Intelligent blood glucose management system for gestational diabetes patients

Abstract

The invention provides an intelligent blood glucose management system for gestational diabetes patients. The intelligent blood glucose management system for the gestational diabetes patient comprises a medical terminal, data communication equipment, a cloud platform, a server and a user terminal, wherein the data communication equipment is used for executing data interaction control among the medical terminal, the cloud platform, the server and the user terminal; the cloud platform is used for performing blood glucose risk prediction on the gestational diabetes patient by using the blood glucose parameter information acquired by the medical terminal in combination with a risk prediction model to obtain a risk prediction result; the server is used for storing and managing the blood glucose parameter information and the corresponding risk prediction result; and the user terminal is used for performing storage management on the currently obtained sugar parameter information corresponding to the gestational diabetes patient and the corresponding risk prediction result, and performing data deletion on the sugar parameter information in the historical record and the corresponding risk prediction result.

Description

Technical Field

[0001] The invention provides an intelligent blood sugar management system for patients with gestational diabetes, belonging to the technical field of intelligent data management. Background Art

[0002] Gestational diabetes is a common metabolic disease in pregnant women, characterized by elevated blood sugar that first appears or is discovered during pregnancy. Due to the physiological changes in the mother and fetus during pregnancy, the management of gestational diabetes is particularly critical, not only related to the health of pregnant women, but also directly affects the development of the fetus and the health status after birth. Therefore, it is crucial to effectively monitor and manage the blood sugar of patients with gestational diabetes. Traditional blood sugar management methods mainly rely on patient self-monitoring and regular hospital check-ups, which have many inconveniences and limitations. For example, patients may cause blood sugar fluctuations due to forgetting to monitor or inaccurate monitoring, and the frequency of regular hospital check-ups is limited, and it is impossible to grasp blood sugar changes in real time. In addition, traditional blood sugar management methods lack the assessment and prediction of individualized risks of patients, making it difficult to formulate targeted management plans. Summary of the invention

[0003] The present invention provides an intelligent blood glucose management system for patients with gestational diabetes, which is used to solve the technical problems existing in the above-mentioned prior art. The technical solutions adopted are as follows:

[0004] An intelligent blood glucose management system for patients with gestational diabetes, the intelligent blood glucose management system for patients with gestational diabetes comprising a medical terminal, a data communication device, a cloud platform, a server and a user terminal; the medical terminal is data-connected to the cloud platform via the data communication device; the cloud platform is data-connected to the server via the data communication device; the server is data-connected to the user terminal via the data communication device;

[0005] Wherein, the data communication device is used to perform data interaction control between the medical terminal, the cloud platform, the server and the user terminal;

[0006] The cloud platform is used to use the blood sugar parameter information obtained by the medical terminal in combination with the risk prediction model to predict the blood sugar risk of patients with gestational diabetes and obtain the risk prediction results;

[0007] The server is used to store and manage blood sugar parameter information and its corresponding risk prediction results;

[0008] The user terminal is used to store and manage the currently acquired sugar parameter information corresponding to the gestational diabetes patient and the corresponding risk prediction results, and to delete the sugar parameter information and the corresponding risk prediction results in the historical records.

[0009] Further, the data communication device includes a first communication device, a second communication device, a third communication device and a communication operation control device;

[0010] Wherein, the first communication device is used to establish a data communication connection between the medical terminal and the cloud platform;

[0011] The second communication device is used to establish a data communication connection between the cloud platform and the server;

[0012] The third communication device is used to establish a data communication connection between the server and the user terminal;

[0013] The communication operation control device is used to control the communication operation of the first communication device, the second communication device and the third communication device, and at the same time, monitor the communication operation quality of the first communication device, the second communication device and the third communication device.

[0014] Furthermore, the communication operation control device controls the communication operation of the first communication device, the second communication device, and the third communication device, including:

[0015] After retrieving the blood sugar parameter information of the gestational diabetes patient, the medical terminal sends a data transmission instruction to the communication operation control device, and sends the blood sugar parameter information of the gestational diabetes patient to the communication operation control device;

[0016] After receiving the blood sugar parameter information of the gestational diabetes patient, the communication operation control device marks the blood sugar parameter information of the gestational diabetes patient to obtain the blood sugar parameter information with a first mark;

[0017] Sending the blood sugar parameter information with the first identifier to the first communication device, and controlling the first communication device to send the blood sugar parameter information with the first identifier to the cloud platform;

[0018] After receiving the blood sugar parameter information with the first identifier, the cloud platform uses the blood sugar parameter information with the first identifier to predict the blood sugar risk of the gestational diabetes patient to obtain a risk prediction result, and identifies the risk prediction result to obtain a risk prediction result with a second identifier;

[0019] After obtaining the risk prediction result with the second identifier, the cloud platform sends the risk prediction result with the second identifier and the blood glucose parameter information with the first identifier to the communication operation control device;

[0020] After receiving the risk prediction result with the second identifier and the blood glucose parameter information with the first identifier, the communication operation control device controls the second communication device to send the risk prediction result with the second identifier and the blood glucose parameter information with the first identifier to the server;

[0021] After receiving the risk prediction result with the second identifier and the blood sugar parameter information with the first identifier, the server stores the risk prediction result with the second identifier and the blood sugar parameter information with the first identifier;

[0022] After the server completes the storage of the risk prediction results with the second identifier and the blood glucose parameter information with the first identifier, the communication operation control device controls the third communication device to send the risk prediction results with the second identifier and the blood glucose parameter information with the first identifier that have been stored in the server to the user terminal.

[0023] Further, monitoring the communication operation quality of the first communication device, the second communication device, and the third communication device includes:

[0024] monitoring first communication operation parameters of the first communication device, the second communication device, and the third communication device in real time, and obtaining operation evaluation basic parameters using the first communication operation parameters and their corresponding weight values;

[0025] real-time monitoring of second communication operating parameters of the first communication device, the second communication device, and the third communication device;

[0026] Using the second communication operation parameter to obtain the penalty factors corresponding to the first communication device, the second communication device, and the third communication device, and using the penalty factors in combination with the operation evaluation basic parameters to obtain the operation evaluation parameters corresponding to the first communication device, the second communication device, and the third communication device;

[0027] Comparing the values ​​of the operation evaluation parameters of the first communication device, the second communication device, and the third communication device with preset evaluation parameter thresholds;

[0028] When the value of the operation evaluation parameter is lower than a preset evaluation parameter threshold, it is determined that the first communication device, the second communication device or the third communication device below the preset evaluation parameter threshold has abnormal communication operation quality, and a quality abnormality alarm is issued.

[0029] Further, real-time monitoring of first communication operation parameters of the first communication device, the second communication device, and the third communication device, and obtaining operation evaluation basic parameters using the first communication operation parameters and their corresponding weight values, include:

[0030] Real-time monitoring of first communication operation parameters of the first communication device, the second communication device, and the third communication device, wherein the first communication operation parameters include data transmission rate, bit error rate, signal strength, bandwidth utilization, and packet loss rate;

[0031] The vector matrix P is constructed using the first communication operation parameter, and the structure of the vector matrix P is as follows:

[0032] P=(P1,P2,P3,P4,P5)

[0033] Wherein, P represents a vector matrix; P1, P2, P3, P4 and P5 represent data transmission rate, bit error rate, signal strength, bandwidth utilization and packet loss rate respectively;

[0034] Extracting weight values ​​corresponding to the data transmission rate, bit error rate, signal strength, bandwidth utilization and packet loss rate;

[0035] The first communication operation parameter is used to obtain operation evaluation basic parameters corresponding to the first communication device, the second communication device, and the third communication device, wherein the operation evaluation basic parameters corresponding to the first communication device, the second communication device, and the third communication device are obtained by the following formula:

[0036]

[0037] Wherein, Q represents the basic parameter for operation evaluation; n represents the parameter type included in the first communication operation parameter, and n=5; P i represents the value corresponding to the i-th parameter type contained in the current first communication operation parameter; P mini represents the minimum value among the historical data values ​​of the i-th parameter type contained in the first communication operation parameter; maxi represents the maximum value among the historical data values ​​of the i-th parameter type contained in the first communication operation parameter; i It represents the weight value corresponding to the i-th parameter type included in the first communication operation parameter; δ represents the sensitivity adjustment coefficient, and the sensitivity adjustment coefficient is a preset coefficient value, which can be set according to the actual application scenario of the communication device.

