Intelligent child rehabilitation management system and method based on big data

Abstract

The invention provides an intelligent child rehabilitation management system and method based on big data, relates to the technical field of data processing, and is used for improving the efficiency and effectiveness of child rehabilitation course formulation. The method comprises the steps of determining a child rehabilitation target according to a child rehabilitation demand and a parent rehabilitation demand; performing data screening on the basic data, the medical evaluation data, the psychological evaluation data, the education background data, the family background data and the rehabilitation record data of the target child through a data screening model corresponding to the child rehabilitation target, and determining weight values corresponding to the screened data; inputting the screening data and the corresponding weight value into a fitting formula corresponding to the child rehabilitation target to obtain the corresponding total duration of the rehabilitation course; and determining a rehabilitation plan course corresponding to the target child based on the total duration of the rehabilitation course, the screening data and the weight value corresponding to the screening data.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to an intelligent child rehabilitation management system and method based on big data. Background Art

[0002] The significance of child rehabilitation management is to help children improve or recover their physical functions, motor skills, language development and other issues. In the process of children's growth, if they encounter physical disabilities, motor problems or intellectual retardation, timely rehabilitation intervention can play a positive role. Child rehabilitation includes treatment in multiple professional fields such as physical therapy, occupational therapy, and speech therapy. Physical therapy focuses on enhancing children's motor and physical functions, occupational therapy helps children develop daily living skills, and speech therapy improves children's language expression and communication skills.

[0003] At present, rehabilitation courses for children are formulated by rehabilitation professionals to help children with rehabilitation training and exercises. However, the formulated rehabilitation courses completely rely on rehabilitation professionals, which will lead to low efficiency and low effectiveness. Therefore, a child rehabilitation management system is urgently needed to solve the problems in the existing technology. Summary of the invention

[0004] The embodiments of the present application provide an intelligent child rehabilitation management system and method based on big data, which are used to improve the efficiency and effectiveness of child rehabilitation course formulation.

[0005] An embodiment of the present invention provides an intelligent child rehabilitation management system based on big data, the system comprising a data acquisition device, a data analysis device and a rehabilitation course formulation device which are sequentially connected in communication, the rehabilitation course formulation device being used to perform the following method: The data acquisition device is used to acquire the target child's basic data, medical assessment data, psychological assessment data, educational background data, family background data, rehabilitation record data, and parents' rehabilitation needs in different time periods; Analyzing the basic data, medical assessment data, psychological assessment data, educational background data, family background data, and rehabilitation record data of the target child by the data analysis device to determine the child's rehabilitation needs; Determine the rehabilitation goals for the child based on the rehabilitation needs of the child and the rehabilitation needs of the parents; The basic data, medical assessment data, psychological assessment data, educational background data, family background data, and rehabilitation record data of the target child are screened through the data screening model corresponding to the child rehabilitation target, and the weight value corresponding to each screening data is determined; wherein different child rehabilitation targets correspond to different data screening models; Inputting the screening data and its corresponding weight value into the fitting formula corresponding to the child rehabilitation goal to obtain the corresponding total duration of the rehabilitation course; The rehabilitation plan course corresponding to the target child is determined based on the total duration of the rehabilitation course, the screening data and its corresponding weight value. The rehabilitation plan course includes course content, course time, course duration and expected results, and the sum of the durations of all rehabilitation plan courses is the total duration of the rehabilitation course.

[0006] In an optional embodiment provided by the present invention, the data analysis device analyzes the basic data, medical assessment data, psychological assessment data, educational background data, family background data, and rehabilitation record data of the target child to determine the child's rehabilitation needs, including: Comparing the medical assessment data and the rehabilitation record data based on the evaluation data table corresponding to the basic data to determine the probability value of belonging to the sports rehabilitation demand; Based on the evaluation data table corresponding to the basic data, the medical assessment data, the educational background data, and the family background data are compared to determine the probability value of intellectual rehabilitation needs; The medical assessment data, the psychological assessment data and the family background data are compared based on the evaluation data table corresponding to the basic data to determine the probability value of belonging to the psychological rehabilitation needs.

[0007] In an optional embodiment provided by the present invention, determining the child's rehabilitation goal according to the child's rehabilitation needs and the parent's rehabilitation needs includes: The child rehabilitation goal is obtained by calculating the parent rehabilitation needs based on the probability value of the motor rehabilitation needs, the probability value of the intellectual rehabilitation needs, the probability value of the psychological rehabilitation needs, and the parent rehabilitation needs.

[0008] In an optional embodiment provided by the present invention, the basic data, medical assessment data, psychological assessment data, educational background data, family background data, and rehabilitation record data of the target child are screened by the data screening model corresponding to the child rehabilitation target, and the weight value corresponding to each screening data is determined, including: Convert the basic data, medical assessment data, psychological assessment data, educational background data, and family background data of the target child in each time period into a feature vector matrix; Inputting the characteristic vector matrix into the data screening model to obtain the rehabilitation prediction data of the corresponding time period; and determining the weight value of each data in the characteristic vector matrix based on the rehabilitation prediction data and the rehabilitation record data; Performing data screening according to the weight value of each data in the eigenvector matrix; The weight values ​​corresponding to each item of filtered data are determined by the weight values ​​of each item of filtered data corresponding to all time periods.

