System and method for coordinated operation of power supply and communication equipment

Abstract

The present application relates to the technical field of equipment collaboration, and discloses a system and method for collaborative work between power supply and communication equipment. The method includes: functional configuration and time slot division of multiple nodes to obtain a power communication collaborative network; real-time monitoring and parameter processing of power grid parameters to obtain comprehensive power grid status information; role allocation and switching control of multiple nodes based on comprehensive power grid status information to obtain a node time slot function allocation scheme; optimization analysis and strategy generation to obtain a global energy allocation plan and its execution strategy; dynamic adjustment of the voltage and power of the node to obtain a dynamic adjustment execution result; based on the dynamic adjustment execution result, distributed collaborative optimization of the multi-node energy management strategy in the power communication collaborative network to obtain a global optimal energy use scheme. The present application can continuously optimize its energy use efficiency and adapt to the ever-changing power grid environment and communication needs.

Description

Technical Field

[0001] The present application relates to the field of device collaboration technology, and in particular to a system and method for collaborative working of a power supply and communication equipment. Background Art

[0002] Traditional power supply systems often operate independently of communication systems and are difficult to adapt to the needs of modern communication networks for dynamic and efficient energy supply. At the same time, the energy consumption of communication equipment is becoming increasingly prominent. How to achieve efficient energy utilization while ensuring communication quality has become a difficult problem that needs to be solved urgently. In addition, with the rise of distributed energy and smart grids, the topology of power supply networks has become increasingly complex. How to achieve collaborative work between multiple nodes and optimal energy allocation has also become an important challenge.

[0003] In this context, traditional power management methods often fail to meet the needs of modern communication networks. They lack the ability to perceive and predict the overall state of the network in real time, and cannot make timely adjustments based on the dynamic changes in communication needs. Secondly, traditional methods usually adopt centralized control strategies, which face scalability and robustness issues in large-scale distributed networks. Existing methods often separate the power supply and communication systems, and cannot achieve deep coordination and optimization between the two systems. Summary of the invention

[0004] The present application provides a system and method for collaborative work between a power supply and communication equipment, which are used to continuously optimize their energy utilization efficiency and adapt to the ever-changing power grid environment and communication needs.

[0005] In a first aspect, the present application provides a method for a power supply to work in coordination with a communication device, the method for a power supply to work in coordination with a communication device comprising:

[0006] Functional configuration and time slot division are performed on multiple nodes to obtain a power supply communication coordination network;

[0007] Performing real-time monitoring and parameter processing of power grid parameters on the power supply communication cooperative network to obtain comprehensive power grid status information;

[0008] Based on the comprehensive power grid status information, multiple nodes in the power supply communication cooperative network are assigned roles and switched to obtain a node time slot function allocation scheme;

[0009] Optimizing and analyzing the node time slot function allocation scheme and generating strategies to obtain a global energy allocation plan and its execution strategy;

[0010] Dynamically adjusting the voltage and power of nodes in the power supply communication cooperative network according to the global energy allocation plan and its execution strategy to obtain a dynamic adjustment execution result;

[0011] According to the dynamic adjustment execution result, distributed collaborative optimization is performed on the multi-node energy management strategy in the power supply communication cooperative network to obtain a global optimal energy usage plan.

[0012] In a second aspect, the present application provides a system for a power supply and a communication device to work in coordination, the system for a power supply and a communication device to work in coordination comprising:

[0013] A configuration module, used for performing function configuration and time slot division on multiple nodes to obtain a power communication coordination network;

[0014] A monitoring module, used to perform real-time monitoring and parameter processing of power grid parameters on the power supply communication cooperative network to obtain comprehensive power grid status information;

[0015] A control module, configured to perform role allocation and switching control on multiple nodes in the power supply communication cooperative network based on the comprehensive power grid state information, and obtain a node time slot function allocation scheme;

[0016] A generation module is used to optimize and analyze the node time slot function allocation scheme and generate strategies to obtain a global energy allocation plan and its execution strategy;

[0017] An adjustment module, used to dynamically adjust the voltage and power of the nodes in the power supply communication cooperative network according to the global energy allocation plan and its execution strategy, and obtain a dynamic adjustment execution result;

[0018] The optimization module is used to perform distributed collaborative optimization on the multi-node energy management strategy in the power supply communication cooperative network according to the dynamic adjustment execution result to obtain the global optimal energy usage plan.

[0019] The third aspect of the present application provides a device for a power supply to work in coordination with a communication device, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the device for a power supply to work in coordination with a communication device executes the above-mentioned method for a power supply to work in coordination with a communication device.

[0020] A fourth aspect of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned method of power supply and communication equipment working in coordination.

[0021] The technical solution provided in this application realizes the dynamic construction and optimization of the power supply communication cooperative network: by configuring the functions and time slot division of multiple nodes, and configuring the communication protocol based on network performance evaluation, a flexible and adjustable power supply communication cooperative network is constructed, providing comprehensive grid state perception and prediction capabilities: through real-time monitoring and parameter processing, combined with time series analysis and state clustering, a comprehensive evaluation and short-term prediction of the grid state is achieved, and the dynamic allocation and smooth switching of node roles are achieved: based on comprehensive grid state information, nodes are dynamically assigned roles and switching control is performed, and the switching frequency is optimized through time continuity analysis and dynamic programming algorithms, thereby improving the stability and efficiency of the network. An optimization solution for global energy distribution is provided: through network energy flow modeling and convex optimization technology, combined with simulation adjustment of random network states, a robust global energy distribution plan and its execution strategy are obtained. Accurate dynamic regulation of node voltage and power is achieved: PID control algorithm and noise suppression technology are used to adjust and optimize the node voltage and power in real time, which improves the accuracy and efficiency of energy transmission, and provides a distributed collaborative optimization energy management strategy: through node efficiency classification, energy bottleneck identification and multi-agent reinforcement learning, distributed collaborative optimization of multi-node energy management strategies in the network is achieved, and the globally optimal energy use plan is obtained, which can continuously optimize its energy use efficiency and adapt to the ever-changing power grid environment and communication needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0023] Figure 1 A schematic diagram of an embodiment of a method for a power supply and a communication device to work in coordination in an embodiment of the present application;

[0024] Figure 2 This is a schematic diagram of an embodiment of a system in which a power supply and a communication device work in coordination in an embodiment of the present application. DETAILED DESCRIPTION

[0025] Embodiments of the present application provide a system and method for power supply and communication equipment to work in coordination. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present 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 here can be implemented in an order other than that illustrated or described here. 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 comprising 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.

[0026] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 , an embodiment of the method for the power supply and the communication device to work together in the embodiment of the present application includes:

[0027] Step S101, performing function configuration and time slot division on multiple nodes to obtain a power supply communication coordination network;

[0028] It is understandable that the execution subject of the present application can be a system in which a power supply and a communication device work together, or a terminal or a server, which is not limited here. The present application embodiment is described by taking a server as the execution subject as an example.

