Intelligent rehabilitation training management system and method based on big data analysis

Abstract

The application discloses an intelligent rehabilitation training management system and method based on big data analysis, and belongs to the technical field of rehabilitation training management; and is used for solving the technical problems of poor rehabilitation training multidimensional supervision and analysis effect and poor adaptive training optimization management effect of different patients in the prior art; through multidimensional data monitoring and statistics and data processing on a target for rehabilitation training, a preliminary rehabilitation training scheme matched with the target is corrected and optimized; through the supervision and analysis of the stage rehabilitation training state of different rehabilitation training stages of the preliminary rehabilitation training scheme of the target based on the supervision and analysis results of the stage rehabilitation training state of different rehabilitation training stages in the early stage, the subsequent rehabilitation training of the target is dynamically optimized and managed adaptively according to the stage rehabilitation training state of different rehabilitation training stages, and the autonomous supervision and expansion and adaptive optimization management of different rehabilitation training stages of the target are realized.

Description

Technical Field

[0001] This invention relates to the field of rehabilitation training management technology, specifically to an intelligent rehabilitation training management system and method based on big data analysis. Background Technology

[0002] Rehabilitation training refers to a series of treatment methods designed to help patients restore physical function, improve their ability to live independently, and improve their quality of life; this treatment is usually conducted under the guidance of a professional rehabilitation physician or physical therapist, targeting functional impairments caused by illness, injury, or surgery.

[0003] Because patients have different ages and physical conditions, their postoperative acute pain manifestations and pain tolerance also vary. The existing single-prescription pain relief method cannot implement targeted and differentiated rehabilitation training management for different patients, resulting in poor rehabilitation training effects for different patients. There are also problems with the multi-dimensional monitoring and analysis of rehabilitation training for different patients and the effectiveness of adaptive training optimization management. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent rehabilitation training management system and method based on big data analysis, which solves the technical problems of poor multi-dimensional monitoring and analysis of rehabilitation training for different patients and poor adaptive training optimization management in existing solutions.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] The intelligent rehabilitation training management system based on big data analysis includes:

[0007] The multidimensional data monitoring and statistics module for rehabilitation training is used to monitor and statistically analyze data from multiple dimensions to achieve the goals of rehabilitation training. It also digitizes and combines the monitoring and statistical data from different dimensions to obtain a rehabilitation training monitoring and processing set.

[0008] The rehabilitation training status monitoring and analysis management module is used to monitor and analyze the rehabilitation training status at different stages of the initial rehabilitation training plan for the target, and to adaptively optimize the target's subsequent rehabilitation training based on the stage-by-stage rehabilitation training status; including:

[0009] Among them, the pain assessment range corresponding to the pain level after the target stage rehabilitation training is obtained by the preliminary monitoring time point assessment based on the centralized sorting of rehabilitation training monitoring and processing. The obtained pain assessment range is then digitally processed and analyzed to obtain the training status identifier and stage rehabilitation training status of the corresponding rehabilitation training stage.

[0010] The system monitors and analyzes the status of subsequent rehabilitation training phases for users, as well as the overall impact of abnormal rehabilitation training. Based on the results of the monitoring and analysis of the overall impact of abnormal rehabilitation training, the system adaptively and dynamically optimizes the initial rehabilitation training plan implemented by the target. Dynamic optimization management includes continuing subsequent phases of rehabilitation training according to the initial rehabilitation training plan, implementing local adjustment plans corresponding to the initial rehabilitation training plan, or implementing overall adjustment plans corresponding to the initial rehabilitation training plan.

[0011] Preferably, when monitoring and statistically analyzing the data of the target's own pain perception dimension, the target's pain perception ability is tested and evaluated to obtain corresponding pain perception data; the pain perception data includes pain error coefficients corresponding to different pain test levels;

[0012] When monitoring and statistically analyzing data on the dimensions of rehabilitation training for the target, the pain level corresponding to the start of rehabilitation training is tested and evaluated. The obtained pain level is then input into the rehabilitation training database for traversal retrieval to obtain the corresponding preliminary rehabilitation training plan.

