Communication unit data transmission optimization method and system for large-scale electric energy data

Abstract

The invention relates to the technical field of data processing, and discloses a communication unit data transmission optimization method and system for large-scale electric energy data. The method comprises the steps that electric energy data features are extracted through a space-time correlation analysis algorithm, and an electric energy data time sequence dependency matrix and a space correlation matrix are constructed; constructing an EQ-learning intelligent scheduling model based on the matrix, and generating a Q value decision table fused with power grid constraints; performing multi-communication-unit path selection by applying the decision table to obtain an optimal transmission path set; a P-DPDK processor is adopted to realize multi-network card concurrent transmission; and performing dynamic load balancing adjustment according to the transmission result, and optimizing the electric energy data transmission performance. According to the invention, the problem that the electric energy data lacks intelligent transmission scheduling and dynamic resource configuration of spatial-temporal correlation perception in the prior art is solved, and the real-time performance, the reliability and the resource utilization efficiency of large-scale electric energy data transmission are improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method and system for optimizing communication unit data transmission for large-scale electric energy data. Background Art

[0002] In existing technologies, data transmission in power systems primarily utilizes traditional SCADA systems and TCP / IP-based communications, with data transmitted via a single network card and a simple priority queue mechanism to process different types of power data. Traditional methods typically classify power data by device type or geographic location, employing fixed transmission paths and static bandwidth allocation strategies. Data processing primarily relies on general load balancing algorithms and standard operating system scheduling mechanisms to allocate computing resources. These existing technologies perform adequately when processing small-scale power data, but with the advancement of smart grid construction and the increasing digitization of power equipment, traditional data transmission methods are gradually experiencing performance bottlenecks.

[0003] The main deficiencies of the existing technology are reflected in the low efficiency of data transmission and irrational resource utilization. First, the traditional single-network card transmission method cannot fully utilize the multi-path concurrent transmission capabilities of modern communication equipment, resulting in low network bandwidth utilization. Network congestion and data packet loss are prone to occur during large-scale burst transmission of electric energy data. Secondly, the existing data classification method is too simple and does not take into account the temporal correlation and spatial correlation characteristics of the electric energy data itself, resulting in highly correlated data being transmitted in a dispersed manner, affecting the integrity and consistency of the data. Thirdly, the traditional path selection algorithm mainly calculates the shortest path based on the physical distance of the network topology, ignoring the impact of the power grid operation status and the importance of electric energy data on the transmission path selection. It is unable to adaptively adjust the transmission strategy in the event of a power grid failure or abnormal state. Summary of the Invention

[0004] The present application provides a communication unit data transmission optimization method and system for large-scale electric energy data, which is used to solve the problem of intelligent transmission scheduling and dynamic resource allocation of electric energy data lacking temporal and spatial correlation perception in the existing technology, and improves the real-time, reliability and resource utilization efficiency of large-scale electric energy data transmission.

[0005] In the first aspect, the present application provides a communication unit data transmission optimization method for large-scale electric energy data, and the communication unit data transmission optimization method for large-scale electric energy data includes: performing feature extraction processing on the electric energy data through a spatiotemporal correlation analysis algorithm to obtain a time dependency matrix and a spatial correlation matrix of the electric energy data; constructing an EQ-learning intelligent scheduling model based on the time dependency matrix and the spatial correlation matrix of the electric energy data to obtain a Q-value decision table that integrates grid constraints; applying the Q-value decision table to multi-communication unit path selection processing to obtain an optimal transmission path set based on the grid topology; performing multi-network card concurrent transmission processing on the optimal transmission path set through a P-DPDK processor to obtain a classified transmission result of an electric energy data packet; performing dynamic load balancing adjustment processing based on the classified transmission result of the electric energy data packet to obtain optimized electric energy data transmission performance parameters.

[0006] In a second aspect, the present application provides a communication unit data transmission optimization system for large-scale electric energy data, the communication unit data transmission optimization system for large-scale electric energy data comprising: The extraction module is used to perform feature extraction processing on the electric energy data through the spatiotemporal correlation analysis algorithm to obtain the temporal dependency matrix and spatial correlation matrix of the electric energy data; A construction module is used to construct an EQ-learning intelligent scheduling model based on the time series dependency matrix and spatial correlation matrix of the electric energy data to obtain a Q-value decision table integrating grid constraints; A selection module, configured to apply the Q-value decision table to a multi-communication unit path selection process to obtain an optimal transmission path set based on a power grid topology; A transmission module, configured to perform multi-network card concurrent transmission processing on the optimal transmission path set through a P-DPDK processor to obtain a classified transmission result of an electric energy data packet; The processing module is used to perform dynamic load balancing adjustment processing according to the classification transmission results of the electric energy data packets to obtain optimized electric energy data transmission performance parameters.

[0007] In a third aspect, a communication unit data transmission optimization device for large-scale electric energy data is provided, comprising: a memory and at least one processor, wherein instructions are stored in the memory; the at least one processor calls the instructions in the memory so that the communication unit data transmission optimization device for large-scale electric energy data executes the above-mentioned communication unit data transmission optimization method for large-scale electric energy data.

[0008] In a fourth aspect, a computer-readable storage medium is provided, wherein instructions are stored in the computer-readable storage medium, which, when executed on a computer, enables the computer to execute the above-mentioned communication unit data transmission optimization method for large-scale electric energy data.

[0009] The technical solution provided in this application uses a spatiotemporal correlation analysis algorithm to extract features from power data, generating a temporal dependency matrix and a spatial correlation matrix. This overcomes the prior art's failure to ignore the inherent correlations of power data, transforming power data transmission from simple point-to-point communication to intelligent transmission based on data features. The EQ-learning intelligent scheduling model incorporates grid constraints, generating a Q-value decision table that optimizes data transmission strategies while ensuring safe and stable grid operation, avoiding the operational risks associated with traditional scheduling algorithms. Applying the Q-value decision table to multi-communication unit path selection yields an optimal transmission path set that not only considers network performance metrics but also leverages the unique characteristics of the grid topology, making transmission path selection more aligned with the actual operational requirements of the power system. The P-DPDK processor's multi-NIC concurrent transmission process overcomes the bandwidth limitations of traditional single-NIC transmission. The classified transmission of power data packets ensures differentiated transmission guarantees for data of different priorities. Dynamic load balancing adaptively adjusts transmission parameters based on the actual grid operating status. The resulting optimized power data transmission performance parameters demonstrate the system's ability to rapidly respond to changes in grid operating conditions, addressing the rigidity of transmission strategies in the face of grid failures or abnormalities in the prior art.

[0010] The spatiotemporal correlation analysis algorithm of this application fully considers the domain characteristics of electric energy data, which has strong temporal and spatial coupling. The autocorrelation function calculation and electrical distance weight analysis in the algorithm design are both specially optimized for the characteristics of power system data. Compared with general reinforcement learning algorithms, the EQ-learning intelligent scheduling model specifically incorporates grid flow constraints and safety and stability requirements. The design of the reward function reflects the power system's strict requirements for data transmission reliability and real-time performance. The P-DPDK processor is deeply optimized for power communication protocols such as IEC 61850. The design of the zero-copy memory management mechanism and the multi-network card load balancing scheduler both take into account the burst and periodic characteristics of electric energy data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0012] Figure 1 A schematic diagram of an embodiment of a method for optimizing communication unit data transmission of large-scale electric energy data in an embodiment of the present application; Figure 2A schematic diagram of an embodiment of a communication unit data transmission optimization system for large-scale electric energy data in an embodiment of the present application; Figure 3 It is a schematic block diagram of the structure of a communication unit data transmission optimization device for large-scale electric energy data in an embodiment of the present invention. DETAILED DESCRIPTION

[0013] The embodiments of the present application provide a method and system for optimizing the transmission of communication unit data of large-scale electric energy data. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0014] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In one embodiment of the present application, an embodiment of a communication unit data transmission optimization method for large-scale electric energy data includes: Step S101: Perform feature extraction processing on the electric energy data using a spatiotemporal correlation analysis algorithm to obtain a temporal dependency matrix and a spatial correlation matrix of the electric energy data; Step S102: constructing an EQ-learning intelligent scheduling model based on the time series dependency matrix and spatial correlation matrix of the electric energy data to obtain a Q-value decision table integrating grid constraints; Step S103: Apply the Q-value decision table to the multi-communication unit path selection process to obtain an optimal transmission path set based on the power grid topology; Step S104: Perform multi-network card concurrent transmission processing on the optimal transmission path set through the P-DPDK processor to obtain a classified transmission result of the electric energy data packet; Step S105: Perform dynamic load balancing adjustment processing according to the classified transmission results of the electric energy data packets to obtain optimized electric energy data transmission performance parameters.

