Edge collaborative communication control method and system based on intelligent gateway

Abstract

The present application provides an edge collaborative communication control method and system based on an intelligent gateway, which relates to the field of intelligent communication technology. The method includes: building an edge collaborative network, collecting monitoring data sets of each edge device, performing collaborative computing task identification, outputting a collaborative task processing queue, obtaining collaborative edge devices, performing visual identification, collecting visual identification results within a preset period, evaluating using a collaborative communication evaluation model, outputting N groups of collaborative edge devices, and controlling the data transmission within each group of collaborative edge devices according to the corresponding communication protocol. This application can solve the technical problem of the prior art that the data processing efficiency is low due to the large data processing delay, which further affects the user experience and system performance. It ensures that the data transmission in the edge collaborative network is both efficient and secure, and further improves the data processing efficiency.

Description

Technical Field

[0001] The present application relates to the field of intelligent communication technology, and in particular to an edge collaborative communication control method and system based on an intelligent gateway. Background Art

[0002] With the explosive growth of IoT devices, how to reduce the burden on central data centers while ensuring data transmission efficiency has become a research focus. As a device connecting the perception layer and the transmission layer, the intelligent gateway plays a vital role. The industrial Internet has very high requirements for real-time and reliability, and the traditional centralized processing method of cloud computing centers is difficult to meet these requirements. Therefore, data processing needs to be performed at the edge of the network to reduce latency.

[0003] Currently, existing cloud computing models have certain limitations when processing data generated by a large number of edge devices.

[0004] In summary, the prior art has a technical problem in which the data processing efficiency is low due to the large data processing delay, which further affects the user experience and system performance. Summary of the invention

[0005] The purpose of this application is to provide an edge collaborative communication control method and system based on an intelligent gateway to solve the technical problem that the prior art has large data processing delays, resulting in low data processing efficiency, which further affects user experience and system performance.

[0006] In view of the above problems, the present application provides an edge collaborative communication control method and system based on an intelligent gateway.

[0007] In the first aspect, the present application provides an edge collaborative communication control method based on an intelligent gateway, and the method is implemented by an edge collaborative communication control system based on an intelligent gateway, wherein the method includes: deploying intelligent gateways and edge computing nodes to build an edge collaborative network, wherein each edge computing node is communicatively connected to an edge device; collecting monitoring data sets of each edge device in the edge collaborative network; performing collaborative computing task identification on the monitoring data set based on the edge collaborative network, and outputting a collaborative task processing queue; analyzing the collaborative task processing queue to obtain collaborative edge devices, visually identifying the collaborative edge devices in the edge collaborative network, and outputting visual identification results; collecting visual identification results within a preset period, evaluating the visual identification results using a collaborative communication evaluation model, and outputting N groups of collaborative edge devices; the intelligent gateway configures the communication protocols corresponding to the N groups of collaborative edge devices to control the data transmission within each group of collaborative edge devices.

[0008] In the second aspect, the present application also provides an edge collaborative communication control system based on an intelligent gateway, which is used to execute the edge collaborative communication control method based on the intelligent gateway as described in the first aspect, wherein the system includes: a network construction module, the network construction module is used to deploy intelligent gateways and edge computing nodes to build an edge collaborative network, wherein each edge computing node is communicatively connected to an edge device; a data set acquisition module, the data set acquisition module is used to collect monitoring data sets of each edge device in the edge collaborative network; a collaborative computing task identification module, the collaborative computing task identification module is used to identify collaborative computing tasks for the monitoring data set based on the edge collaborative network, and output a collaborative task processing queue; a visual identification module, the visual identification module is used to analyze the collaborative task processing queue, obtain collaborative edge devices, visually identify the collaborative edge devices in the edge collaborative network, and output visual identification results; a result evaluation module, the result evaluation module is used to collect visual identification results within a preset period, evaluate the visual identification results using a collaborative communication evaluation model, and output N groups of collaborative edge devices; a data transmission module, the data transmission module is used to configure the communication protocol corresponding to the N groups of collaborative edge devices by the intelligent gateway, and control the data transmission within each group of collaborative edge devices.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] By deploying intelligent gateways and edge computing nodes, an edge collaborative network is constructed, wherein each edge computing node is connected to an edge device for communication; monitoring data sets of each edge device in the edge collaborative network are collected; collaborative computing tasks are identified on the monitoring data sets based on the edge collaborative network, and a collaborative task processing queue is output; the collaborative task processing queue is analyzed to obtain collaborative edge devices, and the collaborative edge devices are visually identified in the edge collaborative network, and the visual identification results are output; the visual identification results within a preset period are collected, and the visual identification results are evaluated using a collaborative communication evaluation model, and N groups of collaborative edge devices are output; the intelligent gateway configures the communication protocols corresponding to the N groups of collaborative edge devices, and controls the data transmission within each group of collaborative edge devices, which effectively solves the technical problem that the existing technology has low data processing efficiency due to large data processing delays, further affecting user experience and system performance. It ensures that data transmission in the edge collaborative network is both efficient and secure, and further improves data processing efficiency.