[0013] The pain level corresponding to the target, the preliminary rehabilitation training plan, and the implementation time of the rehabilitation training are sorted and combined to obtain rehabilitation training monitoring data.

[0014] Preferably, when digitally processing and combining monitoring statistics from different dimensions, several preliminary monitoring time points and corresponding preliminary training pain judgment ranges in the preliminary rehabilitation training plan are obtained based on the implementation time point.

[0015] Furthermore, based on the pain error coefficients corresponding to different pain test levels in the target's pain perception data, the preliminary training pain judgment range corresponding to several preliminary monitoring time points is corrected to obtain the preliminary training pain correction range corresponding to different preliminary monitoring time points.

[0016] By arranging and combining several preliminary monitoring time points and their corresponding preliminary training pain correction ranges in chronological order, a rehabilitation training monitoring and processing set is obtained.

[0017] Preferably, the pain level of the target after corresponding stage rehabilitation training is assessed based on the preliminary monitoring time points of the sorting to obtain the corresponding training pain assessment range. When the obtained training pain assessment range is digitally processed and analyzed for the stage rehabilitation training status, the obtained training pain assessment range is analyzed through the training pain recognition piecewise function and the corresponding training status identifier is output.

[0018] The training status indicator contains values ​​of 0, 1, or 2, which respectively represent the stage of rehabilitation training as normal, partially normal, or abnormal.

[0019] Based on the training status identifier corresponding to the values ​​of 0, 1, or 2, prompts are generated indicating whether the rehabilitation training status is normal, partially normal, or abnormal for the corresponding rehabilitation training stage.

[0020] Preferably, the expression for training the piecewise pain recognition function is: In the formula, XB is the training status identifier of the corresponding rehabilitation training stage; (PFmin, PFmax) is the training pain assessment range of the target in the corresponding rehabilitation training stage, PFmin and PFmax are the minimum and maximum values ​​of the training pain assessment range, respectively; (BFmin, BFmax) is the preliminary training pain correction range of the corresponding rehabilitation training stage, BFmin and BFmax are the minimum and maximum values ​​of the preliminary training pain correction range, respectively.

[0021] Preferably, a first abnormality tracing instruction or a second abnormality tracing instruction is generated based on the training status identifier with a value of 1 or 2, and the user's subsequent rehabilitation training stage is monitored and analyzed based on the first abnormality tracing instruction or the second abnormality tracing instruction to monitor and analyze the abnormal impact of the rehabilitation training.

[0022] Preferably, the training status identifiers corresponding to different preliminary monitoring time points are sorted and combined to obtain a training status processing sequence. When processing and analyzing the overall abnormal impact of rehabilitation training corresponding to the rehabilitation training stage based on the training status processing sequence, the training status processing sequence is traversed and the total number of first training status identifiers with a value of 1 and the total number of second training status identifiers with a value of 2 are counted respectively. The training validity corresponding to the preliminary rehabilitation training plan for the target is obtained by calculation.

[0023] Data analysis of training effectiveness is used to determine the goals and to develop an initial rehabilitation training program that corresponds to the overall training status.

[0024] Preferably, the formula for calculating training validity is as follows: In the formula, a is the weighting coefficient, with a value range of (1, 2]; N1 and N2 are the total number of the first training status identifiers and the total number of the second training status identifiers, respectively; N is the total number of times the target has undergone the preliminary rehabilitation training program; and A is the training validity standard value.

[0025] Preferably, if the training validity is less than 1, the management goal is to continue with the subsequent stages of rehabilitation training in the initial rehabilitation training program;

[0026] If the training validity is greater than or equal to 1 and less than XX*K%, then the management goal is to implement the partial adjustment plan corresponding to the initial rehabilitation training plan; where K is a real number and greater than 100;

[0027] If the training validity is greater than or equal to XX*K%, then the management goal is to implement the overall adjustment plan corresponding to the initial rehabilitation training program.