[0015] It is understandable that the execution subject of this application can be a communication unit data transmission optimization system for large-scale electric energy data, or a terminal or a server, which is not limited here. The embodiment of this application is described by taking the server as the execution subject as an example.

[0016] Specifically, the spatiotemporal correlation analysis algorithm first calculates the autocorrelation function of the active power data, reactive power data, voltage data, and current data collected by communication units such as smart meters and substation monitoring equipment. The autocorrelation function quantifies the temporal dependency by calculating the correlation strength between data at different times. The specific calculation process is to multiply the difference between the power value and the average power value at each moment, sum it, and then divide it by the variance to obtain the temporal correlation strength coefficient. These coefficients constitute the temporal dependency matrix of the electric energy data, and each element in the matrix represents the degree of correlation between the corresponding moments. At the same time, the algorithm calculates the electrical distance of each grid node based on the grid topology. The electrical connection strength is determined by analyzing the resistance and reactance values ​​between nodes. These resistance and reactance values ​​are input into the impedance calculation model for weight calculation to obtain the electrical distance weight coefficient. These weight coefficients constitute the spatial correlation matrix, which reflects the spatial correlation between different grid nodes.

[0017] Based on the aforementioned temporal dependency matrix and spatial correlation matrix, an EQ-learning intelligent scheduling model is constructed. The model first defines a state space set, combining the temporal and spatial characteristics of power data with the grid topology and load level to form state parameters. It then constructs an action space set, including actions for transmission path selection, bandwidth allocation, and priority adjustment. The model evaluates state-action pairs using a specially designed grid-constrained reward function. This reward function consists of four components: a reliability reward calculated based on the data transmission success rate; a power flow constraint reward calculated based on the grid power flow equation to ensure the safety margin of the transmission path; a temporal integrity reward that measures the correctness of the packet arrival sequence; and a spatial correlation reward that evaluates the coordinated transmission of data between related grid nodes. The algorithm then weights these four rewards to produce a comprehensive reward function value, which is then fed into a Q-value update algorithm for iterative training. The Q-value update process adjusts the values ​​in the decision table using a weighted combination of the current reward and the expected future reward. After multiple rounds of iterative training, a Q-value decision table that incorporates grid constraints is formed.

[0018] The trained Q-value decision table is applied to multi-communication unit path selection. First, a network topology diagram of the communication units is constructed based on the Q-value decision table. This topology diagram includes all communication unit nodes and the communication links connecting them. The algorithm comprehensively evaluates each communication link in terms of bandwidth, latency, packet loss rate, and stability, and obtains a weighted evaluation index for each link. These index values ​​are then fed into a modified Dijkstra algorithm, which adds power data feature constraints to the traditional shortest path algorithm. The algorithm calculates candidate transmission paths between communication units by traversing all nodes and edges. The algorithm prioritizes candidate paths based on the power data's temporal dependency matrix, converting the data's importance into a correction factor for the path cost. Finally, the algorithm selects the optimal transmission path set based on the power grid topology by comparing the path costs.

[0019] The P-DPDK processor, a data plane development kit specifically optimized for power communication protocols and featuring deep IEC 61850 protocol optimization, handles concurrent multi-NIC transmissions based on the optimal transmission path set. The processor first configures dedicated receive and transmit queues for each NIC and establishes a mapping table between NICs and transmission paths. Then, based on the power data timing dependency matrix, the power data is classified into three levels of importance: first-level critical data (such as protection control signals), second-level critical data (such as real-time monitoring data), and third-level regular data (such as historical statistics). The packet classifier assigns queues based on the classification identifier and the NIC queue mapping table. Critical data is assigned to dedicated high-performance NIC queues, important data is distributed across multiple NIC queues using redundant transmission, and regular data is distributed using load balancing. The processor implements a zero-copy memory management mechanism, pre-allocating a memory pool to avoid data replication overhead. The multi-NIC load balancing scheduler dynamically adjusts data allocation based on the real-time load of each NIC, ultimately generating the classified power data packet transmission results.

[0020] Dynamic load balancing is performed based on the classified transmission results of power data packets. The algorithm first constructs a power grid operation status monitoring matrix, which records the operating status of each grid node in real time. This matrix includes four types: normal, lightly loaded, heavily loaded, and faulted. Each state corresponds to a different data transmission priority requirement. The algorithm detects and assesses fault severity based on grid node operating parameters. It calculates fault impact factors and severity levels by analyzing indicators such as voltage deviation, frequency fluctuation, and load mutation. The adaptive priority adjustment algorithm recalculates data transmission priority weights based on the fault impact factors and severity levels. When a grid fault is detected, it automatically increases the transmission priority of power data in the faulted area. Based on the adjusted priority weights, the algorithm dynamically allocates CPU core resources, assigning dedicated CPU cores to first-level critical data to ensure exclusive processing power. It establishes CPU affinity binding for second-level critical data, securing processing cores. And it allocates a shared CPU core pool to third-level routine data to achieve resource reuse. A comprehensive transmission performance evaluation model evaluates the performance of resource allocation solutions, deriving optimized power data transmission performance parameters through weighted calculations of throughput ratio, latency ratio, packet loss rate, integrity ratio, and reliability ratio.

[0021] In a specific embodiment, the process of executing step S101 may specifically include the following steps: Perform autocorrelation function calculation on the collected active power data, reactive power data, voltage data and current data to obtain the time series correlation intensity coefficient; Based on the time series correlation strength coefficient, the time series dependency matrix of electric energy data is constructed to obtain the quantitative results of the time-to-time correlation; Calculate the electrical distance of each grid node according to the grid topology to obtain the resistance and reactance values ​​between nodes; The resistance and reactance values ​​between nodes are input into the impedance calculation model for weight calculation to obtain the electrical distance weight coefficient; A spatial correlation matrix is ​​constructed based on the electrical distance weight coefficient to obtain quantitative data of spatial correlation between power grid nodes.

[0022] Specifically, the autocorrelation function identifies the periodic and trend characteristics of the data by calculating the correlation of the power data at different time delays. The specific calculation process is to multiply each value in the collected active power data series with its time-delayed value. The average of all the multiplication results is then divided by the variance of the data series to eliminate dimensionality effects, thereby obtaining a standardized time series correlation strength coefficient. The algorithm performs the same autocorrelation function calculation on reactive power data, voltage data, and current data simultaneously. Each data type generates a series of correlation strength coefficients reflecting its time series characteristics. These coefficients range from negative one to positive one. The closer the value is to positive one, the stronger the positive correlation is, the closer the value is to negative one, the stronger the negative correlation is, and the closer the value is to zero, the less correlation is. The power data collected by the communication unit is processed by the autocorrelation function to form a multidimensional set of time series correlation strength coefficients, each coefficient corresponding to a specific time delay and data type combination.

[0023] The temporal dependency matrix construction process arranges the temporal correlation strength coefficients calculated above into a matrix according to chronological order and data type. The rows of the matrix represent different time points, and the columns represent different time delays. Each element in the matrix stores the correlation strength coefficient between the corresponding time point and the delayed time point. The matrix construction algorithm first creates a two-dimensional array structure with a dimension equal to the number of time points multiplied by the maximum delay time. Each temporal correlation strength coefficient is then assigned to the corresponding position in the array according to its corresponding time coordinate. Lacking positions are filled using linear interpolation to ensure matrix integrity. The quantification of inter-moment correlation is reflected in the distribution pattern of the matrix elements. High values ​​near the diagonal indicate strong short-term correlation, while high values ​​away from the diagonal indicate long-term periodic correlation. The algorithm further normalizes the matrix, mapping all element values ​​to the range of zero to one, to facilitate numerical calculations and convergence analysis in the subsequent processing algorithm.

