Controller remote configuration method and device, equipment and storage medium

Abstract

The invention provides a controller remote configuration method, device and equipment and a storage medium, and the method comprises the steps: collecting configuration intention data of an edge controller in a distributed system, and constructing a configuration intention network of a directed weighted graph structure according to the configuration intention data; according to the configuration intention network, performing conflict detection and influence evaluation on a configuration request of an edge controller to obtain a conflict feature vector; conflict coordination is carried out according to the conflict feature vectors through target compatibility analysis and a game negotiation mechanism, and a coordinated and consistent configuration scheme is obtained; and carrying out transactional deployment on the coordinated and consistent configuration scheme by adopting a two-stage submission protocol to obtain an edge controller configuration result. By establishing the configuration intention network and the conflict coordination mechanism, the conflict problem of the configuration decision of the multi-edge controller is effectively solved, and the stable operation of the system is ensured.

Description

Technical Field

[0001] The present invention relates to the field of remote configuration technology, and in particular to a controller remote configuration method, device, equipment and storage medium. Background Art

[0002] As the scale of distributed systems continues to expand, edge controllers, as an important part of the system, undertake local optimization and control functions. At present, there are two main modes for remote configuration of edge controllers: one is a centralized management mode, where the cloud platform issues configuration instructions uniformly; the other is a completely decentralized management mode, where each edge node makes independent decisions. These two modes have their own advantages in practical applications. Centralized management can ensure global consistency, while decentralized management has strong flexibility.

[0003] However, when edge controllers have AI decision-making capabilities, when multiple edge nodes simultaneously optimize system parameters based on local information, the configuration decisions of each node often result in direct parameter setting conflicts and indirect performance impact conflicts due to the lack of effective conflict detection and coordination mechanisms. These conflicts not only affect the operating efficiency of the system, but may also cause system shocks and even safety hazards. Summary of the invention

[0004] The main purpose of the present invention is to solve the technical problem that there is a lack of effective conflict coordination mechanism for multi-edge controller configuration decisions in existing distributed systems; A first aspect of the present invention provides a controller remote configuration method, the controller remote configuration method comprising: Collecting configuration intention data of edge controllers in a distributed system, and constructing a configuration intention network with a directed weighted graph structure according to the configuration intention data; According to the configuration intent network, conflict detection and impact assessment are performed on the configuration request of the edge controller to obtain a conflict feature vector; Through target compatibility analysis and game negotiation mechanism, conflicts are coordinated according to conflict feature vectors to obtain a coordinated configuration plan; A two-phase commit protocol is used to transactionally deploy the coordinated configuration scheme to obtain an edge controller configuration result.

[0005] Optionally, in a first implementation manner of the first aspect of the present invention, collecting configuration intention data of edge controllers in a distributed system and constructing a configuration intention network with a directed weighted graph structure according to the configuration intention data includes: Collect data on target parameters, adjustment ranges, priority identifiers, and time windows of edge controllers in a distributed system to obtain a configuration intent data set. Extract features from the physical connection relationships, energy flow paths, and historical interaction patterns of the edge controllers according to the configuration intention dataset to obtain an edge controller impact feature matrix; Construct a directed graph with edge controllers as nodes and influence relationships as edges according to the edge controller impact feature matrix, and calculate edge weights based on historical operation data to obtain a configuration intention network.

[0006] Optionally, in the second implementation manner of the first aspect of the present invention, the conflict detection and impact assessment of the configuration requests of the edge controllers according to the configuration intention network to obtain a conflict feature vector includes: Match and filter the configuration requests sent by the edge controllers according to the configuration intention network, identify the associated edge controller groups, and obtain a configuration association set; Cross-compare the parameter contents of the configuration requests according to the configuration association set, detect parameter contradictions and resource competition, and obtain a parameter conflict set; Propagate and analyze the impact paths of the configuration requests in combination with the system physical model according to the configuration association set and the parameter conflict set to obtain an impact assessment matrix; Calculate the severity, impact range, and urgency of the conflict according to the impact assessment matrix to obtain a conflict feature vector.

[0007] Optionally, in the third implementation manner of the first aspect of the present invention, the propagation analysis of the impact paths of the configuration requests in combination with the system physical model according to the configuration association set and the parameter conflict set to obtain an impact assessment matrix includes: Calibrate and identify the parameters of the system physical model according to the electrical characteristic parameters of the edge controllers in the configuration association set, construct a state variable set including voltage, current, and power, and obtain a system mathematical model; For the system mathematical model, in combination with the parameter change information in the configuration request, determine the impact propagation path according to the energy transmission equation and the topological connection relationship to obtain an impact path diagram; According to the impact path diagram, use the conflict information in the parameter conflict set to perform sensitivity analysis on the state quantities on each impact path to obtain a propagation intensity matrix; Perform weighted superposition and normalization processing on the propagation intensity matrix, and correct it in combination with the physical quantity threshold constraint to obtain an impact assessment matrix.

[0008] Optionally, in the fourth implementation manner of the first aspect of the present invention, the conflict coordination according to the conflict feature vector through target compatibility analysis and game negotiation mechanism to obtain a consistent configuration plan includes: Construct a multi-party game revenue function and game constraint conditions according to the conflict feature vector to obtain a game model; Solve the Nash equilibrium using the game model, and according to the virtual incentive mechanism, obtain the candidate configuration scheme set through N - time iterative operations; Perform compatibility analysis on the configuration parameters in the candidate configuration scheme set, and use the minimum target deviation principle to screen the schemes to obtain the optimized configuration scheme; According to the optimized configuration scheme, perform configuration splitting according to the subnet area and timing constraints to obtain a consistent configuration scheme.

[0009] Optionally, in the fifth implementation manner of the first aspect of the present invention, the performing configuration splitting according to the subnet area and timing constraints according to the optimized configuration scheme to obtain a consistent configuration scheme includes: For the controller group in the optimized configuration scheme, construct an affinity matrix based on the electrical distance and impedance coupling degree, and use the spectral clustering algorithm for adaptive partitioning to obtain the initial subnet partitioning result; According to the initial subnet partitioning result, use the node importance and boundary node recognition algorithm to optimize and adjust the partition boundary, and calculate the cross - region coupling coefficient to obtain the configuration subset in the spatial domain; For the configuration instructions in the configuration subset in the spatial domain, use the improved critical path algorithm and resource competition graph to construct a timing dependence network, and introduce a slack time window mechanism to obtain the buffered execution timing; According to the execution timing, combine the fault traceability analysis to determine the rollback point position, and use the hierarchical transaction management mechanism to generate a traceable configuration instruction chain to obtain a consistent configuration scheme.

[0010] Optionally, in the sixth implementation manner of the first aspect of the present invention, the performing transactional deployment on the consistent configuration scheme using the two - phase commit protocol to obtain the edge controller configuration result includes: Use the two - phase commit protocol to send a pre - commit request to all relevant edge controllers, receive and record the prepare status information to obtain the first - phase commit result; According to the first - phase commit result, send a formal commit request to the edge controllers that confirm being ready to obtain the second - phase commit result; According to the second - phase commit result, generate a configuration transaction log and establish a configuration rollback point to obtain the transaction deployment status; Verify the configuration result according to the transaction deployment status, and perform a rollback operation when an abnormality is detected to obtain the edge controller configuration result.

[0011] The second aspect of the present invention provides a controller remote configuration device, and the controller remote configuration device includes: A network construction module, configured to collect configuration intention data of edge controllers in a distributed system, and construct a configuration intention network with a directed weighted graph structure according to the configuration intention data; A conflict detection module, configured to perform conflict detection and impact assessment on configuration requests of edge controllers according to the configuration intention network, and obtain a conflict feature vector; A coordination processing module, configured to perform conflict coordination according to the conflict feature vector through target compatibility analysis and a game negotiation mechanism, and obtain a coordinated configuration scheme; A deployment execution module, configured to perform transactional deployment on the coordinated configuration scheme by using a two-phase commit protocol, and obtain an edge controller configuration result.

