Exercise prescription generation device and method, equipment and storage medium

Abstract

The invention discloses an exercise prescription generation device, method and equipment and a storage medium, and is applied to the field of prescription generation. The device constructs a basic data set by obtaining personal body information and exercise injury information of a target patient, and performs data analysis based on the basic data set and a historical disease data set of the target patient; and a target exercise prescription most conforming to the physical condition of the target patient is screened from the exercise prescriptions. According to the method, the personalized exercise prescription of the patient is automatically generated through the personal body information of the target patient, the exercise injury information and the historical disease information, so that the problems of high workload and low prescription generation efficiency caused by manual analysis of a doctor are avoided, the exercise prescription generation efficiency is improved, and the user experience is improved. And the obtained exercise prescription better conforms to the physical condition of the target patient, and the rehabilitation effect of the patient is improved.

Description

Technical Field

[0001] The present invention relates to the field of prescription generation, and in particular to an exercise prescription generation device, method, equipment and computer-readable storage medium. Background Art

[0002] When issuing exercise prescriptions, doctors usually need to spend a lot of time reviewing patients' medical records, imaging materials and other medical data, and conduct complex analysis to formulate exercise prescriptions for patients. This not only increases the workload of doctors, but also prolongs the time patients have to wait for treatment, reducing treatment efficiency. In addition, traditional exercise prescriptions often use standardized rehabilitation programs, which are difficult to adjust according to individual differences and specific injuries of patients, resulting in poor rehabilitation effects. Although medical institutions have accumulated a large amount of patient data, these data are often not fully utilized, lack of data for effective analysis and application, and cannot provide strong support for the formulation of personalized exercise prescriptions. There is a lack of effective ways to generate exercise prescriptions. Summary of the invention

[0003] The purpose of the present invention is to provide an exercise prescription generation device, method, equipment and computer-readable storage medium, which are applied to the field of prescription generation. The invention automatically generates personalized exercise prescriptions for patients through the personal physical information, sports injury information and historical medical information of the target patient, thereby improving the efficiency of generating exercise prescriptions, and the obtained exercise prescriptions are more in line with the physical condition of the target patient, thereby improving the patient's rehabilitation effect.

[0004] In order to solve the above technical problems, the present invention provides an exercise prescription generating device, comprising:

[0005] The first module is used to obtain personal physical information and sports injury information of the target patient to construct a basic data set, and determine a standard verification action set and a physical function influence coefficient based on the basic data set;

[0006] The second module is used to determine the verification action completion coefficient of the standard verification action set, and determine the motion recovery coefficient based on the body function influence coefficient and the verification action completion coefficient;

[0007] A third module is used to match the available exercise prescription of the target patient based on the basic data set and the verification action completion coefficient;

[0008] A fourth module is used to determine the exercise load influence coefficient of the available exercise prescription based on the exercise recovery coefficient and the rehabilitation movement difficulty coefficient of each type of rehabilitation movement in the available exercise prescription;

[0009] A fifth module is used to determine the effective influence coefficient of the comprehensive rehabilitation action based on the historical disease data set of the target patient, and determine the exercise effectiveness coefficient of the available exercise prescription based on the effective influence coefficient of the comprehensive rehabilitation action and the exercise load influence coefficient;

[0010] The sixth module is used to determine the available exercise prescription whose exercise effectiveness coefficient is greater than the exercise effectiveness coefficient threshold as the target exercise prescription.

[0011] Optionally, the second module includes:

[0012] The first unit is used to obtain the dynamic profile and completion time of the target patient completing various verification actions in the standard verification action set;

[0013] The second unit is used to match the dynamic profile with the standard profile of the standard verification action set to obtain the action completion degree of each verification action;

[0014] A third unit is used for inputting the completion time and the action completion degree into the first model to obtain the output of the verification action completion coefficient;

[0015] The expression of the first model is:

[0016] ;

[0017] Where Acc is the verification action completion coefficient of the standard verification action set, i represents the number of each verification action, k is the total number of each verification action, is the standard completion time of the i-th type of verification action, is the completion time of the i-th type of verification action, is the action completion degree of the i-th type verification action, It is the influencing factor of the preset action completion coefficient.