[0029] Specifically, multiple nodes are configured with functional modules to form a node set including an energy collection circuit, an energy storage unit, a communication module and a power management unit. Each node is configured with different functional modules to enable it to have the ability to collect energy, store energy, communicate and manage power. The network time is evenly divided according to the number of node sets and the network topology structure to obtain a time slot sequence, and each node in the node set is initially assigned a role according to the time slot sequence to generate an initial power supply node set and an initial communication node set. The nodes in the initial power supply node set are mainly responsible for energy collection and supply, while the nodes in the initial communication node set are mainly responsible for communication tasks. According to the initial power supply node set and the initial communication node set, the energy transmission path between nodes is planned to obtain an initial energy transmission topology map, and the initial energy transmission topology map is conflict-detected to identify potential energy transmission path conflicts and generate a conflict node pair list. By planning and conflict-detecting the energy transmission path between nodes, it is ensured that the energy transmission path in the network is reasonable and efficient. According to the conflict node pair list, the roles of the conflicting nodes are redistributed to optimize the power supply node set and the communication node set. Through role redistribution, conflicts in the energy transmission path are eliminated or reduced as much as possible, and the overall efficiency and stability of the network are improved. The optimized power supply node set and communication node set enable each node in the network to perform its assigned tasks more effectively. Based on the optimized power supply node set and communication node set, the capacity of the node's energy storage unit is allocated to obtain the node energy storage configuration scheme. According to the configuration scheme, the power management unit of the node is parameterized to form a dynamic voltage and power regulation system. The system can dynamically adjust the voltage and power of the node according to the actual operation of the network to ensure the stable operation of the network. The dynamic voltage and power regulation system is simulated and tested, and the performance and stability of the system are evaluated through simulation tests, and a network performance evaluation report is generated. According to the evaluation report, the communication protocol of the node's communication module is configured to ensure that the communication module can effectively exchange information with other nodes in the network to form an efficient power communication collaborative network.

[0030] Step S102: Real-time monitoring and parameter processing of power grid parameters are performed on the power supply communication cooperative network to obtain comprehensive power grid status information;

[0031] Specifically, parameter collection is performed on each node in the power communication cooperative network to collect the original power grid parameter data set, including key power grid parameters such as voltage, current, and power. Data cleaning and outlier detection are performed on the original power grid parameter data set to remove noise and abnormal data, ensure the accuracy and integrity of the data, and obtain the preprocessed power grid parameter data set. According to the preprocessed power grid parameter data set, time series analysis of power grid parameters is performed. By analyzing historical data, a power grid parameter change trend model is established to predict the change of power grid parameters in future time slots. Based on the power grid parameter change trend model, short-term power grid parameter prediction results are generated. At the same time, feature extraction and dimensionality reduction processing are performed on the preprocessed power grid parameter data set to simplify the complexity of the data and highlight key features to obtain the power grid state feature vector. Based on the power grid state feature vector, cluster analysis of the power grid state is performed to classify various power grid states and obtain the power grid state category division results to help understand the operation state and characteristics of the power grid under different conditions. Based on the power grid state category division results, the current power grid state is classified to determine the real-time operation state category of the power grid. Combined with the real-time power grid state category, the power grid parameter change trend model and the short-term power grid parameter prediction results, a comprehensive analysis is performed to obtain comprehensive power grid state information.

[0032] Step S103: Based on the comprehensive power grid status information, multiple nodes in the power supply communication cooperative network are assigned roles and switched to obtain a node time slot function allocation scheme;

[0033] Specifically, the comprehensive power grid status information is parsed to extract the power grid energy supply capacity evaluation index and communication demand evaluation index. The power grid energy supply capacity evaluation index helps to evaluate the energy supply capability of each node, while the communication demand evaluation index helps to evaluate the urgency and priority of each communication task in the network. According to the power grid energy supply capacity evaluation index, the nodes in the network are divided into energy levels to form a node energy level distribution diagram, which shows the relative capabilities of different nodes in energy supply. Based on the communication demand evaluation index, the various communication tasks in the network are prioritized, and a communication task priority list is generated to determine which communication tasks need to be completed first when resources are limited. Based on the node energy level distribution diagram and the communication task priority list, preliminary node role allocation is performed to form a node role allocation plan. According to the plan, the node functions in each time slot are preliminarily planned to generate an initial time slot function allocation table. The preliminary planning not only takes into account the energy supply capability of the node, but also the priority of the communication task to ensure the reasonable allocation and efficient use of network resources. The initial time slot function allocation table is analyzed for time continuity, and the periods of frequent node role switching are identified. These frequent node role switching are optimized by using a dynamic programming algorithm to reduce the frequency of node role switching, obtain a smoothed time slot function allocation table, and improve network stability and energy efficiency. Based on the smoothed time slot function allocation table, the role switching timing of the node is adjusted to ensure that the role switching can be accurately performed in actual operation, and the node time slot function allocation plan is obtained, thereby effectively utilizing network resources, improving the overall performance and response speed of the network, and ensuring stable operation under various power grid conditions.

[0034] Step S104: Optimize and analyze the node time slot function allocation scheme and generate strategies to obtain a global energy allocation plan and its execution strategy;

[0035] Specifically, the node time slot function allocation scheme is parsed, and the power supply and communication function matrices of each node in different time periods are extracted from it. These matrices reflect the functional role and status of each node in a specific time slot. According to the function matrix, the energy flow of the entire network is modeled to form a network energy flow diagram, which reveals the path and flow of energy transmission within the network and provides a visual view of the overall energy flow of the network. By calculating the upper limit of energy transmission in the network energy flow diagram and comparing it with the physical and technical constraints in the actual network, the initial energy allocation constraint set is obtained. These constraint sets include the maximum power output limit of the node, the communication bandwidth limit and other physical constraints to ensure that the energy allocation plan is feasible in the actual network environment. The constraint set is processed using convex optimization technology to construct a global energy allocation problem model. The model aims to maximize the network energy utilization efficiency while satisfying all constraints. By solving the model, a preliminary energy allocation plan is generated, and the energy allocation and communication task allocation of each node in different time periods are preliminarily determined. Simulation tests are performed based on the preliminary energy allocation plan. During the simulation process, multiple groups of random network states are generated to simulate different load and fault conditions. According to the simulation results, the preliminary energy allocation plan is refined and adjusted to adapt to the changing network conditions and actual needs, and the target energy allocation plan is obtained. The target energy allocation plan is piecewise linearized to simplify the execution process and generate a specific execution instruction set for each node. The instruction set specifies the energy output and communication task execution mode of each node in different time periods, forming a global energy allocation plan and its execution strategy. This strategy takes into account the energy efficiency of the network and ensures the timeliness and reliability of communication tasks.

