Muscle rehabilitation data management system based on intelligent chip

Abstract

The invention discloses a muscle rehabilitation data management system based on an intelligent chip, and belongs to the technical field of rehabilitation data management. A muscle rehabilitation data management system based on an intelligent chip comprises a data acquisition module, a data management module and a data storage module. The method solves the problem that in the prior art, a rehabilitation training plan of a patient is not adjusted according to the muscle rehabilitation condition of the patient, so that a final rehabilitation strategy is not matched with the patient, and consequently the effort is depleted and even negative effects are generated, the efficiency and quality of rehabilitation treatment are improved, the treatment effect is maximized, and the rehabilitation effect is improved. The rehabilitation process can be better monitored and adjusted, scientificity and effectiveness of a muscle rehabilitation training plan are ensured, a treatment scheme is optimized, the overall effect of rehabilitation treatment is improved, data access behaviors can be monitored in real time, potential security threats can be found and prevented in time, a more refined data security management strategy is implemented, and the rehabilitation effect is improved. And the security of data storage is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of rehabilitation data management, and in particular to a muscle rehabilitation data management system based on a smart chip. Background Art

[0002] The digitization of massive medical information and the electronicization of medical treatment processes have adapted to the current social reality of a rapidly increasing population, an accelerating aging trend, and a growing number of patients with chronic diseases. At the same time, various medical data are growing rapidly, and muscle rehabilitation data is one of them. With the development of big data, data applications are becoming more and more valuable for rehabilitation training. More effective management of muscle rehabilitation data can comprehensively analyze it and mine useful value information to promote the improvement of medical rehabilitation levels.

[0003] A Chinese patent with publication number CN117558449A discloses a medical health data management system based on big data, which includes: an acquisition module, which acquires real-time vital sign data during the monitoring process; a selection module, which acquires preliminary medical diagnosis information of a target patient based on the real-time vital sign data, and selects target monitoring data in the real-time vital sign data based on the preliminary medical diagnosis information, wherein the number of target monitoring data is less than the number of real-time vital sign data; an extraction module, which extracts monitoring values ​​of the target monitoring data, and determines the health status of the target patient in the current monitoring cycle; a prediction module, which predicts items to be inspected in the next monitoring cycle based on the changing trend of the health status and the target monitoring data; and a management terminal, which locks the corresponding inspection instrument based on the items to be inspected, so as to use the inspection instrument to complete the items to be inspected for the target patient in the next monitoring cycle, thereby improving the timeliness of the patient's medical treatment.

[0004] In actual use, the above patent does not adjust the patient's rehabilitation training plan according to the patient's muscle rehabilitation condition, which makes the final rehabilitation strategy incompatible with the patient, thus failing to achieve the desired result or even producing negative effects. Therefore, it does not meet the existing needs. We propose a muscle rehabilitation data management system based on smart chips. Summary of the invention

[0005] The purpose of the present invention is to provide a muscle rehabilitation data management system based on a smart chip, which can evaluate the effect of rehabilitation treatment, optimize the treatment plan, improve the efficiency and quality of rehabilitation treatment, adjust the treatment plan according to the patient's specific situation and treatment effect, ensure the maximization of the treatment effect, and improve the efficiency and quality of rehabilitation treatment. Through data management, the rehabilitation process can be better monitored and adjusted to ensure the scientificity and effectiveness of the muscle rehabilitation training plan. The patient's rehabilitation condition can be fully understood, the muscle rehabilitation data can be compressed and stored using a smart chip, data access behavior can be monitored in real time, potential security threats can be discovered and prevented in a timely manner, and a more refined data security management strategy can be implemented to ensure the security of data storage and solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a muscle rehabilitation data management system based on a smart chip, comprising: The data acquisition module is used to collect the muscle rehabilitation data of the patient, classify the muscle rehabilitation data according to the category of the muscle rehabilitation data, and clean the acquired muscle rehabilitation data of the patient, delete invalid data, and retain valid data; The data management module is used to analyze the rehabilitation index of the acquired patient muscle rehabilitation data, generate abnormal part index change data, and evaluate the patient's muscle rehabilitation status based on the analysis results; The data storage module is used to encode and convert the rehabilitation data to obtain the character string to be compressed, obtain the compressed muscle rehabilitation data according to the encoding results of each character in the character string to be compressed, and encapsulate the compressed muscle rehabilitation information into a muscle rehabilitation data packet, and transmit the data packet to the smart chip for storage.

[0007] Preferably, the data acquisition module includes: The data collection module is used to collect the patient's muscle rehabilitation data and classify it according to the type of muscle rehabilitation data, specifically into patient basic data, patient diagnosis data, daily physiological data, treatment plan, rehabilitation process and treatment effect; The data processing module is used to clean the acquired patient muscle rehabilitation data, delete invalid data, and retain valid data.

[0008] Preferably, the data processing module specifically includes: Clean the acquired muscle rehabilitation data, read the abnormal data in the muscle rehabilitation data and record it as an abnormal value, and compare the abnormal value with the preset calibration value; When the abnormal value is greater than or equal to the calibration value, the muscle rehabilitation data is marked as invalid and deleted from the cleaning; When the abnormal value is less than the calibration value, the muscle rehabilitation data is marked as valid, and the process of the muscle rehabilitation data in the current cleaning is repeated.

