Distribution network transient recording event sample library dynamic maintenance method and computer equipment

Abstract

The invention discloses a dynamic maintenance method for a distribution network transient recording event sample library and computer equipment. The method comprises the steps of obtaining a first preset database; receiving a maintenance operation executed on the first preset database based on the target account; based on the maintenance operation, a log is generated and stored in a second preset database, and the log comprises operation time corresponding to the maintenance operation, an identifier corresponding to the power distribution network transient recording event sample and a target account identifier; based on the preset interval duration, whether an abnormal identifier exists in a first preset database is detected, and the abnormal identifier represents that the identifier of the corresponding power distribution network transient recording event sample does not exist; and under the condition that the abnormal identifier does not exist in the first preset database, determining that the maintenance operation is executed successfully. According to the method, the technical problem that certain risks exist in data errors due to implicit association of waveform data and label information when a power distribution network transient recording event sample library is maintained at present is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system automation, in particular to a distribution network transient recording event sample library dynamic maintenance method and computer equipment. BACKGROUND

[0002] In the field of operation and maintenance of distribution networks, accurately identifying the causes of transient recording event is the key to achieving efficient proactive defect elimination and improving power supply reliability. Currently, artificial intelligence-based transient waveform analysis technology has become the mainstream direction, and the performance of its model is highly dependent on the support of high-quality and large-scale sample libraries. However, there are obvious deficiencies in the construction and maintenance of distribution network transient recording event sample libraries. In the architecture design of some sample libraries, waveform data and label information are stored separately, and only rely on implicit sequential correlation. Such design is prone to data misplacement when performing sample addition and deletion operations, i.e., waveform data and label information lose correspondence, which further affects the consistency of the sample library. For example, if the deletion operation does not follow the principle of "entity first, index later", the label information may be archived, but the corresponding waveform data still exists in the sample library, forming a so-called "broken link" problem.

[0003] At present, there is no effective solution to the above problems. SUMMARY

[0004] The embodiments of the present application provide a distribution network transient recording event sample library dynamic maintenance method and computer equipment to at least solve the technical problem that there is a certain risk of data error due to implicit association of waveform data and label information when maintaining the distribution network transient recording event sample library.

[0005] According to an aspect of an embodiment of the present application, a distribution network transient recording event sample library dynamic maintenance method is provided, comprising: obtaining a first preset database, wherein the first preset database stores a plurality of distribution network transient recording event samples and a plurality of distribution network transient recording event samples each corresponding identifier; receiving a maintenance operation on the first preset database based on a target account, wherein the maintenance operation includes at least one of the following: addition, deletion and modification; based on the maintenance operation, generating a log and storing the log to a second preset database, wherein the log includes the operation time corresponding to the maintenance operation, the identifier corresponding to the distribution network transient recording event sample corresponding to the maintenance operation and the identifier corresponding to the target account; based on a preset interval duration, detecting whether there is an abnormal identifier in the first preset database, wherein the abnormal identifier represents an identifier that does not exist corresponding to the distribution network transient recording event sample; in the case that there is no abnormal identifier in the first preset database, it is determined that the maintenance operation is successfully executed.

[0006] Optionally, the obtaining the first preset database comprises: assigning an identifier to each of the plurality of power grid transient recording event samples; constructing a label index corresponding to each of the plurality of power grid transient recording event samples based on the identifier corresponding to each of the plurality of power grid transient recording event samples; establishing a sample label index library based on the label index corresponding to each of the plurality of power grid transient recording event samples; establishing a physical entity file library for storing waveform data entity files corresponding to each of the plurality of power grid transient recording event samples; and determining the first preset database based on the sample label index library and the physical entity file library.

[0007] Optionally, in the case of the maintenance operation being addition, the method comprises: receiving an added power grid transient recording event sample; assigning a new identifier to the added power grid transient recording event sample, wherein the new identifier is different from the identifiers corresponding to the plurality of power grid transient recording event samples in the first preset database; determining a label index corresponding to the added power grid transient recording event sample based on the new identifier corresponding to the added power grid transient recording event sample; storing a waveform data entity file corresponding to the added power grid transient recording event sample in the physical entity file library; and storing the label index corresponding to the added power grid transient recording event sample in the sample label index library.

