Abnormality handling method, device, medium and product for transferring power link

Abstract

The application discloses an abnormality processing method and device of a power transfer link, a medium and a product. The method comprises the following steps: obtaining metering operation data in the power transfer link, including meter reading data, topology connection data and metering rule parameters; uniformly analyzing entities of the metering operation data to obtain an entity analysis set; constructing a power transfer link graph based on the entity analysis set and the topology connection data; calculating a metering caliber baseline sequence based on the power transfer link graph and the metering rule parameters; constructing an actual metering caliber sequence based on the meter reading data; comparing the metering caliber baseline sequence with the actual metering caliber sequence to obtain a metering difference result; performing layer-by-layer backtracking positioning on the metering difference result in the power transfer link graph to obtain abnormal positioning information; performing risk scoring on the abnormal positioning information to obtain a risk value; and matching the risk value in a preset hierarchical treatment library to obtain an abnormality processing scheme of the power transfer link. The application can improve the accuracy of abnormality processing of the power transfer link.

Description

Technical Field

[0001] This invention relates to the field of power supply anomaly diagnosis technology, and in particular to methods, devices, media and products for handling anomalies in power supply links. Background Technology

[0002] In scenarios such as commercial complexes, industrial parks, office buildings, and apartments, power transfer refers to a power supply model where the power grid company cannot directly supply power to end users. Instead, the property management or park management acts as the power transfer entity, utilizing its own distribution facilities to provide power services to end users, and electricity is metered and allocated using a "master meter - sub-meter" method. Due to the complex structure of the power transfer link and the large number of participants, problems such as imbalance between master and sub-meter readings, abnormal line losses, inconsistent time-period strategies, exceeding capacity and demand limits, incorrect topology configurations, missing meter reading data, or communication anomalies are prone to occur during actual operation. If anomalies are not detected and accurately located in a timely manner, the link's operational status will be unknown, metering data will be unreliable, and responsibility will be unclear, thus affecting the normal operation of power supply management. Therefore, it is necessary to effectively handle anomalies in the power transfer link to ensure the reliability of link operation and the accuracy of metering data.

[0003] In existing technologies, the handling of anomalies in power transfer links mainly relies on manual inspections and post-event verifications, but this approach has significant drawbacks: the structure of power transfer links is complex and dynamically changing, making it difficult for manual methods to fully cover all link nodes, resulting in randomness and lag in anomaly detection; multi-source data in the link is scattered across different systems, making it difficult for manual personnel to conduct systematic correlation analysis, leading to anomaly location relying on experience-based judgments with insufficient accuracy; and the metering rules differ between different metering points, making manual point-by-point verification labor-intensive and prone to omissions, making it difficult to accurately identify the specific link where the anomaly occurred, thus hindering accurate anomaly detection and handling. Summary of the Invention

[0004] This invention provides a method, apparatus, medium, and product for handling abnormalities in power transfer links, which can improve the accuracy of handling abnormalities in power transfer links.

[0005] In a first aspect, an embodiment of the present invention provides a method for handling abnormalities in a power transfer link, including: Acquire metering operation data in the power transfer link, wherein the metering operation data includes meter reading data, topology connection data and metering rule parameters; The metering operation data is subjected to unified entity parsing to obtain an entity parsing set. Using the entity parsing set and the topology connection data, a power transfer link map is constructed. Based on the power transfer link map and the metering rule parameters, a metering baseline sequence is calculated, and based on the meter reading data, an actual metering sequence is constructed. The metering baseline sequence is compared with the actual metering sequence to obtain metering difference results. The metering difference results are used to trace and locate the abnormal location information of the power transfer link layer by layer in the power transfer link map to obtain the abnormal location information. The abnormal location information is risk-scored to obtain a risk value. The risk value is matched with a preset graded handling database to obtain an anomaly handling plan for the power transfer link.

[0006] This approach, by acquiring metering operation data including meter readings, topology connection data, and metering rule parameters from the power transfer link, provides a solid data foundation for unified anomaly handling in the power transfer link, thereby improving the accuracy of anomaly handling. Unified entity parsing of the metering operation data yields an entity parsing set, enabling unified identification and association of scattered entity information within the power transfer link. This provides accurate entity correspondences for subsequent link mapping, ensuring the accuracy of subsequent anomaly handling. Constructing a power transfer link map using the entity parsing set and topology connection data transforms the attribution relationships within the power transfer link into a structured, computable map, providing topological support for subsequent layer-by-layer tracing and positioning. This addresses the issue of relying on experience-based judgment for manually sorting link relationships, thus improving the accuracy of anomaly handling. Calculating the metering baseline sequence based on the power transfer link map and metering rule parameters generates theoretically required metering values ​​according to various rules, providing a standardized reference baseline for subsequent anomaly comparisons. This solves the problem of difficulty in unified judgment due to rule differences at different metering points, further improving the accuracy of power transfer link anomaly handling. Finally, constructing the actual metering sequence based on meter reading data allows for the unified identification and association of scattered entity information within the power transfer link. Initial meter readings are converted into structured measured values, providing actual metering data for subsequent comparison with baseline sequences. This ensures the accuracy and reliability of the difference analysis data, thereby improving the accuracy of handling anomalies in the power transfer link. Comparing the baseline metering sequence with the actual metering sequence yields metering difference results, quantifying the deviation between theoretical and measured values. This provides objective difference data for anomaly identification, improving the accuracy of anomaly handling. Using the metering difference results, layer-by-layer tracing and location are performed in the power transfer link map to obtain anomaly location information. This allows for tracing upstream from the anomaly point, attributing the anomaly to the least probable cause. This invention addresses the difficulty of locating specific links or metering points in existing technologies, thereby improving the accuracy of subsequent handling of abnormal areas. It performs risk scoring on anomaly location information to obtain risk values, which can quantify the severity of anomalies and provide an objective basis for subsequent tiered handling, improving the accuracy of handling power transfer link anomalies. By matching risk values ​​with a pre-set tiered handling database, an anomaly handling plan for the power transfer link is obtained. Different levels of handling actions can be automatically matched based on the risk value, solving the problems of traditional single-level and untargeted handling plans, and improving the accuracy of anomaly handling. This application can improve the accuracy of handling power transfer link anomalies.