[0012] A third aspect of the present invention provides a controller remote configuration device, including: a memory and at least one processor, wherein instructions are stored in the memory, and the memory and the at least one processor are interconnected through a line; the at least one processor calls the instructions in the memory to enable the controller remote configuration device to execute the steps of the above-mentioned controller remote configuration method.

[0013] A fourth aspect of the present invention provides a computer-readable storage medium, wherein instructions are stored in the computer-readable storage medium, and when the instructions are run on a computer, the computer is enabled to execute the steps of the above-mentioned controller remote configuration method.

[0014] The above-mentioned controller remote configuration method, device, equipment and storage medium collect configuration intention data of edge controllers in a distributed system, and construct a configuration intention network with a directed weighted graph structure according to the configuration intention data; perform conflict detection and impact assessment on configuration requests of edge controllers according to the configuration intention network, and obtain a conflict feature vector; perform conflict coordination according to the conflict feature vector through target compatibility analysis and a game negotiation mechanism, and obtain a coordinated configuration scheme; perform transactional deployment on the coordinated configuration scheme by using a two-phase commit protocol, and obtain an edge controller configuration result. The present invention effectively solves the conflict problem of multi-edge controller configuration decision-making by establishing a configuration intention network and a conflict coordination mechanism, and ensures the stable operation of the system.

[0015] Other features and advantages of the present invention will be described in the following specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention are achieved and obtained by the structures specifically pointed out in the specification, claims and drawings.

[0016] To make the above objectives, features and advantages of the present invention more obvious and understandable, the following specific preferred embodiments are given, and in conjunction with the accompanying drawings, the detailed description is as follows. Description of the Drawings

[0017] Figure 1 Schematic diagram of the first embodiment of the controller remote configuration method in the embodiments of the present invention; Figure 2 Schematic diagram of an embodiment of the controller remote configuration device in the embodiments of the present invention; Figure 3 Schematic diagram of an embodiment of the controller remote configuration device in the embodiments of the present invention. Detailed implementation manners

[0018] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0019] The terms "including" and "having" and any variations thereof mentioned in the embodiments of the present invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but optionally further includes other unlisted steps or units, or optionally further includes other steps or units inherent to these processes, methods, products or devices.

[0020] For the convenience of understanding this embodiment, first, a controller remote configuration method disclosed in the embodiments of the present invention will be introduced in detail. The multi-agent includes a question rewriting agent, a document selection agent, an answer generation agent, and a retrieval agent. As Figure 1 shown, the method includes the following steps: 101. Collect the configuration intention data of the edge controllers in the distributed system, and construct a configuration intention network with a directed weighted graph structure according to the configuration intention data; In an embodiment of the present invention, the step of collecting the configuration intention data of the edge controllers in the distributed system and constructing a configuration intention network with a directed weighted graph structure according to the configuration intention data includes: collecting the target parameters, adjustment ranges, priority identifiers, and time windows of the edge controllers in the distributed system to obtain a configuration intention data set; extracting features of the physical connection relationships, energy flow paths, and historical interaction patterns of the edge controllers according to the configuration intention data set to obtain an edge controller influence feature matrix; constructing a directed graph with edge controllers as nodes and influence relationships as edges according to the edge controller influence feature matrix, and calculating the edge weights according to the historical operation data to obtain a configuration intention network.

[0021] Specifically, when collecting data on the target parameters, adjustment range, priority identifier, and time window of edge controllers in a distributed system, first establish connections with each edge controller through a secure communication channel, using standard industrial communication protocols such as OPC UA or Modbus TCP to ensure the reliability and real-time nature of data transmission. In large-scale distributed energy management systems, edge controllers are usually configured with local API interfaces, and the system sends structured query requests through these interfaces to obtain the current configuration intention data of the controllers. For example, for a wind farm controller, collect target parameters such as its power control curve and voltage regulation range; for a storage device controller, collect parameters such as charge and discharge power limits and state adjustment thresholds. In addition to the basic parameters, it is also necessary to collect the adjustment range set for each controller, that is, the upper and lower limit ranges within which the parameters are allowed to vary, which reflects the elastic space of the controller. At the same time, record the priority identifier set by the controller, which is usually represented by an integer value from 1 to 10, and the higher the value, the more important the configuration request and the higher the execution priority. In addition, time window information is also collected, including timing constraints such as the start and end times when the configuration takes effect, the maximum execution time, and the refresh period. These information will directly affect the timing analysis of subsequent configuration conflicts. After all the collected data undergoes format standardization, data type conversion, and validity verification, a structured configuration intention data set is formed and stored in the form of key-value pairs. Each controller corresponds to a configuration intention data object for subsequent processing and analysis.

[0022] Specifically, when extracting features of the physical connection relationship, energy flow path, and historical interaction pattern of the edge controller based on the configuration intention dataset, it is first necessary to process the physical connection relationship, extract electrical connection information from the topological structure database of the energy system, including line connection status, transformer connection relationship, and switchgear status, etc., to construct a basic physical connection matrix. Then analyze the energy flow path, determine the main channels and directions of energy transmission in the system through the power flow calculation model, combined with historical power flow data, and generate an energy flow transfer matrix. This matrix describes the propagation path and attenuation characteristics of unit power change in the system. For example, a 10% power fluctuation in a certain wind farm will cause how much power the connected energy storage device needs to absorb or release. Then mine the historical interaction pattern, extract the records of controller parameter changes in the past 30 - 90 days from the system operation historical database, and apply time series analysis methods to identify the parameter correlation and timing dependence relationship between controllers. When a parameter change of one controller causes a subsequent parameter adjustment of another controller, it indicates that there is an interaction relationship between the two. By calculating the cross-correlation coefficient and Granger causality test of parameter changes, the interaction intensity and directionality between controllers are quantified. In addition, the influence coefficients of environmental factors (such as temperature, light, load, etc.) on each controller are also calculated as external disturbance characteristics. Multidimensional feature fusion is performed on the physical connection matrix, energy flow transfer matrix, and historical interaction matrix to form a comprehensive edge controller influence feature matrix, and each element of this matrix represents the influence degree and characteristics of the source controller on the target controller.

[0023] Specifically, when constructing the configuration intention network based on the edge controller impact feature matrix, each edge controller is first regarded as an independent node, and the node attributes include information such as controller identification, type, geographical location, and the type of energy unit it is responsible for. Then, connection edges between nodes are established according to the non-zero elements in the impact feature matrix, and the direction of the edge is from the impact source node to the affected node, forming an initial directed graph structure. Next, weight assignment is performed for each edge, and the weight value is based on the corresponding element value in the impact feature matrix and calibrated in combination with the measured impact degree in historical operation data. The specific calculation method uses the weighted average algorithm, which weights the impact intensity predicted by the theoretical model and the actual impact intensity statistically analyzed from historical data in a ratio of 7:3 to obtain the final edge weight value. The range of the edge weight value is usually normalized to a real number between 0 and 1, and the larger the value, the more significant the impact. For the controllers of volatile power sources such as wind farms and photovoltaic power plants, their edge weights also need to be dynamically adjusted according to the current meteorological prediction data, increasing their impact weights on relevant nodes under strong wind or strong light conditions. After completing the construction of the basic directed weighted graph, time dimension information also needs to be added, and a time delay attribute is attached to each edge, indicating the average time required for the configuration change of the source node to propagate to the target node, which is extracted from the time series analysis of historical interaction data. The finally formed configuration intention network is a multi-level and dynamically updated graph structure, which not only contains the spatial topological relationship, but also integrates the time dimension and impact intensity information, and completely describes the complex impact relationship network among the controllers in the distributed energy management system.

