Data communication method, system and equipment under disaster condition

Abstract

The invention discloses a data communication method, system and device under a disaster condition, and belongs to the field of power systems, and the method comprises the steps: determining a graph structure model based on a current network topology structure and network topology change information under the disaster condition, carrying out the calculation through a graph theory algorithm, and determining a transmission node and a standby path; if the node connection is abnormal, performing multi-path planning calculation on the standby path to obtain a planning result; splitting the power grid information packet into a plurality of data packets, performing data transmission through a multi-path planning result, and monitoring a transmission progress and arrival confirmation information in real time; and if it is monitored that the transmission is overtime or lost, updating the planning result based on a preset path switching rule to obtain a target planning result, and performing rerouting processing on the remaining data packets according to the target planning result to realize data communication under the disaster condition. And efficient, reliable and stable operation of data communication of the power grid under disaster conditions can be realized.

Description

Technical Field

[0001] The present invention relates to the field of power systems, and in particular to a data communication method, system and equipment under disaster conditions. Background Art

[0002] As critical power infrastructure, the safe and stable operation of power grids under the impact of extreme natural disasters is directly related to socioeconomic development and people's livelihoods. Extreme disasters such as earthquakes, typhoons, and icy conditions can cause widespread damage to power grid communication infrastructure, leading to a loss of connectivity between grid nodes and the inability to effectively transmit data between grid systems. This communication disruption further challenges adaptability to dynamic changes in network topology, making real-time status monitoring and coordinated control impossible, and compromising grid system security.

[0003] Traditional fixed routing strategies cannot cope with the complex situation of random failure and intermittent recovery of communication nodes after a disaster. The uncertainty of network topology directly leads to the difficulty of selecting data transmission paths. A single transmission path is very likely to cause the loss of key scheduling instructions and status information due to node failure, seriously affecting the data communication efficiency of the post-disaster power grid. Summary of the Invention

[0004] The present invention provides a data communication method, system and equipment under disaster conditions, which can solve the problems of difficulty in selecting data transmission paths and low data communication efficiency in power systems under disaster conditions, and realize efficient, reliable and stable operation of data communication in power grid systems.

[0005] The present invention provides a data communication method under disaster conditions, comprising:

[0006] Determine a dynamic graph structure model based on the current network topology and network topology change information, calculate the dynamic graph structure model using a graph theory algorithm, and determine a set of transmission nodes and a set of backup paths, wherein the network topology change information refers to information that indicates a communication interruption caused by a disaster at a node or that signal attenuation exceeds a preset threshold;

[0007] If a transmission node in the transmission node set has an abnormal connection state, performing a multi-path planning calculation on the backup path set by using an ant colony optimization algorithm to obtain a multi-path planning result;

[0008] Splitting the power grid information packet into multiple data packets, transmitting data for each of the data packets using the multi-path planning result, and monitoring the transmission progress and arrival confirmation information of each of the data packets in real time;

[0009] If it is detected that the data packet has a transmission timeout or loss, the multi-path planning result is updated based on the preset path switching rules to obtain the target multi-path planning result. The remaining data packets on the abnormal transmission path corresponding to the data packet with transmission timeout or loss are rerouted according to the target multi-path planning result to achieve data communication under disaster conditions.

[0010] By monitoring the transmission progress and arrival confirmation information of data packets in real time, the embodiments of the present invention can promptly detect transmission problems and make dynamic adjustments, further optimizing the data transmission process and improving communication efficiency. A graph structure model is dynamically generated based on the current network topology and network topology change information, which can reflect changes in network topology in real time, adapt to dynamic changes in the network under disaster conditions, and improve the adaptability of the network topology. When an abnormal connection state of a transmission node is detected, an ant colony optimization algorithm is used to quickly calculate the multipath planning results of the backup path set, which can quickly find an alternative path and reduce path recovery time. The backup path set is pre-calculated using a graph theory algorithm, providing multiple options for path switching and rerouting, thereby enhancing the flexibility and adaptability of the network in the face of topology changes. Once a data packet transmission timeout or loss is detected, the multipath planning results are immediately updated based on preset path switching rules, and the timed-out or lost data packet is rerouted to quickly restore data transmission and significantly improve path recovery speed. The power grid information packet is split into multiple data packets and transmitted via multiple paths, reducing the transmission risk of a single data packet and avoiding the loss of the entire data packet due to a single path failure, thereby improving the success rate and efficiency of data transmission.

[0011] Furthermore, before determining the dynamic graph structure model based on the current network topology structure and the network topology change information, the method further includes:

[0012] Using signal strength detection and connection status scanning, the real-time collected operating status data and communication link connectivity information of each node in the power grid are processed to determine the current node mapping relationship of the current network topology structure, and obtain initial topology structure data and initial topology mapping data;

[0013] Analyze the signal strength and connection status of each node one by one according to the initial topology mapping data, and if an unavailable node corresponding to a signal strength lower than a preset threshold or a connection status display interruption is detected, determine a first distribution feature corresponding to each unavailable node;

[0014] Based on the first distribution feature and the communication link connectivity information, a graph traversal method is used to analyze the current network topology structure to obtain the network topology change information.

[0015] In this way, the graph traversal method is used to quickly analyze the scope of the affected area and the changes in the connection relationship of unavailable nodes, providing clear guidance for subsequent path planning and recovery, and accelerating the speed of post-disaster path recovery; by analyzing the signal strength and connection status of the nodes one by one, unavailable nodes can be accurately identified to avoid wasting communication resources on these nodes, thereby improving post-disaster data communication efficiency; signal strength detection and connection status scanning obtain the operating status data and communication link connectivity information of each node in the power grid in real time, generate initial topology mapping data, provide accurate basic data for the establishment of dynamic graph structure models, and enhance the adaptability of network topology.

[0016] Furthermore, the network topology change information includes connection relationship change information and affected areas, and the dynamic graph structure model is determined based on the current network topology structure and the network topology change information, specifically:

[0017] If it is determined that there is a need for local adjustment of the current network topology structure based on the connection relationship change information of the unavailable nodes in the affected area, then the current node mapping relationship in the initial topology mapping data is updated according to the area range of the affected area and the connection relationship change information of the unavailable nodes in the affected area to obtain an updated node mapping relationship and an updated network topology structure, a preset adjacency matrix is ​​used to store the updated network topology structure, and the initial topology structure data is updated according to the updated network topology structure to obtain updated topology structure data;

[0018] Determining a change timestamp of each of the unavailable nodes based on the updated topology data, obtaining corresponding network topology change information based on the change timestamp, and determining a second distribution feature corresponding to a change trend according to the network topology change information;

[0019] Based on the second distribution feature, combined with the operating status data and the communication link connectivity information, a support vector machine algorithm is used to predict the network topology stability within a preset time period to obtain potential risk nodes;

[0020] An optimization adjustment strategy is determined based on each of the potential risk nodes, and the updated node mapping relationship is iteratively updated according to the optimization adjustment strategy until the network topology data of the current iteration meets the preset stability conditions. The iteration is stopped and the target mapping relationship is obtained, and the dynamic graph structure model is determined based on the target mapping relationship.

[0021] In this way, by dynamically updating network topology data, optimizing network structure, and reducing potential risk nodes in communication paths, the efficiency of post-disaster data communication can be improved; the support vector machine algorithm is used to predict network topology stability, identify potential risk nodes in advance, provide early warning for post-disaster path recovery, and accelerate path recovery; based on the change information of the connection relationship of unavailable nodes in the affected area, local adjustments and dynamic updates are made to ensure that the network topology can quickly adapt to changes under disaster conditions and enhance the adaptability and stability of the network.

[0022] Furthermore, the dynamic graph structure model is calculated by a graph theory algorithm to determine a set of transmission nodes and a set of backup paths, specifically:

[0023] Calculating the connectivity matrix between nodes in the dynamic graph structure model using a graph theory algorithm, analyzing the connection relationship and path reachability between the nodes, and obtaining a plurality of key nodes and a third distribution feature corresponding to each of the key nodes;

[0024] According to the third distribution feature, the node degree and edge weight value of each of the key nodes are analyzed to determine the transmission node set, the backup path set, and the priority sequence between the transmission paths.

[0025] In this way, by analyzing the node degrees and edge weight values ​​of key nodes, the priority sequence of transmission paths can be determined, path selection can be optimized, and the efficiency of data transmission can be improved; the set of backup paths can be pre-calculated using graph theory algorithms, so that when the main path fails, it can quickly switch to the backup path and reduce the path recovery time; the node connection relationship and path reachability can be analyzed through the connectivity matrix between nodes, which can dynamically adapt to changes in network topology and enhance the adaptability of the network.

