A power data intelligent acquisition method and device based on dual-mode communication

Abstract

The application discloses a kind of power data intelligent acquisition method and device based on dual-mode communication, it is related to electric power communication technical field;Method includes, each transmission equipment is determined as node, wireless channel delay and wired channel delay are determined as first channel edge and second channel edge respectively, and target network topology graph is constructed;The noise coefficient value is obtained by calculating target node in target network topology graph, and target node is any one node in target network topology graph;The noise coefficient value of all nodes of target network topology graph is acquired, and constitutes noise node set;Receiving target transmission equipment request information, according to noise node set and preset noise threshold value, final network topology graph is obtained by reconstruction;The optimal transmission path is obtained by path planning to final network topology graph, and optimal transmission path is sent to target transmission equipment, and the power data transmission to power grid is acquired.The application shortens power transmission delay, and then improves the overall efficiency of power transmission.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of electric power communication, in particular to an electric power data intelligent acquisition method and device based on dual-mode communication. BACKGROUND

[0002] Electric power communication undertakes the task of transmitting and interacting relevant information of electric power production and operation in the electric power system, and guarantees that each link of the electric power system can work efficiently and stably. With the help of various communication means such as optical fiber communication, wireless communication and carrier communication, the electric power communication constructs a communication network with wide coverage and high safety and reliability, realizes real-time transmission of various data (such as device operation state data and electric power dispatching instructions) in the electric power system, and forms a multi-dimensional communication system to serve the electric power system and provide solid information transmission guarantee for safe, economic and high-quality operation of the electric power system.

[0003] The prior art (publication number: CN117335906A) discloses a dual-mode communication network channel selection method. First, the electric meter device nodes are numbered by modeling the dual-mode network of the table area. Second, the network inside and between the meter boxes is divided according to the spatial distribution. Then, an adaptive communication mechanism is designed, and a communication link evaluation based on the change rate of the received signal strength is used to realize automatic selection of the dual-mode communication mode. In the case of multiple relay nodes, the best relay node is selected by the maximum minimum signal-to-noise ratio criterion. According to the real-time channel state and network environment, the communication mode and relay path are optimized to improve the completeness rate and real-time performance of data transmission, enhance the reliability and anti-interference ability of the communication network, and support smart grid and distributed energy management applications.

[0004] The above-mentioned patent combines power line carrier and micro-power wireless communication organically to realize adaptive switching, but in actual application, switching delay and noise are generated in the adaptive switching process, which reduces the overall efficiency of electric power transmission. SUMMARY

[0005] The purpose of the present application is to solve the problem of switching delay and noise generated in the adaptive switching process, which reduces the overall efficiency of electric power transmission, and to propose an electric power data intelligent acquisition method and device based on dual-mode communication.

[0006] The purpose of the present application can be achieved by the following technical solutions:

[0007] First, an electric power data intelligent acquisition method based on dual-mode communication is proposed. The electric power control network includes an SDN remote server and a preset number of transmission devices. The SDN end sends control instructions to each transmission device. The method is applied to the SDN remote server, and the method includes:

[0008] acquiring a wireless channel delay and a wired channel delay between each transmission device in a target area;

[0009] determining each transmission device as a node, determining the wireless channel delay and the wired channel delay as a first channel edge and a second channel edge respectively, constructing a target network topology graph according to all nodes, all first channel edges and all second channel edges of the target area; there is a first channel edge and a second channel edge between any two nodes in the target network topology graph;

[0010] calculating a target node in the target network topology graph to obtain a noise coefficient value, the target node being any one node in the target network topology graph; acquiring noise coefficient values of all nodes of the target network topology graph, and forming a noise node set;

[0011] receiving request information of a target transmission device, determining a sending end and a receiving end according to the request information, the target transmission device being any one of a preset number of transmission terminals; and reconstructing a final network topology graph according to the sending end, the receiving end, the noise node set and a preset noise threshold;

[0012] path planning for the final network topology graph to obtain an optimal transmission path;

[0013] sending the optimal transmission path to the target transmission device, so that the target transmission device transmits power data through the power grid.