[0024] 102. According to the configuration intention network, perform conflict detection and impact assessment on the configuration requests of the edge controllers to obtain a conflict feature vector; In an embodiment of the present invention, the performing conflict detection and impact assessment on the configuration requests of the edge controllers according to the configuration intention network to obtain a conflict feature vector includes: matching and screening the configuration requests sent by the edge controllers according to the configuration intention network, identifying the associated edge controller group, and obtaining a configuration association set; cross-comparing the parameter content of the configuration requests according to the configuration association set, detecting parameter contradictions and resource competition, and obtaining a parameter conflict set; performing propagation analysis on the impact path of the configuration requests according to the configuration association set and the parameter conflict set in combination with the system physical model to obtain an impact assessment matrix; calculating the severity, impact range, and urgency of the conflict according to the impact assessment matrix to obtain a conflict feature vector.

[0025] Specifically, when matching and filtering the configuration requests sent by the edge controllers according to the configuration intention network, the system first receives the configuration request information submitted by each edge controller. These requests usually contain elements such as controller identification, target parameters, target values, and time windows. Subsequently, the system queries the graph structure data of the configuration intention network, with the controller node that currently sends the configuration request as the center, and expands the search outward for related nodes. The search process uses the depth-first algorithm to traverse along the edges of the directed graph, and the search depth is usually limited within three hops because the influence beyond three hops in the actual energy system is usually significantly attenuated. During the traversal process, the system sets a threshold filter for the edge weights and only retains the nodes connected by the edges with weight values greater than 0.2. This threshold ensures that only the controllers with truly significant influence relationships are concerned. For the power regulation requests submitted by the wind farm controllers, the system will identify the associated nodes such as the directly connected energy storage controllers, adjacent substation controllers, and protection controllers of the connecting lines. At the same time, the system also needs to consider the overlap of the time windows. Only when the execution time windows of the configuration requests overlap, the relevant controllers truly form a potential conflict relationship. For example, if one controller plans to execute the configuration at 2 am and another plans to execute it at 10 am, even if they are highly correlated in space, they will not be included in the same configuration association set. Through the dual filtering of space and time, the system finally forms a configuration association set, and each element in this set contains a set of edge controllers that have potential influence relationships and overlapping execution times.

[0026] Specifically, when cross-checking the parameter content of a configuration request according to the configuration association set, the system first extracts the configuration request details of all controllers from each configuration association set and constructs a parameter mapping table. This mapping table classifies the parameters according to dimensions such as parameter type, resource attributes, and physical quantities, facilitating the identification of potential parameter-level conflicts. Then the system performs direct parameter conflict detection to identify cases where different target values are proposed for the same parameter. For example, if two adjacent energy storage controllers simultaneously propose different setting values for the maximum power limit of a shared transformer, the system directly marks this as a direct parameter conflict. Next, the system conducts resource competition conflict analysis to identify cases where, although the configuration parameters are different, they actually compete for the same physical resource. In a distributed energy system, typical resource competitions include line capacity, transformer capacity, reactive power compensation capacity, etc. When the configurations of multiple controllers jointly affect the utilization rate of a certain resource and may lead to resource overlimits, the system marks such cases as resource competition conflicts. In addition, the system also needs to detect logical contradictions between parameters and, through a preset parameter relationship rule library, identify cases where there are logical conflicts between different parameters. For example, one controller requests to increase the system frequency while another controller requests to decrease the power generation. Although these two parameters are not directly the same, they are physically related and potentially contradictory. By comprehensively analyzing direct parameter conflicts, resource competition conflicts, and logical contradictions, the system generates a detailed parameter conflict set for each configuration association set, which records information such as the conflicting parameter pairs, conflict types, and preliminary judgment values of conflict severity, providing basic data for subsequent impact assessments.

[0027] Specifically, when conducting propagation analysis on the influence path of a configuration request based on the configuration association set and parameter conflict set, in combination with the system physical model, the system first invokes the steady-state and transient analysis models of the power system, which include detailed network topologies, device parameters, and operating constraints. For a distributed energy system, the model needs to cover the coupling relationships of the power network, thermal network, and pneumatic network, accurately describing the conversion and transfer characteristics of energy among different forms. Subsequently, the system performs real-time calibration of the physical model based on the current operating state and monitoring data to ensure that the model reflects the latest system state. Then, for each configuration change in the parameter conflict set, the system sequentially simulates and executes it in the physical model, tracking the cascading change process of the state magnitudes caused by parameter changes. During this process, the system records the changes in state variables such as voltage, frequency, and power flow at key nodes, and analyzes whether the changes in state variables exceed the safety threshold or approach the system stability boundary. For the power limit request of a wind farm controller, the system will simulate a series of chain reactions such as the change in connection point voltage, the redistribution of line power flow, and the response adjustment of the energy storage system when the wind power output is restricted. At the same time, the system analyzes the time characteristics of the propagation of configuration changes, estimates the time delay for the state changes to propagate from the source to each affected node, and identifies possible time sequence amplification effects or oscillation phenomena. Through this series of simulations and analyses, the system constructs a comprehensive impact assessment matrix, which uses the controllers in the configuration association set as row and column indices, and each element represents the degree, direction, and time characteristics of the impact of the configuration change of the row controller on the key state variables within the jurisdiction of the column controller.

[0028] Specifically, when calculating the severity, impact scope, and urgency of conflicts according to the impact assessment matrix, the system uses multi-dimensional indicators for comprehensive evaluation. First, the severity of the conflict is calculated. This indicator reflects the degree to which the state variables deviate from the safe range caused by the conflict. The system performs weighted summation on the ratio of the change values of each state variable in the impact assessment matrix to its safety limit value. The weight coefficients are determined based on the importance of the state variables to system safety. For example, the percentage of voltage deviation from the nominal value is usually given a higher weight because voltage stability is directly related to system safety. When the calculated severity exceeds 0.8, it indicates that the conflict may lead the system close to an unstable state and needs to be processed preferentially. Next, the impact scope is evaluated. This indicator measures the number of controllers affected by the conflict and the scale of the physical devices they manage. The system counts the number of controllers significantly affected in the impact assessment matrix and also considers the total capacity of the devices managed by these controllers. The impact scope is represented by a normalized value, usually in the range of 0 - 1, and the larger the value, the wider the impact scope. Then, the urgency is analyzed. This indicator reflects the time urgency of conflict resolution. The system comprehensively considers factors such as the time distance when the conflict occurs, the change rate of state variables, and the tolerance time of the system for the conflict. For conflicts with a fast change rate and a small system adjustment margin, the urgency score is higher. Finally, the system combines the three indicators of severity, impact scope, and urgency to form a conflict characteristic vector. This vector comprehensively describes the multi-dimensional characteristics of the conflict and provides a decision-making basis for subsequent conflict coordination. The conflict characteristic vector also includes a conflict type identifier and a list of key affected parameters, facilitating the coordination mechanism to select appropriate processing strategies.

[0029] Furthermore, the propagation analysis of the impact path of the configuration request by combining the configuration association set and the parameter conflict set with the system physical model to obtain the impact assessment matrix includes: calibrating and parameter identifying the system physical model according to the electrical characteristic parameters of the edge controllers in the configuration association set, constructing a set of state variables including voltage, current, and power, and obtaining the system mathematical model; for the system mathematical model, combining the parameter change information in the configuration request, determining the impact propagation path according to the energy transmission equation and the topological connection relationship, and obtaining the impact path diagram; according to the impact path diagram, using the conflict information in the parameter conflict set, performing sensitivity analysis on the state variables on each impact path to obtain the propagation intensity matrix; performing weighted superposition and normalization processing on the propagation intensity matrix, and correcting it in combination with the physical quantity threshold constraint to obtain the impact assessment matrix.

