Optimization Method for Traditional Chinese Medicine Treatment Plan of Knee Osteoarthritis Based on Big Data Analysis

Abstract

The present invention relates to the technical field of medical informatics, specifically an optimization method for traditional Chinese medicine treatment plans for knee osteoarthritis based on big data analysis, including the following steps: Based on knee joint load distribution data, collect knee joint shear stress data, monitor cartilage force gradient data, calculate the force peak value within the gait cycle, and obtain the knee joint biomechanical load balance value. In the present invention, by combining the calculation of shear stress, cartilage force gradient, and gait cycle force peak value, the individual biomechanical characteristics are accurately characterized, the patellar tendon stretch and quadriceps tendon displacement data are analyzed, the instantaneous elastic modulus is identified, the measurement accuracy of tendon adaptive adjustment is improved, the elastic changes of soft tissues are reflected, abnormal data is eliminated using individualized force characteristic data, the disease condition analysis is optimized, and the accuracy of efficacy evaluation is improved. According to the efficacy deviation degree, the rehabilitation training intensity is dynamically optimized, making the treatment plan more adaptable and improving the predictability and personalization level of the treatment effect.

Description

Technical Field

[0001] The present invention relates to the technical field of medical informatics, and particularly to a method for optimizing the traditional Chinese medicine treatment plan for knee osteoarthritis based on big data analysis. Background Art

[0002] The technical field of medical informatics includes related technologies for collecting, storing, managing, analyzing, and applying medical-related information based on computer and data analysis technologies. The core content of this technical field includes directions such as electronic health records, medical image processing, clinical decision support, and medical data mining, aiming to improve the utilization efficiency and accuracy of medical data. Medical informatics involves the collection and integration of patients' health data, including medical record information, diagnosis results, treatment plans, etc., and combines statistical analysis, machine learning, natural language processing, etc. to extract medical knowledge and assist in decision-making. This technical field is widely applied in disease diagnosis, treatment plan optimization, health management, telemedicine, etc., aiming to improve the accuracy and efficiency of medical services.

[0003] Among them, the method for optimizing the traditional Chinese medicine treatment plan for knee osteoarthritis refers to a method for optimizing the treatment process of knee osteoarthritis patients by using large-scale medical data analysis technology. The patent theme covers the collection of disease data, traditional Chinese medicine syndrome differentiation information, historical treatment plans, and efficacy data of knee osteoarthritis patients, and uses pattern recognition, data mining, statistical analysis, etc. to study the relevance of different treatment plans. By constructing a traditional Chinese medicine treatment data set for knee osteoarthritis, the treatment plans for different types of patients are matched and analyzed to explore optimization strategies for individualized treatment plans.

[0004] In the optimization of traditional Chinese medicine treatment plans for knee osteoarthritis in the prior art, there are problems such as insufficient data collection dimensions, limited accuracy of individualized analysis, and insufficient ability to dynamically adjust rehabilitation plans. Currently, data collection mainly relies on medical record records, diagnostic information, and treatment effect feedback, and fails to fully cover key biomechanical indicators such as joint load, shear stress, and tendon compliance, resulting in a lack of refined data support for individualized assessment. Due to the lack of real-time biomechanical data analysis for individuals, the assessment of disease progression mainly relies on speculation based on historical data, making it difficult to accurately predict the trend of patients' condition changes, leading to the lag of plan matching. The adjustment of rehabilitation plans is mainly based on fixed cycles or doctors' empirical judgments, rather than being optimized by combining patients' real-time biomechanical feedback, which affects the timeliness and pertinence of intervention. Due to limited data quality, abnormal data is difficult to effectively eliminate, further reducing the reliability of treatment plans. The efficacy assessment method is single and fails to combine biomechanical parameters to dynamically monitor the trend of efficacy changes, resulting in the difficulty of accurately identifying short-term efficacy fluctuations and affecting the adjustment and optimization of subsequent treatment plans. The prior art has deficiencies in individualized data collection, disease prediction accuracy, and rehabilitation plan optimization, restricting the precision and adaptability of traditional Chinese medicine treatment plans and affecting the stability of the overall treatment effect and the ability to continuously optimize. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies in the prior art and propose an optimization method for traditional Chinese medicine treatment plans for knee osteoarthritis based on big data analysis.

[0006] To achieve the above purpose, the present invention adopts the following technical solutions: An optimization method for traditional Chinese medicine treatment plans for knee osteoarthritis based on big data analysis, including the following steps:

[0007] S1: Based on the knee joint load distribution data, collect knee joint shear stress data, monitor the cartilage force gradient data, calculate the force peak value within the gait cycle, and obtain the knee joint biomechanical load balance value;

[0008] S2: Invoke the knee joint biomechanical load balance value, detect the patellar tendon stretch data and the quadriceps tendon displacement data, identify the instantaneous elastic modulus, screen the tendon compliance recovery characteristic data, and obtain the knee joint tendon compliance change coefficient;

[0009] S3: Invoke the knee joint tendon compliance change coefficient, screen the individualized knee joint force characteristic data, analyze the joint force change rate in different time windows, compare the cartilage wear trend parameters, judge the shear force distribution change characteristics, eliminate abnormal data, and obtain the individualized disease progression trend data set;

[0010] S4: Call the individualized disease progression trend dataset, analyze the efficacy change rate in the differential period, extract the short-term efficacy growth rate data, set the efficacy deviation threshold, judge the efficacy change trend, and combine the expert feedback mechanism to obtain the individualized efficacy deviation degree;

[0011] S5: Based on the individualized efficacy deviation degree, optimize the rehabilitation training intensity distribution, combine expert experience to adjust the individualized rehabilitation training, and obtain the optimized individual treatment plan for knee osteoarthritis.