[0007] Furthermore, the step of performing unified entity parsing on the metering operation data to obtain an entity parsing set specifically includes: Entity extraction is performed on the metering operation data to obtain an entity candidate set, wherein the entity candidate set includes entities with strong identifier fields and entities with weak identifier fields; The entities with strong identifier fields are subjected to rule fusion processing using preset fusion rules to obtain a first entity set; The weakly identified entities are subjected to similarity fusion processing to obtain a second entity set; Based on the first entity set and the second entity set, the entity parsing set is determined.

[0008] This unified entity parsing of metering operation data yields an entity parsing set, which can uniformly identify and associate the scattered entity information in the power transfer link, providing accurate entity correspondences for subsequent link mapping and ensuring the accuracy of subsequent abnormal handling of the power transfer link.

[0009] Furthermore, the step of constructing a power transfer link map using the entity parsing set and the topology connection data specifically includes: Based on the entity parsing set and the topology connection data, a node set and an edge set are generated. The node set includes power transfer main nodes, metering point nodes, spatial unit nodes and end user nodes. The edge set is used to represent the power supply affiliation relationship of each node in the node set. Based on the set of nodes and the set of edges, a power supply link path is constructed; Perform a topology consistency check on the power supply link path and obtain the check result; If the verification result is successful, the power transfer link map is generated based on the power supply link path.

[0010] By constructing a power transfer link map using entity parsing sets and topology connection data, the attribution relationships in the power transfer links can be transformed into a structured and computable map. This provides link topology support for subsequent layer-by-layer tracing and positioning, solves the problem of relying on experience-based judgment to manually sort out link relationships, and thus improves the accuracy of handling abnormalities in power transfer links.

[0011] Furthermore, the calculation of the metering baseline sequence based on the power transfer link map and the metering rule parameters specifically includes: Based on the power transfer link map and the metering rule parameters, determine the time period rule data, tiered rule data and allocation rule data for each metering point in the power transfer link map; For each metering point, the billing period data in the meter reading data corresponding to each metering point is decomposed into time periods using the time period rule data to obtain the decomposition results; The payment period data is segmented into tiers using the tiered rule data to obtain the segmentation results; Based on the allocation rule data and the payment period data, the allocation caliber data is generated; Based on the decomposition results, the segmentation results, and the apportionment caliber data, a baseline sequence of the measurement caliber corresponding to each measurement point is generated.

[0012] This method, based on the power transfer link map and metering rule parameters, calculates the metering baseline sequence, which can generate theoretical metering values ​​according to various rules. This provides a standardized reference baseline for subsequent anomaly comparisons, solves the problem of difficulty in unified judgment caused by differences in rules at different metering points, and further improves the accuracy of handling anomalies in the power transfer link.

[0013] Furthermore, the step of comparing the baseline sequence of the metrological caliber with the actual metrological caliber sequence to obtain the metrological difference result specifically includes: The measurement points in the baseline sequence and the actual measurement sequence are compared in the same dimension, and the deviation rate corresponding to each measurement point is calculated. For each metering point, if the deviation rate corresponding to the current metering point exceeds a preset difference threshold, an abnormal record corresponding to the current metering point is generated based on the metering point identifier and the deviation rate. The abnormal records from each measurement point are summarized to determine the measurement difference results.

[0014] By comparing the baseline sequence of the metrological caliber with the actual metrological caliber sequence, the metrological difference results can be obtained. This can quantify the deviation between the theoretical value and the measured value, provide objective difference data for anomaly identification, and improve the accuracy of anomaly handling.

[0015] Furthermore, after acquiring the metering operation data in the power transfer link, the process also includes: The metering operation data is subjected to field normalization processing to obtain the first dataset; The first dataset is partitioned to obtain the second dataset; Perform data integrity checks on the second dataset and obtain the check results; The quality of the second dataset is labeled using the detection results to obtain the third dataset.

[0016] This process of field standardization, data partitioning, data integrity testing, and quality labeling of metering operation data transforms multi-source heterogeneous raw data into a standardized dataset with a unified format, reasonable partitioning, and quality labels. This provides a high-quality, traceable data foundation for subsequent entity analysis, link graph construction, baseline calculation, and anomaly identification, avoiding misjudgments caused by inconsistent data formats or missing data, thereby improving the accuracy of handling anomalies in power transfer links.

[0017] Furthermore, the step of matching the risk value in a preset graded handling database to obtain an anomaly handling plan for the power transfer link specifically includes: If the risk value is less than a preset first risk threshold, a periodic recalculation scheme is generated; If the risk value is greater than or equal to the first risk threshold and less than the preset second risk threshold, a configuration verification scheme is generated. If the risk value is greater than or equal to the second risk threshold, an emergency alarm scheme is generated; Based on the periodic recalculation scheme, the configuration verification scheme, and the emergency alarm scheme, the anomaly handling scheme is determined.

[0018] This method matches risk values ​​against a pre-defined tiered handling database to obtain anomaly handling plans for power supply links. It can automatically match different levels of handling actions based on risk values, solving the problems of traditional handling plans being singular and lacking specificity, and improving the accuracy of anomaly handling.

[0019] Secondly, an embodiment of the present invention provides an anomaly handling device for a power transfer link, comprising a first module, a second module and a third module; The first module is used to acquire metering operation data in the power transfer link, wherein the metering operation data includes meter reading data, topology connection data and metering rule parameters; The second module is used to perform unified entity parsing on the metering operation data to obtain an entity parsing set. Using the entity parsing set and the topology connection data, a power transfer link map is constructed. Based on the power transfer link map and the metering rule parameters, a metering baseline sequence is calculated, and based on the meter reading data, an actual metering sequence is constructed. The metering baseline sequence is compared with the actual metering sequence to obtain a metering difference result. The metering difference result is used to trace and locate the abnormal location information of the power transfer link layer by layer in the power transfer link map to obtain the abnormal location information. The abnormal location information is then risk-scored to obtain a risk value. The third module is used to match the risk value in a preset graded handling database to obtain an abnormal handling plan for the power supply link.

