Power defect service monitoring method, system, device, storage medium and product

Abstract

The application relates to a power defect service monitoring method, system, device, storage medium and product. The method comprises the following steps: acquiring service association data of a power defect service; the service association data comprises service process data, service involved objects and defect description data; according to the service process data, a time limit and efficiency type exception identification result of the power defect service is determined; according to the service involved objects, a role configuration type exception identification result is determined; according to the defect description data, a data consistency type exception identification result is determined; according to at least one of the time limit and efficiency type exception identification result, the role configuration type exception identification result and the consistency type exception identification result, early warning information is generated; according to the early warning information, a supervision task is generated, and the supervision task is sent to the service involved objects of the power defect service. The method can improve the power defect service monitoring efficiency and precision.

Description

Technical Field

[0001] This application relates to the field of power production data technology, and in particular to a method, system, equipment, storage medium and product for monitoring power defects. Background Technology

[0002] With the development of power production, operation and maintenance, and smart grid technologies, power grid equipment defect management is gradually moving towards informatization and process-oriented approaches. Currently, a complete business chain has been formed for equipment defects, from registration, reporting, review, and planning to on-site handling, acceptance, and archiving. Traditional management methods are no longer adequate for the needs of large-scale equipment operation and maintenance.

[0003] In traditional technologies, defective business data is scattered across multiple independent systems, and anomaly identification mainly relies on manually exporting reports and checking each item for issues such as timeouts and responsibility exceptions.

[0004] However, the current manual verification method has problems such as difficulty in unifying and integrating multi-source data, delay in anomaly detection, low verification efficiency and high cost. Furthermore, anomaly identification and supervision and handling are separated, making it impossible to achieve automatic monitoring and closed-loop management of the entire defective business process. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, system, equipment, storage medium, and product for power defect monitoring that can improve the efficiency and accuracy of power defect monitoring, addressing the aforementioned technical problems.

[0006] Firstly, this application provides a method for monitoring power defect operations, including:

[0007] Acquire business-related data for power defect operations; business-related data includes business process data, business-related objects, and defect description data.

[0008] Based on business process data, determine the timeliness-related anomaly identification results for power defect business; based on the objects involved in the business, determine the role configuration-related anomaly identification results; based on defect description data, determine the data consistency-related anomaly identification results.

[0009] Based on at least one of the following anomaly identification results: timeliness anomaly identification results, role configuration anomaly identification results, and consistency anomaly identification results, generate early warning information;

[0010] Based on the early warning information, a supervision task is generated and then distributed to the relevant business entities involved in the power defect business.

[0011] In one embodiment, an early warning message is generated based on at least one of the following: timeliness-related anomaly identification results, role configuration-related anomaly identification results, and consistency-related anomaly identification results:

[0012] Determine the anomaly type of the anomaly identification results, and generate corresponding warning information based on the anomaly type;

[0013] Accordingly, supervisory tasks are generated based on the early warning information and then distributed to the relevant business entities involved in the power defect business, including:

[0014] Based on the anomaly type of the early warning information, identify the target object that matches the anomaly type from the business-related objects, and issue the supervision task to the target object.

[0015] In one embodiment, the progress of the supervision task is monitored, and upon receiving a handling receipt from the target object, supervision completion information is generated based on the handling receipt.

[0016] Based on the completion information of the supervision, verify whether the handling receipt is consistent with the actual status of the corresponding power defect business, and if consistent, update the abnormal status of the power defect business.

[0017] In one embodiment, based on business process data, the timeliness-related anomaly identification result of the power defect business is determined, including:

[0018] Using the defective equipment identifier as the primary key, business process data, business-related objects, and defect description data are linked and integrated to construct a defective business status view corresponding to power defect business.

[0019] Based on the matching relationship between process node information and timing constraint information in the defect service status view, the timeliness-related anomaly identification results of power defect services are determined.

[0020] In one embodiment, the role configuration anomaly identification result is determined based on the business-related objects, including:

[0021] Perform role mapping on the objects involved in the business to obtain the business role information corresponding to different objects;

[0022] Based on the consistency between the correspondence between different objects and roles in the business role information and the preset role rules, the result of role configuration anomaly identification is determined.

