Ischemic preconditioning training instrument based on 4G mobile communication and app linkage method and system

Abstract

The application provides an ischemic preconditioning training instrument and applet linkage method and system based on 4G mobile communication, relates to the technical field of medical health management, and solves the problems that the traditional ischemic preconditioning training instrument cannot realize remote synchronization and centralized management of training data in a single machine operation mode, lacks security encryption protection for data interaction, lacks intelligent health management service support after training, and is difficult to realize unified supervision of the device and the user state. The application realizes training and physiological data interaction among the device, the cloud and the applet through 4G network encryption, collects and uploads multi-dimensional training and physiological data, realizes data visualization, blood pressure early warning, integral encouragement and supporting health services by the applet. The application realizes remote data linkage of the training instrument and the applet by relying on 4G communication, realizes encrypted transmission to guarantee data security, retains the convenience of single machine training, realizes centralized management of training data and intelligent extension of health services at the same time.

Description

Technical Field

[0001] This invention relates to the field of medical and health management technology, and in particular to a method and system for linking an ischemic preconditioning training device with a mini-program based on 4G mobile communication. Background Technology

[0002] Remote ischemic preconditioning training is an important non-pharmacological intervention for the prevention, adjuvant treatment, and postoperative rehabilitation of cardiovascular and cerebrovascular diseases. Currently, most ischemic preconditioning training devices on the market are standalone devices that can only perform training locally, measure blood pressure and pulse rate, and store a small amount of data. They lack mobile communication modules and remote interaction systems, which presents significant technical shortcomings. Existing devices cannot remotely upload training and physiological data in real time via mobile communication networks. Users cannot conveniently view training and health indicators through mini-programs, and medical staff cannot remotely obtain training status to provide guidance. Training parameters must be manually set and adjusted locally by users, which is cumbersome and relies on users' professional knowledge, resulting in poor adaptability and difficulty in covering training needs of different physical tolerance levels and usage scenarios. Data interaction between devices, the cloud, and users lacks a standardized encryption mechanism, posing security risks to medical data transmission. At the same time, existing devices lack mini-program-linked functions such as training check-in, points incentives, blood pressure warnings, risk assessments, and online health consultations, leading to low user training compliance and preventing the formation of a closed loop in health management services. Furthermore, standalone devices cannot be connected to a unified operation and management platform, making it difficult to achieve centralized device monitoring, user health management, and training data statistical analysis. This hinders doctor-patient collaboration, rehabilitation follow-up, and scientific research data collection, failing to meet the needs of intelligent, remote, and integrated medical and health management. Summary of the Invention

[0003] This invention aims to provide a method and system for linking an ischemic preconditioning training device with a mini-program based on 4G mobile communication. It utilizes multiple locally fixed training modes on the training device to perform stand-alone training. The training device encrypts and uploads training and physiological data via the 4G network; a cloud server handles data decryption, verification, and forwarding; and a mini-program platform enables data display, early warning judgment, points incentives, risk assessment, and health consultation. This addresses the problems of existing ischemic preconditioning training devices in stand-alone operation mode, such as the inability to achieve remote synchronization and centralized management of training data, lack of standardized encryption protection for data interaction, lack of intelligent health management services linked to mini-programs, and difficulty in achieving unified monitoring of device and user status.

[0004] A method for linking an ischemic preconditioning training device with a mini-program based on 4G mobile communication includes: The user starts the ischemia preconditioning training device to perform ischemia preconditioning training; The ischemic preconditioning training device has a built-in 4G mobile communication module. The 4G mobile communication module is debugged and adapted according to the preset application interface specification. During and after training, training process data and user physiological measurement data are collected and uploaded to the cloud server through the 4G mobile communication network in an encrypted transmission method according to the application interface specification. After the cloud server decrypts, verifies, and stores the uploaded data, it forwards the data to the mini-program platform. The mini-program platform parses and displays the received data, and performs early warning judgments, health service linkages, and data synchronization processing.

[0005] Furthermore, the preset application programming interface specification includes the following encrypted transmission and communication rules: The / getDeviceData communication interface is called using a POST request. The communication request header uses the HmacSHA256 algorithm to generate and verify the signature, and the communication request body uses the AES / ECB / PKCS5Padding algorithm to encrypt and decrypt. The communication request header includes a 13-bit timestamp, a replay and backtracking ID nonce, a unique downstream user ID clientId, a signature, and a content type Content-Type: application / json; The signature is a string composed of clientId, a 13-digit timestamp, and nonce, which is then calculated using the HmacSHA256 algorithm with a 24-digit unique key.

[0006] Furthermore, the ischemic preconditioning training device has fixed preset training parameters including automatic mode parameters and parameters for schemes one to four, wherein: The automatic mode parameters are as follows: first, measure the user's blood pressure, then increase the measured blood pressure value by 40 mmHg, inflate for 5 minutes, deflate for 5 minutes, and the total training time is 45 minutes. The parameters for Scheme 1 are: training pressure 180 mmHg, inflation time 3 minutes, deflation time 5 minutes, and total training time 35 minutes. The parameters for Scheme 2 are: training pressure 180 mmHg, inflation time 5 minutes, deflation time 5 minutes, and total training time 45 minutes. The parameters for Scheme 3 are: training pressure 200 mmHg, inflation time 5 minutes, deflation time 5 minutes, and total training time 45 minutes. The parameters for Scheme 4 are: training pressure 220 mmHg, inflation time 5 minutes, deflation time 5 minutes, and total training time 45 minutes.

