Rehabilitation training scheme generation method and device, electronic equipment and medium

Abstract

The invention relates to a rehabilitation training scheme generation method and device, electronic equipment and a medium, and relates to the field of data processing.The method comprises the steps that the maximum exercise load power of a patient during load testing on a power vehicle and a heart rate change diagram and a respiratory rate change diagram of the patient in the testing process are obtained; fusing the heart rate change diagram and the respiratory rate change diagram to obtain a state change diagram of the patient, determining state quality corresponding to a plurality of preset power intervals of the patient from the state change diagram, and determining the number of training stages and the training load power of each training stage based on the state quality and the maximum exercise load power, and generating a rehabilitation training scheme of the patient based on the training load power of each training stage. The method has the effect of accurately formulating the rehabilitation training scheme more suitable for the self condition of the patient.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular, to a method, apparatus, electronic device, and medium for generating a rehabilitation training plan. Background Art

[0002] Currently, for the rehabilitation training of patients' cardiopulmonary function, relevant personnel such as doctors usually formulate rehabilitation training plans for patients based on general principles and their own clinical experience, which rely more on doctors' experience and subjective judgment, lacking accurate quantitative basis and systematic planning. This method not only makes it difficult to ensure the scientificity and rationality of the training plan, but also may lead to poor training effects. Therefore, how to accurately formulate a more suitable rehabilitation training plan for the patient's own situation has become a problem. Summary of the Invention

[0003] In order to accurately formulate a more suitable rehabilitation training plan for the patient's own situation, the present application provides a method, apparatus, electronic device, and medium for generating a rehabilitation training plan.

[0004] In a first aspect, the present application provides a method for generating a rehabilitation training plan, adopting the following technical solution: A method for generating a rehabilitation training plan includes: Obtaining the maximum exercise load power of a patient during a load test on an ergometer, the heart rate change graph and the respiratory rate change graph of the patient during the test; Fusing the heart rate change graph and the respiratory rate change graph to obtain the state change graph of the patient; Determining the state quality corresponding to each of a plurality of preset power intervals of the patient from the state change graph; Based on the state quality and the maximum exercise load power, determining the number of training stages and the training load power of each training stage; Generating a rehabilitation training plan for the patient based on the training load power of each training stage.

[0005] By adopting the above technical solution, obtaining the maximum exercise load power of the patient on the ergometer, the heart rate change graph and the respiratory rate change graph during the test is convenient for subsequent accurate analysis. Both the heart rate change graph and the respiratory rate change graph are specific manifestations of the patient's physical condition and cardiopulmonary function during exercise on the ergometer. Therefore, by fusing the heart rate change graph and the respiratory rate change graph, a comprehensive state change graph of the patient can be obtained. The state quality corresponding to each of the multiple preset power intervals of the patient is determined from the state change graph. Both the state quality and the maximum exercise load power are key factors characterizing the patient's own situation. Therefore, the number of appropriate training stages for the patient and the training load power of each training stage are comprehensively determined according to the state quality and the maximum exercise load power. Finally, a rehabilitation training plan is generated according to the determined training load power of each training stage. Compared with the existing methods for formulating rehabilitation training plans, it combines the change of the patient's physical state quality with the load power during the maximum exercise load power test on the ergometer, making the subsequently determined training plan more accurately adapted to the patient's own situation.

[0006] In another possible implementation manner, the fusing of the heart rate change graph and the respiratory rate change graph to obtain the state change graph of the patient includes: Determining the product of the heart rate value in the heart rate change graph and the respiratory rate value in the respiratory rate change graph at the same moment as the state value of the patient at the same moment; Calculating the first difference between the state value at each moment and the reference state value at the corresponding moment; Generating the state change graph of the patient based on the first difference.

[0007] In another possible implementation manner, the determining of the state quality corresponding to each of the multiple preset power intervals of the patient from the state change graph includes: Mapping the multiple preset power intervals to the state change graph to obtain state change scatter points corresponding to each preset power interval; Performing linear fitting and non-linear fitting on the state change scatter points corresponding to each preset power interval to obtain a linear function and a first curve segment corresponding to each preset power interval, and determining the first slope of each linear function; Obtaining a reference state change graph, which characterizes the state change graph of a healthy population; Mapping the multiple preset power intervals to the reference state change graph to obtain a second curve segment corresponding to each preset power interval; Determining the state quality corresponding to the multiple preset power intervals based on the first slope, the first curve segment and the second curve segment.

[0008] In another possible implementation manner, determining the state quality corresponding to the multiple preset power intervals based on the first slope, the first curve segment, and the second curve segment includes: Performing a linear transformation on each second curve segment to obtain a linear function corresponding to each second curve segment, and determining a second slope of the linear function corresponding to each second curve segment; Calculating a second difference between the first slope and the second slope of each preset power interval; Calculating the similarity between the first curve segment and the second curve segment of each preset power interval; Determining the state quality corresponding to each preset power interval based on the second difference, the similarity, and their respective weights.

[0009] In another possible implementation manner, determining the number of training stages and the training load power of each training stage based on the state quality and the maximum exercise load power includes: Determining a target preset power interval whose state quality is lower than a preset quality threshold, and determining the average state quality of the target preset power interval; Determining the score of the patient based on the number of the target preset power intervals, the average state quality, and the maximum exercise load power; Determining a target preset score interval where the score is located from multiple preset score intervals, each preset score interval corresponding to a preset number of training stages, and determining the preset number corresponding to the target preset score interval as the number of training stages; Determining the training load power of each training stage based on the number of training stages and the maximum exercise load power.