[0026] Furthermore, the multi-path planning calculation is performed on the backup path set by using the ant colony optimization algorithm to obtain a multi-path planning result, which is specifically:

[0027] Obtaining an initial path allocation scheme corresponding to the backup path set, and determining characteristic data of each initial path in the initial path allocation scheme, wherein the characteristic data includes path length, node reliability, and transmission delay;

[0028] Obtaining a path evaluation model, inputting the characteristic data into the path evaluation model so that the path evaluation model calculates the characteristic data of each of the initial paths based on a preset path evaluation function to obtain a weight value corresponding to each of the initial paths; deleting initial paths having weight values ​​less than a preset threshold from the initial path allocation plan, obtaining a target path allocation plan, and determining a weight parameter for each of the target paths based on the weight values ​​of each target path in the target path allocation plan;

[0029] The multi-path planning calculation is performed on the target path allocation scheme by using an ant colony optimization algorithm and weight parameters of each target path to obtain the multi-path planning result.

[0030] In this way, by collecting path feature data and screening it using a path evaluation model, paths with poor performance can be deleted, path performance can be optimized, and communication efficiency can be improved; using the ant colony optimization algorithm to perform multi-path planning on the target path, alternative paths can be quickly found, reducing path recovery time; and path selection can be dynamically adjusted through weight parameters to enhance the adaptability of the network topology to dynamic changes.

[0031] Furthermore, the obtaining of the initial path allocation solution corresponding to the backup path set is specifically as follows:

[0032] Determining an initial connection relationship according to the transmission node set and the backup path set, wherein the initial connection relationship refers to a connection relationship between a transmission node and a backup path;

[0033] Based on the priority sequence and the connectivity matrix, each transmission node in each transmission path is detected, and if there is a transmission node with an abnormal connection status, a path reconstruction mechanism is triggered to reconstruct the path of the transmission node with the abnormal connection status based on the path reconstruction mechanism;

[0034] During the path reconstruction process, availability information of each backup path in the backup path set is obtained in real time, path reachability is calculated based on the availability information, and initial connection relationships of transmission nodes with abnormal connection status are updated based on the path reachability to obtain intermediate connection relationships;

[0035] Using a preset adjacency table to store intermediate connection relationships, and updating the initial topology mapping data to obtain updated topology mapping data;

[0036] Based on the updated topology mapping data, analyzing the correlation between each of the key nodes and each of the transmission nodes during the connection relationship update process, determining whether there is a potential connection risk, and determining a fourth distribution feature corresponding to the risk node;

[0037] Based on the fourth distribution feature and the path reconstruction data, a support vector machine algorithm is used to evaluate the stability of the network topology to obtain a fifth distribution feature corresponding to stability being lower than a preset threshold;

[0038] The intermediate connection relationship and the connectivity matrix are updated by using the fifth distribution feature to obtain the initial path allocation solution for use in multi-path planning calculation of the backup path set.

[0039] In this way, by detecting the node connection status in real time and updating the connection relationship, the path can be dynamically adjusted to avoid wasting communication resources on faulty nodes and improve communication efficiency. When an abnormal node connection status is detected, the path reconstruction mechanism is immediately triggered, and the connection relationship is quickly updated using the availability information of the backup path to speed up path recovery. By analyzing the correlation between key nodes and transmission nodes, the topology mapping data is updated in real time, enhancing the adaptability of the network topology to dynamic changes.

[0040] Furthermore, the data communication method under disaster conditions further includes:

[0041] Based on a network status feedback mechanism, the current network status and performance information of each target path is obtained, target paths whose performance information is less than a preset performance threshold are marked as inefficient paths, and an inefficient path list is determined based on the inefficient paths in the target multi-path planning result;

[0042] For each inefficient node in the inefficient path list, obtaining recovery status information of each inefficient node, and evaluating the recovery potential of the inefficient node based on a historical recovery success rate to obtain a node recovery evaluation result;

[0043] updating the weight parameters of the target path according to the node recovery evaluation results of each of the inefficient nodes to obtain updated weight parameters;

[0044] Iteratively optimizing the path selection strategy according to the updated weight parameters, and using a support vector machine algorithm to predict the path selection of the next state to obtain an optimized path allocation solution;

[0045] Data transmission for the next state is performed according to the optimized path allocation scheme, and performance information of each state is obtained until the performance information is greater than or equal to a preset performance threshold. The iteration is stopped to obtain a target path selection strategy, and data transmission for the next state is performed according to the target path selection strategy.

[0046] In this way, the network status feedback mechanism can be used to obtain path performance information in real time, and inefficient paths can be discovered and optimized in a timely manner to ensure that path performance reaches the preset threshold, thereby improving communication efficiency. Data is transmitted according to the optimized path allocation plan to ensure that data is always transmitted through the path with the best performance, further improving communication efficiency. By marking inefficient paths and determining a list of inefficient paths, paths that need to be optimized can be quickly identified, accelerating path recovery. The support vector machine algorithm is used to predict the path selection in the next state, and the path selection strategy is optimized in advance, further accelerating path recovery. The path weight parameters are dynamically updated based on the recovery evaluation results of inefficient nodes to enhance the adaptability of the network in the face of dynamic changes. By iteratively optimizing the path selection strategy and adjusting the path allocation plan in real time, it is ensured that the network topology can quickly adapt to dynamic changes, further improving the adaptability and stability of the network.

[0047] Furthermore, wherein the power grid information package includes a power grid dispatch instruction set and a monitoring data set, the data communication method under disaster conditions further includes:

[0048] The priority queue management mechanism is used to classify the power grid dispatch instruction set, determine the priority corresponding to each power grid dispatch instruction, and obtain an instruction priority sequence;

[0049] Determine an instruction allocation scheme based on the instruction priority sequence and the target multi-path planning result, and execute corresponding power grid node control operations for power grid dispatch instructions corresponding to emergency instructions based on the instruction allocation scheme, and obtain execution status data corresponding to the power grid dispatch instructions;

[0050] If the execution status data indicates that data transmission is interrupted, switching to a backup path for transmission until the transmission is completed, and collecting the operation results;

[0051] Extracting node status information from the operation result, determining whether the operation result meets a preset threshold condition, and if not, modifying the node status to an abnormal state, and determining an abnormal state list corresponding to each node in the abnormal state;

[0052] For each node in the abnormal status list, determining whether there is evaluation data corresponding to a potential fault at each node, and updating the instruction priority sequence according to the evaluation data to obtain an updated instruction allocation plan;

[0053] According to the updated instruction allocation scheme, the support vector machine algorithm is used to predict the instruction priority sequence of the next state to obtain a prediction result, and the target instruction priority sequence is determined based on the prediction result. The target instruction priority sequence is used to adjust the instruction allocation scheme of the next state.

[0054] In this way, the power grid dispatch instructions are classified and sorted through the priority queue management mechanism to ensure that urgent instructions are transmitted first and important instructions are avoided from being delayed due to queuing, thereby improving communication efficiency; the instruction allocation plan is dynamically adjusted based on the instruction priority sequence and multi-path planning results to optimize the instruction transmission path and further improve communication efficiency; when a data transmission interruption is detected, the backup path is immediately switched for transmission to reduce the transmission interruption time caused by path failure and speed up the path recovery speed; by obtaining execution status data in real time, transmission anomalies are promptly discovered and handled to ensure that instructions can be transmitted quickly and reliably; based on the node status information and potential fault assessment data in the operation results, the instruction priority sequence is dynamically updated to enhance the network's adaptability in the face of dynamic changes; the support vector machine algorithm is used to predict the instruction priority sequence of the next state, and the instruction allocation plan is adjusted in advance to further improve the adaptability and stability of the network topology.

[0055] Another embodiment of the present invention further provides a data communication system under disaster conditions, comprising: a topology module, an initial path planning module, a data transmission module, and a data abnormality transmission processing module;

[0056] The topology module is configured to determine a dynamic graph structure model based on the current network topology and network topology change information, calculate the dynamic graph structure model using a graph theory algorithm, and determine a set of transmission nodes and a set of backup paths, wherein the network topology change information refers to information indicating that a node has experienced a communication interruption caused by a disaster or that signal attenuation is greater than a preset threshold;

[0057] The initial path planning module is configured to perform multi-path planning calculation on the backup path set using an ant colony optimization algorithm to obtain a multi-path planning result if a transmission node in the transmission node set has an abnormal connection state;

[0058] The data transmission module is used to split the power grid information packet into multiple data packets, transmit data for each of the data packets according to the multi-path planning result, and monitor the transmission progress and arrival confirmation information of each of the data packets in real time;

[0059] The data abnormal transmission processing module is used to update the multi-path planning result based on the preset path switching rules if it is detected that the data packet has a transmission timeout or loss, obtain a target multi-path planning result, and reroute the remaining data packets on the abnormal transmission path corresponding to the data packet with transmission timeout or loss according to the target multi-path planning result to achieve data communication under disaster conditions.