[0011] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0013] Figure 1 This is a flow chart of the edge collaborative communication control method based on the intelligent gateway of this application;

[0014] Figure 2 This is a structural diagram of the edge collaborative communication control system based on the intelligent gateway of this application.

[0015] Description of reference numerals:

[0016] Network construction module 11, data set acquisition module 12, collaborative computing task identification module 13, visualization identification module 14, result evaluation module 15, data transmission module 16. DETAILED DESCRIPTION

[0017] This application provides an edge collaborative communication control method and system based on an intelligent gateway to solve the technical problem that the existing technology has a large data processing delay, resulting in low data processing efficiency, which further affects user experience and system performance. It ensures that data transmission in the edge collaborative network is both efficient and secure, and further improves data processing efficiency.

[0018] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0019] Embodiment 1

[0020] Please refer to the attached Figure 1 The present application provides an edge collaborative communication control method based on an intelligent gateway, wherein the method is applied to an edge collaborative communication control system based on an intelligent gateway, and the method specifically comprises the following steps:

[0021] S1: Deploy smart gateways and edge computing nodes to build an edge collaborative network, in which each edge computing node communicates with edge devices.

[0022] Specifically, nodes with computing and storage capabilities are deployed at the edge of the network. These nodes are directly connected to various edge devices, such as sensors, cameras, and industrial equipment, to form a local data processing and communication network. Such a network structure can process data instantly at the source of data generation, reducing the need to transmit data to remote data centers, thereby reducing latency, improving response speed, and alleviating the burden on central data centers. According to the network environment and business needs, select appropriate devices as edge computing nodes. These devices can be dedicated edge computing servers or gateway devices with certain computing capabilities, such as industrial gateways and home gateways. As the core of the edge collaborative network, the intelligent gateway has strong computing power, storage capacity, and network connection capabilities. The intelligent gateway is responsible for managing edge computing nodes, coordinating communications between nodes, and interacting with cloud data centers. Connect edge devices such as sensors to edge computing nodes via wired or wireless means. Edge devices are responsible for collecting data, while edge computing nodes are responsible for processing and analyzing this data. Configure appropriate network protocols for intelligent gateways and edge computing nodes to ensure smooth communication between nodes. These protocols can be TCP / IP, MQTT, CoAP, etc.

[0023] S2: Collect monitoring data sets of each edge device in the edge collaborative network.

[0024] Specifically, in the edge collaborative network, each edge device will continuously collect and monitor data, which are sensor readings, video streams, user interaction information, etc. Edge devices collect data through built-in sensors or external detection devices. After data collection, edge devices or edge computing nodes can perform preliminary preprocessing on the data, such as data cleaning, denoising, format conversion, etc., to ensure data quality and consistency. The preprocessed data needs to be stored in the local storage of the edge device or the storage system of the edge computing node. Storage can use databases, file systems, or other suitable data storage solutions.

[0025] S3: Based on the edge collaborative network, collaborative computing tasks are identified for the monitoring data set, and a collaborative task processing queue is output.

