A 5G+ energy management method and system based on intelligent network

Abstract

The application discloses a 5G+ energy management method and system based on an intelligent network, relates to the technical field of energy management, and has the technical scheme as follows: obtaining equipment operation data and energy demand data of an energy terminal, marking an intelligent network node in 5G communication with the energy terminal as a management node, and obtaining node data of the management node; generating an energy consumption change trajectory of the energy terminal according to the equipment operation data, and collecting a network coverage range of the management node according to the node data; processing and analyzing the energy consumption change trajectory and the network coverage range to obtain a delay optimization coefficient corresponding to a candidate management node; obtaining network performance parameters of the candidate management node, processing and analyzing the energy demand data and the network performance parameters to obtain an energy management coefficient corresponding to the candidate management node; and the effect is to promote energy management to be more intelligent and refined.

Description

Technical Field

[0001] This invention relates to the field of energy management technology, and specifically to a 5G+ energy management method and system based on intelligent networks. Background Technology

[0002] With the increasing number and diversification of energy terminals, traditional management methods are struggling to meet complex needs in the field of energy management. The development of 5G technology brings new opportunities to energy management, as its high bandwidth, low latency, and wide connectivity can overcome the bottlenecks in data transmission and interaction in traditional energy management.

[0003] Energy management faces challenges such as difficulties in data integration and poor adaptability between network nodes and terminal needs. On the one hand, the operation of energy terminals generates massive amounts of data, but there is a lack of effective means to comprehensively collect and analyze it, making it difficult to accurately grasp energy consumption patterns and demands. On the other hand, there are numerous intelligent network nodes with varying network coverage and performance parameters, making it impossible to assess their support capabilities for energy terminals. This results in high energy dispatch latency, low efficiency, and even waste of network resources or failure to meet terminal needs.

[0004] Therefore, it is necessary to build a 5G+ energy management method based on intelligent networks, integrate multi-source data to quantitatively assess node adaptability, optimize energy dispatching processes, and thus improve management efficiency and intelligence level. Summary of the Invention

[0005] The purpose of this invention is to provide a 5G+ energy management method and system based on intelligent networks to solve the problems of high delay and low efficiency in energy dispatching in the prior art.

[0006] To solve the above-mentioned technical problems, the present invention specifically provides the following technical solution:

[0007] A 5G+ energy management method based on intelligent networks, comprising the following steps:

[0008] Acquire equipment operation data and energy demand data from energy terminals, mark smart network nodes that communicate with energy terminals via 5G as management nodes, and acquire node data of management nodes;

[0009] Based on equipment operation data, generate energy consumption change trajectories for energy terminals and manage the network coverage of nodes based on node data collection.

[0010] The delay optimization coefficients corresponding to the candidate management nodes are obtained by processing and analyzing the energy consumption change trajectory and network coverage.

[0011] Obtain the network performance parameters of the candidate management nodes, and process and analyze the energy demand data and network performance parameters to obtain the energy management coefficient corresponding to the candidate management nodes;

[0012] Based on the delay optimization weight and delay optimization coefficient, and the energy management weight and energy management coefficient, the comprehensive management coefficient corresponding to the candidate management node is obtained;

[0013] Based on the comprehensive management coefficient, the target management node corresponding to the energy terminal is obtained, and the energy dispatch instructions of the energy terminal are transmitted and executed according to the target management node.

[0014] As a preferred embodiment of the present invention, the delay optimization coefficient corresponding to the candidate management node is obtained by processing and analyzing the energy consumption change trajectory and network coverage, specifically including the following steps:

[0015] The energy management scope corresponding to the energy terminal is obtained based on the energy consumption change trajectory;

[0016] If the network coverage of the management node completely covers the energy management range of the energy terminal, and the network signal strength meets the preset threshold, then the management node will be marked as the candidate management node corresponding to the energy terminal.

[0017] The response delay trajectory between the candidate management node and the energy terminal is obtained based on the energy consumption change trajectory of the energy terminal;

[0018] The latency optimization coefficients corresponding to the candidate management nodes are obtained based on the response latency trajectory.

[0019] As a preferred embodiment of the present invention, the energy demand data and network performance parameters are processed and analyzed to obtain the energy management coefficient corresponding to the candidate management node, specifically including the following steps:

[0020] Based on energy demand data, the energy type and energy dispatch level of the energy terminal are obtained;

[0021] Set the performance requirement parameters for energy terminals based on energy type and energy dispatch level;

[0022] The energy management coefficient corresponding to the candidate management node is obtained by analyzing the performance requirement parameters and the network performance parameters of the candidate management node. The performance requirement parameters include the bandwidth requirement, real-time requirement, data transmission security requirement, and energy scheduling capacity requirement of the energy terminal. The network performance parameters include the bandwidth capacity, response speed parameter, data encryption level, and maximum scheduling processing volume of the candidate management node.