[0023] In one embodiment, based on the defect description data, the data consistency anomaly identification result is determined, including:

[0024] Text element extraction processing is performed on the defect description data to obtain structured defect element information;

[0025] Based on the comparison results between defect element information and related business data, the data consistency anomaly identification results are determined.

[0026] In one embodiment, after obtaining the timeliness-related anomaly identification results, the role configuration-related anomaly identification results, and the data consistency-related anomaly identification results, the method further includes:

[0027] For multiple anomaly identification results corresponding to the same power defect service, the anomaly identification results are deduplicated and merged, and the merged anomaly identification results are classified into different levels.

[0028] Accordingly, early warning information is generated, including:

[0029] Based on the level of the merged anomaly identification results, a corresponding level of early warning information is generated.

[0030] Secondly, this application also provides a power defect business monitoring system, which includes a cascaded business management subsystem, an online monitoring subsystem, an alarm subsystem, and a supervision subsystem.

[0031] The business management subsystem is used to acquire business-related data for power defect business; business-related data includes business process data, business-related objects, and defect description data.

[0032] The online monitoring subsystem is used to determine the timeliness-related anomaly identification results of power defect business based on business process data; determine the role configuration-related anomaly identification results based on the objects involved in the business; and determine the data consistency-related anomaly identification results based on defect description data.

[0033] The alarm subsystem is used to generate warning information based on at least one of the following: time-sensitive anomaly identification results, role configuration anomaly identification results, and consistency anomaly identification results.

[0034] The supervision subsystem is used to generate supervision tasks based on early warning information and distribute these tasks to the relevant business entities involved in the power defect business.

[0035] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0036] Acquire business-related data for power defect operations; business-related data includes business process data, business-related objects, and defect description data.

[0037] Based on business process data, determine the timeliness-related anomaly identification results for power defect business; based on the objects involved in the business, determine the role configuration-related anomaly identification results; based on defect description data, determine the data consistency-related anomaly identification results.

[0038] Based on at least one of the following anomaly identification results: timeliness anomaly identification results, role configuration anomaly identification results, and consistency anomaly identification results, generate early warning information;

[0039] Based on the early warning information, a supervision task is generated and then distributed to the relevant business entities involved in the power defect business.

[0040] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0041] Acquire business-related data for power defect operations; business-related data includes business process data, business-related objects, and defect description data.

[0042] Based on business process data, determine the timeliness-related anomaly identification results for power defect business; based on the objects involved in the business, determine the role configuration-related anomaly identification results; based on defect description data, determine the data consistency-related anomaly identification results.

[0043] Based on at least one of the following anomaly identification results: timeliness anomaly identification results, role configuration anomaly identification results, and consistency anomaly identification results, generate early warning information;

[0044] Based on the early warning information, a supervision task is generated and then distributed to the relevant business entities involved in the power defect business.

[0045] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0046] Acquire business-related data for power defect operations; business-related data includes business process data, business-related objects, and defect description data.

[0047] Based on business process data, determine the timeliness-related anomaly identification results for power defect business; based on the objects involved in the business, determine the role configuration-related anomaly identification results; based on defect description data, determine the data consistency-related anomaly identification results.

[0048] Based on at least one of the following anomaly identification results: timeliness anomaly identification results, role configuration anomaly identification results, and consistency anomaly identification results, generate early warning information;

[0049] Based on the early warning information, a supervision task is generated and then distributed to the relevant business entities involved in the power defect business.

[0050] The aforementioned power defect monitoring methods, systems, equipment, storage media, and products collect multi-dimensional business-related data, including business process data, business-related objects, and defect description data, and conduct targeted anomaly identification based on these three different data dimensions. This allows for the simultaneous discovery of problems in power defect operations from three independent dimensions: business processing timeliness, personnel role compliance, and defect data quality. This overcomes the limitations of traditional solutions that only conduct audits from a single dimension, resulting in broader anomaly identification coverage and more accurate problem localization. Furthermore, the unified aggregation of multiple anomaly identification results generates early warning information and automatically creates and distributes supervisory tasks to relevant business objects, creating an automated business loop of automatic anomaly identification, early warning push, and supervisory handling. This eliminates the need for manual organization of various issues and manual initiation of supervisory tasks, reducing the workload of manual verification and offline supervision, improving the automation level of power grid defect business supervision and governance, promptly identifying maintenance risks, reducing management oversights, and ensuring the standardized and orderly operation of power grid equipment defect business. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is a flowchart illustrating a power defect monitoring method in one embodiment;