[0012] As a further solution of the present invention, the knee joint biomechanical load balance value includes knee joint load distribution parameters, shear stress parameters, cartilage stress gradient parameters, and gait cycle stress peak parameters. The knee joint tendon compliance change coefficient includes patellar tendon stretch parameters, quadriceps tendon displacement parameters, instantaneous elastic modulus, and tendon compliance recovery characteristic parameters. The individualized disease progression trend dataset includes knee joint stress characteristic parameters, joint stress change rate parameters, cartilage wear trend parameters, and shear force distribution change characteristic parameters. The individualized efficacy deviation degree includes efficacy change rate, short-term efficacy growth rate data, efficacy deviation threshold, and efficacy change trend. The optimized individual treatment plan for knee osteoarthritis includes treatment intervention adjustment amplitude, rehabilitation training intensity distribution, and individualized rehabilitation training adjustment results.

[0013] As a further solution of the present invention, the specific steps for obtaining the knee joint biomechanical load balance value are as follows:

[0014] S111: Based on the knee joint load distribution data, collect the knee joint shear stress data, record the shear stress change trend within the gait cycle, screen the intervals with large rate changes, and obtain the key shear stress change intervals;

[0015] S112: Call the key shear stress change intervals, combine with the monitored cartilage stress gradient data, calculate the stress gradient change rate within the key intervals, analyze the gait phase stress gradient distribution, and obtain the dynamic stress gradient change value;

[0016] S113: Call the dynamic stress gradient change value, identify the stress difference degree within the gait cycle, analyze it in combination with the gait stability parameters, and use the formula:

[0017] ;

[0018] Obtain the knee joint biomechanical load balance value;

[0019] Among them, represents the knee joint biomechanical load balance value, represents the cartilage stress value at the th moment, represents the Instantaneous shear stress value, representing the dynamic change value of the force gradient, representing the gait stability parameter, being the total number of gait cycle data points.

[0020] As a further solution of the present invention, the steps for obtaining the change coefficient of the knee joint tendon compliance are specifically as follows:

[0021] S211: Invoke the knee joint biomechanical load balance value, extract the patellar tendon stretch data and the quadriceps tendon displacement data, analyze the patellar tendon stretch degree and the corresponding displacement ratio of the quadriceps tendon, compare the change amplitude under different load states, and obtain the instantaneous elastic modulus;

[0022] S212: Based on the instantaneous elastic modulus, analyze the change trend at different time points, screen the tendon compliance recovery characteristic data, identify the fluctuation range, and obtain the tendon compliance recovery trend;

[0023] S213: Invoke the tendon compliance recovery trend, analyze the change rate at adjacent time points, normalize the change value of each time period, and use the formula:

[0024] ;

[0025] Calculate to obtain the change coefficient of the knee joint tendon compliance;

[0026] Wherein, represents the change coefficient of the knee joint tendon compliance, represents the tendon compliance recovery value at the t-th time point, represents the tendon compliance recovery value at the previous time point, represents the total number of time points, represents the mean value of the time points.

[0027] As a further solution of the present invention, the steps for obtaining the individualized disease progression trend data set are specifically as follows:

[0028] S311: Invoke the change coefficient of the knee joint tendon compliance, screen the individualized knee joint force characteristic data, calculate the force balance coefficient of the joint differential area, and obtain the individualized knee joint force distribution data;

[0029] S312: Based on the individualized knee joint force distribution data, analyze the joint force change rate in different time windows, analyze the force change degree in different time windows, and use the formula:

[0030] ;

[0031] Calculate the joint force fluctuation index, identify the abnormal area of force fluctuation, and obtain the individualized dynamic change data of joint force;

[0032] Among them, represents the joint force fluctuation index, represents the joint force value within the j-th time window, represents the joint force value of the previous time window, represents the average joint force of the time window, represents the number of time windows;

[0033] S313: According to the individualized dynamic change data of joint force, identify the change characteristics of shear force distribution, screen the abnormal area and eliminate the abnormal force data to obtain the individualized disease progression trend data set.

[0034] As a further solution of the present invention, the specific steps for obtaining the individualized treatment efficacy deviation degree are as follows:

[0035] S411: Call the individualized disease progression trend data set, extract the efficacy change rate, compare the differential period data, screen the differential time period, and obtain the efficacy change rate of the differential period;

[0036] S412: Based on the efficacy change rate of the differential period, analyze the short-term growth rate change, and use the formula:

[0037] ;

[0038] Calculate the short-term efficacy growth rate data, and compare it with the efficacy deviation threshold to obtain the efficacy growth rate deviation ratio;

[0039] Among them, represents the short-term efficacy growth rate data, represents the efficacy change rate of the x-th time period, represents the highest efficacy change rate in the short term, represents the lowest efficacy change rate in the short term, represents the stability factor, represents the total number of time periods;

[0040] S413: Call the efficacy growth rate deviation ratio, judge the change trend of the corresponding threshold, and identify the individualized efficacy change direction according to the deviation direction and amplitude to obtain the individualized treatment efficacy deviation degree.

