaining needle height, and the reference lateral remaining needle angle. The acupuncture depth weight includes the weight of the remaining needle length, the weight of the lateral remaining needle height, and the weight of the lateral remaining needle angle.

[0087] S452. The acupuncture depth analysis coefficient is obtained by performing depth analysis on the remaining needle length, lateral remaining needle height, and lateral remaining needle angle with the corresponding acupoint depth weights and reference acupoint depth data. The depth analysis process represents a comprehensive quantitative processing method that integrates the influence of the remaining needle length, lateral remaining needle height, and lateral remaining needle angle on the accuracy of acupuncture depth, as well as the influence of the relationship between the remaining needle length, lateral remaining needle height, and lateral remaining needle angle on the accuracy of acupuncture depth.

[0088] In one feasible implementation, the specific limiting expression for the acupuncture depth analysis coefficient is as follows (6):

[0089] (6)

[0090] In the formula, n represents the acupoint number. N represents the total number of acupoints. This represents the remaining needle length in the local acupuncture point diagram for the nth acupuncture point. This represents the lateral residual needle height in the local acupuncture point diagram for the nth acupuncture point. This represents the lateral angle of the needle in the local acupuncture point diagram for the nth acupuncture point. Indicates the reference remaining needle length. Indicates the reference side residual needle height. Indicates the reference side residual needle angle. Indicates the weight of the remaining needle length. Indicates the weight of the remaining needle height on the side. Indicates the weight of the side residual needle angle. This represents the individual difference correction factor for the f-th patient to be treated. This represents the acupuncture depth analysis coefficient of the local acupuncture point diagram for the nth acupuncture point.

[0091] In this embodiment, the acupuncture depth analysis coefficient is specifically analyzed by combining the remaining needle length, lateral remaining needle height and lateral remaining needle depth, and the corresponding reference acupuncture point depth data. In the formula, the smaller the relative deviation of each needle depth data, the larger the corresponding acupuncture depth analysis coefficient, indicating that the acupuncture depth conforms to the standard operation method of the treatment acupuncture technique. By analyzing the acupuncture depth analysis coefficient, it is helpful to more objectively reflect the degree of conformity between the doctor's needle depth and the standard treatment acupuncture technique, thereby helping the doctor to more clearly understand the accuracy of the doctor's needle operation technique, and thus adjust in time to improve the accuracy of the acupuncture depth.

[0092] Specifically, the acupoint depth weight is obtained from a preset database, and the acupoint depth weight represents the degree of influence of the acupoint needling depth data on the acupoint needling depth analysis coefficient. There is a unique mapping relationship between the acupoint needling depth data and the acupoint needling depth analysis coefficient, and the value ranges from 0 to 1. In a specific embodiment, the remaining needle length weight, the lateral remaining needle height weight, and the lateral remaining needle angle weight are obtained in real time from the preset database. The remaining needle length weight, the lateral remaining needle height weight, and the lateral remaining needle angle weight represent the degree of influence of the remaining needle length, the lateral remaining needle height, and the lateral remaining needle angle on the acupoint needling depth analysis coefficient, respectively, and the sum of the remaining needle length weight, the lateral remaining needle height weight, and the lateral remaining needle angle weight is 1.

[0093] Specifically, the reference acupoint angle data is obtained from a preset database. In one specific embodiment, the reference acupoint depth data is preset and entered into the preset database by a professional doctor according to the standard operating procedures of the treatment acupuncture method (i.e., Huangdi Neizhen and Zhiguzhen).

[0094] Optionally, the specific process for generating the acupuncture visualization feedback report is as follows: The acupuncture point analysis coefficients of each acupoint are weighted separately with the comprehensive analysis weighting factors obtained from a preset database to obtain a local acupuncture analysis index; the obtained local acupuncture analysis indices are summed and averaged sequentially to obtain a comprehensive acupuncture analysis index, where the comprehensive analysis weighting factors include selection factors, angle factors, and depth factors; the obtained acupoint image data and local acupuncture analysis indices are marked on the local acupuncture point map of the corresponding acupoints, and the marked local acupuncture point maps are combined to obtain a combined marked acupuncture point map; the obtained comprehensive acupuncture analysis index is marked on the combined marked acupuncture point map, and the marked combined marked acupuncture point map is used to generate a visualization feedback report.

