Rehabilitation information cloud sharing method and system

Abstract

The present application provides a rehabilitation information cloud sharing method and system. The method obtains the original rehabilitation information of the target object through a biosensor cluster, sends the original rehabilitation information to a rehabilitation information cloud platform, configures the access right of the original rehabilitation information as the first permission in the rehabilitation information cloud platform, and sends an information processing instruction to at least one processing terminal in the processing terminal cluster, so that the first processing terminal in the processing terminal cluster responds to the information processing instruction, processes the original rehabilitation information, and sends the processed rehabilitation information corresponding to the original rehabilitation information to the rehabilitation information cloud platform, thereby enabling the rehabilitation information cloud platform to configure the access right of the original rehabilitation information and the processed rehabilitation information as the second permission, thereby improving the accuracy and availability of the information disclosed in the rehabilitation information cloud platform, realizing the accurate sharing of rehabilitation information, and facilitating patients and medical personnel to understand the rehabilitation progress in a timely manner.

Description

Technical Field

[0001] The present application relates to data processing technology, and in particular to a rehabilitation information cloud sharing method and system. Background Art

[0002] In the current field of rehabilitation medicine, with the continuous advancement of medical technology and the growing demand for rehabilitation of patients, comprehensive, real-time and accurate management of rehabilitation information is particularly important. Traditional rehabilitation information management methods mainly rely on manual records and paper files, which have problems such as untimely information acquisition, low data processing efficiency, and difficulty in information sharing.

[0003] In recent years, with the rapid development of cloud computing, big data, the Internet of Things and other technologies, new solutions have been provided for rehabilitation information management. By building a rehabilitation information cloud platform, centralized storage, intelligent processing and flexible sharing of rehabilitation information can be achieved, greatly improving the efficiency and security of rehabilitation information management.

[0004] However, in the management of the existing rehabilitation information cloud platform, after uploading the collected raw data to the cloud platform, patients can directly access the data by accessing the cloud platform, but patients are often unable to judge the accuracy of the raw data, and may make misjudgments due to inaccurate raw data. Summary of the invention

[0005] The present application provides a rehabilitation information cloud sharing method and system to improve the accuracy and availability of disclosed information, realize accurate sharing of rehabilitation information, and facilitate patients and medical personnel to understand rehabilitation progress in a timely manner.

[0006] In a first aspect, the present application provides a rehabilitation information cloud sharing method, which is applied to a rehabilitation information management system, wherein the rehabilitation information management system includes: a biosensor cluster, a user terminal cluster, a processing terminal cluster, and a rehabilitation information cloud platform, wherein each biosensor in the biosensor cluster is communicatively connected to the rehabilitation information cloud platform, each user terminal in the user terminal cluster is communicatively connected to the rehabilitation information cloud platform, and each processing terminal in the processing terminal cluster is communicatively connected to the rehabilitation information cloud platform; the method includes:

[0007] Acquire original rehabilitation information of the target object through the biosensor cluster, and send the original rehabilitation information to the rehabilitation information cloud platform, wherein the rehabilitation information includes target identity information and multiple physiological index data of the target object;

[0008] The rehabilitation information cloud platform configures the access permission of the original rehabilitation information as a first permission, and sends an information processing instruction to at least one processing terminal in the processing terminal cluster, wherein the first permission is used to restrict access by all user terminals in the user terminal cluster;

[0009] The first processing terminal in the processing terminal cluster processes the original rehabilitation information in response to the information processing instruction, and sends processed rehabilitation information corresponding to the original rehabilitation information to the rehabilitation information cloud platform;

[0010] The rehabilitation information cloud platform configures the access rights of the original rehabilitation information and the processed rehabilitation information as second permissions, and the second permissions are used to allow the target user terminal corresponding to the target object to access.

[0011] In the above scheme, by constructing a rehabilitation information management system including a biosensor cluster, a user terminal cluster, a processing terminal cluster and a rehabilitation information cloud platform, comprehensive collection, secure processing and accurate sharing of rehabilitation information are achieved. Specifically, the biosensor cluster can be used to obtain the original rehabilitation information of the target object in real time, including the target identity information and multiple physiological index data, to ensure the comprehensiveness and real-time nature of the data. Then, the original rehabilitation information is first configured as the first permission, restricting access to all user terminals except the processing terminal, effectively protecting personal privacy, data security and avoiding the early disclosure of information to be confirmed. After the original rehabilitation information is processed by the processing terminal cluster, processed rehabilitation information is generated, which improves the accuracy and availability of the disclosed information. The processed rehabilitation information is then configured as the second permission, allowing the target user terminal to access, realizing the accurate sharing of rehabilitation information, and facilitating patients and medical personnel to understand the rehabilitation progress in a timely manner.

[0012] Optionally, before the rehabilitation information cloud platform configures the access permission of the original rehabilitation information as the first permission, the method further includes:

[0013] The rehabilitation information cloud platform determines that the first physiological indicator data in the original rehabilitation information is outside the corresponding preset physiological indicator value range, and the first physiological indicator data is one of the physiological indicators in the original rehabilitation information; and / or,

[0014] The rehabilitation information cloud platform obtains the historical rehabilitation information of the target object in the rehabilitation information cloud platform according to the target identity information to form a historical first physiological indicator data sequence, wherein the historical first physiological indicator data sequence is a data sequence in which the historical first physiological indicator data in the historical rehabilitation information are arranged in chronological order from earliest to latest;

[0015] The rehabilitation information cloud platform determines, based on the historical first physiological indicator data sequence, that the first physiological indicator data in the original rehabilitation information is the physiological indicator to be confirmed.

[0016] In the above scheme, by comparing the first physiological indicator data in the original rehabilitation information with the preset physiological indicator value range, possible abnormal data is identified, thereby enhancing the accuracy and reliability of data processing. The historical rehabilitation information of the target object in the rehabilitation information cloud platform is used to form a historical first physiological indicator data sequence, which provides a basis for the prediction and confirmation of subsequent data. By comparing historical data with current data, the physiological indicators to be confirmed are determined, thereby providing a basis for further verification, correction and processing of the data.

[0017] Optionally, the rehabilitation information cloud platform determines, according to the historical first physiological indicator data sequence, the first physiological indicator data in the original rehabilitation information as the physiological indicator to be confirmed, including:

[0018] The rehabilitation information cloud platform determines predicted first physiological indicator data according to the historical first physiological indicator data sequence;

[0019] The rehabilitation information cloud platform determines a first physiological indicator data deviation value according to the predicted first physiological indicator data and the first physiological indicator data;

[0020] If the deviation value of the first physiological indicator data is greater than a preset deviation threshold, the first physiological indicator data is determined to be the physiological indicator to be confirmed.

[0021] In the above scheme, based on the historical first physiological indicator data sequence, the physiological indicator data at the next time point is predicted through a mathematical model, which improves the scientificity and predictability of data processing. In addition, by comparing the predicted data with the measured data, the deviation value is calculated, which provides a quantitative standard for judging whether the data is abnormal. Then, a preset deviation threshold is set. When the deviation value exceeds the threshold, it is judged as a physiological indicator to be confirmed, which improves the accuracy and operability of abnormal judgment.

[0022] Optionally, the historical rehabilitation information only includes rehabilitation information whose access permission in the rehabilitation information cloud platform is the second permission.

