Cardiac surgery patient postoperative cardiopulmonary rehabilitation training guiding and supervising system based on applet

Abstract

The invention discloses a cardiac surgery patient postoperative cardiopulmonary rehabilitation training guidance and supervision system based on an applet, and belongs to the technical field of postoperative cardiopulmonary rehabilitation training. The platform end comprises a patient database, an online service module and a data analysis module; the patient database is used for counting patient rehabilitation data of the target patient in real time; the online service module is used for analyzing medical care resources of a hospital and establishing a medical care guidance information base; the data analysis module is used for analyzing the patient rehabilitation data in the patient database, obtaining a rehabilitation analysis report of the target patient and sending the rehabilitation analysis report to the corresponding user side; the user side comprises a data acquisition module and a guidance module; the data acquisition module is used for acquiring patient detection data of a target patient in real time and sending the patient detection data to the user side; and the guidance module is used for performing medical guidance on the target patient, and the target guidance personnel guide the target patient according to a preset communication mode.

Description

Technical Field

[0001] The present invention belongs to the technical field of postoperative cardiopulmonary rehabilitation training, and specifically relates to a supervision system for postoperative cardiopulmonary rehabilitation training of cardiac surgery patients based on a mini-program. Background Art

[0002] Cardiac surgery is a high-risk and highly complex medical procedure, and the rehabilitation process of postoperative patients is crucial. However, traditional rehabilitation methods often have many deficiencies, such as lack of personalization in rehabilitation plans, difficulty in effectively monitoring the rehabilitation process, and lack of professional rehabilitation guidance for patients. These problems not only affect the rehabilitation effect of patients but also may increase the risk of postoperative complications.

[0003] With the rapid development of mobile Internet technology, the mini-program, as a lightweight application form, is widely popular among users due to its convenience, ease of use, and cross-platform nature. Applying the mini-program to the supervision of postoperative cardiopulmonary rehabilitation training of cardiac surgery patients can achieve comprehensive, real-time, and personalized management of the patient's rehabilitation process.

[0004] Based on this, the present invention provides a supervision system for postoperative cardiopulmonary rehabilitation training of cardiac surgery patients based on a mini-program. Summary of the Invention

[0005] To solve the problems existing in the above solutions, the present invention provides a supervision system for postoperative cardiopulmonary rehabilitation training of cardiac surgery patients based on a mini-program.

[0006] The object of the present invention can be achieved through the following technical solutions:

[0007] A supervision system for postoperative cardiopulmonary rehabilitation training of cardiac surgery patients based on a mini-program includes a platform end and a user end;

[0008] The platform end includes a patient database, an online service module, and a data analysis module;

[0009] The patient database is used to statistically analyze the patient rehabilitation data of target patients in real time, and the patient rehabilitation data includes patient information, doctor detection data, and patient detection data.

[0010] Furthermore, several storage nodes are set in the patient database, and each target patient corresponds to a storage node.

[0011] Furthermore, the users at the user end are verified for target patients according to the patient information stored in the patient database.

[0012] The online service module is used to analyze the medical and nursing resources of the hospital to obtain several medical and nursing guidance personnel, acquire the information of the medical and nursing guidance personnel, where the information of the medical and nursing guidance personnel includes name, age, gender, professional ability, and service scope, and establish a medical and nursing guidance information database according to the information of the medical and nursing guidance personnel.

[0013] Furthermore, the method for analyzing the medical and nursing resources of the hospital includes:

[0014] Acquire the medical and nursing resources of the hospital, and divide the medical and nursing resources into several pieces of medical and nursing information; establish a standard evaluation model, and the expression of the standard evaluation model is:

[0015]

[0016] In the formula: (YH, XD) is the input data, YH is the medical and nursing information, and XD is the selected standard; YH ∈ XD means that the corresponding medical and nursing information meets the selected standard; the output data is the medical and nursing evaluation value BP(YH, XD), and the medical and nursing evaluation value is 1 or 0;

[0017] Analyze the medical and nursing information and the selected standard through the standard evaluation model to obtain the medical and nursing evaluation value of each piece of medical and nursing information;

[0018] Integrate the medical and nursing information with a medical and nursing evaluation value of 1 into an alternative list, and set medical and nursing guidance personnel according to the alternative list.

[0019] The data analysis module is used to analyze the patient rehabilitation data in the patient database to obtain a rehabilitation analysis report of the target patient, where the rehabilitation analysis report includes the status evaluation result and rehabilitation training plan of the target patient; send the rehabilitation analysis report to the corresponding client.