[0030] Specifically, when calibrating and identifying the parameters of the system physical model according to the electrical characteristic parameters of the edge controllers associated with the configuration, first extract the basic electrical parameters of the devices under the jurisdiction of each edge controller from the database of the distributed energy management system, including the rated power, impedance parameters, and excitation system characteristics of the generator sets, the charge and discharge characteristic curves, power conversion efficiency, and capacity limits of the energy storage devices, as well as the key physical quantities such as line impedance, transformer parameters, and bus characteristics. Subsequently, the system calls the real-time measurement data to obtain the measured key state quantities such as voltage, current, active power, and reactive power under the current system operating state, as well as external factors such as environmental conditions like temperature, wind speed, and light intensity. Combine these measured data with the controller parameters and use the weighted least squares method to calibrate the parameters of the system physical model in real time to ensure that the model accurately reflects the current system state. For example, for a wind farm controller, it is necessary to calibrate the deviation between the actual power curve and the theoretical curve of the wind turbines; for an energy storage controller, it is necessary to calibrate the actual state of charge and internal impedance characteristics of the battery pack. After calibration, the system constructs a state space representation containing the key state variables. The state variable set usually includes physical quantities such as the voltage amplitude and phase angle of each node, line current, active power flow, reactive power flow, frequency deviation, etc., as well as device characteristic quantities such as the state of charge and temperature of the energy storage device. For a large-scale distributed energy system, the number of state variables is usually in the order of hundreds to thousands, and these variables together constitute the system state space. Finally, based on the calibrated parameters and the state variable set, the system establishes a complete system mathematical model, which accurately describes the correlation relationship and its evolution law among the physical quantities.

[0031] Specifically, for the system mathematical model, when determining the impact propagation path by combining the parameter change information in the configuration request according to the energy transfer equation and the topological connection relationship, the system first converts each configuration request into a corresponding state variable perturbation. For example, the power limit change requested by the wind farm controller is converted into a change in the power injection of the power generation node, and the adjustment of the charge and discharge strategy of the energy storage controller is converted into a change in the power absorption or release of the energy storage node. Subsequently, the system uses physical laws such as the power flow equation and the law of energy conservation in the energy transfer equation to trace the propagation process of these initial perturbations in the system. In the power system, energy transfer is mainly reflected by the power flow. The system uses a DC power flow or AC power flow calculation model to analyze the power redistribution caused by the initial perturbation. For example, when a wind farm operates with power limitation, its reduced power output will cause other power generation sources in the system to increase their output or the load to reduce power consumption. This power balance adjustment is transmitted to the entire network through the power flow equation. In addition to power transmission, the system also needs to consider the transmission characteristics of other energy forms such as the thermal network and gas network, especially in the integrated energy system. Based on the topological connection relationship, the system identifies the main and secondary paths of energy transfer and constructs a complete impact propagation network. For example, for the power fluctuation of a certain photovoltaic power station, the system will trace its impact path on the connected substation, transmission line, energy storage device and even the load side. These paths include not only direct physical connections but also logical connections formed by the transmission of control signals. Finally, the system generates an impact path diagram, which is represented in the form of a directed weighted graph. The nodes are the key devices or controllers in the system, the edges are the energy or information transfer paths, and the edge weights represent the transfer efficiency or damping characteristics.

[0032] Specifically, when performing sensitivity analysis on the state variables on each impact path using the conflict information in the parameter conflict set according to the impact path diagram, the system first selects key state variables as the observation objects. These state variables usually include safety operation indicators such as node voltage, line power flow, equipment load rate, and system frequency. Then, the system conducts a single-factor sensitivity analysis on the parameter changes of each configuration request, that is, changing one parameter alone while keeping other parameters unchanged, and observing the response changes of each key state variable. The sensitivity calculation uses the differential ratio method or the analytical Jacobian matrix method to obtain the proportional relationship between the parameter change amount and the state variable change amount. For a non-linear system, local linearization is performed near the operating point to ensure the effectiveness of the sensitivity analysis. For example, for the power limit parameter of a wind farm controller, calculate the percentage change in the key node voltage of the system when it changes by 1%, and obtain the sensitivity coefficient of the voltage to the power limit. The sensitivity analysis not only considers the steady-state impact but also needs to evaluate the transient characteristics, such as transient oscillations or changes in dynamic stability margins caused by parameter changes. When dealing with the conflict items in the parameter conflict set, the system needs to pay special attention to the cross-impacts of conflicting parameters on the same state variable, and evaluate the coupling effect and non-linear amplification effect when multiple parameters change simultaneously. For example, when the reactive power outputs of two adjacent wind farm controllers are adjusted simultaneously, their impact on the voltage at the point of common coupling is not simply additive, but the interaction needs to be considered. Through systematic sensitivity analysis, a propagation intensity matrix is finally formed, which describes the influence degree, direction, and propagation characteristics of each configuration parameter change on each key state variable of the system.

[0033] Specifically, when performing weighted superposition and normalization on the propagation intensity matrix and making corrections in combination with physical quantity threshold constraints, the system first sets importance weights for each key state quantity, and these weights reflect the importance of the state quantity to the safe and stable operation of the system. For example, the system frequency is usually given a higher weight because frequency deviation directly affects system stability; the voltage qualification rate is crucial for power quality; and the equipment load rate is closely related to equipment life and safety margin. Subsequently, the system performs weighted processing on each column of the propagation intensity matrix (corresponding to a state quantity), multiplying the sensitivity coefficient by the importance weight of the corresponding state quantity to obtain the weighted sensitivity value. Then, a comprehensive evaluation of multiple state quantities is carried out to summarize the multi-dimensional impacts of each parameter change and form a comprehensive impact index. This process uses weighted summation or multi-objective evaluation methods to ensure that all aspects of the impact are reasonably considered. Then, the calculation results are normalized, and the comprehensive impact index is standardized to the 0-1 interval to facilitate horizontal comparison of the impact degrees of different parameter changes. During the normalization process, the system uses piecewise linear or non-linear mapping functions to ensure higher sensitivity to changes near important critical values. Subsequently, the impact assessment is corrected in combination with the safety threshold constraints of physical quantities. When a parameter change causes a key state quantity to approach or exceed the safety threshold, the corresponding impact assessment value is amplified to reflect potential safety risks. For example, when the line load rate approaches the thermal stability limit, its impact assessment value will be significantly increased. Finally, the system generates a complete impact assessment matrix, which comprehensively describes the comprehensive impact of each configuration request on the system operating state, including multi-dimensional characteristics such as the impact direction, impact intensity, impact range, and risk level.

[0034] 103. Perform conflict coordination based on the conflict feature vector through target compatibility analysis and game negotiation mechanism to obtain a consistent configuration plan; In an embodiment of the present invention, the performing conflict coordination based on the conflict feature vector through target compatibility analysis and game negotiation mechanism to obtain a consistent configuration plan includes: constructing a multi-party game revenue function and game constraint conditions according to the conflict feature vector to obtain a game model; using the game model to solve for the Nash equilibrium, and obtaining a candidate configuration plan set through N iterative operations according to the virtual incentive mechanism; performing compatibility analysis on the configuration parameters in the candidate configuration plan set, and screening the plans using the minimum target deviation principle to obtain an optimized configuration plan; and performing configuration segmentation according to the subnet area and timing constraints according to the optimized configuration plan to obtain a consistent configuration plan.