[0095] In this embodiment, by comprehensively considering and analyzing the acupuncture point images obtained from each local acupuncture point map, the acupuncture point analysis coefficients, and the comprehensive acupuncture analysis index, the data is integrated and marked on the image, and then combined to obtain a combined marked acupuncture point map. This not only completes the integrated presentation of the data, but also accurately presents various types of data in each acupuncture point area, helping doctors to understand the doctor's acupuncture situation more intuitively. Furthermore, this image analysis method of segmentation and then combination not only improves the efficiency of image analysis, but also ensures the accuracy and comprehensiveness of the analysis of each image, thereby obtaining a more comprehensive and detailed acupuncture visualization feedback report, improving the doctor's user experience and the efficiency of treatment data analysis.

[0096] Specifically, the comprehensive analysis weighting factor is obtained from a preset database, and the comprehensive analysis weighting factor represents the degree of influence of the acupuncture point analysis coefficient on the local acupuncture analysis index. Each acupuncture point analysis coefficient has a unique mapping relationship with the acupuncture point depth analysis coefficient, and the value ranges from 0 to 1. In a specific embodiment, the selection factor, angle factor, and depth factor are obtained in real time from the preset database. The selection factor, angle factor, and depth factor represent the degree of influence of the acupuncture point accuracy analysis coefficient, the acupuncture point angle analysis coefficient, and the acupuncture point depth analysis coefficient on the acupuncture point depth analysis coefficient, respectively, and the sum of the selection factor, angle factor, and depth factor is 1.

[0097] This invention addresses the problem in existing technologies of limited analysis of acupoint-related treatment data obtained during the treatment of chronic nonspecific low back pain. It provides a method and system for analyzing treatment data of chronic nonspecific low back pain. The method involves segmenting the acupuncture point map of the patient to obtain a local acupuncture point map. Individual difference data of the patient is then input into a constructed individual difference model to obtain data on influencing factors of individual differences. Corresponding individual difference correction factors are then obtained. These correction factors are used to optimize and adjust the acupuncture point map, resulting in acupuncture point image data. Finally, acupuncture point analysis coefficients are derived from the acupuncture point image data to generate a visual feedback report on acupuncture treatment. This approach achieves a more accurate analysis of acupoint-related treatment data.

[0098] Figure 2 This is a block diagram of a treatment data analysis device for chronic nonspecific low back pain provided in an embodiment of the present invention. This device is used for a treatment data analysis method for chronic nonspecific low back pain. (Refer to...) Figure 2 The device includes an acquisition unit 210, an image segmentation unit 220, a correction unit 230, and a generation unit 240. Wherein:

[0099] The acquisition unit 210 is used to acquire data of the patient to be treated, which includes acupuncture point diagrams, individual difference data, and individual difference factors.

[0100] Image segmentation unit 220 is used to segment the acupuncture point map to obtain a local acupuncture point map, which includes a local top view acupuncture point map and a local side view acupuncture point map.

[0101] The correction unit 230 is used to obtain data on individual difference influencing factors of the patient to be treated based on the individual difference data and the constructed individual difference model, and to obtain corresponding individual difference correction factors based on the individual difference influencing factor data and individual difference factors. The individual difference correction factors are used to reduce the acupuncture point errors of the patient to be treated caused by individual differences.

[0102] The generation unit 240 is used to optimize and adjust the local acupuncture point map through individual difference correction factors to obtain acupuncture point image data, and to obtain acupuncture point analysis coefficients based on the acupuncture point image data to generate an acupuncture point visualization feedback report. The acupuncture point analysis coefficients are used to quantitatively analyze the acupuncture situation of the acupuncture points of the patient to be treated.

[0103] Figure 3 This is a schematic diagram of the structure of a data analysis device for the treatment of chronic nonspecific low back pain provided in an embodiment of the present invention, as shown below. Figure 3 As shown, the data analysis device for the treatment of chronic nonspecific low back pain may include the above-mentioned... Figure 2The illustrated device is a treatment data analysis apparatus for chronic nonspecific low back pain. Optionally, the treatment data analysis apparatus 310 for chronic nonspecific low back pain may include a first processor 2001.

[0104] Optionally, the data analysis device 310 for the treatment of chronic nonspecific low back pain may also include a memory 2002 and a transceiver 2003.

[0105] The first processor 2001, memory 2002, and transceiver 2003 can be connected via a communication bus.

[0106] The following is combined with Figure 3 A detailed introduction to the various components of the data analysis device 310 for the treatment of chronic nonspecific low back pain:

[0107] The first processor 2001 is the control center of the treatment data analysis device 310 for chronic nonspecific low back pain. It can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0108] Optionally, the first processor 2001 can perform various functions of the treatment data analysis device 310 for chronic nonspecific low back pain by running or executing software programs stored in the memory 2002 and calling data stored in the memory 2002.

