Circuit distribution sensing and control method, system, medium and product

Abstract

The invention discloses a circuit distribution perception and control method and system, a medium and a product, and belongs to the field of power distribution automation, and the method comprises the steps: obtaining local electrical parameters reported by each communication module and the number of cooperative nodes; the number of the cooperative nodes is the number of subordinate circuit nodes which are in effective communication and electrical communication with the corresponding circuit nodes; calculating a routing grade score of each node based on the parameter and the number of cooperative nodes, and combining real-time power and circuit topology to construct a power path map with edge weight; determining the priority coefficient of each node according to the real-time power, and constructing an adjacent matrix based on the weight of each edge; and calculating an energy transfer path for the overload node in the adjacent matrix, and generating an adjustment instruction to regulate and control the circuit system. By implementing the method, the technical problem that the power distribution network is difficult to realize millisecond energy path reconstruction in a high dynamic scene due to the lack of modeling of a coupling relationship between node energy flow dynamic characteristics and regulation feasibility in the prior art can be solved.

Description

Technical Field

[0001] This invention belongs to the field of power distribution automation and relates to a circuit distribution sensing and control method, system, medium and product. Background Technology

[0002] With the large-scale integration of distributed energy resources and the increasing popularity of the coordinated operation of virtual power plants and microgrids, low-voltage distribution networks are characterized by a surge in the number of nodes, dynamic and varied energy flow, and frequent fluctuations in local loads, which places higher demands on the real-time perception and autonomous control capabilities of the distribution network.

[0003] Currently, load balancing and topology control in distribution networks mainly rely on centralized systems. These systems construct a global model by collecting measurement data from the entire network and then use reinforcement learning or complex optimization algorithms to generate control strategies. For example, CN120879620A discloses a topology switching control method based on a state-structure joint graph. This method encodes node voltage, current, and topology connections into multi-dimensional state vectors using tensor embedding and employs a deep policy gradient algorithm to solve for the optimal switching path. However, it does not address modeling the coupling relationship between the dynamic characteristics of energy flow and the feasibility of control at nodes. In highly dynamic distributed scenarios, it is difficult to support millisecond-level energy path reconstruction. Summary of the Invention

[0004] This application provides a circuit distribution sensing and control method, system, medium, and product, which can solve the technical problem in the prior art that the lack of modeling of the coupling relationship between the dynamic characteristics of node energy flow and the feasibility of regulation makes it difficult for distribution networks to achieve millisecond-level energy path reconfiguration in high dynamic scenarios.

[0005] To achieve the above objectives, in a first aspect, the present invention provides a circuit distribution sensing and control method, comprising:

[0006] Obtain the local electrical parameters and number of cooperating nodes of each circuit node reported by each communication module on the circuit; wherein, the number of cooperating nodes is the effective number of lower-level modules of the communication module on the communication network; the effective number is the number of lower-level circuit nodes in each lower-level circuit node corresponding to each lower-level module that have an effective circuit connection relationship with the circuit node of the communication module.

[0007] Based on the local electrical parameters and the number of cooperating nodes, the routing level score of each circuit node is calculated, and a power path graph is constructed by combining the real-time power of each circuit node and the circuit topology; wherein each edge in the power path graph is configured with an edge weight calculated from the routing level score.

[0008] Based on the real-time power, the priority coefficient of each circuit node is calculated, and an adjacency matrix is ​​constructed based on the edge weights of the power path graph.

[0009] Based on the priority coefficient, energy transfer paths are calculated for each overloaded circuit node in the adjacency matrix, and corresponding adjustment instructions are generated to adjust the circuit system.

[0010] Compared to existing technologies, the embodiments of this application have the following beneficial effects: By acquiring the local electrical parameters and the number of cooperating nodes reported by each communication module, where the number of cooperating nodes specifically refers to the number of lower-level circuit nodes that have been discovered through communication and confirmed by electrical parameter verification to have a valid circuit connection, the system can distinguish between invalid nodes that are "electrically present but unreachable by communication" or "reachable by communication but disconnected by circuit," thereby including only nodes with real controllable capabilities in subsequent decisions. Furthermore, by integrating local electrical parameters with the number of valid cooperating nodes to calculate a routing level score, the score not only reflects the dynamic characteristics of power flow (such as power change rate and power direction) but also reflects the controllability potential of nodes in the communication-circuit coupling sense. At the same time, a power path graph is constructed by combining real-time power and circuit topology, and the path is incorporated into the edge weights of the graph. By using a rating system, the energy path representation becomes sensitive to the comprehensive control capabilities of nodes. Then, load saturation is calculated based on real-time power, and priority coefficients are derived to quantify each node's ability to accept additional energy. Furthermore, an adjacency matrix is ​​constructed based on the edge weights of the power path graph, providing a structured data foundation for path search. Finally, based on the priority coefficients, energy transfer paths are planned for overloaded nodes in the adjacency matrix, and adjustment commands are generated, achieving closed-loop control of the circuit system. The synergistic effect of these features enables the system to accurately identify controllable nodes, reasonably assess energy transfer feasibility, and generate low-disturbance control strategies in highly dynamic distributed scenarios. This solves the technical problem of existing technologies failing to support millisecond-level energy path reconstruction due to the lack of modeling the coupling relationship between the dynamic characteristics of node energy flow and control feasibility.

[0011] In some embodiments of the first aspect of this application, calculating the routing grade score of each circuit node based on the local electrical parameters and the number of cooperating nodes includes:

[0012] In each of the electrical parameters, the original current signal and original voltage signal of each circuit node are extracted, and moving average filtering and noise reduction processing are performed to obtain current signal data and voltage signal data.

[0013] Based on the current signal data and voltage signal data, the corresponding active power time series data is calculated, and combined with a preset time window, differential calculation is performed on the active power time series data to obtain the power change rate data of each circuit node.

[0014] Based on the active power time-series data, the sign of the active power is determined by applying a sign function to obtain the corresponding energy direction data; where a positive value indicates energy inflow and a negative value indicates energy outflow.

[0015] Based on the maximum value among the power change rate data, the power change rate data of each circuit node is normalized; based on the maximum value among the number of each cooperative node, the number of each cooperative node is normalized; and the normalized power change rate data, the normalized number of cooperative nodes, and the absolute value of the power direction data are weighted and fused to obtain the corresponding routing level score.