[0010] In another possible implementation manner, generating a rehabilitation training plan for the patient based on the training load power of each training stage includes: Determining the preset power interval where each training power is located; If there is a target training power, determining the number of days of the training stage corresponding to the target training power, where the target training power is a training power whose state quality in the preset power interval where it is located is lower than the preset quality threshold; Determining the number of days of the training stage corresponding to each remaining training power except the target training power, where the number of days is a preset number of days; Generating a rehabilitation training plan for the patient based on the number of days of the training stage corresponding to each target training power and the number of days of the training stage corresponding to each remaining reference power.

[0011] In another possible implementation manner, determining the number of days of the training stage corresponding to the target training power includes: Determine a third difference between the state quality of the preset power interval where the target training power is located and a preset quality threshold; Determine a number adjustment value based on the third difference and a preset coefficient; Add the preset number of days to the number adjustment value to obtain the number of days of the training phase corresponding to the target training power.

[0012] In a second aspect, the present application provides a rehabilitation training plan generation device, adopting the following technical solution: A rehabilitation training plan generation device, comprising: A data acquisition module, configured to acquire the maximum exercise load power of a patient during a load test on an exercise bike, the heart rate change graph and the respiratory rate change graph of the patient during the test process; A fusion module, configured to fuse the heart rate change graph and the respiratory rate change graph to obtain the state change graph of the patient; A quality determination module, configured to determine the state quality corresponding to each of a plurality of preset power intervals of the patient from the state change graph; A power determination module, configured to determine the number of training phases and the training load power of each training phase based on the state quality and the maximum exercise load power; A plan generation module, configured to generate a rehabilitation training plan for the patient based on the training load power of each training phase.

[0013] By adopting the above technical solution, the data acquisition module acquires the maximum exercise load power of the patient on the exercise bike, the heart rate change graph and the respiratory rate change graph during the test process, which is convenient for subsequent accurate analysis. Both the heart rate change graph and the respiratory rate change graph are specific manifestations of the patient's physical condition and cardiopulmonary function during exercise on the exercise bike. Therefore, the fusion module can fuse the heart rate change graph and the respiratory rate change graph to obtain a comprehensive state change graph of the patient. The quality determination module determines the state quality corresponding to each of a plurality of preset power intervals of the patient from the state change graph. The state quality and the maximum exercise load power are both key factors characterizing the patient's own situation. Therefore, the power determination module comprehensively determines the number of appropriate training phases of the patient and the training load power of each training phase according to the state quality and the maximum exercise load power. Finally, the plan generation module can generate a rehabilitation training plan according to the determined training load power of each training phase. Compared with the existing methods for formulating rehabilitation training plans, it combines the change of the patient's physical state quality with the load power during the maximum exercise load power test on the exercise bike, making the subsequent determined training plan more accurately adapt to the patient's own situation.

[0014] In another possible implementation, when the fusion module fuses the heart rate change graph and the respiratory rate change graph to obtain the state change graph of the patient, it specifically is used for: Determining the product of the heart rate value in the heart rate change graph and the respiratory rate value in the respiratory rate change graph at the same moment as the state value of the patient at the same moment; Calculating the first difference between the state value at each moment and the reference state value at the corresponding moment; Generating the state change graph of the patient based on the first difference.

[0015] In another possible implementation, when the quality determination module determines the state quality corresponding to each of the multiple preset power intervals of the patient from the state change graph, it specifically is used for: Mapping the multiple preset power intervals to the state change graph to obtain state change scatter points corresponding to each preset power interval; Performing linear fitting and non-linear fitting on the state change scatter points corresponding to each preset power interval to obtain a linear function and a first curve segment corresponding to each preset power interval, and determining the first slope of each linear function; Obtaining a reference state change graph, where the reference state change graph represents the state change graph of a healthy population; Mapping the multiple preset power intervals to the reference state change graph to obtain a second curve segment corresponding to each preset power interval; Determining the state quality corresponding to the multiple preset power intervals based on the first slope, the first curve segment, and the second curve segment.

[0016] In another possible implementation, when the quality determination module determines the state quality corresponding to the multiple preset power intervals based on the first slope, the first curve segment, and the second curve segment, it specifically is used for: Performing linear transformation on each second curve segment to obtain a linear function corresponding to each second curve segment, and determining the second slope of the linear function corresponding to each second curve segment; Calculating the second difference between the first slope and the second slope of each preset power interval; Calculating the similarity between the first curve segment and the second curve segment of each preset power interval; Determining the state quality corresponding to each preset power interval based on the second difference, the similarity, and their respective weights.

[0017] In another possible implementation, when the power determination module determines the number of training stages and the training load power of each training stage based on the state quality and the maximum exercise load power, it specifically is used for: Determine a target preset power range whose state quality is lower than a preset quality threshold, and determine the average state quality of the target preset power range; Based on the number of the target preset power ranges, the average state quality, and the maximum exercise load power, determine the score of the patient; Determine the target preset score range where the score is located from multiple preset score ranges. Each preset score range corresponds to a preset number in a training stage, and determine the preset number corresponding to the target preset score range as the number of the training stage; Based on the number of the training stage and the maximum exercise load power, determine the training load power of each training stage.

[0018] In another possible implementation manner, when generating the rehabilitation training plan for the patient based on the training load power of each training stage, the plan generation module is specifically configured to: Determine the preset power range where each training power is located; If there is a target training power, determine the number of days of the training stage corresponding to the target training power, where the target training power is the training power whose state quality in the preset power range is lower than the preset quality threshold; Determine the number of days of the training stage corresponding to each remaining training power except the target training power, and the number of days is the preset number of days; Generate the rehabilitation training plan for the patient based on the number of days of the training stage corresponding to each target training power and the number of days of the training stage corresponding to each remaining reference power.