[0023] In the above scheme, the scope of historical rehabilitation information is clarified, that is, only rehabilitation information with the second level of access rights in the rehabilitation information cloud platform is included. This limitation helps to ensure the accuracy and relevance of historical data, and only accurate data is used for subsequent predictions, thus providing a more reliable basis for subsequent data analysis and processing.

[0024] Optionally, the rehabilitation information cloud platform determines the predicted first physiological indicator data according to the historical first physiological indicator data sequence, including:

[0025] The rehabilitation information cloud platform determines the predicted first physiological indicator data based on the historical first physiological indicator data sequence, the first characteristic order and the second characteristic order. The first characteristic order is used to determine the number of historical first physiological indicator data in the first physiological indicator data sequence that participate in determining the predicted first physiological indicator data. The second characteristic order is used to determine the number of physiological indicator data deviation characteristic values ​​in the first physiological indicator data sequence that participate in determining the predicted first physiological indicator data.

[0026] In the above scheme, by introducing two parameters, the first characteristic order and the second characteristic order, the calculation process of predicting the first physiological indicator data is made more refined. The two characteristic orders are used to determine the number of historical first physiological indicator data involved in the prediction calculation and the number of physiological indicator data deviation characteristic values, so as to more accurately reflect the change trend of the target object's physiological indicators. In addition, the setting of the first characteristic order and the second characteristic order enables the prediction model to be flexibly adjusted according to the rehabilitation status and physiological indicator change characteristics of different target objects.

[0027] Furthermore, by adjusting these two parameters, the performance of the prediction model can be optimized to make it more adaptable to the data prediction needs of different rehabilitation stages and rehabilitation needs. This enables the platform to more effectively process and analyze a large amount of rehabilitation information data, improving the efficiency and accuracy of data processing. In addition, in the field of rehabilitation medicine, the physiological index data of patients often show complex trends. By introducing the concept of characteristic order, it provides strong support for processing such complex data. Through refined data processing and analysis, it can more accurately reflect the patient's rehabilitation progress and physiological state changes.

[0028] Optionally, the rehabilitation information cloud platform determines the predicted first physiological indicator data according to the historical first physiological indicator data sequence, including:

[0029] The rehabilitation information cloud platform uses formula 1 and according to the historical first physiological indicator data sequence Determining the predicted first physiological indicator data , the formula 1 is:

[0030]

[0031] in, is the first preset constant term, is the first characteristic order, is the second characteristic order, For the The characteristic parameters of the indicators, For the Deviation characteristic parameters.

[0032] In the above scheme, Formula 1 provides a clear quantitative method for predicting the calculation of the first physiological indicator data. Through this formula, the rehabilitation information cloud platform can accurately calculate the predicted value based on the historical first physiological indicator data sequence, the first characteristic order, the second characteristic order, and the characteristic parameters and deviation characteristic parameters of each indicator. This quantitative method reduces human intervention and subjective judgment, and improves the objectivity and accuracy of the prediction results. In addition, the various parameters in Formula 1 (such as the first preset constant term, the first characteristic order, the second characteristic order, the indicator characteristic parameters and the deviation characteristic parameters) can ensure that the prediction model can accurately reflect the changing trend of the target object's physiological indicators by comprehensively considering multiple factors, thereby generating a prediction value that is closer to reality, and improving the accuracy and reliability of the prediction.

[0033] In addition, since the patient's physiological index data is often affected by multiple factors and presents a complex trend of change, the introduction of multiple parameters and characteristic values ​​enables the prediction model to better adapt to this complex situation. Whether it is the periodic changes or sudden fluctuations of physiological indicators, Formula 1 can provide relatively accurate prediction results and provide strong decision-making support for medical personnel.

[0034] Optionally, before determining the predicted first physiological indicator data according to the historical first physiological indicator data sequence, the method further includes:

[0035] The rehabilitation information cloud platform determines the first characteristic order and the second characteristic order according to the historical first physiological indicator data sequence and a preset characteristic function.

[0036] In the above scheme, by determining the first characteristic order and the second characteristic order according to the historical first physiological indicator data sequence and the preset characteristic function, the prediction model can more flexibly adapt to the characteristics of the physiological indicator data of the patient at different stages. This method of dynamically adjusting parameters helps to improve the adaptability and accuracy of the prediction model in different situations. Among them, the first characteristic order and the second characteristic order are key parameters that affect the accuracy of predicting the first physiological indicator data.

[0037] In addition, in the process of building a prediction model, determining the appropriate feature order is an important step. By clarifying this step and giving a specific implementation method, the data processing process is optimized. This not only improves the efficiency of data processing, but also ensures the accuracy and scientificity of data processing. Moreover, at different stages of patient recovery, there may be different physiological indicator data characteristics, so different feature order configurations are required, so that the data basis for prediction can be more reliable.

[0038] Optionally, in the first physiological indicator data sequence according to the historical Determining the predicted first physiological indicator data Previously, it also included:

[0039] The rehabilitation information cloud platform uses formula 2 and according to the historical first physiological indicator data sequence Determine the first characteristic order And the second characteristic order , the formula 2 is:

[0040]

[0041] in, is the second preset constant term, The first historical physiological indicator data sequence The preset characteristic function The maximum eigenvalue of .

[0042] In the above scheme, Formula 2 provides an accurate quantitative method for determining the first feature order and the second feature order. These feature orders have an important impact on the performance of the prediction model because they determine how much historical data and what type of deviation features should be considered in the prediction process. Through Formula 2, the optimal feature order can be objectively calculated based on the historical first physiological indicator data sequence and its characteristic function, reducing the subjectivity and uncertainty of artificial settings. In addition, choosing the right feature order is the key to building a stable prediction model. If the feature order is too high, it may lead to overfitting of the model, that is, the model performs well on the training data, but has poor generalization ability on new data; if the feature order is too low, it may not be able to fully capture the complex changes of the data. Formula 2 determines the feature order by a scientific method, which helps to build a stable and reliable prediction model. By accurately determining the feature order, Formula 2 enables the prediction model to more accurately reflect the trend of changes in the physiological indicators of the target object. This optimization not only improves the accuracy of the prediction, but also makes the prediction results more in line with the actual situation, providing medical personnel with a more reliable basis for decision-making.

[0043] Optionally, in the first physiological indicator data sequence according to the historical Before determining the first characteristic order and the second characteristic order, the method further includes:

[0044] The rehabilitation information cloud platform uses a preset iterative algorithm to calculate the preset characteristic function according to the historical first physiological indicator data sequence. Parameter estimation iterations are performed to determine a maximum eigenvalue, where the maximum eigenvalue is used to determine the first eigenorder and the second eigenorder.

[0045] In the above scheme, the preset feature function is iterated by the preset iterative algorithm to estimate the parameters, which can gradually approach the optimal parameter estimation value, that is, the maximum feature estimation value. This process reduces the error that may be caused by directly setting or selecting parameters based on experience, and improves the accuracy and reliability of parameter estimation. The first feature order and the second feature order are determined based on the accurate maximum feature estimation value, so that the complexity and variability of the data can be fully considered during the construction of the prediction model. This robust model construction method helps to improve the adaptability and generalization ability of the prediction model in practical applications. In addition, through iterative calculation, the selection of feature order can be gradually optimized, thereby improving the efficiency and accuracy of the entire data processing and analysis process. The application of iterative algorithms makes the construction of the prediction model a process of continuous improvement and optimization. With the continuous addition of new data and the accumulation of historical data, the value of the feature order can be updated by re-iterative calculation to further improve the accuracy and stability of the prediction model.

