Communication optimization method, system and equipment of electric energy meter and medium

Abstract

The invention provides a communication optimization method, system and equipment of an electric energy meter and a medium, and relates to the technical field of data processing, in the power supply process of a target power supply area, in the process that a communication node receives power consumption data of a communication node group, power consumption behavior analysis and malicious equipment identification are carried out through a safety verification module; and the electric energy meter communication cloud performs communication load dynamic monitoring and communication load dynamic scheduling of the communication nodes through cooperative operation of the load balancing module and the load scheduling module in the process of receiving the electric energy meter data uploaded by the communication node group transferred by the communication nodes. The technical problems that the data transmission reliability of the electric energy meter is insufficient and the communication safety of the electric energy meter is influenced due to the lack of an effective electric energy meter communication real-time monitoring technology in the prior art are solved. The technical effects of effectively improving the maintenance efficiency of the electric energy meter, the data transmission reliability of the electric energy meter and the communication operation safety of the electric energy meter are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a communication optimization method, system, device and medium for an electric energy meter. Background Art

[0002] In the prior art, the communication system of electric energy meters usually lacks effective real-time monitoring technology, which leads to insufficient reliability of data transmission of electric energy meters, thus affecting the communication security of the entire system. For example, in a large-scale smart grid system, thousands of electric energy meters are distributed in a wide power supply area, and these electric energy meters need to continuously upload data to the central system. However, due to the lack of real-time communication monitoring capabilities in the system, when an electric energy meter or communication node fails, it cannot be perceived and responded in time, which may cause data transmission interruption or delay, or even cause large-scale data loss.

[0003] For example, if a key communication node cannot operate normally due to overload, the meter data will not be transmitted to the cloud in time, resulting in the failure of the communication of the meter in the area. In addition, malicious devices may attack by forging data packets or tampering with communication protocols. Due to the lack of real-time security verification mechanism, the system cannot identify these potential threats in time, affecting the safe operation of the overall power grid.

[0004] Therefore, the lack of real-time monitoring and security verification technology not only reduces the reliability of electricity meter data transmission, but also aggravates communication security risks, seriously affecting the stability of the system and power supply management efficiency. Summary of the invention

[0005] The present application provides a communication optimization method, system, device and medium for an electric energy meter, which is used to solve the technical problem that there is a lack of effective real-time monitoring technology for electric energy meter communication in the prior art, resulting in insufficient reliability of electric energy meter data transmission and affecting the communication security of the electric energy meter.

[0006] In view of the above problems, the present application provides a communication optimization method, system, device and medium for an electric energy meter.

[0007] The first aspect of the present application provides a communication optimization method for an electric energy meter, the method comprising: constructing an electronic fence according to the operation and maintenance time limit of the electric energy meter, and dividing the target power supply area into K operation and maintenance areas; dividing multiple electric energy meters in the target power supply area into K communication node groups according to the distribution of electric energy meters in the target power supply area and the K operation and maintenance areas; configuring K communication nodes for the K communication node groups, and introducing K security verification modules in the K communication nodes, wherein the K security verification modules are constructed and generated using multiple historical communication behaviors of the multiple electric energy meters; pre-constructing an electric energy meter communication cloud, and completing the construction of a distributed communication architecture by connecting the electric energy meter communication cloud with the K communication nodes, wherein the The electric energy meter communication cloud is pre-configured with a load balancing module and a load scheduling module; during the power supply process in the target power supply area, the multiple electric energy meters are constrained by the K communication node groups, and transmit power consumption data to the electric energy meter communication cloud via the K communication nodes; in the process of the K communication nodes receiving the power consumption data of the K communication node groups, the K security verification modules are used to perform power consumption behavior analysis and malicious device identification; in the process of receiving the electric energy meter data uploaded by the K communication node groups transferred by the K communication nodes, the electric energy meter communication cloud performs dynamic monitoring and dynamic scheduling of communication loads of the K communication nodes through the coordinated operation of the load balancing module and the load scheduling module.

[0008] The second aspect of the present application provides a communication optimization system for an electric energy meter, the system comprising: a power supply area division unit, used to construct an electronic fence according to the operation and maintenance time limit of the electric energy meter, and divide the target power supply area into K operation and maintenance areas; a communication node grouping unit, used to divide multiple electric energy meters in the target power supply area into K communication node groups according to the distribution of electric energy meters in the target power supply area and the K operation and maintenance areas; a communication node configuration unit, used to configure K communication nodes for the K communication node groups, and introduce K security verification modules into the K communication nodes, wherein the K security verification modules are constructed and generated using multiple historical communication behaviors of the multiple electric energy meters; a communication architecture construction unit, used to pre-construct an electric energy meter communication cloud, and complete the construction of a distributed communication architecture by connecting the electric energy meter communication cloud with the K communication nodes. , wherein the electric energy meter communication cloud is pre-configured with a load balancing module and a load scheduling module; a communication transmission execution unit is used for, during the power supply process in the target power supply area, the multiple electric energy meters are constrained by the K communication node groups, and the power consumption data is communicated and transmitted to the electric energy meter communication cloud via the K communication nodes; a security verification execution unit is used for, during the process in which the K communication nodes receive the power consumption data of the K communication node groups, to perform power consumption behavior analysis and malicious device identification through the K security verification modules; a channel optimization execution unit is used for, during the process in which the electric energy meter communication cloud receives the electric energy meter data uploaded by the K communication node groups transferred by the K communication nodes, to perform dynamic communication load monitoring and dynamic communication load scheduling of the K communication nodes through the coordinated operation of the load balancing module and the load scheduling module.