[0020] The first module acquires metering operation data, including meter reading data, topology connection data, and metering rule parameters, for the power transfer link. This provides a solid data foundation for unified anomaly handling in the power transfer link, thereby improving the accuracy of anomaly handling. The second module performs unified entity parsing on the metering operation data to obtain an entity parsing set. This allows for unified identification and association of scattered entity information in the power transfer link, providing accurate entity correspondences for subsequent link mapping and ensuring the accuracy of subsequent anomaly handling. Using the entity parsing set and topology connection data to construct the power transfer link map, the attribution relationships in the power transfer link are transformed into a structured, computable map. This provides link topology support for subsequent layer-by-layer tracing and positioning, solving the problem of relying on experience-based judgment for manually sorting link relationships, thus improving the accuracy of anomaly handling. Based on the power transfer link map and metering rule parameters, a metering baseline sequence is calculated. Theoretical metering values ​​can be generated according to various rules, providing a standardized reference baseline for subsequent anomaly comparison. This solves the problem of difficulty in unified judgment due to rule differences at different metering points, further improving the accuracy of power transfer link anomaly handling. Based on meter reading data, an actual metering sequence is constructed. The raw meter reading data is transformed into structured measured values, providing actual metering data for subsequent comparison with the baseline sequence. This ensures the accuracy and reliability of the difference analysis data, thereby improving the accuracy of handling anomalies in the power transfer link. Comparing the metering baseline sequence with the actual metering sequence yields metering difference results, quantifying the deviation between theoretical and measured values. This provides objective difference data for anomaly identification, improving the accuracy of anomaly handling. Using the metering difference results, the power transfer link map is traced layer by layer to obtain anomaly location information. This allows for tracing upstream from the anomaly point, attributing the anomaly to the least solvable cause. The first module identifies the link segment or metering point, addressing the difficulty of locating the specific link where anomalies occur in existing technologies, thereby improving the accuracy of subsequent handling of anomaly areas. The second module performs risk scoring on anomaly location information to obtain risk values, which can quantitatively assess the severity of anomalies, providing an objective basis for subsequent tiered handling and improving the accuracy of handling power supply link anomalies. The third module matches the risk values ​​against a pre-set tiered handling database to obtain anomaly handling plans for power supply links. It can automatically match different levels of handling actions based on the risk values, solving the problems of traditional handling plans being singular and lacking specificity, and improving the accuracy of anomaly handling.

[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 an abnormal handling method for the power supply link.

[0022] Fourthly, another embodiment of the present invention provides a computer program product, including a computer program or instructions, wherein when the computer program or instructions are executed by a communication device, a method for handling abnormalities in a power transfer link is implemented. 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 a method for handling abnormalities in a power transfer link provided in this application; Figure 2 This is a schematic diagram of the structure of an abnormal handling device for a power transfer link provided in this application. 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 field of power supply anomaly diagnosis, the complex structure of power supply links makes them prone to problems such as power imbalance, abnormal line loss, and inconsistent rules. If these issues cannot be detected and located in a timely manner, the operational status will be unknown and the data unreliable. Existing technologies rely on manual inspections and post-event verification, but these methods are insufficient for comprehensive coverage and systematic correlation analysis, leading to anomaly location relying on experience and lacking accuracy.

[0033] See Figure 1 In order to improve the accuracy of handling abnormalities in power transfer links, an embodiment of the present invention provides a method for handling abnormalities in power transfer links, including steps S101 to S103. Step S101: Obtain metering operation data in the power transfer link, wherein the metering operation data includes meter reading data, topology connection data and metering rule parameters; In some embodiments, metering operation data in the power transfer link is acquired, wherein the metering operation data includes meter reading data, topology connection data, and metering rule parameters. Specifically, this includes: generating a compliance authorization record after obtaining user compliance authorization; reading multi-source data from the data lake according to the authorization scope in the authorization record; extracting meter reading data of the master meter and sub-meters, demand curve data, load curve data, and power outage / restoration event records from the read multi-source data, and using these extracted data as meter reading data; extracting equipment ledger records, parent-child relationship records between metering points, ownership relationship records between spatial units and metering points, and management relationship records between the power transfer entity and metering points from the read multi-source data, and using these extracted data as topology connection data; extracting multiplier configuration data, current transformer and voltage transformer configuration data, contract terms information and caliber parameter data, and regional policy caliber data from the read multi-source data, and using these extracted data as metering rule parameters; and summarizing the extracted meter reading data, topology connection data, and metering rule parameters as metering operation data.

[0034] In some embodiments, after acquiring the metering operation data in the power transfer link, the method further includes: performing field normalization processing on the metering operation data to obtain a first dataset; performing data partitioning processing on the first dataset to obtain a second dataset; performing data integrity detection on the second dataset to obtain a detection result; and using the detection result to perform quality marking on the second dataset to obtain a third dataset. Specifically, field normalization processing is performed on each record in the acquired metering operation data, mapping fields with the same meaning from different data sources to standard field names, converting electricity values ​​of different units to standard units, converting timestamps of different time zones to the system standard time zone time, converting text with different character encodings to the system internal encoding, and generating a unified identifier placeholder according to the source identifier. All records after normalization processing are summarized into a first dataset. Logically partitioning the records in the first dataset according to three dimensions: billing period identifier, subject identifier, and metering point identifier, grouping all meter reading records of the same metering point within the same billing period into the same data partition, grouping all metering point configuration records under the same subject into the same data partition, and using the partitioned dataset as the second dataset; traversing the first dataset... For each data partition in the second dataset, the system checks for null values ​​in key fields, whether battery values ​​exceed preset reasonable range boundaries, and whether records with missing values ​​at adjacent time points exist in the time series. Based on the detection results, it generates detection results including field missing rate, numerical anomalous jump rate, and data collection time breakpoint rate. Based on the missing rate and anomalous jump rate in the detection results, the system performs quality labeling on each data partition in the second dataset. When the missing rate is greater than or equal to a preset first quality threshold, the partition is labeled as a low-quality partition. When the missing rate is less than the first quality threshold but greater than or equal to a preset second quality threshold, the partition is labeled as a partition to be verified. When the missing rate is less than the second quality threshold and the anomalous jump rate is less than a preset third quality threshold, the partition is labeled as a high-quality partition. The labeled dataset is then used as the third dataset.

[0035] For example, after quality labeling the second dataset to obtain the third dataset, the process further includes: generating a data quality profile based on the missing rate, abnormal jump rate, and data acquisition time breakpoint rate in the detection results, and binding the data quality profile to the corresponding billing period window; extracting the current metering point configuration information from the second dataset, including the multiplier parameter, current transformer and voltage transformer parameters, wiring method, acquisition channel, and clock source information, and solidifying this configuration information to form a metering point configuration snapshot; extracting the current node set and edge set from the topology connection data, solidifying the node set, edge set, and corresponding version identifier to form a topology snapshot, and associating and storing the metering point configuration snapshot and topology snapshot with the billing period window for subsequent version-based recalculation and traceability.