[0026] Specifically, analyze the characteristics of the monitoring data set, including the size, type, processing complexity, etc. of the data, as well as business requirements, to determine which tasks need to be executed. Assign priorities to each task based on the importance and urgency of the task. For example, tasks with high real-time requirements, such as emergency alarm processing, will be given higher priority. Evaluate the resource status in the edge collaborative network, including computing power, storage space, and energy supply. Assign tasks to appropriate edge computing nodes based on task priority and resource status. This process includes combining factors such as node load, geographic location, and network latency. On each edge computing node, build a collaborative task processing queue based on the task allocation results. This queue can be a priority queue to ensure that high-priority tasks are executed first.

[0027] S4: Analyze the collaborative task processing queue, obtain collaborative edge devices, visually identify the collaborative edge devices in the edge collaborative network, and output the visual identification results.

[0028] Specifically, visual identification helps network administrators and decision makers monitor network status, optimize resource allocation, analyze collaborative task processing queues, and identify the execution status of each task, including task progress, completion status, resource consumption, etc. According to the task processing queue, identify the edge devices involved in collaborative computing. These devices are key devices for performing specific tasks, or devices that need to collaborate with other devices. Visually identify collaborative edge devices in the edge collaborative network based on the role of the device, the type of task, the performance indicators of the device, etc. This is achieved through network management software or dedicated visualization tools. The identification can be in the form of color, icon, label, etc. The visual identification results are output to the network management interface or monitoring system, so that network administrators and decision makers can view the network status and device collaboration in real time.

[0029] S5: Collect visual identification results within a preset period, evaluate the visual identification results using a collaborative communication evaluation model, and output N groups of collaborative edge devices.

[0030] Specifically, according to a preset cycle, for example, every hour, every day, or every week, the visual identification results of each collaborative edge device in the edge collaborative network are collected. These results include the status of the device, performance indicators, task completion status, etc. The collaborative communication evaluation model can evaluate the collaborative effect of edge devices and the overall performance of the network based on the collected data. The collected visual identification results are used as input data, which are preprocessed before being input into the evaluation model. Run the evaluation model to analyze and calculate the input data. The evaluation model will output the communication evaluation results of each group of collaborative edge devices, and select N groups of collaborative edge devices with the best performance or that best meet specific business needs based on the evaluation results.

[0031] S6: The intelligent gateway configures the communication protocols corresponding to the N groups of collaborative edge devices to control data transmission within each group of collaborative edge devices.

[0032] Specifically, the intelligent gateway configures appropriate communication protocols for each group of collaborative edge devices based on the specific needs of the network and the capabilities of the devices. These protocols include TCP / IP, UDP, MQTT, CoAP, etc. For each communication protocol, the intelligent gateway sets appropriate parameters such as port number, encryption method, authentication mechanism, data compression options, etc. The intelligent gateway controls the data transmission within each group of collaborative edge devices based on the configured communication protocol. This includes operations such as routing, forwarding, caching, and synchronization of data. The intelligent gateway manages network traffic to avoid congestion and ensure priority transmission of critical data. Network conditions may change, and the intelligent gateway dynamically adjusts the communication protocol configuration and data transmission strategy to adapt to these changes.

[0033] Furthermore, step S5 of the present application also includes:

[0034] Statistics are performed on the visualization identification results to output L groups of collaborative edge devices; collaborative probability analysis is performed on the L groups of collaborative edge devices to obtain L groups of collaborative probabilities corresponding to the L groups of collaborative edge devices; collaborative edge devices with a probability greater than or equal to an expected probability are screened from the L groups of collaborative probabilities, and the screened M groups of collaborative edge devices are output; a collaborative communication evaluation model is trained, and the M groups of collaborative edge devices are evaluated using the collaborative communication evaluation model, and N groups of collaborative edge devices are output.