[0018] Optionally, the second module includes:

[0019] A fourth unit is used to input the body function influence coefficient and the verification action completion coefficient into the second model to obtain the output motion recovery coefficient;

[0020] The expression of the second model is:

[0021] ;

[0022] Wherein, Rsc is the motion recovery coefficient, e is a natural constant, Acc is the verification action completion coefficient, pf is the body function influence coefficient, is the preset action energy consumption influencing factor, is the preset motion recovery coefficient influencing factor, k is the total number of various verification actions, i is the number of various verification actions, is the action difficulty coefficient of the i-th type of verification action.

[0023] Optionally, the third module includes:

[0024] A fifth unit is used for matching and obtaining the initially screened available prescriptions and the rehabilitation effect set of the initially screened available prescriptions from a database based on the basic data set;

[0025] A sixth unit is used to determine the verification action completion coefficient improvement rate of each of the initially screened available prescriptions based on the pre-treatment verification action completion coefficient and the post-treatment verification action completion coefficient in the rehabilitation effect set;

[0026] The seventh unit is used to screen the available prescriptions for secondary screening from the available prescriptions for primary screening based on the action completion coefficient, and determine the available prescriptions for secondary screening whose verification action completion coefficient improvement rate is greater than a preset verification action completion coefficient improvement rate threshold as the available prescription.

[0027] Optionally, the fourth module includes:

[0028] An eighth unit is used to input the exercise recovery coefficient and the rehabilitation action difficulty coefficient of each type of rehabilitation action in the available exercise prescription into a third model to obtain the output exercise load influence coefficient of the available exercise prescription;

[0029] The expression of the third model is:

[0030] ;

[0031] In the formula, is the exercise load influence coefficient of the mth available exercise prescription, is the number of repetitions of the pth rehabilitation action in the mth available exercise prescription, q is the total number of rehabilitation actions in the mth available exercise prescription, is the rehabilitation action difficulty coefficient of the pth rehabilitation action in the mth available exercise prescription, Rsc is the exercise recovery coefficient, To preset the conversion factor between exercise intensity and energy consumption, It is the preset motion load influence coefficient factor.

[0032] Optionally, the fifth module includes:

[0033] The ninth unit is used to obtain the historical disease data of the target patient; the historical disease data includes the type of disease, the degree of disease and the time of disease;

[0034] A tenth unit is used to construct a disease data set of each disease type based on the historical disease data, and determine a standard rehabilitation treatment influence coefficient of each disease data set;

[0035] The eleventh unit is used to input the standard rehabilitation treatment influence coefficient into the fourth model to obtain the output effective influence coefficient of the comprehensive rehabilitation action;

[0036] The expression of the fourth model is:

[0037] ;

[0038] Where A is the effective influence coefficient of the comprehensive rehabilitation action, For the The standard rehabilitation treatment impact coefficient of the disease data set, is the total number of disease datasets, It is the influencing factor of the effective influence coefficient of the preset comprehensive rehabilitation action.

[0039] Optionally, the fifth module includes:

[0040] A twelfth unit is used to input the comprehensive rehabilitation action effective influence coefficient and the exercise load influence coefficient into a fifth model to obtain the output exercise effective coefficient of the available exercise prescription;

[0041] The expression of the fifth model is:

[0042] ;

[0043] In the formula, is the exercise effectiveness coefficient of the mth available exercise prescription, is the exercise load influence coefficient of the mth available exercise prescription, A is the effective influence coefficient of the comprehensive rehabilitation action, is the improvement rate of the verification action completion coefficient of the mth available exercise prescription, It is the influencing factor of the preset motion effectiveness coefficient.