[0023] As a preferred embodiment of the present invention, the energy management range corresponding to the energy terminal is obtained based on the energy consumption change trajectory, specifically including the following steps:

[0024] Obtain the energy consumption value and timestamp of each trajectory point in the energy consumption change trajectory;

[0025] The first threshold point is set based on the maximum and minimum energy consumption values ​​in the energy consumption data;

[0026] Set a second threshold point based on the earliest and latest timestamps in the timestamps;

[0027] A dynamic management interval is constructed based on the first and second threshold points, and the dynamic management interval is marked as the energy management range corresponding to the energy terminal.

[0028] As a preferred embodiment of the present invention, the response delay trajectory between the candidate management node and the energy terminal is obtained based on the energy consumption change trajectory of the energy terminal, specifically as follows:

[0029] Obtain the real-time response parameters of the candidate management nodes, and based on the real-time response parameters, obtain the node response coordinates of the candidate management nodes in the standard time energy consumption coordinate system;

[0030] Based on the energy consumption change trajectory of the energy terminal, the energy consumption trajectory coordinates of each trajectory point in the energy consumption change trajectory in the standard time energy consumption coordinate system are obtained;

[0031] The response delay values ​​between each trajectory point in the energy consumption change trajectory and the candidate management node are obtained based on the energy consumption trajectory coordinates and node response coordinates;

[0032] Based on the response delay values ​​of each trajectory point in the energy consumption change trajectory and the candidate management node, the response delay trajectory between the candidate management node and the energy terminal is generated.

[0033] As a preferred embodiment of the present invention, the delay optimization coefficient corresponding to the candidate management node is obtained based on the response delay trajectory, specifically including the following steps:

[0034] Set response delay intervals, each with a corresponding delay optimization weight;

[0035] Based on the inclusion relationship between the response delay value and the response delay interval in the response delay trajectory, the response delay trajectory is divided into trajectories to obtain at least one segmented response delay trajectory;

[0036] The piecewise response delay trajectory corresponds to the response delay interval, and the delay optimization weight corresponding to the response delay interval is marked as the target optimization weight of the piecewise response delay trajectory.

[0037] Based on the segmented response delay trajectory and the target optimization weights corresponding to the segmented response delay trajectory, the delay optimization coefficients corresponding to the candidate management nodes are obtained.

[0038] As a preferred embodiment of the present invention, the energy management coefficient corresponding to the candidate management node is obtained based on the analysis of performance requirement parameters and network performance parameters of the candidate management node, specifically including the following steps:

[0039] Set performance evaluation weights; the performance evaluation weights include bandwidth evaluation weights, real-time evaluation weights, security evaluation weights, and capacity evaluation weights;

[0040] If the network performance parameters of the candidate management node and the performance requirements of the energy terminal are both consistent and the matching degree of each parameter exceeds the preset matching threshold, then the candidate management node will be marked as a parameter-adapted node.

[0041] Based on the bandwidth evaluation weight, bandwidth requirements, and bandwidth capacity of the parameter adaptation node, the bandwidth adaptation coefficient of the parameter adaptation node is obtained.

[0042] Based on the real-time evaluation weights, real-time requirements, and the response speed parameters of the parameter adaptation nodes, the real-time adaptation coefficients of the parameter adaptation nodes are obtained.

[0043] Based on the security assessment weights, data transmission security requirements, and the data encryption level of the parameter adaptation nodes, the security adaptation coefficient of the parameter adaptation nodes is obtained.

[0044] Based on the capacity assessment weight, energy dispatch capacity demand, and the maximum dispatch processing capacity of the parameter adaptation node, the capacity adaptation coefficient of the parameter adaptation node is obtained.

[0045] The energy management coefficient corresponding to the candidate management node is obtained based on the bandwidth adaptation coefficient, real-time adaptation coefficient, security adaptation coefficient, and capacity adaptation coefficient. If the candidate management node is a parameter adaptation node, then the candidate management node has an energy management coefficient; if the candidate management node is not a parameter adaptation node, then the candidate management node does not have an energy management coefficient.

[0046] As a preferred embodiment of the present invention, the target management node corresponding to the energy terminal is obtained based on the comprehensive management coefficient, specifically as follows:

[0047] The candidate management node with the highest comprehensive management coefficient is marked as the target management node corresponding to the energy terminal.

[0048] A 5G+ energy management system based on intelligent networks includes:

[0049] Acquisition Module: Acquires equipment operation data and energy demand data from energy terminals, marks smart network nodes that communicate with energy terminals via 5G as management nodes, and acquires node data of management nodes;

[0050] Generation module: Generates energy consumption change trajectory of energy terminal based on equipment operation data, and manages network coverage of node based on node data acquisition and management.

[0051] The first analysis module processes and analyzes the energy consumption change trajectory and network coverage to obtain the delay optimization coefficient corresponding to the candidate management node;

[0052] The second analysis module: obtains the network performance parameters of the candidate management nodes, processes and analyzes the energy demand data and network performance parameters to obtain the energy management coefficients corresponding to the candidate management nodes;

[0053] Processing module: Based on the delay optimization weight and delay optimization coefficient, and the energy management weight and energy management coefficient, the comprehensive management coefficient corresponding to the candidate management node is obtained;

[0054] Execution module: Based on the comprehensive management coefficient, the target management node corresponding to the energy terminal is obtained, and the energy dispatch instructions of the energy terminal are transmitted and executed according to the target management node.