[0109] In a specific implementation, as one example, the first processor 2001 may include one or more CPUs, for example... Figure 3 CPU0 and CPU1 are shown in the diagram.

[0110] In a specific implementation, as one example, the data analysis device 310 for the treatment of chronic nonspecific low back pain may also include multiple processors, for example... Figure 3The first processor 2001 and the second processor 2004 are shown in the diagram. Each of these processors can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). Here, a processor can refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0111] The memory 2002 is used to store the software program that executes the present invention, and is controlled by the first processor 2001 to execute it. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0112] Optionally, the memory 2002 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 2002 may be integrated with the first processor 2001 or may exist independently, and may be connected via the interface circuit of the chronic nonspecific low back pain treatment data analysis device 310. Figure 3 (Not shown in the image) is coupled to the first processor 2001, and this embodiment of the invention does not specifically limit this.

[0113] The transceiver 2003 is used to communicate with network devices or with terminal devices.

[0114] Alternatively, transceiver 2003 may include a receiver and a transmitter. Figure 3 (Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the transmitting function.

[0115] Optionally, the transceiver 2003 can be integrated with the first processor 2001 or exist independently, and can be connected to the interface circuit of the chronic nonspecific low back pain treatment data analysis device 310. Figure 3 (Not shown in the image) is coupled to the first processor 2001, and this embodiment of the invention does not specifically limit this.

[0116] It should be noted that, Figure 3 The structure of the treatment data analysis device 310 for chronic nonspecific low back pain shown in the diagram does not constitute a limitation on the router. Actual knowledge structure identification devices may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0117] Furthermore, the technical effects of the treatment data analysis device 310 for chronic nonspecific low back pain can be referred to the technical effects of the treatment data analysis method for chronic nonspecific low back pain described in the above method embodiments, and will not be repeated here.

[0118] It should be understood that the first processor 2001 in the embodiments of the present invention may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0119] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0120] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0121] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0122] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0123] It should be understood that, in various embodiments of the present invention, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0124] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0125] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0126] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0127] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0128] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0129] If the aforementioned functions are implemented as 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 this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0130] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for analyzing treatment data of chronic nonspecific low back pain, characterized in that, The method includes: S1. Obtain patient data to be treated, including acupuncture point charts, individual difference data, and individual difference factors; the acupuncture point charts are obtained through a scanning device; the individual difference data includes body shape data and skin data, the body shape data includes individual weight, individual height, and individual body fat percentage, and the skin data includes skin thickness, skin elasticity coefficient, and skin surface fluctuation index; the individual difference factors represent the degree of influence of individual difference influencing factor data on individual difference correction factors; S2. The acupuncture point map is segmented to obtain a local acupuncture point map, which includes a local top view acupuncture point map and a local side view acupuncture point map. S3. Based on the individual difference data and the constructed individual difference model, obtain the individual difference influencing factor data of the patient to be treated; based on the individual difference influencing factor data and the individual difference factor, obtain the corresponding individual difference correction factor; the individual difference correction factor is used to reduce the acupuncture point error caused by individual differences in the patient to be treated; the individual difference model is used to establish a unique mapping relationship between the individual difference data and the individual difference influencing factor data. S4. The local acupuncture point map is optimized and adjusted by the individual difference correction factor to obtain acupuncture point image data. Based on the acupuncture point image data, the acupuncture point analysis coefficient is obtained to generate an acupuncture visualization feedback report. The acupuncture point analysis coefficient is used to quantitatively analyze the acupuncture situation of the acupuncture points of the patient to be treated. The step S2 involves segmenting the acupuncture point map to obtain a local acupuncture point map, including: The acupuncture point map is obtained and the corresponding acupuncture points are selected. The needle insertion points in the acupuncture point map are matched with the selected acupuncture points to obtain the acupuncture points. The needle insertion point represents the point where the needle used for treatment contacts the skin of the patient being treated, as extracted by image processing software. Matching the needle insertion points in the acupuncture point map with the selected acupuncture points means comparing and matching the acupuncture points that need to be needled with the currently selected acupuncture points that have already been needled to determine whether all acupuncture points that need to be needled have been needled. The acupuncture points are numbered in the acupuncture point map, and a coordinate system is established with the needle insertion point of each acupuncture point as the origin. The acupuncture point map is segmented based on the number of each acupuncture point to obtain the corresponding local acupuncture point map.