[0046] Optionally, in the first physiological indicator data sequence according to the historical Determine the first characteristic order And the second characteristic order Previously, it also included:

[0047] The rehabilitation information cloud platform uses the historical first physiological indicator data sequence and The preset characteristic function Perform parameter estimation iterations to determine the maximum eigenvalue estimate , the formula 3 is:

[0048]

[0049] in, For the The parameter estimates at iteration , For the The parameter estimates at iteration , For the The parameter estimates at iteration The inverse matrix of the Hessian matrix at , For the The parameter estimates at iteration The characteristic function at The first derivative of .

[0050] In the above scheme, Formula 3 gradually approaches the true parameter value, that is, the maximum eigenvalue, by continuously iteratively updating the parameter estimate. This method reduces the subjectivity and uncertainty of parameter estimation and improves the accuracy and reliability of the estimation. In addition, the iterative process in Formula 3 utilizes the inverse matrix of the Hessian matrix and the first-order derivative information of the eigenfunction, which helps to speed up the convergence speed of the iterative algorithm. Faster convergence speed means that accurate parameter estimates can be obtained faster under limited computing resources, which improves computational efficiency. In addition, during the iteration process, by considering the inverse matrix of the Hessian matrix (characterizing the local curvature of the objective function) at the parameter estimate, Formula 3 can better handle the nonlinear characteristics and multi-extreme value problems of the objective function. This processing method enhances the stability of the algorithm and reduces the risk of falling into a local optimal solution. In addition, based on the parameters accurately estimated by Formula 3, the first characteristic order and the second characteristic order can be more accurately determined, thereby optimizing the performance of the prediction model. This optimization not only improves the accuracy of the prediction, but also makes the prediction model more robust and reliable.

[0051] In a second aspect, the present application provides a rehabilitation information management system, including: a biosensor cluster, a user terminal cluster, a processing terminal cluster and a rehabilitation information cloud platform, wherein each biosensor in the biosensor cluster is communicatively connected to the rehabilitation information cloud platform, each user terminal in the user terminal cluster is respectively communicatively connected to the rehabilitation information cloud platform, and each processing terminal in the processing terminal cluster is respectively communicatively connected to the rehabilitation information cloud platform;

[0052] Acquire original rehabilitation information of the target object through the biosensor cluster, and send the original rehabilitation information to the rehabilitation information cloud platform, wherein the rehabilitation information includes target identity information and multiple physiological index data of the target object;

[0053] The rehabilitation information cloud platform configures the access permission of the original rehabilitation information as a first permission, and sends an information processing instruction to at least one processing terminal in the processing terminal cluster, wherein the first permission is used to restrict access by all user terminals in the user terminal cluster;

[0054] The first processing terminal in the processing terminal cluster processes the original rehabilitation information in response to the information processing instruction, and sends processed rehabilitation information corresponding to the original rehabilitation information to the rehabilitation information cloud platform;

[0055] The rehabilitation information cloud platform configures the access rights of the original rehabilitation information and the processed rehabilitation information as second permissions, and the second permissions are used to allow the target user terminal corresponding to the target object to access.

[0056] Optionally, before the rehabilitation information cloud platform configures the access permission of the original rehabilitation information as the first permission, the method further includes:

[0057] The rehabilitation information cloud platform determines that the first physiological indicator data in the original rehabilitation information is outside the corresponding preset physiological indicator value range, and the first physiological indicator data is one of the physiological indicators in the original rehabilitation information; and / or,

[0058] The rehabilitation information cloud platform obtains the historical rehabilitation information of the target object in the rehabilitation information cloud platform according to the target identity information to form a historical first physiological indicator data sequence, wherein the historical first physiological indicator data sequence is a data sequence in which the historical first physiological indicator data in the historical rehabilitation information are arranged in chronological order from earliest to latest;

[0059] The rehabilitation information cloud platform determines, based on the historical first physiological indicator data sequence, that the first physiological indicator data in the original rehabilitation information is the physiological indicator to be confirmed.

[0060] Optionally, the rehabilitation information cloud platform determines, according to the historical first physiological indicator data sequence, the first physiological indicator data in the original rehabilitation information as the physiological indicator to be confirmed, including:

[0061] The rehabilitation information cloud platform determines predicted first physiological indicator data according to the historical first physiological indicator data sequence;

[0062] The rehabilitation information cloud platform determines a first physiological indicator data deviation value according to the predicted first physiological indicator data and the first physiological indicator data;

[0063] If the deviation value of the first physiological indicator data is greater than a preset deviation threshold, the first physiological indicator data is determined to be the physiological indicator to be confirmed.

[0064] Optionally, the historical rehabilitation information only includes rehabilitation information whose access permission in the rehabilitation information cloud platform is the second permission.

[0065] Optionally, the rehabilitation information cloud platform determines the predicted first physiological indicator data according to the historical first physiological indicator data sequence, including:

[0066] The rehabilitation information cloud platform determines the predicted first physiological indicator data based on the historical first physiological indicator data sequence, the first characteristic order and the second characteristic order. The first characteristic order is used to determine the number of historical first physiological indicator data in the first physiological indicator data sequence that participate in determining the predicted first physiological indicator data. The second characteristic order is used to determine the number of physiological indicator data deviation characteristic values ​​in the first physiological indicator data sequence that participate in determining the predicted first physiological indicator data.

[0067] Optionally, the rehabilitation information cloud platform determines the predicted first physiological indicator data according to the historical first physiological indicator data sequence, including:

[0068] The rehabilitation information cloud platform uses formula 1 and according to the historical first physiological indicator data sequence Determining the predicted first physiological indicator data , the formula 1 is:

[0069]

[0070] in, is the first preset constant term, is the first characteristic order, is the second characteristic order, For the The characteristic parameters of the indicators, For the Deviation characteristic parameters.

[0071] Optionally, before determining the predicted first physiological indicator data according to the historical first physiological indicator data sequence, the method further includes:

[0072] The rehabilitation information cloud platform determines the first characteristic order and the second characteristic order according to the historical first physiological indicator data sequence and a preset characteristic function.

[0073] Optionally, in the first physiological indicator data sequence according to the historical Determining the predicted first physiological indicator data Previously, it also included:

[0074] The rehabilitation information cloud platform uses formula 2 and according to the historical first physiological indicator data sequence Determine the first characteristic order And the second characteristic order , the formula 2 is:

[0075]

[0076] in, is the second preset constant term, The first historical physiological indicator data sequence The preset characteristic function The maximum eigenvalue of .

[0077] Optionally, in the first physiological indicator data sequence according to the historical Before determining the first characteristic order and the second characteristic order, the method further includes:

[0078] The rehabilitation information cloud platform uses a preset iterative algorithm to calculate the preset characteristic function according to the historical first physiological indicator data sequence. Parameter estimation iterations are performed to determine a maximum eigenvalue, where the maximum eigenvalue is used to determine the first eigenorder and the second eigenorder.

[0079] Optionally, in the first physiological indicator data sequence according to the historical Determine the first characteristic order And the second characteristic order Previously, it also included:

[0080] The rehabilitation information cloud platform uses the historical first physiological indicator data sequence and The preset characteristic function Perform parameter estimation iterations to determine the maximum eigenvalue estimate , the formula 3 is:

[0081]

[0082] in, For the The parameter estimates at iteration , For the The parameter estimates at iteration , For the The parameter estimates at iteration The inverse matrix of the Hessian matrix at , For the The parameter estimates at iteration The characteristic function at The first derivative of .

