Personalized rehabilitation treatment recommendation system based on big data analysis

Abstract

The invention discloses a personalized rehabilitation treatment recommendation system based on big data analysis, and relates to the technical field of rehabilitation treatment, and the system comprises a rehabilitation treatment scheme providing module, a rehabilitation treatment scheme alternative module and a rehabilitation treatment scheme recommendation module. A plurality of simulated rehabilitation treatment schemes can be generated through construction of a rehabilitation treatment scene, alternative recommended rehabilitation treatment schemes conforming to rehabilitation treatment standards are quickly screened out through multi-dimensional stereo analysis of each simulated rehabilitation treatment scheme, and a rehabilitation treatment scheme recommendation module is arranged and can be matched with a rehabilitation treatment scheme alternative module, so that the rehabilitation treatment scheme recommendation module can be matched with a rehabilitation treatment scheme recommendation module; and carrying out secondary analysis on the rehabilitation treatment schemes, and selecting a stable and fit rehabilitation treatment scheme from the alternative recommended rehabilitation treatment schemes to treat the patient.

Description

Technical Field

[0001] The present invention relates to the field of rehabilitation therapy technology, and more specifically, to a personalized rehabilitation therapy recommendation system based on big data analysis. Background Art

[0002] With the rapid development of medical technology and the continuous accumulation of medical data, the application of big data analysis in the field of medical health is becoming more and more extensive. At present, doctors will combine big data to analyze some rehabilitation treatment plans suitable for patients, and screen these rehabilitation treatment plans based on experience, but manual screening has problems such as strong subjectivity and low efficiency. Therefore, it is particularly important to propose a personalized rehabilitation treatment recommendation system based on big data analysis. Summary of the invention

[0003] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a personalized rehabilitation treatment recommendation system based on big data analysis.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A personalized rehabilitation treatment recommendation system based on big data analysis, including a rehabilitation treatment plan provision module, a rehabilitation treatment plan selection module, and a rehabilitation treatment plan recommendation module;

[0006] The rehabilitation treatment program providing module is used to construct a rehabilitation treatment scenario and generate multiple simulated rehabilitation treatment programs based on the rehabilitation treatment scenario;

[0007] The rehabilitation treatment plan candidate module is used to obtain the rehabilitation treatment recommendation value of each simulated rehabilitation treatment plan, and based on the comparison result between the rehabilitation treatment recommendation value and the rehabilitation treatment recommendation threshold value, determine whether to mark the simulated rehabilitation treatment plan as an alternative recommended rehabilitation treatment plan;

[0008] The rehabilitation treatment plan recommendation module is used to obtain the rehabilitation treatment recommendation value of the alternative recommended rehabilitation treatment plan, mark the alternative recommended rehabilitation treatment plan with the largest rehabilitation treatment recommendation value as a personalized rehabilitation treatment recommendation plan, and perform rehabilitation treatment on the patient according to the personalized rehabilitation treatment recommendation plan.

[0009] Furthermore, the specific method of constructing a rehabilitation treatment scenario is: obtaining the patient's initial rehabilitation data, synchronously presetting the patient's rehabilitation target data and rehabilitation condition data, and obtaining a rehabilitation treatment scenario based on the initial rehabilitation data, rehabilitation target data and rehabilitation condition data.

[0010] Furthermore, based on the comparison result between the recommended rehabilitation treatment value and the recommended rehabilitation treatment threshold, it is determined whether to mark the simulated rehabilitation treatment plan as an alternative recommended rehabilitation treatment plan. Specifically, the recommended rehabilitation treatment value of each simulated rehabilitation treatment plan is obtained, and the recommended rehabilitation treatment threshold is set. When the recommended rehabilitation treatment value is greater than or equal to the recommended rehabilitation treatment threshold, the simulated rehabilitation treatment plan is marked as an alternative recommended rehabilitation treatment plan. When the recommended rehabilitation treatment value is greater than or equal to the recommended rehabilitation treatment threshold, no treatment is performed.