[0038] Further, using the second communication operation parameter to obtain penalty factors corresponding to the first communication device, the second communication device, and the third communication device includes:

[0039] Extracting second communication operation parameters of the first communication device, the second communication device, and the third communication device; wherein the second communication operation parameters include fluctuation parameters of the bit error rate and the packet loss rate in the first communication operation parameters per unit time, and the electromagnetic interference intensity per unit time to which the first communication device, the second communication device, and the third communication device are subjected;

[0040] The adjustment coefficient is obtained by using the electromagnetic interference intensity per unit time of the first communication device, the second communication device, and the third communication device, wherein the adjustment coefficient is obtained by the following formula:

[0041]

[0042] Wherein, γ(t) represents the adjustment coefficient; exp represents the natural exponential function, which is used to index the influence of electromagnetic interference intensity on the system. This method can ensure the nonlinear increase of the influence of interference intensity on the system; β represents the adjustment factor, and the value range of the adjustment factor is 0.1-10; N represents the number of unit time experienced by the first communication device, the second communication device and the third communication device; e(t i ) represents the electromagnetic interference intensity corresponding to the i-th unit time of the first communication device, the second communication device and the third communication device;

[0043] The penalty factor is obtained by combining the adjustment coefficient with the fluctuation parameters of the bit error rate and the packet loss rate in the first communication operation parameter within each unit time and the adjustment coefficient, wherein the penalty factor is obtained by the following formula:

[0044] F(t)=γ(t)·[w1·ΔP2(t)+w2·ΔP5(t)]

[0045] Right now

[0046]

[0047] Among them, F(t) represents the penalty factor; γ(t) represents the adjustment coefficient; w1 represents the weight value corresponding to the bit error rate fluctuation parameter; w2 represents the weight value corresponding to the packet loss rate fluctuation parameter; ΔP2(t) represents the bit error rate fluctuation parameter; w2 represents the weight value corresponding to the packet loss rate fluctuation parameter; ΔP5(t) represents the packet loss rate fluctuation parameter.

[0048] Further, the penalty factor is combined with the operation evaluation basic parameter to obtain the operation evaluation parameters corresponding to the first communication device, the second communication device and the third communication device, including:

[0049] Extract penalty factors and basic parameters for operation evaluation;

[0050] The penalty factor is combined with the operation evaluation basic parameter to obtain the operation evaluation parameters corresponding to the first communication device, the second communication device and the third communication device, wherein the operation evaluation parameters are obtained by the following formula:

[0051]

[0052] Among them, Q t represents the operation evaluation parameter; n represents the parameter type included in the first communication operation parameter, and n=5; P i represents the value corresponding to the i-th parameter type contained in the current first communication operation parameter; P mini represents the minimum value among the historical data values ​​of the i-th parameter type contained in the first communication operation parameter; maxi represents the maximum value among the historical data values ​​of the i-th parameter type contained in the first communication operation parameter; i represents the weight value corresponding to the i-th parameter type included in the first communication operation parameter; δ represents the sensitivity adjustment coefficient; F(t) represents the penalty factor.

[0053] Furthermore, the cloud platform includes:

[0054] A blood glucose risk prediction module, for predicting blood glucose risk for patients with gestational diabetes using the blood glucose parameter information with the first identifier in combination with a risk prediction model to obtain a risk prediction result; wherein the risk prediction model adopts a convolutional neural network model, and the model structure of the risk prediction model includes a data input end, a TCN feature extraction network, an XGBoost timing prediction layer and a data output end; and the blood glucose parameter information includes fasting blood glucose value, glycosylated hemoglobin, glycosylated albumin, insulin value, blood pressure value and blood lipid value, as well as the data collection timing corresponding to the fasting blood glucose value, glycosylated hemoglobin, glycosylated albumin, insulin value, blood pressure value and blood lipid value;

[0055] A second identification module is used to identify the risk prediction result to obtain a risk prediction result with a second identification;

[0056] The association relationship establishing module is used to establish a calling association relationship between the first identifier and the second identifier.

[0057] Furthermore, the server comprises:

[0058] An information receiving module, used for receiving the risk prediction result with the second identifier and the blood glucose parameter information with the first identifier through a preset interface;

[0059] A format conversion and data verification module, used for performing data format conversion and data content integrity verification on the risk prediction result with the second identifier and the blood glucose parameter information with the first identifier;

[0060] An information pairing module, used for extracting the calling association relationship between the first identifier and the second identifier, and pairing and binding the risk prediction result with the second identifier and the blood glucose parameter information with the first identifier according to the calling association relationship between the first identifier and the second identifier;

[0061] An entry creation module, used to create a record entry for the first identifier and the second identifier;

[0062] The data storage module is used to store the risk prediction results with the second identifier and the blood glucose parameter information with the first identifier that are paired and bound into the record entry.

[0063] Furthermore, the user terminal includes:

[0064] A data receiving module, used for receiving the risk prediction result with the second identifier and the blood glucose parameter information with the first identifier from the server;

[0065] A storage information determination module, used to determine whether the risk prediction results and blood glucose parameter information of the historical records are stored in the database of the user terminal;

[0066] A storage execution module, used for storing the risk prediction result with the second identifier and the blood glucose parameter information with the first identifier in the database of the user terminal when the database of the user terminal does not have the risk prediction result and the blood glucose parameter information of the historical record;

[0067] A data updating module, for writing the currently received risk prediction result with the second identifier and the blood glucose parameter information with the first identifier into the database of the user terminal when the database of the user terminal has risk prediction results and blood glucose parameter information of the historical records, and updating and replacing the risk prediction results and blood glucose parameter information of the historical records;

[0068] A first deletion module, configured to extract the risk prediction results and blood glucose parameter information of the replaced historical records, and delete the risk prediction results and blood glucose parameter information of the replaced historical records from the database of the user terminal;

[0069] A data retrieval and display module, which is used to retrieve the risk prediction results and blood glucose parameter information of the replaced historical records from the server according to the first identifier or the second identifier corresponding to the risk prediction results and blood glucose parameter information of the replaced historical records when the user terminal receives a call instruction for the risk prediction results and blood glucose parameter information of the replaced historical records sent by the user, and display the data on the user terminal;

[0070] The second deletion module is used to cache data within a preset time period after the risk prediction results and blood glucose parameter information of the replaced historical records are displayed, and to delete the risk prediction results and blood glucose parameter information of the replaced historical records in the user terminal at the end of the preset time period.

[0071] Beneficial effects of the present invention:

[0072] The intelligent blood glucose management system for patients with gestational diabetes proposed by the present invention can collect and transmit blood glucose data in real time, ensuring that patients and doctors can understand blood glucose changes in a timely manner and make corresponding adjustments. Through high-precision medical terminals and advanced risk prediction models, the system can provide accurate blood glucose data and risk prediction results, providing a basis for the formulation of treatment plans. The system can formulate personalized monitoring and management plans based on the individual conditions of patients to improve management effects. The user terminal provides a convenient operation interface and data management functions, which facilitates patients and doctors to manage blood glucose and adjust treatment plans anytime and anywhere. Through automated data processing and storage management, the system can reduce the workload of medical staff and improve management efficiency. The system takes strict security measures during data transmission and storage to ensure the security and privacy protection of patient data. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 This is a schematic diagram of the structure of the intelligent blood glucose management system of the present invention;

[0074] Figure 2 This is a control flow chart of the communication operation control device of the present invention;

[0075] Figure 3 The present invention is a flow chart of the communication operation control device for monitoring the communication operation quality. DETAILED DESCRIPTION

[0076] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0077] The embodiment of the present invention provides an intelligent blood sugar management system for patients with gestational diabetes. Figure 1As shown, the intelligent blood glucose management system for gestational diabetes patients includes a medical terminal, a data communication device, a cloud platform, a server and a user terminal; the medical terminal is data-connected to the cloud platform through the data communication device; the cloud platform is data-connected to the server through the data communication device; the server is data-connected to the user terminal through the data communication device;

[0078] Wherein, the data communication device is used to perform data interaction control between the medical terminal, the cloud platform, the server and the user terminal;

[0079] The cloud platform is used to use the blood sugar parameter information obtained by the medical terminal in combination with the risk prediction model to predict the blood sugar risk of patients with gestational diabetes and obtain the risk prediction results;

[0080] The server is used to store and manage blood sugar parameter information and its corresponding risk prediction results;

[0081] The user terminal is used to store and manage the currently acquired sugar parameter information corresponding to the gestational diabetes patient and the corresponding risk prediction results, and to delete the sugar parameter information and the corresponding risk prediction results in the historical records.

[0082] The working principle of the above technical solution is: the medical terminal is used to collect blood glucose parameter information of patients with gestational diabetes in real time. The above equipment usually has the characteristics of high precision and fast response, which can ensure the accuracy of blood glucose data. The data communication equipment is responsible for the data interaction control between the medical terminal, cloud platform, server and user terminal. Through wireless or wired communication methods, real-time data transmission is realized to ensure the integrity and security of the data.