[0028] Intelligent rehabilitation training management methods based on big data analytics include:

[0029] Multi-dimensional data monitoring and statistics are implemented to achieve the goals of rehabilitation training. The monitoring and statistical data of different dimensions are digitally processed and combined to obtain the rehabilitation training monitoring and processing set.

[0030] Based on the rehabilitation training monitoring and processing set, the rehabilitation training status of the target is monitored and analyzed at different stages of the initial rehabilitation training plan. Then, the subsequent rehabilitation training of the target is dynamically optimized and managed adaptively according to the stage rehabilitation training status of different rehabilitation training stages.

[0031] Compared to existing solutions, the beneficial effects achieved by this invention are:

[0032] This invention, through multi-dimensional data monitoring, statistics, and data processing of the rehabilitation training targets, not only corrects and optimizes the initial rehabilitation training plan for target matching, but also provides reliable data support for the monitoring and analysis of the rehabilitation training status of the targets at different stages of rehabilitation training, thereby improving the diversity of data processing and utilization in the early stage of rehabilitation training.

[0033] This invention, based on the results of the monitoring and analysis of the rehabilitation training status at different stages of the initial rehabilitation training plan, implements the monitoring and analysis of the rehabilitation training status at different stages of the target's initial rehabilitation training plan, and adaptively optimizes the subsequent rehabilitation training of the target according to the rehabilitation training status at different stages. This realizes the autonomous monitoring and adaptive optimization management of the target at different stages of rehabilitation training, and improves the effect of multi-dimensional monitoring and analysis of rehabilitation training for different patients as well as the effect of adaptive training optimization management. Attached Figure Description

[0034] The present invention will now be further described with reference to the accompanying drawings.

[0035] Figure 1 This is a block diagram of the intelligent rehabilitation training management system based on big data analysis of the present invention.

[0036] Figure 2 This is a flowchart illustrating the digital processing and combination of monitoring statistics from different dimensions in this invention.

[0037] Figure 3 This is a flowchart of the training state identifier processing and analysis in this invention.

[0038] Figure 4 This is a flowchart of the intelligent rehabilitation training management method based on big data analysis according to the present invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] Example 1: As Figure 1 As shown, the present invention is an intelligent rehabilitation training management system based on big data analysis, including a multi-dimensional data monitoring and statistics module for rehabilitation training and a rehabilitation training status monitoring, analysis and management module.

[0041] The multidimensional data monitoring and statistics module for rehabilitation training is used to monitor and statistically analyze data from multiple dimensions to achieve the goals of rehabilitation training. It digitizes and combines the monitoring data from different dimensions to obtain a rehabilitation training monitoring and processing set; including:

[0042] It should be noted that the target of this invention embodiment can specifically be patients undergoing thoracoscopic surgery. Although patients undergoing thoracoscopic surgery experience significantly less postoperative pain compared to those undergoing open-chest surgery, acute postoperative pain remains one of the most common complaints among patients. Currently, analgesics are still the foundation for postoperative pain control, but they can cause adverse events, including nausea, vomiting, intestinal obstruction, and respiratory depression. Existing non-pharmacological therapies such as music therapy, aromatherapy, and hypnosis are increasingly being used to help patients relieve postoperative pain. The rehabilitation training in this invention embodiment is also a non-pharmacological therapy, specifically music video therapy, which involves playing videos and music for rehabilitation training. The target can be a patient undergoing rehabilitation training using VR glasses.

[0043] When monitoring and statistically analyzing the data of the target's own pain perception dimension, the target's pain perception ability is tested and evaluated to obtain corresponding pain perception data; the pain perception data includes pain error coefficients corresponding to different pain test levels;

[0044] It should be explained that the testing and assessment of the target's pain perception ability can be obtained based on subjective assessment reports and other objective indicators; the pain error coefficient is used to digitally represent the corresponding pain test level, and different pain test levels and their corresponding pain error coefficients can be obtained based on previous sample targets through pain assessment tests.