[0019] In another possible implementation manner, when determining the number of days of the training stage corresponding to the target training power, the plan generation module is specifically configured to: Determine the third difference between the state quality of the preset power range where the target training power is located and the preset quality threshold; Based on the third difference and a preset coefficient, determine an adjustment value of the number of days; Add the preset number of days to the adjustment value of the number of days to obtain the number of days of the training stage corresponding to the target training power.

[0020] In a third aspect, the present application provides an electronic device, adopting the following technical solution: An electronic device, which includes: At least one processor; A memory; At least one application program, where at least one application program is stored in the memory and is configured to be executed by at least one processor, and at least one configuration is for: executing a rehabilitation training plan generation method according to any possible implementation manner shown in the first aspect.

[0021] In a fourth aspect, the present application provides a computer-readable storage medium, adopting the following technical solution: A computer-readable storage medium, when the computer program is executed on a computer, causes the computer to execute the method for generating a rehabilitation training plan according to any one of the first aspects.

[0022] In summary, the present application includes at least one of the following beneficial technical effects: Obtaining the maximum exercise load power of the patient on the ergometer, the heart rate change graph and the respiratory rate change graph during the test is convenient for subsequent accurate analysis. Both the heart rate change graph and the respiratory rate change graph are specific manifestations of the patient's physical condition and cardiopulmonary function during exercise on the ergometer. Therefore, by fusing the heart rate change graph and the respiratory rate change graph, a comprehensive state change graph of the patient can be obtained. The state quality corresponding to each of the patient in multiple preset power intervals is determined from the state change graph. Both the state quality and the maximum exercise load power are key factors characterizing the patient's own situation. Therefore, the number of appropriate training stages for the patient and the training load power for each training stage are comprehensively determined according to the state quality and the maximum exercise load power. Finally, a rehabilitation training plan is generated according to the determined training load power for each training stage. Compared with the existing methods for formulating rehabilitation training plans, it combines the change of the patient's physical state quality with the load power during the maximum exercise load power test on the ergometer, making the subsequent determined training plan more accurately adapt to the patient's own situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a schematic flowchart of a method for generating a rehabilitation training plan according to an embodiment of the present application.

[0024] Figure 2 is a schematic structural diagram of a device for generating a rehabilitation training plan according to an embodiment of the present application.

[0025] Figure 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] The following further details the present application with reference to the accompanying drawings.

[0027] Those skilled in the art can make modifications to this embodiment without creative contributions according to their needs after reading this specification, but they are protected by the Patent Law as long as they are within the scope of the claims of the present application.

[0028] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are some, but not all, of the embodiments of this application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of this application without creative efforts shall fall within the scope of protection of this application.

[0029] In addition, the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, unless otherwise specified.

[0030] The embodiments of this application will be further described in detail below with reference to the accompanying drawings of the specification.

[0031] The embodiments of this application provide a method for generating a rehabilitation training plan, which is executed by an electronic device. The electronic device can be a server or a terminal device. Among them, the server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto. The terminal device and the server can be directly or indirectly connected through wired or wireless communication methods, which are not limited in the embodiments of this application. As Figure 1 shown, the method includes steps S101, S102, S103, S104, and S105, where S101, obtain the maximum exercise load power of the patient during the load test on the ergometer, the heart rate change graph of the patient during the test, and the respiratory rate change graph.

[0032] For the embodiments of the present application, the ergometer bike resembles a normal bike in appearance. Its main purpose is to evaluate cardiopulmonary function and it can output the real-time load power of the ergometer bike. First, the patient conducts a test on the ergometer bike for the maximum exercise load power, that is, the load power starts from 0, and the maximum exercise load power can be the power when the patient reaches the preset heart rate threshold on the ergometer bike, which is also the limit of the load power that the patient can bear. The electronic device is connected to the ergometer bike through a wire, so that the maximum exercise load power can be obtained. The heart rate value of the patient can be obtained by placing a heart rate monitor on the patient's body (at the heart), and the heart rate monitor is connected to the electronic device through a wire, so that the electronic device can obtain the heart rate change graph of the patient during the test. Similarly, medical staff, etc. can set sensors such as pressure sensors and respiratory monitoring belts on the patient, and these sensors are also connected to the electronic device through wires, so that the electronic device can obtain the respiratory rate change graph of the patient during the test.

[0033] S102, fuse the heart rate change graph and the respiratory rate change graph to obtain the state change graph of the patient.

[0034] For the embodiments of the present application, both the heart rate change graph and the respiratory rate change graph characterize the specific changes and manifestations of the patient's physical condition and cardiopulmonary function during the test. Therefore, the electronic device fuses the heart rate change graph and the respiratory rate change graph to obtain a state change graph that characterizes the overall state of the patient. Obtaining the state change graph facilitates subsequent analysis and reduces the amount of data.

[0035] S103, determine the state quality corresponding to each of the multiple preset power intervals of the patient from the state change graph.

[0036] For the embodiments of the present application, the preset power interval can be a power interval set by relevant personnel according to a specified power range, such as a preset power interval starting from 0W according to a range of 3 watts (W), 5W, etc. The electronic device maps the multiple preset power intervals into the state change graph and divides the state change graph, so as to determine the state quality of each preset power interval.

[0037] S104, determine the number of training stages and the training load power of each training stage based on the state quality and the maximum exercise load power.

[0038] For the embodiments of the present application, the state quality of each preset power interval and the maximum exercise load power are both key factors characterizing the patient's own situation. Therefore, combining the state quality of each preset power interval and the maximum exercise load power can accurately determine the number of appropriate training stages for the patient and the training load power of each training stage.