[0016] Compared with existing technologies, the above embodiments have the following beneficial effects: by extracting the original current and voltage signals and performing moving average filtering and noise reduction processing, the interference of sampling noise on power calculation is effectively suppressed. Furthermore, the power change rate data is obtained by differential calculation of active power time series data to quantify the dynamic activity of node energy flow. At the same time, the sign function is applied to determine the positive and negative of active power to clarify the direction of power. Then, normalization is performed based on the maximum power change rate and the maximum number of cooperative nodes in each node to eliminate dimensional differences and achieve cross-node comparability. Finally, the normalized power change rate, the normalized number of cooperative nodes and the absolute value of the power direction data are weighted and fused so that the routing level score can simultaneously characterize the dynamic adjustment capability, the scale of cooperative control and the active state of the node, avoiding scoring deviations caused by inconsistent dimensions or imbalance of index weights.

[0017] In some embodiments of the first aspect of this application, the step of constructing a power path map by combining the real-time power of each circuit node and the circuit topology includes:

[0018] The power path graph is constructed by using each circuit node as a node and the connection relationship between any two circuit nodes as an edge.

[0019] Each node in the graph is configured with corresponding routing grade score, real-time power, and power direction data; each edge is configured with an edge weight calculated by weighting the power change rate data and the routing grade score.

[0020] Compared with existing technologies, the above embodiments have the following beneficial effects: by constructing a power path graph with circuit nodes as graph nodes and electrical connection relationships as edges, the graph truly maps the physical power grid structure. At the same time, routing level scores, real-time power and energy direction data are configured in the nodes to retain multi-dimensional state information, and power change rate and routing level scores are integrated in the edge weights, so that the weight of the energy path not only reflects the current power fluctuation intensity, but also reflects the comprehensive control capability of the node where the path is located, thereby providing a decision basis that is both dynamic and reliable for subsequent path search.

[0021] In some embodiments of the first aspect of this application, calculating the priority coefficient of each circuit node based on the real-time power includes:

[0022] The corresponding load saturation is calculated based on the ratio of the real-time power to the rated power of each circuit node; wherein, the overloaded circuit node is the circuit node whose load saturation is greater than a preset saturation threshold.

[0023] Calculate the difference between the load saturation of each circuit node and the value of 1, and multiply it by the corresponding routing grade score to obtain the priority coefficient.

[0024] Compared with the prior art, the above embodiments have the following beneficial effects: by calculating the ratio of real-time power to rated power, the load saturation is obtained, so as to objectively quantify the current load pressure level of the node, and clearly define the node whose load saturation exceeds the preset saturation threshold as an overloaded circuit node. Then, the product of the difference between the load saturation and the routing level is used as the priority coefficient, so that the higher the priority coefficient, the lighter the current load of the node and the higher the routing level, and the greater the margin of energy transfer that can be taken, thus providing a direct criterion for the target selection of energy transfer path.

[0025] In some embodiments of the first aspect of this application, constructing an adjacency matrix based on the edge weights of the power path graph includes:

[0026] The weights of each edge in the power path graph are used as matrix elements to construct an adjacency matrix; wherein, when two nodes in the power path graph have no edge, the value of the corresponding matrix element is zero.

[0027] Compared with the prior art, the above embodiments have the following beneficial effects: the edge weights in the power path graph are directly used as elements of the adjacency matrix, and the nodes without connections are set to zero, so that the adjacency matrix strictly corresponds to the physical topology of the power grid and the dynamic path weights, providing standard mathematical input for path planning based on graph search algorithms, and ensuring that the path calculation is consistent with the actual energy flow path.

[0028] In some embodiments of the first aspect of this application, the step of calculating energy transfer paths for each overloaded circuit node in the adjacency matrix according to the priority coefficient and generating corresponding adjustment instructions includes:

[0029] For each overloaded circuit node, based on Dijkstra's algorithm, the adjacency matrix is ​​traversed, and the circuit node with the highest current priority coefficient is taken as the destination target. The path target is the one with the minimum sum of the weights of all edges on the path. The energy transfer path from the corresponding overloaded circuit node to the current destination target is calculated.

[0030] Based on the operating status of the circuit nodes corresponding to the source and target nodes of each energy transfer path, corresponding adjustment instructions are generated.

[0031] Compared with existing technologies, the above embodiments have the following beneficial effects: based on Dijkstra's algorithm to traverse the adjacency matrix, with the circuit node with the highest priority coefficient as the destination target and the minimum sum of path edge weights as the optimization objective, the shortest energy transfer path from the overloaded node to the high-potential receiving node is calculated. The edge weights integrate dynamic activity and control capability, and the path selection takes into account both transfer efficiency and system stability. Furthermore, the adjustment instructions are generated by combining the operating status of the source node and the target node, making the instructions physically executable.

[0032] In some embodiments of the first aspect of this application, generating corresponding adjustment instructions based on the operating states of the circuit nodes corresponding to the source and target nodes of each energy transfer path includes:

[0033] Based on the operating status of the circuit nodes corresponding to the source and target nodes of each energy transfer path, the amount of power to be transferred is calculated, and based on the amount of power and the energy transfer path, a corresponding power adjustment command is generated.

[0034] Traverse all non-zero elements in the adjacency matrix and determine whether the weight of the corresponding edge is lower than a preset low-activity threshold. If so, generate a first path switching instruction to disconnect the circuit path corresponding to the edge.

[0035] Traverse the priority coefficients of all circuit nodes, identify high-potential circuit nodes whose priority coefficients are higher than the preset high-potential threshold and whose preset neighborhood contains overloaded circuit nodes, and generate a second path switching instruction for the new energy path based on the circuit connection relationship between the circuit nodes in the neighborhood.

