A communication system and method for diabetes clinic user management

Abstract

The present invention relates to the field of communication adjustment technology, and discloses a communication system and method for diabetic outpatient user management, the system comprising: a collection module configured to collect real-time communication data of multiple communication devices in a diabetes management system. The judgment module is configured to obtain network parameters of each communication device, and the judgment module is further configured to determine a communication fluctuation value based on the relationship between each historical communication data. The prediction module is configured to predict the communication performance change trend of each communication device within a preset time period based on a long short-term memory network, and generate a prediction threshold. The control module is configured to dynamically adjust the communication path according to the relationship between the real-time communication fluctuation value and the prediction threshold. The encryption module is configured to dynamically encrypt the adjusted communication path and data. The present invention can improve the communication efficiency of diabetic outpatient user management by real-time collection of communication data, prediction of performance change trend, dynamic adjustment of communication path, and encryption protection of data.

Description

Technical Field

[0001] The present invention relates to the technical field of communication adjustment, and in particular to a communication system and method for managing users of a diabetes clinic. Background Art

[0002] With the rapid development of medical information technology, the diabetes outpatient management system has gradually introduced intelligent and networked technologies to achieve real-time collection and management of patient health data. However, since the system involves multiple communication devices and a large amount of data interaction, it is easily affected by factors such as network environment fluctuations and equipment failures, resulting in data transmission delays, packet loss or anomalies, which in turn affects the system's operating efficiency and patient service quality. Therefore, how to achieve stable and reliable communication data transmission has become one of the core technical issues of the diabetes management system.

[0003] At present, traditional communication monitoring systems mainly rely on simple threshold judgments or path optimization solutions based on fixed rules, which cannot effectively cope with complex and dynamic communication environments. In addition, traditional methods cannot predict the changing trend of communication performance in advance, and usually take passive measures after problems occur, causing the problem to spread and affecting the overall operation efficiency of the system. At the same time, the diabetes outpatient management system involves a large amount of user health data, which is highly sensitive and has high privacy protection requirements. During the data transmission process, if there is a lack of effective encryption measures, data may be stolen, tampered with or leaked, seriously threatening user privacy and system security.

[0004] Therefore, there is an urgent need to invent a communication system for diabetes clinics to solve the problem that traditional communication monitoring systems cannot actively predict and respond to complex and dynamic communication environments, and lack effective data encryption measures to ensure the security and privacy of sensitive health data. Summary of the invention

[0005] In view of this, the present invention proposes a communication system and method for diabetic outpatient user management, aiming to solve the problem that traditional communication monitoring systems are unable to actively predict and respond to complex and dynamic communication environments, and lack effective data encryption measures to ensure the security and privacy of sensitive health data.

[0006] The present invention proposes a communication system for managing users of a diabetes clinic, comprising:

[0007] Acquisition module, judgment module, prediction module, control module and encryption module;

[0008] The acquisition module is configured to acquire real-time communication data of multiple communication devices in the diabetes management system;

[0009] The judgment module is electrically connected to the acquisition module, and is configured to obtain network parameters of each communication device. The judgment module is also configured to determine a communication fluctuation value according to a relationship between each historical communication data;

[0010] The prediction module is electrically connected to the judgment module and is configured to predict the communication performance change trend of each communication device within a preset period of time based on the long short-term memory network and generate a prediction threshold;

[0011] The control module is electrically connected to the judgment module and the prediction module respectively, and the control module is configured to dynamically adjust the communication path according to the relationship between the real-time communication fluctuation value and the prediction threshold;

[0012] The encryption module is electrically connected to the control module, and is configured to dynamically encrypt the adjusted communication path and data.

[0013] Furthermore, the judging module includes:

[0014] an acquisition unit, electrically connected to the acquisition module, configured to acquire network parameters of each communication device, the network parameters including network bandwidth, network delay, data transmission rate and network interference, and further configured to acquire real-time operation data of the communication device, the real-time operation data including: device load rate, energy consumption data and communication stability;

[0015] an evaluation unit, electrically connected to the acquisition unit, and configured to determine a communication fluctuation value of the communication device according to a relationship between the communication data and the historical communication data;

[0016] A storage unit is electrically connected to the evaluation unit, and the storage unit is configured to store historical communication data and communication fluctuation values ​​of each of the communication devices.

[0017] Furthermore, the prediction module predicts the communication performance change trend of each communication device within a preset period of time based on the long short-term memory network and generates a prediction threshold, including:

[0018] The prediction module is further configured to train a long short-term memory network model based on historical communication data and real-time data;

[0019] The prediction module is further configured to determine the communication fluctuation value and performance trend of the communication device within a preset period of time based on the long short-term memory network model and the real-time communication parameters;

[0020] The prediction module is further configured to determine the prediction threshold based on the relationship between the communication fluctuation value and the performance trend of the communication device within a preset time period.