[0083] After receiving the blood sugar parameter information uploaded by the medical terminal, the cloud platform uses the built-in risk prediction model to process and analyze the blood sugar data. The above model is based on a large amount of clinical data and machine learning algorithms, which can accurately predict the patient's blood sugar risk and generate corresponding risk prediction results.

[0084] The server is used to store and manage the blood sugar parameter information and its corresponding risk prediction results processed by the cloud platform. The server has powerful data processing and storage capabilities, which can ensure long-term storage and rapid retrieval of data.

[0085] The user terminal is specifically a smartphone APP, which is used to provide an interactive interface for patients and doctors. The user terminal can display the currently acquired blood sugar parameter information and its risk prediction results in real time, and allow users to manage historical records, including viewing and deleting. Through the user terminal, patients can keep abreast of their blood sugar status and risk situation, and doctors can also adjust treatment plans based on the data.

[0086] The effect of the above technical solution is: the above system can collect and transmit blood sugar data in real time, ensuring that patients and doctors can understand blood sugar changes in a timely manner and make corresponding adjustments. Through high-precision medical terminals and advanced risk prediction models, the system can provide accurate blood sugar data and risk prediction results to provide a basis for the formulation of treatment plans. The system can formulate personalized monitoring and management plans based on the individual conditions of patients to improve management effects. The user terminal provides a convenient operation interface and data management functions, which facilitates patients and doctors to manage blood sugar and adjust treatment plans anytime and anywhere. Through automated data processing and storage management, the system can reduce the workload of medical staff and improve management efficiency. The system takes strict security measures during data transmission and storage to ensure the security and privacy protection of patient data.

[0087] In summary, the intelligent blood glucose management system for patients with gestational diabetes integrates advanced medical equipment, data communication technology and cloud computing platform to realize the real-time collection, transmission, analysis and storage of patients' blood glucose data, provide patients with personalized blood glucose management plans, and improve treatment effects and patients' quality of life.

[0088] In one embodiment of the present invention, the data communication device includes a first communication device, a second communication device, a third communication device and a communication operation control device;

[0089] Wherein, the first communication device is used to establish a data communication connection between the medical terminal and the cloud platform;

[0090] The second communication device is used to establish a data communication connection between the cloud platform and the server;

[0091] The third communication device is used to establish a data communication connection between the server and the user terminal;

[0092] The communication operation control device is used to control the communication operation of the first communication device, the second communication device and the third communication device, and at the same time, monitor the communication operation quality of the first communication device, the second communication device and the third communication device.

[0093] Among them, Figure 2 As shown, the communication operation control device controls the communication operation of the first communication device, the second communication device and the third communication device, including:

[0094] S1. After retrieving the blood glucose parameter information of the gestational diabetes patient, the medical terminal sends a data transmission instruction to the communication operation control device, and sends the blood glucose parameter information of the gestational diabetes patient to the communication operation control device;

[0095] S2. After receiving the blood sugar parameter information of the gestational diabetes patient, the communication operation control device marks the blood sugar parameter information of the gestational diabetes patient to obtain the blood sugar parameter information with a first mark;

[0096] S3, sending the blood glucose parameter information with the first identifier to the first communication device, and controlling the first communication device to send the blood glucose parameter information with the first identifier to the cloud platform;

[0097] S4. After receiving the blood glucose parameter information with the first identifier, the cloud platform uses the blood glucose parameter information with the first identifier to predict the blood glucose risk of the gestational diabetes patient to obtain a risk prediction result, and identifies the risk prediction result to obtain a risk prediction result with a second identifier;

[0098] S5. After obtaining the risk prediction result with the second identifier, the cloud platform sends the risk prediction result with the second identifier and the blood glucose parameter information with the first identifier to the communication operation control device;

[0099] S6, after receiving the risk prediction result with the second identifier and the blood glucose parameter information with the first identifier, the communication operation control device controls the second communication device to send the risk prediction result with the second identifier and the blood glucose parameter information with the first identifier to the server;

[0100] S7, after receiving the risk prediction result with the second identifier and the blood glucose parameter information with the first identifier, the server stores the risk prediction result with the second identifier and the blood glucose parameter information with the first identifier;

[0101] S8. After the server completes the storage of the risk prediction results with the second identifier and the blood glucose parameter information with the first identifier, the communication operation control device controls the third communication device to send the risk prediction results with the second identifier and the blood glucose parameter information with the first identifier that have been stored in the server to the user terminal.

[0102] The working principle of the above technical solution is as follows: the first communication device is used to establish a stable data communication connection between the medical terminal and the cloud platform. When the medical terminal (such as a blood glucose meter) collects the patient's blood glucose data, the first communication device transmits the above data to the cloud platform in real time through a preset communication protocol (such as Bluetooth, Wi-Fi, etc.). In this process, the first communication device is responsible for data packaging, encryption (if necessary) and transmission to ensure the security and integrity of the data.

[0103] The second communication device is responsible for the data communication connection between the cloud platform and the server. After receiving the data uploaded by the medical terminal, the cloud platform will process and analyze it, and transmit the results (such as blood sugar risk prediction results) and the original data to the server through the second communication device. In this process, the second communication device is also responsible for data encryption, transmission and reception confirmation to ensure data synchronization and consistency between the cloud platform and the server.

[0104] The third communication device establishes a data communication connection between the server and the user terminal. The server sends the stored and managed blood glucose parameter information and its risk prediction results to the user terminal (such as a smartphone APP) through the third communication device. After the user terminal receives the data, it will decrypt, parse and display it for patients and doctors to view. The third communication device also plays the role of a data bridge in this process, ensuring the timely and accurate transmission of data.

[0105] Communication operation control equipment: Control and monitor the entire data communication process. The communication operation control equipment monitors the operation status and communication quality of the first, second, and third communication equipment in real time, including key parameters such as data transmission rate, bit error rate, signal strength, and connection stability. Once an abnormal situation is found (such as connection interruption, data transmission delay, etc.), the communication operation control equipment will immediately take corresponding measures (such as retrying the connection, switching the communication channel, etc.) to restore and ensure the stable operation of the entire data communication system. At the same time, the communication operation control equipment will also record and analyze various data in the communication process to provide data support for system optimization and improvement.

[0106] Specifically, after collecting the blood glucose parameter information of the patient with gestational diabetes, the medical terminal sends a data transmission instruction to the communication operation control device and sends the blood glucose parameter information together. After receiving the data, the communication operation control device identifies it (first identification) to ensure the uniqueness and traceability of the data. The communication operation control device controls the first communication device to send the blood glucose parameter information with the first identification to the cloud platform. After receiving the data, the cloud platform analyzes the blood glucose parameter information using the risk prediction model, generates a risk prediction result, and identifies the result (second identification). The cloud platform sends the risk prediction result with the second identification and the original blood glucose parameter information with the first identification back to the communication operation control device. After receiving the above data, the communication operation control device is ready to further transmit it to the server. The communication operation control device controls the second communication device to send the risk prediction result with the second identification and the blood glucose parameter information with the first identification to the server. After receiving the data, the server performs storage management for subsequent query and analysis. After the server completes data storage, the communication operation control device controls the third communication device to send the stored risk prediction result and blood glucose parameter information to the user terminal. After receiving the data, the user terminal displays it for patients and doctors to view.

[0107] The effect of the above technical solution is as follows: Through specially designed communication equipment and optimized communication protocols, the system can achieve fast and stable data transmission between medical terminals, cloud platforms, servers and user terminals, thereby improving data transmission efficiency. Encryption technology is used in the data transmission process to ensure the security and privacy protection of data during transmission, and prevent data leakage and illegal access. The communication operation control device monitors and controls the communication operation in real time, and can promptly detect and resolve abnormal situations in the communication process, thereby improving the stability and reliability of the entire system. By recording and analyzing various data in the communication process, system administrators can easily troubleshoot communication failures and perform maintenance, thereby reducing maintenance costs and improving maintenance efficiency. By realizing real-time transmission and display of data, the user terminal can provide users with timely and accurate blood sugar data and risk prediction results, optimize user experience and improve user satisfaction.

[0108] At the same time, by identifying the data, the accuracy and traceability of the data during transmission and processing are ensured, avoiding data confusion and errors. The communication operation control device centrally controls the communication process, optimizes the data transmission path and timing, and improves communication efficiency. By real-time monitoring of the communication operation status and quality, the communication operation control device can promptly discover and solve problems in the communication process, improving the stability and reliability of the system. The user terminal can receive accurate risk prediction results and blood glucose parameter information in real time, providing timely and effective data support for patients and doctors, and optimizing the user experience. Based on real-time and accurate blood glucose parameter information and risk prediction results, doctors can make more scientific and reasonable medical decisions, improving treatment effects and patient satisfaction.