[0009] In a third aspect of the present application, the present application provides an electronic device, comprising: a processor; a memory for storing instructions executable by the processor; wherein the processor is used to execute a communication optimization method for an electric energy meter provided in the present application.

[0010] In a fourth aspect of the present application, the present application provides a computer-readable storage medium storing a computer program, and the computer program is used to execute a communication optimization method for an electric energy meter provided by the present application.

[0011] One or more technical solutions provided in this application have at least the following technical effects or advantages: The method provided in the embodiment of the present application constructs an electronic fence according to the operation and maintenance time limit of the electric energy meter, and divides the target power supply area into K operation and maintenance areas; divides the multiple electric energy meters in the target power supply area into K communication node groups according to the distribution of the electric energy meters in the target power supply area and the K operation and maintenance areas; configures K communication nodes for the K communication node groups, and introduces K security verification modules into the K communication nodes, wherein the K security verification modules are constructed and generated using multiple historical communication behaviors of the multiple electric energy meters; pre-constructs an electric energy meter communication cloud, and completes the construction of a distributed communication architecture by connecting the electric energy meter communication cloud with the K communication nodes, wherein the electric energy meter communication cloud is pre-configured A load balancing module and a load scheduling module are provided; during the power supply process in the target power supply area, the multiple electric energy meters are constrained by the K communication node groups, and transmit the power consumption data to the electric energy meter communication cloud through the K communication nodes; during the process of the K communication nodes receiving the power consumption data of the K communication node groups, the K security verification modules are used to analyze the power consumption behavior and identify malicious devices; during the process of the electric energy meter communication cloud receiving the electric energy meter data uploaded by the K communication node groups transferred by the K communication nodes, the load balancing module and the load scheduling module are used to dynamically monitor the communication load of the K communication nodes and dynamically schedule the communication load. The technical effect of effectively improving the maintenance efficiency of the electric energy meter, the reliability of the data transmission of the electric energy meter and the safety of the communication operation of the electric energy meter is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A schematic flow chart of a communication optimization method for an electric energy meter provided in this application.

[0013] Figure 2 A schematic diagram of the structure of a communication optimization system for an electric energy meter provided in this application.

[0014] Figure 3 A schematic diagram of the structure of an electronic device provided in this application.

[0015] Explanation of the accompanying drawings: power supply area division unit 11, communication node grouping unit 12, communication node configuration unit 13, communication architecture construction unit 14, communication transmission execution unit 15, security verification execution unit 16, channel optimization execution unit 17, processor 21, memory 22, input device 23, output device 24. DETAILED DESCRIPTION

[0016] The present application provides a communication optimization method, system, device and medium for electric energy meters, which are used to solve the technical problem that there is a lack of effective real-time monitoring technology for electric energy meter communication in the prior art, resulting in insufficient reliability of electric energy meter data transmission and affecting the communication safety of electric energy meters. The technical effect of effectively improving the maintenance efficiency of electric energy meters, the reliability of electric energy meter data transmission and the safety of electric energy meter communication operation is achieved.

[0017] Below, the technical solutions in the present invention 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 invention, rather than all of the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments described herein. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. It should also be noted that, for the convenience of description, only the parts related to the present invention are shown in the accompanying drawings, rather than all of them.

[0018] Embodiment 1, as Figure 1 As shown, the present application provides a communication optimization method for an electric energy meter, the method comprising: A100: Build an electronic fence based on the operation and maintenance time limit of the electricity meter and divide the target power supply area into K operation and maintenance areas.

[0019] In one embodiment, an electronic fence is constructed according to the operation and maintenance time limit of the electric energy meter, and the target power supply area is divided into K operation and maintenance areas. Step A100 of the method provided in the present application includes: A110: Interactively obtain historical operation and maintenance information, and calculate the average fault operation and maintenance time based on the historical operation and maintenance information to obtain the operation and maintenance time limit of the electric energy meter.

[0020] A120: Interactively obtain the regional transportation network of the target power supply area.

[0021] A130: Perform data fusion on the distribution of the electric energy meters and the regional transportation network to obtain an operation and maintenance transportation model.

[0022] A140: Taking the operation and maintenance time limit of the electric energy meter as a constraint, fitting the operation and maintenance route in the operation and maintenance traffic model, and constructing K electronic fences based on the fitting results to divide the target power supply area into K operation and maintenance areas.

[0023] In one embodiment, data fusion is performed on the distribution of electric energy meters and the regional transportation network to obtain an operation and maintenance transportation model. Step A130 of the method provided in this application includes: A131: Construct a transportation space model based on the regional transportation network.

[0024] A132: Locate the coordinates of multiple operation and maintenance nodes according to the distribution of the electric energy meters in the traffic space model.

[0025] A133: Locate, in the traffic space model, a plurality of traffic network nodes that are closest to the coordinates of the plurality of operation and maintenance nodes in a straight line, and use the plurality of traffic network nodes as a plurality of traffic operation and maintenance nodes.

[0026] A134: Identify the multiple traffic operation and maintenance nodes in the traffic space model to obtain the operation and maintenance traffic model.

[0027] In one embodiment, taking the maintenance time limit of the electric energy meter as a constraint, performing maintenance route fitting in the maintenance traffic model, and constructing K electronic fences based on the fitting results, the method step A140 provided in the present application includes: A141: Enumerate the plurality of traffic operation and maintenance nodes by permutations and combinations to obtain H groups of operation and maintenance node combinations.

[0028] A142: Calculate the traffic time of the H groups of operation and maintenance node combinations based on the operation and maintenance traffic model to obtain H operation and maintenance traffic time.