2. The method for analyzing treatment data of chronic nonspecific low back pain according to claim 1, characterized in that, S3 obtains individual difference influencing factor data for the patient to be treated based on the individual difference data and the constructed individual difference model, and obtains corresponding individual difference correction factors based on the individual difference influencing factor data and individual difference factors, including: S31. Perform data preprocessing on individual difference data, wherein the data preprocessing is used to de-unitize and normalize the individual difference data; S32. Input the individual difference data and individual difference weights into the individual difference model to obtain the corresponding individual difference influencing factor data. The individual difference influencing factor data includes body shape difference factor and skin layer difference factor. The individual difference weights include body shape difference weight and skin difference weight. The body shape difference weights include weight weight, height weight and body fat percentage weight. The skin difference weights include skin thickness weight, skin elasticity weight and skin surface weight. S33. Perform a comprehensive correction operation on the data of factors influencing individual differences using individual difference factors to obtain individual difference correction factors. The comprehensive correction operation represents the mapping method of the degree of influence of body shape difference factors and skin layer difference factors on individual differences.

3. The method for analyzing treatment data of chronic nonspecific low back pain according to claim 1, characterized in that, The S4 step optimizes and adjusts the local acupuncture point map using an individual difference correction factor to obtain acupuncture point image data, including: S41. The height of the remaining needles on the local side of the acupuncture point map is optimized according to the individual difference correction factor to obtain the optimized height of the remaining needles on the local side. The optimization process represents the mapping method between the individual difference correction factor and the height of the remaining needles on the local side of the acupuncture point map. The remaining needles represent needles that have not been fully inserted into the skin. S42. Modify the height of the remaining needles on the local side of the acupuncture point diagram to the optimized height of the remaining needles on the local side.

4. The method for analyzing treatment data of chronic nonspecific low back pain according to claim 3, characterized in that, The acupoint image data includes acupoint accuracy selection data, acupoint needling angle data, and acupoint needling depth data; The acupuncture point analysis coefficients include the acupuncture point accuracy analysis coefficient, the acupuncture point angle analysis coefficient, and the acupuncture point depth analysis coefficient; The acupuncture point analysis coefficients obtained from the acupuncture point image data in S4 include: S43. Based on the data selected for acupoint accuracy, determine the acupoint accuracy analysis coefficient; S44. Based on the acupuncture angle data, determine the acupuncture angle analysis coefficient. S45. Based on the acupuncture depth data, determine the acupuncture depth analysis coefficient.

5. The method for analyzing treatment data of chronic nonspecific low back pain according to claim 4, characterized in that, The S43 method, based on the acupoint accuracy selection data, determines the acupoint accuracy analysis coefficient, including: S431. Obtain corresponding reference acupoint selection data based on acupoint accuracy selection data. The acupoint accuracy selection data includes the number of acupoints, the concentric distance between acupoints, and the number of acupoint deviations. The reference acupoint selection data includes the preset number of acupoints and acupoint selection weights. The acupoint selection weights include the weight of the number of acupoints, the weight of the concentric distance between acupoints, and the weight of the number of acupoint deviations. The number of acupoints indicates the number of acupoints with needle insertion points; the concentric distance between acupoints indicates the distance between the preset needle insertion point and the origin in the coordinate system with the needle insertion point as the origin in the local acupuncture point map; the number of acupoint deviations indicates the number of acupoints outside the area corresponding to the needle insertion point; the preset needle insertion point indicates the center point of the area corresponding to the acupoint to be needled on the standard human acupuncture point map. S432. The deviation between the number of acupoints and the preset number of acupoints is processed to obtain the corresponding deviation of the number of acupoints. S433. The acupoint accuracy is processed by the acupoint number deviation, the concentric distance between acupoints and acupoints and the number of acupoint deviations and the corresponding acupoint selection weights to obtain the acupoint accuracy analysis coefficient. The acupoint accuracy processing refers to the comprehensive quantitative processing of the influence of the acupoint selection accuracy on the degree of influence of the relationship between the acupoint number deviation, the concentric distance between acupoints and acupoints and the number of acupoint deviations on the degree of influence of the acupoint selection accuracy.

