Power distribution network dispatching order ticket checking method and device, medium and product

Abstract

The application discloses a power distribution network order ticket checking method and device, medium and product. The method comprises the following steps: obtaining an order ticket used for controlling a power distribution network operation process; performing semantic analysis on the order ticket to obtain an instruction object used for the power distribution network operation, wherein the instruction object comprises a tower section and an operation object; obtaining a section influence subgraph based on the power distribution network switch state information and the tower section in a preset dynamic topology graph; determining a plurality of associated devices affected by the operation object based on the section influence subgraph; calculating target live line probabilities of the associated devices respectively based on observation data of the associated devices; inputting the instruction object, the section influence subgraph and the target live line probabilities into a preset risk assessment model to obtain a misoperation risk value of the order ticket; and checking a misoperation behavior of the order ticket based on the misoperation risk value. The application can improve the checking accuracy of the misoperation of the power distribution network order ticket in the power distribution network operation scene.

Description

Technical Field

[0001] This invention relates to the field of power systems, and more particularly to methods, devices, media, and products for verifying distribution network dispatch orders. Background Technology

[0002] In power distribution networks, dispatch orders serve as the core safety instruction carrier for dispatching operations, bearing the responsibility for safety control throughout the entire process from instruction generation to on-site execution. Especially in high-risk scenarios involving power outages, switching, and maintenance, the accuracy of the dispatch order content directly determines the personal safety of personnel, equipment protection, and the stability of power grid operation. Multi-dimensional verification of the semantic integrity, equipment correspondence, and status consistency of dispatch orders is a fundamental prerequisite for ensuring the safe and controllable operation of power outage work from instruction issuance to on-site execution.

[0003] Existing dispatch order verification mainly adopts a single-layer static verification method based on structured field matching and format rule verification. However, it has significant limitations: its verification logic relies entirely on the comparison between the text on the order and the static ledger. It cannot understand the equipment operation intentions and relationships implied in natural language instructions, nor can it combine the real-time topology of the power grid with the dynamic energized status of the equipment for linkage safety analysis. As a result, in scenarios requiring power outage operations, the verification results can only guarantee the compliance of the order, but cannot identify deep safety risks such as energized misoperation or accidental entry into energized areas that may be caused by semantic ambiguity, inconsistent equipment status, or misunderstanding of topology connections. This seriously restricts the verification accuracy of erroneous operation behavior on dispatch orders. Summary of the Invention

[0004] This invention provides a method, device, medium, and product for verifying distribution network dispatch tickets, which can improve the verification accuracy of distribution network dispatch ticket misoperation in distribution network operation scenarios.

[0005] In a first aspect, an embodiment of the present invention provides a method for verifying distribution network dispatch orders, comprising: Obtain dispatch orders for controlling the operation process of the power distribution network; The dispatch order is semantically parsed to obtain an instruction object for distribution network operations, wherein the instruction object includes tower sections and operation objects; Based on the distribution network switch status information and the tower section in the preset dynamic topology diagram, a section influence sub-diagram is obtained. Based on the section influence sub-diagram, several related devices affected by the operation object are determined. Based on the observation data of each of the related devices, the target energization probability of each of the related devices is calculated. The command object, the section influence sub-diagram and several target energization probabilities are input into a preset risk assessment model to obtain the misoperation risk value of the dispatch order. The erroneous operation behavior of the transfer order is verified based on the aforementioned erroneous operation risk value.

[0006] By acquiring the dispatch tickets used to control the distribution network operation process, the verification object is clearly identified, ensuring that all subsequent verification steps revolve around the control instructions of the actual distribution network operation, thus guaranteeing high accuracy in the verification work. Semantic parsing of the dispatch tickets yields instruction objects containing tower sections and operation objects, achieving precise conversion from natural language to structured operation intent. This fundamentally overcomes the shortcomings of existing technologies that rely solely on field matching and cannot understand instruction semantics, a primary prerequisite for improving verification accuracy. Based on the distribution network switch status information and tower sections in the preset dynamic topology diagram, a section influence sub-graph is obtained. This maps the tower sections in the dispatch ticket to the physical topology reflecting the real-time connection relationships of the power grid, ensuring that the verification analysis is based on the real and dynamically changing power grid structure. This accurately pinpoints the actual impact range of the operation instructions in the physical power grid, contributing to improved dispatch ticket verification accuracy. Identifying several associated devices based on the section influence sub-graph allows the verification scope to extend beyond the devices directly mentioned on the ticket to encompass all devices in the real-time topology related to the operation area. The device associated with the electrical section improves the comprehensiveness and accuracy of the verification by expanding its coverage. Based on the acquired observation data of each associated device, its energization probability is calculated, realizing the quantitative perception and fusion analysis of the dynamic operating status of the associated devices. This allows the verification to more accurately reflect the true risk level of the equipment at the time of operation, improving the verification accuracy. By inputting the structured command object, the section influence sub-diagram, and the energization probability of each device into a preset risk assessment model, a misoperation risk value is obtained. This achieves a comprehensive quantitative assessment of the dispatch order's operational intent, grid topology association, and real-time equipment status, outputting a specific misoperation risk value. This upgrades risk judgment from qualitative to quantitative, significantly improving the accuracy of misoperation risk differentiation and effectively enhancing the verification accuracy. The misoperation risk value is used to verify the misoperation behavior of dispatch orders. By guiding the verification of misoperation of dispatch orders through the misoperation risk value, it greatly facilitates dispatchers in quickly locating the root cause of the problem and taking targeted measures, ultimately significantly improving the overall verification accuracy of misoperation of dispatch orders in power outage operation scenarios in the distribution network. This application can improve the verification accuracy of misoperation of distribution network dispatch orders in distribution network operation scenarios.