[0014] Optionally, the calculating the target node in the target network topology graph to obtain the noise coefficient value comprises:

[0015] a formula for obtaining the noise coefficient value of the target node is:

[0016] ;

[0017] wherein, represents a noise value of the target node, and is normalized to obtain the noise coefficient value, and is denoted as L; m represents a number of connections between the target node and adjacent nodes, M represents an upper threshold of the number of connections between the target node and adjacent nodes; p0 represents a standard frequency band of the target node, n represents a total number of characteristic frequency bands generated by the target node, pi represents an i-th characteristic frequency band generated by the target node, and e represents a natural constant.

[0018] Optionally, the path planning for the final network topology graph to obtain the optimal transmission path comprises:

[0019] determining a starting point and an ending point according to the final network topology graph, and acquiring all intermediate nodes between the starting point and the ending point;

[0020] dividing each cluster by using a K-means algorithm on the final network topology graph;

[0021] optimizing a target path in the target cluster to obtain a local optimal transmission path; the target cluster is any one of the clusters;

[0022] obtaining the local optimal transmission path corresponding to each cluster, and determining a local optimal transmission path set;

[0023] sorting the local optimal transmission path set in ascending order of fitness value of a target local optimal transmission path to obtain an optimal transmission path; the target local optimal transmission path is any one of the local optimal transmission paths.

[0024] Optionally, the step of dividing each cluster by using a K-means algorithm on the final network topology graph comprises:

[0025] S1: extracting all intermediate nodes in the final network topology graph, performing a K-means algorithm on all intermediate nodes to obtain each initial cluster, and calculating the number of intermediate nodes in each initial cluster;

[0026] S2: migrating the intermediate node closest to the current cluster center from the adjacent cluster to the cluster with a number of intermediate nodes less than a preset average number of nodes;

[0027] S3: repeating step S2 until the number of intermediate nodes in each initial cluster reaches the preset average number of nodes, and outputting each cluster.

[0028] Optionally, the step of optimizing a target path in the target cluster to obtain a local optimal transmission path comprises:

[0029] initializing the target cluster as a population, calculating the fitness of each chromosome in the population by using a fitness formula, and taking the optimal chromosome corresponding to the highest fitness as an evolution target to perform iteration by using a quantum evolution operation; the population comprises a plurality of chromosomes;

[0030] if the quantum genetic algorithm meets a convergence error or reaches a maximum number of iterations, outputting the local optimal transmission path;

[0031] The fitness formula is specifically:

[0032] ;

[0033] wherein, γ represents the fitness, represents the wireless channel delay of the connection edge between the jth node in the target cluster and the previous node; a wired channel delay representing a connection edge between the jth node in the chromosome and a previous node; j=1, 2, …, J; J represents the total number of nodes in the target clustering cluster, YS represents a preset noise threshold value; a noise coefficient value representing the jth node in the target clustering cluster.

[0034] An intelligent power data acquisition device based on dual-mode communication is provided, comprising:

[0035] A data acquisition module: acquiring wireless channel delays and wired channel delays between each transmission device in a target area;

[0036] A network topology graph module: determining each transmission device as a node, determining the wireless channel delays and the wired channel delays as first channel edges and second channel edges respectively, and constructing a target network topology graph according to all nodes, all first channel edges and all second channel edges of the target area; there are first channel edges and second channel edges between any two nodes in the target network topology graph;

[0037] A noise node set module: calculating a noise coefficient value of a target node in the target network topology graph, the target node being any one node in the target network topology graph; acquiring noise coefficient values of all nodes of the target network topology graph and forming a noise node set;

[0038] A final network topology graph module: receiving request information of a target transmission device, determining a sending end and a receiving end according to the request information, the target transmission device being any one of a preset number of transmission terminals; and reconstructing a final network topology graph according to the sending end, the receiving end, the noise node set and a preset noise threshold value;

[0039] A path planning module: performing path planning on the final network topology graph to obtain an optimal transmission path, and sending the optimal transmission path to the target transmission device, so that the target transmission device transmits power data through the power grid.