[0041] As a further solution of the present invention, the specific steps for obtaining the optimized individualized treatment plan for knee osteoarthritis are as follows:

[0042] S511: Based on the individualized efficacy deviation degree, evaluate the adjustment range of the treatment intervention method, call the individual physiological parameters, lesion area characteristic values, and efficacy data, analyze the change rate of the impact of the current treatment intervention on the individual, and obtain the adjustment range correction coefficient;

[0043] S512: Call the adjustment range correction coefficient to optimize the distribution of rehabilitation training intensity, and use the formula:

[0044] ;

[0045] Calculate the optimized intensity distribution value, analyze the individual rehabilitation training adaptability, and obtain the optimized training parameter weights;

[0046] Among them, represents the optimized intensity distribution value, represents the training load, represents the training frequency, represents the training duration, represents the individual's basic recovery ability, represents the target recovery progress, is the influence factor of training load, frequency, and duration, is the influence factor of the individual's basic recovery ability and the target recovery progress;

[0047] S513: Based on the optimized training parameter weights, combine expert experience to adjust the individualized rehabilitation training, adjust the dynamic training cycle, training mode, and training load balance, and obtain the optimized treatment plan for individuals with knee osteoarthritis.

[0048] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0049] In the present invention, by collecting knee joint load distribution data and combining the calculations of shear stress, cartilage stress gradient, and the peak stress within the gait cycle, the biomechanical characteristics of an individual in different motion states can be accurately characterized. Further analyzing the patellar tendon stretch and quadriceps tendon displacement data to identify the instantaneous elastic modulus enables more accurate measurement of tendon adaptive adjustment, effectively reflecting the elastic changes of the knee joint soft tissues during the rehabilitation process. Using the individualized knee joint force characteristic data to compare the joint force change rate, cartilage wear trend, and shear force distribution characteristics, while eliminating abnormal data and optimizing the data quality, makes the disease analysis more targeted. Analyzing the efficacy change rate in different cycles and setting the efficacy deviation threshold improve the accuracy of short-term efficacy evaluation. Based on the individualized efficacy deviation degree, accurately calculating the adjustment range of the treatment intervention method and dynamically optimizing the rehabilitation training intensity make the rehabilitation plan more in line with individual needs, improve the overall rehabilitation effect, and establish an individualized treatment plan for knee osteoarthritis based on accurate measurement, dynamic adjustment, and data-driven optimization, enhancing the adaptability of the treatment plan and the predictability of the efficacy, and effectively improving the scientificity and personalization of the rehabilitation intervention. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is a schematic diagram of the main steps of the present invention;

[0051] Figure 2 is a flowchart for obtaining the knee joint biomechanical load balance value in the present invention;

[0052] Figure 3 is a flowchart for obtaining the knee joint tendon compliance change coefficient in the present invention;

[0053] Figure 4 is a flowchart for obtaining the individualized disease progression trend data set in the present invention;

[0054] Figure 5 is a flowchart for obtaining the individualized efficacy deviation degree in the present invention;

[0055] Figure 6 is a flowchart for obtaining the optimized individualized treatment plan for knee osteoarthritis in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0057] In the description of the present invention, it should be understood that the orientation or positional relationships indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. are based on the orientation or positional relationships shown in the drawings. These are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0058] Embodiment 1

[0059] Please refer to Figure 1 , the present invention provides a technical solution: an optimization method for the traditional Chinese medicine treatment plan of knee osteoarthritis based on big data analysis, including the following steps:

[0060] S1: Based on the knee joint load distribution data, collect the knee joint shear stress data, monitor the cartilage force gradient data, calculate the force peak value within the gait cycle, and obtain the knee joint biomechanical load balance value;

[0061] S2: Call the knee joint biomechanical load balance value, detect the patellar tendon stretch data and the quadriceps tendon displacement data, identify the instantaneous elastic modulus, screen the tendon compliance recovery characteristic data, and obtain the knee joint tendon compliance change coefficient;

[0062] S3: Call the knee joint tendon compliance change coefficient, screen the individualized knee joint force characteristic data, analyze the joint force change rate in the differential time window, compare the cartilage wear trend parameters, judge the shear force distribution change characteristics, eliminate the abnormal data, and obtain the individualized disease progression trend data set;

[0063] S4: Call the individualized disease progression trend data set, analyze the efficacy change rate in the differential cycle, extract the short-term efficacy growth rate data, set the efficacy deviation threshold, judge the efficacy change trend, and combine with the expert feedback mechanism to obtain the individualized efficacy deviation degree;

[0064] S5: Based on the individualized efficacy deviation degree, calculate the adjustment amplitude of the treatment intervention method, optimize the rehabilitation training intensity distribution, combine with expert experience to adjust the individualized rehabilitation training, and obtain the optimized individualized treatment plan for knee osteoarthritis.

[0065] The knee joint biomechanical load balance values include knee joint load distribution parameters, shear stress parameters, cartilage force gradient parameters, and gait cycle force peak parameters. The knee joint tendon compliance change coefficients include patellar tendon stretch parameters, quadriceps tendon displacement parameters, instantaneous elastic modulus, and tendon compliance recovery characteristic parameters. The individualized disease progression trend dataset includes knee joint force characteristic parameters, joint force change rate parameters, cartilage wear trend parameters, and shear force distribution change characteristic parameters. The individualized treatment efficacy deviation includes treatment efficacy change rate, short-term treatment efficacy growth rate data, treatment efficacy deviation threshold, and treatment efficacy change trend. The optimized individual treatment plan for knee osteoarthritis includes treatment intervention adjustment amplitude, rehabilitation training intensity distribution, and individualized rehabilitation training adjustment results.