[0060] By monitoring the transmission progress and arrival confirmation information of data packets in real time, the embodiments of the present invention can promptly detect transmission problems and make dynamic adjustments, further optimizing the data transmission process and improving communication efficiency. A graph structure model is dynamically generated based on the current network topology and network topology change information, which can reflect changes in network topology in real time, adapt to dynamic changes in the network under disaster conditions, and improve the adaptability of the network topology. When an abnormal connection state of a transmission node is detected, an ant colony optimization algorithm is used to quickly calculate the multipath planning results of the backup path set, which can quickly find an alternative path and reduce path recovery time. The backup path set is pre-calculated using a graph theory algorithm, providing multiple options for path switching and rerouting, thereby enhancing the flexibility and adaptability of the network in the face of topology changes. Once a data packet transmission timeout or loss is detected, the multipath planning results are immediately updated based on preset path switching rules, and the timed-out or lost data packet is rerouted to quickly restore data transmission and significantly improve path recovery speed. The power grid information packet is split into multiple data packets and transmitted via multiple paths, reducing the transmission risk of a single data packet and avoiding the loss of the entire data packet due to a single path failure, thereby improving the success rate and efficiency of data transmission.

[0061] Another embodiment of the present invention also provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps of the data communication method under disaster conditions of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for use in the implementation. Obviously, the drawings described below are only some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0063] Figure 1 This is a schematic diagram of the steps of a data communication method under disaster conditions provided by an embodiment of the present invention;

[0064] Figure 2 This is a structural diagram of a data communication system under disaster conditions provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0065] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions in this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0066] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0067] In the description of the embodiments of this application, technical terms such as "first" and "target" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly specify the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "plurality" means more than two, unless otherwise specifically defined.

[0068] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0069] In the description of the embodiments of this application, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0070] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0071] In the description of the embodiments of the present application, unless otherwise expressly specified or limited, technical terms such as "installed," "connected," "connected," and "fixed" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integration; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; internal connections between two components or interactions between two components. Those skilled in the art can understand the specific meanings of the above terms in the embodiments of the present application based on specific circumstances.

[0072] See also Figure 1 To solve the problems of difficulty in selecting a data transmission path and low data communication efficiency in the prior art under disaster conditions, an embodiment of the present invention provides a data communication method under disaster conditions, comprising:

[0073] Step 102: Determine a dynamic graph structure model based on the current network topology and network topology change information, calculate the dynamic graph structure model through a graph theory algorithm, and determine a transmission node set and a backup path set, wherein the network topology change information refers to topology change information corresponding to node communication interruption caused by a disaster or signal attenuation greater than a preset threshold.

[0074] Topology changes include updating node status, adjusting connectivity, and optimizing path selection. Updating node status means marking unavailable nodes as unavailable and reflecting this change in the topology map. Adjusting connectivity involves recalculating connectivity between nodes based on node availability. If a node becomes unavailable, the associated connectivity paths will also be affected and require replanning. Optimizing path selection involves recalculating key transmission nodes and backup paths based on the updated topology to ensure reliable and efficient data transmission. Network topology change information includes not only the topology change timestamp but also information about the changed nodes and other relevant information to fully reflect the dynamic nature of the network topology. This information includes any combination of information: changed node information, connectivity change information, topology change scope, change cause, node recovery information, changes in network performance indicators, or optimization strategies. Changed node information includes node identification, node status, node location, and node attribute changes. Node identification uniquely identifies the node that has changed, such as the node's number, name, or IP address. Node status records the node's current status, such as "unavailable," "weak signal," or "available again." Node location is the location of the node in the network, such as its position in the network topology or its connection to other nodes. Node attribute changes are changes in node attributes (such as signal strength, transmission delay, and load), for example, a node's signal strength drops from a normal value to below a threshold, or a node's load suddenly increases. Change causes include fault type and fault description. Fault type records the specific cause of the topology change, such as natural disasters (earthquakes, typhoons, etc.), equipment failure, signal interference, etc. Fault description describes the specific circumstances of the fault in detail, such as damage to a node's communication module or the destruction of a link's physical line. Node recovery information includes recovery timestamp, recovery status, and recovery measures. The recovery timestamp records the time when the node becomes available again, allowing for analysis of node recovery time. The recovery status records the node's status after recovery, such as whether it has fully recovered or requires further optimization and adjustment. The recovery measures record the recovery measures taken, such as restoring communication through an alternative path or restoring node functionality through equipment repair. Changes in network performance indicators include transmission delay, packet loss rate, and network load. Transmission delay records the change in transmission delay of the link before and after the topology change. Packet loss rate records the change in packet loss rate during data transmission. Network load records changes in network load, such as whether the load on certain nodes or links increases or decreases. Optimization and adjustment strategies include adjustment measures and adjustment results. Adjustment measures record the optimization and adjustment strategies taken to address topology changes, such as replanning paths and adjusting node loads. Adjustment results record the effects of the adjustments, such as whether communication was successfully restored or whether network stability was improved.Monitoring nodes include sensors and monitoring equipment in the power grid, which are responsible for collecting real-time operating status data of the power grid, such as voltage, current, temperature, power, etc.

[0075] In this embodiment, data such as the operating status data and communication link connectivity information of each monitoring node of the power grid under the current state of disaster conditions are obtained, and the current network topology structure is determined based on the obtained operating status data and communication link connectivity information. In the process of constructing the current network topology structure, the above data is continuously acquired and data detection is performed. If it is detected that the node communication is interrupted or the signal attenuation exceeds a preset threshold, the node is marked as an unavailable node, and the topology changes corresponding to the node changes are recorded in real time. The current network topology structure is updated according to the topology changes to obtain the current network topology information, and a dynamic graph structure model is constructed based on the real-time network topology change information. Furthermore, a graph theory algorithm such as an ant colony optimization algorithm can be used to calculate the dynamic graph structure model to determine the transmission node set and backup path set corresponding to the dynamic graph structure model in real time.

[0076] As an example of an embodiment of the present invention, the dynamic graph structure model is calculated by a graph theory algorithm to determine the transmission node set and the backup path set. Specifically, the connectivity matrix between the nodes in the dynamic graph structure model is calculated by a graph theory algorithm, the connection relationship between the nodes and the path reachability are analyzed, and multiple key nodes and the third distribution characteristics corresponding to each of the key nodes are obtained; according to the third distribution characteristics, the node degree and edge weight value of each of the key nodes are analyzed to determine the transmission node set, the backup path set, and the priority sequence between the transmission paths.

[0077] The connectivity matrix is ​​a square matrix whose elements represent the connection status between nodes. A node's degree refers to the number of edges directly connected to that node. Nodes with higher degrees are more important in the network because they connect to more other nodes. Edge weights reflect the quality and importance of connections between nodes. Edges with higher weights typically indicate more reliable connections or more efficient transmission paths. Graph theory algorithms are used to analyze the path reachability between nodes and identify which nodes play a key role in network connectivity.

[0078] In this embodiment, in the power grid network topology management, in the process of constructing a dynamic graph structure model based on the network topology change information obtained in real time, by collecting the connection status data between each node, for example, there are 10 nodes in a certain area, and automatically generating a 10x10 adjacency matrix, where the matrix element value is 1, indicating that the two nodes are directly connected, and the value is 0, indicating that there is no connection. At the same time, a weight is assigned to each edge, for example, the transmission delay of a certain edge is 30.5 milliseconds; for this adjacency matrix, the breadth-first search algorithm in the graph theory algorithm can be used to analyze the connectivity matrix, and the distribution of key nodes is obtained by calculating the connectivity matrix, which means using the graph theory algorithm to analyze the importance of nodes in the current network topology structure, thereby determining which nodes are important. Points play a critical role in network connectivity and stability. Key nodes are identified based on node degree, edge weight, and path reachability. For example, the total number of reachable paths from node A to node B is calculated to be three, with the shortest path having a cumulative weight of 62.7 milliseconds. Matrix operations are then used to determine path reachability, revealing a reachability probability of 0.92 from node C to node D. The degree of each node is calculated. For example, node E has a degree of 5, indicating that it connects to five other nodes. Combined with edge weight analysis, paths with edge weights below 40.2 milliseconds are marked as critical transmission paths. Node E is identified as a key transmission node, and a set of two backup paths is generated, with an average latency of 45.8 milliseconds. Based on this calculation process, key nodes in the current network topology are identified. A comprehensive evaluation of key nodes is performed based on node degree and edge weight values. Priority ranking is performed based on the comprehensive evaluation results, and transmission nodes are determined based on the ranking results. Based on this, a set of transmission nodes and a set of backup paths are determined within the dynamic graph structure model, and a path priority sequence is determined for each transmission path corresponding to each transmission node and backup path.