[0055] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a 5G+ energy management method based on a smart network.

[0056] Compared with the prior art, the present invention has the following advantages:

[0057] This invention, by acquiring equipment operation data, energy demand data, and node data from management nodes, comprehensively grasps the operational status of energy terminals and the resource status of network nodes. In the energy consumption and network analysis phase, it generates energy consumption change trajectories based on equipment operation data and clarifies network coverage by combining node data. This clearly presents energy consumption patterns and network accessibility, making the objects of energy management and the network resources they rely on visible and clear, facilitating advance planning and response to energy consumption fluctuations. Furthermore, by calculating latency optimization coefficients and energy management coefficients, it evaluates management nodes from two key dimensions: latency and energy demand adaptation. The former ensures the timeliness of energy dispatch command transmission, while the latter ensures precise matching between network performance and energy demand, allowing the selection of management nodes to better align with actual energy management needs and improving the efficiency and stability of energy dispatch. By integrating multi-dimensional evaluations such as latency and energy management, the optimal management node is selected, maximizing the advantages of 5G network's low latency and high reliability, ensuring efficient transmission and execution of energy dispatch commands, and achieving rational energy allocation. The entire process forms a complete closed loop from data collection, analysis and evaluation to decision execution, driving energy management towards intelligence and refinement, and effectively improving the overall level and efficiency of energy management. Attached Figure Description

[0058] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0059] Figure 1 This invention provides a schematic diagram illustrating the steps of a 5G+ energy management method based on an intelligent network.

[0060] Figure 2 This invention presents a schematic diagram of a 5G+ energy management system based on an intelligent network. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] The concepts involved in this application will first be described with reference to the accompanying drawings. It should be noted that the following descriptions of various concepts are only for the purpose of making the content of this application easier to understand and do not constitute a limitation on the scope of protection of this application; furthermore, the embodiments and features in the embodiments of this application can be combined with each other unless otherwise specified. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0063] like Figures 1-2 As shown, the embodiment provides a 5G+ energy management method and system based on intelligent networks.

[0064] A 5G+ energy management method based on intelligent networks, comprising the following steps:

[0065] Acquire equipment operation data and energy demand data from energy terminals, mark smart network nodes that communicate with energy terminals via 5G as management nodes, and acquire node data of management nodes;

[0066] Based on equipment operation data, generate energy consumption change trajectories for energy terminals and manage the network coverage of nodes based on node data collection.

[0067] The delay optimization coefficients corresponding to the candidate management nodes are obtained by processing and analyzing the energy consumption change trajectory and network coverage.

[0068] Obtain the network performance parameters of the candidate management nodes, and process and analyze the energy demand data and network performance parameters to obtain the energy management coefficient corresponding to the candidate management nodes;

[0069] Based on the delay optimization weight and delay optimization coefficient, and the energy management weight and energy management coefficient, the comprehensive management coefficient corresponding to the candidate management node is obtained;

[0070] Based on the comprehensive management coefficient, the target management node corresponding to the energy terminal is obtained, and the energy dispatch instructions of the energy terminal are transmitted and executed according to the target management node.

[0071] First, we acquire the equipment operation data and energy demand data of the energy terminal. Simultaneously, we identify the smart network nodes communicating with the energy terminal via 5G as management nodes and acquire their node data. This step gathers foundational information for subsequent analysis. The equipment operation data reflects the terminal's current and past operating status, the energy demand data clarifies the energy supply required by the terminal, and the management node data helps us understand the network nodes we can rely on.

[0072] Based on equipment operation data, an energy consumption change trajectory of the energy terminal is generated, and the network coverage of the management node is managed by collecting node data. The energy consumption change trajectory can intuitively show the energy consumption fluctuation of the energy terminal under different time periods and operating conditions, which is the key to determining the energy consumption pattern; the network coverage defines the spatial area where the management node can effectively play its role. Both provide a basis for subsequent matching of management nodes from the energy side and the network side, respectively.

[0073] By processing and analyzing energy consumption change trajectories and network coverage, latency optimization coefficients for candidate management nodes are obtained. This explores the correlation between energy consumption changes at energy terminals and network coverage of management nodes, examining latency performance in network transmission and other aspects when management nodes meet the energy consumption management needs of energy terminals. By analyzing both, candidate management nodes with potential for latency optimization are selected, and their latency optimization capabilities are quantified.

[0074] The network performance parameters of the candidate management nodes are obtained. Energy demand data and network performance parameters are processed and analyzed to obtain the energy management coefficient corresponding to the candidate management nodes. Network performance parameters include bandwidth, response speed, and other factors related to the quality of energy dispatch command transmission. Combined with energy demand data, the suitability of the candidate management nodes in meeting the energy type and quantity requirements of energy terminals is determined, thereby quantifying the energy management coefficient and measuring the candidate nodes' support capability for energy management.