[0007] Furthermore, the data uploaded by the ischemic preconditioning training device includes basic device data, user identity data, training process data, physiological measurement data, and transmission auxiliary data; The basic equipment data includes equipment ID, equipment model, equipment longitude, equipment latitude, equipment address, and equipment IP; the user identity data includes user name, gender, age, mobile phone number, and ID number. The training process data includes training mode, training pressure, training start timestamp, training end timestamp, average pressure, training error messages, and user-initiated stop flag. The physiological measurement data includes measurement timestamps, left arm error codes, left arm systolic blood pressure, left arm diastolic blood pressure, left arm pulse rate, right arm error codes, right arm systolic blood pressure, right arm diastolic blood pressure, and right arm pulse rate; The transmission auxiliary data includes a 13-bit upload timestamp, page number, page size, total number of data entries, response status code, and response information.

[0008] Furthermore, the data parsing, display, early warning judgment, health service linkage, and data synchronization processing of the mini-program platform specifically include: The training process data and physiological measurement data are used to generate left limb measurement curves, right limb measurement curves, mean arterial pressure curves, and pulse pressure difference curves for visualization. The system makes judgments based on preset warning parameters and configuration rules. The judgment dimensions include the decrease in systolic blood pressure, the decrease in diastolic blood pressure, and the blood pressure range level. The blood pressure range level includes the target range of 90 / 60mmHg-130 / 80mmHg, the high range of 130-139 / 80-89mmHg, and the high-risk range of 140-159 / 90-99mmHg. Based on the judgment results, corresponding health guidance and prompts are pushed out. After training is completed, the training check-in record will be automatically executed, and incentives will be given according to the 7-day cycle points rule. The points rule is 1 point on the first day, 2 points on the second day, 5 points on the third day, 7 points on the fourth day, 10 points on the fifth day, 14 points on the sixth day, and 20 points on the seventh day. If training is interrupted, the points will start accumulating again from 1 point. The integrated cardiovascular and cerebrovascular risk assessment module provides services such as BMI obesity assessment, stroke risk factor assessment, hypertension cardiovascular risk assessment, Framingham stroke assessment, and stroke recurrence prediction assessment. The integrated health consultation module provides access to a doctor list, online text and image consultations, consultation record queries, and 48-hour consultation time limit management services. It synchronously updates user health records, pushes training results, early warning information, and system notification messages, and synchronizes all data to the operation and management terminal for use in digital screen statistics, equipment central station management, patient training central station monitoring, registered personnel management, early warning parameter configuration, and science popularization and education management.

[0009] A system linking an ischemic preconditioning training device and a mini-program based on 4G mobile communication includes: an ischemic preconditioning training device, a cloud server, and a mini-program platform. The ischemic preconditioning training device has a built-in 4G mobile communication module and a parameter storage module. The 4G mobile communication module is debugged and adapted according to the preset application interface specification. The user can directly start the ischemic preconditioning training device locally. The training device calls the preset training parameters stored in the parameter storage module to perform ischemic preconditioning training and collects training process data and user physiological measurement data, which are then uploaded to the cloud server through the 4G mobile communication network. The cloud server is used to communicate with the ischemic preconditioning training device and the mini-program platform using a preset encryption method and according to the application interface specification, to decrypt, verify and store the received data, and to forward the data to the mini-program platform. The mini-program platform is used to receive and parse data forwarded by the cloud server, and to perform data display, early warning judgment and health service linkage processing.

[0010] Furthermore, the ischemic preconditioning training device also includes an encryption module. The encryption module is used to generate and verify the signature of the communication request header using the HmacSHA256 algorithm, and to encrypt and decrypt the communication request body using the AES / ECB / PKCS5Padding algorithm. The cloud server communicates with the ischemia preconditioning training device and the mini-program platform via a POST request, and the communication interface is / getDeviceData; The parameter storage module stores fixed preset training parameters, including automatic mode parameters and parameters for schemes one through four: The automatic mode parameters are as follows: first, measure the user's blood pressure, then increase the measured blood pressure value by 40 mmHg, inflate for 5 minutes, deflate for 5 minutes, and the total training time is 45 minutes. The parameters for Scheme 1 are: training pressure 180 mmHg, inflation time 3 minutes, deflation time 5 minutes, and total training time 35 minutes. The parameters for Scheme 2 are: training pressure 180 mmHg, inflation time 5 minutes, deflation time 5 minutes, and total training time 45 minutes. The parameters for Scheme 3 are: training pressure 200 mmHg, inflation time 5 minutes, deflation time 5 minutes, and total training time 45 minutes. The parameters for Scheme 4 are: training pressure 220 mmHg, inflation time 5 minutes, deflation time 5 minutes, and total training time 45 minutes.

[0011] Furthermore, the mini-program platform includes: The data visualization module is used to generate measurement curves for the left limb, right limb, mean arterial pressure, and pulse pressure difference. The early warning and judgment module is used to push corresponding health guidance and prompt information based on the changes in systolic blood pressure, changes in diastolic blood pressure, and blood pressure zone levels. The points incentive module is used to execute a 7-day cyclical points system: 1 point on the first day, 2 points on the second day, 5 points on the third day, 7 points on the fourth day, 10 points on the fifth day, 14 points on the sixth day, and 20 points on the seventh day. If training is interrupted, the points will start accumulating again from 1 point. The risk assessment module provides services such as BMI obesity assessment, stroke risk factor assessment, hypertension cardiovascular risk assessment, Framingham stroke assessment, and stroke recurrence prediction assessment. The health consultation module is used to provide online text and image consultation, consultation record inquiry, and 48-hour consultation time limit management services. The data synchronization module is used to synchronize data to the operation and management terminal to realize digital screen statistics, equipment central station management, and patient training central station monitoring.

[0012] A storage medium storing a computer program, which, when executed by a processor, implements the above-described method for linking an ischemic preconditioning training device with a mini-program based on 4G mobile communication.

[0013] A computer device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described method for linking an ischemic preconditioning training device with a mini-program based on 4G mobile communication.