[0009] In an optional embodiment provided by the present invention, the determining the weight value of each data in the feature vector matrix based on the rehabilitation prediction data and the rehabilitation record data includes: Calculate the weight values ​​of the data.

[0010] In an optional embodiment provided by the present invention, the determining the weight value of each data in the feature vector matrix based on the rehabilitation prediction data and the rehabilitation record data includes: Obtain the contribution of the i-th data in the characteristic vector matrix to the obtained rehabilitation prediction data , and the mutual information between the i-th data and other data in the obtained rehabilitation prediction data ; Used to represent the mutual dependence of the i-th data and the j-th data in the characteristic vector matrix on the obtained rehabilitation prediction data; According to the contribution and the mutual information Determine the weight value of each data in the eigenvector matrix.

[0011] In an optional embodiment provided by the present invention, the contribution and the mutual information Determining the weight value of each data in the eigenvector matrix includes: Calculating a data difference between the rehabilitation prediction data and the rehabilitation record data; If the data difference is greater than a preset value, the weight value of each data in the eigenvector matrix is ​​calculated; If the data difference is less than a preset value, the weight value of each data in the eigenvector matrix is ​​calculated.

[0012] In an optional embodiment provided by the present invention, the screening data and its corresponding weight value are input into the fitting formula corresponding to the child rehabilitation goal to obtain the corresponding total duration of the rehabilitation course, including: If the child rehabilitation goal is sports rehabilitation demand, the screening data and its corresponding weight value are input into the first fitting formula to obtain the total duration of the sports rehabilitation demand course; If the child rehabilitation goal is intellectual rehabilitation needs, the screening data and its corresponding weight value are input into the second fitting formula to obtain the total duration of the intellectual rehabilitation needs course; If the child rehabilitation goal is psychological rehabilitation needs, the screening data and its corresponding weight value are input into the third fitting formula to obtain the total duration of the psychological rehabilitation needs course.

[0013] In an optional embodiment provided by the present invention, determining the rehabilitation program course corresponding to the target child based on the total duration of the rehabilitation course and the screening data and the corresponding weight value thereof includes: According to the total duration of the sports rehabilitation demand course, the screening data and the corresponding weight values ​​are input into the sports rehabilitation prediction model to obtain a sports rehabilitation plan course, wherein the duration of the sports rehabilitation plan course is the total duration of the sports rehabilitation demand course; According to the total duration of the intellectual rehabilitation required course, the screening data and the corresponding weight values ​​are input into the intellectual rehabilitation prediction model to obtain the intellectual rehabilitation plan course, where the duration of the intellectual rehabilitation plan course is the total duration of the intellectual rehabilitation required course; According to the total duration of the psychological rehabilitation demand course, the screening data and their corresponding weight values ​​are input into the psychological rehabilitation prediction model to obtain the psychological rehabilitation plan course, and the duration of the psychological rehabilitation plan course is the total duration of the psychological rehabilitation demand course.

[0014] An embodiment of the present invention provides an intelligent child rehabilitation management method based on big data, the method comprising: The data acquisition device is used to obtain the target child's basic data, medical assessment data, psychological assessment data, educational background data, family background data, rehabilitation record data, and parents' rehabilitation needs at different time periods; Analyzing the basic data, medical assessment data, psychological assessment data, educational background data, family background data, and rehabilitation record data of the target child by a data analysis device to determine the child's rehabilitation needs; Determine the rehabilitation goals for the child based on the rehabilitation needs of the child and the rehabilitation needs of the parents; The basic data, medical assessment data, psychological assessment data, educational background data, family background data, and rehabilitation record data of the target child are screened through the data screening model corresponding to the child rehabilitation target, and the weight value corresponding to each screening data is determined; wherein different child rehabilitation targets correspond to different data screening models; Inputting the screening data and its corresponding weight value into the fitting formula corresponding to the child rehabilitation goal to obtain the corresponding total duration of the rehabilitation course; The rehabilitation plan course corresponding to the target child is determined based on the total duration of the rehabilitation course, the screening data and its corresponding weight value. The rehabilitation plan course includes course content, course time, course duration and expected results, and the sum of the durations of all rehabilitation plan courses is the total duration of the rehabilitation course.