[0036] Step S105: dynamically adjust the voltage and power of the nodes in the power communication cooperative network according to the global energy allocation plan and its execution strategy to obtain a dynamic adjustment execution result;

[0037] Specifically, the global energy allocation plan and its execution strategy are analyzed to determine the target voltage and power values ​​of each node in each time slot. The target value is set based on the current energy demand and network load conditions, aiming to optimize the overall energy efficiency and performance of the network. According to the target voltage and power values, the parameters of the dynamic voltage and power regulation system of each node are initialized to form the initial configuration set of the regulation system. Based on the initial configuration set of the regulation system, voltage regulation instructions are issued to the node, and the output voltage is adjusted in real time using the PID control algorithm to obtain the node voltage regulation response curve, which reflects the process and effect of the node responding to the regulation instruction. The voltage regulation response curve is analyzed to extract the personalized voltage regulation parameter set of each node. These parameters reflect the characteristics and response behavior of the node. Based on the personalized voltage regulation parameter set, power regulation instructions are issued to the node, and the output power of the node is dynamically adjusted to generate the node power regulation response curve, which shows the change of power output with the regulation instruction and how the node outputs stable power under different conditions. When analyzing the node power regulation response curve, the noise in the power regulation process is suppressed, and the state estimation is performed to obtain the optimized power regulation trajectory. Noise suppression ensures data accuracy, while state estimation helps predict and compensate for possible future load changes. The optimized power regulation trajectory is piecewise linearized to simplify the computational complexity of the regulation process and generate a node power regulation strategy table. This strategy table specifies how nodes should adjust their power output in different situations to achieve optimal energy distribution. Based on the node power regulation strategy table, the voltage and power regulation of the node are jointly optimized to generate an optimized control strategy for the regulation system. This strategy coordinates the voltage and power output between nodes and takes into account the overall stability and efficiency of the network. Based on the optimized control strategy, parallel control instructions are issued to all nodes so that they can be adjusted synchronously to form the execution state of the network-level regulation system. The execution state reflects the operation of the entire network under the current regulation strategy and is the final performance of the dynamic regulation process. The execution state of the network-level regulation system is monitored in real time and data is collected to ensure the effectiveness and accuracy of the regulation process. After the collected data is analyzed, a dynamic regulation execution result report is generated. The report summarizes the performance of each node in the regulation process, the overall energy efficiency improvement of the network, and the parts that may need further optimization.

[0038] Step S106: Based on the dynamic adjustment execution result, a distributed collaborative optimization is performed on the multi-node energy management strategy in the power communication cooperative network to obtain a global optimal energy usage plan.

[0039] Specifically, the dynamic adjustment execution results are analyzed and evaluated to determine the energy efficiency index and energy margin status of each node. The energy efficiency index measures the performance of each node in terms of energy utilization, while the energy margin status shows the margin of the node in terms of energy supply. According to the energy efficiency index and energy margin status, the nodes are clustered and divided into an efficient node set and an inefficient node set to obtain the node efficiency classification results. The efficient node set includes those nodes with high energy utilization efficiency and sufficient energy margin, while the inefficient node set includes nodes with low energy utilization efficiency and insufficient energy margin. According to the node efficiency classification results, the energy transmission path between nodes is replanned, the optimal energy transmission path is calculated, and an updated network energy flow diagram is generated. The diagram shows how energy flows in the network under the new conditions to maximize the overall efficiency. When analyzing the updated network energy flow diagram, the energy bottlenecks in the network are identified, that is, the nodes that limit the overall energy flow and utilization. Through analysis, a list of energy bottleneck nodes is generated, which are the most critical part of energy distribution. For these bottleneck nodes, their existing energy management strategies are locally optimized, their power supply and power consumption modes are adjusted to alleviate the bottleneck effect, and a bottleneck node optimization strategy set is generated to optimize the energy distribution and management of bottleneck nodes in a targeted manner. Based on the bottleneck node optimization strategy set, a distributed reinforcement learning environment is constructed. In this environment, a multi-agent deep Q learning algorithm is used to train the energy management strategy of each node. The multi-agent deep Q learning algorithm allows each node to optimize its energy usage strategy through learning, thereby achieving the overall optimal energy usage plan in the distributed system. Through interaction and learning, each node continuously improves its decision-making quality and optimizes the energy management strategy of the entire network. The energy management strategy of global collaborative optimization is verified and performance evaluated. Taking into account factors such as energy efficiency, communication quality and load balancing, the strategy is optimized and adjusted. Through comprehensive evaluation and optimization, a global optimal energy usage plan is formed. This plan improves the overall energy utilization efficiency of the network and ensures communication quality and balanced distribution of load.

[0040] In the embodiments of the present application, the dynamic construction and optimization of the power supply communication cooperative network is realized: by configuring the functions and time slot division of multiple nodes, and configuring the communication protocol based on network performance evaluation, a flexible and adjustable power supply communication cooperative network is constructed, providing comprehensive grid state perception and prediction capabilities: through real-time monitoring and parameter processing, combined with time series analysis and state clustering, a comprehensive evaluation and short-term prediction of the grid state is achieved, and the dynamic allocation and smooth switching of node roles are achieved: based on comprehensive grid state information, the nodes are dynamically assigned roles and switching control is performed, and the switching frequency is optimized through time continuity analysis and dynamic programming algorithms, thereby improving the stability and efficiency of the network. An optimization scheme for global energy distribution is provided: through network energy flow modeling and convex optimization technology, combined with simulation adjustment of random network states, a robust global energy distribution plan and its execution strategy are obtained. Accurate dynamic regulation of node voltage and power is achieved: PID control algorithm and noise suppression technology are used to adjust and optimize the node voltage and power in real time, which improves the accuracy and efficiency of energy transmission, and provides a distributed collaborative optimization energy management strategy: through node efficiency classification, energy bottleneck identification and multi-agent reinforcement learning, distributed collaborative optimization of multi-node energy management strategies in the network is achieved, and the globally optimal energy use plan is obtained, which can continuously optimize its energy use efficiency and adapt to the ever-changing power grid environment and communication needs.

[0041] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0042] (1) Performing functional module configuration on multiple nodes to obtain a node set having an energy harvesting circuit, an energy storage unit, a communication module, and a power management unit;

[0043] (2) Evenly divide the network time according to the number of node sets and the network topology to obtain a time slot sequence, and perform initial role assignment on each node in the node set to obtain an initial power supply node set and an initial communication node set;

[0044] (3) According to the initial power supply node set and the initial communication node set, the energy transmission path between nodes is planned to obtain an initial energy transmission topology map, and conflict detection is performed on the initial energy transmission topology map to obtain a list of conflicting node pairs;

[0045] (4) Reassign the roles of the conflicting nodes according to the conflicting node pair list to obtain an optimized power supply node set and an optimized communication node set;

[0046] (5) Based on the optimized power supply node set and the optimized communication node set, the capacity of the node energy storage unit is allocated to obtain the node energy storage configuration scheme, and according to the node energy storage configuration scheme, the parameters of the node power management unit are set to obtain the dynamic voltage and power regulation system;

[0047] (6) Conduct simulation tests on the dynamic voltage and power regulation system to obtain a network performance evaluation report. Based on the network performance evaluation report, configure the communication protocol of the node's communication module to obtain a power supply communication coordination network.