[0083] In a third aspect, the present application provides an electronic device, including:

[0084] processor; and,

[0085] A memory, configured to store executable instructions of the processor;

[0086] The processor is configured to perform any possible method described in the first aspect by executing the executable instructions.

[0087] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement any possible method described in the first aspect.

[0088] The rehabilitation information cloud sharing method and system provided in the present application obtain the original rehabilitation information of the target object through a biosensor cluster, and send the original rehabilitation information to a rehabilitation information cloud platform. In the rehabilitation information cloud platform, the access right of the original rehabilitation information is configured as the first permission, and an information processing instruction is sent to at least one processing terminal in the processing terminal cluster, so that the first processing terminal in the processing terminal cluster responds to the information processing instruction, processes the original rehabilitation information, and sends the processed rehabilitation information corresponding to the original rehabilitation information to the rehabilitation information cloud platform, so that the rehabilitation information cloud platform configures the access right of the original rehabilitation information and the processed rehabilitation information as the second permission, thereby improving the accuracy and availability of the information disclosed in the rehabilitation information cloud platform, realizing the accurate sharing of rehabilitation information, and facilitating patients and medical personnel to understand the rehabilitation progress in a timely manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0090] Figure 1 It is a flowchart of a rehabilitation information cloud sharing method according to an example embodiment of the present application;

[0091] Figure 2 is a flowchart of a rehabilitation information cloud sharing method according to another exemplary embodiment of the present application;

[0092] Figure 3 This is a schematic diagram of the structure of a rehabilitation information management system according to an example embodiment of the present application;

[0093] Figure 4 It is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present application.

[0094] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0095] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0096] In order to solve the above problems, the embodiments provided in this application use core components such as a biosensor cluster, a user terminal cluster, a processing terminal cluster, and a rehabilitation information cloud platform to build a complete rehabilitation information management system.

[0097] The original rehabilitation information of patients, including target identity information and multiple physiological index data, is collected through the biosensor cluster to ensure the comprehensiveness and timeliness of the data. In addition, the collected original rehabilitation information is sent to the rehabilitation information cloud platform for unified storage and management, laying the foundation for subsequent data processing and analysis.

[0098] After the data is initially uploaded to the rehabilitation information cloud platform, the platform first configures the access rights of the original rehabilitation information as the first permission, which strictly limits access by all user terminals in the user terminal cluster to ensure data security and avoid early disclosure of information to be confirmed. After the original rehabilitation information is processed by the processing terminal cluster, the rehabilitation information cloud platform configures the access rights of the original rehabilitation information and its processing results as the second permission, allowing the target user terminal corresponding to the target object to access. This hierarchical access control mechanism not only protects patient privacy, but also meets the information needs of different user groups, and can also convey the most accurate information to users.

[0099] In addition, the processing terminal cluster in the system receives information processing instructions from the rehabilitation information cloud platform and performs professional processing on the original rehabilitation information, such as data cleaning and standardization, to improve the availability and accuracy of the data.

[0100] In addition, before configuring access rights, the rehabilitation information cloud platform will perform anomaly detection, such as determining whether the physiological indicator data is within the preset range, or whether there is a significant difference compared with historical data. This helps to detect potential health problems in a timely manner and provide early warnings for medical personnel. Based on historical rehabilitation information and preset characteristic functions, the rehabilitation information cloud platform can use mathematical models to predict future physiological indicator data. This predictive analysis function can also provide a scientific basis for medical personnel to formulate rehabilitation plans.

[0101] In order to further improve the accuracy of prediction, the rehabilitation information cloud platform estimates and iterates the parameters of the preset characteristic function through a preset iterative algorithm to determine the optimal characteristic order and other key parameters. This optimization mechanism enables the prediction model to better adapt to the physiological characteristics of patients at different stages of rehabilitation.

[0102] Based on the patient's historical data and predicted results, the rehabilitation information cloud platform can also provide users with personalized rehabilitation advice and services. This service model not only improves the rehabilitation effect, but also enhances user satisfaction and compliance.

[0103] To sum up, the concept of this application is to build an efficient, safe and intelligent rehabilitation information cloud sharing platform to achieve comprehensive collection, integration, processing, prediction and analysis of patient rehabilitation information, so as to provide patients with more personalized and accurate rehabilitation services.

[0104] Figure 1 FIG. 1 is a flow chart of a rehabilitation information cloud sharing method according to an exemplary embodiment of the present application. Figure 1 As shown, the rehabilitation information cloud sharing method provided in this embodiment includes:

[0105] S101. Acquire original rehabilitation information of a target object through a biosensor cluster, and send the original rehabilitation information to a rehabilitation information cloud platform.

[0106] The rehabilitation information cloud sharing method provided in this embodiment can be applied to a rehabilitation information management system. The rehabilitation information management system includes: a biosensor cluster, a user terminal cluster, a processing terminal cluster and a rehabilitation information cloud platform. Each biosensor in the biosensor cluster is connected to the rehabilitation information cloud platform, each user terminal in the user terminal cluster is connected to the rehabilitation information cloud platform, and each processing terminal in the processing terminal cluster is connected to the rehabilitation information cloud platform. For ease of understanding, the following is a detailed description of each part of the above-mentioned rehabilitation information management system:

[0107] Biosensor cluster: deployed in the rehabilitation environment, used to collect real-time data on various physiological indicators of the target object, such as heart rate, blood pressure, blood oxygen saturation, etc. These biosensors are connected to the rehabilitation information cloud platform via wireless network or wired mode.

[0108] User terminal cluster: including patient personal terminals, used to receive and view rehabilitation information and interact with the rehabilitation information cloud platform. These user terminals are also connected to the rehabilitation information cloud platform through the network.

[0109] Processing terminal cluster: including medical staff terminals, which can be servers set up in hospitals, responsible for processing and analyzing the original rehabilitation information from the biosensor cluster. The processing terminal can be used to transmit data with the rehabilitation information cloud platform through a secure connection.

[0110] Rehabilitation Information Cloud Platform: As the core of the system, it is responsible for storing, managing and sharing rehabilitation information. The cloud platform has high reliability, high availability and security, ensuring data integrity and privacy protection.

[0111] In this step, the original rehabilitation information of the target object is obtained through the biosensor cluster and sent to the rehabilitation information cloud platform. The rehabilitation information includes the target identity information of the target object and multiple physiological indicator data.

[0112] Specifically, when the target object starts rehabilitation training or daily monitoring, the biosensor cluster starts working and collects its physiological index data in real time. At the same time, the system also records the target object's target identity information, such as name, age, gender, rehabilitation plan, etc. The collected original rehabilitation information (including target identity information and physiological index data) is preliminarily processed and packaged. The packaged original rehabilitation information is sent to the rehabilitation information cloud platform through the network for storage.

[0113] S102: The rehabilitation information cloud platform configures the access permission of the original rehabilitation information as the first permission, and sends an information processing instruction to at least one processing terminal in the processing terminal cluster.

[0114] In this step, the rehabilitation information cloud platform configures the access permission of the original rehabilitation information as the first permission, and sends an information processing instruction to at least one processing terminal in the processing terminal cluster. The first permission is used to restrict access by all user terminals in the user terminal cluster.

