Multi-dimensional evaluation system for senile weakness and Parkinson's disease based on intelligent single-chip microcomputer

Abstract

The invention discloses a senile weakness and Parkinson's disease multi-dimensional evaluation system based on an intelligent single-chip microcomputer, and belongs to the technical field of disease evaluation. The senile weakness and Parkinson's disease multi-dimensional evaluation system comprises a data acquisition module used for acquiring patient multi-dimensional real-time data based on artificial intelligence; the data transmission module is used for uploading the multi-dimensional real-time data of the patient based on artificial intelligence to the multi-dimensional evaluation terminal; and the multi-dimensional evaluation terminal is used for processing, analyzing and evaluating the multi-dimensional real-time data of the patient based on artificial intelligence, and determining a multi-dimensional evaluation result of senile weakness and Parkinson's disease based on the intelligent single-chip microcomputer. The problems that real-time monitoring and multi-dimensional evaluation cannot be carried out on senile weakness and Parkinson's disease based on an intelligent single-chip microcomputer in the prior art, and disease deterioration cannot be effectively prevented are solved. Real-time monitoring and multi-dimensional evaluation can be performed on senile weakness and Parkinson's disease on the basis of the intelligent single chip microcomputer, the illness state of a patient can be mastered in time, the patient can be tracked and treated in time, disease progression is slowed down, and deterioration of the illness state can be effectively prevented.

Description

Technical Field

[0001] The present invention relates to the technical field of disease assessment, and in particular to a multi-dimensional assessment system for senile frailty and Parkinson's disease based on an intelligent single chip microcomputer. Background Art

[0002] Senile debility includes cardiovascular system, respiratory system, musculoskeletal system, metabolic system, nervous system and mental health. Parkinson's disease is a neurodegenerative disease characterized by the death of dopaminergic neurons in the central nervous system, which causes a decrease in the activity of the striatal nigrostriatal dopamine pathway, and is mainly manifested as resting tremor, slow movements and postural instability.

[0003] The Chinese patent with publication number CN114023432A discloses a Parkinson's disease follow-up system, including a patient side, a doctor side and a server side, wherein the patient side and the doctor side are connected to the server side; the patient side includes a patient scale module, which pushes the corresponding patient scale to the patient according to the patient's current stage of treatment, obtains the patient scale content filled in by the patient, and uploads it to the server side, and the server side sends the patient scale content to the doctor side; the doctor side includes a doctor scale module, which receives the patient scale content and pushes the doctor scale to the doctor, and obtains the doctor scale content filled in by the doctor and uploads it to the server side; the server side calculates the patient's disease stage according to the patient scale content and the doctor scale content, and sends the disease stage to the patient side and the doctor side; however, the patent has the following defects: The existing system cannot conduct real-time monitoring and multi-dimensional evaluation of frailty in the elderly and Parkinson's disease based on intelligent single-chip microcomputers, and cannot timely grasp the patient's condition, so that the patient cannot receive timely follow-up treatment, resulting in rapid disease progression and failure to effectively prevent the disease from worsening. Summary of the invention

[0004] The purpose of the present invention is to provide a multi-dimensional assessment system for elderly frailty and Parkinson's disease based on an intelligent single-chip microcomputer, which can perform real-time monitoring and multi-dimensional assessment of elderly frailty and Parkinson's disease based on the intelligent single-chip microcomputer, can timely grasp the patient's condition, enable the patient to receive timely follow-up treatment, slow down the progression of the disease, and effectively prevent the deterioration of the disease, thereby solving the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: Multi-dimensional assessment system for frailty and Parkinson's disease in the elderly based on intelligent single chip microcomputer, including: Data collection module, used to collect physiological and behavioral parameters of patients and determine multi-dimensional real-time data of patients based on artificial intelligence; A data transmission module, used to upload the AI-based multidimensional real-time data of patients to the multidimensional evaluation terminal via wireless communication; The multidimensional evaluation terminal is used to process, analyze and evaluate the multidimensional real-time data of patients based on artificial intelligence, determine whether the patient is a frail elderly or Parkinson's patient, and determine the multidimensional evaluation results of frail elderly and Parkinson's disease based on the intelligent single-chip computer.

[0006] Preferably, the data acquisition module comprises: A physiological collection unit, used to collect physiological parameters of patients; Based on the sensors installed on the wearable devices worn by the patients, the patients' heart rate, blood pressure, body temperature, and blood oxygen saturation are monitored in real time and continuously collected to obtain the patients' physiological parameters; A behavior collection unit, used to collect patient behavior parameters; Based on the sensors installed on the wearable devices worn by the patients, the patients' posture, gait, movement, balance, tremor and language expression are monitored in real time and continuously collected to obtain the patients' behavioral parameters; Among them, based on the patient's physiological parameters and patient behavioral parameters, the patient's multi-dimensional real-time data based on artificial intelligence is determined.

[0007] Preferably, the data transmission module includes: A data sending unit, used to send multi-dimensional real-time data of patients based on artificial intelligence; A data receiving unit, used for receiving multi-dimensional real-time data of patients based on artificial intelligence; Among them, according to the multi-dimensional assessment requirements of frailty and Parkinson's disease in the elderly based on intelligent single-chip microcomputer, a data transmission link between the data sending unit and the data receiving unit is established; The data sending unit receives the multi-dimensional real-time data of the patient based on artificial intelligence transmitted from the data acquisition module, and the data sending unit sends the received multi-dimensional real-time data of the patient based on artificial intelligence to the data receiving unit through a wireless network; The data receiving unit receives the multi-dimensional real-time data of the patient based on artificial intelligence transmitted from the data sending unit, and the data receiving unit sends the received multi-dimensional real-time data of the patient based on artificial intelligence to the multi-dimensional evaluation terminal, and the intelligent single-chip microcomputer built into the multi-dimensional evaluation terminal processes, analyzes and evaluates the multi-dimensional real-time data of the patient based on artificial intelligence to determine whether the patient is a senile frail or Parkinson's patient.

[0008] Preferably, the multi-dimensional evaluation terminal comprises: A data processing module is used to clean, convert and extract features of multi-dimensional real-time data of patients based on artificial intelligence, and determine multi-dimensional feature data of patients based on artificial intelligence; The analysis and evaluation module is used to conduct multi-dimensional evaluation of the patient's multi-dimensional feature data based on artificial intelligence, determine whether the patient is a frail or Parkinson's patient, and determine the multi-dimensional evaluation results of frail or Parkinson's disease based on the intelligent single-chip computer.