[0029] A143: Taking the multiple electric energy meters as graph nodes, topologically connecting the multiple electric energy meters according to the H groups of operation and maintenance node combinations, and assigning topological connection weights according to the H operation and maintenance traffic times to obtain an operation and maintenance weighted undirected graph.

[0030] A144: Split the operation and maintenance weighted undirected graph based on the operation and maintenance time limit of the electric energy meter as a constraint to obtain the K electronic fences.

[0031] Specifically, in this embodiment, historical fault and maintenance data related to the electric energy meter are collected through existing operation and maintenance logs, including time records from the discovery of the electric energy meter fault to the actual completion of the repair. The key contents of the data include: operation and maintenance start time, operation and maintenance completion time, geographical location where the fault occurred, road traffic conditions, etc.

[0032] The time of each operation and maintenance is extracted from the historical data of the operation and maintenance log, and the average operation and maintenance time is calculated as the operation and maintenance time limit of the electric energy meter, which is used for subsequent regional division and route fitting. The definition of the operation and maintenance time limit ensures that when the electric energy meter fails (the electric energy meter is maliciously invaded), the operation and maintenance personnel can complete the repair task within the specified time.

[0033] The regional transportation network of the target power supply area is obtained through a transportation management system or a geographic information system (GIS), wherein the regional transportation network is complete transportation network information within the target power supply area.

[0034] Traffic information is structured according to the regional traffic network, and a traffic node and traffic path model is constructed. Each node represents a geographical location or a road intersection, and an edge represents a road. The weight of the edge is assigned based on traffic conditions (such as distance, speed limit), and a traffic space model is obtained.

[0035] The electric energy meter distribution is multiple location information of the multiple electric energy meters in the target power supply area. According to the distribution of the electric energy meters in the traffic space model, multiple operation and maintenance node coordinates are located, and the operation and maintenance node coordinates are the actual geographical locations of the electric energy meters.

[0036] In the traffic space model, multiple traffic network nodes that are closest to the straight-line distances of the multiple operation and maintenance node coordinates are located, and the multiple traffic network nodes are used as multiple traffic operation and maintenance nodes, and the multiple traffic operation and maintenance nodes are used as traffic operation and maintenance nodes of multiple electric energy meters to ensure the accuracy of the operation and maintenance routes.

[0037] The plurality of traffic operation and maintenance nodes are identified in the traffic space model to obtain the operation and maintenance traffic model, which will serve as the basis for subsequent route planning and electronic fence construction.

[0038] The multiple traffic operation and maintenance nodes are enumerated in permutations and combinations to obtain all possible operation and maintenance node combinations, totaling H groups of operation and maintenance node combinations. The H groups of operation and maintenance node combinations will be used to calculate the traffic time of different routes. The traffic time calculation of the H groups of operation and maintenance node combinations is performed in the operation and maintenance traffic model to obtain H operation and maintenance traffic time costs.

[0039] The multiple electric energy meters are used as graph nodes, topological connections are performed on the multiple electric energy meters according to the H groups of operation and maintenance node combinations, and topological connection weights are assigned according to the H operation and maintenance traffic times to obtain an operation and maintenance weighted undirected graph.

[0040] The specific logic of segmenting the operation and maintenance weighted undirected graph with the operation and maintenance time limit of the electric energy meter as a constraint is that if the weight assignment (operation and maintenance traffic time) of the topological connection between two graph nodes exceeds the operation and maintenance time limit of the electric energy meter, then this topological connection cannot exist as a valid connection.

[0041] In this embodiment, the specific method of using the power meter operation and maintenance time limit as a constraint to partition the operation and maintenance weighted undirected graph to obtain the K electronic fences is to use the power meter operation and maintenance time limit to traverse the operation and maintenance weighted undirected graph, remove the topological connections that do not meet the operation and maintenance weighted undirected graph, complete the partitioning of the operation and maintenance weighted undirected graph, and obtain K smaller partitioned weighted undirected graphs. Among them, the topological connection weights of the K partitioned weighted undirected graphs are all less than the power meter operation and maintenance realization.

[0042] Then, according to the graph nodes of the K segmented weighted undirected graphs, the multiple traffic operation and maintenance nodes are divided into K groups of traffic operation and maintenance nodes, and then the top view of the model is segmented in the traffic operation and maintenance model according to the K groups of traffic operation and maintenance nodes to obtain the K electronic fences; The target power supply area is divided according to the traffic route coverage of the K electronic fences to obtain the K operation and maintenance areas.

[0043] This embodiment constructs a traffic model for operation and maintenance time limits by integrating historical operation and maintenance data, regional traffic networks, and the distribution of electric energy meters. It also calculates the combined time consumption of multiple operation and maintenance nodes to ultimately form electronic fences for multiple operation and maintenance areas, thereby achieving the technical effect of ensuring that the electric energy meters in each area can receive operation and maintenance processing within the specified time when a failure occurs, thereby improving the efficiency and responsiveness of operation and maintenance.

[0044] A200: Divide a plurality of electric energy meters in the target power supply area into K communication node groups according to the distribution of electric energy meters in the target power supply area and the K operation and maintenance areas.

[0045] Specifically, in this embodiment, according to the division of the K operation and maintenance areas, the electric energy meters in each area are grouped for communication to obtain the K communication node groups, so that each operation and maintenance area corresponds to a communication node group, and the electric energy meters in each operation and maintenance area transmit data through the same communication node.

[0046] The electric energy meters in each operation and maintenance area will be mapped to the communication node group to which it belongs. In other words, a communication node group is responsible for communicating with multiple electric energy meters, and the distribution of electric energy meters in this communication node group is restricted by the boundaries of the operation and maintenance area.

[0047] A300: configuring K communication nodes for the K communication node groups, and introducing K security verification modules into the K communication nodes, wherein the K security verification modules are constructed and generated using multiple historical communication behaviors of the multiple electric energy meters.