[0035] Specifically, when constructing the multi-party game revenue function and game constraint conditions based on the conflict feature vector, the system first analyzes each dimension feature in the conflict feature vector to extract key information such as the controller identity identifiers of both or multiple parties in the conflict, conflict parameter items, conflict severity, influence range, and urgency. Subsequently, an individual utility function is established for each edge controller participating in the game, and this function quantifies the degree of achievement of the controller's configuration objectives. The utility function usually contains multiple components, such as parameter attainment, energy efficiency, impact on equipment life, and economic cost. For example, for a wind farm controller, its utility function mainly considers factors such as maximizing power generation, grid friendliness, and safe operation of equipment; while the utility function of a energy storage controller focuses on charge-discharge cycle efficiency, battery life protection, and maximizing economic benefits. Based on the utility functions of each controller, the system constructs a complete game revenue function matrix, which describes the utility values obtained by each participant under different configuration combinations. At the same time, the system also needs to set game constraint conditions, which are derived from the safety operation limitations of the physical system, equipment technical parameter boundaries, and regulatory rule requirements, etc. Typical constraints include line transmission capacity limits, upper limits of transformer load ratios, allowable voltage deviation ranges, frequency stability requirements, etc. For distributed energy systems, special conditions such as new energy access ratio limits and energy storage capacity constraints also need to be considered. By combining the revenue function and constraint conditions, the system finally constructs a complete multi-party non-cooperative game model, which accurately describes the decision-making environment where each controller pursues its own utility maximization while being restricted by system constraints.

[0036] Specifically, when using the game model to solve for Nash equilibrium, the system first determines the initial conditions for the solution, including the current parameter settings of each controller, the system operating state, and the external environmental conditions, as the starting point of the game process. Then, the system uses an improved best response dynamic algorithm for iterative solution, which simulates the process of each controller taking turns to make optimal decisions. In each round of iteration, a controller calculates and adjusts its optimal configuration parameters based on the current strategies of other controllers to maximize its own utility function. This rotational decision-making process continues until the strategy choices of all controllers reach a relatively stable state, that is, no controller can improve its own utility by unilaterally changing its strategy. To accelerate convergence and avoid local optima, the system introduces a virtual incentive mechanism, which provides additional utility rewards for cooperative behavior and guides the controllers to make decisions that are more beneficial to the overall situation. The virtual incentive is specifically manifested as a certain virtual compensation given by the system when the controller makes concessions on the configuration parameters. This compensation can be in the form of preferential scheduling rights, capacity reservation, or virtual economic subsidies. For example, when the wind farm controller actively reduces its power output to relieve line overload, the system gives it priority in subsequent scheduling as compensation. Through the guidance of virtual incentives, the game process among controllers is more inclined to seek the global optimal solution. The system sets a maximum number of iterations N (usually 30 - 50 times) or a convergence threshold as the termination condition, and stops the calculation when N iterations are reached or the strategy change is less than the threshold. The multiple convergence results finally obtained form a candidate configuration plan set, and each plan contains the parameter configuration values of all controllers and the corresponding individual and global utility evaluations.

[0037] Specifically, when performing compatibility analysis on the configuration parameters in the candidate configuration solution set, the system first evaluates the technical feasibility of each candidate solution and checks whether the configuration parameters meet all physical constraints and safety boundaries. This process verifies the performance of the solution under different operating conditions by calling a detailed system simulation model. For example, for a system with hybrid access of wind farms and photovoltaic power plants, it is necessary to specifically verify the stability and voltage quality of the system under extreme conditions such as strong wind and sunny days and calm and cloudy days. Subsequently, the system quantitatively evaluates the achievement of the goals of each controller, calculates the deviation between the actual configuration parameters and the ideal target parameters. These deviations are standardized to form a target deviation matrix, and each element in the matrix represents the goal achievement degree of a specific controller under a specific solution. Then, the system uses the principle of minimum target deviation for solution evaluation and screening. This principle aims for the minimum overall deviation rather than the minimum deviation of individual controllers, reflecting the balance between overall fairness and efficiency. Specifically in the calculation, the system performs a weighted sum of the target deviations of all controllers, and the weight coefficients consider factors such as the importance, influence range, and status of the controller in the system. For example, a controller responsible for regulating the voltage of the main line is usually given a higher weight because it is directly related to the system stability. By comparing the weighted total deviations of different candidate solutions, the system selects the solution with the minimum total deviation as the optimized configuration solution. In addition, the system also considers the complexity of solution implementation and adjustment costs, and preferentially selects solutions with smooth parameter changes and moderate adjustment amounts to avoid causing severe disturbances to the system.

[0038] Specifically, when performing configuration slicing according to the subnet area and timing constraints based on the optimization configuration scheme, the system first divides the distributed energy management system into several relatively independent subnet areas based on the physical topology and electrical similarity of the power system. The division basis includes factors such as electrical distance (e.g., line impedance), power flow distribution characteristics, and control area boundaries. For example, devices sharing the same main transformer or located on the same bus are usually assigned to the same subnet. For large-scale wind power bases, subnets may be divided according to the collector lines and booster station areas; for urban distribution networks, subnets may be divided according to feeder loops or substation power supply scopes. After the division is completed, the system classifies the various configuration instructions in the optimization configuration scheme, combines the configuration instructions affecting the same subnet into a configuration subset to ensure coordinated configuration changes within each subnet. Subsequently, the system analyzes the dependency relationships and priorities among the configuration instructions to construct a timing network for configuration execution. The timing analysis considers factors such as the response time of the physical system, the requirements for the start-stop sequence of devices, and the transmission delay of control signals. For example, before adjusting the parameters of a photovoltaic inverter, it is necessary to ensure that the relevant line protection settings have been completed; before adjusting the energy storage charge-discharge strategy, it is necessary to first confirm the normal state of the battery management system. Based on the timing network, the system generates a detailed configuration execution schedule, specifying the execution time, duration, and completion deadline for each configuration instruction. To cope with uncertainties during the execution process, the system also sets appropriate time buffers and defines key checkpoints. When the execution reaches a checkpoint, the system verifies the effects of the completed configurations and decides whether to continue with subsequent configurations or make necessary adjustments. Through subnet division in the spatial domain and timing arrangement in the temporal domain, the system finally forms a structured and coordinated configuration scheme, which is not only technically feasible but also minimizes the risks of system disturbances and conflicts during implementation.

[0039] Furthermore, the process of obtaining a coordinated configuration scheme by performing configuration slicing according to the subnet area and timing constraints based on the optimization configuration scheme includes: for the controller group in the optimization configuration scheme, constructing an affinity matrix based on electrical distance and impedance coupling degree, and using the spectral clustering algorithm for adaptive partitioning to obtain an initial subnet division result; according to the initial subnet division result, using the node importance and boundary node recognition algorithm to optimize and adjust the partition boundaries, and calculating the cross-region coupling coefficient to obtain the configuration subsets in the spatial domain; for the configuration instructions within the configuration subsets in the spatial domain, using an improved critical path algorithm and resource competition graph to construct a timing dependency network, and introducing a slack time window mechanism to obtain a buffered execution timing; according to the execution timing, combining fault tracing analysis to determine the rollback point position, and using a hierarchical transaction management mechanism to generate a traceable configuration instruction chain to obtain a coordinated configuration scheme.

[0040] Specifically, when constructing the affinity matrix for the controller group in the optimized configuration scheme based on the electrical distance and impedance coupling degree, the system first extracts the complete network connection information from the topology database of the energy management system, including line parameters, transformer data, and switch states. Subsequently, the electrical distance between any two controllers is calculated. This indicator reflects the tightness of the electrical connection between the two controllers and is usually represented by the equivalent impedance or transfer impedance. For example, for the electrical distance between wind farm A and energy storage station B, the system considers the equivalent impedance of all possible paths between them and selects the minimum value as their electrical distance. At the same time, the system calculates the impedance coupling degree, which measures the degree of influence of the voltage change of one controller on another controller. In a distributed energy system, the impedance coupling degree is directly related to the sensitivity coefficient in the Jacobian matrix, and the larger the value, the stronger the coupling. The electrical distance and impedance coupling degree form the basis of the affinity between controllers. Then, the system performs dynamic correction in combination with actual operation data. For example, during high-load periods, the equivalent impedance of some lines will be amplified due to the approaching saturation of the thermal limit, reducing the affinity of the relevant nodes. Finally, the system organizes the affinity between controllers into a matrix form, and each element in the matrix represents the association strength between the corresponding two controllers. After completing the construction of the affinity matrix, the system uses the spectral clustering algorithm for network partitioning. This algorithm first calculates the Laplacian matrix of the affinity matrix, then extracts its eigenvectors, and applies K-means clustering in the eigenvector space to automatically identify the natural partition boundaries. The system evaluates the rationality of different partition numbers through the silhouette coefficient, determines the optimal number of subnet partitions, and obtains the initial subnet partition result.