[0009] Preferably, the data processing module further includes: A first quantity extraction module, used to extract the total number of abnormal values; A second quantity extraction module, used for extracting the number of invalid values ​​in the abnormal value; The time interval duration calling module is used to call the collection interval duration between invalid values ​​corresponding to each two adjacent collection moments in the invalid values; An invalid value abnormality degree coefficient acquisition module is used to obtain the invalid value abnormality degree coefficient by using the total number of abnormal values, the number of invalid values ​​in the abnormal values, and the collection interval between the invalid values ​​corresponding to each two adjacent collection moments; The invalid value abnormality coefficient is obtained by the following formula:

[0010] Wherein, E represents the invalid value abnormality coefficient; N represents the total number of the abnormal values; n represents the number of invalid values ​​in the abnormal values; T represents the collection interval between the i-th invalid value and the i+1-th invalid value; T min and T max Indicates the minimum and maximum values ​​of the collection interval corresponding to the invalid values ​​among the n abnormal values; T b Indicates the standard deviation of the collection interval length corresponding to the invalid values ​​among the n outliers; A coefficient comparison module, used for comparing the invalid value abnormality degree coefficient with a preset abnormality degree coefficient threshold; The calibration value control module is used to adjust the calibration value when the invalid value abnormality coefficient exceeds a preset abnormality coefficient threshold, and to screen invalid values ​​according to the adjusted calibration value.

[0011] Preferably, the calibration value control module includes: A calibration value retrieving module, used for retrieving a preset calibration value when the invalid value abnormality coefficient exceeds a preset abnormality coefficient threshold; A first data information extraction module, used to extract the number of data and data values ​​marked as valid in the abnormal value as the first data information; A second data information extraction module is used to extract the number of data and data values ​​determined to be invalid in the abnormal value as the second data information; A calibration value adjustment coefficient acquisition module, used to acquire a calibration value adjustment coefficient using the first data information and the second data information; The calibration value adjustment coefficient is obtained by the following formula:

[0012] Wherein, C represents the calibration value adjustment coefficient; M represents the number of data marked as valid in the abnormal value; n represents the number of invalid values ​​in the abnormal value; R represents the difference between the average value of the valid data and the average value of the invalid data in the abnormal value; V represents the preset calibration value; X min Indicates the minimum value among invalid values; Y max Indicates the maximum value among valid values; A calibration value adjustment module, used to obtain an adjusted calibration value by using the calibration value adjustment coefficient in combination with a preset calibration value; The adjusted calibration value is obtained by the following formula:

[0013] Among them, V t Indicates the adjusted calibration value; V indicates the preset calibration value; X b The standard deviation of the values ​​representing invalid values; Y b Indicates the numerical standard deviation of the effective value; C represents the calibration value adjustment coefficient; The data processing control module is used to perform data processing of abnormal values ​​on the muscle rehabilitation data using the adjusted calibration values.

[0014] Preferably, the data management module includes: A data analysis module is used to analyze the rehabilitation index of the acquired patient muscle rehabilitation data and generate abnormal part index change data; The data evaluation module is used to evaluate the patient's muscle recovery according to the analysis results of the data analysis module.

[0015] Preferably, the data analysis module includes: Extract abnormal rehabilitation indexes from the patient's muscle rehabilitation data to obtain abnormal muscle rehabilitation indexes of the patient; According to the patient's abnormal muscle rehabilitation index, the standard patient's muscle rehabilitation data is associated with abnormal parts to generate abnormal part association data; Perform dynamic change analysis of rehabilitation indicators on abnormal part-related data to generate dynamic change data of abnormal part indicators; Compare the dynamic change data of abnormal part indicators with the standard muscle rehabilitation indicator change data; When the dynamic change data of the abnormal part index is greater than the standard muscle rehabilitation index change data, the abnormal part is marked as a highly abnormal part to obtain the highly abnormal part data; When the dynamic change data of the abnormal part index is smaller than the standard muscle rehabilitation index change data, the abnormal part associated data is marked as a low abnormal part to obtain low abnormal part data.

[0016] Preferably, the data evaluation module comprises: According to the high abnormality part data and the low abnormality part data, respectively generate the high abnormality part rehabilitation adjustment decision data and the low abnormality part rehabilitation adjustment decision data; According to the rehabilitation adjustment decision data of the high-abnormality part and the rehabilitation adjustment decision data of the low-abnormality part, the gap between the current rehabilitation process and the established rehabilitation goal is judged, and the current rehabilitation status of the patient is evaluated; Then, based on the individual differences in the patient's age and gender and medical history, and combined with the patient's current stage of recovery, clear rehabilitation goals are set; Develop a rehabilitation training plan for the patient based on the patient's real-time muscle rehabilitation data and historical data during the rehabilitation process; Finally, the rehabilitation training plan is adjusted individually according to the patient's age, gender and physical condition characteristics combined with the rehabilitation adjustment decision data of high-abnormality parts and low-abnormality parts.