[0024] The electrical distance calculation process quantifies the electrical connectivity between different grid nodes based on the electrical characteristics of the grid topology. This calculation requires detailed topological information, including node connectivity, line parameters, and equipment parameters. The algorithm first extracts the coordinates and connectivity of each grid node from the grid dispatching and management system. It then calculates the resistance and reactance of each transmission line based on the physical characteristics of the line. Resistance is primarily determined by the resistivity of the conductor material, the conductor cross-sectional area, and the line length, while reactance is related to the line's inductance and capacitance. The algorithm employs power flow analysis to determine the electrical distance between nodes by solving the grid's node voltage equations. The calculation treats the grid as a complex impedance network composed of resistance and reactance, with the impedance of each line equal to the complex sum of the resistance and reactance values. The resistance between nodes reflects the loss characteristics of active power transmission, while the reactance reflects the difficulty of reactive power transmission and voltage regulation. Together, these two values ​​determine the strength of the electrical coupling between grid nodes.

[0025] The impedance calculation model's weighted calculation process converts the resistance and reactance values ​​between nodes into weight coefficients for data transmission path selection. This calculation model utilizes the inverse relationship between electrical distance and transmission priority. The model first combines the resistance and reactance values ​​into a complex impedance value. The modulus of the impedance value is then calculated as a measure of electrical distance. A smaller electrical distance indicates a closer electrical connection between two nodes, and the corresponding data transmission priority should be higher. The weight calculation formula uses an inverse transformation to convert electrical distance into a weight coefficient: the weight coefficient equals the inverse of the electrical distance multiplied by a normalization constant. This ensures that pairs of nodes with smaller electrical distances receive larger weight coefficients. The algorithm also incorporates a load level correction factor to adjust weight distribution under different operating conditions. When a node is heavily loaded, its corresponding weight coefficient increases to reflect the urgency of data transmission. The range of the electrical distance weight coefficient is constrained to between zero and one through maximum normalization, facilitating unified processing and comparative analysis with parameters from other optimization algorithms.

[0026] The spatial correlation matrix construction process organizes the calculated electrical distance weight coefficients into a matrix based on the topological relationships of the power grid nodes. The rows and columns of the matrix correspond to different power grid nodes, and the matrix elements store the electrical distance weight coefficients between corresponding node pairs. The matrix construction algorithm uses a sparse matrix storage structure to handle the sparse connectivity characteristics of the power grid topology, storing only non-zero weight coefficients to save storage space and computing resources. The algorithm first determines the locations in the matrix that need to be filled with values ​​based on the power grid topological connectivity relationships, and then fills the corresponding locations with the corresponding electrical distance weight coefficients. The matrix elements corresponding to nodes that are not directly connected are set to zero or the weight coefficients of indirect connections are calculated through multi-hop paths. The quantitative data of the spatial correlation between power grid nodes is reflected through the numerical distribution of the matrix. Elements with larger values ​​in the matrix correspond to pairs of nodes with close electrical connections. These node pairs should adopt a coordinated transmission strategy during data transmission to maintain spatial correlation.

[0027] In a specific embodiment, the process of executing step S102 may specifically include the following steps: Based on the time series dependency matrix and spatial correlation matrix of electric energy data, a state space set is defined to obtain a state parameter combination including the grid topology state and load level level; Construct an action space set based on the combination of state parameters to obtain transmission path selection action, bandwidth allocation action and priority adjustment action; The state parameter combination and action space set are input into the power grid constraint reward function for evaluation processing to obtain the reliability reward value, power flow constraint reward value, time sequence integrity reward value and spatial correlation reward value; Perform weighted summation based on each reward value to obtain the value of the comprehensive reward function; The comprehensive reward function value is input into the Q-value update algorithm for iterative training to obtain the Q-value decision table integrating the power grid constraints.

[0028] Specifically, the state space set definition process uses the time-series dependency matrix and spatial correlation matrix of power data as input data and extracts key characteristic parameters of power grid operation through a data fusion algorithm. The algorithm first extracts the periodic characteristics of the power data from the time-series dependency matrix. By analyzing the distribution pattern of the values ​​in the matrix, it identifies temporal patterns such as daily and weekly load cycles and quantifies these periodic characteristics into time-series state parameters. Simultaneously, the algorithm extracts the connectivity characteristics of the power grid topology from the spatial correlation matrix. By analyzing the distribution of weight coefficients in the matrix, it determines the closeness between power grid nodes and defines groups of closely connected nodes as topological state units. Power grid topological states are defined by enumerating the main operating modes of the power grid, including four basic types: normal operation, maintenance, fault, and load transfer. Each state corresponds to different data transmission requirements and constraints. Load levels are categorized into four levels: light load, medium load, heavy load, and overload, based on the ratio of the real-time load to the rated load. Each level corresponds to different data transmission priorities and bandwidth allocation strategies. The state parameter combination forms a multidimensional state vector by performing Cartesian product operation on the timing state parameters, topological state type and load level grade. Each state vector uniquely describes the operation status and data transmission environment of the power grid at a specific moment.

[0029] The action space set construction process designs corresponding control action types based on the characteristics of state parameter combinations. Each action type corresponds to a specific data transmission optimization strategy. The transmission path selection action defines a set of alternative paths based on the connection topology between communication units. The algorithm traverses all nodes and edges in the power grid communication network to enumerate all possible data transmission paths and classifies and labels the paths based on path length, bandwidth capacity, and reliability indicators. The bandwidth allocation action allocates transmission resources to different data types based on the total bandwidth capacity of network links. The algorithm segments the total bandwidth according to the importance of power data: high-priority bandwidth segments are allocated to critical protection data, medium-priority bandwidth segments are allocated to general monitoring data, and low-priority bandwidth segments are allocated to historical statistical data. The priority adjustment action dynamically adjusts the data transmission priority weights based on changes in the power grid operating state. When the power grid is in a fault state, the algorithm automatically adjusts the data priority of the fault-related area to the highest level. When the power grid is normal, the algorithm executes according to the preset priority allocation scheme. The action space set combines the three basic action types to form composite action vectors. Each composite action vector contains the path selection decision, bandwidth allocation ratio, and priority adjustment coefficient, forming a complete data transmission control strategy.

[0030] The grid constraint reward function evaluation process uses state parameter combinations and action space sets as input parameters and quantifies the performance of different state-action pairs using a multi-objective evaluation system. The reliability reward calculation process measures the success rate of data transmission. The algorithm records the ratio of the number of successfully transmitted packets to the total number of transmitted packets after each state-action pair is executed and uses this ratio as a quantitative measure of reliability. A higher ratio indicates better transmission reliability and a correspondingly larger reward. The power flow constraint reward evaluation process uses the power flow equation to determine the electrical safety of the selected data transmission path. The algorithm solves the power flow equation, including the data transmission load, to calculate the voltage amplitude and phase angle at each node. A positive reward is awarded when all node voltages are within the permitted range and the line power flow does not exceed the thermal limit; otherwise, a negative reward or zero reward is awarded. The timing integrity reward measures the correctness of the time sequence of data packets arriving at the receiving end. The algorithm compares the send and receive timestamps of the packets to determine whether there is out-of-order transmission. A high reward is awarded when the packets arrive in the order they were sent. A penalty reward is imposed based on the degree of out-of-order transmission. The spatial correlation reward value evaluates the collaborative transmission effect of data from related power grid nodes. The algorithm determines whether highly correlated data adopts a collaborative transmission strategy based on the weight coefficient in the spatial correlation matrix. A positive reward value is given when highly correlated data adopts the same or adjacent transmission path, and a negative reward value is given when highly correlated data is transmitted in a dispersed manner.