[0044] In order to solve the above technical problems, the present invention provides a method for generating an exercise prescription, comprising:

[0045] Obtaining personal physical information and sports injury information of the target patient to construct a basic data set, and determining a standard verification action set and a physical function influence coefficient based on the basic data set;

[0046] Determine the verification action completion coefficient of the standard verification action set, and determine the motion recovery coefficient based on the body function influence coefficient and the verification action completion coefficient;

[0047] Matching the available exercise prescription of the target patient based on the basic data set and the verification action completion coefficient;

[0048] Determining the exercise load influence coefficient of the available exercise prescription based on the exercise recovery coefficient and the rehabilitation movement difficulty coefficients of various rehabilitation movements in the available exercise prescription;

[0049] Determine the effective influence coefficient of the comprehensive rehabilitation action based on the historical disease data set of the target patient, and determine the effective coefficient of the exercise prescription of the available exercise prescription based on the effective influence coefficient of the comprehensive rehabilitation action and the exercise load influence coefficient;

[0050] The available exercise prescription whose exercise effectiveness coefficient is greater than an exercise effectiveness coefficient threshold is determined as a target exercise prescription.

[0051] In order to solve the above technical problems, the present invention provides an exercise prescription generating device, comprising:

[0052] Memory, for storing computer programs;

[0053] A processor is used to implement the exercise prescription generation method when executing the computer program.

[0054] In order to solve the above technical problems, the present invention provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, the exercise prescription generation method is implemented.

[0055] It can be seen that the present invention obtains the personal physical information and sports injury information of the target patient to construct a basic data set, determines the standard verification action set and the body function influence coefficient based on the basic data set; determines the verification action completion coefficient of the standard verification action set, and determines the movement recovery coefficient based on the body function influence coefficient and the verification action completion coefficient; matches the available exercise prescription of the target patient based on the basic data set and the verification action completion coefficient; determines the exercise load influence coefficient of the available exercise prescription based on the movement recovery coefficient and the rehabilitation action difficulty coefficient of each type of rehabilitation action in the available exercise prescription; determines the comprehensive rehabilitation action effective influence coefficient based on the historical disease data set of the target patient, and determines the exercise effective coefficient of the available exercise prescription based on the comprehensive rehabilitation action effective influence coefficient and the exercise load influence coefficient; determines the available exercise prescription whose exercise effective coefficient is greater than the exercise effective coefficient threshold as the target exercise prescription. The automatic generation of the patient's personalized exercise prescription is performed through the personal physical information, sports injury information and historical disease information of the target patient, which improves the generation efficiency of the exercise prescription, and the obtained exercise prescription is more in line with the physical condition of the target patient, thereby improving the patient's rehabilitation effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0057] Figure 1 A structural block diagram of an exercise prescription generating device provided by an embodiment of the present invention;

[0058] Figure 2 The present invention provides a flowchart of a method for generating an exercise prescription. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] Traditional exercise prescriptions often use standardized rehabilitation programs, which are difficult to adjust according to individual differences and specific injuries of patients. Although medical institutions have accumulated a large amount of patient data, these data are often not fully utilized, lacking effective data analysis and application, unable to provide strong support for the formulation of personalized exercise prescriptions, and lacking effective auxiliary methods for exercise prescriptions.

[0061] When issuing exercise prescriptions with existing technologies, doctors usually need to spend a lot of time reviewing patients' medical records, imaging materials and other medical data, and perform complex manual analysis before formulating them. This not only increases the workload of doctors, but also prolongs the time patients have to wait for treatment and reduces treatment efficiency. Existing technologies lack personalized testing for patients, and peers lack effective analysis and learning of historical medical record data, making it impossible to formulate personalized and highly specialized exercise prescriptions for patients, which indirectly reduces the rehabilitation effect of patients and prolongs the rehabilitation cycle.

[0062] The present invention assists in generating personalized exercise prescriptions for target patients by obtaining health factors such as the patient's sports injury information, personal physical information, and historical medical information for auxiliary analysis, thereby improving the pertinence and effectiveness of the exercise prescriptions. The present invention can further effectively analyze and utilize a large amount of patient data accumulated in the database of a medical institution, mine useful information in the data, and provide a data basis and theoretical basis for the patient's personalized exercise prescription, thereby making the personalized exercise prescription efficient, scientific, and intelligent, thereby improving the patient's rehabilitation effect, reducing rehabilitation time, and reducing the risk of secondary injuries.