[0048] In one embodiment, K communication nodes are configured for the K communication node groups, and K security verification modules are introduced into the K communication nodes. Step A300 of the method provided by the present application includes: A310: Aggregate the multiple electric energy meters according to device type to obtain M types of sample electric energy meters.

[0049] A320: Update the data groups of the multiple historical communication behaviors according to the M types of sample electric energy meters to obtain M sample communication behaviors.

[0050] A330: construct M optimized safety verification models according to the M sample communication behaviors, and obtain a safety verification model library by associating and storing the M types of sample electric energy meters and the M optimized safety verification models.

[0051] A340: Calling models from the security verification model library according to the K communication node groups to obtain K groups of optimized security verification models.

[0052] A350: Connect the K groups of optimized security verification models in parallel to obtain the K security verification modules, and configure the K security verification modules at the K communication nodes.

[0053] In one embodiment, M optimized safety verification models are constructed according to the M sample communication behaviors, and a safety verification model library is obtained by associating and storing the M sample electric energy meters with the M optimized safety verification models. The method step A330 provided in the present application includes: A331: Obtain M sample annotated behavior data by marking the M sample communication behaviors as malicious behaviors.

[0054] A332: Divide the M sample labeled behavior data based on the time series, and serialize the M sample labeled behavior data based on a predetermined time window to obtain M security verification training data.

[0055] A333: Build a standard security verification model based on the LSTM model, and use the M security verification training data to optimize the malicious behavior recognition accuracy of the standard security verification model to obtain the M optimized security verification models.

[0056] A334: The M types of sample electric energy meters and the M optimized safety verification models are associated and stored to obtain the safety verification model library.

[0057] Specifically, in this embodiment, multiple electric energy meters in the target power supply area are classified according to different types or models of electric energy meters to obtain M types of sample electric energy meters. Each sample electric energy meter represents a type of electric energy meter with similar characteristics (for example, similar communication protocols, the same hardware model, etc.).

[0058] Data is extracted from the multiple electric energy meters to obtain multiple historical communication behaviors, where the historical communication behaviors include historical electricity consumption data, communication frequency, data packet size, transmission protocol, etc. of the electric energy meters.

[0059] According to the M types of sample electric energy meters, multiple historical communication behaviors of multiple electric energy meters are grouped and updated to ensure that each sample electric energy meter has a corresponding historical communication data set to obtain M sample communication behaviors.

[0060] M optimized safety verification models are constructed according to the M sample communication behaviors, and a safety verification model library is obtained by associating and storing the M sample electric energy meters with the M optimized safety verification models. The specific construction process of the safety verification model library is as follows: By labeling the M sample communication behaviors with malicious behaviors, specific malicious behaviors include data anomalies, repeated reporting, communication interruption, forged data, etc., M sample labeled behavior data are obtained. The M sample labeled behavior data provide supervision data for distinguishing normal and abnormal behaviors for subsequent model training.

[0061] The M sample labeled behavior data are segmented based on the time series to form continuous behavior segments, which are convenient for time series analysis. Then, based on a predetermined time window (such as one day or one hour), the segmented data segments of the M sample labeled behavior data are serialized to obtain M security verification training data (time series data). The M security verification training data are used to train the model to ensure that the model can recognize communication patterns that change over time.

[0062] A standard security verification model is constructed using a long short-term memory network (LSTM). LSTM is good at processing time series data and can capture the time dependency of the communication behavior of the electric energy meter. The M security verification training data (serialized training data) are used as training data, and the conventional LSTM model training method is used to optimize the malicious behavior recognition accuracy of the LSTM model. By continuously iterating the model, the accuracy of identifying malicious behaviors in different scenarios is improved, and the M optimized security verification models are obtained.

[0063] The M types of sample electricity meters and the M optimized security verification models are associated and stored to obtain the security verification model library. Each communication node group can subsequently call the corresponding security verification model from this model library when needed to perform real-time monitoring of communication behaviors and malicious identification.

[0064] According to the K communication node groups and the types of electric energy meters they manage, corresponding M optimized security verification models are called from the security verification model library to obtain K groups of optimized security verification models to ensure that the communication nodes use the most appropriate models when identifying malicious behaviors.

[0065] The K groups of optimized safety verification models are connected in parallel to obtain the K safety verification modules, and the K safety verification modules are deployed on the K communication nodes. Each communication node will be responsible for performing safety verification on the electric energy meters within its management scope and monitoring the communication behavior in real time.

[0066] This embodiment achieves the goal that each communication node can quickly and specifically identify malicious behavior when managing the communication behavior of its electric energy meter, thereby ensuring the security and stability of the entire communication network when conducting electric energy meter communications.

[0067] A400: pre-build an electric energy meter communication cloud, and complete the construction of a distributed communication architecture by connecting the electric energy meter communication cloud with K communication nodes, wherein the electric energy meter communication cloud is pre-configured with a load balancing module and a load scheduling module.

[0068] Specifically, in this embodiment, an energy meter communication cloud is constructed, which consists of K server nodes, has real-time data processing and historical data analysis functions, and supports parallel processing of energy meter data in K operation and maintenance areas. K communication nodes are connected to the energy meter communication cloud through a distributed architecture, and each node is responsible for the transmission of energy meter data in its area, and a secure communication protocol is used to ensure data security.

[0069] The electric energy meter communication cloud is pre-configured with a load balancing module and a load scheduling module. The load balancing module is responsible for real-time monitoring of the load status of the communication nodes and grouping the nodes to ensure traffic balance. The load scheduling module adjusts the configuration of the electric energy meter and the node according to the load distribution results, optimizes the overall performance of the system, and performs synchronous updates of the security verification function.