[0021] Furthermore, after the prediction module generates the prediction threshold, it also includes:

[0022] The prediction module is configured to divide the data set according to the time series of each of the historical communication data;

[0023] The prediction module is further configured to cluster each data set and obtain the mean and standard deviation in each cluster;

[0024] The prediction module is further configured to determine a fluctuation threshold according to the mean and the standard deviation:

[0025] ;

[0026] Wherein, Y is the fluctuation threshold, is the mean difference, σ is the standard deviation, and k is the adjustment coefficient;

[0027] The prediction module is further configured to determine whether to adjust the fluctuation threshold according to a relationship between the prediction threshold and a preset prediction threshold.

[0028] Furthermore, when the prediction module determines whether to adjust the fluctuation threshold according to the relationship between the prediction threshold and a preset prediction threshold, it includes:

[0029] The prediction module is further configured to obtain a threshold difference between the prediction threshold and the preset prediction threshold, and determine whether to adjust the fluctuation threshold according to a relationship between a first preset threshold difference and a second preset threshold difference configured by the prediction module:

[0030] When the threshold difference is less than the first preset threshold difference, the prediction module determines not to adjust the fluctuation threshold;

[0031] When the threshold difference is greater than or equal to the first preset threshold difference, and the threshold difference is less than the second preset threshold difference, the prediction module determines the adjustment coefficient as Z1, and adjusts the fluctuation threshold according to the adjustment coefficient Z1;

[0032] When the threshold difference is greater than or equal to the second preset threshold difference, the prediction module determines that the adjustment coefficient is Z2, and adjusts the fluctuation threshold according to the adjustment coefficient Z2;

[0033] The first preset threshold difference is smaller than the second preset threshold difference, and Z1 is smaller than Z2.

[0034] Furthermore, when the control module dynamically adjusts the communication path according to the relationship between the real-time communication fluctuation value and the prediction threshold, it includes:

[0035] The control module is configured to determine whether to dynamically adjust the communication path according to the relationship between the real-time communication fluctuation value and the fluctuation threshold;

[0036] When the real-time communication fluctuation value is greater than the fluctuation threshold, the control module determines that the communication data of the communication device is abnormal communication data, and adjusts the communication path of the communication device;

[0037] When the real-time communication fluctuation value is less than or equal to the fluctuation threshold, the control module determines that the communication data of the communication device is normal data, and does not adjust the communication path of the communication device.

[0038] Further, when the control module determines that the communication data of the communication device is abnormal communication data and adjusts the communication path of the communication device, it includes:

[0039] The control module is further configured to determine a performance score of the communication path according to a relationship between the real-time communication fluctuation value and the fluctuation threshold;

[0040] The control module is further configured to obtain a fluctuation value of each backup communication path, and determine a performance score of each backup communication path according to a relationship between the fluctuation value of the backup communication path and the fluctuation threshold;

[0041] The control module is further configured to sort the performance scores of the communication paths and the performance scores of the backup communication paths in reverse order, and determine the communication path corresponding to the first performance score sort as the adjusted communication path of the communication device;

[0042] The control module is also configured to synchronize the adjusted communication path of the communication device to cloud storage based on edge computing.

[0043] Furthermore, when the control module synchronizes the adjusted communication path of the communication device to cloud storage based on edge computing, it includes:

[0044] The control module is further configured to deploy edge computing nodes on each of the communication devices;

[0045] The control module is also configured to process the communication data of the communication device in real time based on the edge computing node, and send the processed communication data to the cloud for storage.

[0046] Furthermore, when the encryption module dynamically encrypts the adjusted communication path and data, it includes:

[0047] The encryption module is further configured to record the historical score and adjustment record of each communication path based on the blockchain;

[0048] The encryption module is also configured to dynamically authenticate and verify the communication path based on a blockchain contract mechanism.

[0049] Compared with the prior art, the beneficial effect of the present invention is that the real-time communication data of multiple communication devices in the diabetes management system is obtained in real time through the acquisition module, and the historical data is analyzed in combination with the judgment module to determine the communication fluctuation value, thereby realizing comprehensive monitoring of the system communication status. Compared with the traditional solution based on fixed threshold judgment, the solution can more accurately identify potential communication anomalies according to the dynamic change law of historical data, improve the adaptability to complex dynamic communication environments, thereby effectively reducing the delay and packet loss problems in the data transmission process, and improving the stability of the system. Secondly, the prediction module predicts the future communication performance change trend of the communication device based on the long short-term memory network (LSTM) and generates a dynamic prediction threshold. This artificial intelligence-based prediction method can not only identify possible communication fluctuations in advance, but also provide a scientific basis for subsequent communication path adjustments, avoiding the lag problem caused by relying on passive processing in traditional methods, thereby improving response speed and operating efficiency. In addition, the control module dynamically adjusts the communication path according to the comparison result between the real-time communication fluctuation value and the prediction threshold. The dynamic adjustment mechanism can automatically select the best communication path when the network environment changes or the device performance fluctuates, ensuring the continuity and reliability of data transmission. Compared with the traditional path optimization scheme based on fixed rules, this dynamic adjustment mechanism is more flexible and can better adapt to complex and changeable actual application scenarios, effectively ensuring the normal operation of the diabetes outpatient user management system. Finally, the adjusted communication path and data are dynamically encrypted through the encryption module, effectively solving the problem of secure transmission of highly sensitive data involved in the diabetes management system. Dynamic encryption technology can not only prevent data leakage and tampering during transmission, but also enhance the system's defense capabilities against potential network attacks, ensure user privacy and data security, and meet the strict requirements of modern medical information systems for data protection.