[0009] Preferably, the data processing module includes: Data cleaning unit, used to clean multi-dimensional real-time data of patients based on artificial intelligence; Obtain multi-dimensional real-time data of patients based on artificial intelligence; Cleaning of AI-based multi-dimensional real-time patient data, including: Conduct consistency checks on AI-based multi-dimensional real-time patient data; According to the data consistency requirements, check whether there are inconsistent data in the AI-based multidimensional real-time data of patients that are useless for the multidimensional assessment of frailty and Parkinson's disease, and remove the inconsistent data in the AI-based multidimensional real-time data of patients; Check invalid and missing values ​​on AI-based multi-dimensional real-time patient data; According to the requirements of data validity and integrity, check whether there are invalid values ​​and missing values ​​in the multidimensional real-time data of patients based on artificial intelligence that are useless for the multidimensional assessment of frailty and Parkinson's disease in the elderly, and remove the invalid values ​​and missing values ​​in the multidimensional real-time data of patients based on artificial intelligence; Identify multidimensional real-time patient data useful for multidimensional assessment of frailty and Parkinson's disease.

[0010] Preferably, the data processing module further includes: The invalid value compensation processing module is used to determine whether to perform invalid value compensation processing according to the multi-dimensional real-time data of patients useful for multi-dimensional assessment of senile frailty and Parkinson's disease, and includes: A data extraction module, used to extract multidimensional real-time data of patients useful for multidimensional assessment of frailty and Parkinson's disease in the elderly; A data collection time acquisition module is used to acquire the data collection time corresponding to each patient's multidimensional real-time data based on the patient's multidimensional real-time data useful for multidimensional assessment of senile frailty and Parkinson's disease; A patient multidimensional real-time data set acquisition module is used to sort the patient multidimensional real-time data in the order of data collection time to obtain a patient multidimensional real-time data set; An effective data time interval acquisition module is used to obtain the effective data time interval corresponding to each two adjacent multidimensional real-time data of patients according to the data collection time corresponding to each two adjacent multidimensional real-time data of patients in the multidimensional real-time data set of patients; An effective data quality evaluation coefficient acquisition module, used to acquire the effective data quality evaluation coefficient according to the interval between the effective data; The effective data quality evaluation coefficient obtained by the interval between the effective data is obtained by the following formula:

[0011] Where J represents the effective data quality evaluation coefficient; f y represents the frequency of occurrence of the patient multidimensional real-time data contained in the patient multidimensional real-time data set; f m represents the frequency of occurrence of invalid values; n represents the number of valid data time intervals contained in the patient's multidimensional real-time data set; T i represents the interval length corresponding to the i-th valid data time interval; T b represents the standard deviation of the interval length corresponding to n valid data time intervals; T max Indicates the maximum value of the interval length corresponding to n valid data time intervals; T c Indicates the preset time interval reference value; m i represents the number of invalid values ​​that appear in the i-th valid data time interval; T gj represents the data collection time of the jth invalid value appearing in the i-th valid data time interval; T gj+1 Indicates the data collection time of the j+1th invalid value that appears in the i-th valid data time interval; m b Indicates the standard deviation of the number of invalid values ​​that appear in each valid data time interval; m max Indicates the maximum number of invalid values ​​that appear in each valid data time interval; A comparison module, used for comparing the effective data quality evaluation coefficient with a preset evaluation coefficient threshold; A compensation processing determination module, used for determining that invalid value compensation processing is required when the effective data quality evaluation coefficient is lower than a preset evaluation coefficient threshold; The invalid value compensation processing execution module is used to perform invalid value compensation processing on invalid values ​​based on multidimensional real-time data of patients useful for multidimensional assessment of elderly frailty and Parkinson's disease.

[0012] Preferably, the invalid value compensation processing execution module includes: An invalid value data collection time acquisition module is used to extract the data collection time corresponding to each invalid value; A first reference data acquisition module is used to extract valid data whose data collection time is closest to the data collection time corresponding to the invalid value in the patient's multi-dimensional real-time data set as first reference data; A first data subset acquisition module, configured to extract, from the patient multidimensional real-time data set, patient multidimensional real-time data whose difference with the first reference data is lower than a preset difference threshold as a first data subset; A second reference data acquisition module is used to extract valid data whose data collection time is farthest from the data collection time corresponding to the invalid value in the patient's multi-dimensional real-time data set as second reference data; A second data subset acquisition module, configured to extract, from the patient multidimensional real-time data set, patient multidimensional real-time data whose difference with the second reference data is lower than a preset difference threshold as a second data subset; A compensation value acquisition module, configured to acquire a compensation value corresponding to an invalid value by using the first reference data and the second reference data in combination with the multi-dimensional real-time data of the patient included in the first data subset and the second data subset; The compensation value corresponding to the invalid value is obtained by the following formula:

[0013] Among them, X b Indicates the compensation value corresponding to the invalid value; X 01 and X 02 represents the numerical value corresponding to the first reference data and the second reference data; s represents the adjustment coefficient, and the adjustment coefficient is obtained by the following formula:

[0014] Wherein, s represents the adjustment coefficient; x represents the number of data included in the first data subset; y represents the number of data included in the second data subset; X 01 and X 02 represents the numerical value corresponding to the first reference data and the second reference data; X p01 and X p02 represents the average value of data corresponding to the first data subset and the average value of data corresponding to the second data subset; X a represents the data value of the a-th patient multidimensional real-time data in the first data subset; X b represents the data value of the b-th patient multidimensional real-time data in the second data subset; X ax represents the standard deviation of the multidimensional real-time data of the patient in the first data subset; X by represents the standard deviation of the multidimensional real-time data of the patient in the second data subset; The data replacement module is used to replace the invalid value with the compensation value corresponding to the invalid value.

[0015] Preferably, the data processing module further includes: A data conversion unit, used for converting the cleaned multi-dimensional real-time data of the patient; Obtain multidimensional real-time data of patients that is useful for multidimensional assessment of frailty and Parkinson's disease; Convert multidimensional real-time data of patients useful for multidimensional assessment of frailty and Parkinson's disease in the elderly; Standardize the formats of multidimensional real-time patient data useful for multidimensional assessment of frailty and Parkinson's disease in the elderly, eliminate the dimensional differences among multidimensional real-time patient data useful for multidimensional assessment of frailty and Parkinson's disease in the elderly, and determine standardized multidimensional real-time patient data; A feature extraction unit, used for extracting features from the converted multi-dimensional real-time data of the patient; Obtain standardized multi-dimensional real-time data of patients; Extract features from standardized multi-dimensional real-time patient data; Features that can reflect the multidimensional assessment of elderly frailty and Parkinson's disease are extracted from standardized multidimensional real-time patient data, and multidimensional feature data of patients based on artificial intelligence are determined.

