A future state-oriented power grid startup wiring diagram automatic generation method and device

Abstract

This invention relates to the field of power system automation technology, specifically to a method and apparatus for automatically generating power grid startup wiring diagrams for future states. The method includes: acquiring common information model data of the current and future states of the power grid; extracting incremental datasets using a hash digest-based branch comparison algorithm; locating operation boundary nodes in a global graph database based on the incremental datasets, performing connectivity reconstruction on affected local subgraphs to generate future-state local topology models; performing connectivity traversal starting from the physical access points of incremental equipment, outputting the outage range and a list of energized equipment; finally, inheriting the original spatial layout of existing equipment, applying a force-oriented algorithm to calculate the optimal coordinates of newly added graphical elements, generating a power grid startup wiring diagram, and combining it with the regional list for early warning highlighting. This invention reduces the algorithm's time complexity to the local incremental level, achieving efficient updating and dynamic early warning of substation wiring diagrams.

Description

Technical Field

[0001] This invention relates to the field of power system automation technology, specifically to a method and apparatus for automatically generating power grid startup wiring diagrams for future applications. Background Technology

[0002] A power grid main wiring diagram is a graphical representation used to describe the relationships between power generation, transmission, transformation, and distribution, as well as the network topology. It is widely used in power system management, analysis, and calculation, and is an important basic auxiliary tool for dispatching and operation personnel. With the continuous expansion of modern smart grids and the increasing complexity of substation topologies, the workload of creating digital power grid diagrams for corresponding automation and information systems is growing exponentially.

[0003] In traditional power grid dispatching and management models, substation wiring diagrams are primarily drawn and entered manually using drawing tools. However, due to the complexity of power grid graphics and the large number of equipment types, manual maintenance is prone to errors such as missing attributes, incorrect associations, and loose connections, leading to inconsistencies in the diagrams and potential risks to power grid operation. Furthermore, with the increasing frequency of new construction, renovation of old lines, changes in operating modes, and maintenance work in distribution and main grids, the component composition and connection methods of main wiring diagrams are changing frequently. Achieving real-time synchronous updates of substation wiring diagrams requires significant labor and management costs. Although some published patent documents and technical solutions propose methods for automatically generating distribution network wiring diagrams, these methods are mostly limited to extracting connection relationships from a specific wiring diagram format and generating another wiring diagram. Moreover, when dealing with updates to local equipment, it is often necessary to regenerate the entire diagram, disrupting dispatchers' familiarity with the original graphic layout.

[0004] In the full lifecycle management of main equipment in power systems, power grid flow diagrams and wiring diagrams can be divided into various forms from a time dimension, such as historical state, current state, near-term state, and future state. Among them, the "future state" model is mainly used for forward-looking business scenarios such as the preparation of day-ahead power grid dispatch plans, simulation of new equipment commissioning, safety verification of maintenance plans, and analysis of anticipated accidents.

[0005] However, existing power grid topology analysis and mapping systems are primarily designed for static or real-time operational models in the "current state." When dealing with "future state" scenarios such as the commissioning of new substations or the connection of new lines, the system typically needs to merge the engineering data of the new equipment with the basic data of the entire network and perform a full topology reconstruction and state estimation calculation for the entire power grid (often containing tens of millions of electrical nodes and connecting edges). This "full reconstruction" mode, when dealing with large power grids, not only consumes enormous amounts of computational memory but also takes excessively long time, often several minutes or even longer, failing to meet the stringent time constraints of modern power grids for millisecond-level or second-level online intelligent situation assessment. Summary of the Invention

[0006] To overcome the problems of large computational load for future topology reconstruction and inaccurate identification of power outage and energized areas in existing technologies, this solution provides a method and device for automatically generating power grid startup wiring diagrams for future states.

[0007] A method for automatically generating future-oriented power grid startup wiring diagrams includes the following steps: Step S1: Obtain the current state public information model data of the power grid, and simultaneously obtain the future state public information model data superimposed with the commissioning of new equipment or state change plans; Step S2: Compare the current state model with the future state model to extract the incremental dataset containing newly added devices, deleted devices, and devices with changed attributes; Step S3: Based on the incremental dataset, locate the operation boundary node in the global power grid graph database constructed from the current state model, and use graph merging and splitting rules to reconstruct the connectivity of the local subgraph affected by the increment, thereby generating an updated future state local topology model. Step S4: Using the newly commissioned equipment connection points or status change switches in the incremental dataset as the search starting point, perform connectivity traversal tracking in the updated future local topology model, identify and output the list of equipment in the affected power outage area and the list of equipment in the energized area. Step S5: Based on the original spatial coordinate layout of the existing equipment without changes, the coordinates and connection paths of the newly added equipment are calculated using an adaptive layout and wiring algorithm to generate a power grid startup wiring diagram. Based on the list of equipment in the power outage area and the list of equipment in the energized area, the generated wiring diagram is colored with differentiated status and highlighted with early warning.