[0040] Optionally, the noise node set module is further configured to: the formula for acquiring the noise coefficient value of the target node is:

[0041] ;

[0042] wherein, the noise value of the target node, and the noise coefficient value is obtained by normalizing, and is denoted as L; m represents the number of connections between the target node and adjacent nodes, M represents an upper threshold of the number of connections between the target node and adjacent nodes; p0 represents a standard frequency band of the target node, n represents the total number of characteristic frequency bands generated by the target node, pi represents the ith characteristic frequency band generated by the target node, and e represents a natural constant.

[0043] Optionally, the path planning module comprises a start-end point module, a cluster generation module, a local optimal path module and an optimal transmission path module.

[0044] The start-end point module is configured to determine a start point and an end point according to the final network topology graph, and acquire all intermediate nodes between the start point and the end point.

[0045] The cluster generation module is configured to divide the final network topology graph using a K-means algorithm to obtain each cluster.

[0046] The local optimal path module is configured to perform target path optimization on the target cluster to obtain a local optimal transmission path; the target cluster is any one of the clusters.

[0047] The optimal transmission path module is configured to acquire the local optimal transmission path corresponding to each cluster, and determine a local optimal transmission path set; and sort the local optimal transmission path set in ascending order of the fitness value of a target local optimal transmission path to obtain an optimal transmission path; the target local optimal transmission path is any one of the local optimal transmission paths.

[0048] Optionally, the dividing of the final network topology graph using the K-means algorithm to obtain each cluster comprises:

[0049] S1: extracting all intermediate nodes in the final network topology graph, performing the K-means algorithm on all the intermediate nodes to obtain each initial cluster, and calculating the number of intermediate nodes of each initial cluster;

[0050] S2: migrating the intermediate nodes closest to the current cluster center from adjacent clusters to the cluster whose number of intermediate nodes is less than a preset average number of nodes;

[0051] S3: repeating step S2 until the number of intermediate nodes of each initial cluster reaches the preset average number of nodes, and outputting each cluster.

[0052] Optionally, the target path optimization on the target cluster to obtain the local optimal transmission path comprises:

[0053] initializing the target cluster as a population, calculating the fitness of each population through a fitness formula, and taking the optimal chromosome corresponding to the highest fitness as an evolution target to perform iteration through quantum evolution operation; the population comprises a plurality of chromosomes;

[0054] if the quantum genetic algorithm meets a convergence error or reaches a maximum number of iterations, outputting the local optimal transmission path;

[0055] The fitness formula is specifically:

[0056] ;

[0057] wherein, gamma represents fitness, represents the wireless channel delay of the connection edge between the jth node and the previous node in the target cluster; represents the wired channel delay of the connection edge between the jth node and the previous node in the chromosome; j=1, 2, …, J; J represents the total number of nodes in the target cluster, and YS represents a preset noise threshold value; represents the noise coefficient value of the jth node in the target cluster.

[0058] The beneficial effects of the present application are:

[0059] The present application provides a power data intelligent acquisition method based on dual-mode communication, which comprises the following steps: acquiring each transmission device in a target area, acquiring the wireless channel delay and the wired channel delay between each transmission device in the target area; determining each transmission device as a node, determining the wireless channel delay and the wired channel delay as a first channel edge and a second channel edge respectively, and constructing a target network topology graph according to all nodes, all first channel edges and all second channel edges of the target area; there are first channel edges and second channel edges between any two nodes in the target network topology graph; calculating the noise coefficient value of the target node in the target network topology graph, wherein the target node is any one node in the target network topology graph; acquiring the noise coefficient value of all nodes of the target network topology graph, and forming a noise node set; receiving the request information of the target transmission device, determining the sending end and the receiving end according to the request information, wherein the target transmission device is any one of the preset number of transmission terminals; reconstructing the final network topology graph according to the sending end, the receiving end, the noise node set and the preset noise threshold value; performing path planning on the final network topology graph to obtain an optimal transmission path; and transmitting the optimal transmission path to the target transmission device, and transmitting the power data obtained by the target transmission device to the power grid. The present application constructs a target network topology graph by acquiring the wireless and wired channel delays between transmission devices, calculates the noise coefficient of each node and constructs a noise node set, and reconstructs the final network topology graph in combination with the preset threshold value, which can effectively screen out transmission paths with serious noise interference and greatly improve the accuracy of power data transmission. Then, the final network topology graph is subjected to path planning to obtain an optimal transmission path, thereby shortening the power transmission delay and improving the overall efficiency of power transmission. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 A flowchart of a power data intelligent acquisition method based on dual-mode communication provided by the embodiment of the present application is shown in the figure;

[0061] Figure 2 A flowchart of path planning provided by the embodiment of the present application is shown in the figure;

[0062] Figure 3 A framework diagram of an intelligent power data acquisition device based on dual-mode communication is provided for the embodiments of the present application. DETAILED DESCRIPTION

[0063] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0064] The embodiments of the present application provide an intelligent power data acquisition method based on dual-mode communication. Figure 1 , Figure 1 A flowchart of an intelligent power data acquisition method based on dual-mode communication is provided for the embodiments of the present application.

[0065] The power control network comprises an SDN remote server and a preset number of transmission devices; the SDN end sends a control instruction to each transmission device, and a target transmission device controls the power data of the corresponding transmission device according to the control instruction, and the target transmission device is any one of the preset number of transmission terminals;

[0066] The method comprises the following steps:

[0067] Step one: obtaining each transmission device in the target area, and obtaining the wireless channel delay and the wired channel delay between each transmission device in the target area;

[0068] Step two: determining each transmission device as each node, determining the wireless channel delay and the wired channel delay as a first channel edge and a second channel edge respectively, and constructing a target network topology graph according to all nodes, all first channel edges and all second channel edges of the target area; there is a first channel edge and a second channel edge between any two nodes in the target network topology graph;

[0069] Step three: calculating a noise coefficient value of a target node in the target network topology graph to obtain the noise coefficient value, the target node being any one node in the target network topology graph; obtaining the noise coefficient values of all nodes of the target network topology graph, and forming a noise node set;

[0070] Step four: receiving request information of a target transmission device, determining a sending end and a receiving end according to the request information, the target transmission device being any one of the preset number of transmission terminals, and re-construction to obtain a final network topology graph according to the noise node set and a preset noise threshold;

[0071] Step 5: Perform path planning on the final network topology to obtain the optimal transmission path, send the optimal transmission path to the target transmission device, and transmit the power data to the power grid based on the power data obtained by the target transmission device.

[0072] This invention provides a method for intelligent power data acquisition based on dual-mode communication. By acquiring the wireless and wired channel delays between transmission devices to construct a target network topology, calculating the noise coefficient of each node and constructing a set of noisy nodes, and reconstructing the final network topology using a preset threshold, the method effectively filters out transmission paths with severe noise interference, significantly improving the accuracy of power data transmission. Furthermore, path planning is performed on the final network topology to obtain the optimal transmission path, thereby shortening power transmission delay and improving the overall efficiency of power transmission.

[0073] Specifically, the target area can be the power supply area of ​​a substation, etc.; the transmission equipment is used for power transmission information, mainly including modulators, demodulators, etc.