6. The method for analyzing treatment data of chronic nonspecific low back pain according to claim 4, characterized in that, The S44 method, based on acupuncture point angle data, determines the acupuncture point angle analysis coefficient, including: S441. Obtain corresponding reference acupoint angle data based on acupoint needling angle data. The acupoint needling angle data includes the length of the remaining needle, the angle of the remaining needle, the height of the remaining needle on the side, and the overlap of the remaining needles. The reference acupoint angle data includes reference angle data and acupoint angle weights. The reference acupoint angle data includes the length of the reference remaining needle, the angle of the reference remaining needle, the height of the remaining needle on the side, and the overlap of the reference remaining needles. The acupoint angle weights include the weight of the remaining needle length, the weight of the remaining needle angle, the weight of the remaining needle height on the side, and the weight of the remaining needle overlap. The remaining needle length represents the length of the line imaged on the acupuncture point map between the insertion point and the needle that is not fully inserted into the skin. The Hough transform is used to detect the line and calculate its length. The remaining needle angle represents the angle between the remaining needle imaged line and the positive direction of the horizontal axis in the coordinate system of the local acupuncture point map. The lateral remaining needle height represents the distance between the end point of the remaining needle imaged line and the horizontal axis in the coordinate system of the local lateral acupuncture point map. The remaining needle overlap represents the ratio of the overlap length between the remaining needle imaged line and the preset needle line to the preset needle line. The preset needle line represents the imaged line corresponding to the operation technique based on the standard treatment acupuncture method. S442. An angle analysis coefficient for acupuncture point application is obtained by performing angle analysis processing based on the remaining needle length, remaining needle angle, lateral remaining needle height, and remaining needle overlap, along with the corresponding acupoint angle weights and reference acupoint angle data. The angle analysis processing represents a comprehensive quantitative processing method that integrates the influence of remaining needle length, remaining needle angle, lateral remaining needle height, and remaining needle overlap on the accuracy of acupoint application angle, as well as the influence of the interrelationship among remaining needle length, remaining needle angle, lateral remaining needle height, and remaining needle overlap on the accuracy of acupoint application angle.

7. The method for analyzing treatment data of chronic nonspecific low back pain according to claim 4, characterized in that, The S45 method, based on acupuncture depth data, determines the acupuncture depth analysis coefficient, including: S451. Obtain corresponding reference acupoint depth data based on acupoint needling depth data. The acupoint needling depth data includes the remaining needle length, the lateral remaining needle height, and the lateral remaining needle angle. The reference acupoint depth data includes reference acupoint depth data and acupoint depth weight. The reference acupoint depth data includes reference remaining needle length, reference lateral remaining needle height, and reference lateral remaining needle angle. The acupoint depth weight includes remaining needle length weight, lateral remaining needle height weight, and lateral remaining needle angle weight. The length of the remaining needle represents the length of the line imaged on the acupuncture point map, representing the distance between the insertion point and the needle that is not fully inserted into the skin. The Hough transform is used to detect the line and calculate its length. The lateral remaining needle height represents the distance between the end point of the line imaged by the remaining needle and the horizontal axis in the coordinate system of the local lateral acupuncture point map. The lateral remaining needle angle represents the angle between the line imaged by the remaining needle and the horizontal axis in the coordinate system of the local lateral acupuncture point map. S452. The acupuncture depth analysis coefficient is obtained by performing depth analysis on the remaining needle length, lateral remaining needle height, and lateral remaining needle angle with the corresponding acupoint depth weights and reference acupoint depth data. The depth analysis process represents a comprehensive quantitative processing method that integrates the influence of the remaining needle length, lateral remaining needle height, and lateral remaining needle angle on the accuracy of acupuncture depth, as well as the influence of the relationship between the remaining needle length, lateral remaining needle height, and lateral remaining needle angle on the accuracy of acupuncture depth.

8. A treatment data analysis device for chronic nonspecific low back pain, wherein the treatment data analysis device for chronic nonspecific low back pain is used to implement the treatment data analysis method for chronic nonspecific low back pain as described in any one of claims 1-7, characterized in that, The device includes: The acquisition unit is used to acquire data of the patient to be treated, including acupuncture point diagrams, individual difference data, and individual difference factors. An image segmentation unit is used to segment the acupuncture point map to obtain a local acupuncture point map, which includes a local top-view acupuncture point map and a local side-view acupuncture point map. The correction unit is used to obtain data on individual difference influencing factors of the patient to be treated based on the individual difference data and the constructed individual difference model, and to obtain corresponding individual difference correction factors based on the individual difference influencing factor data and individual difference factors. The individual difference correction factors are used to reduce the acupuncture point errors of the patient to be treated caused by individual differences. The generation unit is used to optimize and adjust the local acupuncture point map through individual difference correction factors to obtain acupuncture point image data. Based on the acupuncture point image data, the acupuncture point analysis coefficient is obtained to generate an acupuncture point visualization feedback report. The acupuncture point analysis coefficient is used to quantitatively analyze the acupuncture situation of the acupuncture points of the patient to be treated.

9. A data analysis device for the treatment of chronic nonspecific low back pain, characterized in that, The treatment data analysis device for chronic nonspecific low back pain includes: processor; A memory storing computer-readable instructions that, when executed by the processor, implement the method as described in any one of claims 1 to 7.

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