[0036] Compared with existing technologies, the above embodiments have the following beneficial effects: the amount of power to be transferred is calculated based on the operating status of the nodes at both ends of the energy transfer path, and a power adjustment command is generated in conjunction with the path to ensure that the amount of power transferred matches the current state of the nodes; at the same time, the edge weight is determined by traversing the adjacency matrix to see if it is lower than a preset low-activity threshold. If it is lower, a command to disconnect the corresponding circuit path is generated to promptly remove inefficient or dormant paths and reduce system redundancy losses; furthermore, by identifying high-potential circuit nodes with priority coefficients higher than a preset high-potential threshold and overloaded nodes in their neighborhood, a command to add new energy paths is generated based on their neighborhood connection relationship, actively constructing energy diversion channels between high-potential nodes and overloaded areas, thereby improving the system's dynamic topology reconstruction capability and load balancing level.

[0037] In a second aspect, the present invention also provides a circuit distribution sensing and control system, comprising: a data acquisition module, a map construction module, a calculation module, and an adjustment module;

[0038] The data acquisition module is used to acquire the local electrical parameters and the number of cooperating nodes of each circuit node reported by each communication module on the circuit; wherein, the number of cooperating nodes is the effective number of lower-level modules of the communication module on the communication network; the effective number is the number of lower-level circuit nodes in each lower-level circuit node corresponding to each lower-level module that have an effective circuit connection relationship with the circuit node of the communication module.

[0039] The power path graph construction module is used to calculate the routing level score of each circuit node based on the local electrical parameters and the number of cooperating nodes, and to construct a power path graph by combining the real-time power of each circuit node and the circuit topology; wherein each edge in the power path graph is configured with an edge weight calculated from the routing level score.

[0040] The calculation module is used to calculate the priority coefficient of each circuit node based on the real-time power, and to construct an adjacency matrix based on the edge weights of the power path graph.

[0041] The adjustment module is used to calculate the energy transfer path for each overloaded circuit node in the adjacency matrix according to the priority coefficient, and generate corresponding adjustment instructions to adjust the circuit system.

[0042] Compared to existing technologies, the above embodiments of this application have the following beneficial effects: By acquiring the local electrical parameters and the number of cooperating nodes reported by each communication module, where the number of cooperating nodes specifically refers to the number of lower-level circuit nodes that have been discovered through communication and confirmed by electrical parameter verification to have a valid circuit connection, the system can distinguish between invalid nodes that are "electrically present but unreachable by communication" or "reachable by communication but disconnected by circuit," thereby including only nodes with real controllable capabilities in subsequent decisions. Furthermore, by integrating local electrical parameters with the number of valid cooperating nodes to calculate a routing level score, the score not only reflects the dynamic characteristics of power flow (such as power change rate and power direction) but also reflects the controllability potential of nodes in the communication-circuit coupling sense. At the same time, a power path graph is constructed by combining real-time power and circuit topology, and the edge weights of the graph are incorporated. Routing level scoring makes the energy path representation sensitive to the comprehensive control capabilities of nodes. Then, load saturation is calculated based on real-time power and priority coefficients are derived to quantify the ability of each node to accept additional energy. Subsequently, an adjacency matrix is ​​constructed based on the edge weights of the power path graph, providing a structured data foundation for path search. Finally, based on the priority coefficients, energy transfer paths are planned for overloaded nodes in the adjacency matrix and adjustment instructions are generated to achieve closed-loop control of the circuit system. The synergistic effect of the above features enables the system to accurately identify controllable nodes, reasonably assess the feasibility of energy transfer, and generate low-disturbance control strategies in highly dynamic distributed scenarios. This solves the technical problem of existing technologies that cannot support millisecond-level energy path reconstruction due to the lack of modeling of the coupling relationship between the dynamic characteristics of node energy flow and control feasibility.

[0043] Thirdly, the present invention also provides a computer program product, including a computer program or instructions, characterized in that, when the computer program or instructions are executed, they implement any one of the circuit distribution sensing and control methods of the present invention.

[0044] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any one of the circuit distribution sensing and control methods of the present invention. Attached Figure Description

[0045] Figure 1 This is a flowchart illustrating a circuit distribution sensing and control method provided in some embodiments of the present invention.

[0046] Figure 2 This is a schematic diagram of the structure of a circuit distributed sensing and control system provided in some embodiments of the present invention. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] Example 1:

[0049] Please refer to Figure 1 To address the technical problem in existing technologies where the lack of modeling for the coupling relationship between the dynamic characteristics of node energy flow and the feasibility of regulation makes it difficult to achieve millisecond-level energy path reconfiguration in high-dynamic scenarios, an embodiment of the present invention provides a circuit distribution sensing and control method, comprising steps S1 to S4:

[0050] Step S1: Obtain the local electrical parameters and number of cooperating nodes of each circuit node reported by each communication module on the circuit; wherein, the number of cooperating nodes is the effective number of lower-level modules of the communication module on the communication network; the effective number is the number of lower-level circuit nodes in each lower-level circuit node corresponding to each lower-level module that have an effective circuit connection relationship with the circuit node of the communication module.

[0051] Step S2: Based on the local electrical parameters and the number of cooperating nodes, calculate the routing level score of each circuit node, and construct a power path graph by combining the real-time power of each circuit node and the circuit topology; wherein each edge in the power path graph is configured with an edge weight calculated from the routing level score.

[0052] Furthermore, in step S2, the calculation of the routing grade score can be implemented through the following preferred embodiments, including steps S21-S25, as follows:

[0053] S21: Extract the original current signal and original voltage signal of each circuit node from the electrical parameters, and perform moving average filtering and noise reduction processing to obtain current signal data and voltage signal data.

[0054] S22: Based on the current signal data and voltage signal data, calculate the corresponding active power time series data, and combine it with a preset time window to perform differential calculation on the active power time series data to obtain the power change rate data of each circuit node.

[0055] S23: Based on the active power time-series data, apply a sign function to determine the sign of the active power and obtain the corresponding energy direction data; where a positive value indicates energy inflow and a negative value indicates energy output.

[0056] S24: Based on the maximum value among the power change rate data, normalize the power change rate data of each circuit node; based on the maximum value among the number of each cooperative node, normalize the number of each cooperative node; and weight and fuse the normalized power change rate data, the normalized number of cooperative nodes, and the absolute value of the power direction data to obtain the corresponding routing level score.

[0057] In existing technologies, communication modules are typically unable to identify the local power flow status, nor can they identify their own role in the power path of the power grid, and naturally they do not have the ability to participate in the regulation of energy flow.