[0020] Furthermore, conduct a training evaluation on the generated rehabilitation training plan to obtain a training evaluation result, and the training evaluation result is meeting the training requirements and not meeting the training requirements;

[0021] When the training evaluation result is not meeting the training requirements, delete the rehabilitation training plan in the corresponding rehabilitation analysis report;

[0022] When the training evaluation result is meeting the training requirements, no corresponding operation is performed.

[0023] Furthermore, the method for conducting a training evaluation on the target patient includes:

[0024] Identify the data analysis method, and acquire the analysis material data of the data analysis method, where the analysis material data includes patient rehabilitation data, rehabilitation training plan, and training evaluation result;

[0025] Set several patient classifications and the corresponding starting stages corresponding to the patient classifications according to the material analysis data;

[0026] Match a corresponding patient classification for the target patient, identify the starting stage corresponding to the patient classification, and conduct training evaluation on the target patient according to the starting stage to obtain corresponding training evaluation results.

[0027] Furthermore, the method for setting patient classifications according to material analysis data includes:

[0028] Step SA1: Set status evaluation items, perform feature recognition on the material analysis data according to the status evaluation items to obtain a number of patient status features; regard the patient status features as a status set.

[0029] Step SA2: Conduct combined evaluation between corresponding status sets according to the material analysis data to obtain the combined evaluation results between the corresponding status sets, and the combined evaluation results include meeting the combination requirements and not meeting the combination requirements.

[0030] Combine the status sets with combined evaluation results meeting the combination requirements to obtain a new status set.

[0031] Step SA3: Loop step SA2 until no combination is possible between each status set, and set patient classifications according to the remaining status sets.

[0032] Furthermore, the method for conducting combined evaluation between corresponding status sets according to the material analysis data includes:

[0033] Set training evaluation items, perform feature extraction on the rehabilitation training plan in the material analysis data according to the training evaluation items to obtain corresponding training features; identify the training evaluation results corresponding to the corresponding training features according to the material analysis data, and integrate the training features and the training evaluation results into a reference set.

[0034] Classify the reference set according to the status set to obtain the reference classification corresponding to the corresponding status set.

[0035] Judge whether the corresponding status set meets the combination requirements according to the reference classification.

[0036] The client includes a data collection module and a guidance module;

[0037] The data collection module is used to collect the patient detection data of the target patient in real time and send the patient detection data to the client.

[0038] The guidance module is used to provide medical guidance to the target patient and identify the guidance needs of the target patient; connect to the medical guidance information database on the platform side, input the guidance needs and the target patient information into the medical guidance information database for matching, and obtain the candidate guidance personnel meeting the guidance requirements.

[0039] Generate a guidance list based on the information of potential guidance personnel, display the guidance list to the target patients, and let the target patients determine the target guidance personnel; send the guidance requirements and the information of the target patients to the target guidance personnel, and let the target guidance personnel guide the target patients according to the preset communication method.

[0040] Furthermore, the method for determining potential guidance personnel includes:

[0041] Determine the guidance requirements, establish a guidance verification model according to the guidance requirements, and the expression of the guidance verification model is:

[0042]

[0043] In the formula: (QA, QB, YD i ) is the input data, QA is the guidance requirement, QB is the information of the target patient; YD i represents the corresponding medical staff guidance personnel information in the medical staff guidance information library, i = 1, 2,..., n, and n is the number of medical staff guidance personnel information in the medical staff guidance information library; the output data is the guidance verification value DR(QA, QB, YD i ), and the guidance verification value is 1 or 0;

[0044] Analyze through the guidance verification model to obtain the guidance verification value of the corresponding medical staff guidance personnel information;

[0045] Determine the potential guidance personnel according to the guidance verification value.

[0046] Compared with the prior art, the beneficial effects of the present invention are:

[0047] Through the user terminal, the system can intelligently generate personalized rehabilitation plans according to the specific conditions, physical constitutions and surgical conditions of each patient. This not only ensures the pertinence and effectiveness of the rehabilitation plan, but also greatly improves the acceptance rate and implementation rate of the rehabilitation plan, and helps to accelerate the rehabilitation process of patients. Through the guidance module, patients can obtain targeted rehabilitation guidance and suggestions at any time, solving the problem that patients lack professional guidance in traditional rehabilitation methods. This kind of instant interaction not only enhances the rehabilitation confidence of patients, but also ensures the accuracy and safety of rehabilitation movements. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings.