[0066] Please refer to Figure 2 , and the steps for obtaining the knee joint biomechanical load balance values are specifically as follows:

[0067] S111: Based on the knee joint load distribution data, collect the knee joint shear stress data, record the change trend of the shear stress during the gait cycle, screen the intervals with large rate changes, and obtain the key shear stress change intervals;

[0068] By using pressure sensors to collect the shear stress data of the knee joint during different gait cycles, the sensors are installed at the knee joint of the subject and can record the mechanical changes of the knee joint during walking, running, or activities in real time. This data collection process not only involves data recording but also includes monitoring and preliminary processing of data quality, such as removing abnormal data caused by sensor position offset. By analyzing the change rate of the shear stress within each gait cycle, select the intervals with a significant increase in the change rate as the key research areas. For example, when walking uphill, the shear stress on the knee joint will increase significantly. The selection of the intervals is based on the change trend of the shear stress and the comparative analysis with the previous and subsequent data to obtain the key shear stress change intervals. The results are of great significance for understanding the biomechanical behavior of the knee joint under different motion states.

[0069] S112: Call the key shear stress change intervals, combine with the monitored cartilage force gradient data, calculate the change rate of the force gradient within the key intervals, analyze the force gradient distribution in the gait phase, and obtain the dynamic change value of the force gradient;

[0070] Combined with the real-time monitored cartilage force gradient data, the cartilage force gradient is measured by a set of miniature sensors installed on the cartilage surface. The sensors can accurately measure the force changes on the cartilage during knee flexion or extension. By detailed calculation of the force gradient within the key change intervals, the force gradient distribution in different phases (such as the stance phase and the swing phase) during the gait cycle can be analyzed. This analysis helps to identify the areas with the largest force changes, which are the locations where cartilage damage or diseases are most severe, and obtain the dynamic change value of the force gradient.

[0071] S113: Call the dynamically changing value of the force gradient to identify the degree of force difference within the gait cycle, and analyze it in combination with the gait stability parameter. Use the formula:

[0072] ;

[0073] Obtain the biomechanical load balance value of the knee joint;

[0074] Among them, represents the biomechanical load balance value of the knee joint, represents the cartilage force value at the moment, represents the shear stress value at the moment, represents the dynamically changing value of the force gradient, represents the gait stability parameter, is the total number of data points in the gait cycle;

[0075] In this embodiment, 5 key data points within the gait cycle are used as the calculation basis, that is ;

[0076] In actual tests, assume the collected data is as follows (unit: N, Newton):

[0077] , , , , ;

[0078] , , , , ;

[0079] , , , , ;

[0080] , , , , ;

[0081] First, calculate the numerator part : ;

[0082] Then, calculate the denominator part: ;

[0083] ;

[0084] ;

[0085] ;

[0086] ;

[0087] The final calculated biomechanical load balance value of the knee joint , the force balance condition of the knee joint during this gait cycle. If this value is too high, it means that the force on the knee joint is unbalanced during movement, leading to cartilage damage or biomechanical problems. If this value is close to a set reference value (such as the average value of healthy individuals measured clinically between 3.5 - 5.0), it indicates that the biomechanical load of the knee joint of the subject is within the normal range. In this embodiment, the calculated , indicating that the force balance of the knee joint of this individual is relatively stable, but it is still necessary to further evaluate whether there is a potential injury risk in combination with the individual's exercise status, age, etc.

[0088] Please refer to Figure 3 , the steps for obtaining the change coefficient of the knee joint tendon compliance are specifically as follows:

[0089] S211: Call the biomechanical load balance value of the knee joint, extract the patellar tendon stretch data and the quadriceps tendon displacement data, analyze the patellar tendon stretch degree and the corresponding displacement ratio of the quadriceps tendon, compare the change amplitude under different load states, and obtain the instantaneous elastic modulus;

[0090] By obtaining the patellar tendon stretch data and the quadriceps tendon displacement data, the progress of the patient during the rehabilitation process can be monitored and evaluated. For example, during daily rehabilitation training, the patellar tendon stretch data of the patient's knee joint under different loads is measured by a sensor device, and the quadriceps tendon displacement data is also obtained in a similar way. The data will be used to calculate the patellar tendon stretch degree and the corresponding displacement ratio of the quadriceps tendon. Through the set operation of the data, the change amplitude under different load states can be compared, so as to identify the instantaneous elastic modulus. This process can help doctors judge the recovery of the patient's tendons and muscles, so as to adjust the rehabilitation plan. Through precise data analysis, the instantaneous elastic modulus is obtained as the basis for adjusting the subsequent treatment plan.

[0091] S212: Based on the instantaneous elastic modulus, analyze the change trend at different time points, screen the tendon compliance recovery characteristic data, identify the fluctuation range, and obtain the tendon compliance recovery trend;

[0092] Provide an important index for evaluating tendon compliance. Based on the instantaneous elastic modulus, characteristic data on the recovery of the patient's tendon compliance can be analyzed. By regularly measuring the patient's tendon compliance in the early stage of rehabilitation and using this data as a basis, the trend of data changes at different time points can be monitored to screen out the characteristic data on the recovery of tendon compliance during the recovery process. For example, using data processing software, the collected time series data is screened and calculated to find the data points that meet the rehabilitation standards, calculate the fluctuation range, and determine whether the compliance change interval is within the recovery range based on the data. Finally, the tendon compliance recovery trend is obtained, which can help doctors evaluate the speed and effect of the patient's rehabilitation and provide data support for further treatment.