[0050] On the other hand, the present application also provides a communication method for diabetic outpatient user management, comprising:

[0051] Collect real-time communication data of multiple communication devices in the diabetes management system;

[0052] Obtain network parameters of each communication device, and determine the communication fluctuation value based on the relationship between historical communication data;

[0053] Based on the long short-term memory network, predict the communication performance change trend of each communication device within a preset period of time and generate a prediction threshold;

[0054] Dynamically adjust the communication path based on the relationship between the real-time communication fluctuation value and the prediction threshold;

[0055] Dynamic encryption of adjusted communication paths and data.

[0056] It can be understood that the communication system and method for managing users of a diabetes clinic in the above-mentioned embodiments of the present invention have the same beneficial effects and will not be described in detail. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0058] Figure 1 A functional block diagram of a communication system for managing users of a diabetes clinic provided by an embodiment of the present invention;

[0059] Figure 2 A flowchart of a communication method for managing users in a diabetes clinic provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0061] like Figure 1 As shown, in some embodiments of the present application, this embodiment provides a communication system for diabetic outpatient user management, including: a collection module, a judgment module, a prediction module, a control module and an encryption module.

[0062] Specifically, the acquisition module is configured to collect real-time communication data of multiple communication devices in the diabetes management system. The judgment module is electrically connected to the acquisition module, and the judgment module is configured to obtain network parameters of each communication device. The judgment module is also configured to determine the communication fluctuation value based on the relationship between each historical communication data. The prediction module is electrically connected to the judgment module, and is configured to predict the communication performance change trend of each communication device within a preset period of time based on the long short-term memory network, and generate a prediction threshold. The control module is electrically connected to the judgment module and the prediction module, respectively, and the control module is configured to dynamically adjust the communication path according to the relationship between the real-time communication fluctuation value and the prediction threshold. The encryption module is electrically connected to the control module, and the encryption module is configured to dynamically encrypt the adjusted communication path and data.

[0063] It is understandable that the network parameters and operating status data of multiple communication devices are collected in real time through the acquisition module to provide basic data support for subsequent analysis and prediction. The acquisition module can accurately capture communication data, including key indicators such as network delay, bandwidth, and rate, to ensure the comprehensiveness and real-time nature of the data. The judgment module calculates the communication fluctuation value based on the relationship between the collected real-time data and the historical communication data. This process uses data statistics and relationship analysis technology to effectively identify the fluctuation characteristics in the communication state and lay the foundation for dynamic regulation. The prediction module models and learns historical communication data based on the long short-term memory network (LSTM), predicts the communication performance change trend of each communication device in the future period, and generates a prediction threshold. The LSTM model can capture the time series characteristics in the data and model long-term dependencies, so it is particularly suitable for performance prediction in complex communication environments. Through accurate prediction of future communication status, the system can achieve a transition from passive response to active regulation and identify potential communication risks in advance. The control module and the encryption module together constitute the execution layer of the system. The control module dynamically adjusts the communication path based on the comparison results of the real-time communication fluctuation value and the prediction threshold to ensure the stability and reliability of communication. At the same time, the encryption module dynamically encrypts the adjusted communication path and data, and uses a secure encryption algorithm to protect the data transmission channel to prevent data leakage and tampering during transmission.

[0064] It can be seen that by acquiring the communication data of multiple communication devices in the diabetes management system in real time through the acquisition module, the network operation status of each device can be fully monitored to ensure the timeliness and accuracy of data acquisition. This real-time performance provides high-quality data support for subsequent fluctuation analysis and performance prediction, can quickly identify potential problems in the communication network, and improve the overall operation efficiency of the system. The prediction module introduces an intelligent prediction method based on the long short-term memory network (LSTM), which can perform deep learning on historical communication data, mine the time series characteristics in the data, and accurately predict the changing trend of future communication performance. By generating prediction thresholds, the system can realize early perception and early warning of communication problems, avoiding the performance degradation caused by the spread of problems in traditional systems. This active prediction capability significantly enhances the adaptability and stability of the system in complex dynamic environments. The control module is combined with the encryption module to realize the dynamic optimization of the communication path and the secure encryption of data. The control module dynamically adjusts the communication path according to the relationship between the real-time communication fluctuation value and the prediction threshold, effectively ensuring the communication quality; and the encryption module dynamically encrypts the optimized path and data, enhancing the security of the system and avoiding the leakage or tampering of sensitive user data during transmission.