[0075] Based on the delay optimization weight and coefficient, and the energy management weight and coefficient, the comprehensive management coefficient corresponding to the candidate management node is obtained. Weights are introduced here because delay optimization and energy management have different levels of importance in the overall energy management process. By assigning different weights and combining them with corresponding coefficients, the overall strength of the candidate management node can be assessed more scientifically and comprehensively, ensuring that the assessment results align with actual management needs.

[0076] The target management node corresponding to the energy terminal is determined based on the comprehensive management coefficient. Then, the energy dispatching instructions of the energy terminal are transmitted and executed based on the target management node. The comprehensive management coefficient is a comprehensive reflection of the candidate management nodes. Selecting the one with the best comprehensive management coefficient as the target management node can ensure that the energy dispatching instructions are transmitted and executed under better network conditions and in a way that better meets energy demand. This achieves efficient energy management based on intelligent networks and leveraging the advantages of 5G communication, allowing energy terminals to complete energy dispatching with the assistance of appropriate network nodes, thereby improving the intelligence and efficiency of energy management.

[0077] The energy consumption change trajectory and network coverage are processed and analyzed to obtain the latency optimization coefficient corresponding to the candidate management node. The specific steps include:

[0078] The energy management scope corresponding to the energy terminal is obtained based on the energy consumption change trajectory;

[0079] If the network coverage of the management node completely covers the energy management range of the energy terminal, and the network signal strength meets the preset threshold, then the management node will be marked as the candidate management node corresponding to the energy terminal.

[0080] The response delay trajectory between the candidate management node and the energy terminal is obtained based on the energy consumption change trajectory of the energy terminal;

[0081] The latency optimization coefficients corresponding to the candidate management nodes are obtained based on the response latency trajectory.

[0082] First, the energy management range of the energy terminal is determined by analyzing its energy consumption change trajectory. Then, a preliminary screening of management nodes is conducted. It is checked whether the network coverage of the management node completely covers the determined energy management range, and whether the network signal strength meets preset requirements. If the network coverage of the management node completely covers the energy terminal's energy management range, and the network signal strength meets the preset threshold, then the management node is marked as a candidate management node for the corresponding energy terminal.

[0083] The focus is on the response latency between candidate management nodes and energy terminals. The response latency between candidate management nodes and energy terminals is determined based on the energy consumption change trajectory of the energy terminals. Energy terminals have different energy consumption at different times, corresponding to different energy management operation requirements. There will be a time lag in the candidate management nodes' responses to these requirements. These time lags are arranged in chronological order of energy consumption changes to form a response latency trajectory, reflecting the varying response speed of candidate nodes throughout the entire process of energy terminal energy consumption changes.

[0084] Delay optimization coefficients are calculated based on response delay trajectories. By analyzing the distribution and fluctuations of delay times within these trajectories, the ability of candidate management nodes to reduce response delay is evaluated. For example, some trajectories show consistently low and stable delays, indicating good delay optimization performance and a high corresponding delay optimization coefficient; conversely, if delays fluctuate greatly and high delays occur frequently, the coefficient is low. Suitable nodes are selected based on energy consumption trajectories, ultimately yielding a coefficient that measures the delay optimization capability of each node, providing a crucial basis for selecting the optimal management node in energy management.

[0085] The energy management coefficients for candidate management nodes are obtained by processing and analyzing energy demand data and network performance parameters, specifically including the following steps:

[0086] Based on energy demand data, the energy type and energy dispatch level of the energy terminal are obtained;

[0087] Set the performance requirement parameters for energy terminals based on energy type and energy dispatch level;

[0088] The energy management coefficient corresponding to the candidate management node is obtained by analyzing the performance requirement parameters and the network performance parameters of the candidate management node. The performance requirement parameters include the bandwidth requirement, real-time requirement, data transmission security requirement, and energy scheduling capacity requirement of the energy terminal. The network performance parameters include the bandwidth capacity, response speed parameter, data encryption level, and maximum scheduling processing volume of the candidate management node.

[0089] This application first extracts key information from energy demand data to clarify the energy type and energy dispatch scale required by the energy terminal. The energy dispatch scale refers to how much energy needs to be dispatched. Based on the energy type and dispatch scale, the performance requirement parameters of the energy terminal are set. Different energy types and dispatch scales have different requirements for network transmission bandwidth, real-time performance, security, and dispatch capacity. For example, transmitting a large number of power dispatch instructions may require high bandwidth and high real-time performance, while data transmission of important energy data requires high security. Thus, the performance requirement parameters such as bandwidth requirement, real-time requirement, data transmission security requirement, and energy dispatch capacity requirement are determined.