[0115] Specifically, after receiving the original rehabilitation information, the rehabilitation information cloud platform first configures its access permission as the first permission. This permission strictly restricts access by all user terminals in the user terminal cluster to ensure the security of the data in the preliminary processing stage. The cloud platform sends information processing instructions to at least one processing terminal in the processing terminal cluster. These instructions may include processing requirements and standards for the original rehabilitation information.

[0116] Optionally, before the rehabilitation information cloud platform configures the access permission of the original rehabilitation information as the first permission, the rehabilitation information cloud platform can also determine that the first physiological indicator data in the original rehabilitation information is outside the corresponding preset physiological indicator value range, and the first physiological indicator data is one of the physiological indicators in the original rehabilitation information.

[0117] S103. The first processing terminal in the processing terminal cluster processes the original rehabilitation information in response to the information processing instruction, and sends the processed rehabilitation information corresponding to the original rehabilitation information to the rehabilitation information cloud platform.

[0118] In this step, the first processing terminal in the processing terminal cluster processes the original rehabilitation information in response to the information processing instruction, and sends the processed rehabilitation information corresponding to the original rehabilitation information to the rehabilitation information cloud platform.

[0119] Specifically, after receiving the information processing instruction, the first processing terminal (or designated processing terminal) in the processing terminal cluster begins to professionally process the original rehabilitation information. The processing content includes but is not limited to data cleaning, standardization, outlier detection, etc. And, after the processing is completed, the processed rehabilitation information (i.e., processed rehabilitation information) is sent back to the rehabilitation information cloud platform for storage.

[0120] S104. The rehabilitation information cloud platform configures the access rights to the original rehabilitation information and the processed rehabilitation information as the second permission.

[0121] In this step, the rehabilitation information cloud platform configures the access rights to the original rehabilitation information and the processed rehabilitation information as the second permission, and the second permission is used to allow the target user terminal corresponding to the target object to access.

[0122] The rehabilitation information cloud platform configures the access rights to the original rehabilitation information and the processed rehabilitation information as the second permission. This permission allows the target user terminal corresponding to the target object to access it to meet the patient's personalized information needs. After the permission is upgraded, the target user terminal can access and view its rehabilitation information, including the original data and the processed results, through the rehabilitation information cloud platform. Medical staff can also access relevant information as needed to formulate and adjust rehabilitation plans.

[0123] In this embodiment, the original rehabilitation information of the target object is obtained through a biosensor cluster, and the original rehabilitation information is sent to a rehabilitation information cloud platform. In the rehabilitation information cloud platform, the access permission of the original rehabilitation information is configured as a first permission, and an information processing instruction is sent to at least one processing terminal in the processing terminal cluster, so that the first processing terminal in the processing terminal cluster responds to the information processing instruction, processes the original rehabilitation information, and sends the processed rehabilitation information corresponding to the original rehabilitation information to the rehabilitation information cloud platform, so that the rehabilitation information cloud platform configures the access permission of the original rehabilitation information and the processed rehabilitation information as a second permission, thereby improving the accuracy and availability of the information disclosed in the rehabilitation information cloud platform, realizing the accurate sharing of rehabilitation information, and facilitating patients and medical staff to understand the rehabilitation progress in a timely manner.

[0124] Figure 2FIG. 1 is a flow chart of a method for sharing rehabilitation information cloud according to another exemplary embodiment of the present application. Figure 2 As shown, the rehabilitation information cloud sharing method provided in this embodiment includes:

[0125] S201. Acquire original rehabilitation information of a target object through a biosensor cluster, and send the original rehabilitation information to a rehabilitation information cloud platform.

[0126] In this step, the original rehabilitation information of the target object is obtained through the biosensor cluster and sent to the rehabilitation information cloud platform. The rehabilitation information includes the target identity information of the target object and multiple physiological indicator data.

[0127] Specifically, when the target object starts rehabilitation training or daily monitoring, the biosensor cluster starts working and collects its physiological index data in real time. At the same time, the system also records the target identity information of the target object, such as name, age, gender, rehabilitation plan, etc. The collected original rehabilitation information (including target identity information and physiological index data) is preliminarily processed and packaged. The packaged original rehabilitation information is sent to the rehabilitation information cloud platform through the network for storage.

[0128] S202. The rehabilitation information cloud platform obtains the historical rehabilitation information of the target object in the rehabilitation information cloud platform according to the target identity information to form a historical first physiological indicator data sequence.

[0129] In this step, the rehabilitation information cloud platform obtains the historical rehabilitation information of the target object in the rehabilitation information cloud platform according to the target identity information to form a historical first physiological indicator data sequence. The historical first physiological indicator data sequence is a data sequence in which the historical first physiological indicator data in the historical rehabilitation information are arranged in chronological order from first to last.

[0130] S203: The rehabilitation information cloud platform determines, based on the historical first physiological indicator data sequence, the first physiological indicator data in the original rehabilitation information as the physiological indicator to be confirmed.

[0131] In this step, the rehabilitation information cloud platform determines the predicted first physiological indicator data based on the historical first physiological indicator data sequence. The rehabilitation information cloud platform determines the deviation value of the first physiological indicator data based on the predicted first physiological indicator data and the first physiological indicator data. If the deviation value of the first physiological indicator data is greater than the preset deviation threshold, the first physiological indicator data is determined to be a physiological indicator to be confirmed.

[0132] Furthermore, the above historical rehabilitation information only includes rehabilitation information with the second level of access rights in the rehabilitation information cloud platform. This clarifies the scope of historical rehabilitation information, that is, only rehabilitation information with the second level of access rights in the rehabilitation information cloud platform is included. This limitation helps to ensure the accuracy and relevance of historical data, and only accurate data is used for subsequent predictions, thereby providing a more reliable basis for subsequent data analysis and processing.

[0133] Furthermore, the rehabilitation information cloud platform determines the predicted first physiological indicator data according to the historical first physiological indicator data sequence, including:

[0134] The rehabilitation information cloud platform determines the predicted first physiological indicator data based on the historical first physiological indicator data sequence, the first characteristic order and the second characteristic order. The first characteristic order is used to determine the number of historical first physiological indicator data in the first physiological indicator data sequence that participate in determining the predicted first physiological indicator data. The second characteristic order is used to determine the number of physiological indicator data deviation characteristic values ​​in the first physiological indicator data sequence that participate in determining the predicted first physiological indicator data.

[0135] Optionally, the rehabilitation information cloud platform can use formula 1 and calculate the first physiological index data sequence according to the history Determine the first physiological indicator data for prediction , Formula 1 is:

[0136]

[0137] in, is the first preset constant term, is the first characteristic order, is the second characteristic order, For the The characteristic parameters of the indicators, For the Deviation characteristic parameters.

[0138] It is worth noting that the above-mentioned indicator characteristic parameters and deviation characteristic parameters can be preset artificially based on experience, or can be obtained by performing statistical analysis on historical physiological indicator data series to calculate statistical quantities such as mean value and standard deviation as indicator characteristic parameters. It can also be obtained by fitting historical data using mathematical models (such as linear regression, time series analysis, etc.), so that the parameters in the model (such as slope, intercept, etc.) can be used as indicator characteristic parameters. It can also be obtained by extracting representative or discriminative features from the original data through feature engineering methods as indicator characteristic parameters.