[0065] Specifically, the judgment module includes: an acquisition unit, an evaluation unit and a storage unit. The acquisition unit is electrically connected to the acquisition module, and the acquisition unit is configured to acquire network parameters of each communication device, including network bandwidth, network delay, data transmission rate and network interference. The acquisition unit is also configured to acquire real-time operation data of the communication device, and the real-time operation data includes: device load rate, energy consumption data and communication stability. The evaluation unit is electrically connected to the acquisition unit, and the evaluation unit is configured to determine the communication fluctuation value of the communication device according to the relationship between the communication data and the historical communication data. The storage unit is electrically connected to the evaluation unit, and the storage unit is configured to store the historical communication data and communication fluctuation value of each communication device.

[0066] It can be understood that the acquisition unit is a key component of the judgment module, which acquires the network parameters and operating data of each communication device in real time through electrical connection with the acquisition module. Network parameters such as network bandwidth, network delay, data transmission rate and network interference reflect the network performance of the device, while operating data such as equipment load rate, energy consumption data and communication stability show the operating status of the device. By integrating these multi-dimensional parameters, the acquisition unit can fully capture the operating status of the communication device in the current environment and provide high-quality data input for subsequent evaluation and storage. Secondly, the evaluation unit analyzes the relationship between the communication data based on the real-time communication data provided by the acquisition unit and the historical communication data in the storage unit, thereby calculating the communication fluctuation value. By comparing the real-time data with the historical data, the evaluation unit can accurately identify the abnormal fluctuation of the current operating status of the communication device, providing a reliable basic support for prediction and dynamic adjustment. This joint analysis based on historical and real-time data effectively makes up for the limitations of a single data source and improves the accuracy and credibility of the fluctuation value calculation. Finally, the storage unit, as the core part of data storage, records the historical communication data of each communication device and the communication fluctuation value generated in real time. This module not only provides data basis for subsequent trend prediction and optimization, but also forms a complete communication equipment operation archive through long-term data accumulation. This storage mechanism ensures that the system can trace the historical operation status and provides important support for the continuous improvement and intelligent management of the communication system.

[0067] Specifically, when the prediction module predicts the communication performance change trend of each communication device within a preset period based on the long short-term memory network and generates a prediction threshold, it includes: the prediction module is also configured to train the long short-term memory network model based on historical communication data and real-time data. The prediction module is also configured to determine the communication fluctuation value and performance trend of the communication device within the preset period based on the long short-term memory network model and real-time communication parameters. The prediction module is also configured to determine the prediction threshold based on the relationship between the communication fluctuation value and the performance trend of the communication device within the preset period.

[0068] Specifically, after the prediction module generates the prediction threshold, it also includes: the prediction module is configured to divide the data set according to the time series of each historical communication data. The prediction module is also configured to cluster each data set and obtain the mean and standard deviation in each cluster. The prediction module is also configured to determine the fluctuation threshold according to the mean and standard deviation: . Where Y is the fluctuation threshold, is the mean difference, σ is the standard deviation, and k is the adjustment coefficient. The prediction module is also configured to determine whether to adjust the fluctuation threshold according to the relationship between the prediction threshold and the preset prediction threshold.

[0069] Specifically, when the prediction module determines whether to adjust the fluctuation threshold according to the relationship between the prediction threshold and the preset prediction threshold, it includes: the prediction module is also configured to obtain the threshold difference between the prediction threshold and the preset prediction threshold, and determine whether to adjust the fluctuation threshold according to the relationship between the first preset threshold difference and the second preset threshold difference configured by the prediction module: when the threshold difference is less than the first preset threshold difference, the prediction module determines not to adjust the fluctuation threshold. When the threshold difference is greater than or equal to the first preset threshold difference, and the threshold difference is less than the second preset threshold difference, the prediction module determines that the adjustment coefficient is Z1, and adjusts the fluctuation threshold according to the adjustment coefficient Z1. When the threshold difference is greater than or equal to the second preset threshold difference, the prediction module determines that the adjustment coefficient is Z2, and adjusts the fluctuation threshold according to the adjustment coefficient Z2. Among them, the first preset threshold difference is less than the second preset threshold difference, and Z1 is less than Z2.

[0070] It is understandable that the prediction module is based on the technical principle of the long short-term memory network (LSTM) model, and learns the complex dynamic relationship between historical communication data and real-time data by training the model. The LSTM network is a deep learning algorithm that can capture the long-term dependencies in time series data. The prediction module uses this feature to accurately predict the performance change trend of communication equipment within a preset period by inputting historical data and real-time communication parameters. The core advantage of this method is that it can dynamically adapt to data characteristics and generate highly accurate performance prediction results, laying the foundation for the intelligent regulation of the system. Secondly, the prediction module deeply explores the statistical characteristics of the data through the division and clustering analysis of time series data. Specifically, the time series data is divided into multiple data sets, and each data set is divided into different categories through clustering technology. Based on clustering, the prediction module calculates the mean and standard deviation of each category. These statistics reflect the fluctuation characteristics of data in different categories. By combining the mean and standard deviation, the prediction module can calculate the fluctuation threshold (Y), which contains the adjustment coefficient (k) to dynamically adjust the threshold. This method improves the accuracy of threshold calculation through statistical analysis and enhances the system's adaptability to complex communication environments. Third, the prediction module not only generates a prediction threshold, but also determines whether the fluctuation threshold needs to be adjusted based on the difference between it and the preset prediction threshold. This process is achieved by calculating the threshold difference between the two. When the difference between the prediction threshold and the preset prediction threshold is less than the first preset threshold difference, the system believes that the current fluctuation threshold does not need to be adjusted. This mechanism ensures the stability of the system under normal circumstances and avoids unnecessary adjustments. Fourth, when the difference between the prediction threshold and the preset prediction threshold is greater than or equal to the first preset threshold difference but less than the second preset threshold difference, the prediction module sets the adjustment coefficient to Z1 and adjusts the fluctuation threshold according to the coefficient. This flexible adjustment mechanism enables the system to respond in time when a moderate fluctuation is detected, but the adjustment range remains moderate to avoid causing greater interference to the system as a whole. Finally, when the threshold difference exceeds the second preset threshold difference, the prediction module sets the adjustment coefficient to Z2 and adjusts the fluctuation threshold to a greater extent based on the coefficient. The value of Z2 is greater than Z1, indicating that in high-risk situations, the system adopts a more active adjustment strategy to cope with possible severe communication fluctuations.