[0035] Specifically, the visual identification results collected within the preset period are statistically analyzed through data analysis and reporting tools to identify the performance and working mode of each group of collaborative edge devices. According to the statistical results, L groups of collaborative edge devices with outstanding performance are output. These devices are the combination that works most frequently or has the best effect in the network. Collaborative probability analysis is performed on the L groups of collaborative edge devices to evaluate the possibility of these devices in future collaborative work. Collaborative probability analysis can be performed based on historical collaborative data, communication quality between devices, device load and other factors. Through analysis, a collaborative probability value is calculated for each group of collaborative edge devices, which reflects the expected performance of the device in future collaborative work. Collaborative edge devices with a probability greater than or equal to the expected probability are selected from the L groups of collaborative probabilities. This expected probability is a threshold set according to network performance requirements and business needs. The selected M groups of collaborative edge devices are those combinations of devices with high collaborative probabilities and that meet performance requirements. The collaborative communication evaluation model is trained using historical data and real-time data. This model is an algorithm based on machine learning that can predict the collaborative effect of a combination of devices based on input features. The trained collaborative communication evaluation model is used to evaluate the M groups of collaborative edge devices and output the N groups of optimal collaborative edge devices. These combinations of devices are expected to perform best when working together in a network.

[0036] Furthermore, the present application also includes:

[0037] Among them, the evaluation indicators of the collaborative communication evaluation model include collaborative utilization rate, collaborative expansion degree and collaborative sharing quality; the M groups of collaborative edge devices are evaluated using the collaborative communication evaluation model, and the M groups of communication evaluation indicators corresponding to the M groups of collaborative edge devices are output; based on the M groups of communication evaluation indicators, N groups of collaborative edge devices that are greater than the preset communication evaluation indicators are obtained.

[0038] Specifically, the collaborative utilization rate measures the activity and frequency of edge devices in collaborative work. The collaborative expansion degree evaluates the ability and scope of edge devices to establish collaborative relationships with other devices in a collaborative network. The collaborative sharing quality measures the effect and quality of edge devices sharing data and resources in collaborative work. The evaluation model is used to analyze the M groups of collaborative edge devices selected, and the communication evaluation indicators corresponding to each group of devices are output, including collaborative utilization rate, collaborative expansion degree, and collaborative sharing quality. According to network performance goals and business needs, a preset threshold or standard is set for each evaluation indicator. The evaluation indicators of the M groups of collaborative edge devices are compared with the preset communication evaluation indicators, and those combinations of devices that exceed the preset thresholds in all evaluation indicators are selected. These combinations are regarded as the optimal collaborative edge devices. The final output is N groups of collaborative edge devices that meet the preset communication evaluation indicators. These device combinations can be used for subsequent task allocation, resource optimization, and network management decisions.

[0039] Furthermore, the present application also includes:

[0040] According to the M groups of communication evaluation indicators, obtain the MN group of collaborative edge devices that is less than or equal to the preset communication evaluation indicators; generate a communication control backup library according to the MN group of collaborative edge devices; monitor the communication evaluation indicators of each group of collaborative edge devices in the communication control backup library in real time, and update the N groups of collaborative edge devices accordingly.

[0041] Specifically, based on the output of the collaborative communication evaluation model, MN groups of collaborative edge devices are selected as backup devices. The backup library contains detailed information on each group of devices, such as device ID, location, configuration, capabilities, historical performance data, etc. Use monitoring tools to collect communication evaluation index data for each group of collaborative edge devices in the backup library in real time. Monitored indicators include collaborative utilization rate, collaborative expansion, and collaborative sharing quality. Evaluate the performance of N groups of currently working collaborative edge devices regularly or based on changes in real-time data. If the performance indicator of any device is found to be lower than the preset threshold, or if a device fails, immediately select a suitable device from the backup library for replacement. The update operation ensures that the network is interrupted for the shortest time and has minimal impact on user experience and business processes.

[0042] Furthermore, the present application also includes:

[0043] Obtain resource configuration parameters of the intelligent gateway; establish a first resource constraint function based on the resource configuration parameters; the first resource constraint function performs resource constraint optimization according to the evaluation indicators output by the collaborative communication evaluation model, and outputs N groups of collaborative edge devices obtained by the optimization.