[0041] Specifically, when optimizing and adjusting the partition boundary based on the initial subnet division result using the node importance and boundary node recognition algorithm, the system first calculates the importance index of each controller node. The node importance comprehensively considers multi-dimensional features such as the node's connectivity, traffic centrality, and bridging property, and quantifies the node's status in the network. For example, a substation controller connecting multiple key lines usually has a high connectivity; an energy storage controller located on multiple energy transmission paths has a high traffic centrality; a key switch controller connecting different regions has a high bridging property. Subsequently, the system identifies the nodes on the partition boundary, which are directly connected to the nodes in different subnets. For each boundary node, the system evaluates its belonging degree in the current subnet and the adjacent subnet, and decides whether to adjust the node's belonging by comparing the sum of the affinities between the node and the core nodes of each subnet. When the affinity of a boundary node with the adjacent subnet is significantly higher than that of the current subnet, the system reassigns it to the adjacent subnet. During the adjustment process, the system maintains the connectivity and functional integrity of the subnets, avoiding the formation of isolated nodes or subnets with incomplete functions. After the boundary optimization is completed, the system calculates the cross-region coupling coefficient between subnets, which reflects the degree of mutual influence between subnets. The calculation is based on factors such as the line capacity connecting subnets, the magnitude of power flow, and the interaction frequency of control signals. Subnet pairs with a high coupling coefficient need to be coordinated specifically to ensure the synchronization of configuration changes. Finally, based on the optimized subnet division, the system groups the configuration instructions in the optimized configuration plan according to the subnets to which they belong, forming a spatial domain configuration subset, and each subset corresponds to a relatively independent configuration execution unit.

[0042] Specifically, when constructing a timing dependency network using an improved critical path algorithm and a resource contention graph for configuration instructions within a subset of the spatial domain configuration, the system first analyzes the logical dependency relationships among the configuration instructions to identify which instructions must be completed before others. Such dependency relationships mainly stem from the operation constraints of the physical system and the requirements of the control logic. For example, before adjusting the power limit of a wind farm, it is necessary to ensure that the corresponding energy storage system is in a standby state; before updating the settings of a protection device, the relevant line needs to be transferred to the maintenance mode first. The system represents these dependency relationships as a directed graph, where the nodes are the configuration instructions and the edges represent the precedence constraints. Subsequently, the system identifies the system resources required for each configuration instruction, including communication bandwidth, computing resources, manual operations, etc., and constructs a resource contention graph. In this graph, there are potential execution conflicts among the configuration instructions sharing the same resources. For example, sending a large amount of configuration data to multiple controllers simultaneously may cause communication congestion; configuration instructions requiring on-site operations are limited by the number of staff. By merging the dependency graph and the resource contention graph, the system obtains a complete timing dependency network. Then the system applies the improved critical path algorithm to analyze this network to identify the critical link for configuration execution and its shortest completion time. The improvement lies in that the algorithm takes into account the dynamic changes in resource occupancy and the probability distribution of execution times to generate an execution plan that is more in line with the actual situation. For time-sensitive tasks, the system introduces a slack time window mechanism, which assigns an execution time window rather than a fixed moment to each configuration instruction, allowing flexible adjustment of the execution time according to the actual situation within the window. This mechanism increases the fault tolerance and adaptability of the execution process, especially suitable for application in a changing distributed energy environment. Finally, an execution timing with buffering is obtained, which not only ensures the correct execution order of the configuration instructions but also reserves room for adjustment to cope with uncertainties.

[0043] Specifically, according to the execution timing, when determining the rollback point location in combination with fault traceability analysis and generating a traceable configuration instruction chain using a hierarchical transaction management mechanism, the system first conducts a configuration risk assessment to analyze the possible fault types and their impact ranges during the execution process. Typical faults include communication interruptions, controller response timeouts, parameter setting failures, or device anomalies. Subsequently, the system extracts the fault mode characteristics from the historical fault database and identifies high-risk configuration instructions and their execution links. For these key points, the system sets strategic rollback points in combination with the execution timing as the safe recovery location in case of faults. The setting of rollback points follows the "state consistency principle", that is, after rollback, all components of the system are in a coordinated and consistent state. For example, in the configuration sequence of wind farm control mode switching and energy storage system parameter adjustment, a rollback point is set after completing all pre-preparation work and before officially executing the mode switching to ensure that it can safely return to the preparation stage in case of a fault. After the rollback point is determined, the system uses a hierarchical transaction management mechanism to organize the configuration instructions. In this mechanism, the configuration process is divided into multiple levels of transaction units according to functions and impact ranges, and lower-level transactions are nested within higher-level transactions. Each transaction unit has clear start points, commit points, and rollback processing logics. For example, the configuration process of a subnet may contain transactions of multiple device groups, and each device group transaction may contain transactions of multiple single devices. This hierarchical structure not only ensures overall coordination but also provides refined fault isolation and handling capabilities. When a transaction at a certain level fails, only this transaction and its sub-transactions need to be rolled back without affecting other completed parallel transactions. The system also establishes a detailed transaction log to record information such as the execution status, parameter changes, response results, and timestamps of each configuration instruction, forming a complete traceable configuration instruction chain. This instruction chain is not only used to monitor the configuration execution progress in real time but also supports post-fault analysis and responsibility tracing, enhancing the manageability and transparency of the system.

[0044] 104. Use the two-phase commit protocol to perform transactional deployment on the coordinated configuration scheme to obtain the edge controller configuration result.

[0045] In an embodiment of the present invention, the use of the two-phase commit protocol to perform transactional deployment on the coordinated configuration scheme to obtain the edge controller configuration result includes: sending a pre-commit request to all relevant edge controllers using the two-phase commit protocol, receiving and recording the prepare status information to obtain the first-phase commit result; according to the first-phase commit result, sending a formal commit request to the edge controllers that confirm they are ready to obtain the second-phase commit result; according to the second-phase commit result, generating a configuration transaction log and establishing a configuration rollback point to obtain the transaction deployment status; verifying the configuration result according to the transaction deployment status, and performing a rollback operation when an anomaly is detected to obtain the edge controller configuration result.

[0046] Specifically, when using the two-phase commit protocol to send pre-commit requests to all relevant edge controllers and receiving and recording the prepare status information, the system first parses the coordinated configuration plan into specific configuration instruction packets for each edge controller. Each configuration instruction packet contains key information such as controller identification, configuration parameter list, target value list, execution time window, and timeout limit. Subsequently, the system initiates the first-phase commit process and sends pre-commit requests to all edge controllers involved in the configuration plan in parallel. The pre-commit request carries the complete configuration instruction packet and is accompanied by a transaction identifier used to associate multiple operations of the same configuration transaction. After receiving the pre-commit request, the edge controller first performs a local pre-check to verify the legality, integrity, and executability of the configuration parameters. The pre-check process includes parameter range checking, resource availability checking, and security constraint verification, etc. For example, after receiving a power limit parameter adjustment request, the wind farm controller will check whether the parameter value is within the device allowable range, whether the current device status allows the execution of the adjustment, and whether the adjustment process will trigger the security protection mechanism. After the pre-check is completed, the edge controller locks the relevant resources to ensure that other operations will not change the current state and returns the pre-check result to the central coordination system. The pre-check result contains information such as a status code (such as ready, rejected, or timed out), the available resource status, and the estimated execution time. The coordination system receives and records the response status of all controllers and establishes a complete prepare status table. This table tracks the response of each controller, including the response time, prepare status, and additional information. For controllers that exceed the predetermined response time, the system marks them as timed out and decides whether to resend the request according to the configuration policy. By summarizing and analyzing the response status of all edge controllers, the system obtains the first-phase commit result, which reflects the pre-execution status of the entire configuration transaction.