[0036] This process of field standardization, data partitioning, data integrity testing, and quality labeling of metering operation data transforms multi-source heterogeneous raw data into a standardized dataset with a unified format, reasonable partitioning, and quality labels. This provides a high-quality, traceable data foundation for subsequent entity analysis, link graph construction, baseline calculation, and anomaly identification, avoiding misjudgments caused by inconsistent data formats or missing data, thereby improving the accuracy of handling anomalies in power transfer links.

[0037] Step S102: Perform unified entity parsing on the metering operation data to obtain an entity parsing set. Use the entity parsing set and the topology connection data to construct a power transfer link map. Based on the power transfer link map and the metering rule parameters, calculate the metering baseline sequence. Based on the meter reading data, construct the actual metering sequence. Compare the metering baseline sequence with the actual metering sequence to obtain the metering difference result. Use the metering difference result to trace and locate the abnormal location information of the power transfer link layer by layer in the power transfer link map to obtain the abnormal location information. Perform risk scoring on the abnormal location information to obtain the risk value. In some embodiments, the step of performing unified entity parsing on the metering operation data to obtain an entity parsing set specifically includes: extracting entities from the metering operation data to obtain an entity candidate set, wherein the entity candidate set includes entities with strong identifier fields and entities with weak identifier fields; performing rule fusion processing on the entities with strong identifier fields using preset fusion rules to obtain a first entity set; performing similarity fusion processing on the entities with weak identifier fields to obtain a second entity set; and determining the entity parsing set based on the first entity set and the second entity set. Specifically, the entity information fields are extracted from each record in the metering operation data. When fields such as licenses, contract numbers, and equipment numbers are extracted, these fields are treated as strongly identified entities and placed into the first candidate set. When fields such as entity names, address descriptions, or user names are extracted, these fields are treated as weakly identified entities and placed into the second candidate set. Each strongly identified entity in the first candidate set is iterated over, and entities with the same license are merged into the same entity identifier. Metering point entities with the same equipment number are merged into the same entity identifier. The resulting set of unique entities is used as the first entity set, and the confidence level of the merging operation is recorded. For every two weakly identified entities in the second candidate set, the confidence level is calculated. The edit distance similarity score and the vector space cosine similarity score between the entities are weighted and summed to obtain a comprehensive similarity score. When the comprehensive similarity score exceeds a preset fusion threshold, the two weakly labeled entities are determined to point to the same entity. All weakly labeled entities determined to point to the same entity are merged into a unified entity identifier, and the merged entity set is used as the second entity set. The unified entity identifiers of entities in the first entity set are associated with the unified entity identifiers of entities in the second entity set. When the same entity appears in both the first and second entity sets, the identifier in the first entity set is used for overriding. The complete entity set containing the unified entity identifier and fusion confidence score after association is used as the entity resolution set.

[0038] This unified entity parsing of metering operation data yields an entity parsing set, which can uniformly identify and associate the scattered entity information in the power transfer link, providing accurate entity correspondences for subsequent link mapping and ensuring the accuracy of subsequent abnormal handling of the power transfer link.

[0039] In some embodiments, constructing a power transfer link graph using the entity parsing set and the topology connection data specifically includes: generating a node set and an edge set based on the entity parsing set and the topology connection data, wherein the node set includes power transfer main nodes, metering point nodes, spatial unit nodes, and end-user nodes, and the edge set is used to represent the power supply affiliation relationship of each node in the node set; constructing a power supply link path based on the node set and the edge set; performing a topology consistency check on the power supply link path to obtain a check result; and generating the power transfer link graph based on the power supply link path when the check result is successful.Specifically, the system extracts a unified identifier for each power transfer entity from the entity parsing set and creates a power transfer entity node; it extracts a unified identifier for each metering point and creates a metering point node, while also labeling the metering point as either a master table or a sub-table; it extracts a unified identifier for each spatial unit and creates a spatial unit node; it extracts a unified identifier for each end user and creates an end user node; and it aggregates all created nodes into a node set. The system also reads parent-child relationship records between metering points from the topology connection data, creates a directed edge for each parent-child relationship from the parent metering point node to the child metering point node, and reads the transfer... The system records the management relationships between power supply entities and metering points, creating a directed edge from the power supply entity node to the metering point node for each management relationship. It also reads the attribution relationship records between spatial units and metering points from the topology connection data, creating a directed edge from the metering point node to the spatial unit node for each attribution relationship. Finally, it reads the occupancy relationship records between end users and spatial units from the topology connection data, creating a directed edge from the spatial unit node to the end user node for each occupancy relationship. Each created edge is appended with a metering level identifier, time period policy version number, capacity constraint value, demand constraint value, and applicable information. Periodic information is used to aggregate all created edges into an edge set. Each power transfer main node in the node set is traversed, starting from that main node, and following the direction of the directed edges, the system sequentially searches for the main table node it points to, the sub-table node pointed to by the main table node, the spatial unit node pointed to by the sub-table node, and the end-user node pointed to by the spatial unit node. The found node sequences are then concatenated into a complete power supply link path. For each concatenated power supply link path, a topology consistency check is performed to detect whether there are loop paths where the same node is repeatedly visited, and whether there are starting nodes without incoming edges. For isolated nodes other than endpoint nodes without outgoing edges, check whether there is any over-level connection in the parent-child relationship in the path, and check whether the node hierarchy on the same link is continuous. Generate a verification result based on the detection results. When the verification result is passed, solidify the power supply link path and its corresponding node set, edge set, and topology snapshot version number to generate a power transfer link graph. At the same time, construct a subtree summary index and a branch comparison index. The subtree summary index is used to record the mapping relationship from each master table node to the subordinate sub-table set and terminal set, and the branch comparison index is used to record the comparability relationship between multiple branches under the same subject.

[0040] By constructing a power transfer link map using entity parsing sets and topology connection data, the attribution relationships in the power transfer links can be transformed into a structured and computable map. This provides link topology support for subsequent layer-by-layer tracing and positioning, solves the problem of relying on experience-based judgment to manually sort out link relationships, and thus improves the accuracy of handling abnormalities in power transfer links.