[0071] It can be seen that the prediction module is based on the long short-term memory network (LSTM) model. By training historical communication data and real-time data, it can accurately predict the communication performance change trend of each communication device within the preset period. This method effectively solves the shortcoming of the traditional system that cannot identify communication performance problems in advance, so that the system can issue an early warning before the communication fluctuation occurs, thereby improving the stability and operation efficiency of the communication system. Secondly, through time series division and cluster analysis, the prediction module can deeply mine the historical communication data and obtain the mean and standard deviation of each cluster. This statistical analysis method makes the calculation of the fluctuation threshold more scientific and accurate, and can more truly reflect the characteristics of various types of data. Compared with the traditional fixed rule threshold setting, this dynamic threshold calculation method is more adaptable and can better cope with complex communication environments. Third, the prediction module can dynamically adjust the fluctuation threshold by generating a prediction threshold and comparing it with the preset prediction threshold. This adjustment mechanism can flexibly respond to changes in the actual communication environment. When the communication system is in a normal state, unnecessary adjustments can be avoided; when the communication system faces fluctuation risks, system parameters can be optimized in time, thereby improving the system's adaptability and fault tolerance. Fourth, the prediction module sets multiple adjustment coefficients (such as Z1 and Z2) to cope with different levels of fluctuations by processing threshold differences in a hierarchical manner. This hierarchical adjustment strategy can take appropriate or more active response measures when the communication system faces moderate or severe fluctuations, thereby ensuring that the system's response sensitivity matches the adjustment range, avoiding excessive intervention and preventing the potential impact of severe fluctuations on the system. Finally, the prediction module ensures the flexibility and accuracy of the adjustment mechanism through the differentiated design of the first preset threshold difference and the second preset threshold difference. At the same time, the reasonable configuration of the adjustment coefficients Z1 and Z2 further optimizes the adjustment range of the fluctuation threshold, enabling the system to achieve more accurate and efficient regulation when dealing with complex and changing communication environments, significantly improving the overall reliability and performance of the communication system.

[0072] Specifically, when the control module dynamically adjusts the communication path according to the relationship between the real-time communication fluctuation value and the prediction threshold, it includes: the control module is configured to determine whether to dynamically adjust the communication path according to the relationship between the real-time communication fluctuation value and the fluctuation threshold. When the real-time communication fluctuation value is greater than the fluctuation threshold, the control module determines that the communication data of the communication device is abnormal communication data, and adjusts the communication path of the communication device. When the real-time communication fluctuation value is less than or equal to the fluctuation threshold, the control module determines that the communication data of the communication device is normal data, and does not adjust the communication path of the communication device.

[0073] Specifically, when the control module determines that the communication data of the communication device is abnormal communication data and adjusts the communication path of the communication device, it includes: the control module is also configured to determine the performance score of the communication path according to the relationship between the real-time communication fluctuation value and the fluctuation threshold. The control module is also configured to obtain the fluctuation value of each backup communication path, and determine the performance score of each backup communication path according to the relationship between the fluctuation value of the backup communication path and the fluctuation threshold. The control module is also configured to sort the performance scores of the communication path and the performance scores of each backup communication path in reverse order, and determine the communication path corresponding to the first performance score sorting as the adjusted communication path of the communication device. The control module is also configured to synchronize the adjusted communication path of the communication device to cloud storage based on edge computing.

[0074] Specifically, when the control module synchronizes the adjusted communication path of the communication device to the cloud storage based on edge computing, it includes: the control module is also configured to deploy edge computing nodes on each communication device. The control module is also configured to process the communication data of the communication device in real time based on the edge computing node, and send the processed communication data to the cloud for storage.