[0079] Step 104: If a transmission node in the transmission node set has an abnormal connection state, a multi-path planning calculation is performed on the backup path set using an ant colony optimization algorithm to obtain a multi-path planning result.

[0080] In this embodiment, based on the previously determined path priority sequence for each transmission path and in conjunction with the connectivity matrix, the node status of each key node is detected. If the connection status of a key node is found to be abnormal, a path reconstruction mechanism is triggered to reconstruct the path for the transmission node with the abnormal connection status based on the path reconstruction mechanism. During the path reconstruction process, path reachability is recalculated based on the availability information of each backup path in the backup path set. The initial connection relationship is updated based on the calculation result, and the updated connection relationship is stored using a pre-established adjacency table to obtain updated network topology data and path reconstruction results. Multi-path planning calculations are then performed on the path reconstruction results to obtain multi-path planning results. For example, if the failure of key node E is detected, the path reconstruction mechanism is automatically triggered, and the network path is recalculated using the minimum spanning tree algorithm. For example, the new path delay from node F to node G is 38.4 milliseconds, ensuring smooth data transmission. The path reconstruction results are then correlated with the equipment load data of the power grid. If the load distribution on a path is uneven after reconstruction, for example, the load ratio of node H increases to 78.6%, the data flow allocation strategy is automatically adjusted to reduce its load to 60.3%, ensuring overall network stability.

[0081] Step 106: split the power grid information packet into multiple data packets, transmit data for each of the data packets using the multi-path planning result, and monitor the transmission progress and arrival confirmation information of each of the data packets in real time.

[0082] In this embodiment, a power grid information packet includes a set of power grid dispatch instructions and a set of monitoring data. Data fragmentation technology is used to split the power grid information packet into multiple sub-data packets. A different transmission path is assigned to each sub-data packet based on the multi-path planning results. Path binding and reassembly marking of the data packets are achieved by adding path identifiers and sequence numbers to the packet headers. The transmission progress and arrival confirmation information of each data packet are monitored in real time to retransmit abnormal data segments and ensure the accuracy of data transmission. Furthermore, taking the transmission of power grid information packets between a monitoring center, a dispatch center, and a control center as an example, the dispatch center receives real-time operating status data transmitted by the monitoring center based on the multi-path planning results. The dispatch center formulates dispatch instructions based on this operating status data and transmits them to the control center via the multi-path planning results. The control center responds to the dispatch instructions in real time, converting them into specific control operations and executing them.

[0083] Specifically, command information and monitoring data streams are obtained from the power grid dispatching system, and the information packets are split using data sharding technology to obtain multiple sub-data units. Based on the split sub-data units, a corresponding transmission channel is matched for each sub-data unit in combination with a multi-path planning allocation strategy to determine a channel allocation scheme. For the allocated sub-data units, a path identifier and a sequence number are added to the header of each unit to complete the setting of transmission channel binding and reorganization tags to obtain marked data units. After obtaining the marked data units, the data units are encapsulated using a pre-established transmission protocol. If a path identifier is missing during the encapsulation process, the identifier information is regenerated to obtain encapsulated data units. Based on the encapsulated data units, a data verification mechanism is used to perform consistency checks on the path identifier and sequence number. If the numbering is missequenced, the numbering sequence is adjusted to determine the verified data units. Based on the verified data units, the sending priority of the data units is dynamically adjusted in combination with real-time status information of the transmission channel to obtain an adjusted sending queue. For the adjusted sending queue, the data units are batch-transmitted using a preset scheduling rule. If a channel status is abnormal during transmission, a backup channel is switched to obtain the final transmission result.

[0084] For example, a set of power grid dispatching instructions and a set of monitoring data are obtained from the dispatching center. Assuming that the total data packet size is 10.2MB, including voltage fluctuation data and equipment status information, it is split into 5 sub-data packets using data sharding technology. Each sub-data packet is 2.04MB in size. The splitting algorithm adopts a uniform splitting method to ensure that the number of bytes of each sub-data packet is balanced, and a check value is generated through a hash check (such as the MD5 algorithm). For example, the check value of sub-data packet D1 is X7F3K9, which is recorded in the system log for subsequent integrity verification; according to the pre-calculated multi-path planning results, a different transmission path is assigned to each sub-data packet. Assuming that the planning results contain 3 available paths A, B, and C, whose bandwidths are 5.5Mbps, 4.8Mbps, and 10.2Mbps, respectively. Mbps and 3.2Mbps, using a bandwidth priority allocation algorithm, sub-packet D1 is allocated to path A, D2 and D3 are allocated to path B, and D4 and D5 are allocated to path C. During the allocation process, the congestion rate of the path is analyzed in real time. For example, the congestion rate of path A is 12.3%, which meets the transmission conditions. A path identifier and sequence number are added to the header of each sub-packet. For example, the header information of sub-packet D1 is "Path: A; Seq: 001". The path identifier is bound to the transmission channel, and the sequence number is used for reassembly order at the receiving end. The header information is encrypted using preset encoding rules (such as Base64) to prevent data tampering. At the same time, a reassembly mark table is generated. For example, D1 is marked as "Reassembly Priority: 1" to ensure that the receiving end processes it in order.

[0085] Furthermore, it can be linked with the fault detection module of the power grid. If a sudden interruption occurs on a path (such as path C) and the interruption delay is detected to be 0.15 seconds, the affected sub-packets D4 and D5 will be automatically reallocated to the backup path D (bandwidth 4.0Mbps), and the reassembly mark table will be updated to ensure the continuity of data transmission.

[0086] Step S108: If it is detected that a data packet has been transmitted overtime or lost, the multipath planning result is updated based on the preset path switching rule to obtain a target multipath planning result, and the remaining data packets on the abnormal transmission path corresponding to the data packet with transmission timeout or loss are rerouted according to the target multipath planning result to achieve data communication under disaster conditions.

[0087] In this embodiment, the link status information on the transmission path is obtained through a real-time monitoring mechanism, and the data packet transmission status on each path is continuously tracked to obtain the transmission progress and arrival confirmation data of each path; based on the obtained transmission progress and arrival confirmation data, the data fragment transmission status on each path is analyzed, and if a transmission timeout or data loss is detected on a certain path, the path is marked as an abnormal state, and an abnormal path identifier is obtained; for the marked abnormal path identifier, the remaining data packet information that has not yet been completed on the path is extracted, and combined with the pre-established path switching rules, a new transmission path allocation plan is determined to obtain the target multi-path planning result; using the target multi-path planning result, rerouting is performed on the remaining data packets. The operation is to reallocate the data fragments to the new transmission path to obtain the rerouted data packet grouping; through the rerouted data packet grouping, combined with the real-time monitoring mechanism, the link status of the new path is continuously tracked. If the transmission progress of the new path does not reach the preset threshold, the sending order of the data packets is adjusted to obtain the adjusted sending queue; based on the adjusted sending queue, the arrival confirmation information of the data packet transmission on each new path is collected. If the confirmation information is missing, the data packet transmission request is re-initiated to obtain the final transmission completion record; through the final transmission completion record, the integrity of all data fragments is tested using a data verification tool to determine whether all data packets have arrived at the target node, and obtain a complete transmission verification result.

[0088] For example, a data fragment is sent through paths X, Y, and Z. The delay baseline value of path X is 0.08 seconds, path Y is 0.12 seconds, and path Z is 0.15 seconds. The monitoring mechanism collects transmission status data of each path every 0.05 seconds, including the sending time of the data packet and the reception confirmation signal. Assume that the expected transmission time of a data packet D6 on path X is 0.1 seconds, but the actual monitoring transmission time has reached 0.2 seconds, exceeding the preset timeout threshold of 0.15 seconds. The built-in delay analysis algorithm determines that this is a transmission anomaly and calculates the current packet loss rate of path X to be 8.5%, which is 3 times higher than the normal range. %; immediately initiate rerouting and scan the load conditions of the remaining available paths. It finds that the current load rates of path Y are 22.7% and path Z is 35.4%. Based on the load balancing algorithm, data packets D7 and D8, which were not yet fully transmitted on path X, are reallocated to path Y. At the same time, the transmission log is updated to record the rerouting trigger time as time T1, which took 0.03 seconds. To ensure data integrity, a rerouting notification message is sent to the data receiver. The message size is 0.02MB and contains the new path information of the data packet. A checksum is generated using the SHA-256 algorithm to verify that the message has not been tampered with.

[0089] Furthermore, it can be linked with the network optimization module of the power grid to analyze the cause of the abnormality of path X. It can be found that its delay fluctuation is related to the surge in traffic at a certain node. The priority weight of path X is automatically adjusted from 0.9 to 0.6, reducing the probability of subsequent shard allocation and forming a closed-loop optimization logic.