[0016] Preferably, the analysis and evaluation module includes: Model training unit, used to train multidimensional assessment models for frailty and Parkinson's disease; According to the multi-dimensional assessment needs of elderly frailty and Parkinson's disease based on intelligent single-chip microcomputers, the multi-dimensional historical data of patients are collected, divided, and the training set and test set are determined; Select a neural network model framework suitable for multidimensional assessment of frailty and Parkinson's disease in the elderly based on an intelligent single-chip microcomputer, train the selected neural network model framework suitable for multidimensional assessment of frailty and Parkinson's disease in the elderly based on an intelligent single-chip microcomputer based on a training set, and determine a multidimensional assessment model of frailty and Parkinson's disease in the elderly based on an intelligent single-chip microcomputer; Based on the test set, the performance of the multidimensional assessment model of frailty and Parkinson's disease in the elderly based on the intelligent single-chip microcomputer is tested to determine whether the multidimensional assessment model of frailty and Parkinson's disease in the elderly based on the intelligent single-chip microcomputer can achieve the expected effect and determine the performance test results of the multidimensional assessment model of frailty and Parkinson's disease in the elderly; Based on the performance test results of the multidimensional assessment model of frailty and Parkinson's disease in the elderly, the multidimensional assessment model of frailty and Parkinson's disease in the elderly based on the intelligent single-chip microcomputer was analyzed, the parameters of the multidimensional assessment model of frailty and Parkinson's disease in the elderly based on the intelligent single-chip microcomputer were adjusted and the structure was optimized. After repeated iterations, the optimal multidimensional assessment model of frailty and Parkinson's disease in the elderly was determined.

[0017] Preferably, the analysis and evaluation module further includes: A multidimensional assessment unit, used to perform multidimensional assessment of multidimensional characteristic data of patients based on artificial intelligence; Obtain the optimal multidimensional assessment model for frailty and Parkinson's disease in the elderly; Deploy the optimal multidimensional assessment model for frailty and Parkinson's disease in the actual multidimensional assessment environment for frailty and Parkinson's disease; Input the multidimensional characteristic data of patients based on artificial intelligence into the optimal multidimensional assessment model for frailty and Parkinson's disease in the elderly; Based on the optimal multidimensional assessment model for frailty and Parkinson's disease in the elderly, the multidimensional characteristic data of patients based on artificial intelligence are analyzed and evaluated to determine whether the patient is a frailty and Parkinson's disease patient, and the multidimensional assessment results of frailty and Parkinson's disease in the elderly based on the intelligent single-chip computer are determined; Among them, according to the multi-dimensional assessment results of elderly frailty and Parkinson's disease based on intelligent single-chip microcomputers, personalized intervention measures are set for patients in a timely manner to carry out intelligent intervention and control of patients to meet the needs of different types of elderly frailty and Parkinson's disease patients.

[0018] Compared with the prior art, the present invention has the following beneficial effects: The present invention determines multi-dimensional real-time data of the patient based on artificial intelligence by collecting physiological parameters and behavioral parameters of the patient, uploads the multi-dimensional real-time data of the patient based on artificial intelligence to a multi-dimensional evaluation terminal through wireless communication, processes and analyzes and evaluates the multi-dimensional real-time data of the patient based on artificial intelligence through an intelligent single-chip microcomputer built in the multi-dimensional evaluation terminal, judges whether the patient is a senile frailty and Parkinson's patient, determines the multi-dimensional evaluation result of senile frailty and Parkinson's disease based on the intelligent single-chip microcomputer, can perform real-time monitoring and multi-dimensional evaluation of senile frailty and Parkinson's disease based on the intelligent single-chip microcomputer, can timely grasp the patient's condition, enable the patient to receive timely follow-up treatment, slow down the progression of the disease, and effectively prevent the deterioration of the condition. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 The module framework diagram of the multi-dimensional assessment system for frailty and Parkinson's disease in the elderly based on an intelligent single chip microcomputer of the present invention. DETAILED DESCRIPTION

[0020] 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.

[0021] In order to solve the existing problems that the real-time monitoring and multi-dimensional evaluation of elderly frailty and Parkinson's disease cannot be carried out based on intelligent single-chip microcomputers, the patient's condition cannot be grasped in time, so that the patient cannot receive timely follow-up treatment, resulting in rapid disease progression and ineffective prevention of disease deterioration, please refer to Figure 1, this embodiment provides the following technical solutions: The multi-dimensional evaluation system for elderly frailty and Parkinson's disease based on intelligent single chip microcomputer includes: data acquisition module, data transmission module and multi-dimensional evaluation terminal.

[0022] It should be noted that the patient's physiological parameters and behavioral parameters are collected through the data acquisition module to determine the patient's multidimensional real-time data based on artificial intelligence; the patient's multidimensional real-time data based on artificial intelligence is uploaded to the multidimensional evaluation terminal through the data transmission module; the patient's multidimensional real-time data based on artificial intelligence is processed, analyzed and evaluated through the multidimensional evaluation terminal to determine whether the patient is a senile frailty and Parkinson's patient, and to determine the multidimensional evaluation results of senile frailty and Parkinson's disease based on the intelligent single-chip microcomputer.

[0023] In this embodiment, the data acquisition module includes: A physiological collection unit, used to collect physiological parameters of patients; Based on the sensors installed on the wearable devices worn by the patients, the patients' heart rate, blood pressure, body temperature, and blood oxygen saturation are monitored in real time and continuously collected to obtain the patients' physiological parameters; A behavior collection unit, used to collect patient behavior parameters; Based on the sensors installed on the wearable devices worn by the patients, the patients' posture, gait, movement, balance, tremor and language expression are monitored in real time and continuously collected to obtain the patients' behavioral parameters; Among them, based on the patient's physiological parameters and patient behavioral parameters, the patient's multi-dimensional real-time data based on artificial intelligence is determined.

[0024] It should be noted that by setting up accelerometers, gyroscopes and voice recognition sensors on wearable devices, the patient's posture, gait, movement, balance, jitter and language expression are collected through the accelerometers, gyroscopes and voice recognition sensors, and then the patient's behavioral parameters are determined.