[0053] Figure 2 This is a flowchart illustrating the steps for generating early warning information in one embodiment;

[0054] Figure 3 This is a flowchart illustrating the level classification steps in one embodiment;

[0055] Figure 4 This is a flowchart illustrating the power defect monitoring method in another embodiment;

[0056] Figure 5 This is a structural block diagram of a power defect service monitoring system in one embodiment;

[0057] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0059] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0060] In one exemplary embodiment, such as Figure 1 As shown, a method for monitoring power defects is provided. Taking the application of this method in a power defect monitoring system as an example, the method includes the following steps:

[0061] S110, Obtain business-related data for power defect services.

[0062] Power defect management can be understood as the entire process of business management activities after a defect occurs in power grid equipment during operation, from defect discovery and registration, hierarchical reporting, review and arrangement, plan formulation, on-site handling, to acceptance and archiving. Power defect management may involve multiple business links and various roles.

[0063] The business-related data includes business process data, business-related objects, and defect description data. Business process data describes the flow, time nodes, and status changes of each processing stage from initiation to closure of a power defect business, including at least the processing time, processing status, and processing opinions for each stage. Business-related objects refer to all entities involved in the entire process of power defect business processing, including at least registration personnel, reporting personnel, review personnel, processing supervisors, acceptance personnel, and responsible units. Defect description data describes the attributes and specific content of the defect itself, including at least the defect content description, defect level, defect location, and equipment information.

[0064] In one optional implementation, the business-related data of power defect operations can be obtained by acquiring multi-source heterogeneous data related to defects from multiple business systems such as the power grid management platform, online monitoring system, alarm system, maintenance system, testing system, and supervision system through standardized interface calls, timed batch data synchronization, message queue subscription, or database log capture.

[0065] S120, based on business process data, determine the timeliness-related anomaly identification results for power defect business; based on the objects involved in the business, determine the role configuration-related anomaly identification results; based on defect description data, determine the data consistency-related anomaly identification results.

[0066] Among them, the timeliness-related anomaly identification results can be understood as the identification conclusions obtained by comparing and analyzing the time nodes of each link in the business process with the preset time limit requirements. It can characterize whether there are anomalies in the processing timeliness of power defect business, as well as the specific type of anomaly, the duration of the overdue period and the severity of the anomaly.

[0067] Among them, the role configuration anomaly identification results can be understood as the identification conclusions obtained by comparing and verifying the personnel role allocation in the business-related objects with the preset incompatible role rules. It can characterize whether the personnel role configuration in the power defect business meets the job separation requirements and whether there is a violation of role concurrency.

[0068] Among them, the data consistency anomaly identification results can be understood as the identification conclusions obtained by cross-comparing the defect description data with other related business data. It can characterize whether the data descriptions of the same power defect are consistent in different sources and different links, and whether there are data conflicts or information mismatches.

[0069] In one optional implementation, the defective equipment identifier can be used as the primary key to associate and integrate business process data, business-related objects, and defect description data to construct a defective business status view corresponding to the power defect business; based on the matching relationship between process node information and time-series constraint information in the defective business status view, the timeliness-type anomaly identification result of the power defect business can be determined.

[0070] Among them, the defective device identifier is used to represent the identity information of the defective device. The defective business status view is used to represent a unified business status model that integrates multi-source information throughout the entire process, with each defect as a unit, and can fully reflect the status of the defect from registration to closure.

[0071] Among them, the time-series constraint information is used to characterize the time limit requirements and early warning rules of each processing link of the power defect business, including at least the standard processing time limit of each process node, the advance warning amount for near overdue warning, and the differentiated time limit configuration corresponding to different defect levels.