[0063] The following combination Figure 1 , Figure 1 This is a structural block diagram of an exercise prescription generation device provided by an embodiment of the present invention, and the device may include:

[0064] The first module is used to obtain the personal physical information and sports injury information of the target patient to construct a basic data set, and determine the standard verification action set and the physical function influence coefficient based on the basic data set;

[0065] The second module is used to determine the verification action completion coefficient of the standard verification action set, and determine the movement recovery coefficient based on the body function influence coefficient and the verification action completion coefficient;

[0066] The third module is used to match the available exercise prescriptions for target patients based on the basic data set and the verified action completion coefficient;

[0067] The fourth module is used to determine the exercise load influence coefficient of the available exercise prescription based on the exercise recovery coefficient and the rehabilitation action difficulty coefficient of each type of rehabilitation action in the available exercise prescription;

[0068] The fifth module is used to determine the effective influence coefficient of the comprehensive rehabilitation action based on the historical disease data set of the target patient, and determine the effective coefficient of the exercise prescription based on the effective influence coefficient of the comprehensive rehabilitation action and the exercise load influence coefficient;

[0069] The sixth module is used to determine the available exercise prescription whose exercise effectiveness coefficient is greater than the exercise effectiveness coefficient threshold as the target exercise prescription.

[0070] The first module in this embodiment can be used to obtain the personal physical information and sports injury information of the target patient to construct a basic data set, and determine the standard verification action set and the body function influence coefficient based on the basic data set. This embodiment does not limit the specific type of personal physical information, which can generally include height, weight, age, gender and daily average exercise volume; this embodiment does not limit the specific type of sports injury information, which can generally include injury site, injury type and injury degree. This embodiment can obtain personal physical information and medical records containing sports injury information from the database.

[0071] This embodiment does not limit the specific manner in which the first module determines the standard verification action set and the body function influence coefficient based on the basic data set. The general database stores the standard verification action set corresponding to each injury site, injury type and injury degree, wherein the standard verification action set includes the standard dynamic profile, action difficulty coefficient and standard completion time of each type of verification action. This embodiment can perform screening and matching in the database based on the basic data set of the target patient to obtain the standard verification action set of the target patient. The general database stores the body function assessment standards corresponding to each body function influence coefficient, wherein the body function assessment standards include standard height, standard weight, standard age, standard gender and standard daily average exercise volume. According to the height, weight, age, gender and daily average exercise volume corresponding to the target patient, the body function assessment standards and body function influence coefficient corresponding to each body function influence coefficient are matched, and the body function assessment standards and body function influence coefficient corresponding to the target patient can be obtained.

[0072] The second module in this embodiment can be used to determine the verification action completion coefficient of the standard verification action set. This embodiment does not limit the specific method of determining the verification action completion coefficient of the standard verification action set. Generally, the second module may include:

[0073] The first unit is used to obtain the dynamic profiles and completion times of various verification actions in the standard verification action set completed by the target patient;

[0074] The second unit is used to match the dynamic profile with the standard profile of the standard verification action set to obtain the action completion degree of each verification action;

[0075] The third unit is used to input the completion time and the action completion degree into the first model to obtain the output verification action completion coefficient;

[0076] The expression of the first model is:

[0077] ;

[0078] Where Acc is the verification action completion coefficient of the standard verification action set, i represents the number of each verification action, k is the total number of each verification action, is the standard completion time of the i-th type of verification action, is the completion time of the i-th type of verification action, is the action completion degree of the i-th type of verification action, It is the influencing factor of the preset action completion coefficient.

[0079] In this embodiment, the first unit can be used to obtain the dynamic contours and completion times of various verification actions of the target patient in the standard verification action set. Generally, the body movements of the target patient when performing various verification actions can be captured through a preset motion capture device to obtain the completion time of the target patient corresponding to each verification action and the dynamic contours corresponding to each verification action.

[0080] In this embodiment, the second unit can match the dynamic profile with the standard profile of the standard verification action set to obtain the action completion degree of each verification action. Generally, the dynamic profile of the target patient completing each verification action in the standard verification action set can be matched and analyzed with the standard dynamic profile of each verification action to obtain the action overlap of the target patient completing each verification action, which is recorded as the action completion degree of the target patient completing each verification action.