[0017] Preferably, generating the high abnormality part rehabilitation adjustment decision data and the low abnormality part rehabilitation adjustment decision data respectively according to the high abnormality part data and the low abnormality part data specifically includes: Individualized rehabilitation decision-making is constructed for the data of the highly abnormal parts to obtain individualized rehabilitation decision-making data for the highly abnormal parts; Conduct feedback evaluation on the rehabilitation training videos of the high-abnormality parts to generate positive feedback data and negative feedback data of the rehabilitation of the high-abnormality parts; Adjusting the rehabilitation decision of the individualized high-abnormality part by using the high-abnormality part rehabilitation positive feedback data and the high-abnormality part rehabilitation negative feedback data, thereby generating the high-abnormality part rehabilitation adjustment decision data; The muscle signals of the low abnormal parts are collected according to the low abnormal parts data to obtain the muscle signals of the low abnormal parts; A general rehabilitation decision is constructed for the muscle signals of the low-abnormality part to obtain the rehabilitation decision data of the low-abnormality part; Perform muscle rehabilitation stimulation feedback analysis on muscle response data based on low-abnormality part rehabilitation decision data to generate low-abnormality part muscle stimulation feedback data; The low-abnormality part muscle stimulation feedback data is used to optimize the low-abnormality part rehabilitation decision data, and generate low-abnormality part rehabilitation adjustment decision data.

[0018] Preferably, the data storage module comprises: A data conversion module is used to perform UTF-8 encoding on the muscle rehabilitation data to obtain binary data corresponding to the muscle rehabilitation data, and convert the binary data corresponding to the muscle rehabilitation data into hexadecimal data to obtain a character string to be compressed; A data compression module is used to encode the characters in the character string to be compressed in sequence, and obtain compressed muscle rehabilitation data according to the encoding results of each character in the character string to be compressed; The data encapsulation module is used to encapsulate the compressed muscle rehabilitation information into a muscle rehabilitation data packet, transmit the data packet to the smart chip for storage, and name the data packet after the patient.

[0019] Compared with the prior art, the present invention has the following beneficial effects: The present invention uses muscle rehabilitation data to adjust the muscle rehabilitation training plan in the muscle rehabilitation data management system based on the smart chip of the present invention. The muscle rehabilitation training plan can be adjusted according to the patient's muscle rehabilitation situation, so that the final muscle rehabilitation training plan is not compatible with the patient, thereby failing to achieve the desired result or even producing a negative impact. It can also avoid overtraining and excessive fatigue, adjust the training intensity and frequency according to the patient's specific situation, ensure that the patient maintains enthusiasm and motivation during the rehabilitation process, and ensure the effect of muscle rehabilitation. The muscle rehabilitation data is compressed and stored using a smart chip, which can monitor data access behavior in real time, promptly discover and prevent potential security threats, implement more refined data security management strategies, and ensure the security of data storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a module schematic diagram of the muscle rehabilitation data management system based on smart chip of the present invention; Figure 2 It is a schematic diagram of the method of the muscle rehabilitation data management system based on smart chip of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] In order to solve the problem that the existing patents do not adjust the patient's rehabilitation training plan according to the patient's muscle rehabilitation status during actual use, making the final rehabilitation strategy incompatible with the patient, thus failing to achieve the desired result or even causing negative effects, please refer to Figure 1-Figure 2 , this embodiment provides the following technical solutions: Muscle rehabilitation data management system based on smart chip, including: The data acquisition module is used to collect the muscle rehabilitation data of the patient, classify the muscle rehabilitation data according to the category of the muscle rehabilitation data, and clean the acquired muscle rehabilitation data of the patient, delete invalid data, and retain valid data; The data management module is used to analyze the rehabilitation index of the acquired patient muscle rehabilitation data, generate abnormal part index change data, and evaluate the patient's muscle rehabilitation status based on the analysis results; The data storage module is used to encode and convert the rehabilitation data to obtain the character string to be compressed, obtain the compressed muscle rehabilitation data according to the encoding results of each character in the character string to be compressed, and encapsulate the compressed muscle rehabilitation information into a muscle rehabilitation data packet, and transmit the data packet to the smart chip for storage.

[0023] Data acquisition module, including: The data collection module is used to collect the patient's muscle rehabilitation data and classify it according to the category of muscle rehabilitation data, specifically into patient basic data, patient diagnostic data, daily physiological data, treatment plan, rehabilitation process and treatment effect; the patient's basic data includes the patient's name, gender, age, contact information and other basic information; the patient's diagnostic data includes the patient's primary diagnosis, secondary diagnosis, complications and other information; the treatment plan includes rehabilitation training, physical therapy, drug therapy and other contents; the rehabilitation process includes recording the time when the patient receives treatment, the specific method of treatment, the frequency and duration of treatment and other information; the daily physiological data includes creatine kinase, lactate dehydrogenase and creatine kinase isoenzyme of the patient; The data processing module is used to clean the acquired patient muscle rehabilitation data, delete invalid data, and retain valid data.