[0075] It is understandable that the control module determines whether it is necessary to dynamically adjust the communication path according to the relationship between the real-time communication fluctuation value and the prediction threshold. When the real-time communication fluctuation value is greater than the fluctuation threshold, it indicates that the operation state of the communication device is abnormal, and the control module determines that the communication data is abnormal communication data and starts the communication path adjustment mechanism. The core of this principle is to monitor the communication fluctuation in real time, so as to timely discover the abnormal conditions in the communication system, thereby improving the flexibility and response speed of the system. Secondly, when the control module determines that the communication data of the communication device is abnormal, it further determines the performance score of the communication path by analyzing the relationship between the real-time communication fluctuation value and the fluctuation threshold. This process not only takes into account the real-time fluctuation, but also includes the evaluation of the fluctuation value of the backup communication path. In this way, the performance of multiple backup paths can be comprehensively considered to ensure that when the main communication path is abnormal, it can quickly switch to the backup path with the best performance, thereby ensuring the high reliability and low latency of the communication system. Thirdly, the control module sorts according to the performance scores of all communication paths, and uses the path with the highest score as the adjusted communication path. By sorting the performance scores in reverse order, the system can give priority to the communication path with the most stability and reliability, thereby optimizing the use of communication resources. This mechanism enables rapid path adjustment when communication fluctuations occur, reduces the time of communication interruption, and improves the overall operating efficiency of the system. Fourth, the control module synchronizes the adjusted communication path to cloud storage based on edge computing technology. This process combines local edge computing with cloud storage, and uses edge computing nodes to process real-time data of communication equipment, thereby reducing data transmission delays and improving data processing efficiency. Edge computing nodes ensure the real-time and security of data by processing communication data in real time and transmitting data to the cloud, while avoiding the delay caused by cloud computing. Finally, the control module implements a distributed architecture for data processing by deploying edge computing nodes on each communication device. This distributed processing method not only improves the computing power of the system, but also enables efficient data storage and analysis in multiple locations, reduces the pressure of centralized data processing, and optimizes the data flow path. This technical architecture that combines edge computing with cloud storage enables the communication system to operate faster and more stably in a dynamically changing environment.

[0076] It can be seen that by judging whether the communication path needs to be adjusted based on the relationship between the real-time communication fluctuation value and the fluctuation threshold, the abnormal situation of the communication equipment can be discovered immediately. When the communication fluctuation exceeds the predetermined threshold, the control module can identify the abnormal communication data and adjust the communication path in time, thereby effectively avoiding the possible communication interruption or performance degradation problems, and ensuring the efficient and stable operation of the communication system. Secondly, by dynamically adjusting the communication path, the most suitable backup communication path can be automatically selected to replace the main path with a fault. The control module analyzes the performance score of the communication path and the fluctuation value of the backup path, and sorts the paths according to the score, thereby ensuring the optimal selection of the communication path. This optimization mechanism not only improves the utilization efficiency of communication resources, but also greatly enhances the system's resilience in the face of communication fluctuations, and improves the reliability and sustainability of communication services. Third, the application of edge computing further improves the system's response speed and data processing capabilities. By deploying edge computing nodes in the control module, communication data can be processed in real time locally on the device, avoiding the delay caused by the need to transmit a large amount of data to a remote server. This edge computing architecture effectively reduces the delay caused by data transmission, making the communication path adjustment more timely and accurate, thereby improving the flexibility and response speed of the overall system. Fourth, the synchronization mechanism based on edge computing can quickly upload the adjusted communication path information to the cloud for storage and backup. This not only ensures the consistency and synchronization of cloud data, but also enhances the security and reliability of data. Real-time updates and backups of cloud storage allow recovery to the optimal state at any time, avoiding data loss or information inconsistency caused by single point failures. Finally, through real-time processing and path adjustment of edge computing nodes, communication resources can be dynamically allocated according to the needs and status of different communication devices to avoid excessive consumption or waste of resources. This flexible resource scheduling mechanism not only helps to improve communication efficiency, but also reduces the energy consumption of equipment, improving the overall economy and environmental friendliness of the system.

[0077] Specifically, when the encryption module dynamically encrypts the adjusted communication path and data, the encryption module is further configured to record the historical score and adjustment record of each communication path based on the blockchain. The encryption module is further configured to dynamically authenticate and verify the communication path based on the blockchain contract mechanism.

[0078] It is understandable that the encryption module records the historical scores and adjustment records of each communication path based on blockchain technology, which can ensure the transparency and traceability of the communication path. Through blockchain technology, all changes in communication paths can be tracked and stored, preventing any tampering or malicious behavior during the path adjustment process, and improving the overall security of the communication system. This tamper-proof recording mechanism provides strong support for subsequent audits and security analysis. Secondly, the communication path is dynamically authenticated and verified using the blockchain's contract mechanism, providing a decentralized trust guarantee for the communication system. The encryption module automatically executes the path authentication and verification process through smart contracts, so that the use of all communication paths can automatically perform compliance checks without the need for intermediaries. This mechanism not only improves the credibility of the system, but also avoids the possibility of human intervention, ensuring the authenticity and validity of the communication path. Third, the encryption technology provided by the blockchain strengthens the security protection during data transmission. Each communication path will be encrypted after adjustment, protecting the security of sensitive data during transmission. With the encryption and decentralized storage characteristics of the blockchain, data can be prevented from being tampered with or leaked, thereby effectively protecting the confidentiality and integrity of the communication content. This security measure is particularly important in medical management systems involving personal privacy, such as diabetes clinics. Fourth, through the combination of dynamic encryption and blockchain mechanisms, the system can still maintain the effectiveness and update of encryption measures when facing frequent adjustments to communication paths. Each path adjustment and data change will be automatically recorded and encrypted for protection, ensuring that the security of each communication is updated and verified in a timely manner. This dynamic encryption method improves the security adaptability of the system in complex environments and prevents potential security risks caused by long-term non-update of encryption. Finally, the blockchain-based encryption and authentication mechanism optimizes the trust management of the system. Since blockchain technology can automatically and transparently handle the authentication and verification process of communication paths, the system no longer relies on a single management agency or centralized authentication method, but ensures the credibility and transparency of all path operations through distributed ledgers.