[0090] By monitoring the transmission progress and arrival confirmation information of data packets in real time, the embodiments of the present invention can promptly detect transmission problems and make dynamic adjustments, further optimizing the data transmission process and improving communication efficiency. A graph structure model is dynamically generated based on the current network topology and network topology change information, which can reflect changes in network topology in real time, adapt to dynamic changes in the network under disaster conditions, and improve the adaptability of the network topology. When an abnormal connection state of a transmission node is detected, an ant colony optimization algorithm is used to quickly calculate the multipath planning results of the backup path set, which can quickly find an alternative path and reduce path recovery time. The backup path set is pre-calculated using a graph theory algorithm, providing multiple options for path switching and rerouting, thereby enhancing the flexibility and adaptability of the network in the face of topology changes. Once a data packet transmission timeout or loss is detected, the multipath planning results are immediately updated based on preset path switching rules, and the timed-out or lost data packet is rerouted to quickly restore data transmission and significantly improve path recovery speed. The power grid information packet is split into multiple data packets and transmitted via multiple paths, reducing the transmission risk of a single data packet and avoiding the loss of the entire data packet due to a single path failure, thereby improving the success rate and efficiency of data transmission.

[0091] As an example of an embodiment of the present invention, before determining the dynamic graph structure model based on the current network topology structure and network topology change information, it also includes: using signal strength detection and connection status scanning to process the operating status data and communication link connectivity information of each node of the power grid collected in real time, determining the current node mapping relationship of the current network topology structure, and obtaining initial topology structure data and initial topology mapping data; according to the initial topology mapping data, analyzing the signal strength and connection status of each node one by one, if it is detected that the signal strength is lower than the preset threshold or the connection status shows an unavailable node corresponding to the interruption, then determining the first distribution feature corresponding to each unavailable node; based on the first distribution feature and the communication link connectivity information, using a graph traversal method to analyze the current network topology structure to obtain the network topology change information.

[0092] Among them, the topology change timestamp is used to track the change process of the network topology, which is convenient for subsequent analysis and recovery operations. Through the timestamp, the specific time and sequence of network topology changes can be understood, which helps to better adapt to the dynamically changing network environment. The scope of topology change includes the affected area and local adjustment requirements. The affected area refers to the simultaneous loss of connection of multiple nodes in a certain area, or the topology structure of a sub-network has undergone major changes. The connection change information includes the connection status, the affected links, and the new connection path; the local adjustment requirement is to analyze whether the topology change requires adjustment to the local network, such as redistribution of load or re-planning of paths; the connection status is the change in the connection between nodes, such as connection interruption, connection recovery, or increased connection delay; the affected links are to clarify which links are affected by the topology change, such as link AB interruption and link CD delay increase; the new connection path means that if some links are interrupted, the connection path may be re-planned, and this new path information also needs to be recorded.

[0093] In this embodiment, a multi-source sensor network is used to collect real-time operating status data and communication link connectivity information of each node in the power grid, and the current node mapping relationship of the current network topology is determined by signal strength detection and connection status scanning to obtain initial topology mapping data. Based on the initial topology mapping data, the signal strength and connection status of each node are analyzed one by one in real time. If it is detected that the signal strength of a node is lower than a preset threshold or the connection status is interrupted, the node is marked as unavailable, and the node is determined to be an unavailable node. The first distribution feature is determined based on the distribution of all unavailable nodes. Through the distribution of unavailable nodes, combined with the communication link connectivity information, a graph traversal method is used to analyze the affected areas in the current network topology to determine the scope of the affected areas in the current network topology and the connection changes of the unavailable nodes. According to the scope of the affected areas and the connection changes of the unavailable nodes, the local adjustment requirements of the current network topology can be determined. For example, in the monitoring of power grid operation status, the real-time operation status data of each node in the power grid is first collected through a multi-source sensor network. For example, the temperature value (such as 25.5 degrees Celsius), voltage value (such as 220.3 volts) and current value (such as 5.2 amps) of the node are respectively obtained using temperature sensors, voltage sensors and current sensors. The data is uploaded to the central server every 5 seconds through the communication module. The server uses a time series database to store this data for subsequent analysis; the network topology is constructed through signal strength detection and connection status scanning. Specifically, the RSSI (Received Signal Strength Indicator) value is used to detect the signal strength between nodes. For example, if the threshold is set to -75 decibels, if a node's RSSI value is -80 decibels, its signal is considered weak. Link layer protocols (such as ZigBee) are used to scan connection status every 10 seconds, generating an adjacency matrix to represent topological relationships. Graph theory algorithms (such as Dijkstra's algorithm) are then used to calculate the shortest path to verify connectivity. Analysis results show that a path delay of 50 milliseconds is less than the path delay threshold of 100 milliseconds, indicating that the path is normal. After completing the detection and scanning of each node, a complete mapping of the current network topology is obtained, along with the initial topology map data. If a node's communication is interrupted or its signal strength is less than a preset threshold (for example, an RSSI below -85 decibels or three consecutive no-response events), the node is automatically marked as unavailable, the change is reflected in the dynamic graph model, and the topology change timestamp (such as 2023-10-01 14:30:25) is recorded. According to the distribution of all unavailable nodes in the current network topology, a first distribution feature is obtained. Based on the first distribution feature and the communication link connectivity information, a graph traversal method is used to analyze the current network topology, determine the affected area of ​​the current network topology and connection change information, and record the above event information related to the unavailable nodes through a log system.

[0094] As an example of an embodiment of the present invention, the network topology change information includes connection relationship change information and an affected area, and the dynamic graph structure model is determined based on the current network topology structure and the network topology change information, specifically: if it is determined that there is a need for local adjustment of the current network topology structure based on the connection relationship change information of the unavailable nodes in the affected area, then the current node mapping relationship in the initial topology mapping data is updated according to the area range of the affected area and the connection relationship change information of the unavailable nodes in the affected area to obtain an updated node mapping relationship and an updated network topology structure, a preset adjacency matrix is ​​used to store the updated network topology structure, and the initial topology structure data is updated according to the updated network topology structure to obtain an updated topological structure data; based on the updated topological structure data, determine the change timestamp of each of the unavailable nodes, obtain the corresponding network topology change information based on the change timestamp, and determine the second distribution feature corresponding to the change trend according to the network topology change information; according to the second distribution feature, combined with the operating status data and the communication link connectivity information, use the support vector machine algorithm to predict the network topology stability within a preset time period to obtain potential risk nodes; determine an optimization adjustment strategy according to each of the potential risk nodes, iteratively update the updated node mapping relationship according to the optimization adjustment strategy, until the network topology data of the current iteration meets the preset stability condition, stop the iteration and obtain the target mapping relationship, and determine the dynamic graph structure model according to the target mapping relationship.

[0095] In this embodiment, the distribution of unavailable nodes is combined with the connectivity information of the communication links, and a graph traversal method is used to analyze the affected areas in the current network topology structure, determine the scope of the affected areas and the connection changes of the unavailable nodes, and judge the local adjustment requirements of the current network topology structure; according to the scope and connection changes of the affected areas, the current node mapping relationship of the current network topology structure is dynamically updated, and a pre-established adjacency matrix is ​​used to store the updated network topology structure to obtain the updated node mapping relationship and the updated topology structure data; through the updated topology structure data, the change timestamps of the nodes with signal attenuation and communication interruption in the time dimension are analyzed to obtain the change time The network topology change information corresponding to the stamp is used to determine the second characteristic distribution corresponding to the change trend; based on the second characteristic distribution, combined with the operating status data and the communication link connectivity information, the support vector machine algorithm is used to predict the stability of the network topology within a preset period of time in the future, and the identification results of the potential risk nodes are obtained; through the identification results of each potential risk node, an optimization adjustment strategy for the updated mapping relationship is generated for the weak links of the communication link and signal strength, and the updated node mapping relationship is iteratively updated according to the optimization adjustment strategy until the current optimized network topology meets the preset stability conditions, the iteration is stopped and the target mapping relationship is obtained, and the dynamic graph structure model is determined according to the target mapping relationship. For example, the topology change information is associated with the operating status of the power grid for analysis. If it is found that a node is unavailable, the current value of its neighboring node suddenly increases to 6.5 amperes. It is inferred that there may be load transfer, which in turn triggers the load balancing algorithm to optimize resource allocation and ensure the stability of the power grid.

[0096] As an example of an embodiment of the present invention, the multi-path planning calculation is performed on the backup path set by using the ant colony optimization algorithm to obtain a multi-path planning result, which is specifically:

[0097] An initial path allocation scheme corresponding to the backup path set is obtained, and characteristic data of each initial path in the initial path allocation scheme is determined, wherein the characteristic data includes path length, node reliability, and transmission delay; a path evaluation model is obtained, and the characteristic data is input into the path evaluation model so that the path evaluation model calculates the characteristic data of each initial path based on a preset path evaluation function to obtain a weight value corresponding to each initial path; initial paths with weight values ​​less than a preset threshold are deleted from the initial path allocation scheme to obtain a target path allocation scheme, and weight parameters of each target path are determined based on the weight values ​​of each target path in the target path allocation scheme; a multi-path planning calculation is performed on the target path allocation scheme using an ant colony optimization algorithm and the weight parameters of each target path to obtain the multi-path planning result.