[0048] Specifically, the core functional modules that each node should have are determined, including energy collection circuit, energy storage unit, communication module and power management unit. Each node collects energy from the environment through the energy collection circuit, such as solar energy, wind energy or other forms of renewable energy. The energy storage unit is responsible for storing the collected energy for use when needed. The communication module is used for information transmission and coordination between nodes, while the power management unit is responsible for managing and regulating the energy use and allocation of nodes. The network time is evenly divided according to the number of node sets and the network topology to obtain a time slot sequence. Assuming that there are N nodes, the network time is divided into T time slots, and the length of each time slot is Δt. The initial role allocation is determined by calculating the position and functional requirements of each node in the network, so that some nodes are used as power supply nodes and other nodes are used as communication nodes. The power supply node is mainly responsible for energy collection and transmission, while the communication node is responsible for data transmission and network coordination, and the initial power supply node set and the initial communication node set are obtained. According to the initial set, the energy transmission path between nodes is planned to form an initial energy transmission topology map. The topology map represents the energy transmission path and its direction between each node. In order to ensure the efficiency and conflict-free nature of energy transmission, conflict detection is performed on the initial energy transmission topology map to identify the node pairs that may cause energy transmission conflicts and form a list of conflicting node pairs. After conflict detection, the roles of the conflicting nodes are reallocated according to the list of conflicting node pairs, and the power supply node set and the communication node set are optimized. By reallocation, conflicts in the energy transmission path are reduced or eliminated, and the overall transmission efficiency of the network is improved. Based on the optimized power supply node set and communication node set, the capacity of the node energy storage unit is allocated to form a node energy storage configuration plan. Assume that the energy storage capacity of each node i is , allocated according to their roles and energy requirements in the network. According to the node energy storage configuration scheme, the parameters of the power management unit of each node are set to establish a dynamic voltage and power regulation system. The dynamic regulation of voltage and power enables each node to adjust its output according to the actual energy demand and supply by setting control parameters. In order to verify the effectiveness of the system, a simulation test is carried out on the dynamic voltage and power regulation system. In the simulation test, the performance of the system is evaluated by simulating different network loads and energy collection conditions, and a network performance evaluation report is generated. The report includes indicators such as the energy utilization efficiency of each node under different conditions, the stability and reliability of the entire network. According to the network performance evaluation report, the communication protocol of the node's communication module is configured to ensure efficient and reliable information transmission in the network. The communication protocol configuration includes setting the communication frequency, data transmission method, and error correction mechanism between nodes, so that each node can communicate effectively under different network conditions. For example, assume that a simplified network contains three nodes A, B, and C. The initial energy storage capacity of each node is , and . Nodes A and B are assigned as power supply nodes, and node C as a communication node. In the initial energy transmission topology diagram, node A transmits energy to node B, and node B transmits energy to node C. In the conflict detection, it is found that there is an energy transmission conflict between nodes A and B, and the energy transmission path needs to be adjusted. By reallocating roles, node A is reconfigured as a communication node, and nodes B and C are used as power supply nodes to form an optimized set of power supply and communication nodes. Based on this optimized set, the energy storage capacity is replanned, assuming that the adjusted , and The parameters of the power management unit are set so that the voltage V and power P of each node are dynamically adjusted, as shown in the following formula:

[0049] ;

[0050] in, is the voltage of node i at time t, is the initial voltage, is the adjustment coefficient, The reference power is the actual power of node i at time t. Through simulation tests, the performance of the system under different loads and energy collection conditions is evaluated. The generated network performance evaluation report shows that the system can effectively adjust the voltage and power of each node to ensure the stability and efficiency of the network. Through the configuration of the communication protocol, the establishment of the power communication collaborative network is completed, so that it has efficient energy management and reliable communication functions.

[0051] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0052] (1) Parameters of each node in the power communication cooperative network are collected to obtain the original power grid parameter data set, and data cleaning and outlier detection are performed on the original power grid parameter data set to obtain the preprocessed power grid parameter data set;

[0053] (2) Based on the preprocessed grid parameter data set, a time series analysis of the grid parameters is performed to obtain a grid parameter change trend model. Based on the grid parameter change trend model, the grid parameters of future time slots are predicted to obtain short-term grid parameter prediction results.

[0054] (3) Perform feature extraction and dimensionality reduction on the preprocessed power grid parameter data set to obtain a power grid state feature vector, and perform power grid state clustering analysis based on the power grid state feature vector to obtain the power grid state classification result;

[0055] (4) Based on the grid status classification results, the current grid status is classified to obtain the real-time grid status category. The real-time grid status category, grid parameter change trend model and short-term grid parameter prediction results are comprehensively analyzed to obtain comprehensive grid status information.

[0056] Specifically, appropriate sensors and data acquisition modules are configured for each node. These sensors can monitor the key grid parameters of the nodes in real time, such as voltage (V), current (I), power (P) and frequency (f). The collected raw data constitute the original grid parameter data set. Data cleaning and outlier detection are performed on the original grid parameter data set. During the data cleaning process, obviously wrong or abnormal readings are removed, such as values ​​that exceed the physical upper or lower limits. Outlier detection is the identification and processing of abnormally high or abnormally low readings, which may be caused by equipment failure or external interference. Statistical methods such as mean and standard deviation, or machine learning methods such as isolation forests are used to identify outliers. After cleaning and detection, the preprocessed grid parameter data set is obtained. Based on the preprocessed grid parameter data set, time series analysis is performed on the grid parameters to identify patterns and trends in the data and establish a grid parameter change trend model. Assume that the voltage data sequence is , can be modeled using an autoregressive moving average model:

[0057] ;

[0058] in, is a constant, is the autoregressive parameter, is the moving average parameter, is white noise. By training and adjusting the model, the grid parameters of future time slots are predicted to obtain the short-term grid parameter prediction results. The preprocessed grid parameter data set is subjected to feature extraction and dimensionality reduction processing to reduce the complexity of the data and highlight the key features. Feature extraction includes extracting features such as mean, standard deviation, maximum, and minimum from the data set, while dimensionality reduction processing can be achieved through techniques such as principal component analysis, which reduces high-dimensional feature vectors to low dimensions to obtain grid state feature vectors that describe the state of the grid at different time points. Based on the grid state feature vectors, cluster analysis of the grid state is performed. Using clustering algorithms such as K-means or DBSCAN, the grid state is divided into different categories. For example, a typical classification may be "normal state", "high load state", and "abnormal state". Classification helps to understand the performance of the grid under different conditions. According to the results of the grid state classification, the current grid state is classified to obtain the real-time grid state category. Combining these categories, as well as the grid parameter change trend model and the short-term grid parameter prediction results, a comprehensive analysis is performed to fully understand the current grid operation status and potential problems, and form comprehensive grid state information.

[0059] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0060] (1) Analyze the comprehensive power grid status information to obtain the power grid energy supply capacity evaluation index and communication demand evaluation index;

[0061] (2) Based on the grid energy supply capacity evaluation index, the nodes are divided into energy levels to obtain a node energy level distribution map, and based on the communication demand evaluation index, the network communication tasks are prioritized to obtain a communication task priority list;

[0062] (3) Perform preliminary node role allocation based on the node energy level distribution map and the communication task priority list to obtain a node role allocation plan. According to the node role allocation plan, perform preliminary planning of the node functions in each time slot to obtain an initial time slot function allocation table.