[0039] S105, generate a rehabilitation training plan for the patient based on the training load power of each training stage.

[0040] For the embodiments of the present application, after the electronic device determines the training load power of each training stage, it can sort the load power of each training stage from small to large, and map the sorting result to a preset table template to obtain the patient's rehabilitation training plan. Further, the electronic device can also save the rehabilitation training plan in the preset table template in the form of a spreadsheet or control a printer device to print out the rehabilitation training plan, so as to facilitate relevant personnel to view. Therefore, by combining the change of the physical state quality of the patient with the change of the load power during the maximum exercise load power test on the ergometer, the subsequent determined training plan can be more accurately adapted to the patient's own situation.

[0041] In a possible implementation manner of the embodiments of the present application, in step S102, fusing the heart rate change graph and the respiratory rate change graph to obtain the patient's state change graph specifically includes step S1021 (not shown in the figure), step S1022 (not shown in the figure), and step S1023 (not shown in the figure), where S1021, determine the product of the heart rate value in the heart rate change graph and the respiratory rate value in the respiratory rate change graph at the same moment as the state value of the patient at the same moment.

[0042] For the embodiments of the present application, the electronic device can map the heart rate change graph and the respiratory rate change graph to the same preset coordinate system, so as to determine the heart rate value and the respiratory rate value at the same moment. The electronic device multiplies the heart rate value and the respiratory rate value at the same moment to obtain a product, and uses this product to represent the state value of the patient at each moment.

[0043] S1022, calculate the first difference between the state value at each moment and the reference state value at the corresponding moment.

[0044] For the embodiments of the present application, the reference state value is the state change graph of healthy people during the test on the ergometer. After the electronic device determines the state value of the patient at each moment, it finds the corresponding reference state value in the state change graph of healthy people according to the moment, and then calculates the difference between the state value and the reference state value to obtain the first difference. The difference represents the gap between the state of the patient and the state of healthy people at the same moment.

[0045] S1023, generate the patient's state change graph based on the first difference.

[0046] For the embodiments of the present application, the electronic device maps the first difference at each moment to a preset coordinate system to generate the patient's state change graph. By comparing the patient's state change graph with the state change graph of healthy people, the obtained state change graph representing the gap between the patient and healthy people is more accurate.

[0047] In a possible implementation manner of the embodiment of the present application, determining the state quality corresponding to each of the multiple preset power intervals of the patient from the state change diagram in step S103 specifically includes step S1031 (not shown in the figure), step S1032 (not shown in the figure), step S1033 (not shown in the figure), step S1034 (not shown in the figure), and step S1035 (not shown in the figure), where S1031, map the multiple preset power intervals to the state change diagram to obtain state change scatter points corresponding to each preset power interval.

[0048] For the embodiment of the present application, the electronic device maps the multiple preset power intervals to the state change diagram, that is, divides the state change diagram through the multiple preset power intervals to obtain state change scatter points corresponding to each preset power interval.

[0049] S1032, perform linear fitting and non-linear fitting on the state change scatter points corresponding to each preset power interval to obtain a linear function and a first curve segment corresponding to each preset power interval, and determine the first slope of each linear function.

[0050] For the embodiment of the present application, the electronic device performs linear fitting on the state change scatter points of each preset power interval through the Origin software plug-in, selects the function class to be fitted in the Origin software plug-in to linearly fit into a linear function, and similarly performs non-linear fitting in the Origin software plug-in to obtain the first curve segment corresponding to each preset power interval. The first curve segment represents the specific performance of the patient's state change within the preset power interval. After the electronic device determines the linear function of each preset power interval, it calculates the first slope of the linear function. The first slope represents the change amplitude of the patient's state within the preset power interval. The larger the slope, the greater the corresponding change amplitude, the greater the change of the patient's state, and the greater the trend of getting worse.

[0051] S1033, obtain a reference state change diagram.

[0052] Among them, the reference state change diagram represents the state change diagram of the healthy population.

[0053] For the embodiment of the present application, the reference state change diagram can be obtained by relevant personnel through the maximum exercise load power test of a large number of healthy people, and then the reference state change diagram is stored in the storage medium in the electronic device, so as to facilitate the electronic device to obtain. The reference state change diagram represents the change of the state value of the healthy population with power.

[0054] S1034, map the multiple preset power intervals to the reference state change diagram to obtain a second curve segment corresponding to each preset power interval.

[0055] For the embodiments of the present application, the electronic device maps each preset power interval to the reference state change diagram to obtain the second curve segment of each preset power interval, that is, the state change of healthy people within each preset power interval.

[0056] S1035. Determine the state quality corresponding to multiple preset power intervals based on the first slope, the first curve segment, and the second curve segment.

[0057] For the embodiments of the present application, in summary, the first slope, the first curve segment, and the second curve segment are all key factors characterizing the state quality of each preset power interval. Therefore, the electronic device can comprehensively determine the accurate state quality according to the first slope, the first curve segment, and the second curve segment.

[0058] In a possible implementation manner of the embodiments of the present application, in step S1035, determining the state quality corresponding to multiple preset power intervals based on the first slope, the first curve segment, and the second curve segment specifically includes step Sa (not shown in the figure), step Sb (not shown in the figure), step Sc (not shown in the figure), and step Sd (not shown in the figure), where Sa. Perform a linear transformation on each second curve segment to obtain a linear function corresponding to each second curve segment, and determine the second slope of the linear function corresponding to each second curve segment.