[0049] Figure 1This is the principle block diagram of the present invention. Specific embodiments

[0050] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0051] As Figure 1 shown, the postoperative cardiopulmonary rehabilitation training guidance and supervision system for cardiac surgery patients based on mini programs includes a platform side and a user side;

[0052] The platform side is used by the hospital side and can be established based on existing technologies such as cloud computing and cloud platforms.

[0053] The user side is connected to the platform side and is used by users. Currently, it is generally set up in the form of mini programs. In the future, with the development of technology, other methods can also be used to set up the user side.

[0054] The platform side includes a patient database, an online service module, and a data analysis module;

[0055] The patient database is used to count the rehabilitation data of patients who need to undergo cardiac rehabilitation training. The patient rehabilitation data includes patient information, doctor detection data, patient detection data, and other data related to the patient; doctor detection data refers to the detection data of patients in the hospital, and patient detection data refers to the detection data uploaded by the user side, generally including detection data such as those worn by patients with smart devices and self-detection. The target patient information can be used later to verify the login of users on the user side, that is, to serve the target patients. Whether the corresponding user is a target patient can be determined through the target patient information, and verification can be carried out through medical cards, social security cards, identity cards, etc.

[0056] In one embodiment, several storage nodes are set in the patient database, and each storage node corresponds to a patient who needs to undergo cardiac rehabilitation training. For the sake of distinction, the above patients are marked as target patients.

[0057] The online service module is used to analyze the medical staff resources of the hospital side, determine the medical staff guidance personnel for online duty service for target patients, obtain the information of the medical staff guidance personnel, including relevant information such as name, age, gender, professional ability, and service scope. The service scope indicates which target patients the medical staff guidance personnel are used to serve, such as the target patients treated by a certain doctor. The service scope is specifically set by the medical staff guidance personnel, the hospital side, etc.; establish a medical staff guidance information database based on the information of the medical staff guidance personnel;

[0058] Medical care guidance personnel in the medical care guidance information database will provide medical care guidance according to the medical care guidance personnel determined by the user terminal later, such as video diagnosis, auxiliary guidance, etc.

[0059] In one embodiment, the medical care guidance personnel can be determined through existing methods, such as manual determination according to the hospital's rules and regulations, voluntary registration, and other methods.

[0060] In one embodiment, the method for analyzing the hospital's medical care resources includes:

[0061] Obtain the hospital's medical care resources, mainly including the information of corresponding medical care personnel, such as doctor information, nurse information, trainee information, etc.; set the selection criteria for medical care guidance personnel by the hospital; divide the medical care resources into several pieces of medical care information, classify and summarize the data according to personnel, and form the medical care information of each medical care personnel;

[0062] Establish a standard evaluation model. The standard evaluation model is used to evaluate whether the corresponding medical care information meets the selection criteria, and is trained by setting training data manually. The training data includes medical care information, selection criteria, and whether the medical care information meets the selection criteria. The simplified expression of the standard evaluation model is:

[0063]

[0064] In the formula: (YH, XD) is the input data, YH is the medical care information, and XD is the selection criteria; YH ∈ XD means that the corresponding medical care information meets the selection criteria; the output data is the medical care evaluation value BP(YH, XD), and the medical care evaluation value is 1 or 0;

[0065] Analyze the medical care information and selection criteria through the standard evaluation model to obtain the medical care evaluation value of each medical care information;

[0066] Integrate the medical care information with a medical care evaluation value of 1 into an alternative list, and set the medical care guidance personnel by the hospital according to the alternative list.

[0067] The data analysis module is used to analyze the patient rehabilitation data in the patient database to obtain a rehabilitation analysis report of the target patient. The rehabilitation analysis report includes relevant data such as the status evaluation result and rehabilitation training plan of the target patient, and can be generated according to a preset report template; send the rehabilitation analysis report to the corresponding user terminal.

[0068] In one embodiment, the analysis of patient rehabilitation data can be performed through intelligent analysis based on existing intelligent technologies.

[0069] Exemplarily, an intelligent analysis model is established based on a deep neural network and trained by manually establishing a corresponding training set. The training set includes input data and output data. The input data is patient rehabilitation data, and the output data is a rehabilitation analysis report. Analysis is performed using the successfully trained intelligent analysis model.