[0041] In some embodiments, calculating the metering baseline sequence based on the power transfer link map and the metering rule parameters specifically includes: determining the time period rule data, tiered rule data, and allocation rule data for each metering point in the power transfer link map based on the power transfer link map and the metering rule parameters; for each metering point, using the time period rule data to decompose the billing period data in the meter reading data corresponding to each metering point into time periods to obtain decomposition results; using the tiered rule data to segment the billing period data into tiers to obtain segmentation results; generating the allocation caliber data based on the allocation rule data and the billing period data; and generating the metering baseline sequence corresponding to each metering point based on the decomposition results, the segmentation results, and the allocation caliber data. Specifically, the process iterates through each metering point node in the power transfer link graph, reads the region identifier and user category identifier of the metering point node, synchronously reads the corresponding peak-valley time period division rule from the metering rule parameters based on the region identifier and user category identifier as the time period rule data for the metering point, reads the corresponding tiered electricity price division rule as the tiered rule data for the metering point, and reads the corresponding shared electricity fee calculation method and line loss calculation rule as the allocation rule data for the metering point, and records the rule version number used in this reading; for each metering point node, the total electricity consumption value of the metering point within the specified billing period is obtained, and according to the peak time period boundary, normal time period boundary, and valley time period boundary in the time period rule data of the metering point, the total electricity consumption value is divided into peak time period electricity, normal time period electricity, and valley time period electricity according to the time period boundary, and the electricity consumption of each time period after division is used as the time period decomposition result; based on the metering point The total electricity value is divided into tiers starting from the first tier, based on the boundary values ​​of each tier in the tiered rule data. The electricity value falling into each tier is calculated, and the electricity value of each tier is used as the tier division result. According to the common ratio coefficient or line loss rate coefficient in the allocation rule data of the metering point, combined with the power supply data of the link where the metering point is located, the common electricity and line loss electricity that the metering point should bear in the current billing period are calculated, and the calculated allocation values ​​are used as the allocation caliber data. The electricity of each time period in the time period decomposition result corresponding to the metering point, the electricity of each tier in the tier division result, and the allocation values ​​in the allocation caliber data are assembled into a time series according to the preset caliber sequence template. The parameter version, time period boundary, tiered tier division path and allocation calculation path in the calculation process are fixed, and this sequence is used as the metering caliber baseline sequence of the metering point in the current billing period.

[0042] This method, based on the power transfer link map and metering rule parameters, calculates the metering baseline sequence, which can generate theoretical metering values ​​according to various rules. This provides a standardized reference baseline for subsequent anomaly comparisons, solves the problem of difficulty in unified judgment caused by differences in rules at different metering points, and further improves the accuracy of handling anomalies in the power transfer link.

[0043] In some embodiments, based on the meter reading data, an actual metering caliber sequence is constructed, specifically including: traversing each metering point node in the power transfer link graph, obtaining the node identifier of the metering point node, and extracting the meter reading records, demand curve data, and load curve data of the metering point within a specified billing period from the third dataset according to the node identifier; structurally decomposing the total electricity value in the extracted meter reading records, decomposing the total electricity value into a first electricity value that can be clearly attributed to the meter reading, a second electricity value that can be attributed to policy-permitted power transfer projects, and a third electricity value that cannot be attributed to the meter reading. The third electricity value attribution is clearly defined, and the data source identifier and field mapping path used for each disassembly operation are recorded to generate the disassembly result. When a time breakpoint or missing value is detected in the extracted meter reading record, the electricity value of the same period in the adjacent billing period is used for interpolation, or the electricity value of the missing time point is calculated backward from the load curve data of the metering point. The source mark and confidence mark are marked on the electricity value obtained by interpolation or calculation. The electricity values ​​obtained after the above disassembly and interpolation are assembled into a sequence in chronological order, and this sequence is used as the actual metering caliber sequence of the metering point in the current billing period.

[0044] For example, after constructing the actual measurement caliber sequence, the method further includes calculating the reconstruction confidence of the sequence: determining the source reliability score based on the data source type of each data point in the sequence (such as automatic collection, manual entry, system interface), determining the integrity score based on the completeness of the fields in the sequence, determining the consistency score based on the matching degree between the data in the sequence and related data (such as data from adjacent periods, data from other measurement points in the same link), determining the delay penalty item based on the time difference between the data collection time and the billing deadline, multiplying the source reliability score, integrity score and consistency score by weight coefficients respectively and summing them, then subtracting the product of the delay penalty item and the weight coefficient, and using the calculation result as the reconstruction confidence of the sequence.

[0045] In some embodiments, the relevant formula for calculating the reconstruction confidence of the sequence includes: Formula for calculating reconstruction confidence: ; In the formula, Assess the reliability of the source; Score for completeness; For consistency scoring; This is a delayed penalty item; , , , These are configurable weighting coefficients.

[0046] In some embodiments, comparing the baseline sequence of the measurement caliber with the actual measurement caliber sequence to obtain the measurement difference result specifically includes: comparing each measurement point in the baseline sequence of the measurement caliber and the actual measurement caliber sequence in the same dimension, and calculating the deviation rate corresponding to each measurement point; for each measurement point, if the deviation rate corresponding to the current measurement point exceeds a preset difference threshold, generating an abnormal record corresponding to the current measurement point based on the measurement point identifier of the current measurement point and the deviation rate; summarizing the abnormal records of each measurement point to determine the measurement difference result. Specifically, for each measurement point node, under the same billing period and time period dimension, the baseline normalized caliber index value for that dimension is read from the calculated measurement caliber baseline sequence, and the actual normalized caliber index value for that dimension is read from the constructed actual measurement caliber sequence. The difference between the actual normalized caliber index value and the baseline normalized caliber index value is calculated. This difference is divided by the sum of the absolute value of the baseline normalized caliber index value and a minimum constant. The calculation result is used as the deviation rate of that measurement point under that dimension. It is determined whether the calculated deviation rate is greater than the system's preset difference threshold. If it is greater, an abnormal record is generated based on the node identifier of that measurement point, the deviation rate, and the billing period and time period information corresponding to the deviation rate. If it is less than or equal to, the next measurement point is processed. The abnormal records generated by all measurement points are summarized, and the summarized result is used as the measurement difference result.

[0047] In some embodiments, the formula for comparing the baseline metrological caliber sequence with the actual metrological caliber sequence to obtain the metrological difference result specifically includes: The formula for calculating the deviation rate is: ; In the formula, The deviation rate; These are the actual unitized index values ​​read from the actual measurement caliber sequence; The baseline normalized caliber index value read from the metrological baseline sequence; This is a preset minimum constant.