[0045] The specific value of the pain error coefficient can be determined by a professional technical team in this field in conjunction with existing rehabilitation training standard data in the field of rehabilitation training; the professional technical team can be formed by thoracoscopic surgeons and experts;

[0046] Subjective assessment reports are obtained through subjective assessment methods, including but not limited to:

[0047] Verbal Rating Scales (VRS): Patients describe their pain level using simple words, such as "no pain," "mild pain," "moderate pain," and "severe pain."

[0048] Numerical Rating Scale (NRS): Uses a numerical scale from 0 to 10, where 0 represents "no pain" and 10 represents "most severe pain"; patients choose a number to represent their pain level.

[0049] Visual Analog Scale (VAS): A straight line (usually 10 cm) is used, with one end representing "no pain" and the other end representing "most severe pain"; patients mark the locations on the line that they believe best represent their pain level.

[0050] Facial Expression Pain Rating Scale: This method involves showing patients a series of facial expression images representing different levels of pain and asking them to choose the expression that best reflects their pain level.

[0051] Other objective indicators are obtained through objective evaluation methods, including but not limited to:

[0052] Behavioral observation: Observe the patient's nonverbal behaviors such as body movements, facial expressions, and voice, which can reflect the patient's pain status;

[0053] Physiological indicator monitoring: Monitor changes in physiological indicators such as heart rate, blood pressure, and skin conductance, as these changes may be related to pain;

[0054] In this embodiment of the invention, by testing and evaluating data from the perspective of the target's own pain perception, reliable data support can be provided for targeted optimization and adjustment of the rehabilitation training indicators corresponding to the subsequent target.

[0055] When monitoring and statistically analyzing data on the dimensions of rehabilitation training for the target, the pain level corresponding to the start of rehabilitation training is tested and evaluated. The obtained pain level is then input into the rehabilitation training database for traversal retrieval to obtain the corresponding preliminary rehabilitation training plan. The test and evaluation of the pain level corresponding to the start of rehabilitation training can be achieved using existing pain assessment schemes. The specific implementation steps are not detailed here.

[0056] In addition, the rehabilitation training database pre-stores several different sample pain levels and their corresponding preliminary rehabilitation training plans. The sample pain levels include, but are not limited to, no pain level, mild pain level, moderate pain level, and severe pain level.

[0057] The preliminary rehabilitation training programs corresponding to different pain levels of the samples all include several preliminary monitoring time points and the corresponding preliminary training pain assessment ranges; the specific content of the preliminary rehabilitation training programs corresponding to different pain levels of the samples can be determined by a professional technical team in this field; the unit of the preliminary monitoring time points is accurate to the minute;

[0058] The pain level corresponding to the target, the preliminary rehabilitation training plan, and the time points of rehabilitation training implementation are sorted and combined to obtain rehabilitation training monitoring data.

[0059] like Figure 2 As shown, when digitally processing and combining monitoring statistics from different dimensions, several preliminary monitoring time points and corresponding preliminary training pain judgment ranges in the preliminary rehabilitation training plan are obtained based on the implementation time point.

[0060] Furthermore, based on the pain error coefficients corresponding to different pain test levels in the target's pain perception data, the preliminary training pain judgment range corresponding to several preliminary monitoring time points is corrected to obtain the preliminary training pain correction range corresponding to different preliminary monitoring time points.

[0061] Among them, to correct the initial training pain judgment range, the minimum and maximum values ​​of the initial training pain judgment range can be multiplied by the pain error coefficient respectively, and the minimum and maximum values ​​after multiplication can be combined to obtain the initial training pain correction range.

[0062] It should be noted that, due to differences in age and physical condition among different individuals, their manifestations of acute pain and pain tolerance after thoracoscopic surgery also vary. Therefore, by using data from monitoring and processing the individual's pain perception dimension to refine and optimize the initial rehabilitation training plan, the matching and accuracy of the monitoring and analysis of the individual's rehabilitation training status at different subsequent stages can be effectively improved.