[0008] Preferably, the step of comparing the current state model with the future state model to extract an incremental dataset containing newly added devices, deleted devices, and devices with changed attributes specifically includes: Discretize the device type, identifier, and connection terminal information in the public information model into data tags; In a hierarchical power grid topology, a hash message digest is generated for each level of object, where changes in the data of the lower-level object trigger a cascading update of the hash digest of the upper-level object. Starting from the highest level, the hash digest values ​​of the current state and future state models are compared. If the digest values ​​are the same, branch pruning is performed and the process is skipped. If they are different, the comparison is recursively performed layer by layer until the lower-level device nodes that have changed are extracted, forming the incremental dataset.

[0009] Preferably, the process of generating the future local topological model in step S3 specifically includes: Identify the physical access point of the incremental device in the current state network graph and define the access point as a boundary node; Centered on the boundary node, a preset k-order network neighbor is expanded outward to extract the local influence domain subgraph; In the local influence domain subgraph, based on the node-circuit breaker model, the disjoint-setup algorithm is applied to perform a shrinking and merging operation on bus nodes connected by closed switches, or a splitting operation on nodes separated by open switches, to generate a modified bus-branch model subgraph. The modified bus-branch model subgraph is mapped and overlaid onto the original global power grid graph database.

[0010] Preferably, the specific process of identifying and outputting the list of equipment in the affected power outage area and the list of equipment in the energized area in step S4 is as follows: Push the starting point of the search onto the traversal stack and initialize the set of visited nodes and the list of target regions; When the stack is not empty, pop the current node and get all its physical adjacent nodes; Check the simulated state of the circuit breaker or disconnector corresponding to the edge connecting the current node and the adjacent node in the future state model; If the simulated state is open, the blocking logic is triggered, and the adjacent node is refused to be pushed onto the stack; if the simulated state is closed and the preset voltage level convergence condition is met, the adjacent node is pushed onto the stack, and its device information is recorded in the corresponding target area list, thus ultimately delineating the boundaries of the isolated electrical island and the power outage area.

[0011] Preferably, the specific process of generating the power grid startup wiring diagram in step S5 is as follows: The electrical main wiring pattern of the local topology is identified based on the subgraph isomorphic matching technology, and the corresponding standard primitives are instantiated. Lock the two-dimensional spatial coordinates of existing equipment in the wiring diagram; A force-oriented algorithm is used to establish an attraction and repulsion model between new primitives and existing primitives, and the minimum system energy is calculated iteratively to determine the optimal coordinates of the new primitives. The grid A* path search algorithm is applied, and existing primitives are assigned high obstruction weight parameters to perform intelligent wiring of the shortest path that automatically avoids obstacles.

[0012] Preferably, the force-directed algorithm adds a virtual magnetic grid force field to the traditional gravity-repulsion model. This force field attracts nodes to the nearest integer grid coordinate point to achieve automatic alignment and orthogonalization of the graphics.

[0013] An automatic generation device for power grid startup wiring diagrams for future-oriented applications includes a model parsing module, an incremental identification module, a local topology reconstruction module, a region identification module, and a graphics generation module. The model parsing module is used to acquire and parse the current and future state public information model data of the power grid, and construct a global power grid graph database based on nodes and circuit breakers; The incremental recognition module is used to execute the hash digest branch comparison algorithm to compare and extract the incremental dataset between the current state and the future state. The local topology reconstruction module is used to receive the incremental dataset, perform local connectivity node merging and splitting operations based on the incremental dataset, complete the dynamic correction of the local topology model, and skip the full network reconstruction process; The region identification module is used to perform a heuristic depth-first search algorithm in the corrected topology model, using the connection points of incremental devices as search seeds, to calculate and output the power outage range and the boundary data of the energized area in real time. The graphics generation module is used to generate a graphical network for new devices based on the original spatial coordinate layout of existing devices without changes, using an adaptive layout and wiring algorithm. Based on the output area boundary data, it completes the warning highlighting and status rendering of the wiring diagram on the user's visualization terminal.