[0047] Specifically, when sending a formal submission request to the edge controllers that are confirmed to be ready based on the results of the first-phase submission, the system first comprehensively evaluates the results of the first-phase submission to determine whether the conditions for entering the second phase are met. The evaluation criteria usually include the response rate of necessary controllers, the readiness status of key resources, and the current stability of the system, etc. In a distributed energy management system, all key controllers of the backbone network must be in a ready state to continue the configuration process. When all necessary conditions are met, the system enters the second-phase submission process; if any necessary condition is not met, the system will abort the current transaction and send an abort instruction to all ready controllers to release the reserved resources. After entering the second phase, the system sends a formal submission request to all edge controllers that are confirmed to be ready. This request contains the final execution instructions, precise timestamps, and synchronization markers, indicating the controllers to formally execute the configuration changes. For operations that require precise time synchronization, such as multi-point collaborative switching, the system uses the Global Positioning System clock or the Network Time Protocol to ensure time consistency. After receiving the formal submission request, each edge controller executes the configuration changes according to the predetermined process, including operations such as parameter modification, mode switching, or resource reallocation. After the execution is completed, the controller returns the execution results to the central system, including information such as the success status, actual execution time, and key status variables. For controllers that fail to execute, detailed error codes and fault descriptions are returned for subsequent analysis and processing. The system collects and correlates the execution results of all controllers to form a complete second-phase submission result. This result details the execution status, parameter changes, and abnormal situations of each controller, comprehensively reflecting the execution effect of the configuration transaction.

[0048] Specifically, when generating the configuration transaction log and establishing the configuration rollback point based on the submission results of the second stage, the system first structures and persistently stores the complete submission result data to form the configuration transaction log. This log adopts the time series database format and records the key events, status changes, and operation results of the entire configuration process. The log content includes transaction identification, start time, completion time, list of participating controllers, execution status of each stage, and detailed parameter change records, etc. For large-scale distributed energy systems, the log also contains network topology snapshots and key system status variables, providing complete context information for subsequent analysis. While recording the detailed log, the system extracts key status information to establish the configuration rollback point. The rollback point is a secure checkpoint of the system state, including parameter backups before configuration, resource status mappings, and recovery instruction sets. The system hierarchically organizes the rollback point data, from parameter recovery of a single controller to coordinated rollback of the entire system, constituting a complete recovery mechanism. For example, for parameter changes in the wind farm control system, the rollback point includes core parameters such as the original power curve, reactive power control mode, and protection setting values; for adjustment of the multi-station coordinated control strategy, the rollback point includes the control modes and interaction parameters of all relevant stations. The system distributes the rollback point information to the local storage of relevant controllers and retains a copy in the central system to ensure that local recovery can be performed even in case of network interruption. After the configuration rollback point is established, the system comprehensively evaluates the execution status of all controllers, network communication conditions, and system operation indicators to generate the overall transaction deployment status. This status visually displays the health of the configuration execution with three-level indicators of red, yellow, and green, and is accompanied by a detailed status description, including the number of completed nodes, list of abnormal nodes, and key resource occupancy, etc.

[0049] Specifically, when verifying the configuration result according to the transaction deployment status and performing a rollback operation when an anomaly is detected, the system first initiates a multi-level verification process to confirm the effectiveness of the configuration change and the stability of the system operation. The verification process is carried out from three dimensions: parameter consistency verification, functional verification, and system performance verification. Parameter consistency verification ensures that the configuration instructions have been correctly applied by reading back the actual configuration parameters of each controller and comparing the target values with the actual values. Functional verification checks whether the controller responds correctly according to the new configuration by triggering specific conditions or simulating input signals. For example, after adjusting the voltage control parameters of a wind farm, the system injects a small voltage fluctuation signal to observe whether the adjustment behavior of the controller meets the expectations. System performance verification monitors the changing trends of key performance indicators, such as power quality parameters, system stability margins, and resource utilization rates, to evaluate the impact of the configuration change on the overall system. When an anomaly is detected during the verification process, such as inconsistent parameter application, abnormal function response, or deterioration of performance indicators, the system decides on the corrective measures to be taken based on the severity and scope of the anomaly. For minor anomalies, the system attempts local adjustment or re-application of the configuration; for severe anomalies, a rollback operation is initiated. The rollback operation sends recovery instructions to the relevant controllers according to the pre-defined rollback point information to restore the system to the stable state before the configuration. The rollback process also uses a two-phase protocol to ensure consistency, first confirming the rollback readiness status of all participating nodes and then uniformly performing the recovery operation. For controllers that cannot be remotely recovered, the system generates detailed manual intervention instructions to guide on-site personnel for manual recovery. After completing the verification and necessary corrections, the system generates a final edge controller configuration result report, which details the completion status of the configuration process, the parameter change list, the anomaly handling measures, and the current operating state of the system.

[0050] In this embodiment, the configuration intention data of the edge controllers in the distributed system is collected, and a configuration intention network with a directed weighted graph structure is constructed according to the configuration intention data; according to the configuration intention network, conflict detection and impact assessment are performed on the configuration requests of the edge controllers to obtain a conflict feature vector; through target compatibility analysis and a game negotiation mechanism, conflict coordination is performed according to the conflict feature vector to obtain a coordinated configuration plan; a two-phase commit protocol is used to perform transactional deployment on the coordinated configuration plan to obtain the edge controller configuration result. The present invention effectively solves the conflict problem of multi-edge controller configuration decision-making and ensures the stable operation of the system by establishing a configuration intention network and a conflict coordination mechanism.

[0051] The method for remotely configuring a controller in the embodiments of the present invention has been described above. Next, the device for remotely configuring a controller in the embodiments of the present invention will be described. The device for remotely configuring a controller in the embodiments of the present invention is shown in Figure 2 , and an embodiment of the device for remotely configuring a controller in the embodiments of the present invention includes: A network construction module 201, configured to collect configuration intention data of edge controllers in a distributed system, and construct a configuration intention network with a directed weighted graph structure according to the configuration intention data; A conflict detection module 202, configured to perform conflict detection and impact assessment on configuration requests of edge controllers according to the configuration intention network, so as to obtain a conflict feature vector; A coordination processing module 203, configured to perform conflict coordination according to the conflict feature vector through target compatibility analysis and a game negotiation mechanism, so as to obtain a consistent configuration plan; A deployment execution module 204, configured to perform transactional deployment on the consistent configuration plan by using a two-phase commit protocol, so as to obtain an edge controller configuration result.

[0052] In an embodiment of the present invention, the controller remote configuration device runs the above-mentioned controller remote configuration method. The controller remote configuration device collects configuration intention data of edge controllers in a distributed system, and constructs a configuration intention network with a directed weighted graph structure according to the configuration intention data; performs conflict detection and impact assessment on configuration requests of edge controllers according to the configuration intention network, so as to obtain a conflict feature vector; performs conflict coordination according to the conflict feature vector through target compatibility analysis and a game negotiation mechanism, so as to obtain a consistent configuration plan; performs transactional deployment on the consistent configuration plan by using a two-phase commit protocol, so as to obtain an edge controller configuration result. The present invention effectively solves the conflict problem of multi-edge controller configuration decision-making by establishing a configuration intention network and a conflict coordination mechanism, and ensures the stable operation of the system.