[0063] By arranging and combining several preliminary monitoring time points and their corresponding preliminary training pain correction ranges in chronological order, a rehabilitation training monitoring and processing set is obtained.

[0064] In this embodiment of the invention, by implementing multi-dimensional data monitoring, statistics, and data processing on the target undergoing rehabilitation training, the initial rehabilitation training plan matching the target can be corrected and optimized. At the same time, it can also provide reliable data support for the monitoring and analysis of the target's rehabilitation training status at different stages of rehabilitation training, thereby improving the diversity of data processing and utilization in the early stage of rehabilitation training.

[0065] The rehabilitation training status monitoring and analysis management module is used to monitor and analyze the rehabilitation training status at different stages of the initial rehabilitation training plan for the target, and to adaptively optimize the target's subsequent rehabilitation training based on the stage-by-stage rehabilitation training status; including:

[0066] like Figure 3 As shown, the pain level of the target after corresponding stage rehabilitation training is assessed based on the preliminary monitoring time points of the sorting to obtain the corresponding training pain assessment range. When the obtained training pain assessment range is digitally processed and analyzed for the stage rehabilitation training status, the obtained training pain assessment range is analyzed through the training pain recognition segmentation function and the corresponding training status identifier is output.

[0067] It should be noted that the assessment of pain level after rehabilitation training at the target stage can obtain the corresponding training pain assessment range. The preliminary training pain assessment ranges obtained from different assessment methods can be deduplicated and combined. There are at least two different assessment methods.

[0068] The expression for training the piecewise pain recognition function is: In the formula, XB is the training status identifier of the corresponding rehabilitation training stage; (PFmin, PFmax) is the training pain assessment range of the target in the corresponding rehabilitation training stage, PFmin and PFmax are the minimum and maximum values ​​of the training pain assessment range, respectively; (BFmin, BFmax) is the preliminary training pain correction range of the corresponding rehabilitation training stage, BFmin and BFmax are the minimum and maximum values ​​of the preliminary training pain correction range, respectively.

[0069] It should be noted that the training status identifier is used to integrate and calculate the training data after the target has undergone phased rehabilitation training to digitally represent its phased rehabilitation training status.

[0070] The training status indicator contains values ​​of 0, 1, or 2, which respectively represent the stage of rehabilitation training as normal, partially normal, or abnormal.

[0071] Based on the training status identifier corresponding to the value of 0, 1 or 2, prompts are generated indicating whether the rehabilitation training status is normal, partially normal or abnormal for the corresponding rehabilitation training stage.

[0072] In this embodiment of the invention, by monitoring and digitally processing the rehabilitation training status of the target at different stages of the initial rehabilitation training program, it is possible to obtain the stage rehabilitation training status of the target at different stages of rehabilitation training, and to provide reliable stage processing data support for the analysis of the overall abnormal impact of rehabilitation training at subsequent stages of rehabilitation training.

[0073] Furthermore, a first abnormality tracing instruction or a second abnormality tracing instruction is generated based on the training status identifier with a value of 1 or 2, and the user's subsequent rehabilitation training stage is monitored and analyzed based on the first abnormality tracing instruction or the second abnormality tracing instruction to monitor and analyze the abnormal impact of the rehabilitation training.

[0074] The training status identifiers corresponding to different initial monitoring time points are sorted and combined to obtain a training status processing sequence. When analyzing the overall abnormal impact of rehabilitation training on the rehabilitation training stage based on the training status processing sequence, the training status processing sequence is traversed, and the total number of first training status identifiers with a value of 1 and the total number of second training status identifiers with a value of 2 are counted respectively. This is then processed using the formula... Calculate the training validity XX corresponding to the preliminary rehabilitation training program for the target; where a is the weight coefficient, with a value range of (1, 2]; N1 and N2 are the total number of the first training status identifier and the total number of the second training status identifier, respectively; N is the total number of times the target has undergone the preliminary rehabilitation training program; A is the standard value of training validity, which can be determined based on the design and test data of the preliminary rehabilitation training program in the early stage.