[0048] By comparing the baseline sequence of the metrological caliber with the actual metrological caliber sequence, the metrological difference results can be obtained. This can quantify the deviation between the theoretical value and the measured value, provide objective difference data for anomaly identification, and improve the accuracy of anomaly handling.

[0049] In some embodiments, the metering difference results are used to trace and locate the abnormal location information of the power transfer link layer by layer in the power transfer link map. Specifically, this includes: reading each abnormal record from the metering difference results and obtaining the metering point identifier and deviation rate corresponding to the abnormal record; taking the main table node in the power transfer link map as the root node, traversing all child nodes downwards along the direction of the directed edge; when the metering point corresponding to the abnormal record is detected to be a sub-table node, calculating the first main-sub-table balance error between the sub-table node and its direct superior main table node, continuing to trace upwards, calculating the second main-sub-table balance error between the main table node and all other sub-table nodes at the same level, comparing the magnitude of the first main-sub-table balance error and the second main-sub-table balance error, determining the sub-table node on the branch path with the largest balance error value as the abnormal location node, or when the sum of the errors of a certain lower-level sub-table node cannot explain the error of its superior main table node, determining the superior main table node as the abnormal location node, and encapsulating the located abnormal node identifier, the link path where the abnormal node is located, and the deviation rate corresponding to the abnormal node as abnormal location information.

[0050] In some embodiments, the metering difference results are used to trace and locate the abnormal location information of the power transfer link layer by layer in the power transfer link map, specifically including: Formula for calculating the balance error of the total score table: ; In the formula, The power consumption value of the parent master node; This is the cumulative sum of the electricity consumption of all subordinate sub-nodes; The desired power loss; It is a local minimum constant.

[0051] In some embodiments, risk scoring is performed on the abnormal location information to obtain a risk value, specifically including: mapping the deviation rate in the abnormal location information to a range of 0 to 1 using a Sigmoid compression mapping function, and using the mapping result as a normalized deviation score; obtaining the number of consecutive cycles of the abnormal signal corresponding to the abnormal location information, calculating the ratio of the number of consecutive cycles to a preset reference number of cycles, comparing the ratio with 1 and taking the smaller value, and using the smaller result as the duration score; obtaining the power scale of the link segment corresponding to the abnormal location information within the window, and calculating the power scale and the preset reference number of cycles. The ratio between the reference electricity consumption is set, and this ratio is compared with 1. The smaller value is taken as the impact electricity consumption score. The evidence strength score corresponding to the abnormal location information is obtained, which is determined based on the completeness of the evidence. The uncertainty penalty term is obtained by weighted summation based on the missing rate, metering caliber reconstruction confidence, and configuration change frequency in the data quality profile. The normalized deviation score, duration score, impact electricity consumption score, and evidence strength score are weighted summation, and then the product of the uncertainty penalty term and the weight coefficient is subtracted to obtain the calculation result as the risk value corresponding to the abnormal location information.

[0052] In some embodiments, a risk score is performed on the abnormal location information to obtain a relevant formula for the risk value, specifically including: Formula for calculating risk value: ; In the formula, Risk value; for Compression mapping function; The deviation rate in the abnormal location information; Score based on duration; To affect the battery rating; The strength of evidence is scored based on the completeness of the evidence. This is a penalty item for uncertainty; , , , , These are configurable weighting coefficients; The formula for calculating the duration score: ; In the formula, The number of consecutive cycles triggered by the acquired abnormal signal; This is the preset reference period number; The function is for finding the minimum value; The formula for calculating the battery rating is as follows: ; In the formula, To obtain the scale of electricity; This is the preset reference battery level; The formula for calculating the uncertainty penalty term is: ; In the formula, Reconstruct confidence levels for measurement standards; Missing rate in data quality profile; To configure the change frequency; , and These are configurable weighting coefficients.

[0053] For example, after obtaining the risk value, the process also includes generating abnormal cases and solidifying the evidence chain: the risk value, abnormal location information, deviation rate, duration, scale of affected electricity, and corresponding cause code are encapsulated into an abnormal case object; for the generated abnormal case, the necessary evidence item list is retrieved according to the cause code, and the corresponding evidence is retrieved from the data lake, topology snapshot, configuration snapshot, baseline sequence, and actual sequence; a digest hash value is calculated for each evidence item to form an evidence digest list; the evidence item metadata, evidence digest list, graph version number, rule version number, configuration version number, and model version number are encapsulated into an evidence package; the evidence package is written to a storage medium with tamper-proof capability, and the written voucher and index key are recorded in the audit log; a replay recalculation pointer is generated so that the judgment result can be reproduced according to the same version in the future.

[0054] Step S103: Match the risk value in a preset graded handling database to obtain an anomaly handling plan for the power transfer link; In some embodiments, matching the risk value in a preset graded handling database to obtain an anomaly handling scheme for the power transfer link specifically includes: if the risk value is less than a preset first risk threshold, generating a periodic recalculation scheme; if the risk value is greater than or equal to the first risk threshold and less than a preset second risk threshold, generating a configuration verification scheme; if the risk value is greater than or equal to the second risk threshold, generating an emergency alarm scheme; and determining the anomaly handling scheme based on the periodic recalculation scheme, the configuration verification scheme, and the emergency alarm scheme. Specifically, a preset first risk threshold and a second risk threshold are read from the system configuration, wherein the value of the second risk threshold is greater than the first risk threshold; the calculated risk value is compared with the first risk threshold; when the risk value is less than the first risk threshold, a periodic recalculation scheme is generated, which includes an instruction to put the anomaly location information of this anomaly into an observation queue, and a scheduling instruction to automatically re-execute the comparison, location, and risk scoring process in step S102 when the next metering cycle arrives; when the risk value is greater than or equal to the first risk threshold and less than the second risk threshold, a configuration verification scheme is generated. The plan includes first triggering an automatic verification instruction for the multiplier configuration, time period policy version, and topology attribution of relevant metering points in the abnormal location information; and generating a work order instruction that requires manual on-site verification when the automatic verification cannot resolve the issue. When the risk value is greater than or equal to the second risk threshold, an emergency alarm scheme is generated. This scheme includes a notification instruction to issue an operational alarm simultaneously through SMS and system pop-up channels, and a work order instruction to generate an emergency verification work order with abnormal location information and evidence package, requiring handling within a specified time. The specific scheme generated based on the risk value is output as the abnormal handling scheme.

[0055] This method matches risk values ​​against a pre-defined tiered handling database to obtain anomaly handling plans for power supply links. It can automatically match different levels of handling actions based on risk values, solving the problems of traditional handling plans being singular and lacking specificity, and improving the accuracy of anomaly handling.