[0007] Furthermore, the step of calculating the target electrification probability of each associated device based on the acquired observation data of each associated device specifically includes: acquiring the observation data of each associated device, wherein the observation data is obtained by several independent monitoring devices monitoring the status of the associated devices; for each associated device, performing weighted fusion calculation on the observation data using preset confidence weights corresponding to each monitoring device to obtain the initial electrification probability corresponding to the associated device; performing power connectivity analysis on the associated device using the segment influence subgraph to obtain connectivity analysis results; and correcting the initial electrification probability using the connectivity analysis results to obtain the target electrification probability corresponding to the associated device.

[0008] Based on the observation data obtained from each associated device, the probability of being charged is calculated, thereby realizing the quantitative perception and fusion analysis of the dynamic operating status of the associated devices. This enables the verification to more accurately reflect the true risk level of the equipment at the moment of operation and improves the verification accuracy.

[0009] Furthermore, the step of obtaining a segment influence subgraph based on the distribution network switch status information and the tower segment in the preset dynamic topology graph specifically includes: performing electrical topology matching on the tower segment in the dynamic topology graph to determine the set of topology nodes corresponding to the tower segment; performing network reachability analysis on the set of topology nodes using the distribution network switch status information to obtain reachable nodes and reachable connected edges; and obtaining the segment influence subgraph based on the reachable nodes and reachable connected edges.

[0010] Based on the distribution network switch status information and tower sections in the preset dynamic topology diagram, a section influence sub-diagram is obtained. This can map the tower sections in the dispatch order to the physical topology that reflects the real-time connection relationship of the power grid. This ensures that the verification analysis is based on the real and dynamically changing power grid structure, accurately locks the actual impact range of the operation command in the physical power grid, and helps to improve the verification accuracy of the dispatch order.

[0011] Furthermore, determining the associated devices affected by the operation object based on the segment influence subgraph specifically includes: determining a first set of devices directly connected to the operation object based on the segment influence subgraph; performing a hierarchical expansion search on the first set of devices using a preset expansion rule to obtain a second set of devices; and deduplicating the first set of devices and the second set of devices to obtain the associated devices affected by the operation object.

[0012] By identifying several associated devices based on the segment influence sub-graph, the verification scope is not limited to the devices directly mentioned on the ticket, but covers all devices electrically associated with the operating segment in the real-time topology, thus improving the comprehensiveness and accuracy of the verification in terms of coverage.

[0013] Furthermore, the semantic parsing of the dispatch order to obtain the instruction object for distribution network operations specifically includes: performing domain normalization on the dispatch order to obtain standardized text; semantically encoding the standardized text using a preset language model to obtain a text semantic vector; inputting the text semantic vector into a preset sequence labeling model for named entity recognition to obtain several candidate entities, wherein the candidate entities include equipment entities, operation verbs, and pole / tower code entities; performing syntactic dependency analysis on the standardized text to obtain syntactic analysis results; determining the operation object based on the syntactic analysis results, the equipment entities, and the operation verbs; determining the pole / tower segment corresponding to the pole / tower code entity based on the pole / tower code entity and preset segment boundary trigger words; and integrating the pole / tower segment and the operation object to obtain the instruction object.

[0014] By semantically parsing the dispatch order, an instruction object containing the tower section and the operation object is obtained, realizing the accurate conversion from natural language to structured operation intent. This fundamentally overcomes the shortcomings of existing technologies that rely solely on field matching and cannot understand the semantics of instructions, and is the primary prerequisite for improving verification accuracy.

[0015] Furthermore, the step of inputting the instruction object, the segment influence subgraph, and several target energization probabilities into a preset risk assessment model to obtain the misoperation risk value of the dispatch order specifically includes: constructing a risk feature vector based on the instruction object, the segment influence subgraph, and several target energization probabilities; inputting the risk feature vector into the risk assessment model to perform weighted calculation on the risk feature vector to obtain an initial risk score value; and processing the initial risk score value using an activation function to obtain the misoperation risk value of the dispatch order, wherein the risk assessment model includes the activation function.

[0016] This approach inputs structured command objects, segment influence sub-graphs, and the energization probability of each device into a preset risk assessment model to obtain a misoperation risk value. This achieves a comprehensive quantitative assessment of the command operation intent, grid topology correlation, and real-time equipment status, and outputs a specific misoperation risk value. This upgrades risk judgment from qualitative to quantitative, significantly improving the accuracy of misoperation risk differentiation and effectively enhancing the accuracy of verification.

[0017] Furthermore, the step of verifying the misoperation behavior of the dispatch order based on the misoperation risk value specifically includes: determining whether the misoperation risk value is greater than a preset risk threshold; if it is, determining that the dispatch order has a misoperation risk, and generating a risk location result based on the segment influence sub-map and the electrification probability of each target to verify the misoperation behavior of the dispatch order.

[0018] This method of verifying erroneous operations on dispatch orders based on erroneous operation risk values, and guiding the verification of erroneous operations through erroneous operation risk values, greatly facilitates dispatchers in quickly locating the root cause of problems and taking targeted measures, ultimately significantly improving the overall verification accuracy of erroneous operations on dispatch orders in scenarios where power outages are required in the distribution network.

[0019] Secondly, an embodiment of the present invention provides a verification device for a distribution network dispatch order, comprising a first module, a second module, a third module and a fourth module; The first module is used to obtain a control order for controlling the operation process of the power distribution network; The second module is used to perform semantic parsing on the dispatch order to obtain an instruction object for distribution network operation, wherein the instruction object includes tower sections and operation objects; The third module is used to obtain a section influence sub-map based on the distribution network switch status information and the tower section in the preset dynamic topology diagram, determine a number of related devices affected by the operation object based on the section influence sub-map, calculate the target energization probability of each related device based on the obtained observation data of each related device, and input the instruction object, the section influence sub-map and the number of target energization probabilities into a preset risk assessment model to obtain the misoperation risk value of the dispatch order. The fourth module is used to verify the erroneous operation behavior of the transfer order based on the erroneous operation risk value.