[0014] Compared with existing technologies, this invention achieves significant beneficial effects through the above-mentioned technical solution: This invention utilizes 4G mobile communication to achieve remote data linkage between the ischemic preconditioning training device and the mini-program, enabling real-time remote transmission, encryption verification, and cloud storage of training and physiological data, effectively ensuring the security and integrity of medical data transmission. Simultaneously, the mini-program integrates functions such as data visualization, intelligent blood pressure warning, training point incentives, cardiovascular risk assessment, and online health consultation, significantly improving user training compliance and health management autonomy. Furthermore, the operation and management terminal enables centralized monitoring, statistical analysis, and collaborative medical services for devices, users, and training data, ultimately constructing an intelligent rehabilitation management system encompassing local autonomous training, real-time data upload, intelligent health intervention, and unified cloud management, greatly enhancing the convenience, safety, and management efficiency of ischemic preconditioning training. Attached Figure Description

[0015] Figure 1This is a flowchart of a method for linking an ischemic preconditioning training device with a mini-program based on 4G mobile communication according to the present invention. Figure 2 This is a system framework diagram of an ischemic preconditioning training device and a mini-program linkage system based on 4G mobile communication according to the present invention. Figure 3 This is the main interface of the patient-side mini-program; Figure 4 Bind the device interface to the patient's mini-program; Figure 5 This is the health consultation interface for the patient's mini-program. Figure 6 This is the risk assessment interface for the patient-side mini-program. Figure 7 Training data curve interface for the patient-side mini-program; Figure 8 For the digital screen interface of the mini-program operation and management terminal; Figure 9 This is the interface of the central station for the operation and management of mini-program devices; Figure 10 Training the central station interface for patients in the mini-program's operation and management terminal; Figure 11 This is the configuration interface for early warning parameters in the mini-program's operation and management system. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Reference Figures 1-11 As shown, this invention aims to provide a method for linking an ischemic preconditioning training device with a mini-program based on 4G mobile communication, including: The user starts the ischemia preconditioning training device to perform ischemia preconditioning training; The ischemic preconditioning training device has a built-in 4G mobile communication module. The 4G mobile communication module is debugged and adapted according to the preset application interface specification. During and after training, training process data and user physiological measurement data are collected and uploaded to the cloud server through the 4G mobile communication network in an encrypted transmission method according to the application interface specification. After the cloud server decrypts, verifies, and stores the uploaded data, it forwards the data to the mini-program platform. The mini-program platform parses and displays the received data, and performs early warning judgments, health service linkages, and data synchronization processing.

[0018] Specifically, the ischemic preconditioning training device in this invention locally stores preset training parameters, allowing users to directly start training locally. It utilizes a 4G mobile communication network to achieve real-time acquisition of training process data and user physiological measurement data, which is then uploaded to the cloud via encrypted transmission. By relying on the 4G mobile communication network for real-time acquisition and encrypted uploading of training process data and user physiological measurement data, coupled with data decryption, verification, storage, and forwarding processing on the cloud server, the security and integrity of medical and health data transmission and storage are effectively guaranteed. This breaks down the barrier of traditional stand-alone training devices' inability to remotely synchronize data, achieving real-time interoperability and unified management of training and physiological data. Furthermore, through a mini-program platform, data analysis, display, early warning judgment, and health service linkage processing are implemented, constructing a complete closed loop for remote training control and health management. This allows both users and medical personnel to monitor training status and health data in real time, significantly improving the intelligent control level and health management efficiency of ischemic preconditioning training.

[0019] The fixed preset training parameters of the ischemic preconditioning training device refer to the fixed training parameters that have been written into the local parameter storage module when the device leaves the factory. These parameters cannot be modified remotely through a mini-program / cloud and can only be updated by connecting to a local computer. They are not lost when the power is off and do not require the user to manually adjust the parameters locally. The user only needs to start the device locally, and the device can call the fixed parameters to perform the corresponding ischemic preconditioning training.

[0020] When encrypting the request body, the JSON format data is converted into a string, encrypted using a 24-bit key consistent with the signature, and then encrypted using the AES / ECB / PKCS5Padding algorithm. The encrypted data is then encapsulated in the format {"requestData":"encrypted string"} for transmission. Decryption uses the same algorithm and key to reverse the parsing process.

[0021] Furthermore, the preset application programming interface specification includes the following encrypted transmission and communication rules: The / getDeviceData communication interface is called using a POST request. The communication request header uses the HmacSHA256 algorithm to generate and verify the signature, and the communication request body uses the AES / ECB / PKCS5Padding algorithm to encrypt and decrypt. The communication request header includes a 13-bit timestamp, a replay and backtracking ID nonce, a unique downstream user ID clientId, a signature, and a content type Content-Type: application / json; The signature is a string composed of clientId, a 13-digit timestamp, and nonce, which is then calculated using the HmacSHA256 algorithm with a 24-digit unique key.

[0022] Specifically, the HmacSHA256 algorithm is used for the generation and verification of signatures in communication request headers. As a method of implementing request signatures, it plays a role in identity verification, preventing data tampering, avoiding unauthorized access requests, and ensuring the legitimacy of communication identities and the integrity of data. AES / ECB / PKCS5Padding algorithm: used for encryption and decryption of communication request bodies, encrypting and protecting private information such as transmitted training data and physiological measurement data, preventing leakage and theft during data transmission, and ensuring data transmission security.

[0023] The communication request header contains the following fields, the definitions and uses of which are as follows: 1. timestamp: Request timestamp, 13-digit timestamp, example value: 1730962899993; 2. nonce: Anti-replay and backtracking ID, example value: 5283c0d8325e4ee185da034937b5559; 3. clientId: A unique code for the downstream user. Example value: XY1234567789; 4. signature: Request signature, used for identity verification; 5. Content-Type: Request format, value is application / json.

[0024] The signature is a string composed of clientId, 13-bit timestamp, and nonce, which is then calculated using the HmacSHA256 algorithm with a 24-bit unique key.