[0070] This embodiment will provide a detailed description of the specific application of the load balancing module and the load scheduling module in the subsequent description.

[0071] A500: During the power supply process in the target power supply area, the multiple electric energy meters are constrained by the K communication node groups and transmit power consumption data to the electric energy meter communication cloud via the K communication nodes.

[0072] A600: When the K communication nodes receive the power consumption data of the K communication node groups, the K security verification modules perform power consumption behavior analysis and malicious device identification.

[0073] Specifically, in this embodiment, during the power supply process in the target power supply area, the electric energy meter will periodically collect power consumption data, and transmit the data to the cloud through the communication node group to which it belongs. Specifically, the multiple electric energy meters are constrained by the K communication node groups, and communicate the power consumption data to the electric energy meter communication cloud via the K communication nodes.

[0074] Each communication node is responsible for receiving the electricity consumption data of the electric energy meters in the node group, performing preliminary processing or packaging of the data, and transmitting it to the electric energy meter communication cloud through a secure communication link. Since the K communication nodes are distributed in different operation and maintenance areas, the efficiency and redundancy of the data transmission process are ensured, avoiding the overload and bottleneck problems of a single node.

[0075] In the process of the K communication nodes receiving the power consumption data of the K communication node groups, the K security verification modules are used to analyze the power consumption behavior and identify malicious devices. These security verification modules are configured in A300. Each security verification module uses multiple optimized security verification models constructed for multiple historical communication behaviors of multiple electric energy meters in an operation and maintenance area. The multiple optimized security verification models connected in parallel in the security verification module determine whether multiple electric energy meters in an operation and maintenance area meet the expected normal power consumption behavior by analyzing the data reporting frequency, power consumption mode, power change and other characteristics of the corresponding multiple electric energy meters.

[0076] If the data is found to be inconsistent with historical behavior patterns, or there are sudden fluctuations in electricity consumption, the security verification module will mark these behaviors as suspicious, intercept the data communication upload behavior of the corresponding electricity meter, mark the corresponding electricity meter as a malicious device, and generate an operation and maintenance prompt and send it back to the operation and maintenance area where the electricity meter is located, reminding the operation and maintenance personnel in the corresponding operation and maintenance area to drive to the location of the electricity meter for on-site operation and maintenance.

[0077] A700: When receiving the electric energy meter data uploaded by the K communication node groups relayed by the K communication nodes, the electric energy meter communication cloud performs dynamic monitoring and dynamic scheduling of the communication loads of the K communication nodes through the coordinated operation of the load balancing module and the load scheduling module.

[0078] In one embodiment, the electric energy meter communication cloud receives the electric energy meter data uploaded by the K communication node groups transferred by the K communication nodes, and dynamically monitors and dynamically schedules the communication loads of the K communication nodes through the coordinated operation of the load balancing module and the load scheduling module. The method step A700 provided in this application includes: A710: Predefine operating load indicators and operating load limits.

[0079] A720: The load balancing module collects the operating load performance of the K communication nodes in real time based on the operating load indicators to obtain K real-time load performances, wherein the operating load indicators include CPU usage, memory occupancy, network bandwidth, network delay and data packet loss rate.

[0080] A730: Traversing the K real-time load performances based on the operating load limit, and dividing the K communication nodes into an overload group and a normal load group.

[0081] A740: The load scheduling module performs load scheduling and allocation between the overload group and the normal load group based on the operating load limit as a constraint to obtain a load scheduling and allocation result.

[0082] A750: Update the configuration relationship between the multiple electric energy meters and the K communication nodes according to the load scheduling and allocation result to obtain K updated node groups.

[0083] A760: Call the model in the security verification model library according to the K update node groups, and update the verification function of the K security verification modules.

[0084] Specifically, in this embodiment, the operation load indicators are predefined, and the operation load indicators include CPU usage, memory occupancy, network bandwidth, network delay and data packet loss rate.

[0085] Predefined operating load limits, the operating load limits include predefined limit values ​​for each of the operating load indicators as the upper limit of the node load. When any load indicator of a communication node exceeds the corresponding limit value, the load balancing module will identify the node as "overloaded" and its load needs to be adjusted.

[0086] The load balancing module collects the operating load performance of the K communication nodes in real time based on the operating load index, ensures that the load status of each node is accurately tracked, and obtains K real-time load performances. Each real-time load performance includes real-time data such as the current CPU, memory, bandwidth, delay, etc. of each communication node, which is used as the basis for the subsequent load scheduling module to perform load balancing and scheduling.

[0087] Based on the operating load limit, the K real-time load performances are traversed to determine which nodes have loads exceeding a preset limit value. Nodes exceeding the limit value are classified into an overload group, and other nodes operating normally are classified into a normal load group.

[0088] The load scheduling module is responsible for redistributing the load between the overloaded group and the normal load group. It uses the operating load limit of the node as a constraint to ensure that the traffic is migrated from the overloaded node to the node with lighter load, alleviate the overload problem, and obtain the load scheduling distribution result, which ensures that the load of each node is within a reasonable range, avoiding the overload of a node and causing the system performance to deteriorate.

[0089] According to the results of the load scheduling module, the configuration relationship between each electric energy meter and the communication node is updated. Specifically, the data transmission of some electric energy meters will be transferred from the overloaded communication nodes to the communication nodes with lighter loads. The updated configuration makes the system more reasonable in the distribution of communication traffic, avoiding some nodes from bearing too much data traffic.