[0063] (4) Perform time continuity analysis on the initial time slot function allocation table and use a dynamic programming algorithm to optimize the node role switching frequency to obtain a smoothed time slot function allocation table;

[0064] (5) Based on the smoothed time slot function allocation table, the node role switching timing is adjusted to obtain the node time slot function allocation plan.

[0065] Specifically, the comprehensive power grid status information is analyzed to obtain the power grid energy supply capacity evaluation index and the communication demand evaluation index. The power grid energy supply capacity evaluation index reflects the energy generation and storage capacity of each node in different time periods, while the communication demand evaluation index measures the communication demand intensity and urgency of each node in the network. The power grid energy supply capacity evaluation index and the communication demand evaluation index are obtained from historical data through statistical analysis and machine learning methods. For example, high energy supply nodes and high communication demand nodes are identified through cluster analysis. According to the power grid energy supply capacity evaluation index, the nodes in the network are divided into energy levels to obtain a node energy level distribution map, and the nodes are divided into three energy levels: high, medium, and low. Each level represents the energy supply capacity of the node. For example, assuming that the supply capacities of nodes A, B, C, and D are 80, 60, 40, and 20 (unit: kilowatt-hours), respectively, the energy levels can be defined as follows:

[0066] ;

[0067] in, is the energy level of node i, is the energy supply capacity of node i. Based on this, a node energy level distribution diagram is generated to show the distribution of different nodes in the power grid. At the same time, based on the communication demand evaluation index, the communication tasks in the network are prioritized to obtain a communication task priority list. Each communication task is assigned a priority based on its urgency, data volume, and transmission distance between nodes. Based on the node energy level distribution diagram and the communication task priority list, a preliminary node role allocation is performed, and high energy supply nodes are preferentially allocated as power supply nodes, and nodes with high communication demand are preferentially allocated as communication nodes. A node role allocation scheme is obtained to preliminarily determine the main functional role of each node in different time periods, such as power supply, communication, or both. According to the node role allocation scheme, the node functions in each time slot are preliminarily planned to form an initial time slot function allocation table, which lists the functional role allocation of each node in each time slot. For example, in a certain time period, node A may be responsible for both power supply and communication, while node B is only responsible for power supply. In order to optimize the switching frequency of node roles, the initial time slot function allocation table is analyzed for time continuity to reduce frequent role switching. Frequent role switching will lead to additional energy consumption and potential system instability. A dynamic programming algorithm is used to optimize the node role switching frequency. Dynamic programming finds the optimal switching strategy by breaking down the problem into sub-problems and solving them step by step. is the node role state at time t, and the optimization goal is to minimize the role switching cost:

[0068] ;

[0069] in, For status from Switch to cost. By solving the optimization problem, a smoothed time slot function allocation table is obtained, which significantly reduces the frequent switching of node roles. Based on the smoothed time slot function allocation table, the node role switching timing is adjusted to obtain the final node time slot function allocation scheme, which ensures the efficient operation of nodes in different time periods and takes into account the overall energy efficiency and communication quality of the network. For example, suppose that in a certain power grid system, node A is initially assigned to the power supply role, but frequently switches to the communication role between time T1 and T2. Through optimization, frequent switching is reduced, and node A maintains the power supply role for a longer time, reducing the energy loss and instability caused by switching.

[0070] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0071] (1) Analyze the node time slot function allocation scheme to obtain the power supply and communication function matrix of each node in different time slots, and model the network energy flow based on the power supply and communication function matrix to obtain the network energy flow diagram;

[0072] (2) Based on the network energy flow diagram, the energy transmission upper limit of the power communication cooperative network is calculated, and the energy transmission upper limit is compared and analyzed with the actual network constraints to obtain the initial energy allocation constraint condition set;

[0073] (3) Based on the initial energy allocation constraint set, a global energy allocation problem model is constructed using convex optimization technology, and the global energy allocation problem model is solved to obtain a preliminary energy allocation plan;

[0074] (4) Performing simulation based on the preliminary energy allocation plan to generate multiple sets of random network states, and adjusting the preliminary energy allocation plan according to the multiple sets of random network states to obtain the target energy allocation plan;

[0075] (5) The target energy allocation plan is piecewise linearized and a specific execution instruction set is generated for each node to obtain the global energy allocation plan and its execution strategy.

[0076] Specifically, the power supply and communication tasks of each node in different time periods are recorded to form a power supply and communication function matrix. Each element of the matrix represents the functional status of the node in a specific time slot, such as power supply or communication. By analyzing the function matrix, the energy flow of the network is modeled to form a network energy flow diagram. The network energy flow diagram is constructed based on the power supply and communication status of each node and the energy transmission relationship between nodes. Assume that there is a node set , where each node The state of at time slot t is represented by the matrix A, whose elements Representation Node In time slot t, to node Energy provided. Network energy flow diagram Each edge in Representative Node arrive The energy transfer path, and the weight Represents the size of the transmitted energy. After obtaining the network energy flow diagram, the energy transmission upper limit of the power communication cooperative network is calculated. The energy transmission upper limit represents the maximum energy flow that the network can handle, which is usually determined by factors such as physical limitations, node energy capacity, and communication bandwidth limitations. The formula is expressed as:

[0077] ;

[0078] in, is the upper limit of energy transmission, Is a node The energy capacity, Is a node The upper limit value is compared with other constraints of the actual network to generate an initial energy allocation constraint set. The constraint set covers all factors that affect energy allocation, such as energy storage limitations, transmission efficiency between nodes, etc. Based on the initial energy allocation constraint set, a global energy allocation problem model is constructed using convex optimization technology. Convex optimization is a mathematical optimization method that is suitable for solving optimization problems with convex objective functions and convex constraints. The objective function of the global energy allocation problem can be to maximize energy transmission efficiency or minimize energy loss. Assume For Node The energy output in a specific time slot, the optimization problem can be expressed as:

[0079] ;

[0080] subject to:

[0081] ;

[0082] ;

[0083] By solving the convex optimization problem, a preliminary energy allocation plan is obtained to determine the energy allocation amount of each node in different time periods, so as to utilize network resources in the best way. In order to verify and optimize the preliminary energy allocation plan, simulation tests are carried out. During the simulation process, multiple groups of random network states are generated, such as different energy requirements and communication load conditions. According to the random states, the preliminary energy allocation plan is adjusted to adapt to different network conditions to obtain the target energy allocation plan. The target energy allocation plan is piecewise linearized to simplify the execution process, and a specific execution instruction set for each node is generated to specify the specific energy output and communication tasks of each node in different time periods to form a global energy allocation plan and its execution strategy. For example, assuming that there are three nodes A, B, and C in the network, the preliminary plan may indicate that in time slot 1, node A transmits 30 and 20 units of energy to B and C respectively, and in time slot 2, B transmits 10 units of energy to C. After the simulation test, it was found that in actual situations, the transmission from B to C is limited, so the adjustment plan is that A increases the energy transmission to C in time slot 1 to compensate for the deficiency of B. The resulting instruction set ensures efficient operation of each node under different network conditions, optimizing resource utilization and network performance.