[0025] In this embodiment, the HmacSHA256 signature generation code is used for request header signature generation and verification when the ischemia preconditioning training device / mini-program platform sends a request to the cloud server. Specifically, the following uses Java as an example: public class SHAUtils{ publicstaticStringHMACSHA256(String key, String data)throwsException{ Mac sha256_HMAC = Mac.getInstance("HmacSHA256"); SecretKeySpec secret_key =newSecretKeySpec(key.getBytes("UTF-8"), "HmacSHA256"); sha256_HMAC.init(secret_key); returnHex.encodeHexString(sha256_HMAC.doFinal(data.getBytes("UTF-8"))); } publicstaticvoidmain(String[] args)throwsException { String clientId = "123456"; String timestamp = "1730962899993"; String nonce = "5283c0d8325e4ee185da034937b5559"; String data = clientId + timestamp + nonce; String key = "vXH5ldW2htJbTAOw0TvPo6y3"; System.out.println(HMACSHA256(key, data)); } } The signature output in the above example is: 5d4dce1be98a9bb07ddfa4aac2f14cdd436349c0f0333d24210e7e1103ce5e4b AES encryption / decryption code is used for data uploads from ischemic preconditioning training devices and for request encryption and decryption during communication between the mini-program platform and the cloud. The following example uses Java: public class AESUtil{ / / AES encryption method publicstaticStringencrypt(String data, String key)throwsException { / / Create an AES Cipher object Cipher cipher = Cipher.getInstance("AES / ECB / PKCS5Padding"); / / Initialize encryption mode and key SecretKeySpec secretKey =newSecretKeySpec(key.getBytes(), "AES"); cipher.init(Cipher.ENCRYPT_MODE, secretKey); / / Encrypted data byte[] encryptedBytes = cipher.doFinal(data.getBytes()); / / Encode into Base64 for output returnBase64.getEncoder().encodeToString(encryptedBytes); } / / AES decryption method publicstaticStringdecrypt(String encryptedData, String key)throwsException { / / Create an AES Cipher object Cipher cipher = Cipher.getInstance("AES / ECB / PKCS5Padding"); / / Initialize decryption mode and key SecretKeySpec secretKey =newSecretKeySpec(key.getBytes(), "AES"); cipher.init(Cipher.DECRYPT_MODE, secretKey); / / Decrypt data byte[] decodedBytes = Base64.getDecoder().decode(encryptedData); byte[] decryptedBytes = cipher.doFinal(decodedBytes); returnnewString(decryptedBytes); } publicstaticvoidmain(String[] args) { try{ / / Generate a key or use a custom key (16 bytes long) String key = "vXH5ldW2htJbTAOw0TvPo6y3"; / / The key must be 16 bytes (192 bits). String data = "{\"measureTime\": 1234567890123}"; / / Encryption String encryptedData = AESUtil.encrypt(data, key); System.out.println("Encrypted: " + encryptedData); / / Decryption String decryptedData = AESUtil.decrypt(encryptedData, key); System.out.println("Decrypted: " + decryptedData); }catch(Exception e) { e.printStackTrace(); } } } The encrypted output of the above example is: NawkAfbUcTYyukNbP5Vd5dWUDrKjHzMvGMLkbdMo8vk= The clientId (client code) is the identifier for the mini-program platform / ischemic preconditioning training device. The data (requested data) can be training control parameters, training process data, or physiological measurement data. The key is a unique 24-bit communication key; The device is an ischemic preconditioning training device.

[0026] Furthermore, the ischemic preconditioning training device has fixed preset training parameters including automatic mode parameters and parameters for schemes one to four, wherein: The automatic mode parameters are as follows: first, measure the user's blood pressure, then increase the measured blood pressure value by 40 mmHg, inflate for 5 minutes, deflate for 5 minutes, and the total training time is 45 minutes. The parameters for Scheme 1 are: training pressure 180 mmHg, inflation time 3 minutes, deflation time 5 minutes, and total training time 35 minutes. The parameters for Scheme 2 are: training pressure 180 mmHg, inflation time 5 minutes, deflation time 5 minutes, and total training time 45 minutes. The parameters for Scheme 3 are: training pressure 200 mmHg, inflation time 5 minutes, deflation time 5 minutes, and total training time 45 minutes. The parameters for Scheme 4 are: training pressure 220 mmHg, inflation time 5 minutes, deflation time 5 minutes, and total training time 45 minutes.

[0027] Specifically, the ischemic preconditioning training device of this invention has its automatic mode and standardized training parameters (Schemes 1 to 4) pre-set at the factory. Users can call these pre-set parameters to execute training simply by starting the device locally. It can provide suitable training modes based on different users' physical tolerance and usage needs. The automatic mode can intelligently adjust the training pressure based on the user's measured blood pressure, significantly improving the personalization and adaptability of the training. Schemes 1 to 4 provide a standardized training process with fixed pressure, inflation time, deflation time, and total time, eliminating the need for users to manually adjust parameters locally on the device. This effectively simplifies the operation process, making it especially convenient for elderly and mobility-impaired users. At the same time, the unified and standardized training parameter settings ensure the controllability and stability of the training process, guaranteeing a consistent execution standard for ischemic preconditioning training and meeting the usage needs of different scenarios such as daily health care and rehabilitation training.

[0028] The applicable scenarios and usage requirements for each fixed preset training parameter are as follows: Automatic mode: After powering on, press the "Automatic" button. The system will perform a self-check and start the blood pressure measurement program after 6 seconds. After the measurement is completed, the measurement value will be automatically saved. The instrument will then automatically refer to the measurement results to set training parameters and enter training mode.