[0058] In this application, the communication module, through its built-in real-time electrical parameter acquisition unit, can continuously acquire basic electrical parameters of the local bus or load-side nodes, including voltage U, current I, and power factor P. f Based on this, a low-pass filter is first used for noise reduction, and then the active power is calculated: P = U × I × P f ;

[0059] To determine the direction of electrical energy flow reflected by this power, a sign function is used for direction determination:

[0060] Dir = sign(P); where a positive Dir indicates current inflow (load end) and a negative Dir indicates power output (generator or feeder end). The specific value can be determined by the magnitude of the current or power. This is the basic signal recognition step for subsequent node role determination and energy topology modeling.

[0061] After initially determining the direction of electrical energy, the module continues to model the trend of electrical energy fluctuations using the time-series power sequence P(t). A fixed time sampling window is set as δ. t The rate of power change ΔP(t) at each time point is obtained through differential calculation (the power data can be smoothed using a moving average method before differential calculation):

[0062] ΔP(t)=P(t)-P(t-δ) t );

[0063] ΔP(t) reflects the power fluctuation trend per unit time; a larger value indicates that the node is in a significant flow regulation state. A threshold can be set to determine whether "channel convergence" or "power leakage" behavior exists. This trend can serve as one of the indicators for judging the "activity" or "transfer" of a module's pathway in the network.

[0064] While obtaining the power direction Dir and the power change trend ΔP(t), the module can also obtain the number N (i.e., the number of collaborative nodes mentioned above) of lower-level modules / devices discovered through the communication protocol stack and confirmed by electrical parameter verification as either "circuit connected" or "awaiting activation and connectivity". The logic of electrical parameter verification is as follows: After discovering a lower-level node through the communication protocol stack, the module sends an "electrical parameter feedback request" to that node and simultaneously receives real-time data from its electrical parameter acquisition unit: if the feedback current I≠0 (or voltage U≠0, which must meet a minimum threshold, such as U≥220V×10%), then it is determined that "circuit connected"; if I=0 and U=0, then it is determined that "circuit disconnected" and is not included in N; if I=0 but U≠0 (for example, the electric vehicle communication is connected but the charging gun is not plugged in tightly, and the circuit is awaiting activation), then it is marked as a "node to be activated" and temporarily included in the "reserve queue" of N, and the changes in I / U will be monitored in real time thereafter.

[0065] Here, by verifying electrical parameters, invalid nodes that are "communication connected but circuit disconnected" (such as devices that are powered off but whose communication modules are not offline) can be excluded. Since I=0 and U=0, they are excluded from N, thus preventing the system from including nodes that "cannot receive energy / execute commands" in the routing plan and ensuring the effectiveness of control.

[0066] In addition, dynamic space can be reserved: nodes with "communication connected + circuit waiting to be activated" (such as electric vehicles preparing to charge in V2G scenarios) enter the reserve queue of N because U≠0. The module can pre-mark the routing level and reserve energy paths. Once the circuit is connected (I≠0), it is immediately included in the effective N to realize dynamic routing level marking. Furthermore, it can focus on controllable objects. Compared with simple circuit indicators such as "number of active devices in the branch", N selects nodes that can participate in energy coordinated scheduling and "can communicate (receive scheduling instructions) and have circuit connection (transmit energy)", while "number of active devices in the branch" only counts electrical existence and cannot distinguish whether they have communication control capabilities.

[0067] After obtaining the above data, as in step S42, normalization is performed to eliminate dimensional differences, followed by weighted fusion:

[0068] Where ω1, ω2, and ω3 are empirical weighting coefficients, P max and N max These are the maximum values ​​in the power change rate data (i.e., the maximum power change) and the maximum values ​​in the number of each cooperating node, respectively.

[0069] In this preferred embodiment, by extracting the original current and voltage signals and performing moving average filtering to denoise, the interference of sampling noise on power calculation is effectively suppressed. Furthermore, the power change rate data is obtained by differential calculation of active power time series data to quantify the dynamic activity of node energy flow. At the same time, the sign function is applied to determine the positive or negative of active power to clarify the direction of electrical energy. Then, normalization is performed based on the maximum power change rate and the maximum number of cooperating nodes in each node to eliminate dimensional differences and achieve cross-node comparability. Finally, the absolute values ​​of the normalized power change rate, the normalized number of cooperating nodes, and the electrical energy direction data are weighted and fused so that the routing level score simultaneously represents the dynamic adjustment capability, the scale of cooperating control, and the active state of the node, avoiding scoring deviations caused by inconsistent dimensions or imbalanced index weights.

[0070] Furthermore, the construction of the power path map can be achieved through the following preferred implementation method, including step S25, as follows:

[0071] S25: Construct the power path graph by using each circuit node as a node and the connection relationship between any two circuit nodes as an edge;

[0072] Each node in the graph is configured with corresponding routing grade score, real-time power, and power direction data; each edge is configured with an edge weight calculated by weighting the power change rate data and the routing grade score.

[0073] In this preferred embodiment, a power path graph is constructed using circuit nodes as graph nodes and electrical connections as edges, so that the graph truly maps the physical power grid structure. At the same time, routing level scores, real-time power and energy direction data are configured in the nodes to retain multi-dimensional state information, and the power change rate and routing level scores are integrated into the edge weights, so that the weight of the energy path not only reflects the current power fluctuation intensity, but also reflects the comprehensive control capability of the node where the path is located, thereby providing a decision basis that is both dynamic and reliable for subsequent path search.

[0074] In practical implementation, each node in the graph should include relevant parameters, including routing grade score, real-time power, and energy direction. In the edge set, each edge represents an energy path between nodes. The weight is calculated by weighting the power change rate data of the nodes at both ends of the edge with the routing grade score, which is used to reflect the impact of power fluctuations and routing grade on path priority.

[0075] Step S3: Calculate the priority coefficient of each circuit node based on the real-time power, and construct an adjacency matrix based on the edge weights of the power path graph.

[0076] Furthermore, in step S3, the calculation of the priority coefficient can be implemented through the following preferred embodiments, including steps S31-S32, as follows:

[0077] S31: The corresponding load saturation is calculated based on the ratio of the real-time power to the rated power of each circuit node; wherein, the overloaded circuit node is the circuit node whose load saturation is greater than the preset saturation threshold.