[0074] In one implementation, the noise figure value calculated for the target node in the target network topology graph includes:

[0075] The formula for obtaining the noise figure value of the target node is:

[0076] ;

[0077] in, Represents the noise value of the target node, for The noise figure value is obtained by normalization and denoted as L; m represents the number of connections between the target node and its neighboring nodes, M represents the upper limit threshold of the number of connections between the target node and its neighboring nodes; p0 represents the standard frequency band of the target node, n represents the total number of characteristic frequency bands generated by the target node, pi represents the i-th characteristic frequency band generated by the target node, and e represents the natural constant.

[0078] In one implementation, the characteristic frequency band is a key frequency information segment selected from a complex signal; the characteristic frequency band mainly comes from the characteristic frequencies of various electromagnetic signals and vibration signals generated during the operation of the transmission equipment.

[0079] In one implementation, the final network topology is reconstructed based on the set of noisy nodes and a preset noise threshold, including:

[0080] Obtain the noise coefficient values ​​of each noise node in the noise node set, and compare each noise coefficient value with the preset noise threshold to determine whether to remove the target node;

[0081] The final network topology is obtained by reconstructing the network from the nodes that were not removed.

[0082] The specific process of determining whether to eliminate the target node includes:

[0083] If the noise coefficient value is less than the preset noise threshold, the target node is retained;

[0084] If the noise coefficient value is greater than or equal to the preset noise threshold, the target node is eliminated.

[0085] In an implementation manner, referring to Figure 2 , Figure 2 A flowchart of path planning provided by an embodiment of the application includes path planning of the final network topology graph to obtain an optimal transmission path.

[0086] According to the final network topology graph, a starting point and an ending point are determined, and all intermediate nodes between the starting point and the ending point are obtained;

[0087] The final network topology graph is divided into various clustering clusters using a K-means algorithm;

[0088] A target path optimization is performed on a target clustering cluster to obtain a locally optimal transmission path; the target clustering cluster is any one of the various clustering clusters;

[0089] The locally optimal transmission paths corresponding to the various clustering clusters are obtained, and a locally optimal transmission path set is determined;

[0090] The locally optimal transmission path set is sorted in ascending order of the fitness values of the target locally optimal transmission paths to obtain an optimal transmission path; the target locally optimal transmission path is any one of the locally optimal transmission paths.

[0091] In an implementation manner, the K-means algorithm is used to divide the clustering clusters of the final network topology graph, which can reasonably group the complex power data transmission network and classify the nodes with similar transmission characteristics into one category, thereby reducing the complexity of subsequent path optimization. The target path optimization is performed on each clustering cluster to obtain a locally optimal transmission path, which finely improves the path transmission efficiency in a local range, so that the power data transmission in each clustering cluster can reach a good state.

[0092] In an implementation manner, dividing the final network topology graph using the K-means algorithm to obtain various clustering clusters includes:

[0093] S1: Extracting all intermediate nodes in the final network topology graph, performing a K-means algorithm on all intermediate nodes to obtain various initial clustering clusters, and calculating the number of intermediate nodes of each initial clustering cluster;

[0094] S2: For a cluster with a number of intermediate nodes less than a preset average number of nodes, migrating the intermediate nodes closest to the current cluster centroid from adjacent clusters;

[0095] S3: repeating step S2 until the intermediate nodes of each initial clustering cluster reach the preset average number of nodes, and outputting each clustering cluster.

[0096] In an implementation manner, the target path optimization on the target clustering cluster obtains a local optimal transmission path, and the local optimal transmission path comprises:

[0097] The target clustering cluster is initialized as a population, each fitness is obtained by calculating the population through a fitness formula, and the optimal chromosome corresponding to the highest fitness is taken as an evolution target to be iterated through quantum evolution operation; the population comprises a plurality of chromosomes;

[0098] If the quantum genetic algorithm meets a convergence error or reaches a maximum number of iterations, a local optimal transmission path is outputted;

[0099] The fitness formula is specifically:

[0100] ;

[0101] Wherein, γ represents the fitness, represents a wireless channel delay of a connection edge between the jth node and a previous node in the target clustering cluster; represents a wired channel delay of a connection edge between the jth node and a previous node in the chromosome; j = 1, 2, …, J; J represents a total number of nodes in the target clustering cluster, and Ys represents a preset noise threshold; represents a noise coefficient value of the jth node in the target clustering cluster.