[0008] Optionally, a state machine model is set, wherein the state machine model is used to update states in the label indexes corresponding to the plurality of power grid transient recording event samples in the sample label index library, and the states include online, to be audited, and archived.

[0009] Optionally, in the case of the maintenance operation being deletion, the method comprises: receiving a to-be-deleted identifier input based on a target account; searching for a to-be-deleted power grid transient recording event sample corresponding to the to-be-deleted identifier in the first preset database; and setting a state of the to-be-deleted power grid transient recording event sample to archived based on the state machine model.

[0010] Optionally, in the case of the maintenance operation being addition, the method further comprises: receiving an added power grid transient recording event sample; determining a confidence degree corresponding to the added power grid transient recording event sample; determining whether the confidence degree is higher than a preset threshold; setting a state of the added power grid transient recording event sample to online in the case that the confidence degree is higher than the preset threshold; and setting the state of the added power grid transient recording event sample to to be audited in the case that the confidence degree is not higher than the preset threshold.

[0011] Optionally, based on the label index in the sample label index library, it is judged whether the corresponding waveform data entity file exists in the physical entity file library; based on the waveform data entity file in the physical entity file library, it is judged whether the corresponding label index exists in the sample label index library; in the case that the corresponding waveform data entity file does not exist in the physical entity file library and / or the corresponding label index does not exist in the sample label index library, a warning prompt is generated.

[0012] According to another aspect of the embodiment of the present application, a dynamic maintenance device for a power distribution network transient recording wave event sample library is further provided, comprising: an acquisition module, configured to acquire a first preset database, wherein the first preset database stores a plurality of power distribution network transient recording wave event samples and a plurality of identifiers corresponding to the power distribution network transient recording wave event samples respectively; a receiving module, configured to receive a maintenance operation on the first preset database based on a target account, wherein the maintenance operation comprises at least one of the following: adding, deleting and modifying; a generation module, configured to generate a log based on the maintenance operation and store the log to a second preset database, wherein the log comprises an operation time corresponding to the maintenance operation, an identifier corresponding to the power distribution network transient recording wave event sample corresponding to the maintenance operation and an identifier corresponding to the target account; a detection module, configured to detect whether an abnormal identifier exists in the first preset database based on a preset interval duration, wherein the abnormal identifier represents an identifier for which the corresponding power distribution network transient recording wave event sample does not exist; and a determination module, configured to determine that the maintenance operation is successfully executed in the case that the abnormal identifier does not exist in the first preset database.

[0013] According to still another aspect of the embodiment of the present application, a nonvolatile storage medium is further provided, comprising a stored program, wherein the program, when running, controls a device in which the nonvolatile storage medium is located to execute any one of the power distribution network transient recording wave event sample library dynamic maintenance methods.

[0014] According to still another aspect of the embodiment of the present application, a computer device is further provided, comprising a processor, the processor being configured to run a program, wherein the program, when running, executes any one of the power distribution network transient recording wave event sample library dynamic maintenance methods.

[0015] According to still another aspect of the embodiment of the present application, a computer program product is further provided, comprising a computer program, the computer program being executed by a processor to implement any one of the power distribution network transient recording wave event sample library dynamic maintenance methods.

[0016] In the embodiment of the present application, the network transient recording event sample library dynamic maintenance method is adopted, the first preset database is obtained, wherein the first preset database stores a plurality of power distribution network transient recording event samples and a plurality of power distribution network transient recording event samples corresponding to the respective identifiers; receiving the maintenance operation of the first preset database based on the target account, wherein the maintenance operation includes at least one of the following: adding, deleting and modifying; based on the maintenance operation, generating a log and storing the log to the second preset database, wherein the log includes the operation time corresponding to the maintenance operation, the identifier corresponding to the power distribution network transient recording event sample corresponding to the maintenance operation and the identifier corresponding to the target account; based on the preset interval time, it is detected whether there is an abnormal identifier in the first preset database, wherein the abnormal identifier represents an identifier that does not exist corresponding to the power distribution network transient recording event sample; in the case where there is no abnormal identifier in the first preset database, it is determined that the maintenance operation is successfully executed, and the purpose of storing the power distribution network transient recording event sample in an explicit association relationship is achieved, thereby realizing the technical effect of reducing the risk of data misplacement, and further solving the technical problem that there is a certain risk of data error due to implicit association of waveform data and label information when maintaining the power distribution network transient recording event sample library. BRIEF DESCRIPTION OF DRAWINGS