[0098] In this embodiment, an ant colony optimization algorithm is used to perform a preliminary analysis of a set of backup paths, collect an initial path allocation plan corresponding to the set of backup paths, determine factors such as path length, node reliability, and transmission delay in the initial path allocation plan, and obtain characteristic data for each initial path. Based on the characteristic data, a pre-built path evaluation model is input. Weights are calculated for the backup paths using the path evaluation model, and a weight value for each path is determined based on the combined effects of path length and transmission delay. If the weight value of a backup path falls below a preset threshold, the path is removed from the set of backup paths, and the relevant characteristic data of the removed path is recorded to obtain an updated set of backup paths. For the updated set of backup paths, the updated node reliability between the backup paths is recalculated, and the updated transmission delays of the backup paths are analyzed to obtain an updated path priority sequence. Based on the updated path priority sequence and the node reliability analysis results, the backup paths are dynamically screened to determine a qualified available path combination. Based on the available path combinations, the weight parameters in the path evaluation model are updated to meet the requirements of multi-path planning, thereby obtaining a target path allocation plan. Based on the target path allocation plan, a pre-established database is used to record log information of path removal and screening to obtain multi-path planning data. Specifically, in the path optimization management of the power grid network, the ant colony optimization algorithm is used to perform multi-path planning calculations on the backup path set. For example, a set of 8 backup paths is extracted from the database. For each path, three parameters, path length, node reliability, and transmission delay, are collected. For example, the length of path P1 is 12.5 kilometers, the node reliability score is 0.88, and the transmission delay is 28.3 milliseconds. A path evaluation function is constructed based on these three parameters. The function formula is comprehensive weight value = 0.4×(1 / path length)+0.3×node reliability+0.3×(1 / transmission delay). Through calculation, the comprehensive weight value of path P1 is 0.7 2. Perform similar calculations on other paths. For example, path P2 has a length of 15.2 kilometers, a node reliability of 0.75, a transmission delay of 35.6 milliseconds, and a weight of 0.58. Set a weight threshold of 0.65 to automatically filter out paths with weights below the threshold. For example, path P2 is eliminated, while path P1 is retained. Sort the remaining paths to generate a priority list, and correlate the results with the real-time traffic data of the power grid. If the traffic on a certain path is close to saturation, for example, path P1 accounts for 85.4% of the traffic, part of the data flow is allocated to path P3, which has the second highest weight and accounts for 42.7% of the traffic, thereby achieving traffic balancing.

[0099] As an example of an embodiment of the present invention, the obtaining of the initial path allocation scheme corresponding to the backup path set is specifically as follows:

[0100] An initial connection relationship is determined based on the transmission node set and the backup path set, wherein the initial connection relationship refers to the connection relationship between the transmission node and the backup path; based on the priority sequence and the connectivity matrix, each transmission node in each transmission path is detected, and if there is a transmission node with an abnormal connection state, a path reconstruction mechanism is triggered to reconstruct the path of the transmission node with the abnormal connection state based on the path reconstruction mechanism; during the path reconstruction process, availability information of each backup path in the backup path set is obtained in real time, path reachability is calculated based on the availability information, and the initial connection relationship of the transmission node with the abnormal connection state is updated based on the path reachability to obtain an intermediate connection relationship ; Use a preset adjacency table to store intermediate connection relationships, and update the initial topology mapping data to obtain updated topology mapping data; Based on the updated topology mapping data, analyze the correlation between each key node and each transmission node during the connection relationship update process, determine whether there is a potential connection risk, and determine the fourth distribution feature corresponding to the risk node; Based on the fourth distribution feature and path reconstruction data, use a support vector machine algorithm to evaluate the stability of the network topology, and obtain a fifth distribution feature corresponding to stability lower than a preset threshold; Through the fifth distribution feature, update the intermediate connection relationship and the connectivity matrix to obtain the initial path allocation plan for multi-path planning calculation of the backup path set.

[0101] In this embodiment, an initial connection relationship is determined based on the transmission node set and the backup path set, and availability information of the backup path is obtained based on the path priority sequence; based on the availability information of the backup path, the path reachability is recalculated, and a pre-established adjacency table is used to store the updated connection relationship to obtain updated network topology data; through the updated network topology data, the correlation between key nodes and transmission nodes in the change information is analyzed to determine whether there is a potential connection risk, and the fourth distribution feature corresponding to the distribution range of the risk node is determined; based on the fourth distribution feature and in combination with the path reconstruction records, the support vector machine algorithm is used to evaluate the stability of the network topology to determine the fifth distribution feature; through the fifth distribution feature, the connection relationship between the backup path and the transmission node is dynamically adjusted, the data of the connectivity matrix is ​​updated, and an initial path allocation plan is obtained.

[0102] As an example of an embodiment of the present invention, the data communication method under disaster conditions also includes: based on a network status feedback mechanism, obtaining the current network status and performance information of each target path, marking the target path whose performance information is less than a preset performance threshold as an inefficient path, and determining an inefficient path list based on the inefficient paths in the target multi-path planning result; for each inefficient node in the inefficient path list, obtaining the recovery status information of each inefficient node, and evaluating the recovery potential of the inefficient node based on the historical recovery success rate to obtain a node recovery evaluation result; based on the node recovery evaluation result of each inefficient node, updating the weight parameter of the target path to obtain an updated weight parameter; iteratively optimizing the path selection strategy based on the updated weight parameter, and using a support vector machine algorithm to predict the path selection of the next state to obtain an optimized path allocation scheme; performing data transmission of the next state according to the optimized path allocation scheme, and obtaining performance information of each state until the performance information is greater than or equal to the preset performance threshold, stopping the iteration to obtain the target path selection strategy, and performing data transmission of the next state according to the target path selection strategy.

[0103] In this embodiment, network status and performance information is obtained from each transmission path through a feedback mechanism, and the obtained data is preliminarily sorted to obtain a performance evaluation set of path transmission. Based on the performance evaluation set, a pre-established analysis tool is used to compare the performance information of the path transmission with the current status. If the performance of a certain path is found to be lower than a preset threshold, it is marked as an inefficient path, and a list of inefficient paths is determined. For the list of inefficient paths, node recovery status information is obtained, and a comprehensive evaluation is performed in combination with historical success rate data to determine whether the node has recovery potential, thereby obtaining a node recovery evaluation result. Based on the node recovery evaluation result, the path planning module is used to adjust the weight evaluation model. Reliability parameters are updated to determine an adjusted set of path weights. The path selection strategy is dynamically optimized using the adjusted set of path weights in combination with current status data. The support vector machine algorithm, part of the machine learning algorithm, is used to predict path selection and obtain an optimized path allocation plan. Network status feedback is obtained for subsequent transmission tasks based on the optimized path allocation plan. If the feedback indicates that the path transmission performance does not meet expectations, the path weight set is adjusted again to determine the final path selection strategy. Based on the final path selection strategy, transmission tasks are assigned to the corresponding paths, and the transmission performance information of the assigned paths is continuously monitored to obtain real-time performance feedback data.

[0104] Specifically, in the management of power grid data transmission, the network status feedback mechanism is used to optimize path selection and node management to improve transmission efficiency. The real-time monitoring module collects path transmission performance data. For example, the current delays of paths X, Y, and Z are 12.5 milliseconds, 18.7 milliseconds, and 9.3 milliseconds, respectively. At the same time, the recovery status of nodes N1 and N2 is recorded. The availability of N1 is 98.2%, and that of N2 is 85.6%. These data are transmitted back to the path planning module via the feedback protocol in a 0.01MB message size, which takes 0.03 seconds. Machine learning algorithms, such as the support vector machine model, are used in combination with historical transmission success rate data (path X is 96.4%, Based on the current network status and the calculated weight of path Z, the path weight is updated, and the calculated weight value of path Z is 0.78, which has the highest priority. The update process takes 0.05 seconds. At the same time, the node reliability parameters are adjusted through the algorithm, and the reliability parameter of N2 is increased from 0.65 to 0.72 based on its fault recovery record analysis in the past 24 hours. The subsequent data transmission strategy is dynamically adjusted according to the updated weights and parameters. It is decided to prioritize the next batch of data packets D1 to D5 to path Z, and set a backup path Y for node N2 as a redundancy guarantee. The adjustment process is completed through an automated scheduling algorithm and takes 0.02 seconds.