[0044] Specifically, the resource configuration parameters of the intelligent gateway can be obtained through the management interface or configuration file of the intelligent gateway, including CPU usage, memory occupancy, storage capacity, network interface bandwidth, etc. The resource constraint function is a mathematical model that defines the constraints on the resource configuration of the intelligent gateway, such as maximum computing power, available storage space, maximum energy consumption, etc. The evaluation indicators output by the collaborative communication evaluation model, such as collaborative utilization rate, collaborative expansion degree, and collaborative sharing quality. Use optimization algorithms, such as linear programming, integer programming, genetic algorithms, etc., to solve the resource constraint function. The goal is to minimize resource waste or maximize network performance while satisfying all resource constraints. The optimization algorithm will output N groups of collaborative edge devices that meet resource constraints and perform best in the collaborative communication evaluation model. These device combinations represent the optimal configuration under given resource constraints.

[0045] Furthermore, the present application also includes:

[0046] According to the collaborative communication evaluation model, output and initialize N groups of collaborative edge devices; predict N resource occupancy indicators of the initialized N groups of collaborative edge devices; fit the N resource occupancy indicators, output the resource occupancy fitting indicator, and judge whether the resource occupancy fitting indicator meets the constraint requirements of the first resource constraint function; if it does not meet the constraint requirements of the first resource constraint function, optimize according to the size of the communication evaluation indicator corresponding to the initialized N groups of collaborative edge devices, and obtain two rounds of N groups of collaborative edge devices until the constraint requirements of the first resource constraint function are met.

[0047] Specifically, according to the collaborative communication evaluation model, N groups of collaborative edge devices that are initially screened are output. These device combinations are the best configurations determined based on model evaluation indicators such as collaborative utilization rate, collaborative expansion degree, and collaborative sharing quality. For each group of initialized collaborative edge devices, its resource occupancy indicators are predicted, including CPU utilization, memory utilization, storage occupancy, and network bandwidth usage. The prediction can be performed through historical data analysis, real-time monitoring, or simulation. The predicted N resource occupancy indicators are fitted to obtain the overall trend or pattern of resource occupancy. Fitting can be performed through linear regression, polynomial fitting, machine learning models, etc. The fitting process will output a set of resource occupancy fitting indicators, which represent the expected resource usage of N groups of collaborative edge devices. The resource occupancy fitting indicator is compared with the first resource constraint function of the intelligent gateway to determine whether the constraint requirements are met. If the resource occupancy fitting indicator exceeds the limit of the constraint function, it means that the current device configuration may cause resource overload or waste. If the resource occupancy fitting indicator does not meet the resource constraint, according to the size of the communication evaluation indicator corresponding to the initialized N groups of collaborative edge devices, the device combination is adjusted, the task is reallocated, or the resource usage strategy is optimized for optimization. Through the optimization process, new N groups of collaborative edge device configurations are obtained, which should better meet the resource constraint requirements. The prediction, fitting and judgment processes are repeated until a configuration that meets the first resource constraint function is found.

[0048] Furthermore, step S6 of the present application also includes:

[0049] Identify the supported protocol type of each group of collaborative edge devices in the N groups of collaborative edge devices; if there is an unmatched collaborative edge device group, return empty; if there is a matching collaborative edge device group, obtain the matching communication protocol corresponding to the matching group collaborative edge device according to the supported protocol type, and the intelligent gateway performs collaborative communication configuration according to the matching communication protocol.

[0050] Specifically, for each group of collaborative edge devices, identify the type of communication protocol it supports. This is achieved by querying the hardware specifications, firmware version of the device, or through the API interface provided by the device. If any unmatched collaborative edge device groups are found, that is, the protocol types they support are inconsistent, a null value needs to be returned, indicating that these devices cannot work together. The purpose of returning a null value is to avoid configuration errors and ensure that only device groups with matching protocols can communicate collaboratively. For matching collaborative edge device groups, obtain matching communication protocols based on their protocol types. It can be TCP / IP, UDP, MQTT, CoAP, etc., depending on the capabilities of the device and network requirements. The intelligent gateway configures collaborative communication for the matching collaborative edge device group based on the matching communication protocol. The configuration includes setting communication ports, encryption methods, authentication mechanisms, etc. to ensure the security and efficiency of data transmission.