[0017] The drawings described herein are used to provide further understanding of the present application, and form a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application, and do not constitute an improper limitation of the present application. In the drawings:

[0018] Figure 1 A hardware structure block diagram of a computer terminal for implementing the power distribution network transient recording event sample library dynamic maintenance method is shown;

[0019] Figure 2 A flowchart of the power distribution network transient recording event sample library dynamic maintenance method according to the embodiment of the present application is shown;

[0020] Figure 3 A flowchart of the power distribution network transient recording event sample library dynamic maintenance method according to the optional embodiment of the present application is shown;

[0021] Figure 4 A state machine model diagram according to the optional embodiment of the present application is shown;

[0022] Figure 5 A sample addition operation procedure diagram according to the optional embodiment of the present application is shown;

[0023] Figure 6 A sample modification operation procedure diagram according to the optional embodiment of the present application is shown;

[0024] Figure 7is a sample deletion operation procedure schematic diagram provided according to an optional embodiment of the present application;

[0025] Figure 8 is an "index-entity" bidirectional consistency normalization patrol schematic diagram provided according to an optional embodiment of the present application;

[0026] Figure 9 is a structural block diagram of a device for dynamically maintaining a transient recording event sample library of a distribution network according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] In order to make the personnel in the technical field better understand the present application scheme, the technical scheme in the embodiments of the present application will be clearly and completely described below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all. Based on the embodiments in the present application, all other embodiments obtained by the person skilled in the art without creative labor should belong to the protection scope of the present application.

[0028] It should be noted that the terms "first", "second" and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0029] According to an embodiment of the present application, a method embodiment of a method for dynamically maintaining a transient recording event sample library of a distribution network is provided. It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown herein.

[0030] The method embodiment provided by the first embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 A hardware structural block diagram of a computer terminal for implementing the method for dynamically maintaining a transient recording event sample library of a distribution network is shown. As shown in Figure 1As shown, the computer terminal 10 may include one or more processors (shown as 102a, 102b, ..., 102n in the figure) (the processor may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0031] It should be noted that the aforementioned one or more processors and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10. As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0032] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the dynamic maintenance method of the distribution network transient waveform event sample library in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the aforementioned application program of the dynamic maintenance method of the distribution network transient waveform event sample library. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0033] The display can be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10.

[0034] Figure 2 This is a flowchart illustrating the dynamic maintenance method for the distribution network transient recording event sample library provided by an embodiment of the present invention, as shown below.Figure 2 As shown, the method includes the following steps:

[0035] Step S202: Obtain a first preset database, wherein the first preset database stores multiple power distribution network transient waveform recording event samples and the corresponding identifiers of the multiple power distribution network transient waveform recording event samples.

[0036] In this step, the first preset database is used to store transient waveform recording event samples of the distribution network and their corresponding identifiers. The first preset database may include a physical entity file library and a sample tag index library. The physical entity file library can be used to store the waveform data entity files corresponding to each of the multiple transient waveform recording event samples of the distribution network, and the sample index library can be used to store the tag indexes corresponding to each of the multiple transient waveform recording event samples of the distribution network. Each transient waveform recording event sample stored in the first preset database should be configured with a corresponding and unique identifier. Specifically, a sample database data architecture centered on a "case ID" can be established, assigning a unique identifier to each sample and using it as the primary key connecting the metadata index and the waveform data entity files. This establishes an explicit, robust, and independently addressable strong association between data and tags, laying the foundation for high-reliability maintenance. For example, a globally unique and immutable "case ID" can be determined for each transient waveform recording event sample in the first preset database as its identifier, and this serves as the core identifier for accurate identification and referencing of the sample throughout the system's entire lifecycle.

[0037] Through the above steps, a large number of power distribution network transient waveform event samples and their identifiers can be retrieved efficiently and safely from the first preset database, providing solid data support for subsequent data analysis, model training, fault diagnosis and other applications.