[0070] In one embodiment, due to the large differences in the physical states of some patients, for the rehabilitation training plans generated by intelligent algorithms, due to issues such as the accuracy of algorithms and models, patients at certain stages may not be suitable for applying the rehabilitation training plans generated by intelligent algorithms. For example, patients who have been discharged for a certain period of time may not be suitable for training according to the above-mentioned rehabilitation training plans due to uncertainties about the accuracy of intelligent algorithms, error situations, and their own acceptance abilities. That is, the generated rehabilitation training plans are evaluated for training to obtain training evaluation results, and the training evaluation results are meeting the training requirements and not meeting the training requirements.

[0071] When the training evaluation result is not meeting the training requirements, the rehabilitation training plan in the corresponding rehabilitation analysis report is deleted.

[0072] When the training evaluation result is meeting the training requirements, no corresponding operation is performed.

[0073] In one embodiment, the method for training evaluation of a target patient includes:

[0074] Identify the data analysis method, such as analysis using the intelligent analysis model in the above embodiment. Obtain the analysis material data of the data analysis method, which can be extracted based on historical analysis data or set through existing methods, such as simulating and setting the analysis material data. The analysis material data mainly includes patient rehabilitation data, rehabilitation training plans, training evaluation results, etc. The training evaluation result is whether the rehabilitation training plan can be used for this patient.

[0075] Set several patient classifications and the corresponding starting stages for the corresponding patient classifications according to the material analysis data; the above steps are determined in advance, and the patient classifications and starting stages are updated according to data changes later.

[0076] Determine the patient classification corresponding to the target patient, and perform training evaluation on the target patient according to the starting stage corresponding to the patient classification to obtain the corresponding training evaluation result.

[0077] In one embodiment, the method for setting patient classifications according to the material analysis data includes:

[0078] Step SA1: Set the status evaluation items. The status evaluation items are set according to the patient status and training-related data and are used to distinguish different patient types later. Identify the features of the material analysis data according to the status evaluation items to obtain several patient status features.

[0079] Regard the patient status feature as a status set, that is, this status set only includes one element, which is the patient status feature;

[0080] Step SA2: Conduct a merger evaluation between corresponding status sets based on the material analysis data to obtain the merger evaluation results between the corresponding status sets. The merger evaluation results include meeting the merger requirements and not meeting the merger requirements;

[0081] Merge the status sets whose merger evaluation results meet the merger requirements to obtain a new status set, that is, the union of the two status sets;

[0082] Step SA3: Loop step SA2 until no further mergers are possible between the status sets. Set the patient classification based on the remaining status sets, that is, set the patient classification according to each patient status feature corresponding to the status set.

[0083] In one embodiment, the method for conducting a merger evaluation between corresponding status sets based on the material analysis data includes:

[0084] Set training evaluation items. The training evaluation items are evaluation items that have an impact on patient training, mainly targeting evaluation items such as training methods and training intensities. Extract features from the rehabilitation training plan in the material analysis data according to the training evaluation items to obtain corresponding training features. Identify the training evaluation results corresponding to the corresponding training features based on the material analysis data, and integrate the training features and training evaluation results into a reference set;

[0085] Classify the reference set according to the status set to obtain the reference classification corresponding to the corresponding status set, that is, classify according to the patient status features corresponding to the reference set. As the status set changes, the reference classification will also be updated synchronously;

[0086] Judge whether the corresponding status set meets the merger requirements according to the reference classification, that is, judge according to the differences between the reference sets in the reference classification. The ideal situation is that the two reference classifications are the same. In actual situations, meeting the preset difference range can be regarded as meeting the merger requirements. The platform sets the corresponding merger requirements and judges according to the merger requirements for the reference classification. It is also possible to establish a judgment model based on a CNN network or a DNN network, etc., and train it through an artificial method to establish a corresponding training set. The training set includes input data and output data. The input data is the reference classification for comparison and judgment; the output data is the merger evaluation result. Analyze through the judgment model after successful training.

[0087] In one embodiment, a starting stage for patient classification is set, and the probabilities (training evaluation results) that the patient classification meets the patient usage requirements for different training features are obtained. Then, based on the corresponding probabilities, the starting stage of the patient classification is set. The starting stage means that the patient's state cannot be lower than the patient state characteristics corresponding to the patient classification, and the rehabilitation training plan cannot exceed the training feature. The average, mode, etc. can be used as representatives to set the patient state characteristics and training features corresponding to the starting stage.

[0088] In one embodiment, the starting stage of patient classification can be set based on other existing methods.

[0089] In one embodiment, for the training evaluation of the target patient, an intelligent evaluation model can also be established based on material analysis data, and intelligent evaluation is carried out through the intelligent evaluation model.