[0011] Furthermore, the rehabilitation treatment recommendation value of the rehabilitation treatment plan is obtained by the following method: based on the rehabilitation treatment plan, the patient is subjected to simulated rehabilitation treatment. During the simulated rehabilitation treatment, a preset period interval is set. At each period node of the preset period interval, a simulated rehabilitation monitoring value is obtained, a rehabilitation face frame value SEP is obtained, and a simulated rehabilitation monitoring limit is set. When the simulated rehabilitation monitoring value is greater than or equal to the simulated rehabilitation monitoring limit, no processing is performed. When the simulated rehabilitation monitoring value is less than the simulated rehabilitation monitoring limit, the period node corresponding to the simulated rehabilitation monitoring value is marked as a negative node, and the total number of negative nodes is marked as Tte. The formula is used. The rehabilitation treatment recommendation value Fyp of the rehabilitation treatment plan is obtained, where k1 is the rehabilitation face frame value coefficient and k2 is the total number coefficient of negative nodes.

[0012] Furthermore, the rehabilitation face frame value SEP is obtained in the following manner: a rectangular coordinate system is established with the rehabilitation treatment time in the rehabilitation treatment process as the X-axis and the simulated rehabilitation monitoring value as the Y-axis, the simulated rehabilitation monitoring value corresponding to the periodic node is plotted in the rectangular coordinate system in the form of a point, and two simulated rehabilitation monitoring values ​​of adjacent periodic nodes in the rectangular coordinate system are connected by a straight line. After all the simulated rehabilitation monitoring values ​​are connected, a simulated rehabilitation monitoring broken line is generated, and the two endpoints of the simulated rehabilitation monitoring broken line are perpendicular to the X-axis to obtain two rehabilitation start and end lines. A figure is formed by the simulated rehabilitation monitoring broken line, the two rehabilitation start and end lines and the X-axis, and the total area of ​​the figure is marked as the rehabilitation face frame value SEP.

[0013] Furthermore, the simulated rehabilitation monitoring value is obtained in the following manner: data of various physiological indicators of the patient are collected, and then the physiological indicator scores of various physiological indicators are obtained, and the upper limit score and the lower limit score of the physiological indicator are set. When the physiological indicator score is greater than or equal to the upper limit score of the physiological indicator, the physiological indicator is marked as a positive indicator. When the physiological indicator score is less than or equal to the lower limit score of the physiological indicator, the physiological indicator is marked as a negative indicator. When the physiological indicator score is between the upper limit score of the physiological indicator and the lower limit score of the physiological indicator, the physiological indicator is marked as a standard indicator. The total number of positive indicators is marked as JJz, and the total number of negative indicators is marked as XJz. The accumulated number of disappointments LPg and the standard gap and score SEG are obtained, and the formula is used The simulated rehabilitation monitoring value MND is obtained, where z1 is the total number coefficient of positive indicators, z2 is the total number coefficient of negative indicators, and z3 is the accumulation and number coefficient of disappointment.

[0014] Furthermore, the physiological indicator scores of various physiological indicators are obtained by the following method: obtaining the physiological indicator scoring model corresponding to the physiological indicators of various aspects, inputting the physiological indicator data of each aspect into the corresponding physiological indicator scoring model, each physiological indicator scoring model outputs a physiological indicator score, and then obtaining the physiological indicator scores of various physiological indicators.

[0015] Furthermore, the accumulated disappointment number LPg and the standard gap score SEG are obtained in the following way: all standard indicators are grouped together in pairs, the physiological indicator scores of the two standard indicators in the same group are summed to obtain the standard accumulated score, and a standard accumulated threshold score is set. When the standard accumulated score is greater than or equal to the standard accumulated threshold score, no processing is performed. When the standard accumulated score is less than the standard accumulated threshold score, the accumulated disappointment number is increased once, and the difference between the standard accumulated threshold score and the standard accumulated score is calculated to obtain the standard gap score. The accumulated disappointment number is summed to obtain the accumulated disappointment number LPg, and the standard gap score is summed to obtain the standard gap score SEG.

[0016] Furthermore, the rehabilitation recommendation value of the alternative recommended rehabilitation treatment plan is obtained by the following method: mark the other alternative recommended rehabilitation treatment plans except the alternative recommended rehabilitation treatment plan as reference alternative plans, calculate the difference between the rehabilitation treatment recommendation value of the alternative recommended rehabilitation treatment plan and the rehabilitation treatment recommendation value of the reference alternative plan and take the absolute value to obtain the treatment recommendation floating value SCp, set the treatment recommendation floating value coefficient to ASe, and use the formula The recommended treatment values ​​of the alternative recommended treatment plans are obtained, where p=1, 2, ..., P, and P is the total number of reference alternative plans.