[0079] In the above embodiment, the real-time communication data of multiple communication devices in the diabetes management system are obtained in real time through the acquisition module, and the historical data is analyzed in combination with the judgment module to determine the communication fluctuation value, thereby realizing comprehensive monitoring of the system communication status. Compared with the traditional solution based on fixed threshold judgment, this solution can more accurately identify potential communication anomalies according to the dynamic change law of historical data, improve the adaptability to complex dynamic communication environments, thereby effectively reducing the delay and packet loss problems in the data transmission process, and improving the stability of the system. Secondly, the prediction module predicts the future communication performance change trend of the communication device based on the long short-term memory network (LSTM) and generates a dynamic prediction threshold. This artificial intelligence-based prediction method can not only identify possible communication fluctuations in advance, but also provide a scientific basis for subsequent communication path adjustments, avoiding the lag problem caused by relying on passive processing in traditional methods, thereby improving response speed and operating efficiency. In addition, the control module dynamically adjusts the communication path according to the comparison result of the real-time communication fluctuation value and the prediction threshold. This dynamic adjustment mechanism can automatically select the best communication path when the network environment changes or the device performance fluctuates to ensure the continuity and reliability of data transmission. Compared with the traditional path optimization scheme based on fixed rules, this dynamic adjustment mechanism is more flexible and can better adapt to complex and changeable actual application scenarios, effectively ensuring the normal operation of the diabetes outpatient user management system. Finally, the adjusted communication path and data are dynamically encrypted through the encryption module, effectively solving the problem of secure transmission of highly sensitive data involved in the diabetes management system. Dynamic encryption technology can not only prevent data leakage and tampering during transmission, but also enhance the system's defense capabilities against potential network attacks, ensure user privacy and data security, and meet the strict requirements of modern medical information systems for data protection.

[0080] In another preferred embodiment based on the above embodiment, Figure 2 As shown, this embodiment provides a communication method for diabetes clinic user management, including:

[0081] Step S100: collecting real-time communication data of multiple communication devices in the diabetes management system.

[0082] Step S200: Acquire network parameters of each communication device, and determine a communication fluctuation value based on the relationship between each historical communication data.

[0083] Step S300: Based on the long short-term memory network, predict the communication performance change trend of each communication device within a preset time period and generate a prediction threshold.

[0084] Step S400: dynamically adjust the communication path according to the relationship between the real-time communication fluctuation value and the prediction threshold, and dynamically encrypt the adjusted communication path and data.

[0085] It can be understood that the communication system and method for managing users of a diabetes clinic in the above-mentioned embodiments of the present invention have the same beneficial effects and will not be described in detail.

[0086] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0087] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0088] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0089] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A communication system for diabetic outpatient user management, characterized in that: include: Acquisition module, judgment module, prediction module, control module and encryption module; The acquisition module is configured to acquire real-time communication data of multiple communication devices in the diabetes management system; The judgment module is electrically connected to the acquisition module, and is configured to obtain network parameters of each communication device. The judgment module is also configured to determine a communication fluctuation value according to a relationship between each historical communication data; The prediction module is electrically connected to the judgment module and is configured to predict the communication performance change trend of each communication device within a preset period of time based on the long short-term memory network and generate a prediction threshold; The control module is electrically connected to the judgment module and the prediction module respectively, and the control module is configured to dynamically adjust the communication path according to the relationship between the real-time communication fluctuation value and the prediction threshold; The encryption module is electrically connected to the control module, and the encryption module is configured to dynamically encrypt the adjusted communication path and data; After the prediction module generates the prediction threshold, the method further includes: The prediction module is configured to divide the data set according to the time series of each of the historical communication data; The prediction module is further configured to cluster each data set and obtain the mean and standard deviation in each cluster; The prediction module is further configured to determine a fluctuation threshold according to the mean and the standard deviation: ; Wherein, Y is the fluctuation threshold, is the mean difference, σ is the standard deviation, and k is the adjustment coefficient; The prediction module is further configured to determine whether to adjust the fluctuation threshold according to the relationship between the prediction threshold and a preset prediction threshold; The control module is configured to determine whether to dynamically adjust the communication path according to the relationship between the real-time communication fluctuation value and the fluctuation threshold.