[0072] Specifically, the defective device identifier can be used as the primary key to match and associate business process data, business-related objects, and defect description data scattered from different sources, forming a complete dataset corresponding to each defect. Then, the node information of each link is arranged in series according to the process sequence to construct a defect business status view that includes process nodes, responsible roles, time constraints, business status, and related events. Then, the actual completion time of each process node in the defect business status view is compared one by one with the corresponding time limit requirements in the time sequence constraint information. When the actual time exceeds the time limit requirement or is close to the time limit warning threshold, it is determined that there is a time-sensitive anomaly and the corresponding time-sensitive anomaly identification result is generated.

[0073] In the above process, relying on the defect business status view to collect defect process node data, obtain the time sequence context of the entire defect processing process, and carry out timeliness comparison, it can effectively avoid the omission and misjudgment of timeliness anomalies caused by data fragmentation, while reducing the resource consumption caused by frequent cross-system data retrieval.

[0074] In one optional implementation, role mapping can be performed on the business-related objects to obtain business role information corresponding to different objects; based on the consistency between the correspondence between different objects and roles in the business role information and the preset role rules, the role configuration anomaly identification result can be determined.

[0075] Among them, business role information is used to characterize the job functions and business role identities of each business-related object in the defect handling process, as well as the relationship between each role and the corresponding handling link; preset role rules can be understood as a set of pre-configured rules used to verify the compliance of role configuration, including at least incompatible job separation rules, role permission boundary rules, and role configuration requirements corresponding to different defect levels.

[0076] Specifically, the personnel identifiers in the business-related objects can be standardized and mapped first, uniformly mapping different accounts and different names of the same person in different business systems to a unique personnel identifier; then, according to the processing stage of each person in the defect process, their corresponding business role type is marked, forming a correspondence between personnel and roles; then, this correspondence is compared and verified with the preset incompatible role rules. When the same personnel identifier is found to appear in two or more preset incompatible role positions at the same time, it is determined that there is a role configuration anomaly and the corresponding role configuration anomaly identification result is generated.

[0077] In the above process, standardized role mapping unifies the identification of personnel across multiple systems, avoiding omissions in the identification of concurrent roles due to inconsistent identification; automatic comparison and verification based on preset incompatible role rules replaces manual verification, efficiently identifies abnormal concurrent roles, ensures the compliance of job separation in the defect handling process, and improves inspection efficiency and accuracy.

[0078] In one optional implementation, text element extraction processing can be performed on the defect description data to obtain structured defect element information; based on the comparison results between the defect element information and related business data, the data consistency anomaly identification result can be determined.

[0079] Among them, defect element information can be understood as structured key attribute information extracted from unstructured defect description text, including at least core elements such as the device to which the defect belongs, the location of the defect, the type of defect, the severity of the defect, and the description of the defect phenomenon; related business data is used to characterize structured data from other business systems that are related to the same defect, serving as a reference benchmark for consistency comparison.

[0080] Specifically, key elements such as equipment name, component location, abnormal phenomenon, and severity level can be extracted from the defect description text through keyword matching, named entity recognition, or semantic analysis to form structured defect element information. Then, the defect element information is compared item by item with related business data from other business systems such as maintenance system, alarm system, and testing system. When the same defect has inconsistent equipment information, defect level, or defect description in data from different sources, it is determined that there is a data consistency anomaly and the corresponding data consistency anomaly identification result is generated.

[0081] In the above process, unstructured defect descriptions are transformed into structured element information through text element extraction, which solves the problem that text data is difficult to directly participate in consistency verification. Based on the automatic comparison of structured elements and multi-source associated data, cross-system data inconsistencies can be detected in a timely manner, replacing manual verification, improving the efficiency and accuracy of data quality verification, and ensuring the reliability of defective business data.

[0082] S130, generate warning information based on at least one of the following: timeliness-related anomaly identification results, role configuration-related anomaly identification results, and consistency-related anomaly identification results.

[0083] For example, warning information of the corresponding type can be generated based on the anomaly type of the anomaly identification result. For example, time-sensitive anomalies correspond to timeout warnings, role configuration anomalies correspond to job compliance warnings, and data consistency anomalies correspond to data quality warnings.