[0014] Compared with the prior art, the advantages of this invention are: By accurately extracting incremental data through a hash digest algorithm and performing "local topology dynamic correction" based on a graph database, the time complexity of topology calculation is reduced, enabling topology analysis and updates to be completed within tens of milliseconds, which greatly improves computational efficiency.

[0015] By using a "controlled heuristic deep search algorithm", electrical attribute constraints such as switch physical state and voltage level are directly embedded into the search logic. This not only effectively blocks invalid traversal, but also directly outputs a list of equipment and lines in the power supply range under each voltage level, replacing the inefficient mode of relying on dispatchers to manually search and analyze wiring diagrams one by one.

[0016] This solution retains the coordinates of the old equipment that has not changed during automatic mapping, and only uses a hybrid algorithm of force-guided and orthogonal wiring to calculate the optimal position and routing for the "newly commissioned" equipment. This reduces the cost and error rate of manual maintenance of the map model, while maintaining the scheduler's familiarity with the screen. Attached Figure Description

[0017] Figure 1 This is a flowchart of a method for automatically generating power grid startup wiring diagrams for future-oriented scenarios, as proposed in this invention. Detailed Implementation

[0018] 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. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0019] Example 1: A method for automatically generating future-oriented power grid startup wiring diagrams includes: Step S1: Obtain the current state public information model data of the power grid, and simultaneously obtain the future state public information model data superimposed with the commissioning of new equipment or state change plans.

[0020] Step S2: Using an incremental feature comparison algorithm, compare the current state model with the future state model to extract an incremental dataset containing newly added devices, deleted devices, and devices with changed attributes.

[0021] Step S3: Based on the incremental dataset, locate the operation boundary node in the global power grid graph database constructed from the current state model, and use graph merging and splitting rules to reconstruct the connectivity of the local subgraph affected by the increment, thereby generating an updated future state local topology model.

[0022] Step S4: Using the newly commissioned equipment connection points or state change switches in the incremental dataset as the search starting point, in the updated future local topology model, a depth-first search algorithm with electrical attributes and switch state constraints is used to perform connectivity traversal tracking, automatically identify and output the list of equipment in the affected power outage range and the list of equipment in the energized area.

[0023] Step S5: Inherit the original spatial coordinate layout of the existing equipment without changes, use an adaptive layout and wiring algorithm to calculate the coordinates and connection paths of the newly added equipment, automatically generate a power grid start-up wiring diagram, and perform differentiated status coloring and early warning highlighting on the generated wiring diagram based on the list of equipment in the power outage area and the list of equipment in the energized area.

[0024] Example 2: The Common Information Model (CIM) is the core foundation for data exchange, storage, and application in modern power systems. In order to efficiently process the "future" power grid, the system must have the ability to quickly extract "incremental" elements from massive and complex CIM model data. This is a prerequisite for avoiding full-network computational reconfiguration.

[0025] The current state model is defined as a dataset reflecting the current physical connections and real-time operating status of the power grid. This model's data originates in real-time from SCADA / EMS (Supervisory and Data Acquisition / Energy Management System) and includes basic parameters of all conductive equipment, connection nodes, terminal information, and the real-time closed / open status of switching equipment. The future state model, on the other hand, reflects the expected physical structure and operating mode of the power grid at a specific future timeframe (such as the planned commissioning date of a new 220kV substation). This model, based on the current state, incorporates data changes resulting from infrastructure planning, technological upgrades and expansion projects, and equipment maintenance plans.

[0026] Because the CIM model adopts an object-oriented design philosophy, its data volume is enormous and the relationships between classes are intricate (including dozens of package definitions such as core packages, topology packages, and wiring packages), making direct XML text comparison extremely inefficient. Therefore, this method first discretizes the complex CIM model data type in heterogeneous systems into a flat set of data tags. By transforming core attributes such as device ID, device type, substation, and connected topology nodes into feature vectors, the system establishes a unified mathematical metric space for subsequent rapid comparison.

[0027] If each analysis involves comparing the current and future states of a model containing tens of millions of nodes, unacceptable system overhead will inevitably occur. Therefore, this invention proposes an incremental extraction algorithm based on hierarchical message digests and tree branch comparison to reduce the time complexity of model comparison.