[0025] Among them, the accelerometer is a sensor that measures the acceleration of an object. It works by measuring the acceleration caused by gravity or other forces. It can be used to detect the patient's posture, gait, movement, balance, shaking, etc. In gait analysis, accelerometers and gyroscopes are used to capture motion information by measuring the position and state information of multiple parts of the legs at each moment. After data processing, the simulation and analysis of biomechanics can be completed with the assistance of computer software.

[0026] Among them, the gyroscope is an angular velocity detection device that uses the momentum of a high-speed rotating body to sense the shell's relative inertial space around one or two axes orthogonal to the axis of rotation. The gyroscope can play a key role in gait detection by measuring and maintaining direction and is not affected by environmental factors such as atmosphere and light. It can accurately measure key data such as acceleration and angular velocity during walking. These data are crucial for evaluating key indicators such as walking stability, cadence, and step length.

[0027] Specifically, the applications of gyroscopes in gait detection are as follows: 1) Accurately measure walking data: The gyroscope can accurately measure key data such as acceleration and angular velocity during walking, which are essential for evaluating key indicators such as walking stability, cadence, and step length; 2) Identify walking patterns and abnormalities: By analyzing the data collected by the gyroscope, different walking patterns can be effectively identified, such as normal gait, abnormal gait, etc., which is of great significance for timely detection and correction of bad gait and prevention of sports injuries; 3) Evaluate rehabilitation effect: In rehabilitation medicine, gyroscopes are also widely used to evaluate the rehabilitation effect of patients. By comparing the gait data before and after treatment, doctors can intuitively understand the patient's recovery and adjust the treatment plan.

[0028] Among them, the speech recognition sensor is a sensor that can recognize human speech. It collects sound wave signals, converts the speaker's voice into text or instructions, and then obtains the patient's language expression.

[0029] In this embodiment, the data transmission module includes: A data sending unit, used to send multi-dimensional real-time data of patients based on artificial intelligence; A data receiving unit, used for receiving multi-dimensional real-time data of patients based on artificial intelligence; Among them, according to the multi-dimensional assessment requirements of frailty and Parkinson's disease in the elderly based on intelligent single-chip microcomputer, a data transmission link between the data sending unit and the data receiving unit is established; The data sending unit receives the multi-dimensional real-time data of the patient based on artificial intelligence transmitted from the data acquisition module, and the data sending unit sends the received multi-dimensional real-time data of the patient based on artificial intelligence to the data receiving unit through a wireless network; The data receiving unit receives the multi-dimensional real-time data of the patient based on artificial intelligence transmitted from the data sending unit, and the data receiving unit sends the received multi-dimensional real-time data of the patient based on artificial intelligence to the multi-dimensional evaluation terminal, and the intelligent single-chip microcomputer built into the multi-dimensional evaluation terminal processes, analyzes and evaluates the multi-dimensional real-time data of the patient based on artificial intelligence to determine whether the patient is a senile frail or Parkinson's patient.

[0030] In this embodiment, the multi-dimensional evaluation terminal includes: A data processing module is used to clean, convert and extract features of multi-dimensional real-time data of patients based on artificial intelligence, and determine multi-dimensional feature data of patients based on artificial intelligence; The analysis and evaluation module is used to conduct multi-dimensional evaluation of the patient's multi-dimensional feature data based on artificial intelligence, determine whether the patient is a frail or Parkinson's patient, and determine the multi-dimensional evaluation results of frail or Parkinson's disease based on the intelligent single-chip computer.

[0031] In this embodiment, the data processing module includes: Data cleaning unit, used to clean multi-dimensional real-time data of patients based on artificial intelligence; Obtain multi-dimensional real-time data of patients based on artificial intelligence; Cleaning of AI-based multi-dimensional real-time patient data, including: Conduct consistency checks on AI-based multi-dimensional real-time patient data; According to the data consistency requirements, check whether there are inconsistent data in the AI-based multidimensional real-time data of patients that are useless for the multidimensional assessment of frailty and Parkinson's disease, and remove the inconsistent data in the AI-based multidimensional real-time data of patients; Check invalid and missing values ​​on AI-based multi-dimensional real-time patient data; According to the requirements of data validity and integrity, check whether there are invalid values ​​and missing values ​​in the multidimensional real-time data of patients based on artificial intelligence that are useless for the multidimensional assessment of frailty and Parkinson's disease in the elderly, and remove the invalid values ​​and missing values ​​in the multidimensional real-time data of patients based on artificial intelligence; Identify multidimensional real-time patient data useful for multidimensional assessment of frailty and Parkinson's disease.

[0032] It should be noted that data cleaning refers to the process of processing and organizing raw data to improve the quality and availability of data. It is a key step in data preprocessing, involving discovering and correcting errors in data files, processing invalid values ​​and missing values, etc. to ensure the accuracy and consistency of data.

[0033] Among them, the consistency check is to check whether the data meets the requirements based on the reasonable value range and mutual relationship of each parameter, and to find data that exceeds the normal range, is logically unreasonable or contradictory. For example, a variable measured by a 1-7 scale has a value of 0, and a negative number appears in the weight, which should be regarded as exceeding the normal value range. Computer software such as SPSS, SAS and Excel can automatically identify each variable value that exceeds the range according to the defined value range. Logically inconsistent answers may appear in many forms: for example, many respondents said that they drove to work, but reported that they did not have a car; or respondents reported that they were heavy buyers and users of a certain brand, but at the same time gave a very low score on the familiarity scale.

[0034] Therefore, by cleaning the AI-based multidimensional real-time data of patients, inconsistent data, invalid values ​​and missing values ​​that are useless for the multidimensional assessment of elderly frailty and Parkinson's disease can be removed from the AI-based multidimensional real-time data of patients, thereby determining the multidimensional real-time data of patients that are useful for the multidimensional assessment of elderly frailty and Parkinson's disease, which can improve the processing accuracy and efficiency of subsequent multidimensional real-time data of patients.