[0090] Obtain the network performance parameters of the candidate management nodes, including their bandwidth capacity, response speed, data encryption level, and maximum scheduling throughput. Compare the performance requirements of the energy terminals with the network performance parameters of the candidate management nodes to determine whether the selected nodes can meet the terminal requirements in terms of bandwidth, real-time performance, security, and capacity, and to what extent. Quantify this to derive the energy management coefficient corresponding to the candidate management nodes. This coefficient reflects the candidate nodes' ability to adapt to the energy management needs of the energy terminals, providing a basis for subsequently selecting suitable management nodes for energy scheduling.

[0091] The energy management scope corresponding to the energy terminal is obtained based on the energy consumption change trajectory, which specifically includes the following steps:

[0092] Obtain the energy consumption value and timestamp of each trajectory point in the energy consumption change trajectory;

[0093] The first threshold point is set based on the maximum and minimum energy consumption values ​​in the energy consumption data;

[0094] Set a second threshold point based on the earliest and latest timestamps in the timestamps;

[0095] A dynamic management interval is constructed based on the first and second threshold points, and the dynamic management interval is marked as the energy management range corresponding to the energy terminal.

[0096] First, the energy consumption values ​​and corresponding timestamps of each trajectory point in the energy consumption change trajectory are obtained, recording the energy consumption of the energy terminal at different times. Next, the maximum and minimum energy consumption values ​​are identified from these values ​​to set the first threshold point; these two extreme values ​​reflect the boundaries of the energy terminal's energy consumption fluctuation range. Simultaneously, the earliest and latest timestamps are extracted from the timestamps to set the second threshold point, defining the time span corresponding to the energy consumption data. A dynamic management interval is constructed based on the first and second threshold points. This interval comprehensively covers the key ranges of the energy terminal in both the energy consumption and time dimensions. Finally, this dynamic management interval is marked as the energy management scope corresponding to the energy terminal. This clarifies the key energy consumption and time boundaries that need to be focused on when managing the energy terminal, providing a basis for subsequent management node adaptation and other operations.

[0097] Based on the energy consumption change trajectory of the energy terminal, the response delay trajectory between the candidate management node and the energy terminal is obtained, specifically:

[0098] Obtain the real-time response parameters of the candidate management nodes, and based on the real-time response parameters, obtain the node response coordinates of the candidate management nodes in the standard time energy consumption coordinate system;

[0099] Based on the energy consumption change trajectory of the energy terminal, the energy consumption trajectory coordinates of each trajectory point in the energy consumption change trajectory in the standard time energy consumption coordinate system are obtained;

[0100] The response delay values ​​between each trajectory point in the energy consumption change trajectory and the candidate management node are obtained based on the energy consumption trajectory coordinates and node response coordinates;

[0101] Based on the response delay values ​​of each trajectory point in the energy consumption change trajectory and the candidate management node, the response delay trajectory between the candidate management node and the energy terminal is generated.

[0102] First, obtain the real-time response parameters of the candidate management node. These parameters reflect the node's ability to respond to commands in the current state. Based on this, determine the node's response coordinates in the standard time-energy consumption coordinate system and clarify the location corresponding to its response characteristics.

[0103] Based on the energy consumption change trajectory of the energy terminal, the energy consumption trajectory coordinates of each trajectory point on the trajectory in the standard time energy consumption coordinate system are found. In other words, the coordinate position of the energy terminal under different energy consumption states and corresponding times is determined. In this way, the energy consumption change process of the energy terminal is presented in the form of coordinate points.

[0104] The response delay value between each trajectory point in the energy consumption change trajectory and the candidate management node is calculated using the energy consumption trajectory coordinates and node response coordinates. Since the difference between coordinate points under the same coordinate system can reflect the delay such as the time difference of the response, the delay corresponding to each point is obtained by calculating these differences.

[0105] By sequentially integrating the response delay values ​​of each trajectory point in the energy consumption change trajectory with those of the candidate management node, a response delay trajectory between the candidate management node and the energy terminal is generated. This trajectory clearly shows the dynamic changes in the response delay of the candidate management node throughout the entire process of energy consumption change at the energy terminal, providing intuitive and continuous data for subsequent evaluation of node delay optimization coefficients, etc.

[0106] The latency optimization coefficients for the candidate management nodes are obtained based on the response latency trajectory, specifically including the following steps:

[0107] Set response delay intervals, each with a corresponding delay optimization weight;

[0108] Based on the inclusion relationship between the response delay value and the response delay interval in the response delay trajectory, the response delay trajectory is divided into trajectories to obtain at least one segmented response delay trajectory;

[0109] The piecewise response delay trajectory corresponds to the response delay interval, and the delay optimization weight corresponding to the response delay interval is marked as the target optimization weight of the piecewise response delay trajectory.

[0110] Based on the segmented response delay trajectory and the target optimization weights corresponding to the segmented response delay trajectory, the delay optimization coefficients corresponding to the candidate management nodes are obtained.

[0111] This application first requires pre-setting response latency intervals, with each interval corresponding to a specific latency optimization weight. These intervals are divided based on different requirements and tolerances for response latency in actual energy management scenarios, and the weights reflect the importance of different latency performance to overall energy management optimization. For example, low latency intervals often correspond to higher weights because low latency has a greater impact on the timeliness of energy dispatch.