[0015] The present invention provides an intelligent child rehabilitation management system and method based on big data. First, a data analysis device is used to analyze the basic data, medical assessment data, psychological assessment data, educational background data, family background data, and rehabilitation record data of a target child to determine the child's rehabilitation needs. Then, the child's rehabilitation goals are determined according to the child's rehabilitation needs and the parents' rehabilitation needs. The basic data, medical assessment data, psychological assessment data, educational background data, family background data, and rehabilitation record data of the target child are screened through a data screening model corresponding to the child's rehabilitation goals, and the weight value corresponding to each screened data is determined; then, the screened data and the corresponding weight value are input into a fitting formula corresponding to the child's rehabilitation goal to obtain the corresponding total duration of the rehabilitation course. Finally, based on the total duration of the rehabilitation course and the screened data and the corresponding weight value, the rehabilitation plan course corresponding to the target child is determined. The rehabilitation plan course includes course content, course time, course duration, and expected effect, and the duration of all rehabilitation plan courses is the total duration of the rehabilitation course. Compared with the existing technology that completely relies on rehabilitation professionals to develop rehabilitation courses, this application independently analyzes and screens relevant data based on the acquired children's data, and then determines the rehabilitation plan curriculum based on the screened data. Therefore, this application can improve the efficiency and effectiveness of children's rehabilitation course development. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A structural diagram of an intelligent child rehabilitation management system based on big data provided in this application; Figure 2 A flowchart of an intelligent child rehabilitation management method based on big data provided in this application; Figure 3 A schematic diagram of the structure of a rehabilitation course formulation device provided in this application. DETAILED DESCRIPTION

[0017] In order to better understand the above-mentioned technical scheme, the technical scheme of the embodiments of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical scheme of the embodiments of the present application, rather than limitations on the technical scheme of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments may be combined with each other.

[0018] See also Figure 1, an intelligent child rehabilitation management system based on big data provided by an embodiment of the present invention, the system includes a data acquisition device, a data analysis device and a rehabilitation course formulation device which are sequentially connected in communication. Among them, the data acquisition device is used to acquire the basic data, medical assessment data, psychological assessment data, educational background data, family background data, rehabilitation record data, and parent rehabilitation needs of the target child in different time periods, and transmit the acquired data to the data analysis device; the data analysis device is used to analyze the basic data, medical assessment data, psychological assessment data, educational background data, family background data, and rehabilitation record data of the target child to determine the child's rehabilitation needs, and transmit the determined child's rehabilitation needs to the rehabilitation course formulation device.

[0019] Afterwards, the course formulation device determines the child's rehabilitation goal according to the child's rehabilitation needs and the parent's rehabilitation needs; the basic data, medical assessment data, psychological assessment data, educational background data, family background data, and rehabilitation record data of the target child are screened through the data screening model corresponding to the child's rehabilitation goal, and the weight value corresponding to each screening data is determined; wherein different child rehabilitation goals correspond to different data screening models; the screening data and its corresponding weight value are input into the fitting formula corresponding to the child's rehabilitation goal to obtain the corresponding total duration of the rehabilitation course; based on the total duration of the rehabilitation course and the screening data and its corresponding weight value, the rehabilitation plan course corresponding to the target child is determined, and the rehabilitation plan course includes course content, course time, course duration and expected effect, and the duration of all rehabilitation plan courses is the total duration of the rehabilitation course. Compared with the existing method of completely relying on rehabilitation professionals to formulate rehabilitation courses, this application conducts autonomous analysis and screens relevant data based on the acquired child data, and then determines the rehabilitation plan course based on the screened data, so that the efficiency and effectiveness of children's rehabilitation course formulation can be improved through this application.

[0020] Specific as Figure 2 As shown, this embodiment provides an intelligent child rehabilitation management method based on big data, which is applied to the rehabilitation course formulation device in the above-mentioned intelligent child rehabilitation management system based on big data, and the method execution steps are: S201, obtaining the target child's basic data, medical assessment data, psychological assessment data, educational background data, family background data, rehabilitation record data, and parent rehabilitation needs in different time periods through a data acquisition device.

[0021] Among them, basic data includes basic information such as the child's age, gender, weight, height, etc. These data help to understand the child's basic physiological condition and provide a basis for formulating appropriate rehabilitation courses. For example, children of different ages have differences in motor and cognitive abilities, so rehabilitation courses need to be adjusted appropriately according to age; medical assessment data include assessment reports from pediatricians and rehabilitation experts, such as detailed assessments of motor ability, language ability, cognitive ability, etc. These data are key to setting rehabilitation goals and choosing appropriate rehabilitation courses. For example, if a child has problems with motor ability, then the rehabilitation course plan needs to focus on strengthening sports training; Psychological assessment data include children's mental health status, behavioral characteristics, etc. This information is particularly important for developing behavioral correction programs. If children have psychological problems such as anxiety and depression, then the rehabilitation course plan needs to include psychological counseling and emotional support; Educational background data: Understanding children's educational background, including preschool education experience, academic performance in school, etc., can help understand children's learning ability and ability to adapt to the environment. These data are helpful in developing training plans for children's learning ability and adaptability; Collect information about children's family living environment, such as family structure, economic status, parental education methods, etc. These factors may affect children's rehabilitation process. For example, a harmonious and supportive family environment helps children better participate in rehabilitation training; Rehabilitation record data: If children have received rehabilitation training before, previous rehabilitation records and effect evaluations can serve as important references, which can help the rehabilitation team understand which of the previous rehabilitation methods are effective and which need to be improved. Parent rehabilitation needs are parents' expectations or requirements for the rehabilitation of target children. For example, parents' rehabilitation needs are to improve the motor ability of target children to the average motor level of children of the same age.