[0024] Data processing module, specifically including: Clean the acquired muscle rehabilitation data, read the abnormal data in the muscle rehabilitation data and record it as an abnormal value, and compare the abnormal value with the preset calibration value; When the abnormal value is greater than or equal to the calibration value, the muscle rehabilitation data is marked as invalid and deleted from the cleaning; When the abnormal value is less than the calibration value, the muscle rehabilitation data is marked as valid, and the process of the muscle rehabilitation data in the current cleaning is repeated.

[0025] Specifically, the data processing module further includes: A first quantity extraction module, used to extract the total number of abnormal values; A second quantity extraction module, used for extracting the number of invalid values ​​in the abnormal value; The time interval duration calling module is used to call the collection interval duration between invalid values ​​corresponding to each two adjacent collection moments in the invalid values; An invalid value abnormality degree coefficient acquisition module is used to obtain the invalid value abnormality degree coefficient by using the total number of abnormal values, the number of invalid values ​​in the abnormal values, and the collection interval between the invalid values ​​corresponding to each two adjacent collection moments; The invalid value abnormality coefficient is obtained by the following formula:

[0026] Wherein, E represents the invalid value abnormality coefficient; N represents the total number of the abnormal values; n represents the number of invalid values ​​in the abnormal values; T represents the collection interval between the i-th invalid value and the i+1-th invalid value; T min and T max Indicates the minimum and maximum values ​​of the collection interval corresponding to the invalid values ​​among the n abnormal values; T b Indicates the standard deviation of the collection interval length corresponding to the invalid values ​​among the n outliers; A coefficient comparison module, used for comparing the invalid value abnormality degree coefficient with a preset abnormality degree coefficient threshold; The calibration value control module is used to adjust the calibration value when the invalid value abnormality coefficient exceeds a preset abnormality coefficient threshold, and to screen invalid values ​​according to the adjusted calibration value.

[0027] The technical effect of the above technical solution is that through the first quantity extraction module and the second quantity extraction module, the system can accurately count the number of abnormal values ​​and invalid values ​​therein, which provides accurate basic data for subsequent abnormality assessment. The time interval duration retrieval module further considers the time interval between invalid values, which helps to identify invalid values ​​that appear frequently in a short period of time or do not appear for a long time, so as to more accurately assess the abnormality of the data. The invalid value abnormality degree coefficient acquisition module calculates a quantified invalid value abnormality degree coefficient E by combining multiple factors such as the number of abnormal values, the number of invalid values, and the acquisition interval duration. This coefficient provides an objective measurement standard for evaluating the abnormality of the data. The minimum value, maximum value, and standard deviation of the acquisition interval duration are considered in the formula, which makes the coefficient calculation more comprehensive and can reflect the impact of different acquisition intervals on the degree of abnormality. The coefficient comparison module compares the calculated invalid value abnormality degree coefficient with the preset abnormality degree coefficient threshold, which helps the system to identify abnormal situations in a timely manner. When the abnormality exceeds the threshold, the calibration value control module can automatically adjust the calibration value and perform invalid value screening according to the adjusted calibration value. This dynamic adjustment capability enables the system to adapt to data processing requirements in different situations, improving the flexibility and adaptability of the system. Through modular design, the system can efficiently process large amounts of data and quickly identify and process outliers. The collaborative work between various modules makes the data processing process smoother and improves the overall data processing efficiency.

[0028] In summary, the technical solution has shown significant technical effects in terms of anomaly detection accuracy, anomaly degree quantification, dynamic adjustment capability, and data processing efficiency. These effects have jointly improved the performance of the system, enabling it to more effectively handle anomalies in the data and provide reliable support for subsequent data analysis and decision-making.

[0029] Specifically, the calibration value control module includes: A calibration value retrieving module, used for retrieving a preset calibration value when the invalid value abnormality coefficient exceeds a preset abnormality coefficient threshold; A first data information extraction module, used to extract the number of data and data values ​​marked as valid in the abnormal value as the first data information; A second data information extraction module is used to extract the number of data and data values ​​determined to be invalid in the abnormal value as the second data information; A calibration value adjustment coefficient acquisition module, used to acquire a calibration value adjustment coefficient using the first data information and the second data information; The calibration value adjustment coefficient is obtained by the following formula:

[0030] Wherein, C represents the calibration value adjustment coefficient; M represents the number of data marked as valid in the abnormal value; n represents the number of invalid values ​​in the abnormal value; R represents the difference between the average value of the valid data and the average value of the invalid data in the abnormal value; V represents the preset calibration value; X min Indicates the minimum value among invalid values; Y max Indicates the maximum value among valid values; A calibration value adjustment module, used to obtain an adjusted calibration value by using the calibration value adjustment coefficient in combination with a preset calibration value; The adjusted calibration value is obtained by the following formula:

[0031] Among them, V t Indicates the adjusted calibration value; V indicates the preset calibration value; X b The standard deviation of the values ​​representing invalid values; Y b Indicates the numerical standard deviation of the effective value; C represents the calibration value adjustment coefficient; The data processing control module is used to perform data processing of abnormal values ​​on the muscle rehabilitation data using the adjusted calibration values.