[0090] The user terminal includes a data acquisition module and a guidance module;

[0091] The data acquisition module is used to collect the patient detection data of the target patient in real time, such as heart rate, blood oxygen saturation, and respiratory rate data, which are generally monitored through wearable devices, such as smart bracelets, chest straps, etc., and the patient detection data is sent to the user terminal.

[0092] The guidance module is used to provide medical guidance to the target patient. When the target patient has a need for medical guidance, the guidance needs are input, such as relevant need information such as reasons and purposes, to identify the guidance needs of the target patient; it docks with the medical guidance information database on the platform side, inputs the guidance needs and the target patient information into the medical guidance information database for matching, and obtains medical guidance personnel who meet the guidance requirements, marked as candidate guidance personnel, that is, there can be multiple medical guidance personnel who meet the guidance requirements;

[0093] A guidance directory is generated according to the candidate guidance personnel information and displayed to the target patient, and the target patient determines the target guidance personnel; the guidance needs and the target patient information are sent to the target guidance personnel, and the target guidance personnel provides guidance to the target patient according to the preset communication method, generally video communication.

[0094] In one embodiment, the determination of candidate guidance personnel can be made by matching according to the existing method. Meeting the guidance requirements means being able to have the ability to solve the guidance needs, conform to the service scope, etc., and other requirements can also be added additionally.

[0095] In one embodiment, the method for determining candidate guidance personnel includes:

[0096] Determine the guiding requirements, and establish a guiding verification model according to the guiding requirements, that is, train using the guiding requirements, information of medical staff, guiding needs, and patient information; the expression of the guiding verification model is:

[0097]

[0098] Where: (QA, QB, YD i ) are input data, QA is the guiding need, and QB is the target patient information; YD i represents the corresponding medical staff information in the medical staff guiding information library, i = 1, 2,..., n, and n is the number of medical staff information in the medical staff guiding information library; the output data is the guiding verification value DR(QA, QB, YD i ), and the guiding verification value is 1 or 0;

[0099] Analyze through the guiding verification model to obtain the guiding verification value of the corresponding medical staff information.

[0100] Determine the candidate guiding personnel according to the guiding verification value.

[0101] The above formulas are all calculated by removing the dimension and taking their numerical values. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters and preset thresholds in the formulas are set by those skilled in the art according to the actual situation or obtained by simulating a large amount of data.

[0102] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A postoperative cardiopulmonary rehabilitation training guidance and supervision system for cardiac surgery patients based on mini-programs, characterized in that, It includes a platform side and a user side; The platform side includes a patient database, an online service module, and a data analysis module; The patient database is used to statistically analyze the patient rehabilitation data of target patients in real time. The patient rehabilitation data includes patient information, doctor detection data, and patient detection data; The online service module is used to analyze the medical and nursing resources of the hospital, obtain a number of medical and nursing guidance personnel, obtain the information of the medical and nursing guidance personnel, where the information of the medical and nursing guidance personnel includes name, age, gender, professional ability, and service scope, and establish a medical and nursing guidance information database according to the information of the medical and nursing guidance personnel; The data analysis module is used to analyze the patient rehabilitation data in the patient database to obtain a rehabilitation analysis report of the target patient. The rehabilitation analysis report includes the status evaluation result and rehabilitation training plan of the target patient; Send the rehabilitation analysis report to the corresponding user side; The user side includes a data collection module and a guidance module; The data collection module is used to collect the patient detection data of the target patient in real time and send the patient detection data to the user side; The guidance module is used to provide medical and nursing guidance to the target patient, identify the guidance needs of the target patient; connect to the medical and nursing guidance information database on the platform side, input the guidance needs and target patient information into the medical and nursing guidance information database for matching, and obtain the candidate guidance personnel who meet the guidance requirements; Generate a guidance directory according to the candidate guidance personnel information, display the guidance directory to the target patient, and let the target patient determine the target guidance personnel; Send the guidance needs and target patient information to the target guidance personnel, and let the target guidance personnel provide guidance to the target patient according to the preset communication method.

2. The postoperative cardiopulmonary rehabilitation training guidance and supervision system for cardiac surgery patients based on mini programs according to claim 1, characterized in that There are several storage nodes set in the patient database, and each target patient corresponds to one storage node.

3. The postoperative cardiopulmonary rehabilitation training guidance and supervision system for cardiac surgery patients based on mini programs according to claim 1, characterized in that, Verify the target patients for the users on the user side according to the patient information stored in the patient database.