[0109] In summary, this technical solution realizes intelligent blood sugar management for patients with gestational diabetes through refined data management and efficient communication control, providing strong support for medical decision-making.

[0110] One embodiment of the present invention, as Figure 3 As shown, monitoring the communication operation quality of the first communication device, the second communication device and the third communication device includes:

[0111] Step 1: monitor the first communication operation parameters of the first communication device, the second communication device, and the third communication device in real time, and obtain operation evaluation basic parameters using the first communication operation parameters and their corresponding weight values;

[0112] Step 2: monitor the second communication operation parameters of the first communication device, the second communication device and the third communication device in real time;

[0113] Step 3: using the second communication operation parameter to obtain the penalty factors corresponding to the first communication device, the second communication device and the third communication device, and using the penalty factors in combination with the operation evaluation basic parameters to obtain the operation evaluation parameters corresponding to the first communication device, the second communication device and the third communication device;

[0114] Step 4: comparing the values ​​of the operation evaluation parameters of the first communication device, the second communication device, and the third communication device with a preset evaluation parameter threshold;

[0115] Step 5: When the value of the operation evaluation parameter is lower than a preset evaluation parameter threshold, it is determined that the first communication device, the second communication device or the third communication device whose value is lower than the preset evaluation parameter threshold has abnormal communication operation quality, and an abnormal quality alarm is issued.

[0116] The working principle of the above technical solution is: real-time monitoring of the key operating parameters of the above communication equipment (first communication operating parameters), including data transmission rate, packet loss rate, delay time, etc., which directly reflect the communication performance of the equipment. Using the above parameters and their corresponding weight values ​​(allocated according to the importance of the parameters on the communication quality), a basic operation evaluation parameter is calculated as a benchmark for subsequent evaluation. In addition to the basic parameters, other communication operating parameters (second communication operating parameters) are also monitored in real time. The above parameters include signal strength, error rate, connection stability, etc., which are used to more comprehensively evaluate the communication status of the equipment.

[0117] According to the performance of the second communication operation parameter, the corresponding penalty factor is calculated. If some parameters are beyond the normal range or perform poorly, a higher penalty factor will be given. The penalty factor is combined with the basic operation evaluation parameter to calculate the final operation evaluation parameter. This step is intended to comprehensively consider the impact of various factors on communication quality. The operation evaluation parameter of each communication device is compared with the preset evaluation parameter threshold. The threshold is set according to system requirements, historical data or industry standards, and is used to determine whether the communication operation quality of the device meets the standard.

[0118] If the operation evaluation parameter of a communication device is lower than the preset threshold, it is determined that the device has abnormal communication operation quality. The system will issue a quality abnormality alarm to remind the administrator or relevant personnel to pay attention to and deal with the problem to avoid affecting the stability and reliability of the entire system.

[0119] The effects of the above technical solutions are: through real-time monitoring and evaluation, communication operation quality problems of communication equipment can be discovered in time, which provides the possibility for rapid response and processing. Comprehensive consideration of multiple communication operation parameters and weight values, as well as the introduction of penalty factors, makes the evaluation results more accurate and comprehensive. Through the preset evaluation parameter thresholds, early warning can be given before the problem worsens, avoiding potential risks and losses. The entire monitoring and evaluation process is highly automated, reducing the need for manual intervention and improving work efficiency and accuracy. Through abnormal alarms and records, data support is provided for equipment maintenance and optimization, which helps to improve the overall performance and stability of the system.

[0120] In summary, this technical solution effectively ensures the communication operation quality of communication equipment through a refined monitoring and evaluation mechanism, and provides strong support for the stable operation of the intelligent blood glucose management system for patients with gestational diabetes.

[0121] In one embodiment of the present invention, first communication operation parameters of the first communication device, the second communication device, and the third communication device are monitored in real time, and operation evaluation basic parameters are obtained using the first communication operation parameters and their corresponding weight values, including:

[0122] Step 101: monitor first communication operation parameters of the first communication device, the second communication device, and the third communication device in real time, wherein the first communication operation parameters include data transmission rate, bit error rate, signal strength, bandwidth utilization, and packet loss rate;

[0123] Step 102: construct a vector matrix P using the first communication operation parameter, and the structure of the vector matrix P is as follows:

[0124] P=(P1,P2,P3,P4,P5)

[0125] Wherein, P represents a vector matrix; P1, P2, P3, P4 and P5 represent data transmission rate, bit error rate, signal strength, bandwidth utilization and packet loss rate respectively;

[0126] Step 103: extracting weight values ​​corresponding to the data transmission rate, bit error rate, signal strength, bandwidth utilization and packet loss rate;

[0127] Step 104: Use the first communication operation parameter to obtain operation evaluation basic parameters corresponding to the first communication device, the second communication device, and the third communication device, wherein the operation evaluation basic parameters corresponding to the first communication device, the second communication device, and the third communication device are obtained by the following formula:

[0128]

[0129] Wherein, Q represents the basic parameter for operation evaluation; n represents the parameter type included in the first communication operation parameter, and n=5; P i represents the value corresponding to the i-th parameter type contained in the current first communication operation parameter; P mini represents the minimum value among the historical data values ​​of the i-th parameter type contained in the first communication operation parameter; maxi represents the maximum value among the historical data values ​​of the i-th parameter type contained in the first communication operation parameter; i It represents the weight value corresponding to the i-th parameter type included in the first communication operation parameter; δ represents the sensitivity adjustment coefficient, and the sensitivity adjustment coefficient is a preset coefficient value, which can be set according to the actual application scenario of the communication device.

[0130] The working principle of the above technical solution is: real-time monitoring of the key first communication operation parameters of the above communication equipment, including data transmission rate, bit error rate, signal strength, bandwidth utilization and packet loss rate. The above parameters are constructed into a vector matrix P, in which each row represents a communication device and each column represents a specific communication operation parameter. The weight value corresponding to each communication operation parameter is extracted, and the above weight value reflects the importance of each parameter in evaluating the communication operation quality. By combining the communication operation parameters and their corresponding historical minimum value, historical maximum value, weight value and sensitivity adjustment coefficient, the basic parameters for operation evaluation of each communication device are calculated.

[0131] The effect of the above technical solution is: through real-time monitoring, changes in the communication operation parameters of the communication equipment can be captured immediately, providing a basis for timely response and handling of potential problems. Multiple key communication operation parameters are taken into account, including data transmission rate, bit error rate, signal strength, bandwidth utilization and packet loss rate, so as to comprehensively evaluate the performance of the communication equipment. By introducing the sensitivity adjustment coefficient, the sensitivity of the evaluation can be adjusted according to different application scenarios, so that the evaluation results are more in line with actual needs. The communication operation parameters are normalized using the minimum and maximum values ​​of the historical data, and combined with the weight value, the communication operation quality of each communication device can be more accurately evaluated. The entire monitoring and evaluation process is automated, reducing the need for manual intervention and improving work efficiency and accuracy.

[0132] At the same time, by real-time monitoring of the transmission rates of the first, second, and third communication devices, the data transmission speed can be optimized to ensure the efficiency of communication. By monitoring and calculating the bit error rate and packet loss rate, the system can identify and correct errors in data transmission, reduce information loss during data transmission, and improve communication reliability. By evaluating signal strength and bandwidth utilization, the use of communication channels can be optimized to ensure the stability and efficiency of data transmission. By calculating the weight value and sensitivity adjustment coefficient of the basic parameters of the operation evaluation, the system can adaptively adjust the parameter configuration of each communication device to cope with different communication environments and load conditions, thereby improving the overall performance of the system.

[0133] At the same time, first, the system monitors the communication parameters of the first, second, and third communication devices in real time, including data transmission rate, bit error rate, signal strength, bandwidth utilization, and packet loss rate. These parameters are important indicators for measuring the performance of communication equipment and network conditions. A vector matrix is ​​constructed using the operating parameters of the first communication device. Each element in the matrix represents a different communication parameter (such as data transmission rate, bit error rate, etc.). This matrix is ​​the basis for subsequent calculations. During the data processing process, the accuracy of the system is improved by re-evaluating the weights of each communication parameter. The purpose of this step is to adjust the importance of different communication parameters according to actual conditions to optimize the overall system performance. Finally, the operating evaluation benchmark parameters of each communication device are calculated using the parameters and weights obtained above. At the same time, the above formula involves the normalization of each parameter, and a comprehensive evaluation is performed in combination with historical data.