[0017] Compared with the prior art, the present invention has the following beneficial effects:

[0018] 1. Set up a rehabilitation treatment plan provision module and a rehabilitation treatment plan alternative module. Through the construction of rehabilitation treatment scenarios, multiple simulated rehabilitation treatment plans can be generated, and through multi-dimensional three-dimensional analysis of each simulated rehabilitation treatment plan, alternative recommended rehabilitation treatment plans that meet the rehabilitation treatment standards can be quickly screened out;

[0019] 2. Set up a rehabilitation treatment plan recommendation module, which can be used in conjunction with the rehabilitation treatment plan alternative module to conduct a secondary analysis of the rehabilitation treatment plan, and select a stable and suitable rehabilitation treatment plan from the alternative recommended rehabilitation treatment plans to treat the patient. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a workflow diagram of a personalized rehabilitation treatment recommendation system based on big data analysis;

[0021] Figure 2 A flowchart for generating multiple simulated rehabilitation treatment plans based on rehabilitation treatment scenarios;

[0022] Figure 3 This is a module block diagram of the personalized rehabilitation treatment recommendation system based on big data analysis. DETAILED DESCRIPTION

[0023] Reference Figures 1 to 3 , a personalized rehabilitation treatment recommendation system based on big data analysis, including a rehabilitation treatment plan providing module, a rehabilitation treatment plan alternative module, and a rehabilitation treatment plan recommendation module.

[0024] Rehabilitation treatment plan providing module: obtain the patient's initial rehabilitation data (i.e., the patient's vital signs data, biochemical indicator data, etc. before rehabilitation treatment), synchronously preset the patient's rehabilitation target data (i.e., the preset patient's vital signs data, biochemical indicator data, etc. after completing rehabilitation treatment) and rehabilitation condition data (i.e., the preset patient's environmental data, medication data, training data, etc. during the rehabilitation treatment process. The above data do not only refer to fixed data, but usually refer to a reasonable range, such as medication data refers to the range of medication dosage, the range of medication types, etc.), obtain rehabilitation treatment scenarios based on the initial rehabilitation data, rehabilitation target data, and rehabilitation condition data (the rehabilitation initial data, rehabilitation target data, and rehabilitation condition data are input into the digital twin model to construct a rehabilitation treatment scenario), and generate multiple simulated rehabilitation treatment plans based on the rehabilitation treatment scenarios.

[0025] Rehabilitation treatment plan alternative module: obtain the rehabilitation treatment recommendation value of each simulated rehabilitation treatment plan, set the rehabilitation treatment recommendation threshold (preset value, modified according to actual needs), when the rehabilitation treatment recommendation value is greater than or equal to the rehabilitation treatment recommendation threshold, mark the simulated rehabilitation treatment plan as an alternative recommended rehabilitation treatment plan, when the rehabilitation treatment recommendation value is greater than or equal to the rehabilitation treatment recommendation threshold, no processing is performed.

[0026] The rehabilitation treatment recommendation value of the rehabilitation treatment plan is obtained in the following way: based on the rehabilitation treatment plan, the patient is subjected to simulated rehabilitation treatment. During the simulated rehabilitation treatment process, a preset cycle interval is set (it should be noted here that the preset cycle interval of each rehabilitation treatment plan is the same, so as to facilitate further analysis later). At each cycle node of the preset cycle interval, the simulated rehabilitation monitoring value is obtained. A rectangular coordinate system is established with the rehabilitation treatment time in the rehabilitation treatment process as the X-axis and the simulated rehabilitation monitoring value as the Y-axis. The simulated rehabilitation monitoring values ​​corresponding to the cycle nodes are plotted in the rectangular coordinate system in the form of points, and two simulated rehabilitation monitoring values ​​at adjacent cycle nodes in the rectangular coordinate system are connected by a straight line. Rehabilitation monitoring value, after all the simulated rehabilitation monitoring values ​​are connected, a simulated rehabilitation monitoring line is generated. The two endpoints of the simulated rehabilitation monitoring line are perpendicular to the X-axis to obtain two rehabilitation start and end lines. The simulated rehabilitation monitoring line, the two rehabilitation start and end lines and the X-axis form a graph. The total area of ​​the graph is marked as the rehabilitation surface frame value SEP. The simulated rehabilitation monitoring limit is set (preset value, modified according to actual needs). When the simulated rehabilitation monitoring value is greater than or equal to the simulated rehabilitation monitoring limit, no processing is performed. When the simulated rehabilitation monitoring value is less than the simulated rehabilitation monitoring limit, the period node corresponding to the simulated rehabilitation monitoring value is marked as a negative node, and the total number of negative nodes is marked as Tte. The formula The rehabilitation treatment recommendation value Fyp of the rehabilitation treatment plan is obtained, where k1 is the rehabilitation face frame value coefficient, k2 is the total number coefficient of negative nodes, the value of k1 is 1.21, and the value of k2 is 1.38.