[0056] For example, after generating the anomaly handling plan, the process also includes executing the handling according to the anomaly handling plan, collecting feedback, and iteratively optimizing: The corresponding handling actions are executed sequentially according to the instruction sequence in the anomaly handling plan. After the handling actions are completed, the handling status, on-site verification records, problem confirmation results, and handling completion time are read from the execution results. This feedback information is structured and used as a feedback set to update rule thresholds, cause code mappings, and model parameters. Based on the monitoring signals in the feedback set, the deviation rate threshold and duration threshold are updated by subject and layer. Newly emerging anomaly patterns are added to the cause code dictionary. The weight coefficients in the risk score and the false alarm suppression gating strategy are updated, and the version number is recorded for replay. A statistical report containing the discovery rate, false alarm rate, average handling time, affected electricity volume, affected scope, subject coverage, and anomaly type distribution is output. The compliance authorization record summary, data access and desensitization strategy, topology snapshot version, configuration snapshot version, rule version, threshold version, model version, alarm reach records, and work order handling links are written to the audit log to support auditing and traceability.

[0057] This approach, by acquiring metering operation data including meter readings, topology connection data, and metering rule parameters from the power transfer link, provides a solid data foundation for unified anomaly handling in the power transfer link, thereby improving the accuracy of anomaly handling. Unified entity parsing of the metering operation data yields an entity parsing set, enabling unified identification and association of scattered entity information within the power transfer link. This provides accurate entity correspondences for subsequent link mapping, ensuring the accuracy of subsequent anomaly handling. Constructing a power transfer link map using the entity parsing set and topology connection data transforms the attribution relationships within the power transfer link into a structured, computable map, providing topological support for subsequent layer-by-layer tracing and positioning. This addresses the issue of relying on experience-based judgment for manually sorting link relationships, thus improving the accuracy of anomaly handling. Calculating the metering baseline sequence based on the power transfer link map and metering rule parameters generates theoretically required metering values ​​according to various rules, providing a standardized reference baseline for subsequent anomaly comparisons. This solves the problem of difficulty in unified judgment due to rule differences at different metering points, further improving the accuracy of power transfer link anomaly handling. Finally, constructing the actual metering sequence based on meter reading data allows for the unified identification and association of scattered entity information within the power transfer link. Initial meter readings are converted into structured measured values, providing actual metering data for subsequent comparison with baseline sequences. This ensures the accuracy and reliability of the difference analysis data, thereby improving the accuracy of handling anomalies in the power transfer link. Comparing the baseline metering sequence with the actual metering sequence yields metering difference results, quantifying the deviation between theoretical and measured values. This provides objective difference data for anomaly identification, improving the accuracy of anomaly handling. Using the metering difference results, layer-by-layer tracing and location are performed in the power transfer link map to obtain anomaly location information. This allows for tracing upstream from the anomaly point, attributing the anomaly to the least probable cause. This invention addresses the difficulty of locating specific links or metering points in existing technologies, thereby improving the accuracy of subsequent handling of abnormal areas. It performs risk scoring on anomaly location information to obtain risk values, which can quantify the severity of anomalies and provide an objective basis for subsequent tiered handling, improving the accuracy of handling power transfer link anomalies. By matching risk values ​​with a pre-set tiered handling database, an anomaly handling plan for the power transfer link is obtained. Different levels of handling actions can be automatically matched based on the risk value, solving the problems of traditional single-level and untargeted handling plans, and improving the accuracy of anomaly handling. This application can improve the accuracy of handling power transfer link anomalies.

[0058] See Figure 2 Based on the above method embodiments, corresponding device embodiments are provided; An embodiment of the present invention provides an anomaly handling device for a power transfer link, comprising a first module 100, a second module 200, and a third module 300; The first module 100 is used to acquire metering operation data in the power transfer link, wherein the metering operation data includes meter reading data, topology connection data and metering rule parameters; The second module 200 is used to perform unified entity parsing on the metering operation data to obtain an entity parsing set, construct a power transfer link map using the entity parsing set and the topology connection data, calculate a metering baseline sequence based on the power transfer link map and the metering rule parameters, construct an actual metering sequence based on the meter reading data, compare the metering baseline sequence with the actual metering sequence to obtain metering difference results, use the metering difference results to perform layer-by-layer tracing and positioning in the power transfer link map to obtain abnormal positioning information of the power transfer link, and perform risk scoring on the abnormal positioning information to obtain a risk value. The third module 300 is used to match the risk value in a preset graded handling library to obtain an abnormal handling plan for the power supply link.

[0059] The first module acquires metering operation data, including meter reading data, topology connection data, and metering rule parameters, for the power transfer link. This provides a solid data foundation for unified anomaly handling in the power transfer link, thereby improving the accuracy of anomaly handling. The second module performs unified entity parsing on the metering operation data to obtain an entity parsing set. This allows for unified identification and association of scattered entity information in the power transfer link, providing accurate entity correspondences for subsequent link mapping and ensuring the accuracy of subsequent anomaly handling. Using the entity parsing set and topology connection data to construct the power transfer link map, the attribution relationships in the power transfer link are transformed into a structured, computable map. This provides link topology support for subsequent layer-by-layer tracing and positioning, solving the problem of relying on experience-based judgment for manually sorting link relationships, thus improving the accuracy of anomaly handling. Based on the power transfer link map and metering rule parameters, a metering baseline sequence is calculated. Theoretical metering values ​​can be generated according to various rules, providing a standardized reference baseline for subsequent anomaly comparison. This solves the problem of difficulty in unified judgment due to rule differences at different metering points, further improving the accuracy of power transfer link anomaly handling. Based on meter reading data, an actual metering sequence is constructed. The raw meter reading data is transformed into structured measured values, providing actual metering data for subsequent comparison with the baseline sequence. This ensures the accuracy and reliability of the difference analysis data, thereby improving the accuracy of handling anomalies in the power transfer link. Comparing the metering baseline sequence with the actual metering sequence yields metering difference results, quantifying the deviation between theoretical and measured values. This provides objective difference data for anomaly identification, improving the accuracy of anomaly handling. Using the metering difference results, the power transfer link map is traced layer by layer to obtain anomaly location information. This allows for tracing upstream from the anomaly point, attributing the anomaly to the least solvable cause. The first module identifies the link segment or metering point, addressing the difficulty of locating the specific link where anomalies occur in existing technologies, thereby improving the accuracy of subsequent handling of anomaly areas. The second module performs risk scoring on anomaly location information to obtain risk values, which can quantitatively assess the severity of anomalies, providing an objective basis for subsequent tiered handling and improving the accuracy of handling power supply link anomalies. The third module matches the risk values ​​against a pre-set tiered handling database to obtain anomaly handling plans for power supply links. It can automatically match different levels of handling actions based on the risk values, solving the problems of traditional handling plans being singular and lacking specificity, and improving the accuracy of anomaly handling.