[0134] Real-time monitoring of the operating parameters of multiple communication devices can quickly detect and respond to abnormal situations that occur during the communication process, thereby reducing the probability of data loss and communication interruption and ensuring the continuity and stability of communication. By monitoring and analyzing parameters such as bandwidth utilization and signal strength, the allocation of communication resources can be dynamically adjusted, resource utilization can be optimized, bandwidth waste can be reduced, and the overall performance of the network can be improved. By constructing a vector matrix and re-estimating parameter weights, the system can accurately evaluate the operating status of communication equipment and make optimization decisions. This precise analysis and evaluation helps to improve the intelligence level of the communication system and enhance its adaptability to complex environments. Monitoring parameters such as bit error rate and packet loss rate can timely detect and correct errors in data transmission to ensure data integrity and security. This is particularly important for mission-critical communication systems, such as military communications or financial data transmission. The weight re-estimation and reference to historical data in this solution enable the system to automatically adjust its own parameters according to environmental changes, thereby maintaining optimal performance under different network conditions. This adaptive capability helps to improve the intelligence and flexibility of the system. This technical solution can effectively cope with a variety of complex communication environments, and ensure communication quality under adverse conditions such as high interference and high latency through parameter adjustment and optimization. This is especially critical for communication systems that need to operate in complex environments (such as industrial IoT, smart city infrastructure, etc.). Through remote management units and automated data analysis and adjustment mechanisms, the system can automatically manage and optimize communication processes, reduce manual intervention, and improve the system's automation and operating efficiency.

[0135] In summary, this technical solution effectively improves the communication operation quality monitoring capability of communication equipment by real-time monitoring and comprehensive evaluation of the communication operation parameters of communication equipment, combined with flexible sensitivity adjustment and accurate evaluation algorithm, and provides strong guarantee for the stable operation of the intelligent blood glucose management system for patients with gestational diabetes.

[0136] In one embodiment of the present invention, using the second communication operation parameter to obtain penalty factors corresponding to the first communication device, the second communication device, and the third communication device includes:

[0137] Step 301a, extracting second communication operating parameters of the first communication device, the second communication device, and the third communication device; wherein the second communication operating parameters include fluctuation parameters of the bit error rate and the packet loss rate in the first communication operating parameters per unit time, and the electromagnetic interference intensity per unit time to which the first communication device, the second communication device, and the third communication device are subjected;

[0138] Step 302a: Obtain an adjustment coefficient using the electromagnetic interference intensity per unit time of the first communication device, the second communication device, and the third communication device, wherein the adjustment coefficient is obtained by the following formula:

[0139]

[0140] Wherein, γ(t) represents the adjustment coefficient; exp represents the natural exponential function, which is used to index the influence of electromagnetic interference intensity on the system. This method can ensure the nonlinear increase of the influence of interference intensity on the system; β represents the adjustment factor, and the value range of the adjustment factor is 0.1-10; N represents the number of unit time experienced by the first communication device, the second communication device and the third communication device; e(t i ) represents the electromagnetic interference intensity corresponding to the i-th unit time of the first communication device, the second communication device and the third communication device;

[0141] Step 303a, using the adjustment coefficient combined with the fluctuation parameters of the bit error rate and the packet loss rate in the first communication operation parameter within each unit time combined with the adjustment coefficient to obtain a penalty factor, wherein the penalty factor is obtained by the following formula:

[0142] F(t)=γ(t)·[w1·ΔP2(t)+w2·ΔP5(t)]

[0143] Right now

[0144]

[0145] Among them, F(t) represents the penalty factor; γ(t) represents the adjustment coefficient; w1 represents the weight value corresponding to the bit error rate fluctuation parameter; w2 represents the weight value corresponding to the packet loss rate fluctuation parameter; ΔP2(t) represents the bit error rate fluctuation parameter; w2 represents the weight value corresponding to the packet loss rate fluctuation parameter; ΔP5(t) represents the packet loss rate fluctuation parameter.

[0146] The working principle of the above technical solution is: extract the second communication operation parameters from the first communication device, the second communication device and the third communication device, and the above parameters include the fluctuation parameters of the bit error rate and the packet loss rate in each unit time, and the electromagnetic interference intensity of the device in each unit time. The adjustment coefficient is calculated by a specific formula using the electromagnetic interference intensity of the device in each unit time. The above formula exponentially modifies the impact of the electromagnetic interference intensity on the system through a natural exponential function to ensure the nonlinear increase of the impact of the interference intensity on the system. The range of values ​​of the adjustment factor limits the magnitude of this impact. The adjustment coefficient is combined with the fluctuation parameters of the bit error rate and the packet loss rate, and the penalty factor is calculated by another formula. The calculation of the penalty factor takes into account the importance of the fluctuation parameters of the bit error rate and the packet loss rate (reflected by the weight value), as well as the impact of electromagnetic interference on the above fluctuations (reflected by the adjustment coefficient).

[0147] The effect of the above technical solution is: by considering the fluctuations of bit error rate, packet loss rate and electromagnetic interference intensity, the technical solution can more accurately evaluate the communication stability of communication equipment. This is crucial to ensure the reliability and accuracy of data transmission. The adjustment coefficient is calculated using the natural exponential function, so that the impact of electromagnetic interference on the system can increase nonlinearly, which is more in line with the actual situation. This helps to improve the accuracy and credibility of the evaluation results. The calculation of the penalty factor takes into account the weight values ​​of different parameters and the real-time impact of electromagnetic interference, and can dynamically adjust the penalty intensity of communication equipment. This helps to discover and solve potential problems in a timely manner and prevent problems from worsening. By real-time monitoring and evaluating the communication operation quality of communication equipment, and dynamically adjusting the system configuration or taking corresponding measures according to the evaluation results, the technical solution helps to improve the stability and reliability of the entire system. This is of great significance to ensure the normal operation of the intelligent blood glucose management system for patients with gestational diabetes.

[0148] Copper welding wire, this solution can accurately identify and adjust problems in the communication process by real-time monitoring of parameters such as the bit error rate, packet loss rate and electromagnetic interference intensity of the communication equipment. This method allows the system to maintain stable data transmission in a high-interference environment. Specifically, the system optimizes the operating state of the communication equipment by adjusting the interference intensity parameter (γ(t)) and the bit error rate fluctuation parameter (F(t)) to ensure the reliability and stability of data transmission. Steps 301a and 302a in the scheme involve real-time acquisition and adjustment of the electromagnetic interference intensity, respectively. Through the formula γ(t) = exp(-β·∑e(t_i)), the system can dynamically adjust the anti-interference ability of the communication equipment according to the current changes in electromagnetic interference. In this way, the system can adapt to different interference environments to avoid data loss and transmission errors caused by electromagnetic interference. This adaptive adjustment mechanism is particularly suitable for complex and changing communication environments, such as industrial Internet of Things, smart transportation, etc.

[0149] At the same time, according to the above formula, the system performs weighted calculation on the bit error rate fluctuation parameters and the packet loss rate fluctuation parameters through weights w1 and w2, further improving the control ability of data transmission errors. This weighted calculation allows the system to more flexibly adjust the degree of attention to the bit error rate and packet loss rate according to actual needs, thereby optimizing the communication quality. Due to the introduction of the nonlinear amplification factor exp and the adjustment factor δ, the system has good elasticity and can maintain stable communication performance under different load conditions. This elastic design enables the system to automatically adapt to various extreme environments and emergencies. For example, when the wireless network is suddenly interfered, the system can quickly adjust the parameters and continue to keep the communication unimpeded. This technical solution achieves the optimal management of communication resources through dynamic adjustment and real-time monitoring. For example, by adjusting the adjustment factors γ(t) and β, the system can intelligently adjust the power consumption and communication strategy of the device according to the current network status and interference situation. This resource optimization capability can not only improve the overall performance of the system, but also reduce energy consumption, improve the economy and environmental friendliness of the system.

[0150] Therefore, the system also has better technical effects, including: by adjusting the interference intensity parameters in real time, the system can effectively resist various types of electromagnetic interference and maintain high-quality data communication. By using the combination of weights and adjustment factors, the system can adjust the different attention to bit error rate and packet loss rate according to actual needs, and provide flexible communication optimization solutions. Dynamic optimization of communication equipment parameters enables the system to minimize energy consumption while meeting communication quality. This technical solution significantly improves the communication performance and reliability of the system in complex environments through a series of real-time monitoring, dynamic adjustment and intelligent optimization technologies.

[0151] In summary, this technical solution evaluates the communication operation quality of communication equipment by comprehensively considering multiple factors, and calculates the penalty factor accordingly, providing strong support for improving the overall performance of the system.