[0020] The first module obtains the dispatch order for controlling the distribution network operation process, clearly defining the verification object and ensuring that all subsequent verification steps revolve around the control instructions of the actual distribution network operation, guaranteeing high accuracy in the verification work. The second module performs semantic parsing on the dispatch order to obtain the instruction object containing tower sections and operation objects, achieving accurate conversion from natural language to structured operation intent. This fundamentally overcomes the shortcomings of existing technologies that rely solely on field matching and cannot understand the semantics of instructions, which is the primary prerequisite for improving verification accuracy. The third module, based on the distribution network switch status information and tower sections in the preset dynamic topology diagram, obtains the section influence sub-diagram. This maps the tower sections in the dispatch order to the physical topology reflecting the real-time connection relationship of the power grid, ensuring that the verification analysis is based on the real and dynamically changing power grid structure, accurately locking the actual impact range of the operation instructions in the physical power grid, and helping to improve the verification accuracy of the dispatch order. Based on the section influence sub-diagram, several related devices are identified, allowing the verification scope to extend beyond the devices directly mentioned on the order to include all devices in the real-time topology related to the power grid. The electrical connections of the equipment in this operation section improve the comprehensiveness and accuracy of the verification in terms of coverage. Based on the observation data of each connected device, the energization probability is calculated, realizing the quantitative perception and fusion analysis of the dynamic operating status of the connected devices. This allows the verification to more accurately reflect the true risk level of the equipment at the time of operation, improving the verification accuracy. The structured command object, the section influence sub-diagram, and the energization probability of each device are input into the preset risk assessment model to obtain the misoperation risk value. This realizes the comprehensive quantitative assessment of the dispatch operation intention, the power grid topology connection, and the real-time status of the equipment. The output of specific misoperation risk values ​​upgrades the risk judgment from qualitative to quantitative, greatly improving the accuracy of misoperation risk differentiation and effectively improving the verification accuracy. The fourth module verifies the misoperation behavior of the dispatch order based on the misoperation risk value. By guiding the verification of misoperation of the dispatch order through the misoperation risk value, it greatly facilitates dispatchers to quickly locate the root cause of the problem and take targeted measures, ultimately significantly improving the overall verification accuracy of misoperation of dispatch orders in the scenario of power outage operation in the distribution network.

[0021] Thirdly, another embodiment of the present invention provides a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device or apparatus where the computer-readable storage medium is located to perform a method for verifying a power distribution network dispatch order.

[0022] Fourthly, another embodiment of the present invention provides a computer program product, including a computer program or instructions, which, when executed by a communication device, implements a method for verifying power distribution network dispatch orders. Attached Figure Description

[0023] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating one embodiment of the verification method for a power distribution network dispatch ticket provided in this application; Figure 2 This application provides a schematic diagram of the structure of a verification device for a power distribution network dispatch order. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0027] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0028] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0029] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0030] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0031] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in the embodiments of this application according to the specific circumstances.

[0032] In the power system field, dispatch orders serve as the core carrier of safety instructions, and the verification of erroneous operation risks in their content is crucial to the safety of distribution network operations. Existing technologies employ a single-layer verification method based on structured field matching and static rules for dispatch order verification. However, this method has fundamental limitations: its verification logic relies solely on comparing the order text with static ledgers. It cannot understand the true operational intent and relationships of the instructions, nor can it combine the dynamic topology of the power grid with the real-time status of equipment for linked safety analysis. Consequently, the verification results can only guarantee compliance with the order text, and cannot effectively identify deep-seated erroneous operation risks caused by semantic ambiguity, status discrepancies, or topological misunderstandings. This severely restricts the verification accuracy of dispatch order erroneous operations.

[0033] See Figure 1 In order to improve the verification accuracy of distribution network dispatch tickets in distribution network operation scenarios, an embodiment of the present invention provides a verification method for distribution network dispatch tickets, including steps S101 to S104. Step S101: Obtain a dispatch order for controlling the distribution network operation process; In some embodiments, obtaining a dispatch order for controlling the distribution network operation process specifically includes: receiving a pending dispatch order for controlling the distribution network operation process from the distribution network dispatch management system.

[0034] For example, upon receipt, the transfer order ticket can be formatted, redundant spaces and special symbols can be removed, and a unique identifier can be assigned to each transfer order ticket to facilitate tracking and management throughout the entire process.

[0035] It should be noted that the dispatch order records the operational tasks, involved lines, target towers, equipment types, operation types, and planned execution time in the form of natural language text during the distribution network operation.

[0036] Step S102: Semantic parsing is performed on the dispatch order to obtain an instruction object for distribution network operation, wherein the instruction object includes tower sections and operation objects; In some embodiments, the semantic parsing of the dispatch order to obtain an instruction object for distribution network operations specifically includes: performing domain normalization on the dispatch order to obtain standardized text; semantically encoding the standardized text using a preset language model to obtain a text semantic vector; inputting the text semantic vector into a preset sequence labeling model for named entity recognition to obtain several candidate entities, wherein the candidate entities include equipment entities, operation verbs, and pole / tower code entities; performing syntactic dependency analysis on the standardized text to obtain syntactic analysis results; determining the operation object based on the syntactic analysis results, the equipment entities, and the operation verbs; determining the pole / tower segment corresponding to the pole / tower code entity based on the pole / tower code entity and preset segment boundary trigger words; and integrating the pole / tower segment and the operation object to obtain the instruction object.