[0112] The system focuses on determining which preset interval the response delay value of each point on the trajectory belongs to, dividing the complete response delay trajectory into at least one segmented response delay trajectory. Since there is a one-to-one correspondence between the segmented response delay trajectories and the response delay intervals, the original delay optimization weight associated with each interval is marked as the target optimization weight for the corresponding segmented trajectory. The magnitude of the weight reflects the delay performance represented by that segment and its weight in the overall optimization evaluation. Integrating the characteristics of the segmented response delay trajectories and combining them with the target optimization weights corresponding to each segment, the delay optimization coefficient of the candidate management nodes is obtained through weighted calculations. The delay optimization coefficient considers the node's contribution across different delay performance intervals, directly reflecting the node's ability to reduce response delay and improve the timeliness of energy dispatch, providing crucial quantitative evidence for subsequent selection of suitable energy management nodes and ensuring efficient energy dispatch.

[0113] Based on the performance requirements parameters and the network performance parameters of the candidate management nodes, the energy management coefficients corresponding to the candidate management nodes are obtained. This process includes the following steps:

[0114] Set performance evaluation weights; the performance evaluation weights include bandwidth evaluation weights, real-time evaluation weights, security evaluation weights, and capacity evaluation weights;

[0115] If the network performance parameters of the candidate management node and the performance requirements of the energy terminal are both consistent and the matching degree of each parameter exceeds the preset matching threshold, then the candidate management node will be marked as a parameter-adapted node.

[0116] Based on the bandwidth evaluation weight, bandwidth requirements, and bandwidth capacity of the parameter adaptation node, the bandwidth adaptation coefficient of the parameter adaptation node is obtained.

[0117] Based on the real-time evaluation weights, real-time requirements, and the response speed parameters of the parameter adaptation nodes, the real-time adaptation coefficients of the parameter adaptation nodes are obtained.

[0118] Based on the security assessment weights, data transmission security requirements, and the data encryption level of the parameter adaptation nodes, the security adaptation coefficient of the parameter adaptation nodes is obtained.

[0119] Based on the capacity assessment weight, energy dispatch capacity demand, and the maximum dispatch processing capacity of the parameter adaptation node, the capacity adaptation coefficient of the parameter adaptation node is obtained.

[0120] The energy management coefficient corresponding to the candidate management node is obtained based on the bandwidth adaptation coefficient, real-time adaptation coefficient, security adaptation coefficient, and capacity adaptation coefficient. If the candidate management node is a parameter adaptation node, then the candidate management node has an energy management coefficient; if the candidate management node is not a parameter adaptation node, then the candidate management node does not have an energy management coefficient.

[0121] First, performance evaluation weights are set, assigning different weights to four dimensions: bandwidth, real-time performance, security, and capacity. This reflects the importance of each dimension in energy management. For example, in energy dispatching scenarios with high real-time requirements, the real-time evaluation weight may be higher. This determines whether the candidate management node is a parameter-matched node. The network performance parameters of the candidate management node are compared with the performance requirements of the energy terminal. The network performance parameters are bandwidth capacity, response speed, data encryption level, and maximum dispatch processing volume. The performance requirements of the energy terminal are bandwidth requirements, real-time requirements, data transmission security requirements, and energy dispatching capacity requirements. If the matching degree of each parameter exceeds the preset threshold, it means that the node can well match the energy terminal's network performance requirements, and it is marked as a parameter-matched node. If it does not meet the requirements, it cannot become a parameter-matched node, and there will be no energy management coefficient in the future.

[0122] When calculating the bandwidth adaptation coefficient, bandwidth evaluation weights are combined with reference to the bandwidth requirements of energy terminals and the bandwidth capacity of nodes to determine whether the node bandwidth can meet the terminal requirements and to what extent. The real-time adaptation coefficient is calculated based on real-time evaluation weights, comparing the real-time requirements of terminals with the response speed parameters of nodes to measure the node's adaptability in terms of response timeliness. The security adaptation coefficient is calculated using security evaluation weights, relating the data transmission security requirements of terminals to the data encryption level of nodes to determine the level of adaptation in terms of data transmission security. The capacity adaptation coefficient is calculated using capacity evaluation weights, referencing the energy dispatch capacity requirements of terminals and the maximum dispatch processing capacity of nodes to assess the degree of capacity adaptation.

[0123] The energy management coefficient of the candidate management node is obtained by combining the bandwidth adaptation coefficient, real-time performance adaptation coefficient, security adaptation coefficient, and capacity adaptation coefficient. Only nodes with suitable parameters will have their energy management coefficient calculated. This coefficient is used to determine the subsequent comprehensive management coefficient, providing a basis for selecting the most suitable energy management node and ensuring that energy terminals can efficiently and securely complete energy scheduling and other management tasks with the support of nodes whose network performance is adapted.