[0059] For the embodiments of the present application, the electronic device can select a suitable linear transformation matrix, apply the transformation matrix to the parametric equation of the original curve to obtain a new parametric equation. By selecting two points on the transformed curve (parametric equation), the parameters of the linear function can be solved and finally the linear function can be obtained. After the electronic device determines the linear function of the second curve segment, the second slope can be calculated.

[0060] Sb. Calculate the second difference between the first slope and the second slope of each preset power interval.

[0061] For the embodiments of the present application, the second slope characterizes the state change amplitude of the healthy population within the preset power interval. The electronic device subtracts the first slope and the second slope of the same preset power interval to obtain the second difference of the slopes. The larger the second difference, the greater the gap between the state change amplitude of the patient and the state change amplitude of the healthy population.

[0062] Sc. Calculate the similarity between the first curve segment and the second curve segment of each preset power interval.

[0063] For the embodiments of the present application, the electronic device can convert the first curve segment and the second curve segment into vectors and represent them by vectors. Then, the electronic device inputs the vectors of the two curve segments into the trained network model to calculate the cosine distance to obtain the similarity. The higher the similarity, the closer the state changes of the patient and the healthy population in the same preset power interval are.

[0064] Sd, determine the state quality corresponding to each preset power interval based on the second difference, the similarity, and their respective corresponding weights.

[0065] For the embodiments of the present application, in summary, both the second difference and the similarity are key factors characterizing the state quality of the patient in each preset power interval, and their degrees of influence on the state quality are different. Therefore, relevant personnel can set their respective corresponding weights for the second difference and the similarity. After the electronic device determines the second difference and the similarity, it can call their respective corresponding coefficients to determine a score, which characterizes the state quality of each preset power interval.

[0066] In a possible implementation manner of the embodiments of the present application, in step S104, determine the number of training stages and the training load power of each training stage based on the state quality and the maximum exercise load power, specifically including step S1041 (not shown in the figure), step S1042 (not shown in the figure), step S1043 (not shown in the figure), and step S1044 (not shown in the figure), where S1041, determine the target preset power interval with the state quality lower than the preset quality threshold, and determine the average value of the state quality of the target preset power interval.

[0067] For the embodiments of the present application, the preset quality threshold is used as the demarcation point for whether the state quality meets the qualified requirements. The electronic device compares the state quality of each preset power interval with the preset quality threshold to determine the target preset power interval with the state quality lower than the preset quality threshold. The electronic device calculates the average value of the state quality of the target preset power interval to obtain the average value of the state quality. It is more accurate to use the average value of the state quality to characterize the overall state quality of all target preset power intervals. The lower the average value of the state quality, the worse the state quality of the patient when the state quality is unqualified.

[0068] S1042, determine the score of the patient based on the number of target preset power intervals, the average value of the state quality, and the maximum exercise load power.

[0069] For the embodiments of the present application, the larger the number of target preset power intervals, the greater the probability that the patient's state quality is unqualified. The smaller the maximum exercise load power, the smaller the limit load power that the patient can bear, which also indicates that the patient's state quality is worse. In summary, the number of target preset power intervals, the average state quality, and the maximum exercise load power determine the score of the patient regarding the physical state. Specifically, the above three factors such as the number of target preset power intervals are all key factors affecting the patient's physical condition, and the influencing degrees are different. Therefore, relevant personnel can set their respective corresponding coefficients for the above three factors and store them in the electronic device. The electronic device calls their respective corresponding coefficients to perform weighted calculations on the number of target preset power intervals, the average state quality, and the maximum exercise load power to obtain the score of the patient.

[0070] S1043. Determine the target preset score interval where the score is located from multiple preset score intervals. Each preset score interval corresponds to a preset number in the training stage, and determine the preset number corresponding to the target preset score interval as the number of the training stage.

[0071] For the embodiments of the present application, the electronic device compares the score with each preset score interval to determine the target preset score interval where the score is located. The preset number of each training stage corresponding to each preset score interval is a suitable number set by relevant personnel through a large number of experiments or requirements. Therefore, the electronic device can use the preset number corresponding to the target preset score interval as the number of the training stage for this patient.

[0072] S1044. Determine the training load power of each training stage based on the number of training stages and the maximum exercise load power.

[0073] For the embodiments of the present application, the electronic device can divide the maximum exercise load power by the number of training stages to obtain the training load power of each training stage. For example, if the maximum exercise load is 150W and the determined number of training stages is 10, then the training load power of each training stage is 150÷10 = 15W. That is, the electronic device starts from 0W and increments by 15W to obtain the training load power of each training stage. By determining the appropriate and accurate number of training stages through the score representing the patient's physical condition, and then combining with the maximum exercise load power, the appropriate training load power of each training stage can be determined, so that the training load power increases gradually in a regular manner.

[0074] A possible implementation manner of the embodiments of the present application. In step S106, generating a rehabilitation training plan for the patient based on the training load power of each training stage specifically includes step S1061 (not shown in the figure), step S1062 (not shown in the figure), step S1063 (not shown in the figure), and step S1064 (not shown in the figure), where S1061. Determine the preset power interval where each training power is located.

[0075] For the embodiments of the present application, the electronic device determines the preset power interval where the training power of each training stage is located, so as to facilitate subsequent judgment on whether to adjust the number of training days of the training stage according to the patient's condition within the preset power interval.

[0076] S1062. If there is a target training power, determine the number of days of the training stage corresponding to the target training power.

[0077] Wherein, the target training power is the training power whose state quality in the preset power interval is lower than the preset quality threshold.

[0078] For the embodiments of the present application, if there is a target training power, it indicates that the patient's state quality in the preset power interval of the target training power is poor, and the patient needs to undergo longer training and rehabilitation in the training stage of the target training power so that the patient can better adapt to the target training power and improve the rehabilitation effect of the patient at the target training power.