[0031] The weighted summation process linearly combines four different types of reward values ​​according to preset weight coefficients. The weight coefficients are set based on the actual needs and importance of power grid data transmission. The algorithm first normalizes the four reward values, mapping reward values ​​of different dimensions and numerical ranges to a standard interval between zero and one, eliminating the impact of dimensional differences on the weighted calculation. The algorithm then performs a weighted summation operation in the order of reliability weight, power flow constraint weight, temporal integrity weight, and spatial correlation weight. The reliability weight is set to the highest value to ensure the basic success rate of data transmission, the power flow constraint weight is set to the second highest value to ensure the safety of power grid operation, and the temporal integrity weight and spatial correlation weight are adjusted according to the specific application scenario. The value of the comprehensive reward function is calculated by summing the products of the four component reward values ​​and the corresponding weight coefficients. This value comprehensively reflects the overall performance of a specific state-action pair across multiple evaluation dimensions.

[0032] The Q-value update algorithm uses the temporal difference method (TD) in reinforcement learning to gradually optimize the decision-making strategy. The algorithm uses trial and error and accumulated experience to find the optimal state-action mapping. The algorithm first initializes the Q-value decision table, setting the Q-values ​​of all state-action pairs to zero or a random decimal value, and then begins the iterative training process. In each iteration, the algorithm selects an action based on the current Q-value table. After obtaining the value of the comprehensive reward function, the TD error (TD error) is calculated. This error is equal to the difference between the current Q-value and the maximum future TD value after adding the immediate reward and discount. The algorithm uses a learning rate parameter to control the Q-value update amplitude. The learning rate is set to a small positive number to ensure training stability and convergence. The Q-value update formula adds the current Q-value to the product of the learning rate and the TD error to obtain a new Q-value. This process continues until the change in the Q-value table falls below a preset convergence threshold. After sufficient training, the Q-value decision table, which incorporates grid constraints, can provide the optimal or near-optimal action for each possible state. The magnitude of the TD error in the table directly reflects the expected cumulative reward for the corresponding state-action pair.

[0033] In a specific embodiment, the process of executing step S103 may specifically include the following steps: Based on the Q-value decision table, a communication unit network topology graph is constructed to obtain a network structure including a communication unit node set and a communication link set; Perform bandwidth, delay, packet loss rate, and stability evaluation on each link in the communication link set to obtain a comprehensive link evaluation index value. The link comprehensive evaluation index value is input into the improved Dijkstra algorithm to perform path calculation processing to obtain the candidate transmission path between each communication unit; According to the time series dependency matrix of electric energy data, the priority weight of candidate transmission paths is adjusted to obtain the path cost value considering the importance of data; The optimal path screening process is performed based on the path cost value to obtain the optimal transmission path set based on the power grid topology.

[0034] Specifically, the process of constructing a communication unit network topology map identifies valid connections within the network based on the state-action mapping relationships in a Q-value decision table. This process first extracts all state-action pairs with positive Q-values ​​from the Q-value decision table. These positive Q-values ​​indicate that the corresponding transmission paths have performed well in historical training and are worth retaining. The algorithm then analyzes the path selection information in each state-action pair to determine the connection relationships between communication units. All communication devices involved in data transmission, such as smart meters, substation monitoring equipment, and distribution automation terminals, are abstracted into network nodes, forming a communication unit node set. Each node in the node set is labeled with attributes such as device type, geographic location, processing power, and access bandwidth. This attribute information is derived from real-time query results from the power grid infrastructure database and device management system. The process of constructing a communication link set analyzes the physical and logical connections between nodes to determine available data transmission channels, including direct fiber links, Ethernet switching links, wireless communication links, and power carrier communication links. Each communication link records detailed parameters such as its starting and ending nodes, link type, physical medium, transmission protocol, and capacity limit, forming a complete description of the network structure.

[0035] Communication link assessment performs a multi-dimensional performance evaluation on each link in the network topology. This evaluation process uses a combination of active measurement and passive monitoring to obtain real-time link performance parameters. Bandwidth assessment determines the actual available bandwidth by sending test packets along the link and measuring the transmission rate. The algorithm uses a method that gradually increases the packet size to detect bandwidth bottlenecks. The bandwidth limit is reached when the packet transmission rate no longer increases linearly with packet size. Latency assessment uses round-trip time measurement. The algorithm deploys latency measurement modules at both ends of the link. It determines the one-way transmission delay by sending timestamped probe packets and calculating half the round-trip time. The average of multiple measurements is used as a stable estimate of link latency. Packet loss rate assessment calculates the packet loss ratio by continuously sending a certain number of test packets and counting the number of successful receptions at the receiving end. The algorithm uses a sequence number mechanism to identify lost packets and distinguish between packet loss and out-of-order transmission. Stability assessment quantifies link stability by monitoring the changes in link performance parameters over a long period of time. The algorithm calculates the variance or standard deviation of parameters such as bandwidth, latency, and packet loss rate within a specific time window. Smaller variances indicate more stable link performance. The value of the comprehensive link evaluation index is calculated by weighted summing the four component indicators according to preset weights. The bandwidth weight is set to the highest to ensure data transmission capacity requirements, the latency weight is second to ensure real-time requirements, and the packet loss rate and stability weights are adjusted according to the fault tolerance capability of the application scenario.

[0036] The improved Dijkstra algorithm path calculation process adds special constraints for power data transmission to the traditional shortest path algorithm. The core improvement lies in the redefinition of the path cost function and the addition of constraints. The algorithm first uses the inverse of the link's comprehensive evaluation index as the edge weight. This is because the Dijkstra algorithm seeks the minimum-cost path, and larger evaluation indexes indicate better link performance, corresponding to smaller path costs. During the initialization phase, the algorithm creates a distance array to record the shortest distances from the source node to each node, a predecessor node array to record the predecessor nodes of each node on the shortest path, and a visit flag array to record whether a node has been processed. During the iterative calculation process, the algorithm selects the node with the smallest distance from an unvisited node as the current node. The algorithm then checks whether the path from the current node to its adjacent nodes is better than the known shortest path. If so, the distance array and predecessor node array are updated. The algorithm also improves the path update process by checking grid operational constraints, including load capacity constraints on nodes along the path, transmission capacity constraints on links, and grid security and stability constraints. Only paths that meet all constraints are considered valid candidate paths. The candidate transmission path generation process reconstructs the complete path from the source node to the destination node by backtracking the predecessor node array. The algorithm calculates several candidate paths for each pair of communication units to provide flexibility in path selection.

[0037] The priority weight adjustment process modifies the importance ranking of candidate transmission paths based on the correlation information in the power data timing dependency matrix. This process reflects the impact of timing correlation on path selection in power data transmission. The algorithm first extracts the timing correlation strength coefficient associated with the currently transmitted data from the timing dependency matrix. These coefficients reflect the degree of temporal correlation between the current data and historical data. A high timing correlation strength coefficient indicates that the current data has a strong timing dependency with previously transmitted data and requires the same or similar transmission path to maintain temporal consistency. Based on this, the algorithm assigns a higher priority to candidate paths that share the same path. A low timing correlation strength coefficient indicates that the current data is relatively independent. The algorithm primarily determines the priority weight based on the basic performance indicators of the path. The priority weight is calculated by multiplying the timing correlation coefficient by the path's base cost. A smaller product indicates a lower overall cost for the path after accounting for timing dependencies. The data importance is quantified based on the criticality of the power data in grid operation. Protection and control data is given the highest importance, real-time monitoring data is given medium importance, and historical statistical data is given lower importance. The final calculation result of the path cost value comprehensively considers multiple factors such as the basic transmission performance of the path, timing dependency, and data importance, providing a quantitative basis for path screening.