[0124] The target management node corresponding to the energy terminal is obtained based on the comprehensive management coefficient, specifically:

[0125] The candidate management node with the highest comprehensive management coefficient is marked as the target management node corresponding to the energy terminal.

[0126] In the 5G+ energy management process based on intelligent networks, after calculating the comprehensive management coefficient of each candidate management node, the process proceeds to the stage of determining the target management node. The comprehensive management coefficient is a quantitative indicator that comprehensively reflects the overall performance of the candidate management node in energy management, integrating evaluation results from multiple aspects such as latency optimization and energy management.

[0127] The overall management coefficients of all candidate management nodes are compared. A higher overall management coefficient indicates better overall performance in meeting the energy dispatching needs of the energy terminal, particularly in areas such as latency control and network performance adaptation. Therefore, the candidate management node with the highest overall management coefficient is designated as the target management node for the corresponding energy terminal. This most suitable node is then responsible for transmitting and executing energy dispatching commands from the energy terminal, ensuring efficient and accurate energy management and enabling the energy terminal to achieve reasonable energy dispatching and management with the support of the optimal network node.

[0128] A 5G+ energy management system based on intelligent networks includes:

[0129] Acquisition Module: Acquires equipment operation data and energy demand data from energy terminals, marks smart network nodes that communicate with energy terminals via 5G as management nodes, and acquires node data of management nodes;

[0130] Generation module: Generates energy consumption change trajectory of energy terminal based on equipment operation data, and manages network coverage of node based on node data acquisition and management.

[0131] The first analysis module processes and analyzes the energy consumption change trajectory and network coverage to obtain the delay optimization coefficient corresponding to the candidate management node;

[0132] The second analysis module: obtains the network performance parameters of the candidate management nodes, processes and analyzes the energy demand data and network performance parameters to obtain the energy management coefficients corresponding to the candidate management nodes;

[0133] Processing module: Based on the delay optimization weight and delay optimization coefficient, and the energy management weight and energy management coefficient, the comprehensive management coefficient corresponding to the candidate management node is obtained;

[0134] Execution module: Based on the comprehensive management coefficient, the target management node corresponding to the energy terminal is obtained, and the energy dispatch instructions of the energy terminal are transmitted and executed according to the target management node.

[0135] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a 5G+ energy management method based on a smart network.

[0136] The embodiments and / or implementation methods described above are merely preferred embodiments and / or implementation methods for implementing the technology of the present invention, and are not intended to limit the implementation methods of the technology of the present invention in any way. Any person skilled in the art may make some modifications or alterations to other equivalent embodiments without departing from the scope of the technical means disclosed in the present invention, but these should still be regarded as the technology or embodiments that are substantially the same as the present invention.

[0137] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. The above descriptions are only preferred embodiments of this application. It should be noted that due to the limitations of written expression, while there are objectively infinite specific structures, those skilled in the art can make several improvements, modifications, or changes without departing from the principles of this application, and can also combine the above technical features in an appropriate manner. These improvements, modifications, changes, or combinations, or the direct application of the inventive concept and technical solution to other situations without modification, should all be considered within the scope of protection of this application.