[0032] The technical effect of the above technical solution is: through the first data information extraction module and the second data information extraction module, the system can respectively obtain detailed information of valid data and invalid data, including the number of data and the data value. This provides an accurate data basis for the subsequent calculation of the calibration value adjustment coefficient. The calibration value adjustment coefficient acquisition module uses the difference between valid data and invalid data (such as the number of data, the difference between the average value, etc.) to calculate the adjustment coefficient, so as to more accurately reflect the actual situation of the data. This helps the system to more accurately identify and process abnormal values ​​and improve the accuracy of data processing. The calibration value control module can dynamically adjust the calibration value according to the abnormal degree coefficient of the invalid value. When the abnormal degree is high, the system can automatically adjust the calibration value to adapt to the change of data, so as to maintain the stability and accuracy of data processing. By introducing the calibration value adjustment coefficient and the adjusted calibration value calculation formula, the system can comprehensively consider multiple factors (such as the difference between valid data and invalid data, standard deviation, etc.), so that the adjusted calibration value is more reasonable and accurate. The modular design enables each module to work together to quickly complete the data processing task. In particular, the calibration value adjustment module can quickly calculate the adjusted calibration value based on the adjustment coefficient and the preset calibration value, thereby shortening the data processing time. The data processing control module uses the adjusted calibration value to process the abnormal value of the muscle rehabilitation data, which can efficiently identify and filter out the abnormal values, retain valuable data, and provide strong support for subsequent data analysis and decision-making. This technical solution enhances the system's ability to handle abnormal data by introducing a calibration value control module. Even if there are many abnormal values ​​in the data, the system can maintain the stability and accuracy of data processing by dynamically adjusting the calibration value. In addition, by comprehensively considering multiple factors to calculate the adjustment coefficient and the adjusted calibration value, the system can better adapt to abnormal data of different types and degrees, enhancing the robustness and reliability of the system.

[0033] In summary, this technical solution has shown significant technical effects in terms of accuracy of outlier processing, system adaptability, data processing efficiency, and robustness. These effects have jointly improved the performance of the system, enabling it to more effectively process outliers in muscle rehabilitation data and provide reliable support for subsequent data analysis and decision-making.

[0034] Data management module, including: A data analysis module is used to analyze the rehabilitation index of the acquired patient muscle rehabilitation data and generate abnormal part index change data; The data evaluation module is used to evaluate the patient's muscle recovery according to the analysis results of the data analysis module.

[0035] Data analysis modules, including: Extract abnormal rehabilitation indexes from the patient's muscle rehabilitation data to obtain abnormal muscle rehabilitation indexes of the patient; According to the patient's abnormal muscle rehabilitation index, the standard patient's muscle rehabilitation data is associated with abnormal parts to generate abnormal part association data; Perform dynamic change analysis of rehabilitation indicators on abnormal part-related data to generate dynamic change data of abnormal part indicators; Compare the dynamic change data of abnormal part indicators with the standard muscle rehabilitation indicator change data; When the dynamic change data of the abnormal part index is greater than the standard muscle rehabilitation index change data, the abnormal part is marked as a highly abnormal part to obtain the highly abnormal part data; When the dynamic change data of the abnormal part index is smaller than the standard muscle rehabilitation index change data, the abnormal part associated data is marked as a low abnormal part to obtain low abnormal part data.

[0036] Data evaluation module, including: According to the high abnormality part data and the low abnormality part data, respectively generate the high abnormality part rehabilitation adjustment decision data and the low abnormality part rehabilitation adjustment decision data; According to the rehabilitation adjustment decision data of the high-abnormality part and the rehabilitation adjustment decision data of the low-abnormality part, the gap between the current rehabilitation process and the established rehabilitation goal is judged, and the current rehabilitation status of the patient is evaluated; Then, based on the individual differences in the patient's age and gender and medical history, and combined with the patient's current stage of recovery, clear rehabilitation goals are set; Develop a rehabilitation training plan for the patient based on the patient's real-time muscle rehabilitation data and historical data during the rehabilitation process; Finally, according to the patient's age, gender and physical condition characteristics, combined with the rehabilitation adjustment decision data of high-abnormal parts and the rehabilitation adjustment decision data of low-abnormal parts, the rehabilitation training plan is adjusted individually. The rehabilitation adjustment decision data of high-abnormal parts reduce the patient's muscle rehabilitation training intensity, while the rehabilitation adjustment decision data of low-abnormal parts increase the muscle rehabilitation training intensity.

[0037] Through statistics and analysis of data, we can evaluate the effect of rehabilitation treatment, optimize treatment plans, improve the efficiency and quality of rehabilitation treatment, adjust treatment plans according to the patient's specific situation and treatment effect, ensure maximum treatment effect, and improve the efficiency and quality of rehabilitation treatment. Through data management, we can better monitor and adjust the rehabilitation process to ensure the scientificity and effectiveness of muscle rehabilitation training plans. We can fully understand the patient's rehabilitation situation, optimize treatment plans, and improve the overall effect of rehabilitation treatment.