[0079] S1063. Determine the number of days of the training stage corresponding to each remaining training power except the target training power.

[0080] Wherein, the number of days is the preset number of days.

[0081] For the embodiments of the present application, except for the target training power, the state quality of the patient in the preset power interval corresponding to the remaining training powers is relatively close to that of the healthy population. Therefore, it is sufficient to determine the preset number of days as the number of days for each remaining training power. The preset number of days is the benchmark number of days set by relevant personnel according to the actual situation and a large number of experiments under the condition of relatively good state quality.

[0082] S1064. Generate a rehabilitation training plan for the patient based on the number of days of the training stage corresponding to each target training power and the number of days of the training stage corresponding to each remaining reference power.

[0083] For the embodiments of the present application, after the electronic device determines the number of days of the training stage of each target training power and the number of days of the training stage corresponding to each remaining reference power, a training plan for the patient in the entire rehabilitation stage is obtained. The electronic device can generate a rehabilitation training plan for the patient by sorting in ascending order of the training power. The rehabilitation training plan obtained through the above solution is more in line with the patient's own situation, making the rehabilitation training plan more accurate.

[0084] A possible implementation manner of the embodiments of the present application. Determining the number of days of the training stage corresponding to the target training power in step S1062 specifically includes step one, step two, and step three, wherein Step 1: Determine the third difference between the state quality of the preset power range where the target training power is located and the preset quality threshold.

[0085] For the embodiments of the present application, after the electronic device determines the target training power, in order to better determine the number of days in the training phase corresponding to the target training power, the electronic device determines the third difference between the state quality of the preset power range where the target training power is located and the preset quality threshold. The larger the third difference, the greater the gap between the state quality of the patient in the preset power range and that of the healthy population, and the more days of training and rehabilitation are required.

[0086] Step 2: Determine the number of days adjustment value based on the third difference and the preset coefficient.

[0087] For the embodiments of the present application, the preset coefficient is set by relevant personnel according to the actual situation and a large number of experiments. After the electronic device determines the third difference, multiplying the third difference by the preset coefficient can determine an adjustment value, and the electronic device can round or round up the adjustment value to obtain the final number of days adjustment value.

[0088] Step 3: Add the preset number of days to the number of days adjustment value to obtain the number of days in the training phase corresponding to the target training power.

[0089] For the embodiments of the present application, then the electronic device can obtain the appropriate number of training days for the target training power by adding the preset number of days to the number of days adjustment value, so that the patient can better adapt to and perform rehabilitation training in the training phase of the target training power.

[0090] The above embodiments introduce a rehabilitation training plan generation method from the perspective of the method process. The following embodiments introduce a rehabilitation training plan generation device from the perspective of virtual modules or virtual units. For details, see the following embodiments.

[0091] Embodiments of the present application provide a rehabilitation training plan generation device 20, as Figure 2 shown. The rehabilitation training plan generation device 20 may specifically include: A data acquisition module 201, configured to acquire the maximum exercise load power of the patient during a load test on a power bike, the heart rate change graph of the patient during the test, and the respiratory rate change graph; A fusion module 202, configured to fuse the heart rate change graph and the respiratory rate change graph to obtain a state change graph of the patient; A quality determination module 203, configured to determine the state quality corresponding to each preset power range of the patient from the state change graph; A power determination module 204, configured to determine the number of training phases and the training load power of each training phase based on the state quality and the maximum exercise load power; A solution generation module 205 is configured to generate a rehabilitation training plan for a patient based on the training load power of each training stage.

[0092] An embodiment of the present application discloses a rehabilitation training plan generation device 20. Among them, a data acquisition module 201 acquires the maximum exercise load power of a patient on an ergometer, the heart rate change graph and the respiratory rate change graph during the test for subsequent precise analysis. The heart rate change graph and the respiratory rate change graph are both specific manifestations of the patient's physical condition and cardiopulmonary function during exercise on the ergometer. Therefore, a fusion module 202 fuses the heart rate change graph and the respiratory rate change graph to obtain a comprehensive state change graph of the patient. A quality determination module 203 determines the state quality corresponding to each of the patient in multiple preset power intervals from the state change graph. The state quality and the maximum exercise load power are both key factors characterizing the patient's own situation. Therefore, a power determination module 204 comprehensively determines the number of appropriate training stages for the patient and the training load power of each training stage according to the state quality and the maximum exercise load power. Finally, a solution generation module 205 generates a rehabilitation training plan according to the determined training load power of each training stage. Compared with the existing methods for formulating rehabilitation training plans, it combines the change of the patient's physical state quality with the load power during the maximum exercise load power test on the ergometer, making the subsequent determined training plan more accurately adapted to the patient's own situation.

[0093] In a possible implementation manner of the embodiment of the present application, when the fusion module 202 fuses the heart rate change graph and the respiratory rate change graph to obtain the state change graph of the patient, it is specifically configured to: Determine the product of the heart rate value in the heart rate change graph and the respiratory rate value in the respiratory rate change graph at the same time as the state value of the patient at the same time; Calculate the first difference between the state value at each moment and the reference state value at the corresponding moment; Generate a state change graph of the patient based on the first difference.

[0094] In a possible implementation manner of the embodiment of the present application, when the quality determination module 203 determines the state quality corresponding to each of the patient in multiple preset power intervals from the state change graph, it is specifically configured to: Map multiple preset power intervals to the state change graph to obtain state change scatter points corresponding to each preset power interval; Perform linear fitting and non-linear fitting on the state change scatter points corresponding to each preset power interval to obtain a linear function and a first curve segment corresponding to each preset power interval, and determine the first slope of each linear function; Obtain a reference state change graph, where the reference state change graph characterizes the state change graph of a healthy population; Map multiple preset power intervals to the reference state change diagram to obtain a second curve segment corresponding to each preset power interval; Determine the state quality corresponding to multiple preset power intervals based on the first slope, the first curve segment, and the second curve segment.