[0102] In an implementation manner, the target clustering cluster is initialized as a population, and iteration is performed in combination with a fitness formula and quantum evolution operation, so that the clustering cluster obtains an optimal transmission path. The quantum genetic algorithm outputs a local optimal path according to a convergence error or a maximum number of iterations, thereby guaranteeing the efficiency of optimization and the rationality of termination. The convergence error or the maximum number of iterations is set by a staff member according to historical experience; the fitness formula comprehensively considers the transmission time and power constraints between nodes, so that the optimized path not only pursues high transmission efficiency, but also meets system resource constraints. The local optimal transmission path can be accurately obtained in the target clustering cluster, which lays a foundation for subsequent integration of local optimal paths of each clustering cluster and acquisition of an overall optimal transmission path, thereby improving the efficiency and stability of power data transmission in the entire network topology, enabling terminal equipment to more reliably and quickly receive power data, and providing strong support for efficient operation of a power system.

[0103] Based on the same inventive concept, the embodiments of the present application also provide a power data intelligent acquisition device based on dual-mode communication. Referring to Figure 3 , Figure 3 A structural schematic diagram of a power data intelligent acquisition device based on dual-mode communication provided by the embodiments of the present application comprises:

[0104] a data acquisition module configured to acquire wireless channel delay and wired channel delay between each transmission device in a target area;

[0105] a network topology graph module configured to determine each transmission device as a node, determine the wireless channel delay and the wired channel delay as a first channel edge and a second channel edge respectively, and construct a target network topology graph according to all nodes, all first channel edges and all second channel edges of the target area; there is a first channel edge and a second channel edge between any two nodes in the target network topology graph;

[0106] a noise node set module configured to calculate a noise coefficient value of a target node in the target network topology graph, the target node being any one node in the target network topology graph; acquire noise coefficient values of all nodes of the target network topology graph and form a noise node set;

[0107] a final network topology graph module configured to receive request information of a target transmission device, determine a sending end and a receiving end according to the request information, the target transmission device being any one of the preset number of transmission terminals; and reconstruct a final network topology graph according to the sending end, the receiving end, the noise node set and a preset noise threshold value;

[0108] a path planning module configured to perform path planning on the final network topology graph to obtain an optimal transmission path, and send the optimal transmission path to the target transmission device, so that the target transmission device transmits power data through the power grid.

[0109] The power data intelligent acquisition device based on dual-mode communication provided by the embodiment of the present application can effectively filter out transmission paths with serious noise interference and greatly improve the accuracy of power data transmission by acquiring wireless and wired channel delays between transmission devices to construct a target network topology graph, calculating noise coefficients of each node and constructing a noise node set, and reconstructing a final network topology graph in combination with a preset threshold value. The final network topology graph is then subjected to path planning to obtain an optimal transmission path, thereby shortening the power transmission delay and improving the overall efficiency of power transmission.

[0110] It should be noted that in this document, terms such as "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusion, so that processes, methods, articles or devices including a series of elements not only include those elements, but also include other elements not explicitly listed or inherent to such processes, methods, articles or devices.

[0111] Although embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, replacements and variations can be made to these embodiments without departing from the principles and spirits of the present application.