[0037] Specifically, the dispatch order tickets undergo domain normalization. This process introduces a power domain dictionary and a synonym merging table to merge different expressions in the dispatch order tickets (e.g., pole / tower / number tower, closed / closed / open, etc.) into unified semantic tags. Simultaneously, full-width and half-width characters are uniformly converted, and equipment aliases are mapped and replaced to obtain standardized text. A pre-trained BERT (Bidirectional Encoder Representations from Transformers) is used as the language model to perform sentence-by-sentence semantic encoding on the standardized text, transforming the text into a high-dimensional vector containing contextual semantic information, resulting in a text semantic vector. Finally, BiLSTM+CRF (Bidirectional Long Short-Term Memory + Conditional Random Field) is employed. The Conditional Random Field (CRF) model, used as a sequence labeling model, inputs text semantic vectors into the model. Through its feature extraction and sequence labeling capabilities, it accurately identifies equipment entities (such as circuit breakers, disconnectors, and grounding wires), operational verbs (such as disconnect, close, maintain, and climb towers), tower code entities (such as #15 tower and #18 tower), line entities (such as line 1), constraint entities (such as power outages), and time entities (i.e., work time). Syntactic dependency analysis is performed on the standardized text. By parsing the grammatical relationships such as dominance and modification between words in the standardized text, the association between operational verbs and equipment entities is clarified, yielding syntactic analysis results (e.g., disconnect - circuit breaker, connect - #16 tower). Based on these syntactic analysis results, the equipment entities affected by the operational verbs are determined, and further... The operation object is obtained by integration; preset segment boundary trigger words (e.g., to, to, —, between, and “from…to…”, etc.) are used to identify the range relationship of the tower coded entity based on these trigger words. This includes extracting the start and end numbers of continuous range expressions (e.g., #15 to #18) and converting them into a closed interval set (e.g., [15,18]), generating discrete sets (e.g., {15,16,18}) for discrete expressions (e.g., #15, #16, #18), and forming a mixed set of segment set plus attention point set for mixed expressions (e.g., #15 to #18, with emphasis on #16), thereby determining the tower segment; finally, the determined tower segment, operation object, line entity, operation type, operation time, semantic constraints, and other information are integrated to form a structured instruction object.

[0038] For example, the instruction object can be ,in, For route identification, This refers to the tower section, including the start and end points, the assembly point, and optional points of interest. The objects to be operated on, such as circuit breakers / disconnectors / grounding wires, and their connections. The operation type is categorized as follows: disconnect, close, maintenance, and tower climbing. This is a set of semantic constraints, such as the need for power outages, the need for isolation points, and the prohibition of reverse power transmission paths. To analyze confidence scores or candidate ranking scores; for example, for the natural language text of the dispatch order "Perform a switching operation on the equipment between towers #15 and #18 of line XX, and disconnect the circuit breaker connected to tower #16", the instruction object is D=⟨line. =XX line, tower section =[15,18], Focus={16}, Operation object =Circuit breaker (connected to tower #16), Operation type =Disconnect, time =t, semantic constraint set ={Power outage required, ...}, Analyze confidence level =conf_value>.

[0039] For example, during the semantic parsing process, entity linking and disambiguation operations are also performed. The parsed natural language entities such as "Line 1" and "#16 Tower" are linked to unique identifiers in CIM (Common Information Model) / GIS (Geographic Information System). If there are lines with the same name or towers with the same number, disambiguation is performed using additional context (substation, line section, geographic coordinates and historical dispatch orders, etc.), and Top-k candidate mappings and confidence scores are output to avoid the hidden risk of mapping the wrong object in subsequent topology mappings but the process still running. At the same time, semantic constraint verification is performed to check whether the tower number exists on the line, whether the section is continuous, whether the action type and object type match (for example, disconnection usually corresponds to switch equipment), and whether the time expression is valid. If the verification fails, candidate reordering is triggered or a rollback flag that requires manual review is generated, and the reason for failure is retained as part of the interpretable output.

[0040] By semantically parsing the dispatch order, an instruction object containing the tower section and the operation object is obtained, realizing the accurate conversion from natural language to structured operation intent. This fundamentally overcomes the shortcomings of existing technologies that rely solely on field matching and cannot understand the semantics of instructions, and is the primary prerequisite for improving verification accuracy.

[0041] Step S103: Based on the distribution network switch status information and the tower section in the preset dynamic topology diagram, a section influence sub-diagram is obtained. Based on the section influence sub-diagram, several related devices affected by the operation object are determined. Based on the observation data of each of the related devices, the target energization probability of each of the related devices is calculated. The command object, the section influence sub-diagram and several target energization probabilities are input into a preset risk assessment model to obtain the misoperation risk value of the dispatch order. In some embodiments, obtaining a segment influence subgraph based on the distribution network switch status information and the tower segment in a preset dynamic topology graph specifically includes: performing electrical topology matching on the tower segment in the dynamic topology graph to determine the set of topology nodes corresponding to the tower segment; performing network reachability analysis on the set of topology nodes using the distribution network switch status information to obtain reachable nodes and reachable connected edges; and obtaining the segment influence subgraph based on the reachable nodes and reachable connected edges.

[0042] Specifically, a dynamic topology map is constructed based on the CIM / GIS model. This dynamic topology map includes a set of nodes such as substation busbars, towers, switches, circuit breakers, and branch contacts, as well as a set of edges that change over time. The availability of edges is determined by operational constraints such as switch status, isolation status, and maintenance tags. Each edge carries connection type, directionality (e.g., feeder direction), status (e.g., closed, open, and unknown), weight (impedance, distance, or priority), and reliability. When performing electrical topology matching between the tower sections in the instruction object and the dynamic topology map, the results of the aforementioned entity linking and disambiguation are used. The starting and ending tower nodes corresponding to the section, the tower nodes within the section, and the tower nodes of interest are determined to form a topology node set. During network reachability analysis, the topology node set is used as the seed node, and constrained reachability expansion is performed in the dynamic topology graph (e.g., constraints include: stopping expansion upon encountering an isolation point / disconnecting a switch and expanding only along closed edges, etc.). The connectivity of edges is determined by combining the distribution network switch status information, and all reachable nodes that have electrical connections with the seed node are obtained, as well as the reachable connected edges connecting these nodes. Finally, a section influence subgraph is generated based on the reachable nodes and reachable connected edges.