[0075] It should be noted that training validity is used to calculate and digitally represent the overall training status corresponding to the initial rehabilitation training program for the target.

[0076] When analyzing training validity data to determine the overall training status corresponding to the initial rehabilitation training plan, if the training validity is less than 1, an overall normal training instruction will be generated and prompted, and the management target will continue to carry out subsequent stages of rehabilitation training in the initial rehabilitation training plan.

[0077] If the training validity is greater than or equal to 1 and less than XX*K%, an overall training mild abnormality instruction will be generated and prompted. At the same time, the management goal will implement the corresponding local adjustment plan for the initial rehabilitation training plan. Here, K is a real number greater than 100, specifically 150. The local adjustment plan can be to increase the number of subsequent initial monitoring time points to increase the monitoring and analysis frequency.

[0078] If the training validity is greater than or equal to XX*K%, an overall training severe abnormality instruction will be generated and prompted. At the same time, the management goal will implement the overall adjustment plan corresponding to the initial rehabilitation training plan. Specifically, the overall adjustment plan can adjust the training content of all rehabilitation training stages corresponding to the initial rehabilitation training plan.

[0079] In this embodiment of the invention, based on the results of the monitoring and analysis of the rehabilitation training status at different stages of the initial rehabilitation training plan, the monitoring and analysis of the rehabilitation training status at different stages of the initial rehabilitation training plan for the target is carried out. The subsequent rehabilitation training of the target is dynamically optimized and managed in an adaptive manner according to the rehabilitation training status at different stages. This realizes the autonomous monitoring and expansion and adaptive optimization management of the target at different stages of rehabilitation training, and improves the effect of multi-dimensional monitoring and analysis of rehabilitation training for different patients and the effect of adaptive training optimization management.

[0080] Example 2: Based on the technical solution of Example 1, it further includes:

[0081] The rehabilitation training expansion, optimization, analysis, and management module is used to monitor and digitize the overall training status of all goals during the initial rehabilitation training program process. It also assesses the reliability of the digitized results and adaptively optimizes the initial rehabilitation training program based on the assessment results. This includes:

[0082] The overall training status corresponding to the initial rehabilitation training plan for all objectives is monitored and statistically analyzed. The total number of first objectives (Nc) continuously implemented under the initial rehabilitation training plan, the total number of second objectives (Nj) adjusted to implement corresponding local adjustment plans, and the total number of third objectives (Nt) adjusted to implement corresponding overall adjustment plans are calculated using the formula... Calculate the implementation validity SX corresponding to the preliminary rehabilitation training program; where B is the standard value of implementation validity, which can be determined based on the initial design and test data of the preliminary rehabilitation training program; β is the error correction factor, which can be 0.512.

[0083] When analyzing data to determine the reliability of the initial rehabilitation training program, the implementation validity is analyzed.

[0084] If the effectiveness is 0, then generate reliable training instructions and maintain the existing preliminary rehabilitation training program content;

[0085] If the validity is 1, then generate training reliable mild abnormal instructions and observe the content of the existing preliminary rehabilitation training program;

[0086] If the validity is greater than 1, then a training reliability severe abnormality instruction is generated and the existing preliminary rehabilitation training program is immediately optimized and adjusted.

[0087] Among these measures, the existing preliminary rehabilitation training program will be immediately optimized and adjusted, including but not limited to adjustments to specific rehabilitation training steps and adjustments to the range of preliminary pain assessment corresponding to specific rehabilitation training.

[0088] In this embodiment of the invention, by monitoring the overall training status of all targets in the initial rehabilitation training program process and conducting a reliability assessment of the rehabilitation training, and adaptively optimizing the internal management of the initial rehabilitation training program based on the assessment results, autonomous monitoring and optimization management of the rehabilitation training dimension are achieved, thereby improving the reliability of the subsequent rehabilitation training program implementation.