[0139] Furthermore, before determining the predicted first physiological indicator data according to the historical first physiological indicator data sequence, the method further includes:

[0140] The rehabilitation information cloud platform determines the first characteristic order and the second characteristic order according to the historical first physiological indicator data sequence and the preset characteristic function.

[0141] Optionally, the rehabilitation information cloud platform uses formula 2 and according to the historical first physiological indicator data sequence Determine the first characteristic order and the second characteristic order , Formula 2 is:

[0142]

[0143] in, is the second preset constant term, The first physiological indicator data series in history The preset characteristic function It is worth noting that the above-mentioned preset characteristic function can be one or a combination of linear function, polynomial function, exponential function, logarithmic function, autoregressive model function and neural network related function, which can be selected according to the change characteristics of specific physiological index data.

[0144] Further, according to the historical first physiological indicator data sequence Before determining the first characteristic order and the second characteristic order, the method further includes:

[0145] The rehabilitation information cloud platform uses the preset iterative algorithm to calculate the preset characteristic function based on the historical first physiological indicator data sequence. Parameter estimation iterations are performed to determine a maximum eigenvalue, and the maximum eigenvalue is used to determine a first eigenorder and a second eigenorder.

[0146] Optionally, the rehabilitation information cloud platform uses the historical first physiological indicator data sequence and For preset feature functions Perform parameter estimation iterations to determine the largest eigenvalue estimate , Formula 3 is:

[0147]

[0148] in, For the The parameter estimates at iteration , For the The parameter estimates at iteration , For the The parameter estimates at iteration The inverse matrix of the Hessian matrix at , For the The parameter estimates at iteration The characteristic function at The first derivative of .

[0149] S204: The rehabilitation information cloud platform configures the access permission of the original rehabilitation information as the first permission, and sends an information processing instruction to at least one processing terminal in the processing terminal cluster.

[0150] In this step, the rehabilitation information cloud platform configures the access permission of the original rehabilitation information as the first permission, and sends an information processing instruction to at least one processing terminal in the processing terminal cluster. The first permission is used to restrict access by all user terminals in the user terminal cluster.

[0151] Specifically, after receiving the original rehabilitation information, the rehabilitation information cloud platform first configures its access permission as the first permission. This permission strictly restricts access by all user terminals in the user terminal cluster to ensure the security of the data in the preliminary processing stage. The cloud platform sends information processing instructions to at least one processing terminal in the processing terminal cluster. These instructions may include processing requirements and standards for the original rehabilitation information.

[0152] Optionally, before the rehabilitation information cloud platform configures the access permission of the original rehabilitation information as the first permission, the rehabilitation information cloud platform can also determine that the first physiological indicator data in the original rehabilitation information is outside the corresponding preset physiological indicator value range, and the first physiological indicator data is one of the physiological indicators in the original rehabilitation information.

[0153] S205. The first processing terminal in the processing terminal cluster processes the original rehabilitation information in response to the information processing instruction, and sends the processed rehabilitation information corresponding to the original rehabilitation information to the rehabilitation information cloud platform.

[0154] In this step, the first processing terminal in the processing terminal cluster processes the original rehabilitation information in response to the information processing instruction, and sends the processed rehabilitation information corresponding to the original rehabilitation information to the rehabilitation information cloud platform.

[0155] Specifically, after receiving the information processing instruction, the first processing terminal (or designated processing terminal) in the processing terminal cluster begins to professionally process the original rehabilitation information. The processing content includes but is not limited to data cleaning, standardization, outlier detection, etc. And, after the processing is completed, the processed rehabilitation information (i.e., processed rehabilitation information) is sent back to the rehabilitation information cloud platform for storage.

[0156] S206. The rehabilitation information cloud platform configures the access rights to the original rehabilitation information and the processed rehabilitation information as the second permission.

[0157] In this step, the rehabilitation information cloud platform configures the access rights to the original rehabilitation information and the processed rehabilitation information as the second permission, and the second permission is used to allow the target user terminal corresponding to the target object to access.

[0158] Figure 3 FIG. 1 is a schematic diagram of a rehabilitation information management system according to an exemplary embodiment of the present application. Figure 3 As shown, the rehabilitation information management system 300 provided in this embodiment includes: a biosensor cluster 310, a user terminal cluster 320, a processing terminal cluster 330 and a rehabilitation information cloud platform 340, each biosensor in the biosensor cluster 310 is connected to the rehabilitation information cloud platform 340, each user terminal in the user terminal cluster 320 is connected to the rehabilitation information cloud platform 340, and each processing terminal in the processing terminal cluster 330 is connected to the rehabilitation information cloud platform 340;

[0159] Acquire original rehabilitation information of the target object through the biosensor cluster 310, and send the original rehabilitation information to the rehabilitation information cloud platform 340, wherein the rehabilitation information includes target identity information and multiple physiological index data of the target object;

[0160] The rehabilitation information cloud platform 340 configures the access permission of the original rehabilitation information as a first permission, and sends an information processing instruction to at least one processing terminal in the processing terminal cluster 330, wherein the first permission is used to restrict access by all user terminals in the user terminal cluster 320;

[0161] The first processing terminal in the processing terminal cluster 330 processes the original rehabilitation information in response to the information processing instruction, and sends the processed rehabilitation information corresponding to the original rehabilitation information to the rehabilitation information cloud platform 340;

[0162] The rehabilitation information cloud platform 340 configures the access rights of the original rehabilitation information and the processed rehabilitation information as the second permission, and the second permission is used to allow the target user terminal corresponding to the target object to access.

[0163] Optionally, before the rehabilitation information cloud platform 340 configures the access permission of the original rehabilitation information as the first permission, the method further includes:

[0164] The rehabilitation information cloud platform 340 determines that the first physiological indicator data in the original rehabilitation information is outside the corresponding preset physiological indicator value range, and the first physiological indicator data is one of the physiological indicators in the original rehabilitation information; and / or,

[0165] The rehabilitation information cloud platform 340 obtains the historical rehabilitation information of the target object in the rehabilitation information cloud platform 340 according to the target identity information to form a historical first physiological indicator data sequence, wherein the historical first physiological indicator data sequence is a data sequence in which the historical first physiological indicator data in the historical rehabilitation information are arranged in chronological order from earliest to latest;

[0166] The rehabilitation information cloud platform 340 determines the first physiological indicator data in the original rehabilitation information as the physiological indicator to be confirmed according to the historical first physiological indicator data sequence.

[0167] Optionally, the rehabilitation information cloud platform 340 determines, according to the historical first physiological indicator data sequence, that the first physiological indicator data in the original rehabilitation information is the physiological indicator to be confirmed, including:

[0168] The rehabilitation information cloud platform 340 determines predicted first physiological indicator data according to the historical first physiological indicator data sequence;

[0169] The rehabilitation information cloud platform 340 determines a first physiological indicator data deviation value according to the predicted first physiological indicator data and the first physiological indicator data;

[0170] If the deviation value of the first physiological indicator data is greater than a preset deviation threshold, the first physiological indicator data is determined to be the physiological indicator to be confirmed.

[0171] Optionally, the historical rehabilitation information only includes rehabilitation information with the second access permission in the rehabilitation information cloud platform 340 .

[0172] Optionally, the rehabilitation information cloud platform 340 determines the predicted first physiological indicator data according to the historical first physiological indicator data sequence, including:

[0173] The rehabilitation information cloud platform 340 determines the predicted first physiological indicator data based on the historical first physiological indicator data sequence, the first characteristic order and the second characteristic order. The first characteristic order is used to determine the number of historical first physiological indicator data in the first physiological indicator data sequence that participate in determining the predicted first physiological indicator data. The second characteristic order is used to determine the number of physiological indicator data deviation characteristic values ​​in the first physiological indicator data sequence that participate in determining the predicted first physiological indicator data.