[0084] The system can also generate different levels of warning information based on the severity of the anomaly identification results, such as Level 1, Level 2, and Level 3 warnings. The warning information should include at least the anomaly type, anomaly level, associated defect identifier, responsible party, and handling recommendations. When the same defect simultaneously triggers multiple anomalies, the multiple anomaly identification results can be merged and deduplicated to generate a single comprehensive warning, avoiding duplicate warnings.

[0085] S140 generates a supervision task based on the early warning information and distributes the supervision task to the relevant business entities involved in the power defect business.

[0086] For example, after receiving an early warning message, the system can automatically extract key fields from the early warning message and the corresponding defective business data, and populate them into the supervision task template to generate a supervision task. The supervision task includes at least the associated defect identifier, anomaly type, rectification requirements, rectification deadline, and responsible party.

[0087] Furthermore, based on the anomaly type of the warning information, the responsible entity corresponding to that anomaly type can be matched from the business-related objects, and the supervision task can be accurately assigned to the corresponding responsible unit or person; the assignment method can be at least one of several methods such as system message push, SMS notification, and email reminder. If no feedback is received after the preset handling time limit for the supervision task, an escalation supervision instruction can be automatically generated and assigned to the superior management entity of the responsible entity.

[0088] The aforementioned power defect monitoring method collects multi-dimensional business-related data, including business process data, business-related objects, and defect description data. It then conducts targeted anomaly identification based on these three different data dimensions. This allows for the simultaneous discovery of problems in power defect operations from three independent dimensions: business processing timeliness, personnel role compliance, and defect data quality. This overcomes the limitations of traditional solutions that only conduct audits from a single dimension, resulting in broader anomaly identification coverage and more accurate problem location. Furthermore, the method unifies the collection of anomaly identification results to generate early warning information and automatically creates and distributes supervisory tasks to relevant business objects. This establishes an automated business loop encompassing automatic anomaly identification, early warning push, and supervisory handling. It eliminates the need for manual problem organization and initiation of supervisory tasks, reducing the workload of manual verification and offline supervision. This improves the automation level of power grid defect supervision and governance, enabling timely detection of maintenance risks, reducing management oversights, and ensuring the standardized and orderly operation of power grid equipment defect services.

[0089] Based on the technical solutions of the above embodiments, this application also provides an optional embodiment. In this optional embodiment, the process of generating warning information based on at least one of the time-sensitive anomaly identification results, role configuration anomaly identification results, and consistency anomaly identification results is refined.

[0090] See Figure 2 The steps for generating the warning information shown include:

[0091] S210, determine the anomaly type of the anomaly identification result, and generate corresponding warning information based on the anomaly type.

[0092] For example, the system first classifies the anomaly identification results to determine their anomaly type. The anomaly types include at least time-sensitive anomalies, role configuration anomalies, and data consistency anomalies. Then, it generates corresponding warning information based on different anomaly types. For example, time-sensitive anomalies correspond to timeout warnings, role configuration anomalies correspond to job compliance warnings, and data consistency anomalies correspond to data quality warnings. Different types of warning information are configured with differentiated display styles, push channels, and handling suggestion templates. For example, timeout warnings highlight the overdue duration and deadline, job compliance warnings highlight the separation requirements of the violating role and job, and data quality warnings highlight inconsistent fields and data sources.

[0093] Accordingly, the above-mentioned process of generating supervision tasks based on early warning information and issuing supervision tasks to the business-related entities involved in power defect business includes: identifying target objects that match the anomaly type from the business-related entities based on the anomaly type of the early warning information, and issuing supervision tasks to the target objects.

[0094] For example, the system pre-configures the correspondence between anomaly types and responsible entities. For instance, time-sensitive anomalies correspond to the person in charge and the responsible unit, role configuration anomalies correspond to the audit management personnel and the human resources management department, and data consistency anomalies correspond to the data governance personnel and the data management department. When a certain type of warning information is generated, the system selects the matching responsible unit or person from the business objects involved in the defect as the target object according to the preset correspondence, and accurately sends the supervision task to the corresponding target object, avoiding sending the supervision task to irrelevant personnel and improving the targeting and efficiency of supervision.