[0043] For example, while constructing the segment impact sub-map, the set of reachable paths from the potential power source side (such as the upstream power node or bus node) to the segment node can be recorded to form evidence path candidates, and the set of boundary devices (such as isolation points, switch nodes and circuit breaker nodes) of the impact sub-map can be extracted to serve as the basis for generating suggested isolation points or suggesting additional disconnection steps when determining the risk location results later.

[0044] For example, a dynamic topology graph can be represented as ,in, For a set of nodes, For every moment The changing set of edges; the segment influence subgraph can be represented as ,in, In the constraint set The set of reachable nodes under (such as operating mode, isolation point, maintenance status, and feeder direction, etc.) For a set of topological nodes, This is a dynamic topology graph.

[0045] Based on the distribution network switch status information and tower sections in the preset dynamic topology diagram, a section influence sub-diagram is obtained. This can map the tower sections in the dispatch order to the physical topology that reflects the real-time connection relationship of the power grid. This ensures that the verification analysis is based on the real and dynamically changing power grid structure, accurately locks the actual impact range of the operation command in the physical power grid, and helps to improve the verification accuracy of the dispatch order.

[0046] In some embodiments, determining a plurality of associated devices affected by the operation object based on the segment influence subgraph specifically includes: determining a first set of devices directly connected to the operation object based on the segment influence subgraph; performing a hierarchical expansion search on the first set of devices in combination with a preset expansion rule to obtain a second set of devices; and deduplicating the first set of devices and the second set of devices to obtain a plurality of associated devices affected by the operation object.

[0047] Specifically, a first set of devices (e.g., disconnectors or cable connectors directly connected to the target circuit breaker) is determined for devices directly connected to the operation object within the segment influence subgraph. Pre-defined expansion rules are established, including device type expansion rules (e.g., main device → its protection / measuring device → grounding device → possible bypass / branch devices), electrical connectivity expansion rules (e.g., extending along the adjacent edges of the segment influence subgraph to critical devices at adjacent points), and boundary constraint rules (e.g., marking boundary devices encountered in the segment influence subgraph as boundary devices and stopping cross-extension). The first set of devices is then progressively expanded and searched hierarchically using these pre-defined expansion rules. During the expansion process, device information with conflicting states is merged, and consistency checks are performed to ensure the accuracy of the device information, resulting in a second set of devices. Finally, the first and second sets of devices are deduplicated and integrated to obtain several associated devices affected by the operation object.

[0048] For example, after obtaining the associated devices, it is also possible to record the topology source node to which each associated device is included, which topology edge it is extended from, and whether it is located on the evidence path from the power supply side to the operating section, thereby forming a structured, traceable, and interpretable chain of evidence.

[0049] For example, a progressively hierarchical expansion search can be represented as: ; in, This refers to the preset extension rules, including device type extension rules, electrical connectivity extension rules, and boundary constraint rules. Represents the subgraph of the influence of a section. Indicates the first Layer-related devices.

[0050] By identifying several associated devices based on the segment influence sub-graph, the verification scope is not limited to the devices directly mentioned on the ticket, but covers all devices electrically associated with the operating segment in the real-time topology, thus improving the comprehensiveness and accuracy of the verification in terms of coverage.

[0051] In some embodiments, the step of calculating the target electrification probability of each associated device based on the acquired observation data of each associated device specifically includes: acquiring the observation data of each associated device, wherein the observation data is obtained by several independent monitoring devices monitoring the status of the associated devices; for each associated device, performing weighted fusion calculation on the observation data using preset confidence weights corresponding to each monitoring device to obtain the initial electrification probability corresponding to the associated device; performing power connectivity analysis on the associated device using the segment influence subgraph to obtain connectivity analysis results; and correcting the initial electrification probability using the connectivity analysis results to obtain the target electrification probability corresponding to the associated device.

[0052] Specifically, multi-source observation data corresponding to each associated device is acquired through multiple independent monitoring devices (such as tower online monitoring devices, switch telemetry devices, bus voltage monitors, and line current sensors). After acquiring the observation data, time windows are used to perform time-series alignment of the multi-source observation data for the same associated device, synchronously handling anomalies such as data delays, missing data, and sudden changes to ensure the temporal consistency and reliability of the observation data. For each associated device, according to the preset confidence weights corresponding to each monitoring device, the time-series aligned observation data is weighted and fused to obtain the initial energization probability corresponding to that associated device. Subsequently, the section... The influence subgraph performs power connectivity analysis on associated devices to determine whether there is a closed electrical path between the device and the power supply side. If the device on the path is in a closed state and the voltage and current observation data along the path supports energization, it is determined that the initial energization probability of the associated device needs to be increased (e.g., the increase rate is set to 20%). If the isolation point has been disconnected and the observation data supports power failure, it is determined that the initial energization probability of the associated device needs to be decreased (e.g., the decrease rate is 20%), forming a connectivity analysis result. Finally, the initial energization probability is corrected using the connectivity analysis result (e.g., increased or decreased by 20%) to obtain the target energization probability corresponding to the associated device.

[0053] For example, the observation data can be represented as: ; in, Indicates the data source (online monitoring of towers, telemetry of switches, bus voltage or line current, etc.). For the observed values, The source credibility can be used to determine the credibility weight of each monitoring device.