[0081] In this embodiment, based on the third unit, the target patient's action completion degree and completion time of various verification actions can be input into the first model to obtain the verification action completion coefficient of the target patient output by the first model.

[0082] The second module in this embodiment can be used to determine the motion recovery coefficient based on the body function influence coefficient and the verification action completion coefficient. This embodiment does not limit the specific method of determining the motion recovery coefficient. Generally, the second module may include:

[0083] The fourth unit is used to input the body function influence coefficient and the verification action completion coefficient into the second model to obtain the output motion recovery coefficient;

[0084] The expression of the second model is:

[0085] ;

[0086] In the formula, Rsc is the motion recovery coefficient, e is the natural constant, Acc is the verification action completion coefficient, and pf is the body function influence coefficient. is the preset action energy consumption influencing factor, is the preset motion recovery coefficient influencing factor, k is the total number of various verification actions, i is the number of various verification actions, is the action difficulty coefficient of the i-th type of verification action.

[0087] The third module in this embodiment can match the available exercise prescription of the target patient based on the basic data set and the verification action completion coefficient, that is, the available exercise prescription is obtained by matching and screening the exercise prescriptions stored in the database through the third module. This embodiment is not limited to the specific method of determining the available exercise prescription, and generally the third module may include:

[0088] The fifth unit is used to match the initially screened available prescriptions and the rehabilitation effect set of the initially screened available prescriptions from the database based on the basic data set; specifically, extract each exercise prescription set stored in the database, where the exercise prescription set includes medical records, personal information, each exercise prescription and the rehabilitation effect set corresponding to each exercise prescription, and perform comparative screening based on the basic data set corresponding to the target patient to obtain the initially screened available prescriptions and the rehabilitation effect set of the initially screened available prescriptions corresponding to the target patient.

[0089] The sixth unit is used to determine the improvement rate of the verification movement completion coefficient of each initially screened available prescription based on the pre-treatment verification movement completion coefficient and the post-treatment verification movement completion coefficient in the rehabilitation effect set; specifically, the exercise prescription may include various types of rehabilitation movements corresponding to the rehabilitation treatment, the number of repetitions corresponding to each type of rehabilitation movement, and the difficulty coefficient of the rehabilitation movement corresponding to each type of rehabilitation movement; the rehabilitation effect set includes the verification movement completion coefficient corresponding to before the completion of the rehabilitation treatment and the verification movement completion coefficient after the completion of the rehabilitation treatment, and the improvement rate of the verification movement completion coefficient corresponding to the rehabilitation treatment can be calculated.

[0090] The seventh unit is used to screen the available prescriptions for secondary screening from the available prescriptions for primary screening based on the action completion coefficient, and determine the available prescriptions for secondary screening whose verification action completion coefficient improvement rate is greater than a preset verification action completion coefficient improvement rate threshold as available prescriptions; further, this embodiment can set a preset verification action completion coefficient improvement rate threshold, and determine the available prescriptions for secondary screening whose verification action completion coefficient improvement rate is greater than the preset verification action completion coefficient improvement rate threshold as available prescriptions.

[0091] The fourth module in this embodiment can be used to determine the exercise load influence coefficient of the available exercise prescription based on the exercise recovery coefficient and the rehabilitation action difficulty coefficient of each type of rehabilitation action in the available exercise prescription. This embodiment does not limit the specific method of determining the exercise load influence coefficient of the available exercise prescription. Generally, the fourth module may include:

[0092] The eighth unit is used to input the motion recovery coefficient and the rehabilitation motion difficulty coefficient of each rehabilitation motion in the available motion prescription into the third model to obtain the output motion load influence coefficient of the available motion prescription;

[0093] The expression of the third model is:

[0094] ;

[0095] In the formula, is the exercise load influence coefficient of the mth available exercise prescription, is the number of repetitions of the pth rehabilitation action in the mth available exercise prescription, q is the total number of rehabilitation actions in the mth available exercise prescription, is the difficulty coefficient of the pth rehabilitation action in the mth available exercise prescription, Rsc is the exercise recovery coefficient, To preset the conversion factor between exercise intensity and energy consumption, It is the preset motion load influence coefficient factor.