[0022] S202, analyzing the target child's basic data, medical assessment data, psychological assessment data, educational background data, family background data, and rehabilitation record data through a data analysis device to determine the child's rehabilitation needs.

[0023] Among them, children's rehabilitation needs are rehabilitation needs automatically determined based on analysis of various data of the target children, and these rehabilitation needs are more in line with the children's own conditions.

[0024] Specifically, the data analysis device analyzes the target child's basic data, medical assessment data, psychological assessment data, educational background data, family background data, and rehabilitation record data to determine the child's rehabilitation needs, including: comparing the medical assessment data and the rehabilitation record data based on the evaluation data table corresponding to the basic data to determine the probability value of motor rehabilitation needs; comparing the medical assessment data, the educational background data, and the family background data based on the evaluation data table corresponding to the basic data to determine the probability value of intellectual rehabilitation needs; comparing the medical assessment data, the psychological assessment data, and the family background data based on the evaluation data table corresponding to the basic data to determine the probability value of psychological rehabilitation needs.

[0025] In this embodiment, multiple evaluation data tables can be divided according to age, height, weight, etc., and then the various data of the target child can be compared and analyzed according to the evaluation data table to determine the probability value of the corresponding rehabilitation needs. If the target child is 7 years old, the target child can be analyzed based on the evaluation data table of the 5-10 age group, and the target child can be analyzed through the normal data range corresponding to the medical assessment data and rehabilitation record data of the children in the age group in the evaluation data table to determine the probability value of the motor rehabilitation needs. If the motor ability of the target child is significantly lower than the normal level by comparing the evaluation data table, the probability value of the motor rehabilitation needs can be calculated based on the motor ability score.

[0026] S203, determine the child's rehabilitation goals based on the child's rehabilitation needs and the parents' rehabilitation needs.

[0027] Among them, the child's rehabilitation needs are obtained by actual analysis and calculation based on various data of the target child, and the parent's rehabilitation needs are set by the parents themselves based on the rehabilitation needs of the target child. After obtaining the child's rehabilitation needs and the parent's rehabilitation needs, this embodiment can perform a weighted calculation on the two to obtain the child's rehabilitation goal, or calculate the average value between the two to obtain the child's rehabilitation goal. This embodiment does not make specific limitations on this.

[0028] Specifically, determining the child rehabilitation goal according to the child rehabilitation needs and the parent rehabilitation needs includes: calculating the child rehabilitation goal according to the probability value of the motor rehabilitation needs, the probability value of the intellectual rehabilitation needs, the probability value of the psychological rehabilitation needs, and the parent rehabilitation needs. The child rehabilitation goal may include motor rehabilitation needs, intellectual rehabilitation needs, psychological rehabilitation needs, etc.

[0029] S204, screening the basic data, medical assessment data, psychological assessment data, educational background data, family background data, and rehabilitation record data of the target children through the data screening model corresponding to the child rehabilitation target, and determining the weight value corresponding to each screening data.

[0030] It should be noted that since the target child contains multiple data, and each data plays a different role in evaluating different child rehabilitation goals, this embodiment can filter and screen the data based on the data screening model, and determine the weight value corresponding to each screening data. Among them, different child rehabilitation goals correspond to different data screening models, and the data screening model is a neural network model of an existing structure. Through the neural network model, the importance of each data in obtaining the corresponding prediction result can be obtained, that is, the weight value in this embodiment.

[0031] In an optional embodiment provided by the present application, the basic data, medical assessment data, psychological assessment data, educational background data, family background data, and rehabilitation record data of the target child are screened by the data screening model corresponding to the child rehabilitation target, and the weight value corresponding to each screening data is determined, including: S2041, converting the basic data, medical assessment data, psychological assessment data, educational background data, and family background data of the target child in each time period into a feature vector matrix.

[0032] S2042, inputting the characteristic vector matrix into the data screening model to obtain the rehabilitation prediction data of the corresponding time period; and determining the weight value of each data in the characteristic vector matrix based on the rehabilitation prediction data and the rehabilitation record data.