[0028] When constructing the hierarchical topology of the power grid, the system calculates and extends a "message digest" attribute for each object node (from the bottom layer circuit breakers and buses, to the middle layer voltage levels and bays, and then to the top layer substations and control areas). This digest is generated using secure hash algorithms such as SHA-256. Its core mechanism lies in the fact that any data change of a bottom-level object (such as a change in the state of a switch or the addition of a conductor) will cause its own hash value to change, and this change will propagate upwards along the topology tree hierarchy, causing a chain reaction of updates to the hash digests of all its ancestor nodes.

[0029] Based on the above structure, when performing incremental comparisons of the CIM model between the future and current states, the system executes a branch-pruning comparison algorithm that traverses downwards from the root node. The system first compares the hash digest values ​​of the highest level (e.g., the entire provincial control area). If the hash digest values ​​of the current and future states are completely identical, it mathematically proves that all devices and their topology under that branch have not changed, and the algorithm directly performs "pruning" to skip this step, avoiding unnecessary traversal of a large number of lower-level nodes. If the hash digest values ​​are inconsistent, the algorithm proceeds to the next level (e.g., various municipal bureaus or specific substations) to repeat the hash comparison, recursively layer by layer, until it accurately locates the lowest-level specific device node where the hash value has changed.

[0030] This hierarchical comparison mechanism transforms the blind global search into a locally guided exploration, greatly reducing the number of comparisons and significantly accelerating the efficiency of model assembly into the library. The incremental dataset extracted by this algorithm is divided into three orthogonal subsets: the newly added set (involving the instantiation of new equipment and the establishment of its connection terminals, mainly corresponding to scenarios such as new station commissioning and new line erection), the deleted set (existing equipment objects are marked as invalid, corresponding to scenarios such as equipment decommissioning and line dismantling), and the modified set (the equipment topology remains unchanged, but attribute values ​​such as switch status and impedance parameters change, corresponding to operation mode switching or network reconstruction).

[0031] Example 3: Traditional network topology reconstruction typically employs matrix elimination, node merging, or graph traversal methods. When local branch switch states change or new equipment is added, traditional methods often require rereading the entire network data model and reconstructing the entire network topology matrix. This full reconstruction is computationally complex and extremely inefficient for ultra-large-scale interconnected power grids, failing to meet the demands of online, second-level situational analysis. Therefore, this solution proposes a "local topology correction instead of full reconstruction" architecture, leveraging the dynamic characteristics of a high-performance graph database to achieve real-time evolution and updating of the power grid graph model.

[0032] The proposed solution directly maps the CIM / SG-CIM standard topology model of the power grid to an undirected attribute graph in a graph database. In this graph structure, the vertex set represents all physical conductive devices (such as transformers, buses, circuit breakers, and loads) and logical connection nodes (ConnectivityNodes); the edge set represents the physical or logical electrical connections between device terminals. Compared to the performance degradation caused by complex table join operations in relational databases, graph databases can seamlessly and intuitively represent the natural mesh topology of power systems. Utilizing a native graph storage engine, millisecond-level depth-first traversal access to the target node and its neighboring nodes is possible.

[0033] After acquiring the incremental dataset, the boundary node identification logic is executed first. For devices in the newly added and modified sets, their specific access points in the current network graph are identified through attribute tracing (e.g., a specific circuit breaker node on the 220kV busbar side of a substation to be connected by a newly commissioned line). These existing access points constitute the boundary node set for this topology correction operation. Subsequently, the local influence domain is defined. Specifically, with the boundary node set as the center, k-order network neighbors are expanded outward according to graph theory principles (in practical power grid engineering applications, k=1 or 2 is usually taken), thus forming a small-scale local influence domain subgraph. At the same time, the remaining device nodes and their connections in the entire power grid are retained in a static cache and do not participate in this reconstruction calculation.

[0034] After extracting the local subgraph, local connectivity merging and splitting operations are performed. This process involves transforming from a fine-grained node-circuit breaker model to a bus-branch model. Specifically, an optimized disjoint-setup algorithm is applied to shrink the local nodes. For example, if a newly added circuit breaker in the future model is in the "closed" state and this circuit breaker connects two originally independent bus segments, the system merges these two bus nodes along with the circuit breaker node in the local graph model to generate a new generalized electrical node. Conversely, if the switch state changes from closed to open, node splitting logic is executed.