[0027] The simulated rehabilitation monitoring value is obtained in the following way: collect data of various physiological indicators of the patient (when analyzing the effect of patient rehabilitation treatment, it is necessary to analyze based on the data of the patient's physiological indicators. The data of physiological indicators include multiple aspects, including but not limited to vital signs data, blood indicator data, endocrine indicator data, and biochemical indicator data), and then obtain the physiological indicator scores of various physiological indicators, set the upper limit score of the physiological indicator and the lower limit score of the physiological indicator (the upper limit score of the physiological indicator is greater than the lower limit score of the physiological indicator, both of which are preset scores and are modified according to actual needs). When the physiological indicator score is greater than or equal to the upper limit score of the physiological indicator, the physiological indicator is marked as a positive indicator. When the physiological indicator score is less than or equal to the lower limit score of the physiological indicator, the physiological indicator is marked as a negative indicator. When the physiological indicator score is between the upper limit score of the physiological indicator When the score is between the lower limit score of the physiological index, the physiological index is marked as a standard index, the total number of positive indexes is marked as JJz, and the total number of negative indexes is marked as XJz. All standard indexes are grouped together, and the physiological index scores of the two standard indexes in the same group are summed to obtain a standard accumulation score. A standard accumulation threshold score is set (a preset score is modified according to actual needs). When the standard accumulation score is greater than or equal to the standard accumulation threshold score, no processing is performed. When the standard accumulation score is less than the standard accumulation threshold score, the number of disappointment accumulation is increased once, and the difference between the standard accumulation threshold score and the standard accumulation score is calculated to obtain a standard gap score. The number of disappointment accumulation is summed to obtain the disappointment accumulation sum number LPg. The standard gap score is summed to obtain the standard gap sum score SEG. The formula is used The simulated rehabilitation monitoring value MND was obtained, where z1 is the total number coefficient of positive indicators, z2 is the total number coefficient of negative indicators, and z3 is the coefficient of disappointment accumulation and number. The value of z1 is 0.89, the value of z2 is 0.81, and the value of z3 is 0.96.

[0028] The physiological indicator scores of various aspects of physiological indicators are obtained in the following manner: obtain the physiological indicator scoring models corresponding to various aspects of physiological indicators (each aspect corresponds to an independently trained physiological indicator scoring model), input the physiological indicator data of each aspect into the corresponding physiological indicator scoring model, each physiological indicator scoring model outputs a physiological indicator score, and then obtains the physiological indicator scores of various aspects of physiological indicators.

[0029] Although each aspect of physiological indicators corresponds to an independently trained physiological indicator scoring model, the training method of each physiological indicator scoring model is the same. It only needs to replace the training data accordingly. The following will take vital signs as an example to disclose the construction method of the physiological indicator scoring model: obtain n groups of vital sign data, each group of vital sign data includes but is not limited to temperature data, pulse data, respiratory data, and blood pressure data, build a deep learning model, use the vital sign data as the training data of the deep learning model, assign a physiological indicator score to each training data, divide the n training data into a training set and a validation set in a ratio of 2:3, and iterate through training and validation to obtain a physiological indicator scoring model for vital signs. Among them, the scoring range of the physiological indicator score is (3 to 6). The closer the physiological indicator score is to 6, the better the rehabilitation treatment effect of the patient's physiological indicator, and the closer the physiological indicator score is to 3, the worse the rehabilitation treatment effect of the patient's physiological indicator; the scoring range of the physiological indicator score of each physiological indicator scoring model remains unchanged.

[0030] A rehabilitation treatment plan providing module and a rehabilitation treatment plan alternative module are set up. Through the construction of rehabilitation treatment scenarios, multiple simulated rehabilitation treatment plans can be generated. Through multi-dimensional three-dimensional analysis of each simulated rehabilitation treatment plan, alternative recommended rehabilitation treatment plans that meet the rehabilitation treatment standards can be quickly screened out.

[0031] Rehabilitation treatment plan recommendation module: obtain the recommended rehabilitation value of the alternative recommended rehabilitation plan, mark the alternative recommended rehabilitation plan with the largest recommended rehabilitation value as the personalized rehabilitation treatment recommendation plan, and provide rehabilitation treatment to the patient according to the personalized rehabilitation treatment recommendation plan.