[0093] S213: Invoke the tendon compliance recovery trend, analyze the change rate between adjacent time points, normalize the change values for each time period, using the formula:

[0094] ;

[0095] Perform operations to obtain the knee joint tendon compliance change coefficient;

[0096] Where, represents the knee joint tendon compliance change coefficient, represents the tendon compliance recovery value at the t-th time point, represents the tendon compliance recovery value at the previous time point, represents the total number of time points, represents the mean value of the time points;

[0097] First, invoke the tendon compliance recovery trend. By comparing the changes in tendon compliance at different time points, calculate the change rate between adjacent time points and normalize the change values for different time periods to calculate the overall change coefficient. This process can collect data regularly through medical monitoring devices. For example, during five consecutive days of rehabilitation training, record the tendon compliance values at the end of each day. Suppose the tendon compliance recovery values of a certain patient during the training period are: 2.1, 2.3, 2.6, 2.4, 2.5;

[0098] is the knee joint tendon compliance change coefficient;

[0099] represents the t-th time point tendon compliance recovery value, and the data is {2.1, 2.3, 2.6, 2.4, 2.5};

[0100] represents the tendon compliance recovery value at the previous time point;

[0101] (total number of time points);

[0102] Represents the mean at all time points and is calculated as follows: ;

[0103] First part: Calculate the mean of the rate of change

[0104] ;

[0105] ;

[0106] ;

[0107] Second part: Calculate the squared term of the standard deviation

[0108] ;

[0109] ;

[0110] ;

[0111] Final calculation: ;

[0112] Therefore, the calculated coefficient of change in knee tendon compliance is 0.0275. This value indicates that the change in tendon compliance of the patient during the five-day training period is relatively stable, with a low fluctuation range, providing information on tendon recovery stability for the doctor. It can be combined with subsequent data to compare the effects of different recovery plans on tendon compliance and further adjust the rhythm of rehabilitation training.

[0113] Please refer to Figure 4 , and the specific steps for obtaining the individualized disease progression trend dataset are as follows:

[0114] S311: Call the coefficient of change in knee tendon compliance, screen the individualized knee joint force characteristics data, calculate the force balance coefficient of the joint differential area, and obtain the individualized knee joint force distribution data;

[0115] The knee joint of the subject is non-invasively measured using a tendon compliance detection device to collect compliance data. The tendon response index after different activities is recorded for each experiment. This data is obtained by comparing the differences in tendon tension before and after movement. Through numerical analysis, it is found that the tendon compliance has an obvious changing trend for different movement patterns. The data is classified and processed through a screening algorithm to obtain the tendon compliance characteristics for different movements and different individuals. The data is used to further calculate the balance of joint forces. Based on the tendon compliance and joint force data, the individualized knee joint force distribution data is calculated. This data helps medical staff better understand the force conditions of the knee joint and provides customized rehabilitation suggestions for clinical practice.

[0116] S312: Analyze the joint force change rate in different time windows based on the individualized knee joint force distribution data, analyze the degree of force change within different time windows, and use the formula:

[0117] ;

[0118] Calculate the joint force fluctuation index, identify the abnormal areas of force fluctuation, and obtain the individualized dynamic change data of joint forces;

[0119] Among them, represents the joint force fluctuation index, represents the joint force value in the j-th time window, represents the joint force value in the previous time window, represents the average joint force of the time window, represents the number of time windows;

[0120] Extract the joint force data of the experimental subjects from the database. This data comes from the real-time records of a dynamic gait analyzer or a pressure sensor. Under different gait conditions (such as normal walking, running, and walking with load), the force data in multiple time windows is collected for the subjects. The interval of each window is set to 0.5 seconds. During this time interval, the sensor collects force data points and calculates the average value as the force value of this window. In the data preprocessing stage, first, the obtained force data is denoised to remove abnormal data caused by environmental interference factors. Then, the time windows are divided. Each window contains the force data of a series of time points, and the average force of this window is calculated to form a time series data set. During the time series data analysis process, the force change amount between adjacent time windows is calculated to quantify the force fluctuation situation;

[0121] Suppose the joint force data of 5 time windows is collected, as follows (unit: N): ;

[0122] Calculate the force change amount between adjacent time windows:

[0123] ;

[0124] ;

[0125] ;

[0126] ;

[0127] Calculate the mean force: ;

[0128] Calculate the denominator:

[0129] ;

[0130] ;

[0131] ;

[0132] Calculate the final force fluctuation index: ;

[0133] The calculation shows that the joint force fluctuation index of the current subject in different time windows is 1.25. The result is used to evaluate the stability of joint force. If the M value is large (such as greater than 2.0), it means that the joint force fluctuates violently and there are abnormal force conditions, such as abnormal gait or increased risk of articular cartilage wear. If the M value is small (such as lower than 0.8), it means that the force is relatively stable and cartilage damage is not likely to occur. The joint force fluctuation index is obtained by calculation, abnormal force fluctuation areas are identified, and individualized joint force dynamic change data are obtained.