Claims

1. A 5G+ energy management method based on intelligent networks, characterized in that, The method includes the following steps: Acquire equipment operation data and energy demand data from energy terminals, mark smart network nodes that communicate with energy terminals via 5G as management nodes, and acquire node data of management nodes; Based on equipment operation data, generate energy consumption change trajectories for energy terminals and manage the network coverage of nodes based on node data collection. The energy consumption change trajectory and network coverage are processed and analyzed to obtain the latency optimization coefficient corresponding to the candidate management node. The specific steps include: The energy management scope corresponding to the energy terminal is obtained based on the energy consumption change trajectory; If the network coverage of the management node completely covers the energy management range of the energy terminal, and the network signal strength meets the preset threshold, then the management node will be marked as the candidate management node corresponding to the energy terminal. Based on the energy consumption change trajectory of the energy terminal, the response delay trajectory between the candidate management node and the energy terminal is obtained, specifically: Obtain the real-time response parameters of the candidate management nodes, and based on the real-time response parameters, obtain the node response coordinates of the candidate management nodes in the standard time energy consumption coordinate system; Based on the energy consumption change trajectory of the energy terminal, the energy consumption trajectory coordinates of each trajectory point in the energy consumption change trajectory in the standard time energy consumption coordinate system are obtained; The response delay values ​​between each trajectory point in the energy consumption change trajectory and the candidate management node are obtained based on the energy consumption trajectory coordinates and node response coordinates; Based on the response delay values ​​between each trajectory point in the energy consumption change trajectory and the candidate management node, the response delay trajectory between the candidate management node and the energy terminal is generated. The latency optimization coefficients for the candidate management nodes are obtained based on the response latency trajectory, specifically including the following steps: Set response delay intervals, each with a corresponding delay optimization weight; Based on the inclusion relationship between the response delay value and the response delay interval in the response delay trajectory, the response delay trajectory is divided into trajectories to obtain at least one segmented response delay trajectory; The piecewise response delay trajectory corresponds to the response delay interval, and the delay optimization weight corresponding to the response delay interval is marked as the target optimization weight of the piecewise response delay trajectory. Based on the segmented response delay trajectory and the target optimization weights corresponding to the segmented response delay trajectory, the delay optimization coefficients corresponding to the candidate management nodes are obtained; Obtain the network performance parameters of the candidate management nodes, and process and analyze the energy demand data and network performance parameters to obtain the energy management coefficient corresponding to the candidate management nodes. This includes the following steps: Based on energy demand data, the energy type and energy dispatch level of the energy terminal are obtained; Set the performance requirement parameters for energy terminals based on energy type and energy dispatch level; Based on the performance requirements parameters and the network performance parameters of the candidate management nodes, the energy management coefficients corresponding to the candidate management nodes are obtained. This process includes the following steps: Set performance evaluation weights; the performance evaluation weights include bandwidth evaluation weights, real-time evaluation weights, security evaluation weights, and capacity evaluation weights; If the network performance parameters of the candidate management node and the performance requirements of the energy terminal are both consistent and the matching degree of each parameter exceeds the preset matching threshold, then the candidate management node will be marked as a parameter-adapted node. Based on the bandwidth evaluation weight, bandwidth requirements, and bandwidth capacity of the parameter adaptation node, the bandwidth adaptation coefficient of the parameter adaptation node is obtained. Based on the real-time evaluation weights, real-time requirements, and the response speed parameters of the parameter adaptation nodes, the real-time adaptation coefficients of the parameter adaptation nodes are obtained. Based on the security assessment weights, data transmission security requirements, and the data encryption level of the parameter adaptation nodes, the security adaptation coefficient of the parameter adaptation nodes is obtained. Based on the capacity assessment weight, energy dispatch capacity demand, and the maximum dispatch processing capacity of the parameter adaptation node, the capacity adaptation coefficient of the parameter adaptation node is obtained. The energy management coefficient corresponding to the candidate management node is obtained based on the bandwidth adaptation coefficient, real-time adaptation coefficient, security adaptation coefficient, and capacity adaptation coefficient; if the candidate management node is a parameter adaptation node, then the candidate management node has an energy management coefficient; if the candidate management node is not a parameter adaptation node, then the candidate management node does not have an energy management coefficient. The performance requirements include the bandwidth requirements, real-time requirements, data transmission security requirements, and energy dispatch capacity requirements of the energy terminal; the network performance parameters include the bandwidth capacity, response speed parameters, data encryption level, and maximum dispatch processing capacity of the candidate management nodes. Based on the delay optimization weight and delay optimization coefficient, and the energy management weight and energy management coefficient, the comprehensive management coefficient corresponding to the candidate management node is obtained; Based on the comprehensive management coefficient, the target management node corresponding to the energy terminal is obtained, and the energy dispatch instructions of the energy terminal are transmitted and executed according to the target management node.

2. The 5G+ energy management method based on intelligent networks according to claim 1, characterized in that, The energy management scope corresponding to the energy terminal is obtained based on the energy consumption change trajectory, which specifically includes the following steps: Obtain the energy consumption value and timestamp of each trajectory point in the energy consumption change trajectory; The first threshold point is set based on the maximum and minimum energy consumption values ​​in the energy consumption data; Set a second threshold point based on the earliest and latest timestamps in the timestamps; A dynamic management interval is constructed based on the first and second threshold points, and the dynamic management interval is marked as the energy management range corresponding to the energy terminal.

3. The 5G+ energy management method based on intelligent networks according to claim 2, characterized in that, The target management node corresponding to the energy terminal is obtained based on the comprehensive management coefficient, specifically: The candidate management node with the highest comprehensive management coefficient is marked as the target management node corresponding to the energy terminal.

4. A 5G+ energy management system based on an intelligent network, applied to the 5G+ energy management method based on an intelligent network as described in any one of claims 1-3, characterized in that, include: Acquisition Module: Acquires equipment operation data and energy demand data from energy terminals, marks smart network nodes that communicate with energy terminals via 5G as management nodes, and acquires node data of management nodes; Generation module: Generates energy consumption change trajectory of energy terminal based on equipment operation data, and manages network coverage of node based on node data acquisition and management. The first analysis module processes and analyzes the energy consumption change trajectory and network coverage to obtain the delay optimization coefficient corresponding to the candidate management node; The second analysis module: obtains the network performance parameters of the candidate management nodes, processes and analyzes the energy demand data and network performance parameters to obtain the energy management coefficients corresponding to the candidate management nodes; Processing module: Based on the delay optimization weight and delay optimization coefficient, and the energy management weight and energy management coefficient, the comprehensive management coefficient corresponding to the candidate management node is obtained; Execution module: Based on the comprehensive management coefficient, the target management node corresponding to the energy terminal is obtained, and the energy dispatch instructions of the energy terminal are transmitted and executed according to the target management node.

5. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements a 5G+ energy management method based on a smart network as described in any one of claims 1 to 4.

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