[0174] Optionally, the rehabilitation information cloud platform 340 determines the predicted first physiological indicator data according to the historical first physiological indicator data sequence, including:

[0175] The rehabilitation information cloud platform 340 uses formula 1 and according to the historical first physiological indicator data sequence Determining the predicted first physiological indicator data , the formula 1 is:

[0176]

[0177] in, is the first preset constant term, is the first characteristic order, is the second characteristic order, For the The characteristic parameters of the indicators, For the Deviation characteristic parameters.

[0178] In the above scheme, Formula 1 provides a clear quantitative method for predicting the calculation of the first physiological indicator data. Through this formula, the rehabilitation information cloud platform can accurately calculate the predicted value based on the historical first physiological indicator data sequence, the first characteristic order, the second characteristic order, and the characteristic parameters and deviation characteristic parameters of each indicator. This quantitative method reduces human intervention and subjective judgment, and improves the objectivity and accuracy of the prediction results. In addition, the various parameters in Formula 1 (such as the first preset constant term, the first characteristic order, the second characteristic order, the indicator characteristic parameters and the deviation characteristic parameters) can ensure that the prediction model can accurately reflect the changing trend of the target object's physiological indicators by comprehensively considering multiple factors, thereby generating a prediction value that is closer to reality, and improving the accuracy and reliability of the prediction.

[0179] In addition, since the patient's physiological index data is often affected by multiple factors and presents a complex trend of change, the introduction of multiple parameters and characteristic values ​​enables the prediction model to better adapt to this complex situation. Whether it is the periodic changes or sudden fluctuations of physiological indicators, Formula 1 can provide relatively accurate prediction results and provide strong decision-making support for medical personnel.

[0180] Optionally, before determining the predicted first physiological indicator data according to the historical first physiological indicator data sequence, the method further includes:

[0181] The rehabilitation information cloud platform 340 determines the first characteristic order and the second characteristic order according to the historical first physiological indicator data sequence and a preset characteristic function.

[0182] Optionally, in the first physiological indicator data sequence according to the historical Determining the predicted first physiological indicator data Previously, it also included:

[0183] The rehabilitation information cloud platform 340 uses formula 2 and according to the historical first physiological indicator data sequence Determine the first characteristic order And the second characteristic order , the formula 2 is:

[0184]

[0185] in, is the second preset constant term, The first historical physiological indicator data sequence The preset characteristic function The maximum eigenvalue of .

[0186] In the above scheme, Formula 2 provides an accurate quantitative method for determining the first feature order and the second feature order. These feature orders have an important impact on the performance of the prediction model because they determine how much historical data and what type of deviation features should be considered in the prediction process. Through Formula 2, the optimal feature order can be objectively calculated based on the historical first physiological indicator data sequence and its characteristic function, reducing the subjectivity and uncertainty of artificial settings. In addition, choosing the right feature order is the key to building a stable prediction model. If the feature order is too high, it may lead to overfitting of the model, that is, the model performs well on the training data, but has poor generalization ability on new data; if the feature order is too low, it may not be able to fully capture the complex changes of the data. Formula 2 determines the feature order by a scientific method, which helps to build a stable and reliable prediction model. By accurately determining the feature order, Formula 2 enables the prediction model to more accurately reflect the trend of changes in the physiological indicators of the target object. This optimization not only improves the accuracy of the prediction, but also makes the prediction results more in line with the actual situation, providing medical personnel with a more reliable basis for decision-making.

[0187] Optionally, in the first physiological indicator data sequence according to the historical Before determining the first characteristic order and the second characteristic order, the method further includes:

[0188] The rehabilitation information cloud platform 340 uses a preset iterative algorithm to calculate the preset characteristic function according to the historical first physiological indicator data sequence. Parameter estimation iterations are performed to determine a maximum eigenvalue, where the maximum eigenvalue is used to determine the first eigenorder and the second eigenorder.

[0189] Optionally, in the first physiological indicator data sequence according to the historical Determine the first characteristic order And the second characteristic order Previously, it also included:

[0190] The rehabilitation information cloud platform 340 uses the historical first physiological indicator data sequence and The preset characteristic function Perform parameter estimation iterations to determine the maximum eigenvalue estimate , the formula 3 is:

[0191]

[0192] in, For the The parameter estimates at iteration , For the The parameter estimates at iteration , For the The parameter estimates at iteration The inverse matrix of the Hessian matrix at , For the The parameter estimates at iteration The characteristic function at The first derivative of .

[0193] In the above scheme, Formula 3 gradually approaches the true parameter value, that is, the maximum eigenvalue, by continuously iteratively updating the parameter estimate. This method reduces the subjectivity and uncertainty of parameter estimation and improves the accuracy and reliability of the estimation. In addition, the iterative process in Formula 3 utilizes the inverse matrix of the Hessian matrix and the first-order derivative information of the eigenfunction, which helps to speed up the convergence speed of the iterative algorithm. Faster convergence speed means that accurate parameter estimates can be obtained faster under limited computing resources, which improves computational efficiency. In addition, during the iteration process, by considering the inverse matrix of the Hessian matrix (characterizing the local curvature of the objective function) at the parameter estimate, Formula 3 can better handle the nonlinear characteristics and multi-extreme value problems of the objective function. This processing method enhances the stability of the algorithm and reduces the risk of falling into a local optimal solution. In addition, based on the parameters accurately estimated by Formula 3, the first characteristic order and the second characteristic order can be more accurately determined, thereby optimizing the performance of the prediction model. This optimization not only improves the accuracy of the prediction, but also makes the prediction model more robust and reliable.

[0194] Figure 4 is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present application. Figure 4 As shown, an electronic device 400 provided in this embodiment includes: a processor 401 and a memory 402; wherein:

[0195] The memory 402 is used to store computer programs, and the memory may also be a flash memory.

[0196] The processor 401 is used to execute the execution instructions stored in the memory to implement each step in the above method. For details, please refer to the relevant description in the above method embodiment.

[0197] Optionally, the memory 402 may be independent or integrated with the processor 401 .

[0198] When the memory 402 is a device independent of the processor 401, the electronic device 400 may further include:

[0199] The bus 403 is used to connect the memory 402 and the processor 401 .

[0200] This embodiment further provides a readable storage medium, in which a computer program is stored. When at least one processor of an electronic device executes the computer program, the electronic device executes the methods provided in the above-mentioned various implementation modes.

[0201] This embodiment also provides a program product, which includes a computer program stored in a readable storage medium. At least one processor of the electronic device can read the computer program from the readable storage medium, and at least one processor executes the computer program so that the electronic device implements the methods provided in the above various embodiments.

[0202] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the claims.