[0152] In one embodiment of the present invention, the penalty factor is combined with the operation evaluation basic parameter to obtain the operation evaluation parameters corresponding to the first communication device, the second communication device and the third communication device, including:

[0153] Step 301b, extracting penalty factors and basic parameters for operation evaluation;

[0154] Step 302b: using the penalty factor in combination with the operation evaluation basic parameter to obtain the operation evaluation parameters corresponding to the first communication device, the second communication device, and the third communication device, wherein the operation evaluation parameters are obtained by the following formula:

[0155]

[0156] Among them, Q trepresents the operation evaluation parameter; n represents the parameter type included in the first communication operation parameter, and n=5; P i represents the value corresponding to the i-th parameter type contained in the current first communication operation parameter; P mini represents the minimum value among the historical data values ​​of the i-th parameter type contained in the first communication operation parameter; maxi represents the maximum value among the historical data values ​​of the i-th parameter type contained in the first communication operation parameter; i represents the weight value corresponding to the i-th parameter type included in the first communication operation parameter; δ represents the sensitivity adjustment coefficient; F(t) represents the penalty factor.

[0157] The working principle of the above technical solution is: extract the penalty factor and operation evaluation basic parameters of each communication device from the system. The above parameters are calculated in the previous steps and reflect the communication operation quality and interference of the communication device. The penalty factor is combined with the operation evaluation basic parameters using a specific formula to calculate the operation evaluation parameters of each communication device. The above formula takes into account the type, value, historical minimum value, historical maximum value, weight value, sensitivity adjustment coefficient and penalty factor of the first communication operation parameter.

[0158] The effect of the above technical solution is: by combining the penalty factor with the basic parameters of operation evaluation, the technical solution can more comprehensively evaluate the communication operation quality of the communication equipment. This helps to understand the actual performance of the equipment more accurately and provide more valuable information for subsequent optimization and maintenance. The introduction of the penalty factor enables the evaluation results to dynamically reflect the interference and fluctuations of the communication equipment. This means that the evaluation results are more real-time and accurate, and potential problems can be discovered and solved in a timely manner. By calculating the operation evaluation parameters, the technical solution provides administrators or relevant personnel with intuitive quantitative indicators to determine whether the communication operation quality of the communication equipment meets the standards. This helps to improve decision-making efficiency and reduce the subjectivity and uncertainty of human judgment. By real-time monitoring and evaluation of the communication operation quality of the communication equipment and taking corresponding optimization measures based on the evaluation results, the technical solution helps to improve the stability and reliability of the entire system. This is of great significance for ensuring the normal operation of the intelligent blood glucose management system for patients with gestational diabetes.

[0159] At the same time, by combining multiple parameters and their weights to calculate the operation evaluation parameters of the communication equipment, the solution can provide a more accurate evaluation of the equipment status. This multi-parameter weighting method takes into account the importance of different parameters, making the final evaluation results more representative. The introduction of the penalty factor makes the system more sensitive to abnormal conditions. The penalty factor F(t) can be dynamically adjusted according to the abnormal conditions of the communication environment or the equipment operation status, so as to give a greater weight to the abnormal state in the evaluation process and help to find potential problems in time. At the same time, by dynamically adjusting the weights of each parameter and the sensitivity adjustment coefficient δ, the system can adapt to different communication environments and condition changes and maintain high robustness. This adaptive capability enables the system to maintain good performance in complex and changing communication environments. Accurate status evaluation can help optimize the allocation and management of communication resources. The system can dynamically adjust the resource allocation strategy according to the equipment's operating status and performance evaluation results, improve resource utilization, and reduce waste. The solution can detect abnormal behaviors or potential risks in the communication system in a timely manner and improve the security and reliability of the system. Through accurate evaluation of the equipment status, possible communication failures or attack behaviors can be warned in advance to ensure the security and stability of communication. The automated data analysis and evaluation mechanism of this technical solution can support higher-level intelligent decision-making. Administrators can make equipment management and maintenance decisions based on the evaluation parameters generated by the system, thereby reducing manual intervention and improving management efficiency. Due to the adaptability and flexibility of this technical solution, it is suitable for various communication scenarios, including but not limited to industrial Internet of Things, intelligent transportation systems, smart city infrastructure, etc. These scenarios have high requirements for communication stability and security, and this solution can meet these requirements. By introducing penalty factors and multi-parameter weighted evaluation mechanisms, this technical solution achieves accurate evaluation and monitoring of the operating status of communication equipment, with significant technical effects and application value.

[0160] In summary, this technical solution provides strong support for improving the overall performance of the system by comprehensively considering multiple factors and introducing penalty factors to evaluate the communication operation quality of communication equipment. At the same time, by providing intuitive quantitative indicators and optimization suggestions, this technical solution also helps to improve the decision-making efficiency of administrators and the maintenance level of the system.

[0161] In one embodiment of the present invention, the cloud platform includes:

[0162] A blood glucose risk prediction module, for predicting blood glucose risk for patients with gestational diabetes using the blood glucose parameter information with the first identifier in combination with a risk prediction model to obtain a risk prediction result; wherein the risk prediction model adopts a convolutional neural network model, and the model structure of the risk prediction model includes a data input end, a TCN feature extraction network, an XGBoost timing prediction layer and a data output end; and the blood glucose parameter information includes fasting blood glucose value, glycosylated hemoglobin, glycosylated albumin, insulin value, blood pressure value and blood lipid value, as well as the data collection timing corresponding to the fasting blood glucose value, glycosylated hemoglobin, glycosylated albumin, insulin value, blood pressure value and blood lipid value;

[0163] A second identification module is used to identify the risk prediction result to obtain a risk prediction result with a second identification;

[0164] The association relationship establishing module is used to establish a calling association relationship between the first identifier and the second identifier.

[0165] The working principle of the above technical solution is as follows: the cloud platform first collects blood glucose parameter information with the first identifier, including but not limited to fasting blood glucose value, glycosylated hemoglobin, glycosylated albumin, insulin value, blood pressure value and blood lipid value, etc. At the same time, the data collection time series corresponding to the above parameter information will also be recorded, which is crucial for subsequent time series prediction.

[0166] The collected blood glucose parameter information is input into the risk prediction model, which is a completed model that uses a convolutional neural network (CNN) combined with a temporal convolutional network (TCN) and the XGBoost algorithm. The input end of the model receives data, the TCN feature extraction network is responsible for extracting key features from time series data, and the XGBoost time series prediction layer uses the above features to perform time series prediction of blood glucose risk, and finally gives the risk prediction results through the data output end.

[0167] The second identification module identifies the risk prediction result and assigns it a second identification. This step helps to distinguish different risk prediction results and facilitates subsequent management and analysis.

[0168] The association relationship establishment module is responsible for establishing a call association relationship between the first identifier (i.e., the identifier of the blood glucose parameter information) and the second identifier (i.e., the identifier of the risk prediction result). The establishment of this association relationship enables the cloud platform to trace the source of the risk prediction result and ensure the integrity and traceability of the data.

[0169] The effect of the above technical solution is: the risk prediction model using convolutional neural network and time convolutional network combined with XGBoost algorithm can make full use of the time series characteristics of blood glucose parameter information to improve the accuracy and reliability of blood glucose risk prediction. The cloud platform can collect and process blood glucose parameter information in real time, make instant blood glucose risk prediction, and provide timely decision support for doctors and patients. By establishing a call association relationship between the first identifier and the second identifier, the cloud platform can easily manage and analyze risk prediction results and improve the efficiency and level of medical management. Risk prediction based on the patient's individual blood glucose parameter information helps doctors develop more personalized treatment plans and improve treatment effects and patient satisfaction.

[0170] In summary, this technical solution provides strong support for the blood sugar risk prediction of patients with gestational diabetes by comprehensively using advanced data processing technology and machine learning algorithms. It has significant technical effects and broad application prospects.

[0171] In one embodiment of the present invention, the server comprises:

[0172] An information receiving module, used for receiving the risk prediction result with the second identifier and the blood glucose parameter information with the first identifier through a preset interface;

[0173] A format conversion and data verification module, used for performing data format conversion and data content integrity verification on the risk prediction result with the second identifier and the blood glucose parameter information with the first identifier;

[0174] An information pairing module, used for extracting the calling association relationship between the first identifier and the second identifier, and pairing and binding the risk prediction result with the second identifier and the blood glucose parameter information with the first identifier according to the calling association relationship between the first identifier and the second identifier;

[0175] An entry creation module, used to create a record entry for the first identifier and the second identifier;

[0176] The data storage module is used to store the risk prediction results with the second identifier and the blood glucose parameter information with the first identifier that are paired and bound into the record entry.