[0095] In some embodiments, the progress of the supervision task can also be monitored, and supervision completion information can be generated based on the handling receipt when the target object is received; based on the supervision completion information, it can be verified whether the handling receipt is consistent with the actual status of the corresponding power defect business, and if they are consistent, the abnormal status of the power defect business can be updated.

[0096] In the above embodiments, by generating differentiated early warnings according to the type of anomaly and accurately matching the target objects to assign supervision, the early warning identification and supervision targeting are improved; by verifying the consistency between the handling receipt and the actual status, the supervision is ensured to be truly implemented in a closed loop, thereby improving the overall efficiency and effectiveness of anomaly handling.

[0097] Based on the technical solutions of the above embodiments, this application also provides an optional embodiment. In this optional embodiment, the subsequent steps after obtaining the timeliness-related anomaly identification results, the role configuration-related anomaly identification results, and the data consistency-related anomaly identification results are described.

[0098] See Figure 3The grading steps shown include:

[0099] S310 performs deduplication and merging of multiple anomaly identification results corresponding to the same power defect service, and classifies the merged anomaly identification results into different levels.

[0100] For example, if multiple anomaly identification results with similar sources and duplicate content are detected for the same defect, the duplicates are removed and merged. For the multiple anomalies of different types remaining after merging, grading rules are set according to the anomaly type, the importance of the defect, and the scope of risk impact to classify different anomaly levels. Subsequently, the anomaly level can be combined to determine the urgency of the warning, the priority of supervision, and the push strategy.

[0101] Accordingly, the aforementioned generation of early warning information includes: generating early warning information of a corresponding level based on the level of the merged anomaly identification results.

[0102] For example, the system generates warning information of corresponding levels based on the level of the merged anomaly identification results. Different levels of warnings are configured with different display styles, push frequencies and notification channels. For example, high-level warnings are displayed in red and pushed in real time through multiple channels, medium-level warnings are marked in orange and pushed on a timed basis, and low-level warnings are marked in yellow and included in batch summary notifications. The warning information carries the corresponding anomaly level identifier, which makes it easy for the recipient to quickly judge the urgency and arrange the handling priority.

[0103] In the above embodiments, multiple anomaly identification results of the same defect are deduplicated and merged to avoid information redundancy caused by repeated warnings and multiple supervisions; by classifying the merged results into levels and generating warning information of corresponding levels, differentiated display and hierarchical push can be achieved, making it easier for the recipient to quickly judge the degree of urgency, reasonably arrange the priority of handling, and improve the orderliness and efficiency of anomaly management.

[0104] Based on the technical solutions of the above embodiments, this application also provides an optional embodiment. In this optional embodiment, the power defect service monitoring method provided by this application will be described in detail.

[0105] See Figure 4 The power defect monitoring method shown includes:

[0106] S410, Obtain business-related data for power defect services; business-related data includes business process data, business-related objects, and defect description data.

[0107] For example, business-related data includes: 1. Basic defect information: defect number, defect name, defect content, defect level, affiliated unit, equipment name, equipment code, voltage level; 2. Process time information: registration time, reporting time, review time, planned start time, planned completion time, actual completion time, acceptance time, archiving time; 3. Process node information: current stage, historical flow stages, stage handler, stage handling opinion, node status; 4. Role personnel information: registrant, reporter, reviewer, planner, handling person in charge, acceptance person, supervision person in charge; 5. External related information: equipment alarm, online monitoring anomaly, maintenance record, test record, power outage plan, risk level; 6. Anomaly identification information: anomaly type, anomaly level, hit rule, identification time, trigger source, rule version; 7. Warning and supervision information: warning topic, warning content, warning status, supervision order number, rectification requirements, rectification deadline, handling progress, and feedback result.

[0108] S420: Based on business process data, determine the timeliness-related anomaly identification results for power defect business; based on the objects involved in the business, determine the role configuration-related anomaly identification results; based on defect description data, determine the data consistency-related anomaly identification results.

[0109] For example, the defective equipment identifier can be used as the primary key to associate and integrate business process data, business-related objects, and defect description data to construct a defective business status view corresponding to the power defect business; based on the matching relationship between process node information and time-series constraint information in the defective business status view, the time-related anomaly identification result of the power defect business can be determined.