[0035] Specifically, the data processing module further includes: The invalid value compensation processing module is used to determine whether to perform invalid value compensation processing according to the multi-dimensional real-time data of patients useful for multi-dimensional assessment of senile frailty and Parkinson's disease, and includes: A data extraction module, used to extract multidimensional real-time data of patients useful for multidimensional assessment of frailty and Parkinson's disease in the elderly; A data collection time acquisition module is used to acquire the data collection time corresponding to each patient's multidimensional real-time data based on the patient's multidimensional real-time data useful for multidimensional assessment of senile frailty and Parkinson's disease; A patient multidimensional real-time data set acquisition module is used to sort the patient multidimensional real-time data in the order of data collection time to obtain a patient multidimensional real-time data set; An effective data time interval acquisition module is used to obtain the effective data time interval corresponding to each two adjacent multidimensional real-time data of patients according to the data collection time corresponding to each two adjacent multidimensional real-time data of patients in the multidimensional real-time data set of patients; An effective data quality evaluation coefficient acquisition module, used to acquire the effective data quality evaluation coefficient according to the interval between the effective data; The effective data quality evaluation coefficient obtained by the interval between the effective data is obtained by the following formula:

[0036] Where J represents the effective data quality evaluation coefficient; f yrepresents the frequency of occurrence of the patient multidimensional real-time data contained in the patient multidimensional real-time data set; f m represents the frequency of occurrence of invalid values; n represents the number of valid data time intervals contained in the patient's multidimensional real-time data set; T i represents the interval length corresponding to the i-th valid data time interval; T b represents the standard deviation of the interval length corresponding to n valid data time intervals; T max Indicates the maximum value of the interval length corresponding to n valid data time intervals; T c Indicates the preset time interval reference value; m i represents the number of invalid values ​​that appear in the i-th valid data time interval; T gj represents the data collection time of the jth invalid value appearing in the i-th valid data time interval; T gj+1 Indicates the data collection time of the j+1th invalid value that appears in the i-th valid data time interval; m b Indicates the standard deviation of the number of invalid values ​​that appear in each valid data time interval; m max Indicates the maximum number of invalid values ​​that appear in each valid data time interval; A comparison module, used for comparing the effective data quality evaluation coefficient with a preset evaluation coefficient threshold; A compensation processing determination module, used for determining that invalid value compensation processing is required when the effective data quality evaluation coefficient is lower than a preset evaluation coefficient threshold; The invalid value compensation processing execution module is used to perform invalid value compensation processing on invalid values ​​based on multidimensional real-time data of patients useful for multidimensional assessment of elderly frailty and Parkinson's disease.

[0037] The technical effect of the above technical solution is: through invalid value compensation processing, the missing or invalid parts in the data can be filled, making the data set more complete. This is crucial for subsequent data analysis and evaluation, because incomplete data may lead to inaccurate analysis results. The introduction of the effective data quality evaluation coefficient enables the system to quantify the quality of the evaluation data. By comprehensively considering factors such as the frequency of occurrence of data, the frequency of invalid values, and the statistical characteristics of the effective data time interval, the true quality status of the data can be more accurately reflected. When the data quality evaluation coefficient is lower than the preset threshold, the system can automatically determine and perform compensation processing to optimize the overall data quality. Multidimensional assessment of frailty and Parkinson's disease in the elderly depends on accurate and complete data. Through this technical solution, it can be ensured that the data used for evaluation is more reliable, thereby improving the accuracy of the evaluation. Accurate evaluation results help doctors make more reasonable diagnosis and treatment decisions, thereby improving the health of patients. This technical solution can process data sets containing invalid or missing values, and enhance the fault tolerance and robustness of the system. In practical applications, invalid values ​​may be included in the data set due to various reasons (such as equipment failure, data transmission problems, etc.). This technical solution can automatically detect and process these invalid values ​​to ensure the stable operation of the system. Through modular design, this technical solution can efficiently process large amounts of data. Each submodule has a clear division of labor and works together, making the data processing process smoother and more efficient. This helps shorten data processing time and improve overall work efficiency.

[0038] In summary, the invalid value compensation processing mechanism introduced by this technical solution in the data processing module can significantly improve data integrity, optimize data quality, improve evaluation accuracy, enhance system robustness and improve processing efficiency. The technical effects on these performance indicators are crucial to the accuracy and reliability of multidimensional evaluation of frailty and Parkinson's disease in the elderly.

[0039] Specifically, the invalid value compensation processing execution module includes: An invalid value data collection time acquisition module is used to extract the data collection time corresponding to each invalid value; A first reference data acquisition module is used to extract valid data whose data collection time is closest to the data collection time corresponding to the invalid value in the patient's multi-dimensional real-time data set as first reference data; A first data subset acquisition module, configured to extract, from the patient multidimensional real-time data set, patient multidimensional real-time data whose difference with the first reference data is lower than a preset difference threshold as a first data subset; A second reference data acquisition module is used to extract valid data whose data collection time is farthest from the data collection time corresponding to the invalid value in the patient's multi-dimensional real-time data set as second reference data; A second data subset acquisition module, configured to extract, from the patient multidimensional real-time data set, patient multidimensional real-time data whose difference with the second reference data is lower than a preset difference threshold as a second data subset; A compensation value acquisition module, configured to acquire a compensation value corresponding to an invalid value by using the first reference data and the second reference data in combination with the multi-dimensional real-time data of the patient included in the first data subset and the second data subset; The compensation value corresponding to the invalid value is obtained by the following formula: Among them, X b Indicates the compensation value corresponding to the invalid value; X 01 and X 02 represents the numerical value corresponding to the first reference data and the second reference data; s represents the adjustment coefficient, and the adjustment coefficient is obtained by the following formula:

[0040] Wherein, s represents the adjustment coefficient; x represents the number of data included in the first data subset; y represents the number of data included in the second data subset; X 01 and X 02 represents the numerical value corresponding to the first reference data and the second reference data; X p01 and X p02 represents the average value of data corresponding to the first data subset and the average value of data corresponding to the second data subset; X a represents the data value of the a-th patient multidimensional real-time data in the first data subset; X b represents the data value of the b-th patient multidimensional real-time data in the second data subset; X ax represents the standard deviation of the multidimensional real-time data of the patient in the first data subset; X by represents the standard deviation of the multidimensional real-time data of the patient in the second data subset; The data replacement module is used to replace the invalid value with the compensation value corresponding to the invalid value.