[0032] The rehabilitation recommendation value of the alternative recommended rehabilitation scheme is obtained by the following method: mark the other alternative recommended rehabilitation schemes as reference alternative schemes, calculate the difference between the rehabilitation treatment recommendation value of the alternative recommended rehabilitation scheme and the rehabilitation treatment recommendation value of the reference alternative scheme and take the absolute value to obtain the treatment recommendation floating value SCp, set the treatment recommendation floating value coefficient to ASe, e=1, 2, 3, ...e; AS1<AS2<AS3<...<ASe, set the range of multiple continuous treatment recommendation floating values, including [0, SC1], (SC1, SC2], ..., (SCP-1, SCP], each range of treatment recommendation floating value corresponds to a treatment recommendation floating value coefficient, for example: when SCp∈[0, SC1], the treatment recommendation floating value coefficient is AS1, using the formula The recommended treatment values ​​of the alternative recommended treatment plans are obtained, where p=1, 2, ..., P, and P is the total number of reference alternative plans.

[0033] A rehabilitation treatment plan recommendation module can be set up to cooperate with the rehabilitation treatment plan alternative module to conduct a secondary analysis of the rehabilitation treatment plan and recommend the most stable and suitable rehabilitation treatment plan to the patient for treatment.

[0034] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0035] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0036] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0037] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0038] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0039] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0040] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0041] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A personalized rehabilitation treatment recommendation system based on big data analysis, characterized by: It includes a rehabilitation treatment plan provision module, a rehabilitation treatment plan selection module, and a rehabilitation treatment plan recommendation module; The rehabilitation treatment program providing module is used to construct a rehabilitation treatment scenario and generate multiple simulated rehabilitation treatment programs based on the rehabilitation treatment scenario; The rehabilitation treatment plan candidate module is used to obtain the rehabilitation treatment recommendation value of each simulated rehabilitation treatment plan, and based on the comparison result between the rehabilitation treatment recommendation value and the rehabilitation treatment recommendation threshold value, determine whether to mark the simulated rehabilitation treatment plan as an alternative recommended rehabilitation treatment plan; The rehabilitation treatment plan recommendation module is used to obtain the rehabilitation treatment recommendation value of the alternative recommended rehabilitation treatment plan, mark the alternative recommended rehabilitation treatment plan with the largest rehabilitation treatment recommendation value as a personalized rehabilitation treatment recommendation plan, and perform rehabilitation treatment on the patient according to the personalized rehabilitation treatment recommendation plan.

2. The personalized rehabilitation treatment recommendation system based on big data analysis according to claim 1 is characterized in that: The specific method of constructing a rehabilitation treatment scenario is: obtaining the patient's initial rehabilitation data, synchronously presetting the patient's rehabilitation target data and rehabilitation condition data, and obtaining a rehabilitation treatment scenario based on the initial rehabilitation data, rehabilitation target data and rehabilitation condition data.

3. The personalized rehabilitation treatment recommendation system based on big data analysis according to claim 1 is characterized in that: Based on the comparison result between the recommended rehabilitation treatment value and the recommended rehabilitation treatment threshold, determine whether to mark the simulated rehabilitation treatment plan as an alternative recommended rehabilitation treatment plan. Specifically, obtain the recommended rehabilitation treatment value of each simulated rehabilitation treatment plan, set the recommended rehabilitation treatment threshold, and when the recommended rehabilitation treatment value is greater than or equal to the recommended rehabilitation treatment threshold, mark the simulated rehabilitation treatment plan as an alternative recommended rehabilitation treatment plan; when the recommended rehabilitation treatment value is greater than or equal to the recommended rehabilitation treatment threshold, no treatment is performed.

4. The personalized rehabilitation treatment recommendation system based on big data analysis according to claim 1 is characterized in that: The rehabilitation treatment recommendation value of the rehabilitation treatment plan is obtained in the following way: based on the rehabilitation treatment plan, the patient is simulated for rehabilitation treatment. During the simulated rehabilitation treatment, a preset period interval is set. At each period node of the preset period interval, a simulated rehabilitation monitoring value is obtained, a rehabilitation face frame value SEP is obtained, and a simulated rehabilitation monitoring limit is set. When the simulated rehabilitation monitoring value is greater than or equal to the simulated rehabilitation monitoring limit, no processing is performed. When the simulated rehabilitation monitoring value is less than the simulated rehabilitation monitoring limit, the period node corresponding to the simulated rehabilitation monitoring value is marked as a negative node, and the total number of negative nodes is marked as Tte. The formula is used. The rehabilitation treatment recommendation value Fyp of the rehabilitation treatment plan is obtained, where k1 is the rehabilitation face frame value coefficient and k2 is the total number coefficient of negative nodes.