[0105] Furthermore, to form a closed-loop logic, it can be linked with the power grid load balancing module to analyze the current network load distribution. The occupancy rate of path Z is 22.4%, confirming that its carrying capacity is sufficient to ensure transmission stability after the strategy adjustment.

[0106] As an example of an embodiment of the present invention, the power grid information packet includes a power grid dispatch instruction set and a monitoring data set, and the data communication method under disaster conditions also includes: using a priority queue management mechanism to classify and process the power grid dispatch instruction set, determining the priority corresponding to each power grid dispatch instruction, and obtaining an instruction priority sequence; based on the instruction priority sequence and the target multi-path planning result, determining an instruction allocation scheme, and based on the instruction allocation scheme, executing corresponding power grid node control operations for the power grid dispatch instruction corresponding to the emergency instruction, and obtaining execution status data corresponding to the power grid dispatch instruction; if the execution status data shows that data transmission is interrupted, switching to a backup path for transmission until the transmission is completed, and collecting operation result; extract node status information from the operation result, determine whether the operation result meets the preset threshold condition, and if not, modify the node status to an abnormal state, and determine the abnormal state list corresponding to each node in the abnormal state; for each node in the abnormal state list, determine whether there is evaluation data corresponding to a potential fault for each node, update the instruction priority sequence according to the evaluation data, and obtain an updated instruction allocation scheme; according to the updated instruction allocation scheme, use a support vector machine algorithm to predict the instruction priority sequence of the next state to obtain a prediction result, and determine a target instruction priority sequence according to the prediction result, and the target instruction priority sequence is used to adjust the instruction allocation scheme for the next state.

[0107] In this embodiment, after obtaining the complete reorganized dispatching instructions and monitoring data, a priority queue management mechanism is used to classify and process different types of power grid control instructions. If an emergency dispatching instruction is received, the corresponding power grid node control operation is immediately executed, and the execution result is fed back to the dispatching center through the same multi-path transmission mechanism. Specifically, after obtaining the monitoring data and dispatching instructions transmitted from each power grid node, a pre-established classification model is used to identify the instruction type to determine whether the instruction is an emergency instruction. If it is an emergency instruction, it immediately enters the priority processing process to obtain an instruction priority sequence; according to the instruction priority sequence, the corresponding power grid node control operation is triggered for the emergency instruction, and an execution signal is sent to the target node through a preset control interface to determine whether the operation has been successfully issued; the execution status data after the operation is issued is obtained, and the execution status data is distributed to the dispatching center through a multi-path transmission mechanism to determine whether the data transmission is complete. If an interruption occurs during the transmission process, it switches to the backup path to obtain a transmission completion confirmation; according to the transmission completion confirmation, the feedback mechanism is used to organize the operation results, extract key status information from it, and determine the operation Whether the result meets the preset threshold requirements, if not, it is marked as an abnormal state and a list of abnormal states is determined; for the abnormal state list, the operating status of the power grid nodes is compared through historical data analysis tools to obtain evaluation data on whether there are potential faults in the nodes, determine whether the priority of the control instructions needs to be adjusted, and obtain an adjusted instruction allocation plan; based on the adjusted instruction allocation plan, the support vector machine algorithm is used to predict and optimize the processing order of subsequent instructions, extract the optimal processing path from the prediction results, and determine the final instruction execution sequence; for the final instruction execution sequence, the adjusted control instructions are sent to each power grid node through a multi-path transmission mechanism, and feedback data on the node execution status is continuously obtained to determine whether the feedback data is stable. If it is unstable, it is recorded as a pending issue and a real-time monitoring log is obtained.

[0108] For example, in the management of power grid control instructions, the size of the power grid information packet is 0.25MB, and the data collection takes 0.08 seconds. The priority queue management mechanism is then used to classify and process the dispatch instructions. A sorting algorithm based on time sensitivity is used to divide the instructions into three categories: emergency, general, and low priority. For example, among the currently received instructions, the response time requirement for the emergency dispatch instruction A1 is 0.1 seconds, the general instruction B1 is 1.5 seconds, and the low priority instruction C1 is 5.0 seconds. For the emergency dispatch instruction A1, the power grid node control operation is immediately triggered, and the preset quick response is called. The algorithm calculates that the load adjustment value of the target node M1 needs to be reduced from 75.3% to 60.8%, and generates a control signal. The signal takes 0.05 seconds to transmit to node M1. The execution result shows that the load adjustment is successful, and the actual value is reduced to 61.2%. The error rate is 0.4%, which is within the expected range. The execution result is fed back to the dispatch center through the multi-path transmission mechanism. Two paths P1 and P2 are selected for redundant transmission. The delay of P1 is 8.7 milliseconds and that of P2 is 10.2 milliseconds. P1 is selected as the main path. The data packet size is 0.03MB, and the feedback takes 0.04 seconds.

[0109] Furthermore, to ensure closed-loop management, the system was linked with the grid monitoring system to analyze the real-time operating data of node M1. It was found that its voltage fluctuation rate was 1.8%, which was lower than the safety threshold of 2.5%. After confirming that there were no abnormalities in stability after the operation, the analysis was completed.

[0110] Through the above process, fully automated processing from instruction classification to execution feedback is achieved, ensuring the efficiency and reliability of power grid control.

[0111] like Figure 2 As shown, based on the above method embodiment, a corresponding system embodiment is provided; an embodiment of the present invention provides a data communication system 200 under disaster conditions, including: a topology module 202, an initial path planning module 204, a data transmission module 206 and a data abnormal transmission processing module 208;

[0112] The topology module 202 is configured to determine a dynamic graph structure model based on the current network topology and network topology change information, and calculate the dynamic graph structure model using a graph theory algorithm to determine a set of transmission nodes and a set of backup paths, wherein the network topology change information refers to information indicating that a node has experienced a communication interruption caused by a disaster or that signal attenuation exceeds a preset threshold.

[0113] The initial path planning module 204 is configured to perform multi-path planning calculations on the backup path set using an ant colony optimization algorithm to obtain a multi-path planning result if a transmission node in the transmission node set has an abnormal connection state;

[0114] The data transmission module 206 is used to split the power grid information packet into multiple data packets, transmit the data packets according to the multi-path planning results, and monitor the transmission progress and arrival confirmation information of each data packet in real time;

[0115] The data abnormal transmission processing module 208 is used to update the multi-path planning result based on the preset path switching rules if it is detected that the data packet has a transmission timeout or loss, obtain a target multi-path planning result, and re-route the remaining data packets on the abnormal transmission path corresponding to the data packet with transmission timeout or loss according to the target multi-path planning result to achieve data communication under disaster conditions.

[0116] It can be understood that the above-mentioned system embodiment corresponds to the method embodiment of the present invention, which can implement any of the above-mentioned method embodiments of the present invention to provide a data communication method under disaster conditions.

[0117] It should be noted that the system embodiments described above are merely illustrative, and some or all of the modules may be selected to achieve the objectives of the present embodiments as needed. Furthermore, in the drawings of the system embodiments provided herein, the connection relationships between modules indicate that they have communication connections, which may be implemented as one or more communication buses or signal lines. Persons of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0118] Based on the above-mentioned embodiment of the data communication method under disaster conditions, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the data communication method under disaster conditions of any embodiment of the present invention is implemented.

[0119] For example, in this embodiment, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more module elements may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device.

[0120] The terminal device may be a computing device such as a desktop computer, a notebook computer, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0121] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, connecting various parts of the entire terminal device using various interfaces and lines.

[0122] Based on the above-mentioned method embodiments, another embodiment of the present invention provides a computer-readable storage medium, including a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the data communication method under disaster conditions described in any one of the above-mentioned method embodiments of the present invention.

[0123] Wherein, the module / unit integrated in the device / terminal equipment, if implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0124] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A data communication method under disaster conditions, characterized in that: include: Determine a dynamic graph structure model based on the current network topology and network topology change information, calculate the dynamic graph structure model using a graph theory algorithm, and determine a set of transmission nodes and a set of backup paths, wherein the network topology change information refers to information that indicates a communication interruption caused by a disaster at a node or that signal attenuation exceeds a preset threshold; If a transmission node in the transmission node set has an abnormal connection state, performing a multi-path planning calculation on the backup path set by using an ant colony optimization algorithm to obtain a multi-path planning result; Splitting the power grid information packet into multiple data packets, transmitting data for each of the data packets using the multi-path planning result, and monitoring the transmission progress and arrival confirmation information of each of the data packets in real time; If it is detected that the data packet has a transmission timeout or loss, the multi-path planning result is updated based on the preset path switching rules to obtain the target multi-path planning result. The remaining data packets on the abnormal transmission path corresponding to the data packet with transmission timeout or loss are rerouted according to the target multi-path planning result to achieve data communication under disaster conditions.