[0041] The technical effect of the above technical solution is: the technical solution provides rich data support for the calculation of the compensation value by extracting the valid data closest and farthest from the invalid value data collection time as reference data, and constructing the first data subset and the second data subset. The compensation value is calculated by the above formula using the first reference data, the second reference data and the data in the two data subsets. Compared with simple interpolation or mean substitution, this method can more accurately reflect the true value of the invalid value. Through the calculation and replacement of the compensation value, the invalid value is replaced by a more reasonable value, making the entire data set more consistent and coherent in terms of numerical value. This helps to reduce data fluctuations and anomalies caused by invalid values ​​and improve the stability and reliability of the data set. The technical solution can process data sets containing invalid values ​​and make the data sets more complete and reliable through compensation processing. This enhances the fault tolerance and robustness of the system when processing incomplete or abnormal data, so that the system can operate stably under various complex situations. The above technical solution helps to ensure the accuracy of the compensation value. Through modular design, each submodule can be processed in parallel, thereby improving the overall processing efficiency. The adjustment coefficient formula in this technical solution takes into account multiple factors, including the size of the data subset, the data mean, the data standard deviation, etc., which makes the calculation of the compensation value more flexible and adaptable. In different data sets and scenarios, this technical solution can perform compensation processing according to the actual situation of the data to ensure the accuracy and reliability of the results. Multidimensional assessment of frailty and Parkinson's disease in the elderly depends on accurate and complete data sets. By compensating for invalid values ​​through this technical solution, it can be ensured that the data used for evaluation is more reliable and accurate. This helps to improve the accuracy of the evaluation and provide doctors with more reliable diagnostic basis and treatment recommendations.

[0042] In summary, the technical solution achieves accurate compensation processing of invalid values ​​through a series of submodules in the invalid value compensation processing execution module, improves the accuracy and reliability of data processing, enhances the robustness and adaptability of the system, and optimizes processing efficiency. The technical effects on these performance indicators are crucial to the accuracy and reliability of multidimensional assessment of frailty and Parkinson's disease in the elderly.

[0043] In this embodiment, the data processing module further includes: A data conversion unit, used for converting the cleaned multi-dimensional real-time data of the patient; Obtain multidimensional real-time data of patients that is useful for multidimensional assessment of frailty and Parkinson's disease; Convert multidimensional real-time data of patients useful for multidimensional assessment of frailty and Parkinson's disease in the elderly; Standardize the formats of multidimensional real-time patient data useful for multidimensional assessment of frailty and Parkinson's disease in the elderly, eliminate the dimensional differences among multidimensional real-time patient data useful for multidimensional assessment of frailty and Parkinson's disease in the elderly, and determine standardized multidimensional real-time patient data; A feature extraction unit, used for extracting features from the converted multi-dimensional real-time data of the patient; Obtain standardized multi-dimensional real-time data of patients; Extract features from standardized multi-dimensional real-time patient data; Features that can reflect the multidimensional assessment of elderly frailty and Parkinson's disease are extracted from standardized multidimensional real-time patient data, and multidimensional feature data of patients based on artificial intelligence are determined.

[0044] In this embodiment, the analysis and evaluation module includes: Model training unit, used to train multidimensional assessment models for frailty and Parkinson's disease; According to the multi-dimensional assessment needs of elderly frailty and Parkinson's disease based on intelligent single-chip microcomputers, the multi-dimensional historical data of patients are collected, divided, and the training set and test set are determined; Select a neural network model framework suitable for multidimensional assessment of frailty and Parkinson's disease in the elderly based on an intelligent single-chip microcomputer, train the selected neural network model framework suitable for multidimensional assessment of frailty and Parkinson's disease in the elderly based on an intelligent single-chip microcomputer based on a training set, and determine a multidimensional assessment model of frailty and Parkinson's disease in the elderly based on an intelligent single-chip microcomputer; Based on the test set, the performance of the multidimensional assessment model of frailty and Parkinson's disease in the elderly based on the intelligent single-chip microcomputer is tested to determine whether the multidimensional assessment model of frailty and Parkinson's disease in the elderly based on the intelligent single-chip microcomputer can achieve the expected effect and determine the performance test results of the multidimensional assessment model of frailty and Parkinson's disease in the elderly; Based on the performance test results of the multidimensional assessment model of frailty and Parkinson's disease in the elderly, the multidimensional assessment model of frailty and Parkinson's disease in the elderly based on the intelligent single-chip microcomputer was analyzed, the parameters of the multidimensional assessment model of frailty and Parkinson's disease in the elderly based on the intelligent single-chip microcomputer were adjusted and the structure was optimized. After repeated iterations, the optimal multidimensional assessment model of frailty and Parkinson's disease in the elderly was determined.

[0045] In this embodiment, the analysis and evaluation module further includes: A multidimensional assessment unit, used to perform multidimensional assessment of multidimensional characteristic data of patients based on artificial intelligence; Obtain the optimal multidimensional assessment model for frailty and Parkinson's disease in the elderly; Deploy the optimal multidimensional assessment model for frailty and Parkinson's disease in the actual multidimensional assessment environment for frailty and Parkinson's disease; Input the multidimensional characteristic data of patients based on artificial intelligence into the optimal multidimensional assessment model for frailty and Parkinson's disease in the elderly; Based on the optimal multidimensional assessment model for frailty and Parkinson's disease in the elderly, the multidimensional characteristic data of patients based on artificial intelligence are analyzed and evaluated to determine whether the patient is a frailty and Parkinson's disease patient, and the multidimensional assessment results of frailty and Parkinson's disease in the elderly based on the intelligent single-chip computer are determined; Among them, when the patient is an elderly frail or Parkinson's disease patient, the patient will be warned, and personalized intervention measures will be set for the patient in time to carry out intelligent intervention and control of the patient to meet the needs of different types of elderly frail and Parkinson's disease patients. At the same time, the patient will be tracked and the intervention measures will be adjusted in real time according to the patient's feedback. This can help doctors diagnose the disease more accurately and develop personalized treatment plans. It provides patients and their families with real-time health information feedback, which is conducive to adjusting lifestyles and preventing the disease from worsening.

[0046] 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.

[0047] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-dimensional assessment system for elderly frailty and Parkinson's disease based on intelligent single-chip microcomputer, characterized by: include: Data collection module, used to collect physiological and behavioral parameters of patients and determine multi-dimensional real-time data of patients based on artificial intelligence; A data transmission module, used to upload the AI-based multidimensional real-time data of patients to the multidimensional evaluation terminal via wireless communication; The multidimensional evaluation terminal is used to process, analyze and evaluate the multidimensional real-time data of patients based on artificial intelligence, determine whether the patient is a frail elderly or Parkinson's patient, and determine the multidimensional evaluation results of frail elderly and Parkinson's disease based on the intelligent single-chip computer.