[0038] According to the high abnormality part data and the low abnormality part data, the high abnormality part rehabilitation adjustment decision data and the low abnormality part rehabilitation adjustment decision data are generated respectively, specifically including: Individualized rehabilitation decision-making is constructed for the data of the highly abnormal parts to obtain individualized rehabilitation decision-making data for the highly abnormal parts; Conduct feedback evaluation on the rehabilitation training videos of the high-abnormality parts to generate positive feedback data and negative feedback data of the rehabilitation of the high-abnormality parts; Adjusting the rehabilitation decision of the individualized high-abnormality part by using the high-abnormality part rehabilitation positive feedback data and the high-abnormality part rehabilitation negative feedback data, thereby generating the high-abnormality part rehabilitation adjustment decision data; The muscle signals of the low abnormal parts are collected according to the low abnormal parts data to obtain the muscle signals of the low abnormal parts; A general rehabilitation decision is constructed for the muscle signals of the low-abnormality part to obtain the rehabilitation decision data of the low-abnormality part; Perform muscle rehabilitation stimulation feedback analysis on muscle response data based on low-abnormality part rehabilitation decision data to generate low-abnormality part muscle stimulation feedback data; The low-abnormality part muscle stimulation feedback data is used to optimize the low-abnormality part rehabilitation decision data, and generate low-abnormality part rehabilitation adjustment decision data.

[0039] Data storage module, including: A data conversion module is used to perform UTF-8 encoding on the muscle rehabilitation data to obtain binary data corresponding to the muscle rehabilitation data, and convert the binary data corresponding to the muscle rehabilitation data into hexadecimal data to obtain a character string to be compressed; A data compression module, used for sequentially encoding the characters in the string to be compressed, obtaining compressed muscle rehabilitation data according to the encoding results of each character in the string to be compressed, starting from the first character in the string to be compressed, sequentially encoding each character in the string to be compressed until the encoding of the last character in the string to be compressed is completed, so as to obtain compressed muscle rehabilitation data according to the encoding results of each character in the string to be compressed; The data encapsulation module is used to encapsulate the compressed muscle rehabilitation information into a muscle rehabilitation data packet, transmit the data packet to the smart chip for storage, and name the data packet after the patient.

[0040] Compressing muscle rehabilitation data and storing it on smart chips can improve data access speed, monitor and analyze storage resource usage in real time, and automatically allocate and optimize resources, thereby significantly reducing data access latency and improving data access speed. It can also enhance data security and privacy protection, monitor data access behavior in real time, promptly detect and prevent potential security threats, and implement more refined security management strategies, greatly reducing operation and maintenance costs and difficulty, and reducing the risk of human operation; at the same time, it can achieve data interconnection and sharing.

[0041] Working principle: When using the muscle rehabilitation data management system based on smart chip of the present invention, according to Figure 1 and Figure 2 , including the following steps: Step 1: Collect the patient's muscle rehabilitation data, classify it according to the category of muscle rehabilitation data, clean the acquired patient muscle rehabilitation data, delete invalid data, and retain valid data; Step 2: Extract abnormal rehabilitation indicators from the patient's muscle rehabilitation data to generate abnormal part-related data, analyze the dynamic changes of rehabilitation indicators on the abnormal part-related data, and determine high-abnormal part data and low-abnormal part data; Step 3: Determine the gap between the current rehabilitation process and the established rehabilitation goals, evaluate the patient's current rehabilitation status, set clear rehabilitation goals based on the patient's current rehabilitation stage, and develop a rehabilitation training plan for the patient based on the patient's real-time muscle rehabilitation data and historical data during the rehabilitation process; Step 4: According to the patient's age, gender and physical condition characteristics, combined with the rehabilitation adjustment decision data of the high-abnormality part and the rehabilitation adjustment decision data of the low-abnormality part, the rehabilitation training plan is adjusted individually, and the muscle rehabilitation training intensity of the patient is reduced according to the rehabilitation adjustment decision data of the high-abnormality part, and the muscle rehabilitation training intensity is increased according to the rehabilitation adjustment decision data of the low-abnormality part; Step 5: Encode and convert the muscle rehabilitation data and the adjusted rehabilitation training plan to obtain the character string to be compressed. Starting from the first character in the character string to be compressed, encode each character in the character string to be compressed in turn until the encoding of the last character in the character string to be compressed is completed, so as to obtain the compressed muscle rehabilitation data according to the encoding results of each character in the character string to be compressed, encapsulate the compressed muscle rehabilitation information into a muscle rehabilitation data packet, and transmit the data packet to the smart chip for storage.