[0035] After completing the local calculations, the updated local subgraph is mapped back to the global graph database to replace the old topological fragments, thereby quickly piecing together the latest future topological graph.

[0036] Example 4: Power grid dispatching regulations require that when new equipment is connected to the grid, the impact of operational actions on the power supply status must be accurately determined. To address this, this solution proposes an electrical connectivity tracing technology based on a fusion of depth-first search and breadth-first search, enabling the automated definition of "outage areas" and "energized zones."

[0037] The accuracy of the algorithm depends on the reasonable selection of the search starting point. The system can automatically anchor the search starting point in the updated future state graph model according to different business application scenarios: when performing energized area (or power supply range) tracking, the starting node is usually the equivalent power source node, the generator set outgoing line terminal, or the high-voltage side energized busbar of the upstream substation; when performing power outage range risk assessment, the starting node is the planned circuit breaker to be opened, the preset fault line endpoint, or the port of a newly connected device that has not yet undergone energization. After determining the starting point, the traversal stack is initialized, and two independent dynamic linked lists are established to store the "energized area equipment list" and the "power outage affected equipment list," respectively.

[0038] Then, a heuristic depth-first search algorithm is used for traversal: the currently visited graph node is set as... The system retrieves all nodes connected to the graph database using the graph database's native adjacency query interface. There exists a set of physically adjacent nodes connected by an edge. Before deciding whether to push adjacent nodes onto the access stack, perform "switch status check" and "voltage level over-limit interception". If the connection... and In the CIM model, the edge is defined as a "circuit breaker" or "disconnector," and the simulated state data of that switch in the future state model is extracted in real time. If the state flag is "open," it indicates that the physical electrical connectivity of the path has been severed, and the search algorithm will immediately trigger blocking logic on this topology branch, terminating the exploration and refusing to push the adjacent node onto the stack. If the state flag is "closed," and the voltage level attribute of the target node meets the preset convergence conditions (for example, setting this impact analysis to only track up to the 220kV voltage level, and triggering interception if the adjacent node is 110kV), the algorithm will push it onto the stack to continue the exploration.

[0039] The traversal process ends when the search stack is cleared. All device nodes successfully accessed and recorded by the system together form one or more closed topological connectivity components, i.e., "electric islands".

[0040] The system compares the changes in the topology connectivity graph before and after new equipment commissioning or switch status changes to identify the set of nodes where state transitions have occurred. For a topology branch that was originally a energized area, if it loses path connectivity with all defined power sources due to a future operation (such as opening a specific circuit breaker), all equipment, lines, and load nodes within that topology branch are marked as "outage areas," and a structured list of equipment affected by the outage is output. Conversely, a list of "energized areas" resulting from the commissioning of new equipment is output.

[0041] Example 5: After obtaining the incremental topology and regional status list, the high-dimensional abstract graph data structure needs to be transformed into a two-dimensional substation wiring diagram that conforms to the visual habits of power engineers. Traditional automation systems often regenerate the entire diagram when facing model updates, resulting in the complete loss of the original manual layout and abrupt changes in the positions of graphical elements, greatly increasing the visual cognitive burden and operational risks for dispatchers. To address this, this solution employs intelligent layout and state evolution rendering for incremental equipment.

[0042] To ensure that the generated wiring diagrams comply with State Grid and regional industry standards, the system pre-constructs a standard primitive symbol library (containing SVG vector graphics of various transformers, circuit breakers, and disconnectors) and a standard bay template database that conform to the General Model Description Specification for Power Grids (CIM / G) and the Graphical Description Specification for Power Systems (CIM / E). During the rendering phase, the "subgraph isomorphic matching" technique from graph theory is first applied to extract and compare the structural features of topology segments that have undergone local modifications. The system automatically identifies, through algorithms, whether the main electrical wiring of a newly commissioned substation belongs to the "single busbar segmentation," "double busbar with bypass," or the more complex "3 / 2 (one and a half) circuit breaker wiring" mode. Once the specific topology feature vector is identified, the system automatically calls the corresponding standard graphic template for primitive instantiation.

[0043] During the layout phase, for existing substations or line equipment that have not changed, their original two-dimensional spatial coordinate information is retained and locked to ensure the stability of the main framework of the panoramic wiring diagram and to guarantee the dispatcher's familiarity with the screen. Only newly commissioned equipment extracted in the incremental identification phase is sent to the layout engine for coordinate calculation.