2. The multi-dimensional assessment system for senile frailty and Parkinson's disease based on intelligent single-chip microcomputer according to claim 1 is characterized in that: The data acquisition module comprises: A physiological collection unit, used to collect physiological parameters of patients; Based on the sensors installed on the wearable devices worn by the patients, the patients' heart rate, blood pressure, body temperature, and blood oxygen saturation are monitored in real time and continuously collected to obtain the patients' physiological parameters; A behavior collection unit, used to collect patient behavior parameters; Based on the sensors installed on the wearable devices worn by the patients, the patients' posture, gait, movement, balance, tremor and language expression are monitored in real time and continuously collected to obtain the patients' behavioral parameters; Among them, based on the patient's physiological parameters and patient behavioral parameters, the patient's multi-dimensional real-time data based on artificial intelligence is determined.

3. The multi-dimensional assessment system for senile frailty and Parkinson's disease based on intelligent single-chip microcomputer according to claim 2 is characterized in that: The data transmission module comprises: A data sending unit, used to send multi-dimensional real-time data of patients based on artificial intelligence; A data receiving unit, used for receiving multi-dimensional real-time data of patients based on artificial intelligence; Among them, according to the multi-dimensional assessment requirements of frailty and Parkinson's disease in the elderly based on intelligent single-chip microcomputer, a data transmission link between the data sending unit and the data receiving unit is established; The data sending unit receives the multi-dimensional real-time data of the patient based on artificial intelligence transmitted from the data acquisition module, and the data sending unit sends the received multi-dimensional real-time data of the patient based on artificial intelligence to the data receiving unit through a wireless network; The data receiving unit receives the multi-dimensional real-time data of the patient based on artificial intelligence transmitted from the data sending unit, and the data receiving unit sends the received multi-dimensional real-time data of the patient based on artificial intelligence to the multi-dimensional evaluation terminal, and the intelligent single-chip microcomputer built into the multi-dimensional evaluation terminal processes, analyzes and evaluates the multi-dimensional real-time data of the patient based on artificial intelligence to determine whether the patient is a senile frail or Parkinson's patient.

4. The multi-dimensional assessment system for senile frailty and Parkinson's disease based on intelligent single chip microcomputer according to claim 3 is characterized in that: The multi-dimensional evaluation terminal comprises: A data processing module is used to clean, convert and extract features of multi-dimensional real-time data of patients based on artificial intelligence, and determine multi-dimensional feature data of patients based on artificial intelligence; The analysis and evaluation module is used to conduct multi-dimensional evaluation of the patient's multi-dimensional feature data based on artificial intelligence, determine whether the patient is a frail or Parkinson's patient, and determine the multi-dimensional evaluation results of frail or Parkinson's disease based on the intelligent single-chip computer.

5. The multi-dimensional assessment system for senile frailty and Parkinson's disease based on intelligent single chip microcomputer according to claim 4 is characterized in that: The data processing module comprises: Data cleaning unit, used to clean multi-dimensional real-time data of patients based on artificial intelligence; Obtain multi-dimensional real-time data of patients based on artificial intelligence; Cleaning of AI-based multi-dimensional real-time patient data, including: Conduct consistency checks on AI-based multi-dimensional real-time patient data; According to the data consistency requirements, check whether there are inconsistent data in the AI-based multidimensional real-time data of patients that are useless for the multidimensional assessment of frailty and Parkinson's disease, and remove the inconsistent data in the AI-based multidimensional real-time data of patients; Check invalid and missing values ​​on AI-based multi-dimensional real-time patient data; According to the requirements of data validity and integrity, check whether there are invalid values ​​and missing values ​​in the multidimensional real-time data of patients based on artificial intelligence that are useless for the multidimensional assessment of frailty and Parkinson's disease in the elderly, and remove the invalid values ​​and missing values ​​in the multidimensional real-time data of patients based on artificial intelligence; Identify multidimensional real-time patient data useful for multidimensional assessment of frailty and Parkinson's disease.

6. The multi-dimensional assessment system for senile frailty and Parkinson's disease based on intelligent single-chip microcomputer according to claim 5 is characterized in that: The data processing module further includes: The invalid value compensation processing module is used to determine whether to perform invalid value compensation processing according to the multi-dimensional real-time data of patients useful for multi-dimensional assessment of senile frailty and Parkinson's disease, and includes: A data extraction module, used to extract multidimensional real-time data of patients useful for multidimensional assessment of frailty and Parkinson's disease in the elderly; A data collection time acquisition module is used to acquire the data collection time corresponding to each patient's multidimensional real-time data based on the patient's multidimensional real-time data useful for multidimensional assessment of senile frailty and Parkinson's disease; A patient multidimensional real-time data set acquisition module is used to sort the patient multidimensional real-time data in the order of data collection time to obtain a patient multidimensional real-time data set; An effective data time interval acquisition module is used to obtain the effective data time interval corresponding to each two adjacent multidimensional real-time data of patients according to the data collection time corresponding to each two adjacent multidimensional real-time data of patients in the multidimensional real-time data set of patients; An effective data quality evaluation coefficient acquisition module, used to acquire the effective data quality evaluation coefficient according to the interval between the effective data; The effective data quality evaluation coefficient obtained by the interval between the effective data is obtained by the following formula: Where J represents the effective data quality evaluation coefficient; f y represents the frequency of occurrence of the patient multidimensional real-time data contained in the patient multidimensional real-time data set; f m represents the frequency of occurrence of invalid values; n represents the number of valid data time intervals contained in the patient's multidimensional real-time data set; T i represents the interval length corresponding to the i-th valid data time interval; T b represents the standard deviation of the interval length corresponding to n valid data time intervals; T max Indicates the maximum value of the interval length corresponding to n valid data time intervals; T c Indicates the preset time interval reference value; m i represents the number of invalid values ​​that appear in the i-th valid data time interval; T gj represents the data collection time of the jth invalid value appearing in the i-th valid data time interval; T gj+1 Indicates the data collection time of the j+1th invalid value that appears in the i-th valid data time interval; m b Indicates the standard deviation of the number of invalid values ​​that appear in each valid data time interval; m max Indicates the maximum number of invalid values ​​that appear in each valid data time interval; A comparison module, used for comparing the effective data quality evaluation coefficient with a preset evaluation coefficient threshold; A compensation processing determination module, used for determining that invalid value compensation processing is required when the effective data quality evaluation coefficient is lower than a preset evaluation coefficient threshold; The invalid value compensation processing execution module is used to perform invalid value compensation processing on invalid values ​​based on multidimensional real-time data of patients useful for multidimensional assessment of elderly frailty and Parkinson's disease.