[0078] S32: Calculate the difference between the load saturation of each circuit node and 1, and multiply it by the corresponding routing grade score as the priority coefficient.

[0079] In practice, the priority coefficient is used to measure the load capacity of a node. First, the load saturation is calculated, which represents the percentage of load that the node is currently undertaking. The difference between this and 1 is then multiplied by the node's routing grade score. The larger the value, the more additional energy the node can handle (high routing grade + low load saturation).

[0080] In this preferred embodiment, the load saturation is obtained by calculating the ratio of real-time power to rated power to objectively quantify the current load pressure level of the node. The node with a load saturation exceeding a preset saturation threshold is defined as an overloaded circuit node. The product of the difference between the load saturation and the routing level is used as a priority coefficient. The higher the priority coefficient, the lighter the current load of the node and the higher the routing level, and the greater the margin for energy transfer. This provides a direct criterion for the target selection of energy transfer paths.

[0081] Furthermore, the construction of the adjacency matrix can be achieved through the following preferred implementation method, including step S33, as follows:

[0082] S33: Use the weights of each edge in the power path graph as matrix elements to construct an adjacency matrix; wherein, when two nodes in the power path graph do not have an edge, the value of the corresponding matrix element is zero.

[0083] In practice, the adjacency matrix A is n*n, where n is the number of nodes. When there is a path between nodes i and j, the corresponding element A[i, j] in the adjacency matrix is ​​the edge weight in the graph; otherwise, it is 0.

[0084] In this preferred embodiment, the edge weights in the power path graph are directly used as elements of the adjacency matrix, and unconnected nodes are set to zero. This ensures that the adjacency matrix strictly corresponds to the physical topology of the power grid and the dynamic path weights, providing standard mathematical input for path planning based on graph search algorithms and ensuring that the path calculation is consistent with the actual energy flow path.

[0085] Step S4: Based on the priority coefficient, calculate the energy transfer path for each overloaded circuit node in the adjacency matrix, and generate the corresponding adjustment command to adjust the circuit system.

[0086] Furthermore, the generation of the adjustment instruction can be achieved through the following preferred implementation method, including steps S41-S42, as follows:

[0087] S41: For each overloaded circuit node, based on Dijkstra's algorithm, traverse the adjacency matrix, take the circuit node with the highest current priority coefficient as the destination target, and take the path target with the minimum sum of the weights of all edges on the path, and calculate the corresponding energy transfer path from the overloaded circuit node to the current destination target.

[0088] S42: Based on the operating status of the circuit nodes corresponding to the source node and target node of each energy transfer path, generate corresponding adjustment instructions.

[0089] In this preferred embodiment, the adjacency matrix is ​​traversed based on Dijkstra's algorithm. The circuit node with the highest priority coefficient is taken as the destination target, and the sum of the edge weights of the path is minimized as the optimization objective. This realizes the calculation of the shortest energy transfer path from the overloaded node to the high-potential receiving node. The edge weights integrate dynamic activity and control capability, and the path selection takes into account both transfer efficiency and system stability. Furthermore, the operating status of the source node and the target node is combined to generate adjustment instructions, making the instructions physically executable.

[0090] Furthermore, step S42 can be implemented through the following preferred embodiments, including steps S421-S423, as follows:

[0091] S421: Calculate the amount of power to be transferred based on the operating status of the circuit nodes corresponding to the source and target nodes of each energy transfer path, and generate a corresponding power adjustment command based on the amount of power and the energy transfer path.

[0092] S422: Traverse all non-zero elements in the adjacency matrix and determine whether the weight of the corresponding edge is lower than the preset low activity threshold. If so, generate a first path switching instruction to disconnect the circuit path corresponding to the edge.

[0093] S423: Traverse the priority coefficients of all circuit nodes, identify high-potential circuit nodes whose priority coefficients are higher than the preset high-potential threshold and whose preset neighborhood contains overloaded circuit nodes, and generate a second path switching instruction for the new energy path based on the circuit connection relationship between the circuit nodes in the neighborhood.

[0094] In this preferred embodiment, the amount of power to be transferred is calculated based on the operating status of the nodes at both ends of the energy transfer path, and a power adjustment command is generated in conjunction with the path to ensure that the amount of power transferred matches the current state of the nodes. At the same time, the edge weight is determined by traversing the adjacency matrix to see if it is lower than a preset low-activity threshold. If it is lower, a command to disconnect the corresponding circuit path is generated to cut off inefficient or dormant paths in a timely manner and reduce system redundancy losses. Furthermore, high-potential circuit nodes with priority coefficients higher than a preset high-potential threshold and overloaded nodes in their neighborhood are identified, and a command to add new energy paths is generated based on their neighborhood connection relationship. This actively constructs energy diversion channels between high-potential nodes and overloaded areas, improving the system's dynamic topology reconstruction capability and load balancing level.

[0095] In practice, the Dijkstra algorithm is used to traverse the adjacency matrix to find the shortest path between each overloaded node (e.g., the load saturation level is greater than 0.8) and the optimal node that can take over the load, thus obtaining the corresponding path matrix (composed of the nodes that pass through the two points and have the minimum sum of the weights of the edges on the path).

[0096] For power adjustment, for example, if the load saturation needs to be reduced to 70%, first calculate the amount of power that the overloaded node needs to transfer, and then generate the corresponding power adjustment command. The command includes the current overloaded node, the amount of power to be transferred, and the shortest path corresponding to the transferred power.

[0097] For path switching, if the weight of an edge in the graph is very small and below the preset threshold, it indicates that the power fluctuation is too small or the routing level is too low, and the path can be disconnected; or, if the priority coefficient of a node is very high and the node load of a node in the surrounding area is very high, the two nodes can be connected to create an energy path between the two nodes and build an energy diversion channel.