[0038] The optimal path selection process determines the optimal transmission path between each pair of communication units based on the relationship between path costs. This selection process utilizes a multi-objective optimization approach to balance transmission performance and resource utilization efficiency. The algorithm first sorts all candidate transmission paths in ascending order of path cost. The path with the lowest cost is considered the optimal choice. However, the algorithm also considers resource conflicts and load balancing between paths. When multiple paths share the same link resources, the algorithm uses a load-sharing strategy to distribute data of different priorities to different paths for transmission, avoiding resource waste caused by overloading a single path while other paths remain idle. The path selection process also considers the redundancy requirements of the power grid topology. The algorithm selects two sets of primary and backup paths for critical power data transmission tasks. The backup path can immediately take over data transmission tasks in the event of a failure of the primary path. The optimal transmission path set based on the power grid topology contains the best path selection solutions between all communication unit pairs. Each path solution contains detailed information about all nodes, links, transmission parameters, and backup options along the path.

[0039] In a specific embodiment, the process of executing step S104 may specifically include the following steps: Based on the optimal transmission path set, a dedicated receiving queue and sending queue are configured for each network card, and a network card queue mapping relationship table is obtained; The importance level of the electric energy data is divided according to the electric energy data time series dependency matrix to obtain the classification identification of the first-level key data, the second-level important data and the third-level regular data; Input the classification identifier and the network card queue mapping relationship table into the data packet classifier for queue allocation processing, and obtain the network card queue allocation plan corresponding to each priority data; Based on the network card queue allocation scheme, a zero-copy memory management mechanism is built to obtain the pre-allocated memory pool and buffer management parameters; The pre-allocated memory pool and buffer management parameters are input into the multi-network card load balancing scheduler for concurrent transmission processing to obtain the classified transmission results of the power data packets.

[0040] Specifically, the NIC queue configuration process allocates dedicated data processing queues to each network interface card (NIC) based on path information from the optimal transmission path set. This configuration process first analyzes the starting node, destination node, and intermediate hop node information for each path in the path set. Then, based on the NIC's physical connectivity, it determines which paths require data transmission through a specific NIC. A dedicated receive queue is a buffer area within the NIC hardware used to temporarily store incoming packets. Each receive queue has an independent memory address space and descriptor ring structure. The queue depth is typically set to 1,024 descriptors to balance memory usage and buffering capacity. A transmit queue is a buffer area within the NIC hardware used to temporarily store outgoing packets. Its structure is similar to that of the receive queue, but the data flow is in the opposite direction. The algorithm allocates a different number of transmit queues to each NIC based on the bandwidth requirements and priority of the transmission path. A NIC queue mapping table records the correspondence between transmission paths and NIC queues. Each entry in the table contains fields such as the path identifier, NIC identifier, queue number, and queue type. This mapping table serves as an important reference for subsequent packet classification and scheduling. The queue configuration process also involves setting queue priorities. High-priority queues receive more processing time and bandwidth resources, while low-priority queues yield to high-priority data flows when the network is congested.

[0041] The importance classification process for power data determines the transmission priority of different types of power data based on the correlation strength information in the time dependency matrix. This classification process first extracts the correlation coefficient between each type of power data and key system operating parameters from the time dependency matrix. Level 1 critical data includes information directly impacting the safe and stable operation of the power grid, such as protection trip signals, emergency control commands, and fault recording data. These data exhibit strong correlations with key parameters such as grid frequency and voltage amplitude in the time dependency matrix, with correlation coefficients typically exceeding 0.9. Level 2 critical data includes information that impacts grid efficiency and equipment health, such as real-time load monitoring data, power quality data, and equipment status information. These data have correlation coefficients with grid operating parameters ranging from 0.5 to 0.9, requiring timely transmission but allowing for a certain delay. Level 3 routine data includes information with less impact on real-time operations, such as historical load statistics, electricity metering data, and maintenance records. These data typically have correlation coefficients below 0.5 and require less stringent transmission delay requirements. The classification identifier generation process automatically identifies the data type by analyzing the energy data's header information and data content. The algorithm uses a combination of pattern matching and feature extraction to determine the data's importance based on the packet's source address, destination address, protocol type, and payload content. The identifier uses a three-bit binary encoding scheme, where 100 represents level 1 critical data, 010 represents level 2 important data, and 001 represents level 3 normal data. This encoding scheme facilitates rapid identification and processing at the hardware level.

[0042] The packet classifier queue allocation process matches the energy data's classification identifier with the network interface card (NIC) queue mapping table to determine which NIC and queue each packet should be processed. The classifier uses a hash table structure to store classification rules, using the packet's classification identifier and transmission path information as the key and the corresponding NIC queue information as the lookup result. This structure supports fast, linear-time lookups. The queue allocation algorithm first determines the packet's priority level based on its classification identifier, then determines the transmission path based on the packet's destination address. It then searches the NIC and queue information corresponding to this path from the NIC queue mapping table. When multiple packets compete for the same queue resources, the algorithm employs a priority scheduling strategy, prioritizing high-priority packets and processing lower-priority packets only when queues are free. The NIC queue allocation scheme details the processing queue arrangements for each type of energy data, including dedicated queues for high-performance NICs for critical data at the first level, multiple queues for redundant transmission for important data at the second level, and shared queues for load-balanced distribution of regular data at the third level. The allocation scheme also takes into account the load balancing problem of queues. The algorithm dynamically monitors the utilization rate of each queue and automatically redirects part of the data flow to a backup queue with a lower load when the load of a queue is too high.

[0043] The zero-copy memory management mechanism is constructed based on the memory requirements of the network card queue allocation scheme to optimize memory operation efficiency during data transmission. The core concept of this mechanism is to avoid repeated data copying between user space and kernel space. A preallocated memory pool is a large, contiguous memory area pre-allocated at program startup. This memory pool is divided into several fixed-size memory blocks, each corresponding to the maximum size of a network packet. Memory pool management utilizes a ring buffer structure, using head and tail pointers to track the location of available memory blocks. When a packet needs to be sent, a memory block is retrieved from the head pointer location; after the packet is sent, the memory block is returned to the tail pointer location. Buffer management parameters include key configuration information such as memory block size, total memory pool capacity, allocation strategy, and reclaim strategy. These parameters are optimized based on the transmission characteristics of power data and the network card hardware capabilities. The algorithm allocates memory pools of different sizes to different data types based on the transmission volume requirements of different priority levels. Larger memory pools are allocated to first-level critical data to ensure transmission continuity, while smaller memory pools are allocated to third-level regular data to conserve memory resources. The zero-copy mechanism uses direct memory access technology to allow the network card hardware to directly read and write pre-allocated memory areas, avoiding the overhead of copying data between different memory areas and significantly reducing CPU usage and transmission latency.

[0044] The multi-NIC load balancing scheduler's concurrent transmission process uses pre-allocated memory pools and buffer management parameters as a scheduling basis, enabling coordinated work and load sharing across multiple NICs. The scheduler uses a round-robin scheduling algorithm to periodically check the status of each NIC's queue, including key metrics such as queue length, processing rate, error statistics, and resource usage. The scheduling period is typically set to 100 milliseconds to balance response speed and system overhead. The load balancing algorithm dynamically adjusts data allocation based on the real-time load of each NIC. When a NIC is overloaded, the algorithm automatically reduces the amount of data allocated to that NIC while increasing the amount allocated to NICs with lower loads. Concurrent transmission control utilizes multi-threading technology to enable simultaneous operation of different NICs. Each NIC corresponds to an independent processing thread, and threads coordinate communication through shared memory and semaphores. The scheduler also implements fault detection and automatic recovery. If a hardware failure or performance degradation is detected on a NIC, the algorithm automatically migrates the data flow on that NIC to another functioning NIC for continued transmission. The classified transmission results of power packets record detailed information such as the transmission path, NIC used, queue number, transmission time, and transmission status for each packet.

[0045] In a specific embodiment, the process of executing step S105 may specifically include the following steps: Based on the classified transmission results of electric energy data packets, a grid operation status monitoring matrix is ​​constructed to obtain grid node operation parameters including normal state, light load state, heavy load state and fault state; Perform fault detection and severity assessment based on grid node operating parameters to obtain fault impact factors and severity level values; The fault impact factor and severity level values ​​are input into the adaptive priority adjustment algorithm for weight calculation to obtain the dynamically adjusted data transmission priority weight; Dynamically allocate CPU core resources based on data transmission priority weights to obtain resource configuration solutions for key data dedicated cores, important data bound cores, and regular data shared cores; The resource allocation plan is input into the comprehensive evaluation model of transmission performance for performance calculation and processing to obtain the optimized power data transmission performance parameters.