[0029] Option 1: Suitable for elderly users starting out. The method of use is once in the morning and once in the afternoon, with an interval of at least 3 hours between sessions. After 1-3 days of training with Option 1, if fully tolerated, switch to Option 2 for long-term training, with at least 5-7 days of training per week and no more than 2 days of interruption. Elderly individuals or children who cannot tolerate Option 2 treatment can choose Option 1 for long-term treatment.

[0030] Option 2: Train once in the morning and once in the afternoon each day, with an interval of at least 3 hours between training sessions. Train 5-7 days a week, with no more than 2 days of interruption. Each course of treatment lasts 6 months, and long-term treatment yields better results.

[0031] Option 3: For those who have partially completed Option 2 treatment for more than 6 months, Option 3 can be implemented to further improve treatment effectiveness. Training will be conducted twice daily, once in the morning and once in the afternoon, with at least a 3-hour interval between sessions. Training will be conducted 5-7 days per week, with a one-week break between every two treatment courses. Long-term treatment yields better results.

[0032] Option 4: For use when training requires wearing thicker clothing due to low temperatures. Dosage, intensity, and frequency are the same as above.

[0033] Furthermore, the data uploaded by the ischemic preconditioning training device includes basic device data, user identity data, training process data, physiological measurement data, and transmission auxiliary data; The basic equipment data includes equipment ID, equipment model, equipment longitude, equipment latitude, equipment address, and equipment IP; the user identity data includes user name, gender, age, mobile phone number, and ID number. The training process data includes training mode, training pressure, training start timestamp, training end timestamp, average pressure, training error messages, and user-initiated stop flag. The physiological measurement data includes measurement timestamps, left arm error codes, left arm systolic blood pressure, left arm diastolic blood pressure, left arm pulse rate, right arm error codes, right arm systolic blood pressure, right arm diastolic blood pressure, and right arm pulse rate; The transmission auxiliary data includes a 13-bit upload timestamp, page number, page size, total number of data entries, response status code, and response information.

[0034] Specifically, this invention can comprehensively collect and upload basic device data, user identity data, training process data, physiological measurement data, and transmission auxiliary data through an ischemic preconditioning training device. It comprehensively covers device identification, location information, user basic information, training status, physiological indicators of left and right limbs, error information, timestamps, and paginated transmission-related data. This enables complete collection and remote transmission of training data across all dimensions. It provides comprehensive and detailed data support for data analysis, visualization, and early warning judgment on the mini-program platform, and also provides complete data evidence for medical personnel to remotely view user training details, changes in physiological indicators, and dynamic updates to health records. Simultaneously, complete data collection and transmission ensures accurate statistics and effective supervision of device operation status and user training status by the operation management end, avoiding problems such as incomplete training monitoring and inaccurate health assessments caused by data gaps, thus guaranteeing the accuracy and completeness of remote training control and health management.

[0035] Training error messages: trainError, 0 for correct results, otherwise error messages, length not exceeding 100 characters.

[0036] User-initiated stop flag: stopFlag, 1 indicates user-initiated stop, 0 or null indicates normal termination.

[0037] Measurement timestamp: measureTime, the measurement time, is a 13-digit timestamp.

[0038] Data interaction between the front-end and back-end is accomplished through JSON data format, and the request method is POST. The specific request body needs to convert the JSON of the API document into a string and encrypt it using AES.

[0039] Furthermore, the data parsing, display, early warning judgment, health service linkage, and data synchronization processing of the mini-program platform specifically include: The training process data and physiological measurement data are used to generate left limb measurement curves, right limb measurement curves, mean arterial pressure curves, and pulse pressure difference curves for visualization. The system makes judgments based on preset warning parameters and configuration rules. The judgment dimensions include the decrease in systolic blood pressure, the decrease in diastolic blood pressure, and the blood pressure range level. The blood pressure range level includes the target range of 90 / 60mmHg-130 / 80mmHg, the high range of 130-139 / 80-89mmHg, and the high-risk range of 140-159 / 90-99mmHg. Based on the judgment results, corresponding health guidance and prompts are pushed out. After training is completed, the training check-in record will be automatically executed, and incentives will be given according to the 7-day cycle points rule. The points rule is 1 point on the first day, 2 points on the second day, 5 points on the third day, 7 points on the fourth day, 10 points on the fifth day, 14 points on the sixth day, and 20 points on the seventh day. If training is interrupted, the points will start accumulating again from 1 point. The integrated cardiovascular and cerebrovascular risk assessment module provides services such as BMI obesity assessment, stroke risk factor assessment, hypertension cardiovascular risk assessment, Framingham stroke assessment, and stroke recurrence prediction assessment. The integrated health consultation module provides access to a doctor list, online text and image consultations, consultation record queries, and 48-hour consultation time limit management services. It synchronously updates user health records, pushes training results, early warning information, and system notification messages, and synchronizes all data to the operation and management terminal for use in digital screen statistics, equipment central station management, patient training central station monitoring, registered personnel management, early warning parameter configuration, and science popularization and education management.

[0040] Specifically, this invention can utilize a mini-program platform to display training and physiological data in multi-dimensional visualization curves, allowing users to intuitively view changes in their training and physiological indicators. Simultaneously, it intelligently judges blood pressure changes and ranges according to preset warning parameters and pushes corresponding health guidance prompts. Combined with a 7-day cyclical points incentive mechanism, it effectively improves user training adherence. It also provides users with diversified cardiovascular risk assessment services and time-limited online health consultation services, achieving integrated linkage of training monitoring, health warnings, rehabilitation incentives, risk assessment, and doctor-patient communication. Furthermore, it can synchronize all data to the operation management terminal, supporting real-time monitoring and management of digital large-screen statistics, equipment central stations, and patient training central stations. This constructs a complete health management closed loop integrating data display, intelligent warnings, incentive guidance, health services, and unified supervision, comprehensively improving the remote management efficiency of ischemic preconditioning training and the user's health management experience.