[0177] The working principle of the above technical solution is as follows: the information receiving module receives data from the cloud platform or other data sources through a preset interface, including the risk prediction results with the second identifier and the blood glucose parameter information with the first identifier. The format conversion and data verification module processes the received data. First, it performs data format conversion to ensure that the data meets the processing standards inside the server. Secondly, it verifies the integrity of the data content to check whether the data is lost or damaged during transmission. The information pairing module extracts the call association relationship between the first identifier and the second identifier. This association relationship is established in the cloud platform and is used to link the risk prediction results with the corresponding blood glucose parameter information. According to this association relationship, the module pairs and binds the risk prediction results with the second identifier and the blood glucose parameter information with the first identifier. The entry establishment module establishes a record entry in the database of the server for each pair of paired data (i.e., a first identifier and a second identifier). The above entry is used to store and manage this pair of data. The data storage module stores the paired and bound data (the risk prediction results with the second identifier and the blood glucose parameter information with the first identifier) ​​in the previously established record entry. In this way, the server saves a complete and related data set for subsequent analysis and processing.

[0178] The effect of the above technical solution is: by pairing and binding the risk prediction results and blood glucose parameter information and storing them in the record entries, the server realizes data integration. This makes the data more orderly and easy to manage. By establishing a call association relationship between the first identifier and the second identifier and storing it in the record entry, the server realizes data traceability. This helps to track the source and destination of the data when needed. Through data format conversion and data content integrity verification, the server ensures the accuracy and integrity of the received data. This helps to prevent the data from being tampered with or damaged during transmission. By storing the paired and bound data in the record entry, the server facilitates subsequent data analysis, processing and report generation. This makes it easier for doctors or researchers to obtain and use this data.

[0179] In summary, this technical solution provides strong support for blood glucose management in patients with gestational diabetes by integrating and processing risk prediction results and blood glucose parameter information from the cloud platform. It improves the integration, traceability and security of data and facilitates subsequent data processing and analysis.

[0180] In one embodiment of the present invention, the user terminal includes:

[0181] A data receiving module, used for receiving the risk prediction result with the second identifier and the blood glucose parameter information with the first identifier from the server;

[0182] A storage information determination module, used to determine whether the risk prediction results and blood glucose parameter information of the historical records are stored in the database of the user terminal;

[0183] A storage execution module, used for storing the risk prediction result with the second identifier and the blood glucose parameter information with the first identifier in the database of the user terminal when the database of the user terminal does not have the risk prediction result and the blood glucose parameter information of the historical record;

[0184] A data updating module, for writing the currently received risk prediction result with the second identifier and the blood glucose parameter information with the first identifier into the database of the user terminal when the database of the user terminal has risk prediction results and blood glucose parameter information of the historical records, and updating and replacing the risk prediction results and blood glucose parameter information of the historical records;

[0185] A first deletion module, configured to extract the risk prediction results and blood glucose parameter information of the replaced historical records, and delete the risk prediction results and blood glucose parameter information of the replaced historical records from the database of the user terminal;

[0186] A data retrieval and display module, which is used to retrieve the risk prediction results and blood glucose parameter information of the replaced historical records from the server according to the first identifier or the second identifier corresponding to the risk prediction results and blood glucose parameter information of the replaced historical records when the user terminal receives a call instruction for the risk prediction results and blood glucose parameter information of the replaced historical records sent by the user, and display the data on the user terminal;

[0187] The second deletion module is used to cache data within a preset time period after the risk prediction results and blood glucose parameter information of the replaced historical records are displayed, and to delete the risk prediction results and blood glucose parameter information of the replaced historical records in the user terminal at the end of the preset time period.

[0188] The working principle of the above technical solution is as follows: the data receiving module is responsible for receiving the risk prediction results with the second identifier and the blood glucose parameter information with the first identifier from the server. The storage information determination module checks whether the risk prediction results and blood glucose parameter information of the historical records are already stored in the database of the user terminal. If there are no historical records in the database, the storage execution module stores the received risk prediction results and blood glucose parameter information in the database. If there are historical records in the database, the data update module writes the currently received information into the database and replaces the old historical records. The first deletion module is responsible for extracting the replaced historical records and deleting this information in the database. When the user sends a call instruction, the data retrieval and display module requests the replaced historical records from the server according to the first identifier or the second identifier, and displays them on the user terminal. The second deletion module caches data within a preset time period after the replaced historical records are displayed, and deletes this information at the end of the time period.

[0189] The effect of the above technical solution is that the user terminal can receive and update the risk prediction results and blood glucose parameter information from the server in real time to ensure the synchronization and accuracy of the data. By replacing the old historical records, the user terminal can keep the database tidy and orderly, avoiding data redundancy and confusion. Users can call and view the replaced historical records at any time, which provides flexibility and convenience for data access. Data deletion after a preset time period helps to protect the user's privacy and data security and reduce the risk of long-term storage of sensitive information. Through the data caching and deletion mechanism, the user terminal can manage its storage resources more effectively and avoid unnecessary waste of resources.

[0190] In summary, this technical solution achieves real-time data synchronization, historical management, user access, security and privacy protection, and resource optimization by optimizing the data processing flow of the user terminal, providing more convenient and safe blood sugar management services for patients with gestational diabetes.

[0191] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if the above-mentioned modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include the above-mentioned modifications and variations.

Claims

1. An intelligent blood sugar management system for patients with gestational diabetes, characterized in that: The intelligent blood glucose management system for gestational diabetes patients comprises a medical terminal, a data communication device, a cloud platform, a server and a user terminal; the medical terminal is connected to the cloud platform through the data communication device; the cloud platform is connected to the server through the data communication device; the server is connected to the user terminal through the data communication device; Wherein, the data communication device is used to perform data interaction control between the medical terminal, the cloud platform, the server and the user terminal; The cloud platform is used to use the blood sugar parameter information obtained by the medical terminal in combination with the risk prediction model to predict the blood sugar risk of patients with gestational diabetes and obtain the risk prediction results; The server is used to store and manage blood sugar parameter information and its corresponding risk prediction results; The user terminal is used to store and manage the currently acquired sugar parameter information corresponding to the gestational diabetes patient and the corresponding risk prediction results, and to delete the sugar parameter information and the corresponding risk prediction results in the historical records.

2. The intelligent blood glucose management system for gestational diabetes mellitus patients according to claim 1, characterized in that: The data communication equipment includes a first communication equipment, a second communication equipment, a third communication equipment and a communication operation control equipment; Wherein, the first communication device is used to establish a data communication connection between the medical terminal and the cloud platform; The second communication device is used to establish a data communication connection between the cloud platform and the server; The third communication device is used to establish a data communication connection between the server and the user terminal; The communication operation control device is used to control the communication operation of the first communication device, the second communication device and the third communication device, and at the same time, monitor the communication operation quality of the first communication device, the second communication device and the third communication device.

3. The intelligent blood glucose management system for gestational diabetes mellitus patients according to claim 2, characterized in that: The communication operation control device controls the communication operation of the first communication device, the second communication device, and the third communication device, including: After retrieving the blood sugar parameter information of the gestational diabetes patient, the medical terminal sends a data transmission instruction to the communication operation control device, and sends the blood sugar parameter information of the gestational diabetes patient to the communication operation control device; After receiving the blood sugar parameter information of the gestational diabetes patient, the communication operation control device marks the blood sugar parameter information of the gestational diabetes patient to obtain the blood sugar parameter information with a first mark; Sending the blood sugar parameter information with the first identifier to the first communication device, and controlling the first communication device to send the blood sugar parameter information with the first identifier to the cloud platform; After receiving the blood sugar parameter information with the first identifier, the cloud platform uses the blood sugar parameter information with the first identifier to predict the blood sugar risk of the gestational diabetes patient to obtain a risk prediction result, and identifies the risk prediction result to obtain a risk prediction result with a second identifier; After obtaining the risk prediction result with the second identifier, the cloud platform sends the risk prediction result with the second identifier and the blood glucose parameter information with the first identifier to the communication operation control device; After receiving the risk prediction result with the second identifier and the blood glucose parameter information with the first identifier, the communication operation control device controls the second communication device to send the risk prediction result with the second identifier and the blood glucose parameter information with the first identifier to the server; After receiving the risk prediction result with the second identifier and the blood sugar parameter information with the first identifier, the server stores the risk prediction result with the second identifier and the blood sugar parameter information with the first identifier; After the server completes the storage of the risk prediction results with the second identifier and the blood glucose parameter information with the first identifier, the communication operation control device controls the third communication device to send the risk prediction results with the second identifier and the blood glucose parameter information with the first identifier that have been stored in the server to the user terminal.