[0044] The layout engine employs a force-oriented algorithm. In a virtual two-dimensional coordinate system, the algorithm treats each newly connected electrical device node as a point mass carrying the same polarity of charge, with electrostatic repulsion between them following Coulomb's law (ensuring mutual repulsion between device primitives and avoiding overlap and visual crowding). Simultaneously, the lines representing electrical connections between devices are treated as springs obeying Hooke's law, with attractive forces between them (ensuring interconnected devices are as close as possible in space). By iteratively calculating the equilibrium state where the total system energy tends to be minimized, the algorithm automatically derives the optimal geometric coordinate position for new devices. Based on this, the system superimposes a virtual magnetic grid force field, which attracts nodes to the nearest integer grid coordinate point, achieving automatic alignment and orthogonality of the graphics. For drawing connection lines, the system applies the A* path search algorithm in the gridded space and assigns a "high obstruction weight" parameter to locked primitives, ensuring that newly generated cable or overhead line trajectories bypass existing devices, achieving a wiring effect with no overlapping lines, minimal intersections, and the shortest overall path.

[0045] After the graphics are generated, logical rendering is performed. For all relevant equipment elements and connections within the identified "energized area," the system follows the standard color scheme for the relevant voltage level (e.g., 500kV network rendered in standard red, 220kV network rendered in purple, 110kV rendered in red, etc.) for layer overlay and real-time dynamic pipeline rendering. For areas identified as "outage zones" affected by operations, the system triggers a warning highlight rendering mechanism, using gray dashed outlines or specific eye-catching warning animations (e.g., a breathing, flashing yellow border with a semi-transparent mask) for visual isolation. This strong visual contrast allows dispatching and operation personnel to quickly identify the scope of future operations when faced with complex power grid startup wiring diagrams, reducing the probability of serious accidents such as mis-dispatching and misoperation.

[0046] Example 6: An automatic generation device for power grid startup wiring diagrams for future-oriented applications includes a model parsing module, an incremental identification module, a local topology reconstruction module, a region identification module, and a graphics generation module. The model parsing module is used to acquire and parse the current and future state public information model data of the power grid, and construct a global power grid graph database based on nodes and circuit breakers; The incremental recognition module is used to execute the hash digest branch comparison algorithm to compare and extract the incremental dataset between the current state and the future state. The local topology reconstruction module is used to receive the incremental dataset, perform local connectivity node merging and splitting operations based on the incremental dataset, complete the dynamic correction of the local topology model, and skip the full network reconstruction process; The region identification module is used to perform a heuristic depth-first search algorithm in the corrected topology model, using the connection points of incremental devices as search seeds, to calculate and output the power outage range and the boundary data of the energized area in real time. The graphics generation module is used to generate a graphical network for new devices based on the original spatial coordinate layout of existing devices without changes, using an adaptive layout and wiring algorithm. Based on the output area boundary data, it completes the warning highlighting and status rendering of the wiring diagram on the user's visualization terminal.

[0047] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0048] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for future state oriented power grid start-up diagram automatic generation, characterized in that, Includes the following steps: Step S1: Obtain the current state public information model data of the power grid, and simultaneously obtain the future state public information model data superimposed with the commissioning of new equipment or state change plans; Step S2: Compare the current state model with the future state model to extract the incremental dataset containing newly added devices, deleted devices, and devices with changed attributes; Step S3: Based on the incremental dataset, locate the operation boundary node in the global power grid graph database constructed from the current state model, and use graph merging and splitting rules to reconstruct the connectivity of the local subgraph affected by the increment, thereby generating an updated future state local topology model. Step S4: Using the newly commissioned equipment connection points or status change switches in the incremental dataset as the search starting point, perform connectivity traversal tracking in the updated future local topology model, identify and output the list of equipment in the affected power outage area and the list of equipment in the energized area. Step S5: Based on the original spatial coordinate layout of the existing equipment without changes, the coordinates and connection paths of the newly added equipment are calculated using an adaptive layout and wiring algorithm to generate a power grid startup wiring diagram. Based on the list of equipment in the power outage area and the list of equipment in the energized area, the generated wiring diagram is colored with differentiated status and highlighted with early warning.