[0033] In an optional embodiment provided in the present application, determining the weight value of each data item in the feature vector matrix based on the rehabilitation prediction data and the rehabilitation record data includes: The weight values ​​of the data are calculated by the following formula: ; ; in, is the weight value of the j-th data in the eigenvector matrix, is the number of time periods, m is the number of data in the eigenvector matrix, is the predicted contribution value of the j-th item in the eigenvector matrix corresponding to the i-th time period in the obtained rehabilitation prediction data, is the actual contribution value of the j-th item in the eigenvector matrix corresponding to the i-th time period in the corresponding rehabilitation record data, is a constant coefficient.

[0034] In another optional embodiment provided in the present application, the weight value of each data in the feature vector matrix is ​​determined based on the rehabilitation prediction data and the rehabilitation record data, including: obtaining the contribution of the i-th data in the feature vector matrix to the obtained rehabilitation prediction data , and the mutual information between the i-th data and other data in the obtained rehabilitation prediction data ; It is used to represent the mutual dependence of the i-th data and the j-th data in the characteristic vector matrix on the obtained rehabilitation prediction data; according to the contribution and the mutual information Determine the weight value of each data in the eigenvector matrix.

[0035] Specifically, according to the contribution and the mutual information Determining the weight value of each data in the characteristic vector matrix includes: calculating the data difference between the rehabilitation prediction data and the rehabilitation record data; if the data difference is greater than a preset value, then using the formula Calculate the weight value of each data in the eigenvector matrix; if the data difference is less than the preset value, use the formula Calculate the weight value of each data in the eigenvector matrix, where: is a constant coefficient, and .

[0036] S2043, performing data screening according to the weight value of each data item in the eigenvector matrix.

[0037] S2044, determining the weight value corresponding to each item of filtered data through the weight values ​​of each item of filtered data corresponding to all time periods.

[0038] In this embodiment, after obtaining the weight values ​​of each item of filtered data corresponding to all time periods, the weight value of each item of data may be averaged to obtain the weight value corresponding to the filtered data.

[0039] S205, inputting the screened data and its corresponding weight value into the fitting formula corresponding to the child rehabilitation goal to obtain the corresponding total duration of the rehabilitation course.

[0040] Specifically, the screening data and the corresponding weight values ​​are input into the fitting formula corresponding to the child's rehabilitation goal to obtain the corresponding total duration of the rehabilitation course, including: if the child's rehabilitation goal is motor rehabilitation needs, the screening data and the corresponding weight values ​​are input into the first fitting formula to obtain the total duration of the motor rehabilitation needs course; if the child's rehabilitation goal is intellectual rehabilitation needs, the screening data and the corresponding weight values ​​are input into the second fitting formula to obtain the total duration of the intellectual rehabilitation needs course; if the child's rehabilitation goal is psychological rehabilitation needs, the screening data and the corresponding weight values ​​are input into the third fitting formula to obtain the total duration of the psychological rehabilitation needs course.

[0041] S206: Determine the rehabilitation program course corresponding to the target child based on the total duration of the rehabilitation course, the screening data and the corresponding weight value.

[0042] The rehabilitation plan course includes course content, course time, course duration and expected results, and the sum of the duration of all rehabilitation plan courses is the total duration of the rehabilitation course.

[0043] In an optional embodiment provided in the present application, the rehabilitation plan course corresponding to the target child is determined based on the total duration of the rehabilitation course and the screening data and the corresponding weight values, including: inputting the total duration of the motor rehabilitation requirement course and the screening data and the corresponding weight values ​​into the motor rehabilitation prediction model to obtain the motor rehabilitation plan course, and the duration of the motor rehabilitation plan course is the total duration of the motor rehabilitation requirement course; inputting the total duration of the intellectual rehabilitation requirement course and the screening data and the corresponding weight values ​​into the intellectual rehabilitation prediction model to obtain the intellectual rehabilitation plan course, and the duration of the intellectual rehabilitation plan course is the total duration of the intellectual rehabilitation requirement course; inputting the total duration of the psychological rehabilitation requirement course and the screening data and the corresponding weight values ​​into the psychological rehabilitation prediction model to obtain the psychological rehabilitation plan course, and the duration of the psychological rehabilitation plan course is the total duration of the psychological rehabilitation requirement course.

[0044] Among them, the sports rehabilitation prediction model, the intellectual rehabilitation prediction model and the psychological rehabilitation prediction model are trained based on a large amount of sample data and the rehabilitation course labels corresponding to the sample data. The different data used between the models is the total course duration, that is, when training the sports rehabilitation prediction model, it is necessary to use sample data containing the total duration of the rehabilitation course to complete the training.