[0041] Specifically, the four types of physiological curves include: (1) Left limb measurement curve: The curves showing the changes in blood pressure and heart rate of the left limb over time.

[0042] (2) Right limb measurement curve: The curves showing the changes in blood pressure and heart rate of the right limb over time.

[0043] (3) Mean arterial pressure curve: This curve shows the change of the mean blood pressure of both limbs over time.

[0044] (4) Pulse pressure difference curve: This curve shows the change of the difference between systolic and diastolic blood pressure over time.

[0045] The blood pressure warning logic is triggered based on two conditions: blood pressure range and the magnitude of blood pressure change, as detailed below: Blood pressure zone determination: (1) Standard range: 90 / 60 mmHg - 130 / 80 mmHg; (2) Higher range: 130-139 / 80-89 mmHg; (3) High-risk range: 140-159 / 90-99 mmHg.

[0046] Conditions for triggering the reduction: (1) Systolic blood pressure drop > 5 mmHg; (2) Diastolic blood pressure drop > 3 mmHg.

[0047] Warning content push notification: When blood pressure is within the above range or the reduction reaches the target, the system will push corresponding health guidance prompts, such as: (1) Blood pressure target achieved: "Your blood pressure has reached the target after training. Continue to persist with Xuan Yi Tong blood vessel training and maintain good living habits." (2) High / High Risk Warning: "Your blood pressure is high. We recommend a low-salt, low-fat diet and consult your doctor to adjust your treatment plan."

[0048] The points system operates on a 7-day cycle to incentivize users to persist in training. If training is interrupted, points are accumulated again starting from 1.

[0049] A system linking an ischemic preconditioning training device and a mini-program based on 4G mobile communication includes: an ischemic preconditioning training device, a cloud server, and a mini-program platform. The ischemic preconditioning training device has a built-in 4G mobile communication module and a parameter storage module. The 4G mobile communication module is debugged and adapted according to the preset application interface specification. The user can directly start the ischemic preconditioning training device locally. The training device calls the preset training parameters stored in the parameter storage module to perform ischemic preconditioning training and collects training process data and user physiological measurement data, which are then uploaded to the cloud server through the 4G mobile communication network. The cloud server is used to communicate with the ischemic preconditioning training device and the mini-program platform using a preset encryption method and according to the application interface specification, to decrypt, verify and store the received data, and to forward the data to the mini-program platform. The mini-program platform is used to receive and parse data forwarded by the cloud server, and to perform data display, early warning judgment and health service linkage processing.

[0050] Specifically, this invention utilizes a linked system consisting of an ischemic preconditioning training device, a cloud server, and a mini-program platform. Relying on the built-in 4G mobile communication module of the training device, a stable remote communication link is established, reliably enabling real-time transmission of training process and physiological measurement data. This completely breaks the limitation of traditional standalone training devices being unable to achieve remote linkage. The cloud server can uniformly decrypt, verify, store, and forward interactive data, ensuring the standardization, stability, and security of the overall system's data interaction. The mini-program platform can complete integrated management and control of data parsing and processing, visualization, early warning judgment, and health service linkage, forming a complete closed loop from data collection and transmission to intelligent processing.

[0051] Furthermore, the ischemic preconditioning training device also includes an encryption module. The encryption module is used to generate and verify the signature of the communication request header using the HmacSHA256 algorithm, and to encrypt and decrypt the communication request body using the AES / ECB / PKCS5Padding algorithm. The cloud server communicates with the ischemia preconditioning training device and the mini-program platform via a POST request, and the communication interface is / getDeviceData; The parameter storage module stores fixed preset training parameters, including automatic mode parameters and parameters for schemes one through four: The automatic mode parameters are as follows: first, measure the user's blood pressure, then increase the measured blood pressure value by 40 mmHg, inflate for 5 minutes, deflate for 5 minutes, and the total training time is 45 minutes. The parameters for Scheme 1 are: training pressure 180 mmHg, inflation time 3 minutes, deflation time 5 minutes, and total training time 35 minutes. The parameters for Scheme 2 are: training pressure 180 mmHg, inflation time 5 minutes, deflation time 5 minutes, and total training time 45 minutes. The parameters for Scheme 3 are: training pressure 200 mmHg, inflation time 5 minutes, deflation time 5 minutes, and total training time 45 minutes. The parameters for Scheme 4 are: training pressure 220 mmHg, inflation time 5 minutes, deflation time 5 minutes, and total training time 45 minutes.

[0052] Specifically, this invention configures an encryption module and a training parameter storage module in the ischemia preconditioning training device. It employs the HmacSHA256 algorithm for communication signature verification and the AES / ECB / PKCS5Padding algorithm for data encryption and decryption, creating a dual security protection mechanism. Combined with standardized POST request methods and the / getDeviceData communication interface for data interaction, it effectively ensures the trustworthiness of identity, the integrity of data, and the security of information during the transmission of training data and physiological measurement data, avoiding problems such as leakage, tampering, or replay attacks of medical and health data. At the same time, relying on the automatic mode fixed by the training parameter storage module and the standardized training parameters of Schemes 1 to 4, it can directly call and stably execute training processes with different training pressures, inflation times, deflation times, and total times locally, without requiring users to manually adjust parameters locally on the device.

[0053] Furthermore, the mini-program platform includes: The data visualization module is used to generate measurement curves for the left limb, right limb, mean arterial pressure, and pulse pressure difference. The early warning and judgment module is used to push corresponding health guidance and prompt information based on the changes in systolic blood pressure, changes in diastolic blood pressure, and blood pressure zone levels. The points incentive module is used to execute a 7-day cyclical points system: 1 point on the first day, 2 points on the second day, 5 points on the third day, 7 points on the fourth day, 10 points on the fifth day, 14 points on the sixth day, and 20 points on the seventh day. If training is interrupted, the points will start accumulating again from 1 point. The risk assessment module provides services such as BMI obesity assessment, stroke risk factor assessment, hypertension cardiovascular risk assessment, Framingham stroke assessment, and stroke recurrence prediction assessment. The health consultation module is used to provide online text and image consultation, consultation record inquiry, and 48-hour consultation time limit management services. The data synchronization module is used to synchronize data to the operation and management terminal to realize digital screen statistics, equipment central station management, and patient training central station monitoring.