[0203] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A rehabilitation information cloud sharing method, characterized in that: Applied to a rehabilitation information management system, the rehabilitation information management system comprises: a biosensor cluster, a user terminal cluster, a processing terminal cluster and a rehabilitation information cloud platform, each biosensor in the biosensor cluster is communicatively connected to the rehabilitation information cloud platform, each user terminal in the user terminal cluster is communicatively connected to the rehabilitation information cloud platform, and each processing terminal in the processing terminal cluster is communicatively connected to the rehabilitation information cloud platform; the method comprises: Acquire original rehabilitation information of the target object through the biosensor cluster, and send the original rehabilitation information to the rehabilitation information cloud platform, wherein the rehabilitation information includes target identity information and multiple physiological index data of the target object; The rehabilitation information cloud platform configures the access permission of the original rehabilitation information as a first permission, and sends an information processing instruction to at least one processing terminal in the processing terminal cluster, wherein the first permission is used to restrict access by all user terminals in the user terminal cluster; The first processing terminal in the processing terminal cluster processes the original rehabilitation information in response to the information processing instruction, and sends processed rehabilitation information corresponding to the original rehabilitation information to the rehabilitation information cloud platform; The rehabilitation information cloud platform configures the access rights of the original rehabilitation information and the processed rehabilitation information as a second permission, wherein the second permission is used to allow the target user terminal corresponding to the target object to access; Before the rehabilitation information cloud platform configures the access permission of the original rehabilitation information as the first permission, the method further includes: The rehabilitation information cloud platform determines that the first physiological indicator data in the original rehabilitation information is outside the corresponding preset physiological indicator value range, and the first physiological indicator data is one of the physiological indicators in the original rehabilitation information; and / or, The rehabilitation information cloud platform obtains the historical rehabilitation information of the target object in the rehabilitation information cloud platform according to the target identity information to form a historical first physiological indicator data sequence, wherein the historical first physiological indicator data sequence is a data sequence in which the historical first physiological indicator data in the historical rehabilitation information are arranged in chronological order from earliest to latest; The rehabilitation information cloud platform determines, according to the historical first physiological indicator data sequence, the first physiological indicator data in the original rehabilitation information as the physiological indicator to be confirmed; The rehabilitation information cloud platform determines, according to the historical first physiological indicator data sequence, the first physiological indicator data in the original rehabilitation information as the physiological indicator to be confirmed, including: The rehabilitation information cloud platform determines predicted first physiological indicator data according to the historical first physiological indicator data sequence; The rehabilitation information cloud platform determines a first physiological indicator data deviation value according to the predicted first physiological indicator data and the first physiological indicator data; If the deviation value of the first physiological indicator data is greater than a preset deviation threshold, determining the first physiological indicator data as the physiological indicator to be confirmed; The rehabilitation information cloud platform determines the predicted first physiological indicator data according to the historical first physiological indicator data sequence, including: The rehabilitation information cloud platform uses formula 1 and according to the historical first physiological indicator data sequence Determining the predicted first physiological indicator data , the formula 1 is: in, is the first preset constant term, is the first characteristic order, is the second characteristic order, For the The characteristic parameters of the indicators, For the Deviation characteristic parameters; In the first physiological indicator data sequence according to the historical Determining the predicted first physiological indicator data Previously, it also included: The rehabilitation information cloud platform uses formula 2 and according to the historical first physiological indicator data sequence Determine the first characteristic order And the second characteristic order , the formula 2 is: in, is the second preset constant term, The first historical physiological indicator data sequence The preset characteristic function The maximum eigenvalue of .

2. The rehabilitation information cloud sharing method according to claim 1, characterized in that: The historical rehabilitation information only includes rehabilitation information whose access permission in the rehabilitation information cloud platform is the second permission.

3. The rehabilitation information cloud sharing method according to claim 1, characterized in that: In the first physiological indicator data sequence according to the historical Before determining the first characteristic order and the second characteristic order, the method further includes: The rehabilitation information cloud platform uses a preset iterative algorithm to calculate the preset characteristic function according to the historical first physiological indicator data sequence. Parameter estimation iterations are performed to determine a maximum eigenvalue, where the maximum eigenvalue is used to determine the first eigenorder and the second eigenorder.

4. A rehabilitation information management system, characterized in that: include: A biosensor cluster, a user terminal cluster, a processing terminal cluster and a rehabilitation information cloud platform, wherein each biosensor in the biosensor cluster is communicatively connected to the rehabilitation information cloud platform, each user terminal in the user terminal cluster is respectively communicatively connected to the rehabilitation information cloud platform, and each processing terminal in the processing terminal cluster is respectively communicatively connected to the rehabilitation information cloud platform; Acquire original rehabilitation information of the target object through the biosensor cluster, and send the original rehabilitation information to the rehabilitation information cloud platform, wherein the rehabilitation information includes target identity information and multiple physiological index data of the target object; The rehabilitation information cloud platform configures the access permission of the original rehabilitation information as a first permission, and sends an information processing instruction to at least one processing terminal in the processing terminal cluster, wherein the first permission is used to restrict access by all user terminals in the user terminal cluster; The first processing terminal in the processing terminal cluster processes the original rehabilitation information in response to the information processing instruction, and sends processed rehabilitation information corresponding to the original rehabilitation information to the rehabilitation information cloud platform; The rehabilitation information cloud platform configures the access rights of the original rehabilitation information and the processed rehabilitation information as a second permission, wherein the second permission is used to allow the target user terminal corresponding to the target object to access; Before the rehabilitation information cloud platform configures the access permission of the original rehabilitation information as the first permission, the method further includes: The rehabilitation information cloud platform determines that the first physiological indicator data in the original rehabilitation information is outside the corresponding preset physiological indicator value range, and the first physiological indicator data is one of the physiological indicators in the original rehabilitation information; and / or, The rehabilitation information cloud platform obtains the historical rehabilitation information of the target object in the rehabilitation information cloud platform according to the target identity information to form a historical first physiological indicator data sequence, wherein the historical first physiological indicator data sequence is a data sequence in which the historical first physiological indicator data in the historical rehabilitation information are arranged in chronological order from earliest to latest; The rehabilitation information cloud platform determines, according to the historical first physiological indicator data sequence, the first physiological indicator data in the original rehabilitation information as the physiological indicator to be confirmed; The rehabilitation information cloud platform determines, according to the historical first physiological indicator data sequence, the first physiological indicator data in the original rehabilitation information as the physiological indicator to be confirmed, including: The rehabilitation information cloud platform determines predicted first physiological indicator data according to the historical first physiological indicator data sequence; The rehabilitation information cloud platform determines a first physiological indicator data deviation value according to the predicted first physiological indicator data and the first physiological indicator data; If the deviation value of the first physiological indicator data is greater than a preset deviation threshold, determining the first physiological indicator data as the physiological indicator to be confirmed; The rehabilitation information cloud platform determines the predicted first physiological indicator data according to the historical first physiological indicator data sequence, including: The rehabilitation information cloud platform uses formula 1 and according to the historical first physiological indicator data sequence Determining the predicted first physiological indicator data , the formula 1 is: in, is the first preset constant term, is the first characteristic order, is the second characteristic order, For the The characteristic parameters of the indicators, For the Deviation characteristic parameters; In the first physiological indicator data sequence according to the historical Determining the predicted first physiological indicator data Previously, it also included: The rehabilitation information cloud platform uses formula 2 and according to the historical first physiological indicator data sequence Determine the first characteristic order And the second characteristic order , the formula 2 is: in, is the second preset constant term, The first historical physiological indicator data sequence The preset characteristic function The maximum eigenvalue of .

5. An electronic device, characterized in that: include: processor; as well as, A memory, configured to store executable instructions of the processor; The processor is configured to perform the method of any one of claims 1 to 3 by executing the executable instructions.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 3 when executed by a processor.

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