[0046] Specifically, the grid operation status monitoring matrix construction process analyzes the real-time operating status of each grid node based on transmission statistics from classified power data packets. The matrix infers the load level and operating status of grid nodes by analyzing the transmission frequency, data content variations, and transmission path distribution of data packets. The algorithm first extracts data packet statistics for each grid node from the classified transmission results, including key metrics such as the total number of packets sent, the total number of packets received, packet size distribution, and transmission interval. These statistics directly reflect the node's data activity and load trends. The normal state identification process analyzes the periodicity and stability of node packet transmissions. When a node's packet transmission frequency remains within a preset range and the transmission interval fluctuates minimally, the algorithm marks the node as operating normally. Lightly loaded states are identified by detecting stable transmissions below a normal threshold, indicating that the node's electrical load is low but the equipment is operating normally. Heavy loaded states are identified by detecting a packet transmission frequency significantly above a normal threshold and containing a large amount of load data, indicating that the node is carrying a heavy electrical load and requires close monitoring. Fault conditions are identified by detecting interruptions in data packet transmission, a surge in transmission error rates, or fault alarm information in the data content, indicating a device failure or abnormal operation at the node. Grid node operating parameters are generated by combining these four status information with node attributes such as electrical parameters, geographic location, and device type to form multidimensional parameter vectors. Each vector comprehensively describes the current operating status of the corresponding node.

[0047] Fault detection and severity assessment processes conduct in-depth analysis of abnormalities in grid node operating parameters. This process uses a combination of rule-based expert knowledge and statistical anomaly detection to identify and quantify fault severity. The fault detection algorithm first establishes a baseline model of normal grid operation. This model, based on historical operating data, derives the normal parameter ranges for each node under different time periods and load conditions. Fault detection is triggered when real-time node parameters exceed the baseline range. The algorithm employs a multi-level detection mechanism. It first detects anomalies at a single node, comparing the deviation of the node's current parameters with historical statistical values ​​to determine whether a local anomaly exists. It then performs regional anomaly detection, analyzing the correlation of parameters between adjacent nodes to determine whether a regional fault exists. The severity assessment process quantifies fault severity based on the scope of the fault, its duration, and the threat to grid stability. The algorithm employs a hierarchical assessment strategy, categorizing faults into four levels: device-level faults, line-level faults, substation-level faults, and regional-level faults. The fault impact factor is determined by calculating the electrical correlation between the faulty node and other healthy nodes. A higher correlation indicates a greater fault scope and a higher impact factor. The severity level is rated on a 10-point scale, with levels one to three representing minor faults, four to six representing general faults, seven to nine representing serious faults, and ten representing an extremely serious fault. The rating is based on comprehensive factors including the load capacity affected by the fault, the number of users involved, and the difficulty of repair.

[0048] The adaptive priority adjustment algorithm uses the fault impact factor and severity level as input parameters for weight calculation. It uses a dynamic weighting mechanism to re-determine the transmission priority of different types of power data. The algorithm first establishes a mapping between fault severity and priority adjustment magnitude. This mapping is based on practical experience with power grid dispatch operations and safety and stability requirements. Lower fault severity levels result in smaller priority adjustments, while higher fault severity levels result in significantly larger adjustments. The weight calculation uses an exponential weighting approach, using the fault impact factor as the base and the severity level as the exponent to calculate a priority adjustment coefficient. This coefficient is then multiplied by the original priority weight to produce the adjusted new weight. The algorithm also considers the varying importance of different types of power data to fault response. Protection and control data maintains the highest priority under any fault conditions. The priority of monitoring data is dynamically adjusted based on its relevance to the fault area. Historical data is appropriately lowered during faults to free up transmission resources. The dynamically adjusted data transmission priority weights form a new weighting scheme that automatically adjusts data transmission strategies based on the actual power grid operation, optimizing overall transmission efficiency while ensuring the transmission of critical data. The weight adjustment process also includes a time decay mechanism. As fault handling progresses and the grid status recovers, the adjusted weights gradually return to normal levels, avoiding long-term abnormal weight distribution affecting daily operations.

[0049] Dynamic CPU core resource allocation reconfigures computing resources based on adjusted data transfer priority weights. This allocation process controls CPU core usage for data processing tasks of different priorities through operating system-level resource management interfaces. The dedicated core allocation process for critical data binds first-level critical data processing tasks to specific CPU cores. These cores are isolated from other tasks, ensuring real-time and stable critical data processing. The algorithm uses CPU affinity to restrict critical data processing processes to designated CPU cores, while also implementing a highest-priority scheduling policy to ensure these processes preempt the execution of lower-priority tasks. The bound core allocation process for critical data binds second-level critical data processing tasks to a separate set of CPU cores. These cores primarily process critical data but relinquish some resources when demand for critical data processing surges. This binding mechanism employs a soft binding strategy, allowing for limited task migration and load redistribution when system load is uneven. The shared core allocation process for regular data assigns third-level regular data processing tasks to the remaining CPU core pool. These cores use a time-sliced ​​round-robin scheduling approach to handle multiple regular data tasks. This allows for faster processing when the core pool load is low, while a task queuing mechanism ensures sequential processing when the load is high. The resource allocation plan records in detail the resource usage parameters corresponding to each type of data, such as the CPU core number, scheduling priority, memory allocation quota, and I / O bandwidth limit. The plan can be dynamically adjusted according to the power grid operation status and fault conditions.

[0050] The comprehensive transmission performance evaluation model uses the resource allocation plan as input and quantifies the overall effectiveness of data transmission optimization through the calculation of multi-dimensional performance indicators. The evaluation model encompasses five key dimensions: throughput, latency, reliability, resource utilization, and energy consumption. Each dimension has its own calculation method and evaluation criteria. Throughput is calculated by counting the number of successfully transmitted power packets and the total number of data bytes per unit time. The algorithm calculates the throughput of key data, important data, and regular data separately, then performs a weighted average based on the weights to produce a comprehensive throughput metric. Latency is calculated by measuring the end-to-end transmission time of data packets from sender to receiver. This includes components such as network transmission delay, queue waiting delay, and processing delay. The algorithm uses statistical methods to calculate statistical characteristics such as average delay, maximum delay, and delay jitter. Reliability measures transmission quality by calculating parameters such as data transmission success rate, error rate, and retransmission rate. The algorithm analyzes anomalies such as packet loss, damage, and out-of-order transmission, calculating the frequency and impact of each anomaly. Resource utilization metrics assess the rationality of resource allocation by monitoring system resource consumption, including CPU core utilization, memory usage, and network bandwidth utilization. The optimized power data transmission performance parameter is calculated by comprehensively calculating the indicator values ​​of five dimensions according to preset weights. This parameter comprehensively reflects the overall transmission performance level after dynamic load balancing adjustments.

[0051] In a specific embodiment, the process of dynamically allocating CPU core resources based on data transmission priority weights may specifically include the following steps: Perform dedicated core allocation processing on the first-level critical data according to the data transmission priority weight, and obtain the exclusive CPU core identifier corresponding to the protection control data; Perform core binding processing on the secondary important data and CPU affinity binding algorithm to obtain the binding relationship table between real-time monitoring data and specific CPU cores; Perform shared core allocation based on the load characteristics of the three-level regular data to obtain a shared CPU core pool corresponding to historical statistical data; Build a core scheduling priority queue based on the exclusive CPU core identifier, binding relationship table, and shared CPU core pool to obtain the core access timing arrangement for data with different priorities; The core access timing arrangement is input into the CPU resource manager for resource configuration processing, and a resource configuration scheme for a key data dedicated core, an important data binding core and a conventional data sharing core is obtained.