[0090] According to the change of the communication node of the electric energy meter communication transmission application, the electric energy meter connected by the original K communication nodes is updated to obtain K update node groups. The K update node groups can ensure a more balanced load and improve the overall performance and stability of the system.

[0091] After the K communication node groups are updated, the load scheduling module will re-call the security verification model library and load or update the corresponding security verification module for each updated node group to ensure that data verification and malicious behavior detection remain accurate and efficient.

[0092] In this embodiment, the load balancing module is responsible for real-time monitoring of the load status of each communication node and collecting the performance data of the node such as CPU, memory, network bandwidth, etc.; while the load scheduling module performs dynamic scheduling between overload and normal load groups to relieve the load pressure of the node. According to the scheduling results, the configuration relationship between the electric energy meter and the communication node is dynamically updated to form a new communication node group, and on this basis, the security verification model library is called to update and optimize the verification function of the communication node, achieving the technical effect of ensuring the security and stability of the distributed communication architecture.

[0093] This embodiment achieves significant improvements in the performance, efficiency, safety, and stability of the entire electric energy meter communication system by reasonably dividing the operation and maintenance areas, optimizing the configuration of communication nodes, introducing a security verification module, and cloud-based load balancing and scheduling. This series of optimization measures has effectively improved the maintenance efficiency of the electric energy meter, the reliability of data transmission, and the operational safety of the overall system, ensuring the technical effect of intelligent management and rapid response of the power supply area.

[0094] Embodiment 2, based on the same inventive concept as the communication optimization method of an electric energy meter in the above embodiment, Figure 2 As shown, the present application provides a communication optimization system for an electric energy meter, wherein the system comprises: The power supply area division unit 11 is used to construct an electronic fence according to the operation and maintenance time limit of the electric energy meter, and divide the target power supply area into K operation and maintenance areas.

[0095] The communication node grouping unit 12 is used to group multiple electric energy meters in the target power supply area into K communication node groups according to the distribution of electric energy meters in the target power supply area and the K operation and maintenance areas.

[0096] The communication node configuration unit 13 is used to configure K communication nodes for the K communication node groups and introduce K security verification modules into the K communication nodes, wherein the K security verification modules are constructed and generated using multiple historical communication behaviors of the multiple electric energy meters.

[0097] The communication architecture construction unit 14 is used to pre-construct an electric energy meter communication cloud, and complete the construction of a distributed communication architecture by connecting the electric energy meter communication cloud with K communication nodes, wherein the electric energy meter communication cloud is pre-configured with a load balancing module and a load scheduling module.

[0098] The communication transmission execution unit 15 is used for, during the power supply process in the target power supply area, the multiple electric energy meters are constrained by the K communication node groups and transmit power consumption data to the electric energy meter communication cloud via the K communication nodes.

[0099] The security verification execution unit 16 is used to perform power usage behavior analysis and malicious device identification through the K security verification modules during the process in which the K communication nodes receive the power usage data of the K communication node groups.

[0100] The channel optimization execution unit 17 is used for the electric energy meter communication cloud to dynamically monitor and dynamically schedule the communication load of the K communication nodes through the coordinated operation of the load balancing module and the load scheduling module during the process of receiving the electric energy meter data uploaded by the K communication node groups relayed by the K communication nodes.

[0101] In one embodiment, the power supply area division unit 11 includes: Interactively obtain historical operation and maintenance information, and calculate the mean fault operation and maintenance time based on the historical operation and maintenance information to obtain the operation and maintenance time limit of the electric energy meter; interactively obtain the regional transportation network of the target power supply area; perform data fusion on the distribution of the electric energy meters and the regional transportation network to obtain an operation and maintenance traffic model; take the operation and maintenance time limit of the electric energy meter as a constraint, perform operation and maintenance route fitting in the operation and maintenance traffic model, and construct K electronic fences based on the fitting results to divide the target power supply area into K operation and maintenance areas.

[0102] In one embodiment, the power supply area division unit 11 includes: A traffic space model is constructed according to the regional traffic network; multiple operation and maintenance node coordinates are located according to the distribution of the electric energy meters in the traffic space model; multiple traffic network nodes that are closest to the multiple operation and maintenance node coordinates in a straight line are located in the traffic space model, and the multiple traffic network nodes are used as multiple traffic operation and maintenance nodes; the multiple traffic operation and maintenance nodes are identified in the traffic space model to obtain the operation and maintenance traffic model.

[0103] In one embodiment, the power supply area division unit 11 includes: The multiple traffic operation and maintenance nodes are enumerated in permutations and combinations to obtain H groups of operation and maintenance node combinations; the traffic time consumption of the H groups of operation and maintenance node combinations is calculated based on the operation and maintenance traffic model to obtain H operation and maintenance traffic time consumptions; the multiple electric energy meters are used as graph nodes, the multiple electric energy meters are topologically connected according to the H groups of operation and maintenance node combinations, and topological connection weights are assigned according to the H operation and maintenance traffic time consumptions to obtain an operation and maintenance weighted undirected graph; the operation and maintenance weighted undirected graph is divided according to the operation and maintenance time limit of the electric energy meter as a constraint to obtain the K electronic fences.

[0104] In one embodiment, the communication node configuration unit 13 includes: Aggregate the multiple electric energy meters according to device type to obtain M types of sample electric energy meters; update the multiple historical communication behaviors according to the M types of sample electric energy meters to obtain M sample communication behaviors; construct M optimized safety verification models according to the M sample communication behaviors, and obtain a safety verification model library by associating and storing the M types of sample electric energy meters and the M optimized safety verification models; call models from the safety verification model library according to the K communication node groups to obtain K groups of optimized safety verification models; connect the K groups of optimized safety verification models in parallel to obtain the K safety verification modules, and configure the K safety verification modules at the K communication nodes.