[0134] S313: According to the individualized joint force dynamic change data, identify the shear force distribution change characteristics, screen abnormal areas and remove abnormal force data, and obtain an individualized disease progression trend data set;

[0135] Advanced mechanical simulation software is used to simulate the shear force distribution of the data, analyze the shear force characteristics of each area, identify and eliminate data areas that are beyond the normal range, and the shear force distribution pattern reveals potential cartilage damage areas. The data is further processed by algorithms to eliminate abnormal data caused by equipment errors or non-pathological changes, and ultimately generate an individualized disease progression trend data set. The data set provides dynamic monitoring information on knee joint lesions, allowing medical experts to observe the progression trend of the disease and adjust treatment plans accordingly to ensure that treatment measures are synchronized with changes in the disease and optimize the rehabilitation process.

[0136] See also Figure 5 , the specific steps for obtaining the individualized efficacy deviation are as follows:

[0137] S411: Call the individualized disease progression trend data set, extract the rate of change of treatment effect, compare the differential cycle data, screen the differential time periods, and obtain the rate of change of treatment effect in the differential cycles;

[0138] First, it is necessary to obtain the rate of change of treatment effect in each time period by calling the individualized disease progression trend data set. By comparing the treatment effect data in different time periods, the periods with significant differences can be screened out. The process involves set operations and comparisons of a large amount of historical treatment effect data. For example, in a clinical trial on knee osteoarthritis, by comparing the treatment effect data of patients in different months before and after receiving traditional Chinese medicine treatment, the months showing significant improvement in treatment effect are determined, and then these months are screened as the key research periods. Such analysis can help researchers more accurately evaluate the effect of treatment regimens to obtain the rate of change of treatment effect in the differential cycles.

[0139] S412: Based on the rate of change of treatment effect in the differential cycles, analyze the short-term growth rate change, and use the formula:

[0140] ;

[0141] Calculate the short-term treatment effect growth rate data, and compare it with the treatment effect deviation threshold to obtain the treatment effect growth rate deviation ratio;

[0142] Among them, represents the short-term treatment effect growth rate data, represents the rate of change of treatment effect in the x-th time period, represents the highest rate of change of treatment effect in the short term, represents the lowest rate of change of treatment effect in the short term, represents the stability factor, represents the total number of time periods;

[0143] To evaluate the impact of the treatment regimen on patients with knee osteoarthritis, extract the rate of change of treatment effect in different time periods, that is, the degree of improvement of treatment effect in each period. The rate of change of treatment effect can be calculated through indicators such as the change rate of the patient's functional score and the pain relief index. Suppose in a clinical trial, the rates of change of treatment effect in three months are , , , indicating the improvement situation of patients in different stages;

[0144] represents the rate of change of treatment effect in a certain time period;

[0145] and represent the maximum and minimum rates of change of treatment effect in the short term respectively;

[0146] Represents the stability factor, which is used to measure the fluctuation of the change rate of the treatment effect;

[0147] In the current situation, , ;

[0148] Calculate the numerator part: ;

[0149] Calculate the denominator part, assuming : ;

[0150] Final calculation: ;

[0151] The result shows that the data of the change rate of the treatment effect in the short term is -0.1253, indicating that the treatment effect of this patient shows a downward trend recently. By comparing with the set treatment effect deviation threshold (such as ±0.1), it is judged whether it is necessary to adjust the treatment plan, and the deviation ratio of the treatment effect change rate is obtained for subsequent individualized analysis.

[0152] S413: Call the deviation ratio of the treatment effect change rate, judge the change trend of the corresponding threshold, identify the individualized treatment effect change direction according to the deviation direction and amplitude, and obtain the individualized treatment effect deviation degree;

[0153] Use the deviation ratio of the treatment effect change rate to judge the change trend of the treatment effect, calculate the individualized treatment effect deviation degree according to the deviation direction and amplitude. The process involves further analysis and calculation of the deviation ratio of the treatment effect change rate. The specific actions include comparing the measured treatment effect data with the threshold to judge whether it exceeds the normal change range. For example, in a certain treatment trial, if the treatment effect deviation ratio of a patient exceeds the preset 5% threshold, it indicates that the change trend of his treatment effect is different from the conventional treatment response and further individualized adjustment is required. In this way, researchers can accurately control and adjust the treatment plan to ensure that each patient can obtain the most suitable treatment for their condition, thereby obtaining the individualized treatment effect deviation degree.

[0154] Please refer to Figure 6 , and the specific steps for obtaining the optimized individualized treatment plan for knee osteoarthritis are as follows:

[0155] S511: Based on the individualized treatment effect deviation degree, evaluate the adjustment amplitude of the treatment intervention method, call the individual physiological parameters, characteristic values of the lesion area, and treatment effect data, and analyze the change rate of the impact of the current treatment intervention on the individual to obtain the adjustment amplitude correction coefficient;

[0156] First, call individual physiological parameters such as weight, age, and characteristic values of the lesion area such as joint wear degree and inflammation indicators, as well as historical treatment efficacy data including the responsiveness and recovery rate of past treatments, and comprehensively analyze the data to determine the difference between the current treatment plan and the individual's actual needs. Further calculate the adjustment amplitude correction coefficient for treatment intervention. This coefficient is the result of adjustment based on the disease progression speed and individual differences. For example, for a 45-year-old patient with chronic knee arthritis, according to his disease course and past treatment responses, the adjustment amplitude correction coefficient is calculated, indicating the need to increase the frequency and intensity of physical therapy.