[0054] Specifically, this invention, through the data visualization, early warning judgment, points incentive, risk assessment, health consultation, and data synchronization modules integrated into the mini-program platform, can transform training and physiological measurement data into intuitive limb measurements, arterial pressure, and pulse pressure curves for users to clearly view. It intelligently pushes corresponding health guidance prompts based on the magnitude and level of blood pressure changes, effectively improving user training compliance and continuity through a 7-day cyclical points system. Simultaneously, it provides users with diversified cardiovascular and cerebrovascular risk assessment services and standardized time-limited online health consultation services. Furthermore, it can synchronize all data to the operation and management terminal, enabling digital large-screen statistics, equipment central station management, and patient training central station monitoring. This makes the health management service for remote ischemic preconditioning training more comprehensive and intelligent, strengthening users' self-management capabilities and providing efficient centralized monitoring methods for medical and management personnel, further improving the integrated service system of remote training and health management.

[0055] A storage medium storing a computer program, which, when executed by a processor, implements the above-described method for linking an ischemic preconditioning training device with a mini-program based on 4G mobile communication.

[0056] A computer device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described method for linking an ischemic preconditioning training device with a mini-program based on 4G mobile communication.

[0057] Specifically, this invention provides a storage medium and a computer device for the adaptive linkage method. The storage medium stores a computer program that can be called and run by a processor. The computer device, through its memory and processor, runs the corresponding computer program and can completely execute the entire process logic of the ischemic preconditioning training device and mini-program linkage method based on 4G mobile communication. It can solidify and stably run the entire set of technical solutions, including the execution of locally fixed parameters, 4G encrypted communication transmission, multi-dimensional data collection and uploading, mini-program data parsing and display, intelligent blood pressure warning, and linkage of various health services. It can flexibly adapt to different types of hardware terminals and service platforms, greatly improving the versatility, portability, and scenario adaptability of the technical solution. It facilitates the storage, circulation, and batch deployment of the program, and also benefits subsequent function iterations and version updates. This lays a solid foundation for the large-scale implementation and multi-terminal adaptation application of the ischemic preconditioning training device and mini-program linkage solution in actual medical and health management scenarios.

[0058] This invention is not limited to the above-described embodiments. Without departing from the spirit and essence of this invention, those skilled in the art can make equivalent substitutions or improvements to the sequence of steps, parameter settings, and specific implementation forms, all of which should fall within the protection scope of the claims of this invention.

Claims

1. A method for linking an ischemic preconditioning training device with a mini-program based on 4G mobile communication, characterized in that, include: The user starts the ischemia preconditioning training device to perform ischemia preconditioning training; The ischemic preconditioning training device has a built-in 4G mobile communication module. The 4G mobile communication module is debugged and adapted according to the preset application interface specification. During and after training, training process data and user physiological measurement data are collected and uploaded to the cloud server through the 4G mobile communication network in an encrypted transmission method according to the application interface specification. After the cloud server decrypts, verifies, and stores the uploaded data, it forwards the data to the mini-program platform. The mini-program platform parses and displays the received data, and performs early warning judgments, health service linkages, and data synchronization processing.

2. The method for linking an ischemic preconditioning training device with a mini-program based on 4G mobile communication according to claim 1, characterized in that, The preset application interface specification includes the following encrypted transmission and communication rules: The / getDeviceData communication interface is called using a POST request. The communication request header uses the HmacSHA256 algorithm to generate and verify the signature, and the communication request body uses the AES / ECB / PKCS5Padding algorithm to encrypt and decrypt. The communication request header includes a 13-bit timestamp, a replay and backtracking ID nonce, a unique downstream user ID clientId, a signature, and a content type Content-Type: application / json; The signature is a string composed of clientId, a 13-digit timestamp, and nonce, which is then calculated using the HmacSHA256 algorithm with a 24-digit unique key.

3. The method for linking an ischemic preconditioning training device with a mini-program based on 4G mobile communication according to claim 2, characterized in that, The ischemia preconditioning training device has fixed preset training parameters, including automatic mode parameters and parameters for schemes one to four, wherein: The automatic mode parameters are as follows: first, measure the user's blood pressure, then increase the measured blood pressure value by 40 mmHg, inflate for 5 minutes, deflate for 5 minutes, and the total training time is 45 minutes. The parameters for Scheme 1 are: training pressure 180 mmHg, inflation time 3 minutes, deflation time 5 minutes, and total training time 35 minutes. The parameters for Scheme 2 are: training pressure 180 mmHg, inflation time 5 minutes, deflation time 5 minutes, and total training time 45 minutes. The parameters for Scheme 3 are: training pressure 200 mmHg, inflation time 5 minutes, deflation time 5 minutes, and total training time 45 minutes. The parameters for Scheme 4 are: training pressure 220 mmHg, inflation time 5 minutes, deflation time 5 minutes, and total training time 45 minutes.

4. The method for linking an ischemic preconditioning training device with a mini-program based on 4G mobile communication according to claim 3, characterized in that, The data uploaded by the ischemic preconditioning training device includes basic device data, user identity data, training process data, physiological measurement data, and transmission auxiliary data. The basic equipment data includes equipment ID, equipment model, equipment longitude, equipment latitude, equipment address, and equipment IP; the user identity data includes user name, gender, age, mobile phone number, and ID number. The training process data includes training mode, training pressure, training start timestamp, training end timestamp, average pressure, training error messages, and user-initiated stop flag. The physiological measurement data includes measurement timestamps, left arm error codes, left arm systolic blood pressure, left arm diastolic blood pressure, left arm pulse rate, right arm error codes, right arm systolic blood pressure, right arm diastolic blood pressure, and right arm pulse rate. The transmission auxiliary data includes a 13-bit upload timestamp, page number, page size, total number of data entries, response status code, and response information.