[0105] In one embodiment, the communication node configuration unit 13 includes: By labeling the M sample communication behaviors as malicious behaviors, M sample labeled behavior data are obtained; the M sample labeled behavior data are segmented based on time series, and the M sample labeled behavior data are serialized based on a predetermined time window to obtain M security verification training data; a standard security verification model is constructed based on an LSTM model, and the malicious behavior recognition accuracy of the standard security verification model is optimized using the M security verification training data to obtain the M optimized security verification models; the M types of sample electricity meters and the M optimized security verification models are associated and stored to obtain the security verification model library.

[0106] In one embodiment, the channel optimization execution unit 17 includes: Predefine operating load indicators and operating load limits; the load balancing module collects operating load performance of the K communication nodes in real time based on the operating load indicators to obtain K real-time load performances, wherein the operating load indicators include CPU usage, memory occupancy, network bandwidth, network delay and data packet loss rate; traverse the K real-time load performances based on the operating load limit, and divide the K communication nodes into an overload group and a normal load group; the load scheduling module performs inter-group load scheduling and allocation between the overload group and the normal load group with the operating load limit as a constraint to obtain a load scheduling and allocation result; according to the load scheduling and allocation result, update the configuration relationship between the multiple electric energy meters and the K communication nodes to obtain K updated node groups; according to the K updated node groups, perform model calls in the safety verification model library to update the verification function of the K safety verification modules.

[0107] Embodiment 3, based on the same inventive concept as the communication optimization method of an electric energy meter in the above embodiment, this embodiment provides an electronic device, Figure 3 The schematic diagram of the structure of the electronic device provided in the third embodiment of the present invention shows a block diagram of an exemplary electronic device suitable for implementing the implementation mode of the present invention. Figure 3 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention. Figure 3 As shown, the electronic device includes a processor 21, a memory 22, an input device 23 and an output device 24; the number of processors 21 in the electronic device can be one or more. Figure 3 Taking a processor 21 as an example, the processor 21, memory 22, input device 23 and output device 24 in the electronic device can be connected through a bus or other means. Figure 3 The example of connecting through bus is taken in the following.

[0108] Embodiment 4, based on the same inventive concept as the communication optimization method of an electric energy meter in the above-mentioned embodiment, this embodiment provides a computer-readable storage medium, which can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the communication optimization method of an electric energy meter in the embodiment of the present application. The processor executes various functional applications and data processing of the computer device by running the software programs, instructions and modules stored in the memory, that is, realizing the communication optimization method of the electric energy meter mentioned above.

[0109] Any of the methods or steps described above may be stored as computer instructions or programs in various types of computer memories, and the computer instructions or programs may be recognized by various types of computer processors to implement any of the methods or steps described above.

[0110] Based on the above specific embodiments of the present invention, any improvements and modifications made to the present invention by those skilled in the art without departing from the principles of the present invention shall fall within the patent protection scope of the present invention.

Claims

1. A communication optimization method for an electric energy meter, characterized in that: The method comprises: Build an electronic fence based on the operation and maintenance time limit of the electric energy meter and divide the target power supply area into K operation and maintenance areas; According to the distribution of electric energy meters in the target power supply area and the K operation and maintenance areas, multiple electric energy meters in the target power supply area are divided into K communication node groups; configuring K communication nodes for the K communication node groups, and introducing K security verification modules into the K communication nodes, wherein the K security verification modules are constructed and generated by using a plurality of historical communication behaviors of the plurality of electric energy meters; Pre-constructing an electric energy meter communication cloud, and completing the construction of a distributed communication architecture by connecting the electric energy meter communication cloud with K communication nodes, wherein the electric energy meter communication cloud is pre-configured with a load balancing module and a load scheduling module; During the power supply process in the target power supply area, the multiple electric energy meters are constrained by the K communication node groups and transmit power consumption data to the electric energy meter communication cloud via the K communication nodes; During the process of the K communication nodes receiving the power consumption data of the K communication node groups, the K security verification modules perform power consumption behavior analysis and malicious device identification; In the process of receiving the electric energy meter data uploaded by the K communication node groups relayed by the K communication nodes, the electric energy meter communication cloud performs dynamic monitoring and dynamic scheduling of the communication loads of the K communication nodes through the coordinated operation of the load balancing module and the load scheduling module.

2. A communication optimization method for an electric energy meter as claimed in claim 1, characterized in that: An electronic fence is constructed according to the operation and maintenance time limit of the electric energy meter, and the target power supply area is divided into K operation and maintenance areas. The method includes: Interactively obtain historical operation and maintenance information, and calculate the mean value of fault operation and maintenance time based on the historical operation and maintenance information to obtain the operation and maintenance time limit of the electric energy meter; Interactively obtain the regional transportation network of the target power supply area; Performing data fusion on the distribution of the electric energy meters and the regional transportation network to obtain an operation and maintenance transportation model; Taking the maintenance time limit of the electric energy meter as a constraint, the maintenance route is fitted in the maintenance traffic model, and K electronic fences are constructed based on the fitting result to divide the target power supply area into K maintenance areas.

3. A communication optimization method for an electric energy meter as claimed in claim 2, characterized in that: Data fusion is performed on the electric energy meter distribution and the regional transportation network to obtain an operation and maintenance transportation model. The method includes: constructing a transportation space model according to the regional transportation network; Locating coordinates of multiple operation and maintenance nodes according to the distribution of the electric energy meters in the traffic space model; Locate a plurality of traffic network nodes in the traffic space model that are closest to the coordinates of the plurality of operation and maintenance nodes in a straight line, and use the plurality of traffic network nodes as a plurality of traffic operation and maintenance nodes; The plurality of traffic operation and maintenance nodes are identified in the traffic space model to obtain the operation and maintenance traffic model.