[0089] Example 3: As Figure 4 As shown, this invention is an intelligent rehabilitation training management method based on big data analysis, comprising:

[0090] Multi-dimensional data monitoring and statistics are implemented to achieve the goals of rehabilitation training. The monitoring and statistical data of different dimensions are digitally processed and combined to obtain the rehabilitation training monitoring and processing set.

[0091] Based on the rehabilitation training monitoring and processing set, the rehabilitation training status of the target is monitored and analyzed at different stages of the initial rehabilitation training plan. Then, the subsequent rehabilitation training of the target is dynamically optimized and managed adaptively according to the stage rehabilitation training status of different rehabilitation training stages.

[0092] Furthermore, the formulas mentioned above are all numerical calculations obtained by removing dimensions and using simulation software to obtain a formula that is closest to the real situation, based on the collection of a large amount of data.

[0093] In the several embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0094] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0095] Furthermore, the functional modules in the various embodiments of this invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated module can be implemented in hardware or in a combination of hardware and software functional modules.

[0096] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the essential characteristics of the present invention.

[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An intelligent rehabilitation training management system based on big data analysis, characterized in that: include: The multidimensional data monitoring and statistics module for rehabilitation training is used to monitor and statistically analyze data from multiple dimensions to achieve the goals of rehabilitation training. It also digitizes and combines the monitoring and statistical data from different dimensions to obtain a rehabilitation training monitoring and processing set. The rehabilitation training status monitoring and analysis management module is used to monitor and analyze the rehabilitation training status of the target at different stages of the initial rehabilitation training plan, and to dynamically optimize the target's subsequent rehabilitation training based on the stage rehabilitation training status of different rehabilitation training stages. include: Among them, the pain assessment range corresponding to the pain level after the target stage rehabilitation training is obtained by the preliminary monitoring time point assessment based on the centralized sorting of rehabilitation training monitoring and processing. The obtained pain assessment range is then digitally processed and analyzed to obtain the training status identifier and stage rehabilitation training status of the corresponding rehabilitation training stage. The system monitors and analyzes the status of subsequent rehabilitation training phases for users, as well as the overall impact of abnormal rehabilitation training. Based on the results of the monitoring and analysis of the overall impact of abnormal rehabilitation training, the system adaptively and dynamically optimizes the initial rehabilitation training plan implemented by the target. Dynamic optimization management includes continuing subsequent phases of rehabilitation training according to the initial rehabilitation training plan, implementing local adjustment plans corresponding to the initial rehabilitation training plan, or implementing overall adjustment plans corresponding to the initial rehabilitation training plan.

2. The intelligent rehabilitation training management system based on big data analysis according to claim 1, characterized in that, When monitoring and statistically analyzing the data of the target's own pain perception dimension, the target's pain perception ability is tested and evaluated to obtain corresponding pain perception data; the pain perception data includes pain error coefficients corresponding to different pain test levels; When monitoring and statistically analyzing data on the dimensions of rehabilitation training for the target, the pain level corresponding to the start of rehabilitation training is tested and evaluated. The obtained pain level is then input into the rehabilitation training database for traversal retrieval to obtain the corresponding preliminary rehabilitation training plan. The pain level corresponding to the target, the preliminary rehabilitation training plan, and the implementation time of the rehabilitation training are sorted and combined to obtain rehabilitation training monitoring data.

3. The intelligent rehabilitation training management system based on big data analysis according to claim 2, characterized in that, When digitally processing and combining monitoring statistics from different dimensions, several preliminary monitoring time points and corresponding preliminary training pain judgment ranges in the preliminary rehabilitation training plan are obtained based on the implementation time point. Furthermore, based on the pain error coefficients corresponding to different pain test levels in the target's pain perception data, the preliminary training pain judgment range corresponding to several preliminary monitoring time points is corrected to obtain the preliminary training pain correction range corresponding to different preliminary monitoring time points. By arranging and combining several preliminary monitoring time points and their corresponding preliminary training pain correction ranges in chronological order, a rehabilitation training monitoring and processing set is obtained.