[0096] The fifth module in this embodiment can be used to determine the effective influence coefficient of the comprehensive rehabilitation action based on the historical disease data set of the target patient. This embodiment does not limit the specific method of determining the effective influence coefficient of the comprehensive rehabilitation action. Generally, the fifth module may include:

[0097] The ninth unit is used to obtain the historical disease data of the target patient; the historical disease data includes various disease types, disease severity and disease time; specifically, the present embodiment can extract the historical disease data of the target patient from the database, and establish various disease data sets corresponding to the target patient according to various disease types, disease severity and disease time corresponding to the target patient, that is, each disease type can correspond to one disease data set;

[0098] The tenth unit is used to construct a disease data set of each disease type based on the historical disease data, and determine the standard rehabilitation treatment impact coefficient of each disease data set; this embodiment can extract the standard rehabilitation treatment impact coefficient corresponding to each disease data set in the database, and screen and match the disease data set of the target patient to obtain the standard rehabilitation treatment impact coefficient of each disease data set of the target patient;

[0099] The eleventh unit is used to input the standard rehabilitation treatment influence coefficient into the fourth model to obtain the output comprehensive rehabilitation action effective influence coefficient;

[0100] The expression of the fourth model is:

[0101] ;

[0102] Where A is the effective influence coefficient of comprehensive rehabilitation action, For the The standard rehabilitation treatment effect coefficient for the disease data set, is the total number of disease datasets, It is the influencing factor of the effective influence coefficient of the preset comprehensive rehabilitation action.

[0103] The fifth module in this embodiment can determine the effective coefficient of the available exercise prescription based on the effective influence coefficient of the comprehensive rehabilitation action and the influence coefficient of the exercise load. This embodiment does not limit the specific method of determining the effective coefficient of the available exercise prescription. Generally, the fifth module may include:

[0104] The twelfth unit is used to input the comprehensive rehabilitation action effective influence coefficient and the exercise load influence coefficient into the fifth model to obtain the output exercise effective coefficient of the available exercise prescription;

[0105] The expression of the fifth model is:

[0106] ;

[0107] In the formula, is the exercise effectiveness coefficient of the mth available exercise prescription, is the exercise load influence coefficient of the mth available exercise prescription, A is the effective influence coefficient of the comprehensive rehabilitation action, is the improvement rate of the verification action completion coefficient of the mth available exercise prescription, It is the influencing factor of the preset motion effectiveness coefficient.

[0108] The sixth module in this embodiment can be used to determine the available exercise prescription whose exercise effectiveness coefficient is greater than the exercise effectiveness coefficient threshold as the target exercise prescription. Specifically, this embodiment can determine the exercise effectiveness coefficient threshold, compare and analyze the exercise effectiveness coefficient of the available prescription with the preset exercise effectiveness coefficient threshold, and screen the available exercise prescription greater than the exercise effectiveness coefficient threshold as the target exercise prescription. The target exercise prescription in this embodiment can be uploaded to the processing center to assist doctors in formulating personalized exercise prescriptions for patients. Furthermore, this embodiment can also upload the basic data set and target exercise prescription corresponding to the target patient to the database.

[0109] Based on the above embodiments, the present invention automatically generates personalized exercise prescriptions for patients through the target patient's personal physical information, sports injury information and historical medical information, thereby improving the efficiency of generating exercise prescriptions. The obtained exercise prescriptions are more in line with the target patient's physical condition, thereby improving the patient's rehabilitation effect.