4. A communication optimization method for an electric energy meter as claimed in claim 3, characterized in that: Taking the maintenance time limit of the electric energy meter as a constraint, fitting the maintenance route in the maintenance traffic model, and constructing K electronic fences based on the fitting results, the method includes: Enumerate the plurality of traffic operation and maintenance nodes by permutations and combinations to obtain H groups of operation and maintenance node combinations; Calculate the traffic time of the H groups of operation and maintenance node combinations based on the operation and maintenance traffic model to obtain H operation and maintenance traffic time; Taking the multiple electric energy meters as graph nodes, topologically connecting the multiple electric energy meters according to the H groups of operation and maintenance node combinations, and assigning topological connection weights according to the H operation and maintenance traffic times, to obtain an operation and maintenance weighted undirected graph; The operation and maintenance weighted undirected graph is segmented based on the operation and maintenance time limit of the electric energy meter as a constraint to obtain the K electronic fences.

5. The communication optimization method of an electric energy meter according to claim 1, characterized in that: K communication nodes are configured for the K communication node groups, and K security verification modules are introduced into the K communication nodes. The method includes: Aggregating the multiple electric energy meters according to device types to obtain M types of sample electric energy meters; According to the M types of sample electric energy meters, the data groups of the multiple historical communication behaviors are updated to obtain M sample communication behaviors; Constructing M optimized safety verification models according to the M sample communication behaviors, and obtaining a safety verification model library by associating and storing the M types of sample electric energy meters and the M optimized safety verification models; Calling a model from the security verification model library according to the K communication node groups to obtain K groups of optimized security verification models; The K groups of optimized security verification models are connected in parallel to obtain the K security verification modules, and the K security verification modules are configured at the K communication nodes.

6. A communication optimization method for an electric energy meter as claimed in claim 5, characterized in that: M optimized safety verification models are constructed according to the M sample communication behaviors, and a safety verification model library is obtained by associating and storing the M types of sample electric energy meters and the M optimized safety verification models. The method includes: By marking the M sample communication behaviors as malicious behaviors, M sample marked behavior data are obtained; Segment the M sample annotated behavior data based on a time series, and serialize the M sample annotated behavior data based on a predetermined time window to obtain M security verification training data; Building a standard security verification model based on the LSTM model, and optimizing the malicious behavior recognition accuracy of the standard security verification model using the M security verification training data to obtain the M optimized security verification models; The M types of sample electric energy meters and the M optimized safety verification models are associated and stored to obtain the safety verification model library.

7. The communication optimization method of an electric energy meter according to claim 5, characterized in that: The electric energy meter communication cloud receives the electric energy meter data uploaded by the K communication node groups and forwarded by the K communication nodes, and dynamically monitors and dynamically schedules the communication loads of the K communication nodes through the coordinated operation of the load balancing module and the load scheduling module. The method includes: Predefine operating load indicators and operating load limits; The load balancing module collects the operation load performance of the K communication nodes in real time based on the operation load index to obtain K real-time load performances, wherein the operation load index includes CPU usage, memory occupancy, network bandwidth, network delay and data packet loss rate; Traversing the K real-time load performances based on the operating load limit, dividing the K communication nodes into an overload group and a normal load group; The load scheduling module performs load scheduling and allocation between the overload group and the normal load group based on the operating load limit as a constraint to obtain a load scheduling and allocation result; According to the load scheduling and allocation result, the configuration relationship between the plurality of electric energy meters and the K communication nodes is updated to obtain K update node groups; Model calls are made in the security verification model library according to the K update node groups, and verification functions of the K security verification modules are updated.

8. A communication optimization system for an electric energy meter, characterized in that: A communication optimization method for an electric energy meter according to any one of claims 1 to 7, the system comprising: The power supply area division unit is used to construct an electronic fence according to the operation and maintenance time limit of the electric energy meter, and divide the target power supply area into K operation and maintenance areas; a communication node grouping unit, configured to group a plurality of electric energy meters in the target power supply area into K communication node groups according to the distribution of electric energy meters in the target power supply area and the K operation and maintenance areas; A communication node configuration unit, configured to configure K communication nodes for the K communication node groups, and introduce K security verification modules into the K communication nodes, wherein the K security verification modules are constructed and generated using a plurality of historical communication behaviors of the plurality of electric energy meters; A communication architecture construction unit, used for pre-constructing an electric energy meter communication cloud, and completing the construction of a distributed communication architecture by connecting the electric energy meter communication cloud with K communication nodes, wherein the electric energy meter communication cloud is pre-configured with a load balancing module and a load scheduling module; a communication transmission execution unit, configured to, during the power supply process in the target power supply area, enable the multiple electric energy meters to perform power consumption data communication transmission to the electric energy meter communication cloud via the K communication nodes with the K communication node groups as constraints; A security verification execution unit, used for performing power usage behavior analysis and malicious device identification through the K security verification modules during the process in which the K communication nodes receive the power usage data of the K communication node groups; The channel optimization execution unit is used for the electric energy meter communication cloud to dynamically monitor and dynamically schedule the communication load of the K communication nodes through the coordinated operation of the load balancing module and the load scheduling module during the process of receiving the electric energy meter data uploaded by the K communication node groups relayed by the K communication nodes.

9. An electronic device, characterized in that: The electronic device comprises: a processor; and a memory for storing instructions executable by the processor; wherein the processor is used to execute a communication optimization method for an electric energy meter as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, so that the computer program is used to execute the communication optimization method of an electric energy meter as described in any one of claims 1 to 7.

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