[0098] In summary, compared with the prior art, the above embodiments of this application have the following beneficial effects: By acquiring the local electrical parameters and the number of cooperating nodes reported by each communication module, where the number of cooperating nodes specifically refers to the number of lower-level circuit nodes that have been discovered through communication and confirmed by electrical parameter verification to have a valid circuit connection, the system can distinguish between invalid nodes that are "electrically present but unreachable by communication" or "reachable by communication but disconnected by circuit," thereby including only nodes with real controllable capabilities in subsequent decisions. Furthermore, by integrating local electrical parameters with the number of valid cooperating nodes to calculate the routing level score, the score not only reflects the dynamic characteristics of power flow (such as power change rate and power direction) but also reflects the controllability potential of nodes in the communication-circuit coupling sense. At the same time, a power path graph is constructed by combining real-time power and circuit topology, and the edge weights of the graph are integrated with the power path graph. The ingress route level scoring makes the energy path representation sensitive to the comprehensive control capabilities of nodes. Then, the load saturation is calculated based on real-time power and priority coefficients are derived to quantify the ability of each node to accept additional energy. Subsequently, an adjacency matrix is ​​constructed based on the edge weights of the power path graph, providing a structured data foundation for path search. Finally, based on the priority coefficients, energy transfer paths are planned for overloaded nodes in the adjacency matrix and adjustment instructions are generated to achieve closed-loop control of the circuit system. The synergistic effect of the above features enables the system to accurately identify controllable nodes, reasonably assess the feasibility of energy transfer, and generate low-disturbance control strategies in highly dynamic distributed scenarios. This solves the technical problem of existing technologies that cannot support millisecond-level energy path reconstruction due to the lack of modeling of the coupling relationship between the dynamic characteristics of node energy flow and control feasibility.

[0099] Example 2:

[0100] Please refer to Figure 2 Based on the same inventive concept, the present invention discloses a circuit distribution sensing and control system, comprising: a data acquisition module M1, a map construction module M2, a calculation module M3, and an adjustment module M4;

[0101] The data acquisition module M1 is used to acquire the local electrical parameters and the number of cooperating nodes of each circuit node reported by each communication module on the circuit; wherein, the number of cooperating nodes is the effective number of lower-level modules of the communication module on the communication network; the effective number is the number of lower-level circuit nodes in each lower-level circuit node corresponding to each lower-level module that have an effective circuit connection relationship with the circuit node of the communication module.

[0102] The graph construction module M2 is used to calculate the routing level score of each circuit node based on the local electrical parameters and the number of cooperating nodes, and to construct a power path graph by combining the real-time power of each circuit node and the circuit topology; wherein each edge in the power path graph is configured with an edge weight calculated from the routing level score.

[0103] Furthermore, the map construction module M2 includes: a denoising unit, an electrical parameter calculation unit, a symbol judgment unit, and a grade scoring calculation unit;

[0104] The denoising unit is used to extract the original current signal and original voltage signal of each circuit node from each of the electrical parameters, and perform moving average filtering denoising processing to obtain current signal data and voltage signal data.

[0105] The electrical parameter calculation unit is used to calculate the corresponding active power time series data based on the current signal data and voltage signal data, and to perform differential calculation on the active power time series data in combination with a preset time window to obtain the power change rate data of each circuit node.

[0106] The sign determination unit is used to determine the sign of the active power based on the active power time-series data and apply a sign function to obtain the corresponding energy direction data; wherein, a positive value indicates energy inflow and a negative value indicates energy outflow.

[0107] The rating calculation unit is used to normalize the power change rate data of each circuit node based on the maximum value of each power change rate data; normalize the number of each cooperative node based on the maximum value of each cooperative node number; and weight and fuse the normalized power change rate data, the normalized cooperative node number, and the absolute value of the power direction data to obtain the corresponding routing rating.

[0108] In this preferred embodiment, by extracting the original current and voltage signals and performing moving average filtering to denoise, the interference of sampling noise on power calculation is effectively suppressed. Furthermore, the power change rate data is obtained by differential calculation of active power time series data to quantify the dynamic activity of node energy flow. At the same time, the sign function is applied to determine the positive or negative of active power to clarify the direction of electrical energy. Then, normalization is performed based on the maximum power change rate and the maximum number of cooperating nodes in each node to eliminate dimensional differences and achieve cross-node comparability. Finally, the absolute values ​​of the normalized power change rate, the normalized number of cooperating nodes, and the electrical energy direction data are weighted and fused so that the routing level score simultaneously represents the dynamic adjustment capability, the scale of cooperating control, and the active state of the node, avoiding scoring deviations caused by inconsistent dimensions or imbalanced index weights.

[0109] Furthermore, the map construction module M2 also includes: a map definition unit;

[0110] The graph definition unit is used to construct the power path graph by using each circuit node as a node and the connection relationship between any two circuit nodes as an edge.

[0111] Each node in the graph is configured with corresponding routing grade score, real-time power, and power direction data; each edge is configured with an edge weight calculated by weighting the power change rate data and the routing grade score.

[0112] In this preferred embodiment, a power path graph is constructed using circuit nodes as graph nodes and electrical connections as edges, so that the graph truly maps the physical power grid structure. At the same time, routing level scores, real-time power and energy direction data are configured in the nodes to retain multi-dimensional state information, and the power change rate and routing level scores are integrated into the edge weights, so that the weight of the energy path not only reflects the current power fluctuation intensity, but also reflects the comprehensive control capability of the node where the path is located, thereby providing a decision basis that is both dynamic and reliable for subsequent path search.

[0113] The calculation module M3 is used to calculate the priority coefficient of each circuit node based on the real-time power, and to construct an adjacency matrix based on the edge weights of the power path graph.

[0114] Furthermore, the calculation module M3 includes: a saturation calculation unit and a difference calculation unit;

[0115] The saturation calculation unit is used to calculate the corresponding load saturation based on the ratio of the real-time power to the rated power of each circuit node; wherein the overloaded circuit node is a circuit node whose load saturation is greater than a preset saturation threshold.

[0116] The difference calculation unit is used to calculate the difference between the load saturation of each circuit node and the value of 1, and multiply the difference with the corresponding routing grade score as the priority coefficient.

[0117] In this preferred embodiment, the load saturation is obtained by calculating the ratio of real-time power to rated power to objectively quantify the current load pressure level of the node. The node with a load saturation exceeding a preset saturation threshold is defined as an overloaded circuit node. The product of the difference between the load saturation and the routing level is used as a priority coefficient. The higher the priority coefficient, the lighter the current load of the node and the higher the routing level, and the greater the margin for energy transfer. This provides a direct criterion for the target selection of energy transfer paths.