[0084] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0085] (1) Analyze the global energy allocation plan and its execution strategy to obtain the target voltage and power value of each node in each time slot;

[0086] (2) Initialize the parameters of the dynamic voltage and power regulation system of each node according to the target voltage and power values ​​to obtain the initial configuration set of the regulation system;

[0087] (3) Based on the initial configuration set of the regulation system, a voltage regulation instruction is issued to the node, and the output voltage is adjusted in real time using the PID control algorithm to obtain the node voltage regulation response curve;

[0088] (4) Analyze the node voltage regulation response curve to obtain a personalized voltage regulation parameter set, and issue power regulation instructions to the node and dynamically adjust the output power based on the personalized voltage regulation parameter set to obtain the node power regulation response curve;

[0089] (5) Based on the node power regulation response curve, noise suppression and state estimation are performed on the power regulation process to obtain the optimized power regulation trajectory, and the optimized power regulation trajectory is piecewise linearized to obtain the node power regulation strategy table;

[0090] (6) According to the node power regulation strategy table, the voltage and power regulation of the node are jointly optimized to obtain the regulation system optimization control strategy. Based on the regulation system optimization control strategy, parallel control instructions are issued to the nodes to obtain the network-level regulation system execution status;

[0091] (7) Conduct real-time monitoring and data collection on the execution status of the network-level regulation system to obtain a dynamic regulation execution result report.

[0092] Specifically, the global energy allocation plan and its execution strategy are analyzed to obtain the target voltage and power value of each node in each time slot. The target value reflects the network's expected output to each node at different time points to ensure the overall stability and efficiency of the network. For example, if a node The target voltage at time slot t is and the target power is . These target values ​​are based on the tasks and roles of each node in the global energy allocation plan. According to the target voltage and power values, the parameters of the dynamic voltage and power regulation system of each node are initialized to generate the initial configuration set of the regulation system. The configuration set includes the initial parameter settings of the PID controller, such as proportional (P), integral (l), and differential (D) gains. The initialization process is determined based on the physical characteristics of the node and the target voltage and power values. For example, the response time, maximum voltage output range, and power capacity of the node need to be taken into account. After the initial configuration is completed, voltage regulation instructions are issued to each node, and the output voltage is adjusted in real time using the PID control algorithm. The PID controller adjusts the output by adjusting its parameters so that the actual voltage As close to the target voltage as possible The PID control formula is:

[0093] ;

[0094] in, is the error value, They are proportional, integral, and differential gains, respectively. By adjusting these parameters in real time, the node voltage regulation response curve is obtained, which shows the changes in node voltage during the regulation process. The voltage regulation response curve is analyzed to extract the personalized voltage regulation parameter set of each node, including the optimized PID gain parameter, which reflects the optimal voltage regulation behavior of the node under different load conditions. According to the personalized parameter set, the power regulation instruction is issued to the node, and the output power is dynamically adjusted to generate the node power regulation response curve. The response curve shows the dynamic changes of the node when adjusting the power output, which helps to identify key problems in the power regulation process, such as delay and overshoot. When analyzing the power regulation response curve, noise suppression and state estimation are performed to ensure the accuracy and stability of the regulation process. Noise suppression can use Kalman filters or low-pass filters to reduce measurement errors and system noise. State estimation helps predict future power demand and system response, so as to adjust the regulation strategy. Through these steps, the optimized power regulation trajectory is obtained. The optimized power regulation trajectory is piecewise linearized to simplify the execution process and generate a node power regulation strategy table. The strategy table describes the power output behavior that each node should take under different conditions, such as the maximum output power limit under high load. These strategies help ensure the stability of the network under various operating conditions. Based on the node power regulation strategy table, the voltage and power regulation of the node are jointly optimized to obtain the optimal control strategy of the regulation system. This strategy optimizes the overall performance of the network by coordinating the output of voltage and power. The goal of joint optimization is to minimize energy loss and maximize network efficiency. For example, using the objective function To express the optimization goal:

[0095] ;

[0096] in, and are weight coefficients, representing the importance of voltage and power errors respectively. Based on the optimization control strategy of the regulation system, parallel control instructions are issued to all nodes. These instructions ensure that all nodes are adjusted synchronously to achieve the expected voltage and power output state, forming the execution state of the network-level regulation system. In this process, data is monitored and collected in real time to evaluate the actual performance of the system and adjust the necessary control parameters. Through data collection, a dynamic regulation execution result report is generated to describe the actual performance of each node, the fluctuation of voltage and power, and abnormal events during the regulation process.

[0097] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0098] (1) Analyze and evaluate the dynamic adjustment execution results to obtain the energy efficiency index and energy margin status of each node;

[0099] (2) Based on the energy efficiency index and energy surplus status, cluster analysis is performed on the nodes and the nodes are divided into a set of efficient nodes and a set of inefficient nodes to obtain the node efficiency classification results;

[0100] (3) Based on the node efficiency classification results, the energy transmission path between nodes is replanned and the optimal energy transmission path is calculated to obtain an updated network energy flow diagram;

[0101] (4) Analyze the updated network energy flow graph and identify the energy bottlenecks in the network to obtain a list of energy bottleneck nodes;

[0102] (5) Locally optimize the energy management strategies of the nodes in the energy bottleneck node list and adjust the power supply and power consumption modes to obtain the bottleneck node optimization strategy set;

[0103] (6) Based on the bottleneck node optimization strategy set, a distributed reinforcement learning environment is constructed and a multi-agent deep Q-learning algorithm is used to train the energy management strategy of each node to obtain a global collaborative optimization energy management strategy;

[0104] (7) Verify and evaluate the performance of the global collaborative optimization energy management strategy, and optimize it by comprehensively considering energy efficiency, communication quality, and load balancing factors to obtain the global optimal energy usage plan.

[0105] Specifically, the energy efficiency index and energy margin status of each node are extracted. The energy efficiency index can be calculated by the following formula:

[0106] ;

[0107] in, is the energy efficiency of node i, , out represents the energy output of the node, , in represents the energy input of the node. The energy margin state reflects the ratio of the remaining energy storage capacity of the node to its maximum capacity, which is expressed by the following formula:

[0108] ;

[0109] in, is the energy margin state of node i, is the current remaining energy of the node, is the maximum energy capacity of the node. According to the efficiency index and margin status, the nodes are clustered. Using the K-means clustering algorithm, the nodes are divided into a set of efficient nodes and a set of inefficient nodes. Assuming that the goal of clustering is to minimize the sum of squared errors within the class, the optimization objective formula of clustering is:

[0110] ;

[0111] Where k is the number of clusters, It is clusters, is the eigenvector of the ith node, It is The center of each cluster is selected. Based on the node efficiency classification results, the energy transmission paths between nodes are replanned to optimize the network energy flow. This involves the calculation of maximizing the energy transmission efficiency and minimizing the transmission loss. A flow network model is used, in which the weight of the edge represents the amount of energy transmission. The optimization objective can be expressed as:

[0112] ;

[0113] subject to:

[0114] ;

[0115] ;