[0051] Furthermore, the present application also includes: after controlling the data transmission within each group of collaborative edge devices, one of the edge devices completes the computing task and then uploads it to the smart gateway.

[0052] Specifically, the intelligent gateway controls the data transmission between each group of collaborative edge devices to ensure that the data is transmitted according to the predetermined communication protocol and path. Including the interaction between multiple edge devices, the intelligent gateway can make the data reach the destination at the right time and order. The intelligent gateway assigns computing tasks to each group of collaborative edge devices based on the collaborative communication evaluation model and resource constraint function. Computing tasks include data analysis, model training, prediction generation, etc., which can be distributed and executed on different edge devices. After receiving the computing task, the collaborative edge device starts to execute the task. The computing task is completed on the edge device, which can reduce the amount of data transmission and improve the response speed. Once the computing task is completed, the edge device responsible for completing the computing task uploads the result data to the intelligent gateway.

[0053] In summary, the edge collaborative communication control method based on the intelligent gateway provided by this application has the following technical effects:

[0054] By deploying intelligent gateways and edge computing nodes, an edge collaborative network is constructed, wherein each edge computing node is connected to an edge device for communication; monitoring data sets of each edge device in the edge collaborative network are collected; collaborative computing tasks are identified on the monitoring data sets based on the edge collaborative network, and a collaborative task processing queue is output; the collaborative task processing queue is analyzed to obtain collaborative edge devices, and the collaborative edge devices are visually identified in the edge collaborative network, and the visual identification results are output; the visual identification results within a preset period are collected, and the visual identification results are evaluated using a collaborative communication evaluation model, and N groups of collaborative edge devices are output; the intelligent gateway configures the communication protocols corresponding to the N groups of collaborative edge devices, and controls the data transmission within each group of collaborative edge devices, which effectively solves the technical problem that the existing technology has low data processing efficiency due to large data processing delays, further affecting user experience and system performance. It ensures that data transmission in the edge collaborative network is both efficient and secure, and further improves data processing efficiency.

[0055] Embodiment 2

[0056] Based on the same inventive concept as the edge collaborative communication control method based on the intelligent gateway in the aforementioned embodiment, the present application also provides an edge collaborative communication control system based on the intelligent gateway, please refer to the attached Figure 2 , the system comprising:

[0057] The network construction module 11 is used to deploy intelligent gateways and edge computing nodes to build an edge collaborative network, wherein each edge computing node is communicatively connected with an edge device.

[0058] The data set acquisition module 12 is used to acquire monitoring data sets of each edge device in the edge collaborative network.

[0059] The collaborative computing task identification module 13 is used to identify collaborative computing tasks for the monitoring data set based on the edge collaborative network and output a collaborative task processing queue.

[0060] The visualization identification module 14 is used to analyze the collaborative task processing queue, obtain collaborative edge devices, visually identify the collaborative edge devices in the edge collaborative network, and output visualization identification results.

[0061] The result evaluation module 15 is used to collect visual identification results within a preset period, evaluate the visual identification results using a collaborative communication evaluation model, and output N groups of collaborative edge devices.

[0062] The data transmission module 16 is used for the intelligent gateway to configure the communication protocols corresponding to the N groups of collaborative edge devices and control the data transmission within each group of collaborative edge devices.

[0063] Furthermore, the result evaluation module 15 in the system is also used for:

[0064] Statistics are performed on the visualization identification results to output L groups of collaborative edge devices; collaborative probability analysis is performed on the L groups of collaborative edge devices to obtain L groups of collaborative probabilities corresponding to the L groups of collaborative edge devices; collaborative edge devices with a probability greater than or equal to an expected probability are screened from the L groups of collaborative probabilities, and the screened M groups of collaborative edge devices are output; a collaborative communication evaluation model is trained, and the M groups of collaborative edge devices are evaluated using the collaborative communication evaluation model, and N groups of collaborative edge devices are output.

[0065] Furthermore, the system further comprises an evaluation index output module, wherein the evaluation index output module is used to:

[0066] Among them, the evaluation indicators of the collaborative communication evaluation model include collaborative utilization rate, collaborative expansion degree and collaborative sharing quality; the M groups of collaborative edge devices are evaluated using the collaborative communication evaluation model, and the M groups of communication evaluation indicators corresponding to the M groups of collaborative edge devices are output; based on the M groups of communication evaluation indicators, N groups of collaborative edge devices that are greater than the preset communication evaluation indicators are obtained.