[0038] Step S204: Receive maintenance operations on the first preset database performed based on the target account, wherein the maintenance operations include at least one of the following: adding, deleting, and modifying.

[0039] In this step, users of the target account can initiate maintenance requests to the first preset database through a front-end interface (such as a web application, desktop client, or mobile application). These requests can include operations such as adding samples, deleting samples, or modifying sample information. Upon receiving an operation request from the target account, the specific instructions and parameters in the request are first parsed to determine what type of maintenance operation the user intends to perform. The login status of the target account can be verified to ensure that the operation request comes from an authenticated and legitimate user. Then, it is checked whether the user has the permission to perform the operation specified in the request. For example, only specific roles (such as administrators, senior maintenance personnel, etc.) can perform deletion operations. For add or modify operations, the data submitted by the user can be preprocessed, including data format validation, content validation, and data conversion, to ensure that the data conforms to database storage standards. Based on the permission verification results and the preprocessed data, the database operation is executed. For add operations, the system writes the sample information and identifier to the database; for modify operations, the system updates the existing sample information; for delete operations, the system marks the sample status as "Archived" without physically deleting it.

[0040] By following the steps above, maintenance operations on the first preset database based on the target account can be handled safely and efficiently, maintaining the health of the sample database.

[0041] Step S206: Based on the maintenance operation, generate a log and store the log in the second preset database. The log includes the operation time corresponding to the maintenance operation, the identifier corresponding to the distribution network transient waveform event sample corresponding to the maintenance operation, and the identifier corresponding to the target account.

[0042] In this step, logs are generated based on maintenance operations and stored in a second preset database. This is a crucial step to ensure the traceability of the data maintenance process and to facilitate operation auditing. This process not only records the time and nature of the operation but also associates it with the target account performing the operation and the identifiers of the affected transient waveform samples, providing a detailed chain of evidence for subsequent auditing, troubleshooting, and data recovery. When a maintenance operation (such as adding, deleting, or modifying) is received in the first preset database, the log generation mechanism is immediately triggered. Once generated, the log cannot be altered. Log records should include the following key components:

[0043] Operation time: A timestamp accurate to the millisecond level, recording the execution time of the operation.

[0044] Maintenance operation type: Describes the nature of the operation, such as "INSERT", "UPDATE", "SOFT_DELETE", etc.

[0045] Distribution network transient recording event sample identifier: also known as "case ID", is used to identify which sample was affected by the operation.

[0046] Target account identifier: A unique identifier for the user performing the operation, which helps determine who is responsible for the operation.

[0047] Logs should be stored in a second preset database, which should be physically isolated from the first preset database to avoid mixing operation logs with business data and enhance data security. Once logs are written to the database, it should be ensured that they cannot be modified or deleted.

[0048] Through the above process, based on the maintenance operations of the target account on the first preset database, detailed operation logs can be generated and these logs can be stored securely and reliably in the second preset database, thus building a comprehensive audit and traceability mechanism and strengthening the security and transparency of data management.

[0049] Step S208: Based on a preset interval, detect whether there is an anomaly identifier in the first preset database, wherein the anomaly identifier represents an identifier for which there is no corresponding distribution network transient waveform recording event sample.

[0050] In this step, based on a preset interval, the system detects whether there are any abnormal markers in the first preset database, especially those markers that indicate that there are no corresponding transient waveform recording event samples in the distribution network. This ensures the integrity and consistency of the database data and aims to proactively discover and handle data inconsistencies that may be caused by various reasons (such as misoperation, system failure, etc.), thereby ensuring the long-term healthy operation of the database and data quality.

[0051] To ensure the health of the power distribution network transient waveform event sample database, routine inspections can be performed at preset intervals to detect inconsistencies between the metadata master index and waveform data entities. Specifically, the inspection process is initiated at preset intervals (e.g., 2:00 AM daily). By setting up a routine inspection process, the system can proactively and continuously monitor data integrity and respond quickly to any potential problems, thereby ensuring the long-term stability of the sample database and data quality.