4. The intelligent blood glucose management system for gestational diabetes mellitus patients according to claim 2, characterized in that: Monitoring the communication operation quality of the first communication device, the second communication device, and the third communication device includes: monitoring first communication operation parameters of the first communication device, the second communication device, and the third communication device in real time, and obtaining operation evaluation basic parameters using the first communication operation parameters and their corresponding weight values; real-time monitoring of second communication operating parameters of the first communication device, the second communication device, and the third communication device; Using the second communication operation parameter to obtain the penalty factors corresponding to the first communication device, the second communication device, and the third communication device, and using the penalty factors in combination with the operation evaluation basic parameters to obtain the operation evaluation parameters corresponding to the first communication device, the second communication device, and the third communication device; Comparing the values ​​of the operation evaluation parameters of the first communication device, the second communication device, and the third communication device with preset evaluation parameter thresholds; When the value of the operation evaluation parameter is lower than a preset evaluation parameter threshold, it is determined that the first communication device, the second communication device or the third communication device below the preset evaluation parameter threshold has abnormal communication operation quality, and a quality abnormality alarm is issued.

5. The intelligent blood sugar management system for gestational diabetes mellitus patients according to claim 4, characterized in that: Real-time monitoring of first communication operation parameters of the first communication device, the second communication device, and the third communication device, and obtaining operation evaluation basic parameters using the first communication operation parameters and their corresponding weight values, including: Real-time monitoring of first communication operation parameters of the first communication device, the second communication device, and the third communication device, wherein the first communication operation parameters include data transmission rate, bit error rate, signal strength, bandwidth utilization, and packet loss rate; The vector matrix P is constructed using the first communication operation parameter, and the structure of the vector matrix P is as follows: P=(P1,P2,P3,P4,P5) Wherein, P represents a vector matrix; P1, P2, P3, P4 and P5 represent data transmission rate, bit error rate, signal strength, bandwidth utilization and packet loss rate respectively; Extracting weight values ​​corresponding to the data transmission rate, bit error rate, signal strength, bandwidth utilization and packet loss rate; The first communication operation parameter is used to obtain operation evaluation basic parameters corresponding to the first communication device, the second communication device, and the third communication device, wherein the operation evaluation basic parameters corresponding to the first communication device, the second communication device, and the third communication device are obtained by the following formula: Wherein, Q represents the basic parameter for operation evaluation; n represents the parameter type included in the first communication operation parameter, and n=5; P i represents the value corresponding to the i-th parameter type contained in the current first communication operation parameter; P mini represents the minimum value among the historical data values ​​of the i-th parameter type contained in the first communication operation parameter; maxi represents the maximum value among the historical data values ​​of the i-th parameter type contained in the first communication operation parameter; i represents the weight value corresponding to the i-th parameter type included in the first communication operation parameter; δ represents the sensitivity adjustment coefficient.

6. The intelligent blood sugar management system for gestational diabetes mellitus patients according to claim 4, characterized in that: Using the second communication operation parameter to obtain penalty factors corresponding to the first communication device, the second communication device, and the third communication device includes: Extracting second communication operation parameters of the first communication device, the second communication device, and the third communication device; wherein the second communication operation parameters include fluctuation parameters of the bit error rate and the packet loss rate in the first communication operation parameters per unit time, and the electromagnetic interference intensity per unit time to which the first communication device, the second communication device, and the third communication device are subjected; The adjustment coefficient is obtained by using the electromagnetic interference intensity per unit time of the first communication device, the second communication device, and the third communication device, wherein the adjustment coefficient is obtained by the following formula: Wherein, γ(t) represents the adjustment coefficient; exp represents the natural exponential function; β represents the adjustment factor, and the value range of the adjustment factor is 0.1-10; N represents the number of unit times experienced by the first communication device, the second communication device and the third communication device; e(t i ) represents the electromagnetic interference intensity corresponding to the i-th unit time of the first communication device, the second communication device and the third communication device; The penalty factor is obtained by combining the adjustment coefficient with the fluctuation parameters of the bit error rate and the packet loss rate in the first communication operation parameter within each unit time and the adjustment coefficient, wherein the penalty factor is obtained by the following formula: F(t)=γ(t)·[w1·ΔP2(t)+w2·ΔP5(t)] Right now Among them, F(t) represents the penalty factor; γ(t) represents the adjustment coefficient; w1 represents the weight value corresponding to the bit error rate fluctuation parameter; w2 represents the weight value corresponding to the packet loss rate fluctuation parameter; ΔP2(t) represents the bit error rate fluctuation parameter; w2 represents the weight value corresponding to the packet loss rate fluctuation parameter; ΔP5(t) represents the packet loss rate fluctuation parameter.

7. The intelligent blood glucose management system for gestational diabetes mellitus patients according to claim 4, characterized in that: The penalty factor is combined with the operation evaluation basic parameter to obtain the operation evaluation parameters corresponding to the first communication device, the second communication device, and the third communication device, including: Extract penalty factors and basic parameters for operation evaluation; The penalty factor is combined with the operation evaluation basic parameter to obtain the operation evaluation parameters corresponding to the first communication device, the second communication device and the third communication device, wherein the operation evaluation parameters are obtained by the following formula: Among them, Q t represents the operation evaluation parameter; n represents the parameter type included in the first communication operation parameter, and n=5; P i represents the value corresponding to the i-th parameter type contained in the current first communication operation parameter; P mini represents the minimum value among the historical data values ​​of the i-th parameter type contained in the first communication operation parameter; maxi represents the maximum value among the historical data values ​​of the i-th parameter type contained in the first communication operation parameter; i represents the weight value corresponding to the i-th parameter type included in the first communication operation parameter; δ represents the sensitivity adjustment coefficient; F(t) represents the penalty factor.

8. The intelligent blood glucose management system for gestational diabetes mellitus patients according to claim 3, characterized in that: The cloud platform includes: A blood glucose risk prediction module, for predicting blood glucose risk for patients with gestational diabetes using the blood glucose parameter information with the first identifier in combination with a risk prediction model to obtain a risk prediction result; wherein the risk prediction model adopts a convolutional neural network model, and the model structure of the risk prediction model includes a data input end, a TCN feature extraction network, an XGBoost timing prediction layer and a data output end; and the blood glucose parameter information includes fasting blood glucose value, glycosylated hemoglobin, glycosylated albumin, insulin value, blood pressure value and blood lipid value, as well as the data collection timing corresponding to the fasting blood glucose value, glycosylated hemoglobin, glycosylated albumin, insulin value, blood pressure value and blood lipid value; A second identification module is used to identify the risk prediction result to obtain a risk prediction result with a second identification; The association relationship establishing module is used to establish a calling association relationship between the first identifier and the second identifier.

9. The intelligent blood sugar management system for gestational diabetes mellitus patients according to claim 1, characterized in that: The server comprises: An information receiving module, used for receiving the risk prediction result with the second identifier and the blood glucose parameter information with the first identifier through a preset interface; A format conversion and data verification module, used for performing data format conversion and data content integrity verification on the risk prediction result with the second identifier and the blood glucose parameter information with the first identifier; An information pairing module, used for extracting the calling association relationship between the first identifier and the second identifier, and pairing and binding the risk prediction result with the second identifier and the blood glucose parameter information with the first identifier according to the calling association relationship between the first identifier and the second identifier; An entry creation module, used to create a record entry for the first identifier and the second identifier; The data storage module is used to store the risk prediction results with the second identifier and the blood glucose parameter information with the first identifier that are paired and bound into the record entry.

10. The intelligent blood sugar management system for gestational diabetes mellitus patients according to claim 1, characterized in that: The user terminal comprises: A data receiving module, used for receiving the risk prediction result with the second identifier and the blood glucose parameter information with the first identifier from the server; A storage information determination module, used to determine whether the risk prediction results and blood glucose parameter information of the historical records are stored in the database of the user terminal; A storage execution module, used for storing the risk prediction result with the second identifier and the blood glucose parameter information with the first identifier in the database of the user terminal when the database of the user terminal does not have the risk prediction result and the blood glucose parameter information of the historical record; A data updating module, for writing the currently received risk prediction result with the second identifier and the blood glucose parameter information with the first identifier into the database of the user terminal when the database of the user terminal has risk prediction results and blood glucose parameter information of the historical records, and updating and replacing the risk prediction results and blood glucose parameter information of the historical records; A first deletion module, configured to extract the risk prediction results and blood glucose parameter information of the replaced historical records, and delete the risk prediction results and blood glucose parameter information of the replaced historical records from the database of the user terminal; A data retrieval and display module, which is used to retrieve the risk prediction results and blood glucose parameter information of the replaced historical records from the server according to the first identifier or the second identifier corresponding to the risk prediction results and blood glucose parameter information of the replaced historical records when the user terminal receives a call instruction for the risk prediction results and blood glucose parameter information of the replaced historical records sent by the user, and display the data on the user terminal; The second deletion module is used to cache data within a preset time period after the risk prediction results and blood glucose parameter information of the replaced historical records are displayed, and to delete the risk prediction results and blood glucose parameter information of the replaced historical records in the user terminal at the end of the preset time period.

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