[0157] S512: Call the adjustment amplitude correction coefficient to optimize the intensity distribution of rehabilitation training, using the formula:

[0158] ;

[0159] Calculate the optimized intensity distribution value, analyze the individual rehabilitation training adaptability, and obtain the optimized training parameter weights;

[0160] Among them, represents the optimized intensity distribution value, represents the training load, represents the training frequency, represents the training duration, represents the individual's basic recovery ability, represents the target recovery progress, is the influencing factor of training load, frequency, and duration, is the influencing factor of the individual's basic recovery ability and the target recovery progress;

[0161] Calculate the training load, training frequency, and training duration to ensure that the patient's training intensity meets their individual needs. Collect the patient's individual physiological data, such as weight 80 kg, age 55 years old, characteristic values of the lesion area (joint space 2.5 mm, inflammation score 4.2), historical treatment efficacy data (exercise recovery rate in the past 3 months 75%), and then calculate the basic recovery ability based on the data and the target recovery progress , where the basic recovery ability can be determined by the result of the exercise tolerance test. Assume that the knee joint exercise tolerance score of this patient is 3.8 (full score 5), and the target recovery progress is evaluated from the rehabilitation goal set by the doctor. Assume it is 4.5 (full score 5). Calculate the training load (daily walking distance), training frequency (weekly training times), training duration (duration of each training session), and set the training load of this patient to walk 3.5 km each time, the training frequency to 5 times a week, and the duration of each training to 40 minutes;

[0162] Substitute specific numerical values for calculation. Assume that the training load impact factor , the training frequency impact factor , the training duration impact factor , the individual's basic recovery ability impact factor , the target recovery progress impact factor , then the calculation is as follows:

[0163] ;

[0164] ;

[0165] ;

[0166] ;

[0167] Finally, the optimized training intensity allocation value is calculated. This value indicates that the patient's current training load, training frequency, and training duration have been optimized and adjusted, making the training plan more in line with their individual needs, thereby further improving the adaptability of rehabilitation training. Based on this value, the individual rehabilitation training adaptability is calculated, and the training parameters are adjusted accordingly to obtain the optimized training parameter weights.

[0168] S513: Based on the optimized training parameter weights, combined with expert experience, perform individualized rehabilitation training adjustments, adjust the dynamic training cycle, training mode, and training load balance to obtain an optimized individual treatment plan for knee osteoarthritis;

[0169] Regarding the adjustments to the dynamic training cycle, training mode, and training load balance, analyze the optimized intensity allocation value. If the optimized intensity allocation value indicates that the current training frequency is too high, reduce the frequency while increasing the duration of each training session to adapt to the patient's actual recovery ability. Through such adjustments, the load of each training cycle can be precisely controlled, and finally an optimized individual treatment plan for knee osteoarthritis can be generated or obtained. For example, an individualized treatment plan designed for a patient with acute symptoms will emphasize reducing the exercise intensity in the short term to relieve symptoms and prevent further joint damage.

[0170] The above is only a preferred embodiment of the present invention and does not impose other forms of limitation on the present invention. Any person skilled in the relevant art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for optimizing the traditional Chinese medicine treatment plan for knee osteoarthritis based on big data analysis, characterized in that, It includes the following steps: S1: Based on the knee joint load distribution data, collect the knee joint shear stress data, monitor the cartilage force gradient data, calculate the peak force within the gait cycle, and obtain the knee joint biomechanical load balance value; S2: Invoke the knee joint biomechanical load balance value, detect the patellar tendon stretch data and the quadriceps tendon displacement data, identify the instantaneous elastic modulus, screen the tendon compliance recovery characteristic data, and obtain the knee joint tendon compliance change coefficient; The specific steps for obtaining the knee joint tendon compliance change coefficient are as follows: S211: Invoke the knee joint biomechanical load balance value, extract the patellar tendon stretch data and the quadriceps tendon displacement data, analyze the patellar tendon stretch degree and the corresponding displacement ratio of the quadriceps tendon, compare the change amplitude under different load states, and obtain the instantaneous elastic modulus; S212: Based on the instantaneous elastic modulus, analyze the change trend at different time points, screen the tendon compliance recovery characteristic data, identify the fluctuation range, and obtain the tendon compliance recovery trend; S213: Invoke the tendon compliance recovery trend, analyze the change rate at adjacent time points, normalize the change values for each time period, and use the formula: ； Calculate to obtain the knee joint tendon compliance change coefficient; Among them, represents the knee joint tendon compliance change coefficient, represents the tendon compliance recovery value at the t-th time point, represents the tendon compliance recovery value at the previous time point, represents the total number of time points, represents the mean value of time points; S3: Invoke the knee joint tendon compliance change coefficient, screen the individualized knee joint force characteristic data, analyze the joint force change rate in different time windows, compare the cartilage wear trend parameters, judge the shear force distribution change characteristics, eliminate abnormal data, and obtain the individualized disease progression trend data set; The specific steps for obtaining the individualized disease progression trend data set are as follows: S311: Invoke the knee joint tendon compliance change coefficient, screen the individualized knee joint force characteristic data, calculate the force balance coefficient in the joint differential area, and obtain the individualized knee joint force distribution data; S312: Based on the individualized knee joint force distribution data, analyze the joint force change rate in different time windows, analyze the force change degree within different time windows, and use the formula: ； Calculate the joint force fluctuation index, identify the force fluctuation abnormal area, and obtain the individualized joint force dynamic change data; Among them, represents the joint force fluctuation index, represents the joint force value within the j-th time window, represents the joint force value of the previous time window, represents the average joint force of the time window, represents the number of time windows; S313: According to the individualized joint force dynamic change data, identify the shear force distribution change characteristics, screen the abnormal area and eliminate the force abnormal data, and obtain the individualized disease progression trend data set; S4: Invoke the individualized disease progression trend data set, analyze the efficacy change rate in different cycles, extract the short-term efficacy growth rate data, set the efficacy deviation threshold, judge the efficacy change trend, and combine with the expert feedback mechanism to obtain the individualized efficacy deviation degree; S5: Based on the individualized efficacy deviation degree, optimize the rehabilitation training intensity distribution, combine with expert experience to adjust the individualized rehabilitation training, and obtain the optimized individual treatment plan for knee osteoarthritis.