[0042] In summary, the muscle rehabilitation data management system based on the smart chip of the present invention uses muscle rehabilitation data to adjust the muscle rehabilitation training plan, improves the adaptability of the rehabilitation training plan, and can adjust the muscle rehabilitation training plan according to the patient's muscle rehabilitation situation, so that the final muscle rehabilitation training plan is not compatible with the patient, thereby failing or even having a negative impact. It can also avoid overtraining and excessive fatigue, adjust the training intensity and frequency according to the patient's specific situation, ensure that the patient maintains enthusiasm and motivation during the rehabilitation process, and ensure the effect of muscle rehabilitation. Through statistics and analysis of data, the effect of rehabilitation treatment can be evaluated, the treatment plan can be optimized, and the efficiency and quality of rehabilitation treatment can be improved. According to the patient's specific situation and treatment effect, the treatment plan is adjusted to ensure that the treatment effect is maximized, and the efficiency and quality of rehabilitation treatment are improved. Through data management, the rehabilitation process can be better monitored and adjusted to ensure the scientificity and effectiveness of the muscle rehabilitation training plan, the patient's rehabilitation situation can be fully understood, and the muscle rehabilitation data can be compressed and stored using a smart chip. It can monitor data access behavior in real time, timely discover and prevent potential security threats, implement more refined data security management strategies, and ensure the security of data storage.

[0043] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0044] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. Muscle rehabilitation data management system based on smart chip, characterized by: include: The data acquisition module is used to collect the muscle rehabilitation data of the patient, classify the muscle rehabilitation data according to the category of the muscle rehabilitation data, and clean the acquired muscle rehabilitation data of the patient, delete invalid data, and retain valid data; The data management module is used to analyze the rehabilitation index of the acquired patient muscle rehabilitation data, generate abnormal part index change data, and evaluate the patient's muscle rehabilitation status based on the analysis results; The data storage module is used to encode and convert the rehabilitation data to obtain the character string to be compressed, obtain the compressed muscle rehabilitation data according to the encoding results of each character in the character string to be compressed, and encapsulate the compressed muscle rehabilitation information into a muscle rehabilitation data packet, and transmit the data packet to the smart chip for storage.

2. The muscle rehabilitation data management system based on smart chip according to claim 1, characterized in that: The data acquisition module comprises: The data collection module is used to collect the patient's muscle rehabilitation data and classify it according to the type of muscle rehabilitation data, specifically into patient basic data, patient diagnosis data, daily physiological data, treatment plan, rehabilitation process and treatment effect; The data processing module is used to clean the acquired patient muscle rehabilitation data, delete invalid data, and retain valid data.

3. The muscle rehabilitation data management system based on smart chip according to claim 2 is characterized in that: The data processing module specifically includes: Clean the acquired muscle rehabilitation data, read the abnormal data in the muscle rehabilitation data and record it as an abnormal value, and compare the abnormal value with the preset calibration value; When the abnormal value is greater than or equal to the calibration value, the muscle rehabilitation data is marked as invalid and deleted from the cleaning; When the abnormal value is less than the calibration value, the muscle rehabilitation data is marked as valid, and the process of the muscle rehabilitation data in the current cleaning is repeated.

4. The muscle rehabilitation data management system based on smart chip according to claim 3 is characterized in that: The data processing module specifically includes: A first quantity extraction module, used to extract the total number of abnormal values; A second quantity extraction module, used for extracting the number of invalid values ​​in the abnormal value; The time interval duration calling module is used to call the collection interval duration between invalid values ​​corresponding to each two adjacent collection moments in the invalid values; An invalid value abnormality degree coefficient acquisition module is used to obtain the invalid value abnormality degree coefficient by using the total number of abnormal values, the number of invalid values ​​in the abnormal values, and the collection interval between the invalid values ​​corresponding to each two adjacent collection moments; The invalid value abnormality coefficient is obtained by the following formula: Wherein, E represents the invalid value abnormality coefficient; N represents the total number of the abnormal values; n represents the number of invalid values ​​in the abnormal values; T represents the collection interval between the i-th invalid value and the i+1-th invalid value; T min and T max Indicates the minimum and maximum values ​​of the collection interval corresponding to the invalid values ​​among the n abnormal values; T b Indicates the standard deviation of the collection interval length corresponding to the invalid values ​​among the n outliers; A coefficient comparison module, used for comparing the invalid value abnormality degree coefficient with a preset abnormality degree coefficient threshold; The calibration value control module is used to adjust the calibration value when the invalid value abnormality coefficient exceeds a preset abnormality coefficient threshold, and to screen invalid values ​​according to the adjusted calibration value.

5. The muscle rehabilitation data management system based on smart chip according to claim 4 is characterized in that: The calibration value control module includes: A calibration value retrieving module, used for retrieving a preset calibration value when the invalid value abnormality coefficient exceeds a preset abnormality coefficient threshold; A first data information extraction module, used to extract the number of data and data values ​​marked as valid in the abnormal value as the first data information; A second data information extraction module is used to extract the number of data and data values ​​determined to be invalid in the abnormal value as the second data information; A calibration value adjustment coefficient acquisition module, used to acquire a calibration value adjustment coefficient using the first data information and the second data information; The calibration value adjustment coefficient is obtained by the following formula: Wherein, C represents the calibration value adjustment coefficient; M represents the number of data marked as valid in the abnormal value; n represents the number of invalid values ​​in the abnormal value; R represents the difference between the average value of the valid data and the average value of the invalid data in the abnormal value; V represents the preset calibration value; X min Indicates the minimum value among invalid values; Y max Indicates the maximum value among valid values; A calibration value adjustment module, used to obtain an adjusted calibration value by using the calibration value adjustment coefficient in combination with a preset calibration value; The adjusted calibration value is obtained by the following formula: Among them, V t Indicates the adjusted calibration value; V indicates the preset calibration value; X b The standard deviation of the values ​​representing invalid values; Y b Indicates the numerical standard deviation of the effective value; C represents the calibration value adjustment coefficient; The data processing control module is used to perform data processing of abnormal values ​​on the muscle rehabilitation data using the adjusted calibration values.