[0095] In a possible implementation manner of the embodiment of the present application, when the quality determination module 203 determines the state quality corresponding to multiple preset power intervals based on the first slope, the first curve segment, and the second curve segment, it is specifically configured to: Perform a linear transformation on each second curve segment to obtain a linear function corresponding to each second curve segment, and determine a second slope of the linear function corresponding to each second curve segment; Calculate a second difference between the first slope and the second slope of each preset power interval; Calculate the similarity between the first curve segment and the second curve segment of each preset power interval; Determine the state quality corresponding to each preset power interval based on the second difference, the similarity, and their respective weights.

[0096] In a possible implementation manner of the embodiment of the present application, when the power determination module 204 determines the number of training stages and the training load power of each training stage based on the state quality and the maximum exercise load power, it is specifically configured to: Determine a target preset power interval whose state quality is lower than a preset quality threshold, and determine the average value of the state quality of the target preset power interval; Determine the score of the patient based on the number of target preset power intervals, the average value of the state quality, and the maximum exercise load power; Determine the target preset score interval where the score is located from multiple preset score intervals, each preset score interval corresponds to a preset number of training stages, and determine the preset number corresponding to the target preset score interval as the number of training stages; Determine the training load power of each training stage based on the number of training stages and the maximum exercise load power.

[0097] In a possible implementation manner of the embodiment of the present application, when the solution generation module 205 generates a rehabilitation training solution for the patient based on the training load power of each training stage, it is specifically configured to: Determine the preset power interval where each training power is located; If there is a target training power, determine the number of days of the training stage corresponding to the target training power, where the target training power is the training power whose state quality in the preset power interval where it is located is lower than the preset quality threshold; Determine the number of days of the training stage corresponding to each remaining training power except the target training power, and the number of days is the preset number of days; Generate a rehabilitation training plan for the patient based on the number of days of the training phase corresponding to each target training power and the number of days of the training phase corresponding to each remaining reference power.

[0098] In a possible implementation manner of the embodiment of the present application, when determining the number of days of the training phase corresponding to the target training power, the scheme generation module 205 is specifically configured to: Determine the third difference between the state quality of the preset power interval where the target training power is located and the preset quality threshold; Determine a number of days adjustment value based on the third difference and a preset coefficient; Add the preset number of days to the number of days adjustment value to obtain the number of days of the training phase corresponding to the target training power.

[0099] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process of a rehabilitation training plan generation device 20 described above can refer to the corresponding process in the foregoing method embodiment, and will not be elaborated herein.

[0100] In the embodiment of the present application, an electronic device is provided, such as Figure 3 shown Figure 3 The electronic device 30 shown includes: a processor 301 and a memory 303. Among them, the processor 301 and the memory 303 are connected, such as connected through a bus 302. Optionally, the electronic device 30 may further include a transceiver 304. It should be noted that in actual applications, the transceiver 304 is not limited to one, and the structure of the electronic device 30 does not constitute a limitation to the embodiment of the present application.

[0101] The processor 301 may be a CPU (Central Processing Unit, central processor), a general-purpose processor, a DSP (Digital Signal Processor, data signal processor), an ASIC (Application Specific Integrated Circuit, application-specific integrated circuit), an FPGA (Field Programmable Gate Array, field programmable gate array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can implement or execute various exemplary logic blocks, modules and circuits described in combination with the disclosure of the present application. The processor 301 may also be a combination for implementing a computing function, such as a combination including one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0102] The bus 302 may include a path for transmitting information between the above components. The bus 302 can be a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus 302 can be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 3 only a thick line is used in Figure 3 , but it does not mean that there is only one bus or one type of bus.

[0103] The memory 303 can be a ROM (Read Only Memory), or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory), or other types of dynamic storage devices that can store information and instructions. It can also be an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory), or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0104] The memory 303 is used to store the application program code for executing the solution of this application, and is controlled by the processor 301 for execution. The processor 301 is used to execute the application program code stored in the memory 303 to implement the content shown in the foregoing method embodiments.

[0105] Among them, the electronic device includes but is not limited to: mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Tablet Computers), PMPs (Portable Multimedia Players), in-vehicle terminals (such as in-vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. It can also be a server, etc. Figure 3 The shown electronic device is only an example and should not impose any limitations on the functions and usage scopes of the embodiments of this application.

[0106] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program runs on a computer, the computer can execute the corresponding content in the foregoing method embodiment. Compared with the related art, obtaining the maximum exercise load power of a patient on an ergometer, the heart rate change graph and the respiratory rate change graph during the test is convenient for subsequent accurate analysis. The heart rate change graph and the respiratory rate change graph are both specific manifestations of the patient's physical condition and cardiopulmonary function during exercise on the ergometer. Therefore, by fusing the heart rate change graph and the respiratory rate change graph, a comprehensive state change graph of the patient can be obtained. The state quality corresponding to each of multiple preset power intervals of the patient is determined from the state change graph. Both the state quality and the maximum exercise load power are key factors characterizing the patient's own situation. Therefore, the number of appropriate training stages for the patient and the training load power of each training stage are comprehensively determined according to the state quality and the maximum exercise load power. Finally, a rehabilitation training plan is generated according to the determined training load power of each training stage. Compared with the existing method of formulating a rehabilitation training plan, it combines the change of the patient's physical state quality with the load power during the maximum exercise load power test on the ergometer, making the subsequently determined training plan more accurately adapted to the patient's own situation.