[0052] Specifically, the process of detecting anomaly identifiers is divided into two main directions: performing "index-entity" integrity verification starting from the metadata master index library, and performing "entity-index" validity verification starting from the waveform data entity file library, that is, checking whether each identifier has a corresponding distribution network transient waveform recording event sample and checking whether each distribution network transient waveform recording event sample has a corresponding identifier.

[0053] Through the above process, data inconsistency issues can be proactively detected and addressed, providing an important guarantee for the long-term healthy operation and maintenance of the power distribution network transient waveform event sample library.

[0054] Step S210: If no abnormality identifier exists in the first preset database, the maintenance operation is determined to have been successfully executed.

[0055] In this step, if no abnormal identifiers are detected in the first preset database, it can be determined that the maintenance operation has been successfully executed. The first preset database may contain two types of abnormalities: "disconnection" and "orphan" issues. "Disconnection" refers to the existence of an identifier but no corresponding distribution network transient waveform event sample; "orphan" refers to the existence of a distribution network transient waveform event sample but no corresponding identifier.

[0056] In summary, a maintenance operation can only be considered successfully executed if the first preset database contains no abnormal identifiers, all samples and their identifiers are consistent, and the entire maintenance process is accurately recorded. This mechanism provides a solid foundation for the dynamic maintenance of the sample database, ensuring data integrity and the accuracy of the algorithm model.

[0057] The method for maintaining the transient waveform recording event sample library in the distribution network ensures an explicit and strong correlation between waveform data and tag information by constructing a sample data architecture centered on "case ID," thus resolving the vulnerability of data association and avoiding the risk of data misalignment caused by addition and deletion operations. Through a standardized transactional maintenance process, including log-first processing, state machine management, and non-destructive execution of sample addition, modification, and deletion operations, reliable management of the entire sample lifecycle is achieved, resolving the "black box" and high-risk issues of the maintenance process and ensuring full traceability and reversibility of changes. Simultaneously, through a mandatory logging mechanism and independently stored immutable logs, the impact and responsibility of any maintenance operation are clearly recorded, further guaranteeing data integrity. Under the "index-entity" bidirectional consistency routine inspection mechanism, the system can proactively detect and repair data inconsistencies, especially high-risk "broken chain" issues. Through automatic archiving and log generation, data self-purification is achieved, resolving the passive nature of data integrity assurance and ensuring the long-term healthy operation of the sample library and a high level of data quality, providing a solid foundation for the continuous optimization of intelligent algorithm models. In summary, the maintenance method proposed in this invention effectively overcomes the key defects in the prior art and significantly improves the maintenance efficiency and data reliability of the transient waveform event sample library of the distribution network.

[0058] Through the above steps, the goal of storing transient waveform recording event samples of the distribution network with explicit association can be achieved, thereby reducing the risk of data misalignment. This solves the technical problem that there is a certain risk of data error due to the implicit association between waveform data and tag information when maintaining the transient waveform recording event sample library of the distribution network.

[0059] As an optional embodiment, obtaining a first preset database includes: assigning identifiers to multiple distribution network transient waveform recording event samples respectively; constructing a tag index corresponding to each of the multiple distribution network transient waveform recording event samples based on the identifiers corresponding to each of the multiple distribution network transient waveform recording event samples; establishing a sample tag index library based on the tag indexes corresponding to each of the multiple distribution network transient waveform recording event samples; establishing a physical entity file library for storing waveform data entity files corresponding to each of the multiple distribution network transient waveform recording event samples; and determining the first preset database based on the sample tag index library and the physical entity file library.

[0060] Optionally, a sample database data architecture can be constructed with identity identifiers at its core. A sample database data architecture centered on "case ID" is established, assigning a unique identity identifier to each sample and using it as the primary key connecting the metadata index and waveform data entity files. This establishes an explicit, robust, and independently addressable strong association between data and tags, resulting in a sample tag index database, laying the foundation for highly reliable maintenance.

[0061] Specifically, for each transient waveform event sample in the sample library, a globally unique and persistent "case ID" is determined and assigned. This "case ID" serves as the core identifier of the sample throughout its entire lifecycle and is used for precise referencing and association in all subsequent operations. The tag index in the sample tag index library contains fields that are divided into at least three categories:

[0062] (1) Tag information: Business fields that describe the physical attributes of transient events, including at least: system grounding operation mode, fault line, fault occurrence time, fault cause verified by experts, and fault equipment type.