4. The intelligent rehabilitation training management system based on big data analysis according to claim 3, characterized in that, Based on the initial monitoring time points of the sorting, the pain level of the target after the corresponding stage of rehabilitation training is assessed to obtain the corresponding training pain assessment range. When the obtained training pain assessment range is digitally processed and analyzed for the stage of rehabilitation training status, the obtained training pain assessment range is analyzed through the training pain recognition segmentation function and the corresponding training status identifier is output. The training status indicator contains values ​​of 0, 1, or 2, which respectively represent the stage of rehabilitation training as normal, partially normal, or abnormal. Based on the training status identifier corresponding to the values ​​of 0, 1, or 2, prompts are generated indicating whether the rehabilitation training status is normal, partially normal, or abnormal for the corresponding rehabilitation training stage.

5. The intelligent rehabilitation training management system based on big data analysis according to claim 4, characterized in that, The expression for training the piecewise pain recognition function is: In the formula, XB is the training status identifier of the corresponding rehabilitation training stage; (PFmin, PFmax) is the training pain assessment range of the target in the corresponding rehabilitation training stage, PFmin and PFmax are the minimum and maximum values ​​of the training pain assessment range, respectively; (BFmin, BFmax) is the preliminary training pain correction range of the corresponding rehabilitation training stage, BFmin and BFmax are the minimum and maximum values ​​of the preliminary training pain correction range, respectively.

6. The intelligent rehabilitation training management system based on big data analysis according to claim 4, characterized in that, Based on the training status identifier with a value of 1 or 2, a first abnormality tracing instruction or a second abnormality tracing instruction is generated respectively. Based on the first abnormality tracing instruction or the second abnormality tracing instruction, the monitoring and analysis of the user's subsequent rehabilitation training stage status and the monitoring and analysis of the overall abnormal impact of rehabilitation training are carried out.

7. The intelligent rehabilitation training management system based on big data analysis according to claim 6, characterized in that, The training status identifiers corresponding to different initial monitoring time points are sorted and combined to obtain the training status processing sequence. When processing and analyzing the overall abnormal impact of rehabilitation training corresponding to the rehabilitation training stage based on the training status processing sequence, the training status processing sequence is traversed and the total number of first training status identifiers with a value of 1 and the total number of second training status identifiers with a value of 2 are counted respectively. And by calculating the target, the training validity of the preliminary rehabilitation training plan is obtained; Data analysis of training effectiveness is used to determine the goals and to develop an initial rehabilitation training program that corresponds to the overall training status.

8. The intelligent rehabilitation training management system based on big data analysis according to claim 7, characterized in that, The formula for calculating training validity is: In the formula, a is the weighting coefficient, with a value range of (1, 2]; N1 and N2 are the total number of the first training status identifiers and the total number of the second training status identifiers, respectively; N is the total number of times the target has undergone the preliminary rehabilitation training program; and A is the training validity standard value.

9. The intelligent rehabilitation training management system based on big data analysis according to claim 7, characterized in that, If the training validity is less than 1, the management goal is to continue with the subsequent stages of rehabilitation training in the initial rehabilitation training program; If the training validity is greater than or equal to 1 and less than XX*K%, then the management goal is to implement the partial adjustment plan corresponding to the initial rehabilitation training plan; where K is a real number and greater than 100; If the training validity is greater than or equal to XX*K%, then the management goal is to implement the overall adjustment plan corresponding to the initial rehabilitation training program.

10. A smart rehabilitation training management method based on big data analysis, using the smart rehabilitation training management system based on big data analysis as described in any one of claims 1-9, characterized in that, include: Multi-dimensional data monitoring and statistics are implemented to achieve the goals of rehabilitation training. The monitoring and statistical data of different dimensions are digitally processed and combined to obtain the rehabilitation training monitoring and processing set. Based on the rehabilitation training monitoring and processing set, the rehabilitation training status of the target is monitored and analyzed at different stages of the initial rehabilitation training plan. Then, the subsequent rehabilitation training of the target is dynamically optimized and managed adaptively according to the stage rehabilitation training status of different rehabilitation training stages.

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