5. The method for linking an ischemic preconditioning training device with a mini-program based on 4G mobile communication according to claim 4, characterized in that, The data parsing, display, early warning judgment, health service linkage, and data synchronization processing of the mini-program platform specifically include: The training process data and physiological measurement data are used to generate measurement curves for the left limb, right limb, mean arterial pressure, and pulse pressure difference for visualization. The system makes judgments based on preset warning parameters and configuration rules. The judgment dimensions include the decrease in systolic blood pressure, the decrease in diastolic blood pressure, and the blood pressure range level. The blood pressure range level includes the target range of 90 / 60mmHg-130 / 80mmHg, the high range of 130-139 / 80-89mmHg, and the high-risk range of 140-159 / 90-99mmHg. Based on the judgment results, corresponding health guidance and prompts are pushed out. After training is completed, the training check-in record will be automatically executed, and incentives will be given according to the 7-day cycle points rule. The points rule is 1 point on the first day, 2 points on the second day, 5 points on the third day, 7 points on the fourth day, 10 points on the fifth day, 14 points on the sixth day, and 20 points on the seventh day. If training is interrupted, the points will start accumulating again from 1 point. The integrated cardiovascular and cerebrovascular risk assessment module provides services such as BMI obesity assessment, stroke risk factor assessment, hypertension cardiovascular risk assessment, Framingham stroke assessment, and stroke recurrence prediction assessment. The integrated health consultation module provides access to a doctor list, online text and image consultations, consultation record queries, and 48-hour consultation time limit management services. It synchronously updates user health records, pushes training results, early warning information, and system notification messages, and synchronizes all data to the operation and management terminal for use in digital screen statistics, equipment central station management, patient training central station monitoring, registered personnel management, early warning parameter configuration, and science popularization and education management.

6. A system linking an ischemic preconditioning training device and a mini-program based on 4G mobile communication, characterized in that, include: Ischemic preconditioning training device, cloud server, and mini-program platform. The ischemic preconditioning training device has a built-in 4G mobile communication module and a parameter storage module. The 4G mobile communication module is debugged and adapted according to the preset application interface specification. The user can directly start the ischemic preconditioning training device locally. The training device calls the preset training parameters stored in the parameter storage module to perform ischemic preconditioning training and collects training process data and user physiological measurement data, which are then uploaded to the cloud server through the 4G mobile communication network. The cloud server is used to communicate with the ischemic preconditioning training device and the mini-program platform using a preset encryption method and according to the application interface specification, to decrypt, verify and store the received data, and to forward the data to the mini-program platform. The mini-program platform is used to receive and parse data forwarded by the cloud server, and to perform data display, early warning judgment and health service linkage processing.

7. The ischemic preconditioning training device and mini-program linkage system based on 4G mobile communication according to claim 6, characterized in that, The ischemic preconditioning training device also includes an encryption module. The encryption module is used to generate and verify the signature of the communication request header using the HmacSHA256 algorithm, and to encrypt and decrypt the communication request body using the AES / ECB / PKCS5Padding algorithm. The cloud server communicates with the ischemia preconditioning training device and the mini-program platform via a POST request, and the communication interface is / getDeviceData; The parameter storage module stores fixed preset training parameters, including automatic mode parameters and parameters for schemes one through four: The automatic mode parameters are as follows: first, measure the user's blood pressure, then increase the measured blood pressure value by 40 mmHg, inflate for 5 minutes, deflate for 5 minutes, and the total training time is 45 minutes. The parameters for Scheme 1 are: training pressure 180 mmHg, inflation time 3 minutes, deflation time 5 minutes, and total training time 35 minutes. The parameters for Scheme 2 are: training pressure 180 mmHg, inflation time 5 minutes, deflation time 5 minutes, and total training time 45 minutes. The parameters for Scheme 3 are: training pressure 200 mmHg, inflation time 5 minutes, deflation time 5 minutes, and total training time 45 minutes. The parameters for Scheme 4 are: training pressure 220 mmHg, inflation time 5 minutes, deflation time 5 minutes, and total training time 45 minutes.

8. The ischemic preconditioning training device and mini-program linkage system based on 4G mobile communication according to claim 6, characterized in that, The mini-program platform includes: The data visualization module is used to generate measurement curves for the left limb, right limb, mean arterial pressure, and pulse pressure difference. The early warning and judgment module is used to push corresponding health guidance and prompt information based on the changes in systolic blood pressure, changes in diastolic blood pressure, and blood pressure zone levels. The points incentive module is used to execute a 7-day cyclical points system: 1 point on the first day, 2 points on the second day, 5 points on the third day, 7 points on the fourth day, 10 points on the fifth day, 14 points on the sixth day, and 20 points on the seventh day. If training is interrupted, the points will start accumulating again from 1 point. The risk assessment module provides services such as BMI obesity assessment, stroke risk factor assessment, hypertension cardiovascular risk assessment, Framingham stroke assessment, and stroke recurrence prediction assessment. The health consultation module is used to provide online text and image consultation, consultation record inquiry, and 48-hour consultation time limit management services. The data synchronization module is used to synchronize data to the operation and management terminal to realize digital screen statistics, equipment central station management, and patient training central station monitoring.

9. A storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for linking the ischemic preconditioning training device and the applet based on 4G mobile communication as described in any one of claims 1-5.

10. A computer device, characterized in that, include: The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the ischemic preconditioning training device and mini-program linkage method based on 4G mobile communication as described in any one of claims 1-5.

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