[0052] Specifically, the first-level critical data dedicated core allocation process identifies protection and control data requiring exclusive processing resources based on the highest-weighted identifier in the data transmission priority weights. This process first selects data types with a weight of 1.0 from the priority weight table. These data types include critical information directly related to grid safety and stability, such as power grid protection trip signals, emergency load shedding instructions, and fault isolation control commands. The algorithm queries the list of currently available CPU cores through the operating system's processor affinity interface and then selects the best-performing core as the dedicated core for critical data processing. Selection criteria include core frequency, cache size, and instruction execution efficiency. The dedicated core allocation process uses a hard binding method, forcing the critical data processing process to execute on a designated core by setting a CPU mask while simultaneously prohibiting other processes from accessing that core to ensure exclusive resource use. The exclusive CPU core identifier corresponding to the protection and control data is recorded as a combination of core number and binding status. Each identifier contains detailed information such as the core's physical number, logical number, binding process identifier, and resource reservation status. The algorithm also establishes a core health monitoring mechanism to regularly check the operating status of the dedicated core, including parameters such as temperature, load rate, and error count. When a core abnormality is detected, it automatically switches to the backup core to continue processing critical data.

[0053] The secondary critical data core binding process uses a CPU affinity binding algorithm to establish soft bindings between real-time monitoring data and specific CPU cores. Unlike hard binding for dedicated cores, this algorithm allows for limited adjustments and migrations when system load is uneven. The CPU affinity binding algorithm first analyzes the processing characteristics of real-time monitoring data, including key attributes such as data arrival frequency, processing complexity, and memory access patterns. Based on these characteristics, it then selects the most appropriate CPU core for each type of monitoring data. The algorithm employs a load-aware binding strategy, prioritizing computationally intensive monitoring data to high-performance cores and allocating I / O-intensive monitoring data to cores with larger caches. The binding table is constructed using a hash table structure to store the mapping between data types and core numbers. Each entry in the table records parameters such as the data type identifier, bound core number, binding strength, and adjustment permissions. Binding strength reflects the tightness of the binding relationship; higher strength values ​​indicate more stable bindings, making them less susceptible to being broken by the system scheduler. The binding relationship table between real-time monitoring data and specific CPU cores also includes a dynamic adjustment mechanism. When it is detected that the load on a core is too high or the performance has degraded, the algorithm automatically migrates part of the monitoring data bound to the core to continue processing on the core with lower load, maintaining the continuity of data processing and the consistency of the state during the migration process.

[0054] The three-level shared core allocation process for regular data builds a multi-core shared processing resource pool based on the load characteristics of regular data. This process fully considers the processing characteristics of regular data, such as historical statistical data, electricity metering data, and equipment maintenance records. The load characteristic analysis process determines resource requirements by analyzing parameters such as processing time distribution, memory usage patterns, and computational complexity. The algorithm finds that regular data, characterized by short processing times and small memory usage but large data volumes, is well-suited for batch and parallel processing. The shared core allocation algorithm constructs a core pool from the system's remaining CPU cores. The cores in the pool use a combination of time-slicing and priority scheduling to process multiple regular data tasks. The shared CPU core pool corresponding to the historical statistical data includes configuration parameters such as the core pool number, number of cores, scheduling policy, and load balancing algorithm. The core pool utilizes a dynamic scaling mechanism. When regular data processing demand increases, new cores are automatically added from idle cores in the system. When processing demand decreases, some cores are released for use by other tasks. The algorithm also establishes a load balancing mechanism within the core pool. By monitoring the task queue length and processing rate of each core, it dynamically adjusts task allocation to ensure a balanced load across the cores within the core pool.

[0055] The core scheduling priority queue construction process integrates information from dedicated core identifiers, binding tables, and the shared core pool to form a unified CPU core access scheduling framework. The construction algorithm first determines the highest-priority access rights for critical data based on the exclusive CPU core identifiers. These cores prioritize critical data tasks under all circumstances and do not accept scheduling requests from other types of tasks. The algorithm then uses the binding table to determine medium-priority access rights for important data. These data receive priority processing on a specific core but must share core resources with a small number of other tasks. Finally, the algorithm determines low-priority access rights for regular data based on the shared core pool. These data are processed within the core pool on a first-come, first-served basis and with fair scheduling. Core access scheduling for data of different priorities utilizes a multi-level queue scheduling algorithm, which maintains a separate task queue for each priority level. Tasks in high-priority queues always take precedence over those in lower-priority queues. The scheduling also considers the real-time requirements of tasks. For critical data tasks with strict latency requirements, the algorithm adopts an earliest-deadline-first scheduling strategy, while for regular data tasks with more relaxed latency requirements, a fair round-robin scheduling strategy is used. The algorithm establishes a preemption mechanism. When a high-priority task arrives, it can preempt the executing low-priority task. The preempted task suspends execution and saves the state, waiting to resume execution at the next scheduling opportunity.

[0056] The CPU resource manager's resource configuration process uses core access scheduling as configuration instructions and implements CPU core allocation and scheduling policies through the operating system's underlying interfaces. The resource manager first parses the configuration parameters in the scheduling, including information such as the core allocation plan, scheduling policy, and priority settings. It then calls the operating system's processor management interface to perform specific configuration operations. The critical data dedicated core configuration process ensures that a designated core processes only critical data tasks by setting CPU affinity masks and scheduling policies. This configuration involves disabling general scheduling for the core, setting real-time scheduling priorities, and configuring interrupt handling policies. The critical data bound core configuration process establishes a prioritized binding relationship between data and cores through soft affinity settings, while maintaining a certain degree of scheduling flexibility to accommodate system load fluctuations. The general data shared core configuration process establishes a core pool management structure to centrally schedule and manage multiple core resources. This includes operations such as initializing the core pool, configuring the load balancer, and setting the task scheduler. The final output of the resource configuration plan contains complete information about each CPU core's specific purpose, the type of data it processes, scheduling priorities, and performance monitoring parameters. This plan can guide the execution of actual power data processing tasks and optimize resource usage.

[0057] The above describes the communication unit data transmission optimization method for large-scale electric energy data in the embodiment of the present application. The following describes the communication unit data transmission optimization system for large-scale electric energy data in the embodiment of the present application. Figure 2 In one embodiment of the present application, a communication unit data transmission optimization system for large-scale electric energy data includes: The extraction module is used to perform feature extraction processing on the electric energy data through the time-space correlation analysis algorithm to obtain the time series dependency matrix and spatial correlation matrix of the electric energy data; A construction module is used to build an EQ-learning intelligent scheduling model based on the time series dependency matrix and spatial correlation matrix of power data, and obtain a Q-value decision table that integrates grid constraints; A selection module, configured to apply the Q-value decision table to a multi-communication unit path selection process to obtain an optimal transmission path set based on a power grid topology; A transmission module, configured to perform multi-network card concurrent transmission processing on the optimal transmission path set through a P-DPDK processor to obtain a classified transmission result of an electric energy data packet; The processing module is used to perform dynamic load balancing adjustment processing according to the classification transmission results of the electric energy data packets to obtain optimized electric energy data transmission performance parameters.

[0058] above Figure 2From the perspective of modular functional entities, the communication unit data transmission optimization system for medium and large-scale electric energy data in the embodiment of the present invention is described in detail. The following describes in detail the communication unit data transmission optimization device for large-scale electric energy data in the embodiment of the present invention from the perspective of hardware processing.

[0059] Reference Figure 3 In an embodiment of the present invention, a communication unit data transmission optimization device for large-scale electric energy data is also provided. The communication unit data transmission optimization device for large-scale electric energy data can be a server, and its internal structure can be as follows: Figure 3 As shown. The communication unit data transmission optimization device for large-scale electric energy data includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the computer-designed processor is used to provide computing and control capabilities. The memory of the communication unit data transmission optimization device for large-scale electric energy data includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the communication unit data transmission optimization device for large-scale electric energy data is used to store the corresponding data in this embodiment. The network interface of the communication unit data transmission optimization device for large-scale electric energy data is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0060] Those skilled in the art will understand that Figure 3 The structure shown in is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the communication unit data transmission optimization device for large-scale electric energy data to which the solution of the present invention is applied.