[0063] (2) Management information: Management fields to support the maintenance and traceability of the sample database, including at least: record version number, creator identifier, creation timestamp, last modifier identifier and last modification timestamp, etc.

[0064] (3) Status information: This is a status field that defines the lifecycle state of a sample. Its core is to introduce a state machine model for fine-grained management of samples.

[0065] For the physical entity file library, the transient waveform data of each sample can be stored atomically as an independent physical entity file, and a strong association can be established between the file name and the "case ID".

[0066] Preferably, each waveform data entity file is named with its corresponding "case ID" and stored in a binary format that supports efficient concurrent read and write operations to maximize data loading speed. Meanwhile, to ensure that downstream algorithm modules can directly call the data, the waveform data stored in the entity file library are all standardized analysis waveforms that have undergone uniform standardization processing (including but not limited to time window alignment, sampling rate normalization, and numerical normalization), avoiding the need for the algorithm to repeatedly perform preprocessing operations each time it is called, thus improving the overall system operating efficiency.

[0067] As an optional embodiment, when the maintenance operation is new, the method includes: receiving new distribution network transient waveform recording event samples; assigning a new identifier to the new distribution network transient waveform recording event sample, wherein the new identifier is different from the identifiers corresponding to each of the multiple distribution network transient waveform recording event samples in the first preset database; determining the tag index corresponding to the new distribution network transient waveform recording event sample based on the new identifier corresponding to the new distribution network transient waveform recording event sample; storing the waveform data entity file corresponding to the new distribution network transient waveform recording event sample into a physical entity file library; and storing the tag index corresponding to the new distribution network transient waveform recording event sample into a sample tag index library.

[0068] Optionally, for adding new samples of transient waveform recording events in the distribution network, after receiving new sample data, a globally unique identifier is assigned to it, ensuring that this identifier is different from the sample identifiers already existing in the metadata master index, thereby achieving data uniqueness and traceability. Next, the new sample and its identifier are stored in the metadata master index, i.e., the first preset database, establishing a stable and explicit association between the sample and tag information. This design principle not only simplifies the management of the sample library but also enhances data reliability and consistency. By ensuring that each sample has a unique identifier and establishes a direct connection with the waveform data entity, the system can accurately identify and locate each sample, providing a solid foundation for subsequent modification, deletion, and other maintenance operations. The implementation of this system effectively avoids data misalignment and situations where index records exist but waveform data entities are missing, improving the efficiency and security of online dynamic maintenance of the sample library, and also providing high-quality, standardized data support for training intelligent diagnostic algorithm models.

[0069] As an optional embodiment, a state machine model is set up, wherein the state machine model is used to update the state in the tag index corresponding to each of the multiple distribution network transient waveform event samples in the sample tag index library, and the state includes online, pending review and archived.

[0070] Optionally, the core of a state machine model is defining states, transitions between states, and transition conditions. For a transient waveform recording sample in a distribution network, its states may include:

[0071] Online (Active): The samples have been validated and can be used for training or analysis of the algorithm model.

[0072] Pending_Review: Some information in the sample may be incomplete or pending confirmation and requires manual review by experts.

[0073] Archived: The sample has been logically deleted or archived and is no longer involved in the online algorithm's operation, but the data is still retained for auditing or recovery.

[0074] The corresponding state transition rules can be:

[0075] From online to pending review: When the labeling information of a sample changes or the data quality is questioned, the sample status can be changed from online to pending review, awaiting further manual review.

[0076] From pending review to online: If the sample information is confirmed to be complete and correct by manual review, the sample status can be changed from pending review back to online.

[0077] From online or pending review to archived: When a sample is deemed no longer applicable (e.g., outdated data, contains erroneous information), its status can be transitioned to archived. This is typically achieved through a "soft deletion" mechanism, where the data is not physically deleted, but rather its status is changed to indicate that the data is unavailable.