[0060] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the abnormal handling method for the power supply link provided by any of the above-described method embodiments of the present invention.

[0061] 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.

[0062] Based on the above-described embodiment of a method for handling abnormalities in a power transfer link, 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 handling abnormalities in a power transfer link according to any embodiment of the present invention.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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 an abnormal handling method for a power transfer link as described in any of the above-described method embodiments of the present invention.

[0067] 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.

[0068] 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 an abnormal handling method for a power transfer link.

[0069] 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 handling abnormalities in a power transfer link, characterized in that, include: Acquire metering operation data in the power transfer link, wherein the metering operation data includes meter reading data, topology connection data and metering rule parameters; The metering operation data is subjected to unified entity parsing to obtain an entity parsing set. Using the entity parsing set and the topology connection data, a power transfer link map is constructed. Based on the power transfer link map and the metering rule parameters, a metering baseline sequence is calculated, and based on the meter reading data, an actual metering sequence is constructed. The metering baseline sequence is compared with the actual metering sequence to obtain metering difference results. The metering difference results are used to trace and locate the abnormal location information of the power transfer link layer by layer in the power transfer link map to obtain the abnormal location information. The abnormal location information is risk-scored to obtain a risk value. The risk value is matched with a preset graded handling database to obtain an anomaly handling plan for the power transfer link.

2. The abnormal handling method for the power transfer link as described in claim 1, characterized in that, The process of performing unified entity parsing on the metering operation data to obtain an entity parsing set specifically includes: Entity extraction is performed on the metering operation data to obtain an entity candidate set, wherein the entity candidate set includes entities with strong identifier fields and entities with weak identifier fields; The entities with strong identifier fields are subjected to rule fusion processing using preset fusion rules to obtain a first entity set; The weakly identified entities are subjected to similarity fusion processing to obtain a second entity set; Based on the first entity set and the second entity set, the entity parsing set is determined.

3. The abnormal handling method for the power transfer link as described in claim 1, characterized in that, The process of constructing a power transfer link map using the entity parsing set and the topology connection data specifically includes: Based on the entity parsing set and the topology connection data, a node set and an edge set are generated. The node set includes power transfer main nodes, metering point nodes, spatial unit nodes and end user nodes. The edge set is used to represent the power supply affiliation relationship of each node in the node set. Based on the set of nodes and the set of edges, a power supply link path is constructed; Perform a topology consistency check on the power supply link path and obtain the check result; If the verification result is successful, the power transfer link map is generated based on the power supply link path.

4. The abnormal handling method for the power transfer link as described in claim 1, characterized in that, The calculation of the metering baseline sequence based on the power transfer link map and the metering rule parameters specifically includes: Based on the power transfer link map and the metering rule parameters, determine the time period rule data, tiered rule data and allocation rule data for each metering point in the power transfer link map; For each metering point, the billing period data in the meter reading data corresponding to each metering point is decomposed into time periods using the time period rule data to obtain the decomposition results; The payment period data is segmented into tiers using the tiered rule data to obtain the segmentation results; Based on the allocation rule data and the payment period data, the allocation caliber data is generated; Based on the decomposition results, the segmentation results, and the apportionment caliber data, a baseline sequence of the measurement caliber corresponding to each measurement point is generated.

5. The abnormal handling method for the power transfer link as described in claim 1, characterized in that, The step of comparing the baseline sequence of the metrological caliber with the actual metrological caliber sequence to obtain the metrological difference result specifically includes: The measurement points in the baseline sequence and the actual measurement sequence are compared in the same dimension, and the deviation rate corresponding to each measurement point is calculated. For each metering point, if the deviation rate corresponding to the current metering point exceeds a preset difference threshold, an abnormal record corresponding to the current metering point is generated based on the metering point identifier and the deviation rate. The abnormal records from each measurement point are summarized to determine the measurement difference results.

6. The abnormal handling method for the power transfer link as described in claim 1, characterized in that, After acquiring the metering operation data in the power transfer link, the method further includes: The metering operation data is subjected to field normalization processing to obtain the first dataset; The first dataset is partitioned to obtain the second dataset; Perform data integrity checks on the second dataset and obtain the check results; The quality of the second dataset is labeled using the detection results to obtain the third dataset.

7. The abnormal handling method for the power transfer link as described in claim 1, characterized in that, The step of matching the risk value in a preset graded handling database to obtain an anomaly handling plan for the power transfer link specifically includes: If the risk value is less than a preset first risk threshold, a periodic recalculation scheme is generated; If the risk value is greater than or equal to the first risk threshold and less than the preset second risk threshold, a configuration verification scheme is generated. If the risk value is greater than or equal to the second risk threshold, an emergency alarm scheme is generated; Based on the periodic recalculation scheme, the configuration verification scheme, and the emergency alarm scheme, the anomaly handling scheme is determined.

8. A fault handling device for a power transfer link, characterized in that, It includes Module 1, Module 2, and Module 3; The first module is used to acquire metering operation data in the power transfer link, wherein the metering operation data includes meter reading data, topology connection data and metering rule parameters; The second module is used to perform unified entity parsing on the metering operation data to obtain an entity parsing set. Using the entity parsing set and the topology connection data, a power transfer link map is constructed. Based on the power transfer link map and the metering rule parameters, a metering baseline sequence is calculated, and based on the meter reading data, an actual metering sequence is constructed. The metering baseline sequence is compared with the actual metering sequence to obtain a metering difference result. The metering difference result is used to trace and locate the abnormal location information of the power transfer link layer by layer in the power transfer link map to obtain the abnormal location information. The abnormal location information is then risk-scored to obtain a risk value. The third module is used to match the risk value in a preset graded handling database to obtain an abnormal handling plan for the power supply link.

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 abnormal handling method for the power transfer link 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, they implement the abnormal handling method for the power transfer link as described in any one of claims 1 to 7.

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