4. The postoperative cardiopulmonary rehabilitation training guidance and supervision system for cardiac surgery patients based on mini-programs according to claim 1, characterized in that, The method for analyzing the medical and nursing resources of the hospital includes: Obtain the medical and nursing resources of the hospital, divide the medical and nursing resources into several medical and nursing information; establish a standard evaluation model, and the expression of the standard evaluation model is: In the formula: (YH, XD) is the input data, YH is the medical and nursing information, XD is the selected standard; YH ∈ XD means that the corresponding medical and nursing information meets the selected standard; the output data is the medical and nursing evaluation value BP(YH, XD), and the medical and nursing evaluation value is 1 or 0; Analyze the medical and nursing information and the selected standard through the standard evaluation model to obtain the medical and nursing evaluation value of each medical and nursing information; Integrate the medical and nursing information with a medical and nursing evaluation value of 1 into an alternative list, and set up medical and nursing guidance personnel according to the alternative list.

5. The postoperative cardiopulmonary rehabilitation training guidance and supervision system for cardiac surgery patients based on mini programs according to claim 1, wherein Conduct a training evaluation on the generated rehabilitation training plan to obtain a training evaluation result, and the training evaluation result is meeting the training requirements and not meeting the training requirements; When the training evaluation result is not meeting the training requirements, delete the rehabilitation training plan in the corresponding rehabilitation analysis report; When the training evaluation result is meeting the training requirements, no corresponding operation is performed.

6. The postoperative cardiopulmonary rehabilitation training guidance and supervision system for cardiac surgery patients based on mini-programs according to claim 5, wherein The method for conducting a training evaluation on the target patient includes: Identify the data analysis method, obtain the analysis material data of the data analysis method, and the analysis material data includes patient rehabilitation data, rehabilitation training plan, and training evaluation result; Set a number of patient classifications and the starting stages corresponding to the respective patient classifications according to the material analysis data; Match the corresponding patient classification for the target patient, identify the starting stage corresponding to the patient classification, and conduct training evaluation on the target patient according to the starting stage to obtain the corresponding training evaluation result.

7. The postoperative cardiopulmonary rehabilitation training guidance and supervision system for cardiac surgery patients based on mini-programs according to claim 6, characterized in that, The method for setting patient classifications according to the material analysis data includes: Step SA1: Set state evaluation items, perform feature recognition on the material analysis data according to the state evaluation items to obtain a number of patient state features; regard the patient state features as a state set; Step SA2: Conduct a combined evaluation between the corresponding state sets according to the material analysis data to obtain the combined evaluation result between the corresponding state sets, and the combined evaluation result includes meeting the combination requirements and not meeting the combination requirements; Combine the state sets with the combined evaluation result of meeting the combination requirements to obtain a new state set; Step SA3: Loop step SA2 until no two state sets can be combined, and set patient classifications according to the remaining state sets.

8. The postoperative cardiopulmonary rehabilitation training guidance and supervision system for cardiac surgery patients based on mini programs according to claim 7, characterized in that, The method for conducting a combined evaluation between the corresponding state sets according to the material analysis data includes: Set training evaluation items, perform feature extraction on the rehabilitation training plan in the material analysis data according to the training evaluation items to obtain the corresponding training features; identify the training evaluation result corresponding to the corresponding training features according to the material analysis data, and integrate the training features and the training evaluation results into a reference set; Classify the reference set according to the state sets to obtain the reference classification corresponding to the corresponding state sets; Judge whether the corresponding state sets meet the combination requirements according to the reference classification.

9. The postoperative cardiopulmonary rehabilitation training guidance and supervision system for cardiac surgery patients based on mini-programs according to claim 1, characterized in that, The method for determining the candidate instructor includes: Determine the guidance requirements, establish a guidance verification model according to the guidance requirements, and the expression of the guidance verification model is: Where: (QA, QB, YD i ) is the input data, QA is the guidance requirement, and QB is the target patient information; YD i represents the corresponding medical staff guidance information in the medical staff guidance information database, where i = 1, 2,..., n, and n is the number of medical staff guidance information in the medical staff guidance information database; the output data is the guidance verification value DR(QA, QB, YD i ), and the guidance verification value is 1 or 0; Conduct analysis through the guidance verification model to obtain the guidance verification value of the corresponding medical staff guidance information; Determine the candidate instructor according to the guidance verification value.

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