[0110] The following combination Figure 2 , Figure 2 A flowchart of a method for generating an exercise prescription provided by an embodiment of the present invention, the method may include:

[0111] S101: Obtaining personal physical information and sports injury information of a target patient to construct a basic data set, and determining a standard verification action set and a physical function influence coefficient based on the basic data set;

[0112] S102: determining a verification action completion coefficient of the standard verification action set, and determining a motion recovery coefficient based on the body function influence coefficient and the verification action completion coefficient;

[0113] S103: matching the available exercise prescription of the target patient based on the basic data set and the verification action completion coefficient;

[0114] S104: determining an exercise load influence coefficient of the available exercise prescription based on the exercise recovery coefficient and the rehabilitation movement difficulty coefficients of various rehabilitation movements in the available exercise prescription;

[0115] S105: determining an effective influence coefficient of a comprehensive rehabilitation action based on the historical disease data set of the target patient, and determining an effective exercise coefficient of the available exercise prescription based on the effective influence coefficient of the comprehensive rehabilitation action and the exercise load influence coefficient;

[0116] S106: Determine the available exercise prescription whose exercise effectiveness coefficient is greater than an exercise effectiveness coefficient threshold as a target exercise prescription.

[0117] Based on the above embodiments, the present invention automatically generates personalized exercise prescriptions for patients through the target patient's personal physical information, sports injury information and historical medical information, thereby improving the efficiency of generating exercise prescriptions. The obtained exercise prescriptions are more in line with the target patient's physical condition, thereby improving the patient's rehabilitation effect.

[0118] Based on the above embodiments, the present invention also provides an exercise prescription generation device, which may include a memory and a processor, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the steps provided in the above embodiments can be implemented. Of course, the device may also include various necessary network interfaces, power supplies, and other components.

[0119] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by an execution terminal or a processor, the method provided in the embodiment of the present invention can be implemented; the storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0120] In this article, relational terms such as first and second, etc. are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

Claims

1. An exercise prescription generating device, characterized in that: include: The first module is used to obtain personal physical information and sports injury information of the target patient to construct a basic data set, and determine a standard verification action set and a physical function influence coefficient based on the basic data set; The second module is used to determine the verification action completion coefficient of the standard verification action set, and determine the motion recovery coefficient based on the body function influence coefficient and the verification action completion coefficient; A third module is used to match the available exercise prescription of the target patient based on the basic data set and the verification action completion coefficient; A fourth module is used to determine the exercise load influence coefficient of the available exercise prescription based on the exercise recovery coefficient and the rehabilitation movement difficulty coefficient of each type of rehabilitation movement in the available exercise prescription; A fifth module is used to determine the effective influence coefficient of the comprehensive rehabilitation action based on the historical disease data set of the target patient, and determine the exercise effectiveness coefficient of the available exercise prescription based on the effective influence coefficient of the comprehensive rehabilitation action and the exercise load influence coefficient; The sixth module is used to determine the available exercise prescription whose exercise effectiveness coefficient is greater than the exercise effectiveness coefficient threshold as the target exercise prescription.

2. The exercise prescription generating device according to claim 1, characterized in that: The second module comprises: The first unit is used to obtain the dynamic profile and completion time of the target patient completing various verification actions in the standard verification action set; The second unit is used to match the dynamic profile with the standard profile of the standard verification action set to obtain the action completion degree of each verification action; A third unit is used for inputting the completion time and the action completion degree into the first model to obtain the output of the verification action completion coefficient; The expression of the first model is: ； Where Acc is the verification action completion coefficient of the standard verification action set, i represents the number of each verification action, k is the total number of each verification action, is the standard completion time of the i-th type of verification action, is the completion time of the i-th type of verification action, is the action completion degree of the i-th type verification action, It is the influencing factor of the preset action completion coefficient.

3. The exercise prescription generating device according to claim 1, characterized in that: The second module comprises: A fourth unit is used to input the body function influence coefficient and the verification action completion coefficient into the second model to obtain the output motion recovery coefficient; The expression of the second model is: ； Wherein, Rsc is the motion recovery coefficient, e is a natural constant, Acc is the verification action completion coefficient, pf is the body function influence coefficient, is the preset action energy consumption influencing factor, is the preset motion recovery coefficient influencing factor, k is the total number of various verification actions, i is the number of various verification actions, is the action difficulty coefficient of the i-th type of verification action.