[0054] For example, if there is a large conflict between multi-source observation data, conflict markers can be output and conflict source information can be retained as part of subsequent risk interpretation. At the same time, the most likely path of evidence of charge can be further inferred by combining the target charge probability of all associated devices.

[0055] It should be noted that weighted fusion can also employ DS evidence theory fusion or Bayesian update methods.

[0056] Based on the observation data obtained from each associated device, the probability of being charged is calculated, thereby realizing the quantitative perception and fusion analysis of the dynamic operating status of the associated devices. This enables the verification to more accurately reflect the true risk level of the equipment at the moment of operation and improves the verification accuracy.

[0057] In some embodiments, the step of inputting the instruction object, the segment influence subgraph, and several target energization probabilities into a preset risk assessment model to obtain the misoperation risk value of the dispatch order specifically includes: constructing a risk feature vector based on the instruction object, the segment influence subgraph, and several target energization probabilities; inputting the risk feature vector into the risk assessment model to perform weighted calculation on the risk feature vector to obtain an initial risk score value, and processing the initial risk score value using an activation function to obtain the misoperation risk value of the dispatch order, wherein the risk assessment model includes the activation function. Specifically, a risk feature vector is constructed based on the instruction object, the segment influence subgraph, and the target energization probabilities of each associated device; the constructed risk feature vector is input into the risk assessment model so that the risk assessment model performs a weighted summation operation on each feature value in the risk feature vector with the corresponding weight parameter to obtain an initial risk score value; subsequently, the initial risk score value is nonlinearly transformed using a Sigmoid activation function to map it to the [0,1] interval to obtain the misoperation risk value of the dispatch order.

[0058] In some embodiments, the formula for inputting the instruction object, the segment influence sub-graph, and several target energization probabilities into a preset risk assessment model to obtain the misoperation risk value of the dispatch order specifically includes: ; In the formula, For the Sigmoid function; This is the weight parameter matrix; This is the risk feature vector.

[0059] It should be noted that the risk assessment model is built on a rule-model fusion architecture and pre-configured with the model's feature weight parameters and Sigmoid activation function.

[0060] This approach inputs structured command objects, segment influence sub-graphs, and the energization probability of each device into a preset risk assessment model to obtain a misoperation risk value. This achieves a comprehensive quantitative assessment of the command operation intent, grid topology correlation, and real-time equipment status, and outputs a specific misoperation risk value. This upgrades risk judgment from qualitative to quantitative, significantly improving the accuracy of misoperation risk differentiation and effectively enhancing the accuracy of verification.

[0061] Step S104: Verify the erroneous operation behavior of the transfer order based on the erroneous operation risk value; In some embodiments, the step of verifying the erroneous operation behavior of the dispatch order based on the erroneous operation risk value specifically includes: determining whether the erroneous operation risk value is greater than a preset risk threshold; if it is, determining that the dispatch order has a erroneous operation risk, and generating a risk location result based on the segment influence sub-map and the electrification probability of each target, so as to verify the erroneous operation behavior of the dispatch order. Specifically, a risk threshold is first preset (which can be set comprehensively based on the safety level requirements of distribution network operations, the statistical results of historical misoperation data, and the actual needs of on-site operation and maintenance, and is usually between 0.5 and 0.8). The calculated misoperation risk value is compared with the preset risk threshold. If the misoperation risk value is greater than or equal to the preset risk threshold, it is directly determined that the dispatch order has a misoperation risk. At this time, the electrical connection relationship between the equipment is analyzed by combining the section influence sub-diagram. The risk source is accurately located based on the target energization probability of each associated equipment, and the specific risk tower, risk equipment, and risk electrical path are identified. The risk location result is generated (for example, "The energization probability of the circuit breaker associated with tower #16 is 0.93, and there is a reachable path from the bus node to the section. The isolation point is not locked / not disconnected, and the risk level is high") to guide relevant personnel to verify the misoperation behavior of the dispatch order.

[0062] For example, while generating the risk location results, a minimum evidence sub-map can be generated. This sub-map is the smallest sub-map containing the risk source, key connecting paths and boundary equipment extracted from the segment impact sub-map. It can serve as an interpretable basis for risk determination, facilitating manual review and subsequent accountability.

[0063] For example, multiple candidate solutions can be generated based on risk type and boundary device set, such as adding isolation point disconnection steps, supplementing voltage testing / grounding operations, adjusting operation time to power outage period, changing operation sequence or alternative path, etc. Each suggestion includes the operation object of the action sequence and the expected elimination path; further, a secondary topology and state verification can be performed on each candidate suggestion, and the corresponding switch state and isolation state can be assumed and applied in the dynamic topology diagram to obtain a new dynamic topology diagram and section influence sub-diagram. The real-time energized state matching and risk judgment process of step S103 is repeated to quickly evaluate the risk value after applying the suggestion, and only suggestions that can reduce the risk value to below the safety threshold are output, and the output is based on the risk reduction margin.

[0064] In some embodiments, the verification of the misoperation behavior of the dispatch order based on the misoperation risk value further includes: if the misoperation risk value is less than a preset risk threshold, it is determined that there is no misoperation risk in the dispatch order and the operation can be performed normally. The verification result will be synchronously fed back to the distribution network dispatch management system and recorded and archived with the unique identifier of the dispatch order for easy subsequent traceability and verification.

[0065] This method of verifying erroneous operations on dispatch orders based on erroneous operation risk values, and guiding the verification of erroneous operations through erroneous operation risk values, greatly facilitates dispatchers in quickly locating the root cause of problems and taking targeted measures, ultimately significantly improving the overall verification accuracy of erroneous operations on dispatch orders in scenarios where power outages are required in the distribution network.