7. The multi-dimensional assessment system for senile frailty and Parkinson's disease based on intelligent single chip microcomputer according to claim 6 is characterized in that: The invalid value compensation processing execution module includes: An invalid value data collection time acquisition module is used to extract the data collection time corresponding to each invalid value; A first reference data acquisition module is used to extract valid data whose data collection time is closest to the data collection time corresponding to the invalid value in the patient's multi-dimensional real-time data set as the first reference data; A first data subset acquisition module, configured to extract, from the patient multidimensional real-time data set, patient multidimensional real-time data whose difference with the first reference data is lower than a preset difference threshold as a first data subset; A second reference data acquisition module is used to extract valid data whose data collection time is farthest from the data collection time corresponding to the invalid value in the patient's multi-dimensional real-time data set as second reference data; A second data subset acquisition module, configured to extract, from the patient multidimensional real-time data set, patient multidimensional real-time data whose difference with the second reference data is lower than a preset difference threshold as a second data subset; A compensation value acquisition module, configured to acquire a compensation value corresponding to an invalid value by using the first reference data and the second reference data in combination with the multi-dimensional real-time data of the patient included in the first data subset and the second data subset; The compensation value corresponding to the invalid value is obtained by the following formula: Among them, X b Indicates the compensation value corresponding to the invalid value; X 01 and X 02 represents the numerical value corresponding to the first reference data and the second reference data; s represents the adjustment coefficient, and the adjustment coefficient is obtained by the following formula: Wherein, s represents the adjustment coefficient; x represents the number of data included in the first data subset; y represents the number of data included in the second data subset; X 01 and X 02 represents the numerical value corresponding to the first reference data and the second reference data; X p01 and X p02 represents the average value of data corresponding to the first data subset and the average value of data corresponding to the second data subset; X a represents the data value of the a-th patient multidimensional real-time data in the first data subset; X b represents the data value of the b-th patient multidimensional real-time data in the second data subset; X ax represents the standard deviation of the multidimensional real-time data of the patient in the first data subset; X by represents the standard deviation of the multidimensional real-time data of the patient in the second data subset; The data replacement module is used to replace the invalid value with the compensation value corresponding to the invalid value.

8. The multi-dimensional assessment system for senile frailty and Parkinson's disease based on intelligent single chip microcomputer according to claim 5 is characterized in that: The data processing module further includes: A data conversion unit, used for converting the cleaned multi-dimensional real-time data of the patient; Obtain multidimensional real-time data of patients that is useful for multidimensional assessment of frailty and Parkinson's disease; Convert multidimensional real-time data of patients useful for multidimensional assessment of frailty and Parkinson's disease in the elderly; Standardize the formats of multidimensional real-time patient data useful for multidimensional assessment of frailty and Parkinson's disease in the elderly, eliminate the dimensional differences among multidimensional real-time patient data useful for multidimensional assessment of frailty and Parkinson's disease in the elderly, and determine standardized multidimensional real-time patient data; A feature extraction unit, used for extracting features from the converted multi-dimensional real-time data of the patient; Obtain standardized multi-dimensional real-time data of patients; Extract features from standardized multi-dimensional real-time patient data; Features that can reflect the multidimensional assessment of elderly frailty and Parkinson's disease are extracted from standardized multidimensional real-time patient data, and multidimensional feature data of patients based on artificial intelligence are determined.

9. The multi-dimensional assessment system for frailty and Parkinson's disease in the elderly based on intelligent single chip microcomputer according to claim 7, characterized in that: The analysis and evaluation module comprises: Model training unit, used to train multidimensional assessment models for frailty and Parkinson's disease; According to the multi-dimensional assessment needs of elderly frailty and Parkinson's disease based on intelligent single-chip microcomputers, the multi-dimensional historical data of patients are collected, divided, and the training set and test set are determined; Select a neural network model framework suitable for multidimensional assessment of frailty and Parkinson's disease in the elderly based on an intelligent single-chip microcomputer, train the selected neural network model framework suitable for multidimensional assessment of frailty and Parkinson's disease in the elderly based on an intelligent single-chip microcomputer based on a training set, and determine a multidimensional assessment model of frailty and Parkinson's disease in the elderly based on an intelligent single-chip microcomputer; Based on the test set, the performance of the multidimensional assessment model of frailty and Parkinson's disease in the elderly based on the intelligent single-chip microcomputer is tested to determine whether the multidimensional assessment model of frailty and Parkinson's disease in the elderly based on the intelligent single-chip microcomputer can achieve the expected effect and determine the performance test results of the multidimensional assessment model of frailty and Parkinson's disease in the elderly; Based on the performance test results of the multidimensional assessment model of frailty and Parkinson's disease in the elderly, the multidimensional assessment model of frailty and Parkinson's disease in the elderly based on the intelligent single-chip microcomputer was analyzed, the parameters of the multidimensional assessment model of frailty and Parkinson's disease in the elderly based on the intelligent single-chip microcomputer were adjusted and the structure was optimized. After repeated iterations, the optimal multidimensional assessment model of frailty and Parkinson's disease in the elderly was determined.

10. The multi-dimensional assessment system for frailty and Parkinson's disease in the elderly based on intelligent single chip microcomputer according to claim 9, characterized in that: The analysis and evaluation module further includes: A multidimensional assessment unit, used to perform multidimensional assessment of multidimensional characteristic data of patients based on artificial intelligence; Obtain the optimal multidimensional assessment model for frailty and Parkinson's disease in the elderly; Deploy the optimal multidimensional assessment model for frailty and Parkinson's disease in the actual multidimensional assessment environment for frailty and Parkinson's disease; Input the multidimensional characteristic data of patients based on artificial intelligence into the optimal multidimensional assessment model for frailty and Parkinson's disease in the elderly; Based on the optimal multidimensional assessment model for frailty and Parkinson's disease in the elderly, the multidimensional characteristic data of patients based on artificial intelligence are analyzed and evaluated to determine whether the patient is a frailty and Parkinson's disease patient, and the multidimensional assessment results of frailty and Parkinson's disease in the elderly based on the intelligent single-chip computer are determined; Among them, according to the multi-dimensional assessment results of elderly frailty and Parkinson's disease based on intelligent single-chip microcomputers, personalized intervention measures are set for patients in a timely manner to carry out intelligent intervention and control of patients to meet the needs of different types of elderly frailty and Parkinson's disease patients.

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