[0067] Furthermore, the system further includes an edge device update module, and the edge device update module is used to:

[0068] According to the M groups of communication evaluation indicators, obtain the MN group of collaborative edge devices that is less than or equal to the preset communication evaluation indicators; generate a communication control backup library according to the MN group of collaborative edge devices; monitor the communication evaluation indicators of each group of collaborative edge devices in the communication control backup library in real time, and update the N groups of collaborative edge devices accordingly.

[0069] Furthermore, the system further comprises a resource constraint optimization module, and the resource constraint optimization module is used to:

[0070] Obtain resource configuration parameters of the intelligent gateway; establish a first resource constraint function based on the resource configuration parameters; the first resource constraint function performs resource constraint optimization according to the evaluation indicators output by the collaborative communication evaluation model, and outputs N groups of collaborative edge devices obtained by the optimization.

[0071] Furthermore, the system further comprises a resource fitting index determination module, and the resource occupancy index determination module is used to:

[0072] According to the collaborative communication evaluation model, output and initialize N groups of collaborative edge devices; predict N resource occupancy indicators of the initialized N groups of collaborative edge devices; fit the N resource occupancy indicators, output the resource occupancy fitting indicator, and judge whether the resource occupancy fitting indicator meets the constraint requirements of the first resource constraint function; if it does not meet the constraint requirements of the first resource constraint function, optimize according to the size of the communication evaluation indicator corresponding to the initialized N groups of collaborative edge devices, and obtain two rounds of N groups of collaborative edge devices until the constraint requirements of the first resource constraint function are met.

[0073] Furthermore, the data transmission module 16 in the system is also used for:

[0074] Identify the supported protocol type of each group of collaborative edge devices in the N groups of collaborative edge devices; if there is an unmatched collaborative edge device group, return empty; if there is a matching collaborative edge device group, obtain the matching communication protocol corresponding to the matching group collaborative edge device according to the supported protocol type, and the intelligent gateway performs collaborative communication configuration according to the matching communication protocol.

[0075] Furthermore, the system also includes a data upload module, which is used to control the data transmission within each group of collaborative edge devices, and then upload the data to the intelligent gateway after one of the edge devices completes the computing task.

[0076] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. Figure 1 The edge collaborative communication control method based on the intelligent gateway and the specific examples in the first embodiment are also applicable to the edge collaborative communication control system based on the intelligent gateway in the present embodiment. Through the above detailed description of the edge collaborative communication control method based on the intelligent gateway, those skilled in the art can clearly know the edge collaborative communication control system based on the intelligent gateway in the present embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0077] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0078] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. An edge collaborative communication control method based on an intelligent gateway, characterized in that: The method comprises: Deploy smart gateways and edge computing nodes to build an edge collaborative network, where each edge computing node communicates with edge devices; Collecting monitoring data sets of each edge device in the edge collaborative network; Based on the edge collaborative network, collaborative computing tasks are identified for the monitoring data set, and a collaborative task processing queue is output; Analyze the collaborative task processing queue, obtain collaborative edge devices, visually identify the collaborative edge devices in the edge collaborative network, and output the visual identification results; Collect visual identification results within a preset period, evaluate the visual identification results using a collaborative communication evaluation model, and output N groups of collaborative edge devices; The intelligent gateway configures the communication protocols corresponding to the N groups of collaborative edge devices to control data transmission within each group of collaborative edge devices; The visual identification result is evaluated by using a collaborative communication evaluation model, the method comprising: Perform statistics on the visual identification results and output L groups of collaborative edge devices; Performing a collaboration probability analysis on the L groups of collaborative edge devices to obtain L group collaboration probabilities corresponding to the L groups of collaborative edge devices; Selecting collaborative edge devices with a probability greater than or equal to an expected probability from the L groups of collaborative probabilities, and outputting the selected M groups of collaborative edge devices; A collaborative communication evaluation model is trained, the M groups of collaborative edge devices are evaluated using the collaborative communication evaluation model, and N groups of collaborative edge devices are output.