[0066] By acquiring the dispatch tickets used to control the distribution network operation process, the verification object is clearly identified, ensuring that all subsequent verification steps revolve around the control instructions of the actual distribution network operation, thus guaranteeing high accuracy in the verification work. Semantic parsing of the dispatch tickets yields instruction objects containing tower sections and operation objects, achieving precise conversion from natural language to structured operation intent. This fundamentally overcomes the shortcomings of existing technologies that rely solely on field matching and cannot understand instruction semantics, a primary prerequisite for improving verification accuracy. Based on the distribution network switch status information and tower sections in the preset dynamic topology diagram, a section influence sub-graph is obtained. This maps the tower sections in the dispatch ticket to the physical topology reflecting the real-time connection relationships of the power grid, ensuring that the verification analysis is based on the real and dynamically changing power grid structure. This accurately pinpoints the actual impact range of the operation instructions in the physical power grid, contributing to improved dispatch ticket verification accuracy. Identifying several associated devices based on the section influence sub-graph allows the verification scope to extend beyond the devices directly mentioned on the ticket to encompass all devices in the real-time topology related to the operation area. The device associated with the electrical section improves the comprehensiveness and accuracy of the verification by expanding its coverage. Based on the acquired observation data of each associated device, its energization probability is calculated, realizing the quantitative perception and fusion analysis of the dynamic operating status of the associated devices. This allows the verification to more accurately reflect the true risk level of the equipment at the time of operation, improving the verification accuracy. By inputting the structured command object, the section influence sub-diagram, and the energization probability of each device into a preset risk assessment model, a misoperation risk value is obtained. This achieves a comprehensive quantitative assessment of the dispatch order's operational intent, grid topology association, and real-time equipment status, outputting a specific misoperation risk value. This upgrades risk judgment from qualitative to quantitative, significantly improving the accuracy of misoperation risk differentiation and effectively enhancing the verification accuracy. The misoperation risk value is used to verify the misoperation behavior of dispatch orders. By guiding the verification of misoperation of dispatch orders through the misoperation risk value, it greatly facilitates dispatchers in quickly locating the root cause of the problem and taking targeted measures, ultimately significantly improving the overall verification accuracy of misoperation of dispatch orders in power outage operation scenarios in the distribution network. This application can improve the verification accuracy of misoperation of distribution network dispatch orders in distribution network operation scenarios.

[0067] See Figure 2 Based on the above method embodiments, corresponding device embodiments are provided; An embodiment of the present invention provides a verification device for a distribution network dispatch order, comprising a first module 100, a second module 200, a third module 300 and a fourth module 400; The first module 100 is used to obtain a dispatch order for controlling the operation process of the power distribution network; The second module 200 is used to perform semantic parsing on the dispatch order to obtain an instruction object for distribution network operation, wherein the instruction object includes tower sections and operation objects; The third module 300 is used to obtain a section influence sub-map based on the distribution network switch status information and the tower section in the preset dynamic topology diagram, determine a number of related devices affected by the operation object based on the section influence sub-map, calculate the target energization probability of each related device based on the obtained observation data of each related device, and input the instruction object, the section influence sub-map and the number of target energization probabilities into a preset risk assessment model to obtain the misoperation risk value of the dispatch order. The fourth module 400 is used to verify the erroneous operation behavior of the transfer order based on the erroneous operation risk value.

[0068] The first module obtains the dispatch order for controlling the distribution network operation process, clearly defining the verification object and ensuring that all subsequent verification steps revolve around the control instructions of the actual distribution network operation, guaranteeing high accuracy in the verification work. The second module performs semantic parsing on the dispatch order to obtain the instruction object containing tower sections and operation objects, achieving accurate conversion from natural language to structured operation intent. This fundamentally overcomes the shortcomings of existing technologies that rely solely on field matching and cannot understand the semantics of instructions, which is the primary prerequisite for improving verification accuracy. The third module, based on the distribution network switch status information and tower sections in the preset dynamic topology diagram, obtains the section influence sub-diagram. This maps the tower sections in the dispatch order to the physical topology reflecting the real-time connection relationship of the power grid, ensuring that the verification analysis is based on the real and dynamically changing power grid structure, accurately locking the actual impact range of the operation instructions in the physical power grid, and helping to improve the verification accuracy of the dispatch order. Based on the section influence sub-diagram, several related devices are identified, allowing the verification scope to extend beyond the devices directly mentioned on the order to include all devices in the real-time topology related to the power grid. The electrical connections of the equipment in this operation section improve the comprehensiveness and accuracy of the verification in terms of coverage. Based on the observation data of each connected device, the energization probability is calculated, realizing the quantitative perception and fusion analysis of the dynamic operating status of the connected devices. This allows the verification to more accurately reflect the true risk level of the equipment at the time of operation, improving the verification accuracy. The structured command object, the section influence sub-diagram, and the energization probability of each device are input into the preset risk assessment model to obtain the misoperation risk value. This realizes the comprehensive quantitative assessment of the dispatch operation intention, the power grid topology connection, and the real-time status of the equipment. The output of specific misoperation risk values ​​upgrades the risk judgment from qualitative to quantitative, greatly improving the accuracy of misoperation risk differentiation and effectively improving the verification accuracy. The fourth module verifies the misoperation behavior of the dispatch order based on the misoperation risk value. By guiding the verification of misoperation of the dispatch order through the misoperation risk value, it greatly facilitates dispatchers to quickly locate the root cause of the problem and take targeted measures, ultimately significantly improving the overall verification accuracy of misoperation of dispatch orders in the scenario of power outage operation in the distribution network.

[0069] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the method of verifying distribution network dispatch tickets provided by any of the above-described method embodiments of the present invention.