Claims

1. A method for intelligent acquisition of power data based on dual-mode communication, characterized in that, The power control network includes an SDN remote server and a preset number of transmission devices. The SDN end sends control commands to each transmission device. The method is applied to an SDN remote server, and the method includes: Obtain the wireless channel latency and wired channel latency between each transmission device in the target area; Each transmission device is designated as a node, and the wireless channel delay and the wired channel delay are designated as the first channel edge and the second channel edge, respectively. A target network topology is constructed based on all nodes, all first channel edges, and all second channel edges in the target area. A first channel edge and a second channel edge exist between any two nodes in the target network topology. The noise figure value is calculated for any node in the target network topology graph. The noise figure values ​​of all nodes in the target network topology graph are obtained and a noise node set is constructed. Receive request information from the target transmission device, determine the sender and receiver based on the request information, wherein the target transmission device is any one of a preset number of transmission terminals; reconstruct the final network topology based on the sender, receiver, noise node set and preset noise threshold; The optimal transmission path is obtained by performing path planning on the final network topology; The optimal transmission path is sent to the target transmission device so that the target transmission device can transmit power data through the power grid.

2. The intelligent power data acquisition method based on dual-mode communication according to claim 1, characterized in that, The noise figure values ​​obtained by calculating the target nodes in the target network topology graph include: The formula for obtaining the noise figure value of the target node is: ； Among them, L f Represents the noise value of the target node, for L f The noise figure value is obtained by normalization and denoted as L; m represents the number of connections between the target node and its neighboring nodes, M represents the upper limit threshold of the number of connections between the target node and its neighboring nodes; p0 represents the standard frequency band of the target node, n represents the total number of characteristic frequency bands generated by the target node, pi represents the i-th characteristic frequency band generated by the target node, and e represents the natural constant.

3. The intelligent power data acquisition method based on dual-mode communication according to claim 1, characterized in that, The process of obtaining the optimal transmission path by performing path planning on the final network topology includes: Determine the starting point and ending point based on the final network topology, and obtain all intermediate nodes between the starting point and ending point; The final network topology was divided into clusters using the K-means algorithm. Local optimal transmission paths are obtained by optimizing the target clusters; the target cluster is any one of the various clusters. Obtain the locally optimal transmission path corresponding to each cluster and determine the set of locally optimal transmission paths; The optimal transmission path is obtained by sorting the set of locally optimal transmission paths in ascending order according to the fitness value of the target locally optimal transmission path; the target locally optimal transmission path is any one of the locally optimal transmission paths in the set.

4. The intelligent power data acquisition method based on dual-mode communication according to claim 3, characterized in that, The K-means algorithm is used to partition the final network topology graph to obtain various clusters, including: S1: Extract all intermediate nodes in the final network topology graph, perform K-means algorithm on all intermediate nodes to obtain each initial cluster, and calculate the number of intermediate nodes in each initial cluster. S2: For clusters with fewer intermediate nodes than the preset average number of nodes, migrate the intermediate node closest to the centroid of the current cluster from the neighboring clusters; S3: Repeat step S2 until the intermediate nodes of each initial cluster reach the preset average number of nodes, and output each cluster.

5. The intelligent power data acquisition method based on dual-mode communication according to claim 3, characterized in that, Optimizing the target path for the target cluster to obtain the locally optimal transmission path includes: The target cluster is initialized as a population, and the fitness of each population is calculated using the fitness formula. The optimal chromosome corresponding to the highest fitness is used as the evolutionary target and iteratively performed through quantum evolution operations. The population contains multiple chromosomes. If the quantum genetic algorithm satisfies the convergence error or reaches the maximum number of iterations, it outputs a locally optimal transmission path. The fitness formula is as follows: ； Where γ represents fitness, The wireless channel delay represents the connection edge between the j-th node and its predecessor node in the target cluster. YS represents the wired channel delay of the connection edge between the j-th node and its predecessor node in the chromosome; j = 1, 2, ..., J; J represents the total number of nodes in the target cluster; YS represents the preset noise threshold; Pz j This represents the noise coefficient value of the j-th node in the target cluster.