[0061] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the steps of the communication unit data transmission optimization method for large-scale electric energy data.

[0062] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0063] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the existing technology, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a large-scale electric energy data communication unit data transmission optimization device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program code.

[0064] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A communication unit data transmission optimization method for large-scale electric energy data, characterized in that: The method comprises: The power data is processed for feature extraction using a spatiotemporal correlation analysis algorithm to obtain the power data temporal dependency matrix and spatial correlation matrix. An EQ-learning intelligent scheduling model is constructed based on the time series dependency matrix and spatial correlation matrix of the electric energy data to obtain a Q-value decision table integrating grid constraints; Applying the Q-value decision table to multi-communication unit path selection processing to obtain an optimal transmission path set based on the power grid topology; Performing multi-network card concurrent transmission processing on the optimal transmission path set through the P-DPDK processor to obtain a classified transmission result of the electric energy data packet; Dynamic load balancing adjustment processing is performed according to the classified transmission results of the electric energy data packets to obtain optimized electric energy data transmission performance parameters.

2. The communication unit data transmission optimization method for large-scale electric energy data according to claim 1, characterized in that: The feature extraction process of the electric energy data is performed by the spatiotemporal correlation analysis algorithm to obtain the electric energy data time series dependency matrix and the spatial correlation matrix, including: Perform autocorrelation function calculation on the collected active power data, reactive power data, voltage data and current data to obtain the time series correlation intensity coefficient; Based on the time series correlation strength coefficient, a time series dependency matrix of electric energy data is constructed to obtain a quantitative result of time-to-time correlation; Calculate the electrical distance of each grid node according to the grid topology to obtain the resistance and reactance values ​​between nodes; The resistance value and reactance value between the nodes are input into the impedance calculation model for weight calculation processing to obtain an electrical distance weight coefficient; A spatial correlation matrix is ​​constructed based on the electrical distance weight coefficient to obtain quantitative data of spatial correlation between power grid nodes.

3. The communication unit data transmission optimization method for large-scale electric energy data according to claim 1, characterized in that: The EQ-learning intelligent scheduling model is constructed according to the electric energy data time series dependency matrix and the spatial correlation matrix to obtain a Q-value decision table integrating grid constraints, including: Defining a state space set based on the electric energy data time series dependency matrix and the spatial correlation matrix to obtain a state parameter combination including a grid topology state and a load level grade; Constructing an action space set based on the state parameter combination to obtain a transmission path selection action, a bandwidth allocation action, and a priority adjustment action; Inputting the state parameter combination and the action space set into the power grid constraint reward function for evaluation processing to obtain a reliability reward value, a power flow constraint reward value, a time sequence integrity reward value, and a spatial correlation reward value; Perform weighted summation based on each reward value to obtain the value of the comprehensive reward function; The comprehensive reward function value is input into the Q-value update algorithm for iterative training processing to obtain a Q-value decision table integrating power grid constraints.

4. The communication unit data transmission optimization method for large-scale electric energy data according to claim 1, characterized in that: The applying the Q value decision table to the multi-communication unit path selection process to obtain an optimal transmission path set based on the power grid topology includes: Constructing a communication unit network topology diagram based on the Q-value decision table to obtain a network structure including a communication unit node set and a communication link set; Performing bandwidth, delay, packet loss rate, and stability evaluation on each link in the communication link set to obtain a comprehensive link evaluation index value; Inputting the link comprehensive evaluation index value into the improved Dijkstra algorithm to perform path calculation processing to obtain candidate transmission paths between each communication unit; performing priority weight adjustment processing on the candidate transmission paths according to the electric energy data time sequence dependency matrix to obtain a path cost value considering the importance of the data; An optimal path screening process is performed based on the path cost value to obtain an optimal transmission path set based on the power grid topology.

5. The communication unit data transmission optimization method for large-scale electric energy data according to claim 1, characterized in that: The performing multi-network card concurrent transmission processing on the optimal transmission path set by the P-DPDK processor to obtain a classified transmission result of the electric energy data packet includes: Based on the optimal transmission path set, a dedicated receiving queue and a sending queue are configured for each network card, and a network card queue mapping relationship table is obtained; The importance level of the electric energy data is divided according to the electric energy data time series dependency matrix to obtain classification identifications of first-level key data, second-level important data and third-level regular data; Inputting the classification identifier and the network card queue mapping relationship table into the data packet classifier for queue allocation processing to obtain a network card queue allocation plan corresponding to each priority data; Building a zero-copy memory management mechanism based on the network card queue allocation scheme to obtain pre-allocated memory pool and buffer management parameters; The pre-allocated memory pool and buffer management parameters are input into a multi-network card load balancing scheduler for concurrent transmission processing to obtain a classified transmission result of the electric energy data packet.

6. The communication unit data transmission optimization method for large-scale electric energy data according to claim 5, characterized in that: The dynamic load balancing adjustment process is performed according to the classification transmission result of the electric energy data packet to obtain the optimized electric energy data transmission performance parameters, including: Based on the classification and transmission results of the electric energy data packets, a grid operation status monitoring matrix is ​​constructed to obtain grid node operation parameters including normal state, light load state, heavy load state and fault state; Perform fault detection and severity assessment based on the grid node operating parameters to obtain fault impact factors and severity level values; Inputting the fault impact factor and severity level value into the adaptive priority adjustment algorithm for weight calculation processing to obtain a dynamically adjusted data transmission priority weight; Dynamically allocating CPU core resources based on the data transmission priority weights to obtain a resource configuration scheme for key data dedicated cores, important data bound cores, and conventional data shared cores; The resource configuration scheme is input into the transmission performance comprehensive evaluation model for performance calculation processing to obtain optimized power data transmission performance parameters.

7. The communication unit data transmission optimization method for large-scale electric energy data according to claim 6, characterized in that: The CPU core resources are dynamically allocated based on the data transmission priority weights to obtain a resource configuration scheme for a key data dedicated core, an important data bound core, and a conventional data shared core, including: Perform dedicated core allocation processing on the first-level critical data according to the data transmission priority weight, and obtain an exclusive CPU core identifier corresponding to the protection control data; Perform core binding processing on the secondary important data and the CPU affinity binding algorithm to obtain a binding relationship table between the real-time monitoring data and the specific CPU core; Performing shared core allocation processing based on the load characteristics of the three-level conventional data to obtain a shared CPU core pool corresponding to historical statistical data; Building a core scheduling priority queue based on the exclusive CPU core identifier, the binding relationship table, and the shared CPU core pool to obtain a core access timing arrangement for data of different priorities; The core access timing arrangement is input into a CPU resource manager for resource configuration processing, and a resource configuration scheme for a key data dedicated core, an important data binding core, and a conventional data sharing core is obtained.

8. A communication unit data transmission optimization system for large-scale electric energy data, characterized in that: A method for optimizing communication unit data transmission for large-scale electric energy data according to any one of claims 1 to 7, wherein the communication unit data transmission optimization system for large-scale electric energy data comprises: The extraction module is used to perform feature extraction processing on the electric energy data through the time-space correlation analysis algorithm to obtain the time series dependency matrix and spatial correlation matrix of the electric energy data; A construction module is used to construct an EQ-learning intelligent scheduling model based on the time series dependency matrix and spatial correlation matrix of the electric energy data to obtain a Q-value decision table integrating grid constraints; A selection module, configured to apply the Q-value decision table to a multi-communication unit path selection process to obtain an optimal transmission path set based on a power grid topology; A transmission module, configured to perform multi-network card concurrent transmission processing on the optimal transmission path set through a P-DPDK processor to obtain a classified transmission result of an electric energy data packet; The processing module is used to perform dynamic load balancing adjustment processing according to the classification transmission results of the electric energy data packets to obtain optimized electric energy data transmission performance parameters.

9. A communication unit data transmission optimization device for large-scale electric energy data, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the communication unit data transmission optimization method for large-scale electric energy data according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the processor executes the communication unit data transmission optimization method for large-scale electric energy data according to any one of claims 1 to 7.

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