[0045] An embodiment of the present application provides an intelligent child rehabilitation management method based on big data. First, a data analysis device is used to analyze the basic data, medical assessment data, psychological assessment data, educational background data, family background data, and rehabilitation record data of the target child to determine the child's rehabilitation needs. Then, the child's rehabilitation goals are determined according to the child's rehabilitation needs and the parents' rehabilitation needs. The basic data, medical assessment data, psychological assessment data, educational background data, family background data, and rehabilitation record data of the target child are screened through a data screening model corresponding to the child's rehabilitation goals, and the weight value corresponding to each screened data is determined; then, the screened data and its corresponding weight value are input into a fitting formula corresponding to the child's rehabilitation goal to obtain the corresponding total duration of the rehabilitation course; finally, based on the total duration of the rehabilitation course and the screened data and its corresponding weight value, the rehabilitation plan course corresponding to the target child is determined. The rehabilitation plan course includes course content, course time, course duration, and expected effect, and the duration of all rehabilitation plan courses is the total duration of the rehabilitation course. Compared with the existing technology that completely relies on rehabilitation professionals to develop rehabilitation courses, this application independently analyzes and screens relevant data based on the acquired children's data, and then determines the rehabilitation plan curriculum based on the screened data. Therefore, this application can improve the efficiency and effectiveness of children's rehabilitation course development.

[0046] It should be understood that the order of execution of the steps in the above embodiment does not necessarily 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 embodiment of the present invention.

[0047] In one embodiment, a rehabilitation course formulation device is provided, which corresponds one-to-one to the intelligent child rehabilitation management method based on big data in the above embodiment. Figure 3 As shown, the functional modules of the rehabilitation course formulation device are described in detail as follows: An acquisition module 31 is used to acquire basic data, medical assessment data, psychological assessment data, educational background data, family background data, rehabilitation record data, and parents' rehabilitation needs of target children in different time periods through a data acquisition device; A determination module 32 is used to analyze the basic data, medical assessment data, psychological assessment data, educational background data, family background data, and rehabilitation record data of the target child through a data analysis device to determine the child's rehabilitation needs; The determination module 32 is further used to determine the child's rehabilitation goal according to the child's rehabilitation needs and the parent's rehabilitation needs; The screening module 33 is used to screen the basic data, medical assessment data, psychological assessment data, educational background data, family background data, and rehabilitation record data of the target child through the data screening model corresponding to the child rehabilitation target, and determine the weight value corresponding to each screening data; wherein different child rehabilitation targets correspond to different data screening models; A fitting module 34, for inputting the screening data and the corresponding weight values ​​into a fitting formula corresponding to the child rehabilitation goal to obtain the corresponding total duration of the rehabilitation course; The determination module 32 is also used to determine the rehabilitation plan course corresponding to the target child based on the total duration of the rehabilitation course and the screening data and its corresponding weight value. The rehabilitation plan course includes course content, course time, course duration and expected results, and the sum of the durations of all rehabilitation plan courses is the total duration of the rehabilitation course.

[0048] In an optional embodiment provided by the present invention, the determination module 32 is specifically configured to: Comparing the medical assessment data and the rehabilitation record data based on the evaluation data table corresponding to the basic data to determine the probability value of belonging to the sports rehabilitation demand; Based on the evaluation data table corresponding to the basic data, the medical assessment data, the educational background data, and the family background data are compared to determine the probability value of intellectual rehabilitation needs; The medical assessment data, the psychological assessment data and the family background data are compared based on the evaluation data table corresponding to the basic data to determine the probability value of belonging to the psychological rehabilitation needs.

[0049] In an optional embodiment provided by the present invention, the determination module 32 is specifically configured to: The child rehabilitation goal is obtained by calculating the parent rehabilitation needs based on the probability value of the motor rehabilitation needs, the probability value of the intellectual rehabilitation needs, the probability value of the psychological rehabilitation needs, and the parent rehabilitation needs.

[0050] In an optional embodiment provided by the present invention, the screening module 33 is specifically used for: Convert the basic data, medical assessment data, psychological assessment data, educational background data, and family background data of the target child in each time period into a feature vector matrix; Inputting the characteristic vector matrix into the data screening model to obtain the rehabilitation prediction data of the corresponding time period; and determining the weight value of each data in the characteristic vector matrix based on the rehabilitation prediction data and the rehabilitation record data; Performing data screening according to the weight value of each data in the eigenvector matrix; The weight values ​​corresponding to each item of filtered data are determined by the weight values ​​of each item of filtered data corresponding to all time periods.

[0051] In an optional embodiment provided by the present invention, the determination module 32 is specifically configured to calculate the weight value of each data item by the following formula: ; ; in, is the weight value of the j-th data in the eigenvector matrix, is the number of time periods, m is the number of data in the eigenvector matrix, is the predicted contribution value of the j-th item in the eigenvector matrix corresponding to the i-th time period in the obtained rehabilitation prediction data, is the actual contribution value of the j-th item in the eigenvector matrix corresponding to the i-th time period in the corresponding rehabilitation record data, is a constant coefficient.

[0052] In an optional embodiment provided by the present invention, the determination module 32 is specifically configured to: Obtain the contribution of the i-th data in the characteristic vector matrix to the obtained rehabilitation prediction data , and the mutual information between the i-th data and other data in the obtained rehabilitation prediction data ; Used to represent the mutual dependence of the i-th data and the j-th data in the characteristic vector matrix on the obtained rehabilitation prediction data; According to the contribution and the mutual information Determine the weight value of each data in the eigenvector matrix.