6. A power data intelligent acquisition device based on dual-mode communication, characterized in that, The device includes: Data acquisition module: Acquires the wireless channel latency and wired channel latency between various transmission devices in the target area; Network topology module: Each transmission device is identified as a node, and the wireless channel delay and the wired channel delay are identified as the first channel edge and the second channel edge, respectively. A target network topology is constructed based on all nodes, all first channel edges, and all second channel edges in the target area; a first channel edge and a second channel edge exist between any two nodes in the target network topology. Noise Node Set Module: Calculates the noise figure value of the target node in the target network topology graph, where the target node is any node in the target network topology graph; obtains the noise figure values ​​of all nodes in the target network topology graph and constructs a noise node set; The final network topology module receives request information from the target transmission device, determines the sender and receiver based on the request information, where the target transmission device is any one of a preset number of transmission terminals; and reconstructs the final network topology based on the sender, receiver, noise node set, and preset noise threshold. Path planning module: Performs path planning on the final network topology to obtain the optimal transmission path, and sends the optimal transmission path to the target transmission device so that the target transmission device can transmit power data through the power grid.

7. The intelligent power data acquisition device based on dual-mode communication according to claim 6, characterized in that, The noise node set module is further configured to: obtain the noise figure value of the target node using the following formula: ； Among them, L f Represents the noise value of the target node, for L f The noise figure value is obtained by normalization and denoted as L; m represents the number of connections between the target node and its neighboring nodes, M represents the upper limit threshold of the number of connections between the target node and its neighboring nodes; p0 represents the standard frequency band of the target node, n represents the total number of characteristic frequency bands generated by the target node, pi represents the i-th characteristic frequency band generated by the target node, and e represents the natural constant.

8. The intelligent power data acquisition device based on dual-mode communication according to claim 6, characterized in that, The path planning module includes: a start-end point module, a cluster generation module, a local optimal path module, and an optimal transmission path module. The start and end point module is used to determine the start and end points according to the final network topology map and obtain all intermediate nodes between the start and end points. The cluster generation module is used to divide the final network topology graph into clusters using the K-means algorithm. The local optimal path module is used to optimize the target path for the target cluster to obtain the local optimal transmission path; the target cluster is any one of the clusters. The optimal transmission path module is used to obtain the local optimal transmission path corresponding to each cluster and determine the set of local optimal transmission paths; sort the set of local optimal transmission paths in ascending order according to the fitness value of the target local optimal transmission path to obtain the optimal transmission path; the target local optimal transmission path is any one of the local optimal transmission paths in the set of local optimal transmission paths.

9. A power data intelligent acquisition device based on dual-mode communication according to claim 8, characterized in that, The K-means algorithm is used to partition the final network topology graph to obtain various clusters, including: S1: Extract all intermediate nodes in the final network topology graph, perform K-means algorithm on all intermediate nodes to obtain each initial cluster, and calculate the number of intermediate nodes in each initial cluster. S2: For clusters with fewer intermediate nodes than the preset average number of nodes, migrate the intermediate node closest to the centroid of the current cluster from the neighboring clusters; S3: Repeat step S2 until the intermediate nodes of each initial cluster reach the preset average number of nodes, and output each cluster.

10. A power data intelligent acquisition device based on dual-mode communication according to claim 8, characterized in that, Optimizing the target path for the target cluster to obtain the locally optimal transmission path includes: The target cluster is initialized as a population, and the fitness of each population is calculated using the fitness formula. The optimal chromosome corresponding to the highest fitness is used as the evolutionary target and iteratively performed through quantum evolution operations. The population contains multiple chromosomes. If the quantum genetic algorithm satisfies the convergence error or reaches the maximum number of iterations, it outputs a locally optimal transmission path. The fitness formula is as follows: ； Where γ represents fitness, The wireless channel delay represents the connection edge between the j-th node and its predecessor node in the target cluster. YS represents the wired channel delay of the connection edge between the j-th node and its predecessor node in the chromosome; j = 1, 2, ..., J; J represents the total number of nodes in the target cluster; YS represents the preset noise threshold; Pz j This represents the noise coefficient value of the j-th node in the target cluster.

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