6. The muscle rehabilitation data management system based on smart chip according to claim 1, characterized in that: The data management module comprises: A data analysis module is used to analyze the rehabilitation index of the acquired patient muscle rehabilitation data and generate abnormal part index change data; The data evaluation module is used to evaluate the patient's muscle recovery according to the analysis results of the data analysis module.

7. The muscle rehabilitation data management system based on smart chip according to claim 3 is characterized in that: The data analysis module comprises: Extract abnormal rehabilitation indexes from the patient's muscle rehabilitation data to obtain abnormal muscle rehabilitation indexes of the patient; According to the patient's abnormal muscle rehabilitation index, the standard patient's muscle rehabilitation data is associated with abnormal parts to generate abnormal part association data; Perform dynamic change analysis of rehabilitation indicators on abnormal part-related data to generate dynamic change data of abnormal part indicators; Compare the dynamic change data of abnormal part indicators with the standard muscle rehabilitation indicator change data; When the dynamic change data of the abnormal part index is greater than the standard muscle rehabilitation index change data, the abnormal part is marked as a highly abnormal part to obtain the highly abnormal part data; When the dynamic change data of the abnormal part index is smaller than the standard muscle rehabilitation index change data, the abnormal part associated data is marked as a low abnormal part to obtain low abnormal part data.

8. The muscle rehabilitation data management system based on smart chip according to claim 3 is characterized in that: The data evaluation module comprises: According to the high abnormality part data and the low abnormality part data, respectively generate the high abnormality part rehabilitation adjustment decision data and the low abnormality part rehabilitation adjustment decision data; According to the rehabilitation adjustment decision data of the high-abnormality part and the low-abnormality part, the gap between the current rehabilitation process and the established rehabilitation goal is judged, and the current rehabilitation status of the patient is evaluated; Then, based on the patient’s age and gender, individual differences in medical history, and the patient’s current stage of recovery, clear rehabilitation goals are set; Develop a rehabilitation training plan for the patient based on the patient's real-time muscle rehabilitation data and historical data during the rehabilitation process; Finally, the rehabilitation training plan is adjusted individually according to the patient's age, gender and physical condition characteristics combined with the rehabilitation adjustment decision data of high-abnormality parts and low-abnormality parts.

9. The muscle rehabilitation data management system based on smart chip according to claim 8, characterized in that: The generating of the high abnormality part rehabilitation adjustment decision data and the low abnormality part rehabilitation adjustment decision data according to the high abnormality part data and the low abnormality part data respectively specifically includes: Individualized rehabilitation decision-making is constructed for the data of the highly abnormal parts to obtain individualized rehabilitation decision-making data for the highly abnormal parts; Conduct feedback evaluation on the rehabilitation training videos of the high-abnormality parts to generate positive feedback data and negative feedback data of the rehabilitation of the high-abnormality parts; Adjusting the rehabilitation decision of the individualized high-abnormality part based on the high-abnormality part rehabilitation positive feedback data and the high-abnormality part rehabilitation negative feedback data, and generating the high-abnormality part rehabilitation adjustment decision data; The muscle signals of the low abnormal parts are collected according to the low abnormal parts data to obtain the muscle signals of the low abnormal parts; A general rehabilitation decision is constructed for the muscle signals of the low-abnormality part to obtain the rehabilitation decision data of the low-abnormality part; Perform muscle rehabilitation stimulation feedback analysis on muscle response data based on low-abnormality part rehabilitation decision data to generate low-abnormality part muscle stimulation feedback data; The low-abnormality part muscle stimulation feedback data is used to optimize the low-abnormality part rehabilitation decision data, and generate low-abnormality part rehabilitation adjustment decision data.

10. The muscle rehabilitation data management system based on smart chip according to claim 1, characterized in that: The data storage module comprises: A data conversion module is used to perform UTF-8 encoding on the muscle rehabilitation data to obtain binary data corresponding to the muscle rehabilitation data, and convert the binary data corresponding to the muscle rehabilitation data into hexadecimal data to obtain a character string to be compressed; A data compression module is used to encode the characters in the character string to be compressed in sequence, and obtain compressed muscle rehabilitation data according to the encoding results of each character in the character string to be compressed; The data encapsulation module is used to encapsulate the compressed muscle rehabilitation information into a muscle rehabilitation data packet, transmit the data packet to the smart chip for storage, and name the data packet after the patient.

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