2. The method according to claim 1, characterized in that The collaborative communication evaluation model is used to evaluate the M groups of collaborative edge devices. include: The evaluation indicators of the collaborative communication evaluation model include collaborative utilization rate, collaborative expansion degree and collaborative sharing quality; Evaluate the M groups of collaborative edge devices using a collaborative communication evaluation model, and output M groups of communication evaluation indicators corresponding to the M groups of collaborative edge devices; According to the M groups of communication evaluation indicators, N groups of collaborative edge devices that are greater than preset communication evaluation indicators are obtained.

3. The method according to claim 2, characterized in that According to the M group communication evaluation index, obtaining the MN group collaborative edge device that is less than or equal to the preset communication evaluation index; Generate a communication control standby library according to the MN group collaborative edge device; The communication evaluation index of each group of collaborative edge devices in the communication control standby library is monitored in real time, and the N groups of collaborative edge devices are updated accordingly.

4. The method according to claim 1, characterized in that The collaborative communication evaluation model is used to evaluate the M groups of collaborative edge devices, and the method further includes: Obtain resource configuration parameters of the intelligent gateway; Establishing a first resource constraint function according to the resource configuration parameters; The first resource constraint function performs resource constraint optimization according to the evaluation index output by the collaborative communication evaluation model, and outputs N groups of collaborative edge devices obtained by the optimization.

5. The method according to claim 4, characterized in that The first resource constraint function performs resource constraint optimization according to the evaluation index output by the cooperative communication evaluation model, and the method includes: Outputting and initializing N groups of collaborative edge devices according to the collaborative communication evaluation model; Predicting N resource occupancy indicators of the initialized N groups of collaborative edge devices; Fit the N resource occupancy indicators, output the resource occupancy fitting indicator, and determine whether the resource occupancy fitting indicator meets the constraint requirements of the first resource constraint function. If it does not meet the constraint requirements of the first resource constraint function, optimize according to the size of the communication evaluation indicator corresponding to the initialized N groups of collaborative edge devices, and obtain two rounds of N groups of collaborative edge devices until the constraint requirements of the first resource constraint function are met.

6. The method according to claim 1, characterized in that After the intelligent gateway configures the communication protocol corresponding to the N groups of collaborative edge devices, it also includes: Identify a supported protocol type of each group of collaborative edge devices in the N groups of collaborative edge devices; If there is an unmatched collaborative edge device group, the return value is empty; If there is a matching collaborative edge device group, the matching communication protocol corresponding to the collaborative edge device in the matching group is obtained according to the supported protocol type, and the intelligent gateway performs collaborative communication configuration according to the matching communication protocol.

7. The method according to claim 1, characterized in that After controlling the data transmission within each group of collaborative edge devices, one of the edge devices completes the computing task and then uploads it to the smart gateway.

8. The edge collaborative communication control system based on intelligent gateway is characterized by: For implementing the steps of the method according to any one of claims 1 to 7, the system comprises: A network construction module, which is used to deploy intelligent gateways and edge computing nodes to build an edge collaborative network, wherein each edge computing node is communicatively connected with an edge device; A data set acquisition module, the data set acquisition module is used to collect monitoring data sets of each edge device in the edge collaborative network; A collaborative computing task identification module, the collaborative computing task identification module is used to identify collaborative computing tasks for the monitoring data set based on the edge collaborative network, and output a collaborative task processing queue; A visualization identification module, the visualization identification module is used to analyze the collaborative task processing queue, obtain collaborative edge devices, visually identify the collaborative edge devices in the edge collaborative network, and output a visualization identification result; A result evaluation module, which is used to collect visual identification results within a preset period, evaluate the visual identification results using a collaborative communication evaluation model, and output N groups of collaborative edge devices; A data transmission module, wherein the data transmission module is used for the intelligent gateway to configure the communication protocols corresponding to the N groups of collaborative edge devices and control the data transmission within each group of collaborative edge devices.

Images