2. The communication system for diabetes clinic user management as claimed in claim 1, characterized in that: The judging module comprises: an acquisition unit, electrically connected to the acquisition module, configured to acquire network parameters of each communication device, the network parameters including network bandwidth, network delay, data transmission rate and network interference, and further configured to acquire real-time operation data of the communication device, the real-time operation data including: device load rate, energy consumption data and communication stability; an evaluation unit, electrically connected to the acquisition unit, and configured to determine a communication fluctuation value of the communication device according to a relationship between the communication data and the historical communication data; A storage unit is electrically connected to the evaluation unit, and the storage unit is configured to store historical communication data and communication fluctuation values ​​of each of the communication devices.

3. The communication system for diabetes clinic user management as claimed in claim 2, characterized in that: The prediction module predicts the communication performance change trend of each communication device within a preset period of time based on the long short-term memory network and generates a prediction threshold, including: The prediction module is further configured to train a long short-term memory network model based on historical communication data and real-time data; The prediction module is further configured to determine the communication fluctuation value and performance trend of the communication device within a preset period of time based on the long short-term memory network model and the real-time communication data; The prediction module is further configured to determine the prediction threshold based on the relationship between the communication fluctuation value and the performance trend of the communication device within a preset time period.

4. The communication system for diabetes clinic user management as claimed in claim 1, characterized in that: When the prediction module determines whether to adjust the fluctuation threshold according to the relationship between the prediction threshold and the preset prediction threshold, it includes: The prediction module is further configured to obtain a threshold difference between the prediction threshold and the preset prediction threshold, and determine whether to adjust the fluctuation threshold according to a relationship between a first preset threshold difference and a second preset threshold difference configured by the prediction module: When the threshold difference is less than the first preset threshold difference, the prediction module determines not to adjust the fluctuation threshold; When the threshold difference is greater than or equal to the first preset threshold difference, and the threshold difference is less than the second preset threshold difference, the prediction module determines the adjustment coefficient as Z1, and adjusts the fluctuation threshold according to the adjustment coefficient Z1; When the threshold difference is greater than or equal to the second preset threshold difference, the prediction module determines that the adjustment coefficient is Z2, and adjusts the fluctuation threshold according to the adjustment coefficient Z2; The first preset threshold difference is smaller than the second preset threshold difference, and Z1 is smaller than Z2.

5. The communication system for diabetes clinic user management as claimed in claim 4, characterized in that: The control module dynamically adjusts the communication path according to the relationship between the real-time communication fluctuation value and the prediction threshold value, including: The control module is configured to determine whether to dynamically adjust the communication path according to the relationship between the real-time communication fluctuation value and the fluctuation threshold; When the real-time communication fluctuation value is greater than the fluctuation threshold, the control module determines that the communication data of the communication device is abnormal communication data, and adjusts the communication path of the communication device; When the real-time communication fluctuation value is less than or equal to the fluctuation threshold, the control module determines that the communication data of the communication device is normal data, and does not adjust the communication path of the communication device.

6. The communication system for diabetes clinic user management as claimed in claim 5, characterized in that: When the control module determines that the communication data of the communication device is abnormal communication data and adjusts the communication path of the communication device, it includes: The control module is further configured to determine a performance score of the communication path according to a relationship between the real-time communication fluctuation value and the fluctuation threshold; The control module is further configured to obtain a fluctuation value of each backup communication path, and determine a performance score of each backup communication path according to a relationship between the fluctuation value of the backup communication path and the fluctuation threshold; The control module is further configured to sort the performance scores of the communication paths and the performance scores of the backup communication paths in reverse order, and determine the communication path corresponding to the first performance score sort as the adjusted communication path of the communication device; The control module is also configured to synchronize the adjusted communication path of the communication device to cloud storage based on edge computing.

7. The communication system for diabetes clinic user management as claimed in claim 6, characterized in that: When the control module synchronizes the adjusted communication path of the communication device to cloud storage based on edge computing, it includes: The control module is further configured to deploy edge computing nodes on each of the communication devices; The control module is also configured to process the communication data of the communication device in real time based on the edge computing node, and send the processed communication data to the cloud for storage.

8. The communication system for diabetes clinic user management as claimed in claim 1, characterized in that: When the encryption module dynamically encrypts the adjusted communication path and data, it includes: The encryption module is further configured to record the historical score and adjustment record of each communication path based on the blockchain; The encryption module is also configured to dynamically authenticate and verify the communication path based on a blockchain contract mechanism.

9. A communication method for managing users of a diabetes clinic, applicable to a communication system for managing users of a diabetes clinic as claimed in any one of claims 1 to 8, characterized in that: include: Collect real-time communication data of multiple communication devices in the diabetes management system; Obtain network parameters of each communication device, and determine the communication fluctuation value based on the relationship between historical communication data; Based on the long short-term memory network, predict the communication performance change trend of each communication device within a preset period of time and generate a prediction threshold; Dynamically adjust the communication path based on the relationship between the real-time communication fluctuation value and the prediction threshold; Dynamic encryption of adjusted communication paths and data.

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