[0116] in, is from node i to node The energy flow, E is the edge set in the network, is the capacity limit of node i, It is the edge After obtaining the updated network energy flow graph, analyze it to identify energy bottlenecks in the network. Energy bottlenecks refer to nodes or paths that limit the overall performance of the network. After identifying these bottlenecks, a list of energy bottleneck nodes is generated, and local optimization energy management strategy adjustments are made for these nodes. Adjustments may include changing the power supply and power consumption patterns of nodes to optimize their energy usage. The goal of optimization is to alleviate the bottleneck effect and ensure the balance and efficiency of the network. On this basis, a multi-agent deep Q learning algorithm is used for optimization. A distributed reinforcement learning environment is constructed to optimize the energy management strategy of each node through collaborative learning of multiple agents. The basic formula of Q learning is:

[0117] ;

[0118] in, is the action selected in state s The Q value, is the learning rate, is the discount factor, It’s an instant reward. is the next state. Through learning, a global collaborative optimization energy management strategy is generated. These strategies are verified and performance evaluated, including comprehensive considerations of energy efficiency, communication quality, and load balancing. The global optimal energy usage plan is obtained to ensure the best performance and resource utilization under various network conditions.

[0119] The above describes the method for the power supply and the communication device to work together in the embodiment of the present application. The following describes the system for the power supply and the communication device to work together in the embodiment of the present application. Figure 2 In the embodiment of the present application, an embodiment of a system in which a power supply and a communication device work in coordination includes:

[0120] Configuration module 201, used to perform function configuration and time slot division on multiple nodes to obtain a power supply communication coordination network;

[0121] Monitoring module 202, used to perform real-time monitoring and parameter processing of power grid parameters on the power supply communication cooperative network to obtain comprehensive power grid status information;

[0122] The control module 203 is used to perform role allocation and switching control on multiple nodes in the power supply communication cooperative network based on the comprehensive power grid state information to obtain a node time slot function allocation scheme;

[0123] The generation module 204 is used to optimize and analyze the node time slot function allocation scheme and generate strategies to obtain a global energy allocation plan and its execution strategy;

[0124] The adjustment module 205 is used to dynamically adjust the voltage and power of the nodes in the power communication cooperative network according to the global energy allocation plan and its execution strategy to obtain a dynamic adjustment execution result;

[0125] The optimization module 206 is used to perform distributed collaborative optimization on the multi-node energy management strategy in the power communication cooperative network according to the dynamic adjustment execution result, so as to obtain the global optimal energy usage plan.

[0126] Through the cooperation of the above components, the dynamic construction and optimization of the power communication cooperative network is realized: by configuring the functions and time slot division of multiple nodes, and configuring the communication protocol based on network performance evaluation, a flexible and adjustable power communication cooperative network is built, providing comprehensive grid state perception and prediction capabilities: through real-time monitoring and parameter processing, combined with time series analysis and state clustering, a comprehensive evaluation and short-term prediction of the grid state are achieved, and the dynamic allocation and smooth switching of node roles are achieved: based on comprehensive grid state information, the nodes are dynamically assigned roles and switched, and the switching frequency is optimized through time continuity analysis and dynamic programming algorithms, which improves the stability and efficiency of the network. An optimization scheme for global energy allocation is provided: through network energy flow modeling and convex optimization technology, combined with simulation adjustment of random network states, a robust global energy allocation plan and its execution strategy are obtained. Accurate dynamic regulation of node voltage and power is achieved: PID control algorithm and noise suppression technology are used to adjust and optimize the node voltage and power in real time, which improves the accuracy and efficiency of energy transmission, and provides a distributed collaborative optimization energy management strategy: through node efficiency classification, energy bottleneck identification and multi-agent reinforcement learning, distributed collaborative optimization of multi-node energy management strategies in the network is achieved, and the globally optimal energy use plan is obtained, which can continuously optimize its energy use efficiency and adapt to the ever-changing power grid environment and communication needs.

[0127] The present application also provides a device for a power supply to work in coordination with a communication device, wherein the device for a power supply to work in coordination with a communication device comprises a memory and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the processor executes the steps of the method for a power supply to work in coordination with a communication device in the above-mentioned embodiments.

[0128] The present application also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein instructions are stored in the computer, and when the instructions are executed on a computer, the computer executes the steps of the method for the power supply and the communication device to work in coordination.

[0129] Those skilled in the art can 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.

[0130] 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 application is essentially or the part that contributes to the prior art 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, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. 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 codes.

[0131] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application 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 embodiments of the present application.

Claims

1. A method for cooperating between a power supply and a communication device, characterized in that: The method comprises: Functional configuration and time slot division are performed on multiple nodes to obtain a power communication collaborative network; specifically, the following steps are performed: functional module configuration is performed on multiple nodes to obtain a node set having an energy collection circuit, an energy storage unit, a communication module and a power management unit; network time is evenly divided according to the number of the node set and the network topology to obtain a time slot sequence, and initial role assignment is performed on each node in the node set to obtain an initial power supply node set and an initial communication node set; energy transmission paths between nodes are planned according to the initial power supply node set and the initial communication node set to obtain an initial energy transmission topology map, and conflict detection is performed on the initial energy transmission topology map to obtain A list of conflicting node pairs; reallocating the roles of conflicting nodes according to the conflicting node pair list to obtain an optimized power supply node set and an optimized communication node set; allocating the capacity of the energy storage unit of the node based on the optimized power supply node set and the optimized communication node set to obtain a node energy storage configuration scheme, and setting parameters for the power management unit of the node according to the node energy storage configuration scheme to obtain a dynamic voltage and power regulation system; performing simulation tests on the dynamic voltage and power regulation system to obtain a network performance evaluation report, and performing communication protocol configuration on the communication module of the node according to the network performance evaluation report to obtain a power communication collaborative network; Performing real-time monitoring and parameter processing of power grid parameters on the power supply communication cooperative network to obtain comprehensive power grid status information; Based on the comprehensive power grid status information, multiple nodes in the power supply communication cooperative network are assigned roles and switched to obtain a node time slot function allocation scheme; Optimizing and analyzing the node time slot function allocation scheme and generating strategies to obtain a global energy allocation plan and its execution strategy; Dynamically adjusting the voltage and power of nodes in the power supply communication cooperative network according to the global energy allocation plan and its execution strategy to obtain a dynamic adjustment execution result; According to the dynamic adjustment execution result, distributed collaborative optimization is performed on the multi-node energy management strategy in the power supply communication cooperative network to obtain a global optimal energy usage plan.

2. The method for cooperating between a power supply and a communication device according to claim 1, characterized in that: The real-time monitoring and parameter processing of the power supply communication cooperative network to obtain comprehensive power grid status information includes: Performing parameter collection on each node in the power supply communication cooperative network to obtain an original power grid parameter data set, and performing data cleaning and outlier detection on the original power grid parameter data set to obtain a preprocessed power grid parameter data set; According to the preprocessed power grid parameter data set, a time series analysis is performed on the power grid parameters to obtain a power grid parameter change trend model, and based on the power grid parameter change trend model, the power grid parameters of future time slots are predicted to obtain a short-term power grid parameter prediction result; Performing feature extraction and dimensionality reduction processing on the preprocessed power grid parameter data set to obtain a power grid state feature vector, and performing power grid state clustering analysis based on the power grid state feature vector to obtain a power grid state classification result; Based on the grid state classification result, the current grid state is classified to obtain the real-time grid state category, and the real-time grid state category, the grid parameter change trend model and the short-term grid parameter prediction result are comprehensively analyzed to obtain comprehensive grid state information.