[0053] In an optional embodiment provided by the present invention, the determination module 32 is specifically configured to: Calculating a data difference between the rehabilitation prediction data and the rehabilitation record data; If the data difference is greater than the preset value, the formula Calculate the weight value of each data in the eigenvector matrix; If the data difference is less than the preset value, the formula Calculate the weight value of each data in the eigenvector matrix, where: is a constant coefficient, and .

[0054] In an optional embodiment provided by the present invention, the fitting module 34 is specifically used for: If the child rehabilitation goal is sports rehabilitation demand, the screening data and its corresponding weight value are input into the first fitting formula to obtain the total duration of the sports rehabilitation demand course; If the child rehabilitation goal is intellectual rehabilitation needs, the screening data and its corresponding weight value are input into the second fitting formula to obtain the total duration of the intellectual rehabilitation needs course; If the child rehabilitation goal is psychological rehabilitation needs, the screening data and its corresponding weight value are input into the third fitting formula to obtain the total duration of the psychological rehabilitation needs course.

[0055] In an optional embodiment provided by the present invention, the determination module 32 is specifically configured to: According to the total duration of the sports rehabilitation demand course, the screening data and the corresponding weight values ​​are input into the sports rehabilitation prediction model to obtain a sports rehabilitation plan course, wherein the duration of the sports rehabilitation plan course is the total duration of the sports rehabilitation demand course; According to the total duration of the intellectual rehabilitation required course, the screening data and the corresponding weight values ​​are input into the intellectual rehabilitation prediction model to obtain the intellectual rehabilitation plan course, where the duration of the intellectual rehabilitation plan course is the total duration of the intellectual rehabilitation required course; According to the total duration of the psychological rehabilitation demand course, the screening data and their corresponding weight values ​​are input into the psychological rehabilitation prediction model to obtain the psychological rehabilitation plan course, and the duration of the psychological rehabilitation plan course is the total duration of the psychological rehabilitation demand course.

[0056] For the specific limitations of the rehabilitation course formulation device, please refer to the limitations of the intelligent child rehabilitation management method based on big data above, which will not be repeated here. Each module in the above-mentioned device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0057] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0058] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. An intelligent child rehabilitation management system based on big data, characterized in that: The system comprises a data acquisition device, a data analysis device and a rehabilitation course formulation device which are sequentially connected in communication, and the rehabilitation course formulation device is used to perform the following method: The data acquisition device is used to acquire the target child's basic data, medical assessment data, psychological assessment data, educational background data, family background data, rehabilitation record data, and parents' rehabilitation needs in different time periods; Analyzing the basic data, medical assessment data, psychological assessment data, educational background data, family background data, and rehabilitation record data of the target child by the data analysis device to determine the child's rehabilitation needs; Determine the rehabilitation goals for the child based on the rehabilitation needs of the child and the rehabilitation needs of the parents; The basic data, medical assessment data, psychological assessment data, educational background data, family background data, and rehabilitation record data of the target child are screened through the data screening model corresponding to the child rehabilitation target, and the weight value corresponding to each screening data is determined; wherein different child rehabilitation targets correspond to different data screening models; Inputting the screening data and its corresponding weight value into the fitting formula corresponding to the child rehabilitation goal to obtain the corresponding total duration of the rehabilitation course; The rehabilitation plan course corresponding to the target child is determined based on the total duration of the rehabilitation course, the screening data and its corresponding weight value. The rehabilitation plan course includes course content, course time, course duration and expected results, and the sum of the durations of all rehabilitation plan courses is the total duration of the rehabilitation course.

2. The system according to claim 1, characterized in that The data analysis device analyzes the basic data, medical assessment data, psychological assessment data, educational background data, family background data, and rehabilitation record data of the target child to determine the child's rehabilitation needs, including: Comparing the medical assessment data and the rehabilitation record data based on the evaluation data table corresponding to the basic data to determine the probability value of belonging to the sports rehabilitation demand; Based on the evaluation data table corresponding to the basic data, the medical assessment data, the educational background data, and the family background data are compared to determine the probability value of intellectual rehabilitation needs; The medical assessment data, the psychological assessment data and the family background data are compared based on the evaluation data table corresponding to the basic data to determine the probability value of belonging to the psychological rehabilitation needs.

3. The system according to claim 2, characterized in that Determining the child's rehabilitation goals based on the child's rehabilitation needs and the parent's rehabilitation needs includes: The child rehabilitation goal is obtained by calculating the parent rehabilitation needs based on the probability value of the motor rehabilitation needs, the probability value of the intellectual rehabilitation needs, the probability value of the psychological rehabilitation needs, and the parent rehabilitation needs.