[0110] For example, role mapping can be performed on business-related objects to obtain business role information corresponding to different objects; based on the consistency between the correspondence between different objects and roles in the business role information and the preset role rules, the role configuration class anomaly identification result can be determined.

[0111] For example, text element extraction processing can be performed on the defect description data to obtain structured defect element information; based on the comparison results between the defect element information and related business data, the data consistency anomaly identification results can be determined.

[0112] S430 performs deduplication and merging of multiple anomaly identification results corresponding to the same power defect service, and classifies the merged anomaly identification results into different levels.

[0113] S440: Determine the anomaly type of the anomaly identification result, and generate a corresponding type of early warning information based on the anomaly type; generate an early warning information of the corresponding level based on the level of the merged anomaly identification result.

[0114] S450 identifies target objects matching the anomaly type from the business-related objects based on the anomaly type in the early warning information, and issues the supervision task to the target object.

[0115] S460 monitors the progress of tasks and generates completion information based on the feedback from the target entity.

[0116] S470, based on the supervision completion information, verifies whether the handling receipt is consistent with the actual status of the corresponding power defect business, and if consistent, updates the abnormal status of the power defect business.

[0117] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0118] Based on the same inventive concept, this application also provides a power defect service monitoring system for implementing the power defect service monitoring method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more power defect service monitoring system embodiments provided below can be found in the limitations of the power defect service monitoring method described above, and will not be repeated here.

[0119] In one exemplary embodiment, such as Figure 5 As shown, a power defect business monitoring system is provided, including: a business management subsystem 510, an online monitoring subsystem 520, an alarm subsystem 530, and a supervision subsystem 540, wherein:

[0120] The business management subsystem 510 is used to acquire business-related data for power defect business; business-related data includes business process data, business-related objects, and defect description data.

[0121] The online monitoring subsystem 520 is used to determine the timeliness-related anomaly identification results of power defect business based on business process data; determine the role configuration-related anomaly identification results based on the objects involved in the business; and determine the data consistency-related anomaly identification results based on defect description data.

[0122] The alarm subsystem 530 is used to generate early warning information based on at least one of the following: the results of time-sensitive anomaly identification, the results of role configuration anomaly identification, and the results of consistency anomaly identification.

[0123] The supervision subsystem 540 is used to generate supervision tasks based on early warning information and distribute the supervision tasks to the relevant business entities involved in the power defect business.

[0124] In one embodiment, the alarm subsystem is specifically used to determine the anomaly type of the anomaly identification result and generate a corresponding type of early warning information based on the anomaly type; correspondingly, the supervision subsystem is specifically used to identify the target object matching the anomaly type from the business-related objects based on the anomaly type of the early warning information and to issue the supervision task to the target object.

[0125] In one embodiment, the supervision subsystem is also used to monitor the progress of the supervision task, and generate supervision completion information based on the handling receipt when a handling receipt is received from the target object; based on the supervision completion information, it verifies whether the handling receipt is consistent with the actual status of the corresponding power defect business, and updates the abnormal status of the power defect business if they are consistent.

[0126] In one embodiment, the online monitoring subsystem includes a first identification unit, which is used to associate and integrate business process data, business-related objects and defect description data with the defective equipment identifier as the primary key to construct a defective business status view corresponding to the power defect business; and to determine the timeliness-type anomaly identification result of the power defect business based on the matching relationship between process node information and time-series constraint information in the defective business status view.

[0127] In one embodiment, the online monitoring subsystem includes a second identification unit, which is used to perform role mapping processing on business-related objects to obtain business role information corresponding to different objects; and to determine the role configuration anomaly identification result based on the consistency between the correspondence between different objects and roles in the business role information and the preset role rules.

[0128] In one embodiment, the online monitoring subsystem includes a third identification unit, which is used to perform text element extraction processing on the defect description data to obtain structured defect element information; and to determine the data consistency anomaly identification result based on the comparison results between the defect element information and the associated business data.

[0129] In one embodiment, the online monitoring subsystem is further configured to perform deduplication and merging of multiple anomaly identification results corresponding to the same power defect service, and classify the merged anomaly identification results into levels; correspondingly, the alarm subsystem is configured to generate warning information of the corresponding level based on the level of the merged anomaly identification results.