[0107] It should be understood that although the steps in the flowchart of the accompanying drawings are shown in sequence according to the arrows, these steps do not necessarily have to be executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps is not strictly restricted by order, and they can be executed in other orders. Moreover, at least some of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages. These sub-steps or stages do not necessarily have to be executed at the same time, but can be executed at different times. Their execution order does not necessarily have to be sequential, but can be executed alternately or alternately with at least a part of other steps or sub-steps or stages of other steps.

[0108] The above are only some embodiments of the present application. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present application, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present application.

Claims

1. A method for generating a rehabilitation training program, characterized in that: include: Obtain the patient's maximum exercise load power when performing a load test on a power bike, the patient's heart rate change graph during the test, and the respiratory rate change graph; Fusing the heart rate change graph and the respiratory rate change graph to obtain a state change graph of the patient; Determining the state quality corresponding to each of the plurality of preset power intervals of the patient from the state change diagram; Determine the number of training phases and the training load power of each training phase based on the state quality and the maximum exercise load power; A rehabilitation training program for the patient is generated based on the training load power of each training phase.

2. A method for generating a rehabilitation training program according to claim 1, characterized in that: The step of fusing the heart rate change graph and the respiratory rate change graph to obtain the patient's state change graph includes: Determine the product of the heart rate value in the heart rate variation graph and the respiratory rate value in the respiratory rate variation graph at the same moment as the state value of the patient at the same moment; Calculate the first difference between the state value at each moment and the reference state value at the corresponding moment; A state change diagram of the patient is generated based on the first difference.

3. A method for generating a rehabilitation training program according to claim 1, characterized in that: The determining, from the state change diagram, the state qualities corresponding to each of the plurality of preset power intervals of the patient includes: Mapping the multiple preset power intervals to the state change graph to obtain state change scatter points corresponding to each preset power interval; Performing linear fitting and nonlinear fitting on the state change scatter points corresponding to each preset power interval to obtain a linear function and a first curve segment corresponding to each preset power interval, and determining a first slope of each linear function; Acquire a baseline state change graph, wherein the baseline state change graph represents a state change graph of a healthy population; Mapping the plurality of preset power intervals to the reference state change diagram to obtain a second curve segment corresponding to each preset power interval; The state qualities corresponding to the plurality of preset power intervals are determined based on the first slope, the first curve segment, and the second curve segment.

4. A method for generating a rehabilitation training program according to claim 3, characterized in that: The determining, based on the first slope, the first curve segment, and the second curve segment, of the state qualities corresponding to the plurality of preset power intervals includes: Performing a linear transformation on each second curve segment to obtain a linear function corresponding to each second curve segment, and determining a second slope of the linear function corresponding to each second curve segment; Calculating a second difference between the first slope and the second slope in each preset power interval; Calculating the similarity between the first curve segment and the second curve segment in each preset power interval; The state quality corresponding to each preset power interval is determined based on the second difference, the similarity and the respective corresponding weights.

5. A method for generating a rehabilitation training program according to claim 1, characterized in that: The determining the number of training stages and the training load power of each training stage based on the state quality and the maximum exercise load power includes: Determine a target preset power interval in which the state quality is lower than a preset quality threshold, and determine an average state quality of the target preset power interval; Determining the patient's score based on the number of the target preset power intervals, the state quality average, and the maximum exercise load power; Determine a target preset score interval where the score is located from a plurality of preset score intervals, each preset score interval corresponds to a preset number of training stages, and determine the preset number corresponding to the target preset score interval as the number of training stages; The training load power of each training phase is determined based on the number of the training phases and the maximum exercise load power.

6. A method for generating a rehabilitation training program according to claim 5, characterized in that: Generating a rehabilitation training program for the patient based on the training load power of each training phase, including: Determine the preset power zone for each training power; If there is a target training power, determining the number of days of the training phase corresponding to the target training power, wherein the target training power is a training power in which the state quality of the preset power interval is lower than a preset quality threshold; Determine the number of days of the training phase corresponding to each remaining training power except the target training power, where the number of days is a preset number of days; A rehabilitation training program for the patient is generated based on the number of days of the training phase corresponding to each target training power and the number of days of the training phase corresponding to each remaining baseline power.

7. A method for generating a rehabilitation training program according to claim 6, characterized in that: The step of determining the number of days of the training phase corresponding to the target training power comprises: Determine a third difference between the state quality of the preset power interval where the target training power is located and a preset quality threshold; Determine a day adjustment value based on the third difference and a preset coefficient; The preset number of days is added to the day adjustment value to obtain the number of days of the training phase corresponding to the target training power.

8. A rehabilitation training program generating device, characterized in that: include: A data acquisition module is used to obtain the patient's maximum exercise load power when the load test is performed on the power bike, the patient's heart rate change graph and respiratory rate change graph during the test; A fusion module, used for fusing the heart rate change graph and the respiratory rate change graph to obtain a state change graph of the patient; A quality determination module, used to determine the state quality corresponding to each of the plurality of preset power intervals of the patient from the state change diagram; A power determination module, used to determine the number of training stages and the training load power of each training stage based on the state quality and the maximum exercise load power; A program generation module is used to generate a rehabilitation training program for the patient based on the training load power of each training stage.

9. An electronic device, characterized in that: It includes: at least one processor; Memory; At least one application, wherein the at least one application is stored in the memory and is configured to be executed by the at least one processor, and the at least one application is used to execute a rehabilitation training program generation method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed in a computer, the computer is caused to execute a method for generating a rehabilitation training program according to any one of claims 1 to 7.

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