[0118] Furthermore, the computing module M3 also includes: a matrix construction unit;

[0119] The matrix construction unit is used to construct an adjacency matrix by taking the weights of each edge in the power path graph as matrix elements; wherein, when two nodes in the power path graph do not have an edge, the value of the corresponding matrix element is zero.

[0120] In this preferred embodiment, the edge weights in the power path graph are directly used as elements of the adjacency matrix, and unconnected nodes are set to zero. This ensures that the adjacency matrix strictly corresponds to the physical topology of the power grid and the dynamic path weights, providing standard mathematical input for path planning based on graph search algorithms and ensuring that the path calculation is consistent with the actual energy flow path.

[0121] The adjustment module M4 is used to calculate the energy transfer path for each overloaded circuit node in the adjacency matrix according to the priority coefficient, and generate the corresponding adjustment command to adjust the circuit system.

[0122] Furthermore, the adjustment module M4 includes: a path calculation unit and an instruction generation unit;

[0123] The path calculation unit is used to calculate the energy transfer path from the overloaded circuit node to the current endpoint target for each overloaded circuit node by traversing the adjacency matrix based on Dijkstra's algorithm, taking the circuit node with the highest current priority coefficient as the endpoint target and the path target with the minimum sum of the weights of all edges on the path.

[0124] The instruction generation unit is used to generate corresponding adjustment instructions based on the operating status of the circuit nodes corresponding to the source nodes and target nodes of each energy transfer path.

[0125] In this preferred embodiment, the adjacency matrix is ​​traversed based on Dijkstra's algorithm. The circuit node with the highest priority coefficient is taken as the destination target, and the sum of the edge weights of the path is minimized as the optimization objective. This realizes the calculation of the shortest energy transfer path from the overloaded node to the high-potential receiving node. The edge weights integrate dynamic activity and control capability, and the path selection takes into account both transfer efficiency and system stability. Furthermore, the operating status of the source node and the target node is combined to generate adjustment instructions, making the instructions physically executable.

[0126] Furthermore, the instruction generation unit includes: a first instruction generation subunit, a second instruction generation subunit, and a third instruction generation subunit;

[0127] The first instruction generation subunit is used to calculate the amount of power to be transferred based on the operating status of the circuit nodes corresponding to the source node and target node of each energy transfer path, and generate a corresponding power adjustment instruction based on the amount of power and the energy transfer path.

[0128] The second instruction generation subunit is used to traverse all non-zero elements in the adjacency matrix, determine whether the corresponding edge weight is lower than a preset low activity threshold, and if so, generate a first path switching instruction to disconnect the circuit path corresponding to the edge.

[0129] The third instruction generation subunit is used to traverse the priority coefficients of all circuit nodes, identify high-potential circuit nodes whose priority coefficients are higher than a preset high-potential threshold and whose preset neighborhood contains overloaded circuit nodes, and generate a second path switching instruction for adding energy paths based on the circuit connection relationship between each circuit node in the neighborhood.

[0130] In this preferred embodiment, the amount of power to be transferred is calculated based on the operating status of the nodes at both ends of the energy transfer path, and a power adjustment command is generated in conjunction with the path to ensure that the amount of power transferred matches the current state of the nodes. At the same time, the edge weight is determined by traversing the adjacency matrix to see if it is lower than a preset low-activity threshold. If it is lower, a command to disconnect the corresponding circuit path is generated to cut off inefficient or dormant paths in a timely manner and reduce system redundancy losses. Furthermore, high-potential circuit nodes with priority coefficients higher than a preset high-potential threshold and overloaded nodes in their neighborhood are identified, and a command to add new energy paths is generated based on their neighborhood connection relationship. This actively constructs energy diversion channels between high-potential nodes and overloaded areas, improving the system's dynamic topology reconstruction capability and load balancing level.

[0131] In summary, compared with the prior art, the embodiments of this application have the following beneficial effects: By acquiring the local electrical parameters and the number of cooperating nodes reported by each communication module, where the number of cooperating nodes specifically refers to the number of lower-level circuit nodes that have been discovered through communication and confirmed by electrical parameter verification to have a valid circuit connection, the system can distinguish between invalid nodes that are "electrically present but unreachable by communication" or "reachable by communication but disconnected by circuit," thereby including only nodes with real controllable capabilities in subsequent decisions. Furthermore, by integrating local electrical parameters with the number of valid cooperating nodes to calculate the routing level score, the score not only reflects the dynamic characteristics of power flow (such as power change rate and power direction) but also reflects the controllability potential of nodes in the communication-circuit coupling sense. At the same time, a power path graph is constructed by combining real-time power and circuit topology, and the edge weights of the graph are incorporated. Routing level scoring makes the energy path representation sensitive to the comprehensive control capabilities of nodes. Then, load saturation is calculated based on real-time power and priority coefficients are derived to quantify the ability of each node to accept additional energy. Subsequently, an adjacency matrix is ​​constructed based on the edge weights of the power path graph, providing a structured data foundation for path search. Finally, based on the priority coefficients, energy transfer paths are planned for overloaded nodes in the adjacency matrix and adjustment instructions are generated to achieve closed-loop control of the circuit system. The synergistic effect of the above features enables the system to accurately identify controllable nodes, reasonably assess the feasibility of energy transfer, and generate low-disturbance control strategies in highly dynamic distributed scenarios. This solves the technical problem of existing technologies that cannot support millisecond-level energy path reconstruction due to the lack of modeling of the coupling relationship between the dynamic characteristics of node energy flow and control feasibility.

[0132] Example 3:

[0133] This invention also provides a computer program product, including a computer program or instructions, capable of running on a computing device or stored in any available medium. When the computer program product is run on at least one computing device, it causes the at least one computing device to execute any of the circuit distribution sensing and control methods of this invention.

[0134] Example 4:

[0135] This invention also provides a computer-readable storage medium storing at least one executable instruction that, when executed on a circuit distributed sensing and control system, causes the circuit distributed sensing and control system to perform one of the circuit distributed sensing and control methods described in any of the above method embodiments.