[0078] Specifically, the label index can include a state field corresponding to each sample to record the current state of the sample. A dedicated transactional operation process can be designed for state transitions to ensure that each state transition is atomic—either completely successful or not executed at all. This can be achieved through the database's transaction management mechanism. When a maintenance operation triggers a state change, the current state and the target state should be checked first, and then the state transition operation should be executed according to the rules of the state machine model. This can be implemented through a state transition function in the programming code, which checks the transition conditions and calls the corresponding database transaction. After each state transition, the system should generate an operation log, recording the details of the state change, including the time of the operation, the account that performed the operation, the target sample identifier, and the situation before and after the state change. This log should be stored in a separate log table in a second pre-defined database and cannot be modified or deleted once generated to ensure the integrity and traceability of the operation history.

[0079] The introduction of a state machine model is used to update the states of multiple distribution network transient waveform samples in the first preset database. These states include online, pending review, and archived. The state machine model enables fine-grained management of the sample lifecycle. Through state transitions rather than physical deletion, it supports non-destructive and reversible management of samples, ensuring the security and flexibility of the sample library maintenance process. This design can proactively identify the intention to change sample data and dynamically assess the impact of the sample on downstream algorithms based on the importance of the changes, thereby determining whether the sample needs to enter the pending review state or be directly archived. The implementation effect of the state machine model is that it can effectively avoid the loss of historical data caused by physical deletion operations, ensuring the long-term health and data integrity of the sample library. At the same time, it supports restoring samples to the online state as needed, improving the traceability and efficiency of sample library maintenance. In this optional embodiment, the introduction of the state machine model further enhances the system's control over sample states, enabling the sample library to adapt to constantly changing maintenance needs and providing stable data assurance for continuous algorithm optimization.

[0080] As an optional embodiment, when the maintenance operation is deletion, the method includes: receiving a deletion identifier based on the target account input; searching for a distribution network transient waveform event sample to be deleted corresponding to the deletion identifier in a first preset database; and setting the state corresponding to the distribution network transient waveform event sample to be deleted to archived based on a state machine model.

[0081] Optionally, for deletion operations during sample library maintenance, the system receives a deletion identifier based on the target account input; this identifier is the sample's "case ID." Subsequently, in the first preset database, i.e., the metadata master index, the system searches for the distribution network transient waveform recording sample record corresponding to the deletion identifier. Unlike traditional physical deletion operations, this embodiment uses a state machine model to change the lifecycle state field of the sample to be deleted from its current state (e.g., online or pending review) to an archived state. This state transition operation only changes the state information in the record without involving any physical operations on the waveform data entity file, ensuring data reversibility and integrity. Through the soft deletion mechanism of the state machine model, the system can not only accurately record the sample's change history but also, when necessary, restore the sample's state from archived to online or pending review through a reverse operation, i.e., state transition, thereby achieving flexible management and long-term retention of sample data. This maintenance method effectively avoids the drawbacks of data loss and irreversible operation that may result from hard deletion, ensuring the long-term stability of the sample library and the traceability of the data. In other embodiments not shown, a similar process can be used to implement the modification and addition of samples using a state machine model, ensuring the safety and controllability of the maintenance process and data consistency.

[0082] As an optional embodiment, when the maintenance operation is new, it further includes: receiving new distribution network transient waveform recording event samples; determining the confidence level corresponding to the new distribution network transient waveform recording event samples; determining whether the confidence level is higher than a preset threshold; if the confidence level is higher than the preset threshold, setting the status of the new distribution network transient waveform recording event samples to online; if the confidence level is not higher than the preset threshold, setting the status of the new distribution network transient waveform recording event samples to pending review.