[0070] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0071] Based on the above-described embodiment of a method for verifying distribution network dispatch tickets, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a method for verifying distribution network dispatch tickets according to any embodiment of the present invention.

[0072] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0073] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0074] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0075] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute a method for verifying a power distribution network dispatch ticket as described in any of the above-described method embodiments of the present invention.

[0076] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0077] Based on the above-described method embodiments, another embodiment of the present invention provides a computer program product, including a computer program or instructions, which, when executed by a communication device, implements a method for verifying power distribution network dispatch orders.

[0078] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for checking a dispatch order of a power distribution network, characterized by, include: Obtain dispatch orders for controlling the operation process of the power distribution network; The dispatch order is semantically parsed to obtain an instruction object for distribution network operations, wherein the instruction object includes tower sections and operation objects; Based on the distribution network switch status information and the tower section in the preset dynamic topology diagram, a section influence sub-diagram is obtained. Based on the section influence sub-diagram, several related devices affected by the operation object are determined. Based on the observation data of each of the related devices, the target energization probability of each of the related devices is calculated. The command object, the section influence sub-diagram and several target energization probabilities are input into a preset risk assessment model to obtain the misoperation risk value of the dispatch order. The erroneous operation behavior of the transfer order is verified based on the aforementioned erroneous operation risk value.

2. The method of claim 1, wherein the command ticket is a command ticket for a power distribution network. The calculation of the target charge probability of each associated device based on the acquired observation data of each associated device specifically includes: Acquire observation data for each of the associated devices, wherein the observation data is obtained by several independent monitoring devices monitoring the status of the associated devices; For each of the associated devices, the observation data is weighted and fused using preset confidence weights corresponding to each of the monitoring devices to obtain the initial charge probability corresponding to the associated device; The power connectivity analysis of the associated equipment is performed using the segment influence subgraph to obtain the connectivity analysis results; The initial charged probability is corrected using the connectivity analysis results to obtain the target charged probability corresponding to the associated device.

3. The method for verifying distribution network dispatch orders as described in claim 1, characterized in that, Based on the distribution network switch status information and the tower sections in the preset dynamic topology diagram, a section influence sub-diagram is obtained, specifically including: The pole / tower section is electrically topologically matched in the dynamic topology graph to determine the set of topology nodes corresponding to the pole / tower section. Using the power distribution network switch status information, network reachability analysis is performed on the set of topological nodes to obtain reachable nodes and reachable connected edges; Based on the reachable nodes and reachable connected edges, the segment influence subgraph is obtained.

4. The method for verifying distribution network dispatch orders as described in claim 1, characterized in that, The step of determining several associated devices affected by the operation object based on the segment influence sub-graph specifically includes: Based on the segment influence subgraph, a first set of devices directly connected to the operation object is determined; A second device set is obtained by performing a hierarchical expansion search on the first device set in accordance with preset expansion rules; The first device set and the second device set are deduplicated to obtain a number of associated devices affected by the operation object.

5. The method for verifying distribution network dispatch orders as described in claim 1, characterized in that, The semantic parsing of the dispatch order to obtain an instruction object for distribution network operations specifically includes: The transfer order is normalized by domain to obtain a standardized text; The standardized text is semantically encoded using a pre-defined language model to obtain a text semantic vector; The text semantic vector is input into a preset sequence labeling model for named entity recognition to obtain several candidate entities, including device entities, operation verbs, and pole / tower code entities. Syntactic dependency relation analysis was performed on the standardized text to obtain the syntactic analysis results. Based on the syntactic analysis results, the device entity, and the operation verb, the operation object is determined; Based on the tower coding entity and the preset segment boundary trigger words, the tower segment corresponding to the tower coding entity is determined; The tower section and the operation object are integrated to obtain the instruction object.

6. The method for verifying distribution network dispatch orders as described in claim 1, characterized in that, The step of inputting the instruction object, the segment influence sub-graph, and several target energization probabilities into a preset risk assessment model to obtain the misoperation risk value of the dispatch order specifically includes: Based on the instruction object, the segment influence sub-graph, and several target electrification probabilities, a risk feature vector is constructed. The risk feature vector is input into the risk assessment model to perform weighted calculations on the risk feature vector to obtain an initial risk score value. The initial risk score value is then processed using an activation function to obtain the misoperation risk value of the transfer order ticket. The risk assessment model includes the activation function.

7. The method for verifying distribution network dispatch orders as described in claim 1, characterized in that, The verification of the erroneous operation behavior of the transfer order based on the erroneous operation risk value specifically includes: Determine whether the risk value of the misoperation is greater than a preset risk threshold; If the conditions are met, it is determined that the dispatch order has a risk of misoperation, and a risk location result is generated based on the segment influence sub-graph and the electrification probability of each target to verify the misoperation behavior of the dispatch order.

8. A verification device for distribution network dispatch orders, characterized in that, It includes Module 1, Module 2, Module 3, and Module 4; The first module is used to obtain a control order for controlling the operation process of the power distribution network; The second module is used to perform semantic parsing on the dispatch order to obtain an instruction object for distribution network operation, wherein the instruction object includes tower sections and operation objects; The third module is used to obtain a section influence sub-map based on the distribution network switch status information and the tower section in the preset dynamic topology diagram, determine a number of related devices affected by the operation object based on the section influence sub-map, calculate the target energization probability of each related device based on the obtained observation data of each related device, and input the instruction object, the section influence sub-map and the number of target energization probabilities into a preset risk assessment model to obtain the misoperation risk value of the dispatch order. The fourth module is used to verify the erroneous operation behavior of the transfer order based on the erroneous operation risk value.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device or apparatus containing the computer-readable storage medium to perform the verification method for distribution network dispatch orders as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by the communication device, the method for verifying the power distribution dispatch order as described in any one of claims 1 to 7 is implemented.

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