[0130] Each module in the aforementioned power defect monitoring system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0131] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6 As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a power defect monitoring method. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0132] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0133] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0134] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0135] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0136] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0137] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0138] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0139] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for monitoring power defect operations, characterized in that, The method, applied to a power defect monitoring system, includes: Acquire business-related data for power defect services; the business-related data includes business process data, business-related objects, and defect description data. Based on the business process data, determine the timeliness-related anomaly identification results for the power defect business; based on the objects involved in the business, determine the role configuration-related anomaly identification results; based on the defect description data, determine the data consistency-related anomaly identification results. Based on at least one of the timeliness-related anomaly identification results, the role configuration-related anomaly identification results, and the consistency-related anomaly identification results, an early warning message is generated; Based on the early warning information, a supervision task is generated and the supervision task is issued to the relevant business entities involved in the power defect business.

2. The method according to claim 1, characterized in that, The step of generating early warning information based on at least one of the timeliness-related anomaly identification results, the role configuration-related anomaly identification results, and the consistency-related anomaly identification results includes: Determine the anomaly type of the anomaly identification result, and generate a corresponding type of early warning information based on the anomaly type; Accordingly, the step of generating a supervision task based on the early warning information and issuing the supervision task to the relevant business entities involved in the power defect service includes: Based on the anomaly type of the warning information, identify the target object that matches the anomaly type from the business-related objects, and issue the supervision task to the target object.

3. The method according to claim 2, characterized in that, The method further includes: Monitor the progress of the supervised tasks, and generate supervision completion information based on the feedback from the target object upon receiving the feedback. Based on the supervision completion information, verify whether the handling receipt is consistent with the actual status of the corresponding power defect service, and if consistent, update the abnormal status of the power defect service.

4. The method according to claim 1, characterized in that, The step of determining the timeliness-related anomaly identification result of the power defect service based on the business process data includes: Using the defective equipment identifier as the primary key, the business process data, the business-related objects, and the defect description data are associated and integrated to construct a defective business status view corresponding to the power defect business; Based on the matching relationship between process node information and timing constraint information in the defect service status view, the timeliness-type anomaly identification result of the power defect service is determined.

5. The method according to claim 1, characterized in that, The step of determining the role configuration anomaly identification result based on the business-related objects includes: Perform role mapping on the objects involved in the business to obtain business role information corresponding to different objects; Based on the consistency between the correspondence between different objects and roles in the business role information and the preset role rules, the role configuration anomaly identification result is determined.

6. The method according to claim 1, characterized in that, The step of determining the data consistency anomaly identification result based on the defect description data includes: Text element extraction processing is performed on the defect description data to obtain structured defect element information; Based on the comparison results between the defect element information and the associated business data, the data consistency anomaly identification result is determined.

7. The method according to any one of claims 1-6, characterized in that, After obtaining the timeliness-related anomaly identification results, the role configuration-related anomaly identification results, and the data consistency-related anomaly identification results, the method further includes: For multiple anomaly identification results corresponding to the same power defect service, the anomaly identification results are deduplicated and merged, and the merged anomaly identification results are classified into different levels. Accordingly, the generation of early warning information includes: Based on the level of the merged anomaly identification results, a corresponding level of early warning information is generated.

8. A power defect monitoring system, characterized in that, The power defect business monitoring system includes a cascaded business management subsystem, an online monitoring subsystem, an alarm subsystem, and a supervision subsystem; The business management subsystem is used to acquire business-related data for power defect business; the business-related data includes business process data, business-related objects, and defect description data. The online monitoring subsystem is used to determine the timeliness-related anomaly identification result of the power defect business based on the business process data; and to determine the role configuration-related anomaly identification result based on the objects involved in the business. Based on the defect description data, determine the data consistency anomaly identification results; The alarm subsystem is used to generate warning information based on at least one of the time-sensitive anomaly identification results, the role configuration anomaly identification results, and the consistency anomaly identification results; The supervision subsystem is used to generate supervision tasks based on the early warning information and to distribute the supervision tasks to the business-related objects of the power defect business.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-7.

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