[0053] Above Figure 2 The controller remote configuration device in the embodiment of the present invention is described in detail from the perspective of modular functional entities. Below, the controller remote configuration device in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0054] Figure 3FIG. 0 is a schematic structural diagram of a controller remote configuration device provided by an embodiment of the present invention. The controller remote configuration device 300 may vary greatly due to different configurations or performances, and may include one or more processors (central processing units, CPUs) 310 (for example, one or more processors) and a memory 320, and one or more storage media 330 for storing application programs 333 or data 332 (for example, one or more mass storage device terminals). Among them, the memory 320 and the storage media 330 may be transient storage or persistent storage. The program stored in the storage media 330 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the controller remote configuration device 300. Further, the processor 310 may be configured to communicate with the storage media 330 and execute a series of instruction operations in the storage media 330 on the controller remote configuration device 300 to implement the steps of the above-mentioned controller remote configuration method.

[0055] The controller remote configuration device 300 may further include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input / output interfaces 360, and / or one or more operating systems 331, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art can understand that Figure 3 The shown structural diagram of the controller remote configuration device does not limit the controller remote configuration device provided by the present invention, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0056] The present invention also provides a computer-readable storage medium. The computer-readable storage medium may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are run on a computer, the computer is caused to execute the steps of the controller remote configuration method.

[0057] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described system or device and unit can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0058] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, 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 and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0059] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of various embodiments of the present invention.

Claims

1. A controller remote configuration method, characterized in that: The controller remote configuration method comprises: Collecting configuration intention data of edge controllers in a distributed system, and constructing a configuration intention network with a directed weighted graph structure according to the configuration intention data; According to the configuration intent network, conflict detection and impact assessment are performed on the configuration request of the edge controller to obtain a conflict feature vector; Through target compatibility analysis and game negotiation mechanism, conflicts are coordinated according to conflict feature vectors to obtain a coordinated configuration plan; A two-phase commit protocol is used to transactionally deploy the coordinated configuration scheme to obtain an edge controller configuration result.

2. The controller remote configuration method according to claim 1, characterized in that: The collecting of configuration intention data of edge controllers in a distributed system and constructing a configuration intention network with a directed weighted graph structure according to the configuration intention data includes: Collect data on target parameters, adjustment ranges, priority identifiers, and time windows of edge controllers in a distributed system to obtain a configuration intent data set. According to the configuration intention data set, feature extraction is performed on the physical connection relationship, energy flow path, and historical interaction mode of the edge controller to obtain an edge controller impact feature matrix; According to the edge controller influence feature matrix, a directed graph is constructed in which nodes are edge controllers and edges are influence relationships, and edge weights are calculated according to historical operation data to obtain a configuration intention network.

3. The controller remote configuration method according to claim 1, characterized in that: The configuration request of the edge controller is subjected to conflict detection and impact assessment according to the configuration intention network to obtain a conflict feature vector including: According to the configuration intent network, the configuration request sent by the edge controller is matched and filtered, the associated edge controller group is identified, and the configuration association set is obtained; According to the configuration association set, cross-comparison is performed on the parameter contents of the configuration request to detect parameter contradictions and resource competition, and obtain a parameter conflict set; According to the configuration association set and the parameter conflict set, a propagation analysis is performed on the impact path of the configuration request in combination with the system physical model to obtain an impact assessment matrix; According to the impact assessment matrix, the severity, impact scope and urgency of the conflict are calculated to obtain a conflict feature vector.

4. The controller remote configuration method according to claim 3, characterized in that: The influence path of the configuration request is propagated and analyzed according to the configuration association set and the parameter conflict set in combination with the system physical model to obtain the influence assessment matrix, including: According to the electrical characteristic parameters of the edge controller in the configuration association set, the system physical model is calibrated and parameter identified, a state variable set including voltage, current and power is constructed, and the system mathematical model is obtained; For the system mathematical model, combined with the parameter change information in the configuration request, the impact propagation path is determined according to the energy transmission equation and the topological connection relationship to obtain an impact path diagram; According to the impact path diagram, using the conflict information in the parameter conflict set, sensitivity analysis is performed on the state quantity on each impact path to obtain a propagation intensity matrix; The propagation intensity matrix is ​​subjected to weighted superposition and normalization processing, and is corrected in combination with a physical quantity threshold constraint to obtain an impact assessment matrix.

5. The controller remote configuration method according to claim 1, characterized in that: The conflict coordination is performed according to the conflict feature vector through the target compatibility analysis and game negotiation mechanism to obtain a coordinated configuration scheme including: According to the conflict characteristic vector, the multi-party game benefit function and game constraints are constructed to obtain the game model; The game model is used to solve the Nash equilibrium, and according to the virtual incentive mechanism, a candidate configuration solution set is obtained through N iterative operations; Performing compatibility analysis on the configuration parameters in the candidate configuration solution set, screening the solutions using the minimum target deviation principle, and obtaining an optimized configuration solution; According to the optimized configuration scheme, configuration segmentation is performed according to subnet areas and timing constraints to obtain a coordinated and consistent configuration scheme.

6. The controller remote configuration method according to claim 5, characterized in that: According to the optimized configuration scheme, the configuration is divided according to the subnet area and the timing constraint to obtain a coordinated configuration scheme including: For the controller group in the optimal configuration scheme, an affinity matrix is ​​constructed based on electrical distance and impedance coupling, and a spectral clustering algorithm is used for adaptive partitioning to obtain the initial subnet partitioning result. According to the initial subnet division result, the partition boundary is optimized and adjusted by using the node importance and boundary node identification algorithm, and the cross-region coupling coefficient is calculated to obtain the spatial domain configuration subset; For the configuration instructions in the spatial domain configuration subset, an improved critical path algorithm and a resource competition graph are used to construct a timing dependency network, and a relaxation time window mechanism is introduced to obtain a buffered execution timing; According to the execution sequence, the rollback point position is determined in combination with the fault tracing analysis, and a hierarchical transaction management mechanism is used to generate a traceable configuration instruction chain to obtain a coordinated and consistent configuration solution.

7. The controller remote configuration method according to claim 1, characterized in that: The two-phase commit protocol is used to transactionally deploy the coordinated configuration scheme to obtain the edge controller configuration result, including: Use the two-phase commit protocol to send pre-commit requests to all relevant edge controllers, receive and record the readiness status information, and obtain the first-phase commit results; According to the submission result of the first stage, a formal submission request is sent to the edge controller that is confirmed to be ready to obtain the submission result of the second stage; According to the submission result of the second phase, a configuration transaction log is generated and a configuration rollback point is established to obtain the transaction deployment status; The configuration result is verified according to the transaction deployment status, and a rollback operation is performed when an abnormality is detected to obtain the edge controller configuration result.

8. A controller remote configuration device, characterized in that: The controller remote configuration device comprises: A network construction module, used to collect configuration intention data of edge controllers in a distributed system, and to construct a configuration intention network with a directed weighted graph structure according to the configuration intention data; A conflict detection module, used to perform conflict detection and impact assessment on the configuration request of the edge controller according to the configuration intention network, and obtain a conflict feature vector; The coordination processing module is used to perform conflict coordination according to the conflict feature vector through target compatibility analysis and game negotiation mechanism to obtain a coordinated configuration plan; The deployment execution module is used to perform transactional deployment on the coordinated configuration scheme using a two-phase commit protocol to obtain an edge controller configuration result.

9. A controller remote configuration device, characterized in that: The controller remote configuration device includes: 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 controller remote configuration device to perform the steps of the controller remote configuration method as described in any one of claims 1-7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the steps of the controller remote configuration method as described in any one of claims 1 to 7 are implemented.

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