4. The exercise prescription generating device according to claim 1, characterized in that: The third module comprises: A fifth unit is used for matching and obtaining the initially screened available prescriptions and the rehabilitation effect set of the initially screened available prescriptions from a database based on the basic data set; A sixth unit is used to determine the verification action completion coefficient improvement rate of each of the initially screened available prescriptions based on the pre-treatment verification action completion coefficient and the post-treatment verification action completion coefficient in the rehabilitation effect set; The seventh unit is used to screen the available prescriptions for secondary screening from the available prescriptions for primary screening based on the action completion coefficient, and determine the available prescriptions for secondary screening whose verification action completion coefficient improvement rate is greater than a preset verification action completion coefficient improvement rate threshold as the available prescription.

5. The exercise prescription generating device according to claim 1, characterized in that: The fourth module comprises: An eighth unit is used to input the exercise recovery coefficient and the rehabilitation action difficulty coefficient of each type of rehabilitation action in the available exercise prescription into a third model to obtain the output exercise load influence coefficient of the available exercise prescription; The expression of the third model is: ； In the formula, is the exercise load influence coefficient of the mth available exercise prescription, is the number of repetitions of the pth rehabilitation action in the mth available exercise prescription, q is the total number of rehabilitation actions in the mth available exercise prescription, is the rehabilitation action difficulty coefficient of the pth rehabilitation action in the mth available exercise prescription, Rsc is the exercise recovery coefficient, To preset the conversion factor between exercise intensity and energy consumption, It is the preset motion load influence coefficient factor.

6. The exercise prescription generating device according to claim 1, characterized in that: The fifth module comprises: The ninth unit is used to obtain the historical disease data of the target patient; the historical disease data includes the type of disease, the degree of disease and the time of disease; A tenth unit is used to construct a disease data set of each disease type based on the historical disease data, and determine a standard rehabilitation treatment influence coefficient of each disease data set; The eleventh unit is used to input the standard rehabilitation treatment influence coefficient into the fourth model to obtain the output effective influence coefficient of the comprehensive rehabilitation action; The expression of the fourth model is: ； Where A is the effective influence coefficient of the comprehensive rehabilitation action, For the The standard rehabilitation treatment impact coefficient of the disease data set, is the total number of disease datasets, It is the influencing factor of the effective influence coefficient of the preset comprehensive rehabilitation action.

7. The exercise prescription generating device according to claim 1, characterized in that: The fifth module comprises: A twelfth unit is used to input the comprehensive rehabilitation action effective influence coefficient and the exercise load influence coefficient into a fifth model to obtain the output exercise effective coefficient of the available exercise prescription; The expression of the fifth model is: ； In the formula, is the exercise effectiveness coefficient of the mth available exercise prescription, is the exercise load influence coefficient of the mth available exercise prescription, A is the effective influence coefficient of the comprehensive rehabilitation action, is the improvement rate of the verification action completion coefficient of the mth available exercise prescription, It is the influencing factor of the preset motion effectiveness coefficient.

8. A method for generating an exercise prescription, characterized in that: include: Obtaining personal physical information and sports injury information of the target patient to construct a basic data set, and determining a standard verification action set and a physical function influence coefficient based on the basic data set; Determine the verification action completion coefficient of the standard verification action set, and determine the motion recovery coefficient based on the body function influence coefficient and the verification action completion coefficient; Matching the available exercise prescription of the target patient based on the basic data set and the verification action completion coefficient; Determining the exercise load influence coefficient of the available exercise prescription based on the exercise recovery coefficient and the rehabilitation movement difficulty coefficients of various rehabilitation movements in the available exercise prescription; Determine the effective influence coefficient of the comprehensive rehabilitation action based on the historical disease data set of the target patient, and determine the effective coefficient of the exercise prescription of the available exercise prescription based on the effective influence coefficient of the comprehensive rehabilitation action and the exercise load influence coefficient; The available exercise prescription whose exercise effectiveness coefficient is greater than an exercise effectiveness coefficient threshold is determined as a target exercise prescription.

9. An exercise prescription generating device, characterized in that: include: Memory, for storing computer programs; A processor, configured to implement the exercise prescription generating method as claimed in claim 8 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the exercise prescription generation method as claimed in claim 8 is implemented.

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