2. The method for automatic generation of future state grid switching diagram according to claim 1, characterized in that, The comparison between the current state model and the future state model extracts an incremental dataset containing newly added devices, deleted devices, and devices with changed attributes, specifically including: Discretize the device type, identifier, and connection terminal information in the public information model into data tags; In a hierarchical power grid topology, a hash message digest is generated for each level of object, where changes in the data of the lower-level object trigger a cascading update of the hash digest of the upper-level object. Starting from the highest level, the hash digest values ​​of the current state and future state models are compared. If the digest values ​​are the same, branch pruning is performed and the process is skipped. If they are different, the comparison is recursively performed layer by layer until the lower-level device nodes that have changed are extracted, forming the incremental dataset.

3. The method for automatically generating future-oriented power grid startup wiring diagrams according to claim 1, characterized in that, The process of generating the future local topological model in step S3 specifically includes: Identify the physical access point of the incremental device in the current state network graph and define the access point as a boundary node; Centered on the boundary node, a preset k-order network neighbor is expanded outward to extract the local influence domain subgraph; In the local influence domain subgraph, based on the node-circuit breaker model, the disjoint-setup algorithm is applied to perform a shrinking and merging operation on bus nodes connected by closed switches, or a splitting operation on nodes separated by open switches, to generate a modified bus-branch model subgraph. The modified bus-branch model subgraph is mapped and overlaid onto the original global power grid graph database.

4. The method for automatically generating future-oriented power grid startup wiring diagrams according to claim 1, characterized in that, The specific process for identifying and outputting the list of equipment in the affected power outage area and the list of equipment in the energized area in step S4 is as follows: Push the starting point of the search onto the traversal stack and initialize the set of visited nodes and the list of target regions; When the stack is not empty, pop the current node and get all its physical adjacent nodes; Check the simulated state of the circuit breaker or disconnector corresponding to the edge connecting the current node and the adjacent node in the future state model; If the simulated state is disconnected, the blocking logic is triggered, and the adjacent node is refused to be pushed onto the stack. If the simulation state is closed and meets the preset voltage level convergence condition, the adjacent node is pushed onto the stack and its device information is recorded in the corresponding target area list, thus ultimately delineating the boundaries of the isolated electrical island and the power outage area.

5. The method for automatically generating future-oriented power grid startup wiring diagrams according to claim 1, characterized in that, The specific process for generating the power grid startup wiring diagram in step S5 is as follows: The electrical main wiring pattern of the local topology is identified based on the subgraph isomorphic matching technology, and the corresponding standard primitives are instantiated. Lock the two-dimensional spatial coordinates of existing equipment in the wiring diagram; A force-oriented algorithm is used to establish an attraction and repulsion model between new primitives and existing primitives, and the minimum system energy is calculated iteratively to determine the optimal coordinates of the new primitives. The grid A* path search algorithm is applied, and existing primitives are assigned high obstruction weight parameters to perform intelligent wiring of the shortest path that automatically avoids obstacles.

6. The method for automatically generating future-oriented power grid startup wiring diagrams according to claim 5, characterized in that, The force-guided algorithm adds a virtual magnetic grid force field to the traditional gravity-repulsion model. This force field attracts nodes to the nearest integer grid coordinate point to achieve automatic alignment and orthogonalization of the graphics.

7. A future-oriented automatic power grid startup wiring diagram generation device, characterized in that, It includes a model parsing module, an incremental recognition module, a local topology reconstruction module, a region recognition module, and a graphics generation module; The model parsing module is used to acquire and parse the current and future state public information model data of the power grid, and construct a global power grid graph database based on nodes and circuit breakers; The incremental recognition module is used to execute the hash digest branch comparison algorithm to compare and extract the incremental dataset between the current state and the future state. The local topology reconstruction module is used to receive the incremental dataset, perform local connectivity node merging and splitting operations based on the incremental dataset, complete the dynamic correction of the local topology model, and skip the full network reconstruction process; The region identification module is used to perform a heuristic depth-first search algorithm in the corrected topology model, using the connection points of incremental devices as search seeds, to calculate and output the power outage range and the boundary data of the energized area in real time. The graphics generation module is used to generate a graphical network for new devices based on the original spatial coordinate layout of existing devices without changes, using an adaptive layout and wiring algorithm. Based on the output area boundary data, it completes the warning highlighting and status rendering of the wiring diagram on the user's visualization terminal.

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