2. The method for optimizing the traditional Chinese medicine treatment plan for knee osteoarthritis based on big data analysis according to claim 1, characterized in that, The knee joint biomechanical load balance value includes knee joint load distribution parameters, shear stress parameters, cartilage stress gradient parameters, and peak force parameters during the gait cycle. The knee joint tendon compliance change coefficient includes patellar tendon stretch parameters, quadriceps tendon displacement parameters, instantaneous elastic modulus, and tendon compliance recovery characteristic parameters. The individualized disease progression trend dataset includes knee joint force characteristics parameters, joint force change rate parameters, cartilage wear trend parameters, and shear force distribution change characteristic parameters. The individualized treatment efficacy deviation degree includes treatment efficacy change rate, short-term treatment efficacy growth rate data, treatment efficacy deviation threshold, and treatment efficacy change trend. The optimized individual treatment plan for knee osteoarthritis includes treatment intervention adjustment amplitude, rehabilitation training intensity distribution, and individualized rehabilitation training adjustment result.

3. The method for optimizing the traditional Chinese medicine treatment plan for knee osteoarthritis based on big data analysis according to claim 1, wherein, The specific steps for obtaining the knee joint biomechanical load balance value are as follows: S111: Based on the knee joint load distribution data, collect the knee joint shear stress data, record the change trend of shear stress during the gait cycle, screen the intervals with large rate changes, and obtain the key shear stress change intervals. S112: Call the key shear stress change intervals, combine with the monitored cartilage stress gradient data, calculate the stress gradient change rate within the key intervals, analyze the stress gradient distribution during the gait phase, and obtain the dynamic stress gradient change value. S113: Call the dynamic stress gradient change value, identify the degree of force difference during the gait cycle, analyze it in combination with the gait stability parameters, and use the formula: ； to obtain the knee joint biomechanical load balance value. in, Represents the biomechanical load balance value of the knee joint. Representative Cartilage force value at each moment, Representative Shear stress value at time Represents the dynamic change value of the force gradient, represents the gait stability parameter, is the total number of gait cycle data points.

4. The method for optimizing the traditional Chinese medicine treatment plan for knee osteoarthritis based on big data analysis according to claim 1, characterized in that The specific steps for obtaining the individualized treatment efficacy deviation degree are as follows: S411: Call the individualized disease progression trend dataset, extract the treatment efficacy change rate, compare the differential cycle data, screen the differential time periods, and obtain the treatment efficacy change rate during the differential cycles. S412: Based on the treatment efficacy change rate during the differential cycles, analyze the short-term growth rate change, and use the formula: ； to calculate the short-term treatment efficacy growth rate data, and compare it with the treatment efficacy deviation threshold to obtain the treatment efficacy growth rate deviation ratio. Among them, represents the short-term efficacy growth rate data, represents the efficacy change rate at the x-th time period, represents the highest efficacy change rate in the short term, represents the lowest efficacy change rate in the short term, represents the stability factor, represents the total number of time periods; S413: Call the treatment efficacy growth rate deviation ratio, judge the change trend of the corresponding threshold, identify the individualized treatment efficacy change direction according to the deviation direction and amplitude, and obtain the individualized treatment efficacy deviation degree.

5. The method for optimizing the traditional Chinese medicine treatment plan for knee osteoarthritis based on big data analysis according to claim 4, wherein, The specific steps for obtaining the optimized individual treatment plan for knee osteoarthritis are as follows: S511: Based on the individualized treatment efficacy deviation degree, evaluate the adjustment amplitude of the treatment intervention method, call the individual physiological parameters, lesion area characteristic values, and treatment efficacy data, analyze the change rate of the impact of the current treatment intervention on the individual, and obtain the adjustment amplitude correction coefficient. S512: Call the adjustment amplitude correction coefficient, optimize the rehabilitation training intensity distribution, and use the formula: ； to calculate the optimized intensity distribution value, analyze the individual rehabilitation training adaptability, and obtain the optimized training parameter weight. Among them, represents the optimized intensity allocation value, represents the training load, represents the training frequency, represents the training duration, represents the individual's basic recovery ability, represents the target recovery progress, is the influencing factor of training load, frequency, and duration, is the influencing factor of the individual's basic recovery ability and the target recovery progress; S513: Based on the optimized training parameter weight, combine with expert experience to perform individualized rehabilitation training adjustment, adjust the dynamic training cycle, training mode, and training load balance, and obtain the optimized individual treatment plan for knee osteoarthritis.

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