2. The data communication method under disaster conditions according to claim 1, characterized in that: Before determining the dynamic graph structure model based on the current network topology structure and the network topology change information, the method further includes: Using signal strength detection and connection status scanning, the real-time collected operating status data and communication link connectivity information of each node in the power grid are processed to determine the current node mapping relationship of the current network topology structure, and obtain initial topology structure data and initial topology mapping data; Analyze the signal strength and connection status of each node one by one according to the initial topology mapping data, and if an unavailable node corresponding to a signal strength lower than a preset threshold or a connection status display interruption is detected, determine a first distribution feature corresponding to each unavailable node; Based on the first distribution feature and the communication link connectivity information, a graph traversal method is used to analyze the current network topology structure to obtain the network topology change information.

3. The data communication method under disaster conditions according to claim 2, characterized in that: in, The network topology change information includes connection relationship change information and affected areas. The dynamic graph structure model is determined based on the current network topology structure and the network topology change information, specifically: If it is determined that there is a need for local adjustment of the current network topology structure based on the connection relationship change information of the unavailable nodes in the affected area, then the current node mapping relationship in the initial topology mapping data is updated according to the area range of the affected area and the connection relationship change information of the unavailable nodes in the affected area to obtain an updated node mapping relationship and an updated network topology structure, a preset adjacency matrix is ​​used to store the updated network topology structure, and the initial topology structure data is updated according to the updated network topology structure to obtain updated topology structure data; Determining a change timestamp of each of the unavailable nodes based on the updated topology data, obtaining corresponding network topology change information based on the change timestamp, and determining a second distribution feature corresponding to a change trend according to the network topology change information; Based on the second distribution feature, combined with the operating status data and the communication link connectivity information, a support vector machine algorithm is used to predict the network topology stability within a preset time period to obtain potential risk nodes; An optimization adjustment strategy is determined based on each of the potential risk nodes, and the updated node mapping relationship is iteratively updated according to the optimization adjustment strategy until the network topology data of the current iteration meets the preset stability conditions. The iteration is stopped and the target mapping relationship is obtained, and the dynamic graph structure model is determined based on the target mapping relationship.

4. The data communication method under disaster conditions according to claim 2, wherein: The dynamic graph structure model is calculated by a graph theory algorithm to determine the transmission node set and the backup path set, specifically: Calculating the connectivity matrix between nodes in the dynamic graph structure model using a graph theory algorithm, analyzing the connection relationship and path reachability between the nodes, and obtaining a plurality of key nodes and a third distribution feature corresponding to each of the key nodes; According to the third distribution feature, the node degree and edge weight value of each of the key nodes are analyzed to determine the transmission node set, the backup path set, and the priority sequence between the transmission paths.

5. The data communication method under disaster conditions according to claim 4, characterized in that: The multi-path planning calculation is performed on the backup path set by the ant colony optimization algorithm to obtain a multi-path planning result, which is specifically: Obtaining an initial path allocation scheme corresponding to the backup path set, and determining characteristic data of each initial path in the initial path allocation scheme, wherein the characteristic data includes path length, node reliability, and transmission delay; Obtaining a path evaluation model, inputting the characteristic data into the path evaluation model so that the path evaluation model calculates the characteristic data of each of the initial paths based on a preset path evaluation function to obtain a weight value corresponding to each of the initial paths; deleting initial paths having weight values ​​less than a preset threshold from the initial path allocation plan, obtaining a target path allocation plan, and determining a weight parameter for each of the target paths based on the weight values ​​of each target path in the target path allocation plan; The multi-path planning calculation is performed on the target path allocation scheme by using an ant colony optimization algorithm and weight parameters of each target path to obtain the multi-path planning result.

6. The data communication method under disaster conditions according to claim 5, characterized in that: The obtaining of the initial path allocation solution corresponding to the backup path set is specifically as follows: Determining an initial connection relationship according to the transmission node set and the backup path set, wherein the initial connection relationship refers to a connection relationship between a transmission node and a backup path; Based on the priority sequence and the connectivity matrix, each transmission node in each transmission path is detected, and if there is a transmission node with an abnormal connection status, a path reconstruction mechanism is triggered to reconstruct the path of the transmission node with the abnormal connection status based on the path reconstruction mechanism; During the path reconstruction process, availability information of each backup path in the backup path set is obtained in real time, path reachability is calculated based on the availability information, and initial connection relationships of transmission nodes with abnormal connection status are updated based on the path reachability to obtain intermediate connection relationships; Using a preset adjacency table to store intermediate connection relationships, and updating the initial topology mapping data to obtain updated topology mapping data; Based on the updated topology mapping data, analyzing the correlation between each of the key nodes and each of the transmission nodes during the connection relationship update process, determining whether there is a potential connection risk, and determining a fourth distribution feature corresponding to the risk node; Based on the fourth distribution feature and the path reconstruction data, a support vector machine algorithm is used to evaluate the stability of the network topology to obtain a fifth distribution feature corresponding to stability being lower than a preset threshold; The intermediate connection relationship and the connectivity matrix are updated by using the fifth distribution feature to obtain the initial path allocation solution for use in multi-path planning calculation of the backup path set.

7. The data communication method under disaster conditions according to claim 6, characterized in that: The data communication method under disaster conditions further includes: Based on a network status feedback mechanism, the current network status and performance information of each target path is obtained, target paths whose performance information is less than a preset performance threshold are marked as inefficient paths, and an inefficient path list is determined based on the inefficient paths in the target multi-path planning result; For each inefficient node in the inefficient path list, obtaining recovery status information of each inefficient node, and evaluating the recovery potential of the inefficient node based on a historical recovery success rate to obtain a node recovery evaluation result; updating the weight parameters of the target path according to the node recovery evaluation results of each of the inefficient nodes to obtain updated weight parameters; Iteratively optimizing the path selection strategy according to the updated weight parameters, and using a support vector machine algorithm to predict the path selection of the next state to obtain an optimized path allocation solution; Data transmission for the next state is performed according to the optimized path allocation scheme, and performance information of each state is obtained until the performance information is greater than or equal to a preset performance threshold. The iteration is stopped to obtain a target path selection strategy, and data transmission for the next state is performed according to the target path selection strategy.

8. The data communication method under disaster conditions according to claim 1, wherein: in, The power grid information package includes a power grid dispatch instruction set and a monitoring data set, and the data communication method under disaster conditions further includes: The priority queue management mechanism is used to classify the power grid dispatch instruction set, determine the priority corresponding to each power grid dispatch instruction, and obtain an instruction priority sequence; Determine an instruction allocation scheme based on the instruction priority sequence and the target multi-path planning result, and execute corresponding power grid node control operations for power grid dispatch instructions corresponding to emergency instructions based on the instruction allocation scheme, and obtain execution status data corresponding to the power grid dispatch instructions; If the execution status data indicates that data transmission is interrupted, switching to a backup path for transmission until the transmission is completed, and collecting the operation results; Extracting node status information from the operation result, determining whether the operation result meets a preset threshold condition, and if not, modifying the node status to an abnormal state, and determining an abnormal state list corresponding to each node in the abnormal state; For each node in the abnormal status list, determining whether there is evaluation data corresponding to a potential fault at each node, and updating the instruction priority sequence according to the evaluation data to obtain an updated instruction allocation plan; According to the updated instruction allocation scheme, the support vector machine algorithm is used to predict the instruction priority sequence of the next state to obtain a prediction result, and the target instruction priority sequence is determined based on the prediction result. The target instruction priority sequence is used to adjust the instruction allocation scheme of the next state.

9. A data communication system under disaster conditions, characterized in that: include: Topology module, initial path planning module, data transmission module and data abnormality transmission processing module; The topology module is configured to determine a dynamic graph structure model based on the current network topology and network topology change information, calculate the dynamic graph structure model using a graph theory algorithm, and determine a set of transmission nodes and a set of backup paths, wherein the network topology change information refers to information indicating that a node has experienced a communication interruption caused by a disaster or that signal attenuation is greater than a preset threshold; The initial path planning module is configured to perform multi-path planning calculation on the backup path set using an ant colony optimization algorithm to obtain a multi-path planning result if a transmission node in the transmission node set has an abnormal connection state; The data transmission module is used to split the power grid information packet into multiple data packets, transmit data for each of the data packets according to the multi-path planning result, and monitor the transmission progress and arrival confirmation information of each of the data packets in real time; The data abnormal transmission processing module is used to update the multi-path planning result based on the preset path switching rules if it is detected that the data packet has a transmission timeout or loss, obtain a target multi-path planning result, and reroute the remaining data packets on the abnormal transmission path corresponding to the data packet with transmission timeout or loss according to the target multi-path planning result to achieve data communication under disaster conditions.

10. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the method for data communication under disaster conditions according to any one of claims 1 to 8 is implemented.

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