5. The personalized rehabilitation treatment recommendation system based on big data analysis according to claim 4 is characterized in that: The rehabilitation face frame value SEP is obtained in the following way: a rectangular coordinate system is established with the rehabilitation treatment time in the rehabilitation treatment process as the X-axis and the simulated rehabilitation monitoring value as the Y-axis, the simulated rehabilitation monitoring value corresponding to the periodic node is plotted in the rectangular coordinate system in the form of a point, and the two simulated rehabilitation monitoring values ​​of adjacent periodic nodes in the rectangular coordinate system are connected by a straight line. After all the simulated rehabilitation monitoring values ​​are connected, a simulated rehabilitation monitoring broken line is generated, and the two endpoints of the simulated rehabilitation monitoring broken line are perpendicular to the X-axis to obtain two rehabilitation start and end lines. A figure is formed by the simulated rehabilitation monitoring broken line, the two rehabilitation start and end lines and the X-axis, and the total area of ​​the figure is marked as the rehabilitation face frame value SEP.

6. The personalized rehabilitation treatment recommendation system based on big data analysis according to claim 4 is characterized in that: The simulated rehabilitation monitoring value is obtained in the following way: collect the data of various physiological indicators of the patient, and then obtain the physiological indicator scores of various physiological indicators, set the upper limit score and the lower limit score of the physiological indicator, when the physiological indicator score is greater than or equal to the upper limit score of the physiological indicator, mark the physiological indicator as a positive indicator, when the physiological indicator score is less than or equal to the lower limit score of the physiological indicator, mark the physiological indicator as a negative indicator, when the physiological indicator score is between the upper limit score and the lower limit score of the physiological indicator, mark the physiological indicator as a standard indicator, mark the total number of positive indicators as JJz, mark the total number of negative indicators as XJz, obtain the disappointment accumulation and number LPg and the standard gap and score SEG, and use the formula The simulated rehabilitation monitoring value MND is obtained, where z1 is the total number coefficient of positive indicators, z2 is the total number coefficient of negative indicators, and z3 is the accumulation and number coefficient of disappointment.

7. The personalized rehabilitation treatment recommendation system based on big data analysis according to claim 6, characterized in that: The physiological indicator scores of various physiological indicators are obtained in the following manner: obtaining the physiological indicator scoring models corresponding to various physiological indicators, inputting the physiological indicator data of each aspect into the corresponding physiological indicator scoring model, each physiological indicator scoring model outputs a physiological indicator score, and then obtaining the physiological indicator scores of various physiological indicators.

8. The personalized rehabilitation treatment recommendation system based on big data analysis according to claim 6, characterized in that: The accumulated disappointment number LPg and the standard gap score SEG are obtained in the following way: all standard indicators are grouped together in pairs, the physiological indicator scores of the two standard indicators in the same group are summed to obtain the standard accumulated score, and the standard accumulated threshold score is set. When the standard accumulated score is greater than or equal to the standard accumulated threshold score, no processing is performed. When the standard accumulated score is less than the standard accumulated threshold score, the accumulated disappointment number is increased once, and the difference between the standard accumulated threshold score and the standard accumulated score is calculated to obtain the standard gap score. The accumulated disappointment number is summed to obtain the accumulated disappointment number LPg, and the standard gap score is summed to obtain the standard gap score SEG.

9. The personalized rehabilitation treatment recommendation system based on big data analysis according to claim 1, characterized in that: The recommended rehabilitation value of the alternative recommended rehabilitation plan is obtained by the following method: mark all the alternative recommended rehabilitation plans other than the alternative recommended rehabilitation plan as reference alternative plans, calculate the difference between the recommended rehabilitation value of the alternative recommended rehabilitation plan and the recommended rehabilitation value of the reference alternative plan and take the absolute value to obtain the recommended treatment floating value SCp, set the recommended treatment floating value coefficient to ASe, and use the formula The recommended treatment values ​​of the alternative recommended treatment plans are obtained, where p=1, 2, ..., P, and P is the total number of reference alternative plans.

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