4. The system according to claim 1, characterized in that The data screening model corresponding to the child rehabilitation target is used to screen the basic data, medical assessment data, psychological assessment data, educational background data, family background data, and rehabilitation record data of the target child, and determine the weight value corresponding to each screening data, including: Convert the basic data, medical assessment data, psychological assessment data, educational background data, and family background data of the target child in each time period into a feature vector matrix; Inputting the characteristic vector matrix into the data screening model to obtain the rehabilitation prediction data of the corresponding time period; and determining the weight value of each data in the characteristic vector matrix based on the rehabilitation prediction data and the rehabilitation record data; Performing data screening according to the weight value of each data in the eigenvector matrix; The weight values ​​corresponding to each item of filtered data are determined by the weight values ​​of each item of filtered data corresponding to all time periods.

5. The system according to claim 4, characterized in that The step of determining the weight value of each item of data in the feature vector matrix based on the rehabilitation prediction data and the rehabilitation record data comprises: Calculate the weight value of each item of data.

6. The system according to claim 4, characterized in that The step of determining the weight value of each item of data in the feature vector matrix based on the rehabilitation prediction data and the rehabilitation record data comprises: Obtain the contribution of the i-th data in the characteristic vector matrix to the obtained rehabilitation prediction data , and the mutual information between the i-th data and other data in the obtained rehabilitation prediction data ; Used to represent the mutual dependence of the i-th data and the j-th data in the characteristic vector matrix on the obtained rehabilitation prediction data; According to the contribution and the mutual information Determine the weight value of each data in the eigenvector matrix.

7. The system according to claim 6, characterized in that According to the contribution and the mutual information Determining the weight value of each data in the eigenvector matrix includes: Calculating a data difference between the rehabilitation prediction data and the rehabilitation record data; If the data difference is greater than a preset value, the weight value of each data in the eigenvector matrix is ​​calculated; If the data difference is less than a preset value, the weight value of each data in the eigenvector matrix is ​​calculated.

8. The system according to any one of claims 2 to 7, characterized in that: The step of inputting the screening data and the corresponding weight values ​​into the fitting formula corresponding to the child rehabilitation goal to obtain the corresponding total duration of the rehabilitation course includes: If the child rehabilitation goal is sports rehabilitation demand, the screening data and its corresponding weight value are input into the first fitting formula to obtain the total duration of the sports rehabilitation demand course; If the child rehabilitation goal is intellectual rehabilitation needs, the screening data and its corresponding weight value are input into the second fitting formula to obtain the total duration of the intellectual rehabilitation needs course; If the child rehabilitation goal is psychological rehabilitation needs, the screening data and its corresponding weight value are input into the third fitting formula to obtain the total duration of the psychological rehabilitation needs course.

9. The system according to claim 8, characterized in that The step of determining the rehabilitation course corresponding to the target child based on the total duration of the rehabilitation course and the screening data and the corresponding weight values ​​thereof includes: According to the total duration of the sports rehabilitation demand course, the screening data and the corresponding weight values ​​are input into the sports rehabilitation prediction model to obtain a sports rehabilitation plan course, wherein the duration of the sports rehabilitation plan course is the total duration of the sports rehabilitation demand course; According to the total duration of the intellectual rehabilitation required course, the screening data and the corresponding weight values ​​are input into the intellectual rehabilitation prediction model to obtain the intellectual rehabilitation plan course, where the duration of the intellectual rehabilitation plan course is the total duration of the intellectual rehabilitation required course; According to the total duration of the psychological rehabilitation demand course, the screening data and their corresponding weight values ​​are input into the psychological rehabilitation prediction model to obtain the psychological rehabilitation plan course, and the duration of the psychological rehabilitation plan course is the total duration of the psychological rehabilitation demand course.

10. An intelligent child rehabilitation management method based on big data, characterized in that: The method comprises: The data acquisition device is used to obtain the target child's basic data, medical assessment data, psychological assessment data, educational background data, family background data, rehabilitation record data, and parents' rehabilitation needs at different time periods; Analyzing the basic data, medical assessment data, psychological assessment data, educational background data, family background data, and rehabilitation record data of the target child by a data analysis device to determine the child's rehabilitation needs; Determine the rehabilitation goals for the child based on the rehabilitation needs of the child and the rehabilitation needs of the parents; The basic data, medical assessment data, psychological assessment data, educational background data, family background data, and rehabilitation record data of the target child are screened through the data screening model corresponding to the child rehabilitation target, and the weight value corresponding to each screening data is determined; wherein different child rehabilitation targets correspond to different data screening models; Inputting the screening data and its corresponding weight value into the fitting formula corresponding to the child rehabilitation goal to obtain the corresponding total duration of the rehabilitation course; The rehabilitation plan course corresponding to the target child is determined based on the total duration of the rehabilitation course, the screening data and its corresponding weight value. The rehabilitation plan course includes course content, course time, course duration and expected results, and the sum of the durations of all rehabilitation plan courses is the total duration of the rehabilitation course.

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