3. The method for cooperating between a power supply and a communication device according to claim 1, characterized in that: The method of performing role allocation and switching control on multiple nodes in the power supply communication cooperative network based on the comprehensive power grid state information to obtain a node time slot function allocation scheme includes: Analyzing the comprehensive power grid status information to obtain power grid energy supply capacity evaluation indicators and communication demand evaluation indicators; According to the power grid energy supply capacity evaluation index, the nodes are divided into energy levels to obtain a node energy level distribution map, and based on the communication demand evaluation index, the network communication tasks are prioritized to obtain a communication task priority list; Perform preliminary node role allocation based on the node energy level distribution map and the communication task priority list to obtain a node role allocation scheme, and perform preliminary planning of node functions in each time slot according to the node role allocation scheme to obtain an initial time slot function allocation table; Performing time continuity analysis on the initial time slot function allocation table and optimizing the node role switching frequency using a dynamic programming algorithm to obtain a smoothed time slot function allocation table; Based on the smoothed time slot function allocation table, the node role switching timing is adjusted to obtain a node time slot function allocation scheme.

4. The method for cooperating between a power supply and a communication device according to claim 1, characterized in that: The optimization analysis and strategy generation of the node time slot function allocation scheme to obtain a global energy allocation plan and its execution strategy include: The node time slot function allocation scheme is analyzed to obtain a power supply and communication function matrix of each node in different time slots, and network energy flow modeling is performed according to the power supply and communication function matrix to obtain a network energy flow diagram; Calculating the energy transmission upper limit of the power communication cooperative network based on the network energy flow diagram, and comparing and analyzing the energy transmission upper limit with the actual network constraints to obtain an initial energy allocation constraint condition set; According to the initial energy allocation constraint condition set, a global energy allocation problem model is constructed using convex optimization technology, and the global energy allocation problem model is solved to obtain a preliminary energy allocation plan; Performing simulation based on the preliminary energy allocation plan to generate multiple groups of random network states, and adjusting the preliminary energy allocation plan according to the multiple groups of random network states to obtain a target energy allocation plan; The target energy allocation plan is subjected to piecewise linearization processing, and a specific execution instruction set for each node is generated to obtain a global energy allocation plan and its execution strategy.

5. The method for cooperating between power supply and communication equipment according to claim 1, characterized in that: The step of dynamically adjusting the voltage and power of nodes in the power supply communication cooperative network according to the global energy allocation plan and its execution strategy to obtain a dynamic adjustment execution result includes: Analyze the global energy allocation plan and its execution strategy to obtain the target voltage and power value of each node in each time slot; Initializing parameters of a dynamic voltage and power regulation system of each node according to the target voltage and power values ​​to obtain an initial configuration set of the regulation system; Based on the initial configuration set of the regulation system, a voltage regulation instruction is issued to the node, and the output voltage is adjusted in real time using a PID control algorithm to obtain a node voltage regulation response curve; Analyze the node voltage regulation response curve to obtain a personalized voltage regulation parameter set, and issue a power regulation instruction to the node and dynamically adjust the output power according to the personalized voltage regulation parameter set to obtain a node power regulation response curve; Based on the node power regulation response curve, noise suppression and state estimation are performed on the power regulation process to obtain an optimized power regulation trajectory, and piecewise linearization processing is performed on the optimized power regulation trajectory to obtain a node power regulation strategy table; According to the node power regulation strategy table, the voltage and power regulation of the node are jointly optimized to obtain the regulation system optimization control strategy, and based on the regulation system optimization control strategy, parallel control instructions are issued to the nodes to obtain the network-level regulation system execution state; The execution status of the network-level regulation system is monitored in real time and data is collected to obtain a dynamic regulation execution result report.

6. The method for cooperating between a power supply and a communication device according to claim 1, characterized in that: The method of performing distributed collaborative optimization on the multi-node energy management strategy in the power communication cooperative network according to the dynamic adjustment execution result to obtain a global optimal energy usage plan includes: Analyzing and evaluating the dynamic adjustment execution results to obtain energy efficiency indicators and energy margin status of each node; According to the energy efficiency index and the energy surplus state, cluster analysis is performed on the nodes and the nodes are divided into an efficient node set and an inefficient node set to obtain a node efficiency classification result; Based on the node efficiency classification result, the energy transmission path between nodes is replanned and the optimal energy transmission path is calculated to obtain an updated network energy flow diagram; Analyze the updated network energy flow diagram and identify energy bottlenecks in the network to obtain an energy bottleneck node list; Locally optimize the energy management strategies of the nodes in the energy bottleneck node list and adjust the power supply and power consumption modes to obtain a bottleneck node optimization strategy set; Based on the bottleneck node optimization strategy set, a distributed reinforcement learning environment is constructed and the energy management strategy of each node is trained using a multi-agent deep Q learning algorithm to obtain a global collaborative optimization energy management strategy; The global collaborative optimization energy management strategy is verified and performance evaluated, and energy efficiency, communication quality and load balancing factors are comprehensively optimized to obtain the global optimal energy usage plan.

7. A system for power supply and communication equipment working in cooperation, characterized in that: A method for performing collaborative work between a power supply and a communication device according to any one of claims 1 to 6, the system comprising: A configuration module, used for performing function configuration and time slot division on multiple nodes to obtain a power communication coordination network; A monitoring module, used to perform real-time monitoring and parameter processing of power grid parameters on the power supply communication cooperative network to obtain comprehensive power grid status information; A control module, configured to perform role allocation and switching control on multiple nodes in the power supply communication cooperative network based on the comprehensive power grid state information, and obtain a node time slot function allocation scheme; A generation module is used to optimize and analyze the node time slot function allocation scheme and generate strategies to obtain a global energy allocation plan and its execution strategy; An adjustment module, used to dynamically adjust the voltage and power of the nodes in the power supply communication cooperative network according to the global energy allocation plan and its execution strategy, and obtain a dynamic adjustment execution result; The optimization module is used to perform distributed collaborative optimization on the multi-node energy management strategy in the power supply communication cooperative network according to the dynamic adjustment execution result to obtain the global optimal energy usage plan.

8. A device for power supply and communication equipment working in cooperation, characterized in that: The device for supplying power to cooperate with the communication device comprises: a memory and at least one processor, wherein the memory stores instructions; The at least one processor calls the instructions in the memory to enable the device for cooperating between a power supply and a communication device to perform the method for cooperating between a power supply and a communication device according to any one of claims 1 to 6.

9. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the method for cooperative operation of a power supply and a communication device according to any one of claims 1 to 6 is implemented.

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