[0136] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. Similarly, for the purpose of simplification and aiding understanding of one or more aspects of the invention, in the above description of exemplary embodiments of this application, various features of the embodiments are sometimes grouped together in a single embodiment, figure, or description thereof. The claims, which follow the detailed description, are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of this application.

[0137] Those skilled in the art will understand that the modules in the system of the embodiments can be adaptively changed and placed in one or more systems different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components, except that at least some of such features and / or processes or units are mutually exclusive.

Claims

1. A circuit distribution awareness and control method, characterized by, The method comprises the following steps: obtaining local electrical parameters and the number of cooperative nodes of each circuit node reported by each communication module on the acquisition circuit; wherein the number of cooperative nodes is the effective number of subordinate modules of the communication module on the communication network; the effective number is the number of subordinate circuit nodes corresponding to each subordinate module that has an effective circuit communication relationship with the circuit node of the communication module; based on each local electrical parameter and the number of cooperative nodes, calculating the routing level score of each corresponding circuit node, and combining the real-time power of each circuit node and the circuit topology to construct a power path atlas; wherein each edge in the power path atlas is configured with an edge weight calculated from the routing level score; according to the real-time power, calculating the priority coefficient of each circuit node, and based on the edge weight of the power path atlas, constructing an adjacency matrix; according to the priority coefficient, calculating the energy transfer path for each overloaded circuit node in the adjacency matrix, and generating corresponding adjustment instructions to adjust the circuit system.

2. A method of circuit distribution awareness and control as claimed in claim 1, wherein, The method comprises the following steps: in each of the electrical parameters, extract the original current signal and the original voltage signal of each circuit node, and perform sliding average filtering denoising processing to obtain current signal data and voltage signal data; based on the current signal data and the voltage signal data, calculate the active power time series data, and combine the preset time window to perform difference calculation on the active power time series data to obtain the power change rate data of each circuit node; based on the active power time series data, apply the sign function to determine the positive and negative of the active power to obtain the corresponding power direction data; wherein a positive value indicates power inflow and a negative value indicates power outflow; based on the maximum value in each power change rate data, normalize the power change rate data of each circuit node; based on the maximum value in each number of cooperative nodes, normalize each number of cooperative nodes; and weight and fuse the normalized power change rate data, the normalized number of cooperative nodes and the absolute value of the power direction data to obtain the corresponding routing level score.

3. A method of circuit distribution awareness and control as claimed in claim 2, wherein, The method comprises the following steps: constructing the power path atlas by taking each circuit node as a node and the connection relationship between any two circuit nodes as an edge; wherein each node in the atlas is configured with the corresponding routing level score, real-time power and power direction data; each edge is configured with an edge weight calculated by weighting the power change rate data and the routing level score.

4. A method of circuit distribution awareness and control as claimed in claim 3, wherein, The method comprises the following steps: calculating the load saturation degree of each circuit node according to the ratio of the real-time power to the rated power; wherein the overloaded circuit node is the circuit node with a load saturation degree greater than a preset saturation threshold; respectively calculating the product of the difference between the load saturation degree of each circuit node and one and the corresponding routing level score as the priority coefficient.

5. A method of circuit distribution awareness and control as claimed in claim 4, wherein, The edge weights in the power path graph are used to construct an adjacency matrix, including: The edge weights in the power path graph are used to construct an adjacency matrix, and the matrix elements are the edge weights in the power path graph; when there is no edge between two nodes in the power path graph, the value of the corresponding matrix element is zero.

6. A method of circuit distribution awareness and control as claimed in claim 5, wherein, The energy transfer path for each overloaded circuit node in the adjacency matrix is calculated according to the priority coefficient, and the corresponding adjustment instruction is generated, including: For each overloaded circuit node, the adjacency matrix is traversed based on the Dijkstra algorithm, and the energy transfer path from the corresponding overloaded circuit node to the current end target is calculated, where the end target is the circuit node with the maximum current priority coefficient, and the sum of the edge weights on the path is the path target. Based on the running states of the circuit nodes corresponding to the source node and the target node of each energy transfer path, the corresponding adjustment instruction is generated.

7. A method of circuit distribution awareness and control as claimed in claim 6, wherein, The adjustment instruction is generated based on the running states of the circuit nodes corresponding to the source node and the target node of each energy transfer path, including: The amount of power to be transferred is calculated according to the running states of the circuit nodes corresponding to the source node and the target node of each energy transfer path, and the corresponding power adjustment instruction is generated based on the amount of power and the energy transfer path; All non-zero elements in the adjacency matrix are traversed to determine whether the corresponding edge weight is lower than a preset low activity threshold, and if so, a first path switching instruction to disconnect the circuit path corresponding to the edge is generated; All priority coefficients of the circuit nodes are traversed to identify high-potential circuit nodes with priority coefficients higher than a preset high-potential threshold and with overloaded circuit nodes in the preset neighborhood, and a second path switching instruction to add a new energy path is generated based on the circuit connection relationship between the circuit nodes in the neighborhood.

8. A circuit distribution awareness and control system, characterized by, It includes: a data acquisition module, a graph construction module, a calculation module, and an adjustment module; The data acquisition module is configured to acquire local electrical parameters and the number of cooperative nodes of each circuit node reported by each communication module on the circuit; the number of cooperative nodes is the effective number of subordinate modules of the communication module on the communication network; the effective number is the number of subordinate circuit nodes corresponding to each subordinate module that have effective circuit communication relationship with the circuit nodes of the communication module; The graph construction module is configured to calculate the routing level score of each circuit node based on the local electrical parameters and the number of cooperative nodes, and construct a power path graph in combination with the real-time power of each circuit node and the circuit topology; each edge in the power path graph is configured with an edge weight calculated from the routing level score; The calculation module is configured to calculate the priority coefficient of each circuit node according to the real-time power, and construct an adjacency matrix based on the edge weights of the power path graph; The adjustment module is configured to calculate the energy transfer path for each overloaded circuit node in the adjacency matrix according to the priority coefficient, and generate the corresponding adjustment instruction to adjust the circuit system.

9. A computer program product comprising computer programs or instructions, characterized in that, The computer program or instructions, when executed, implement a circuit distribution awareness and control method as claimed in any of claims 1-7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program, when executed by a processor, implements a circuit distribution awareness and control method as claimed in any of claims 1-7.

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