[0083] Optionally, when maintenance operations involve adding new distribution network transient waveform samples, all data of the samples to be added are first received. Subsequently, a confidence level test can be performed on the newly added distribution network transient waveform event samples, i.e., the quality of the sample waveform and the completeness of the information are checked to determine whether its confidence level is higher than a preset threshold. If the confidence level exceeds the threshold, the sample's status is set to online, immediately available for use by downstream intelligent diagnostic algorithms; conversely, if the confidence level does not meet the preset standard, the sample is marked as pending review and enters an isolation state awaiting further evaluation by domain experts. This differentiated sample status management mechanism based on account confidence level ensures both the rapid entry and effective utilization of high-confidence samples and the effective isolation of low-confidence samples by setting a pending review status, reducing the negative impact of potential labeling errors on the algorithm model, thereby supporting the high quality and sustainable evolution of the sample library. In this embodiment, by dynamically adjusting the initial lifecycle state of samples through real-time evaluation of the operator's historical performance and sample quality, front-end control of sample validity is achieved, providing a key guarantee for the stable operation of the algorithm model. In addition, logging and state transition mechanisms can be set up to ensure that even in scenarios where the confidence assessment is not up to standard, the operational intent and sample information are fully recorded, providing a basis for subsequent traceability and decision-making.

[0084] As an optional implementation, based on the tag index in the sample tag index library, it is determined whether a corresponding waveform data entity file exists in the physical entity file library; based on the waveform data entity file in the physical entity file library, it is determined whether a corresponding tag index exists in the sample tag index library; if a corresponding waveform data entity file does not exist in the physical entity file library and / or a corresponding tag index does not exist in the sample tag index library, an early warning prompt is generated.

[0085] Optionally, routine bidirectional consistency checks between the index and entities can be performed. An automated background check service is deployed to routinely cross-check the "Case ID" master index repository and the waveform data entity file repository, enabling proactive detection and early warning of data inconsistencies such as "broken links" and "orphan" data, ensuring the long-term health and integrity of the sample repository. Based on this information, the system can intelligently generate early warning prompts, notifying maintenance personnel to review potential data inconsistencies, such as "broken links" or "orphan" issues. This process not only strengthens the system's monitoring of sample data integrity but also improves the response speed and processing efficiency of maintenance personnel, effectively preventing the negative impact of data misalignment or loss on the health of the sample repository, and ensuring the continuity of sample repository maintenance operations and data consistency.

[0086] The following is a specific example. Figure 3 This is a flowchart of a dynamic maintenance method for a distribution network transient recording event sample library provided by an optional embodiment of the present invention, such as... Figure 3 As shown, it includes:

[0087] A: Sample database data architecture construction centered on identity identifiers: Establish a sample database data architecture centered on "case IDs", assign a unique identity identifier to each sample, and use it as the primary key to connect the metadata index and waveform data entity files, thereby establishing an explicit, stable and independently addressable strong association between data and tags, laying the foundation for highly reliable maintenance.

[0088] Specifically, for each transient waveform event sample in the sample library, a globally unique and persistent "case ID" is determined and assigned. This "case ID" serves as the core identifier of the sample throughout its entire system lifecycle and is used for precise referencing and association in all subsequent operations. A structured metadata master index is established to centrally and systematically store and manage various types of information for each sample. The index uses the "case ID" as the unique primary key, and its fields are divided into at least three categories:

[0089] (1) Tag information: Business fields that describe the physical attributes of transient events, including at least: system grounding operation mode, fault line, fault occurrence time, fault cause verified by experts, and fault equipment type.

[0090] (2) Management information: Management fields to support the maintenance and traceability of the sample database, including at least: record version number, creator identifier, creation timestamp, last modifier identifier and last modification timestamp, etc.

[0091] (3) State Information: This is a state field that defines the lifecycle state of a sample. Its core is the introduction of a state machine model for refined sample management. Preferably, Figure 4This is a state machine model diagram provided according to an optional embodiment of the present invention, such as... Figure 4 As shown, this state machine includes at least the following three states:

[0092] Online (Active): This indicates that the sample has passed verification, the data is complete and valid, and it is a high-quality "activated" sample. Samples in this state will serve as valid data sources for training or matching downstream intelligent diagnostic algorithm models.

[0093] Pending Review: This indicates that during the data entry or modification process, the system's built-in verification mechanism assessed the data quality or tag information confidence level of the sample. If the result fell below a preset threshold, the sample was automatically placed into this isolation state, awaiting final decision from domain experts. Samples in this state will be isolated by the system and will not participate in the downstream algorithm's computation process.

[0094] Archived means that the sample has been archived by operations personnel for a specific reason (such as outdated data or inconsistency with reality). Samples in this state do not participate in algorithm calculations, but all their data and records are still completely retained in the system for historical tracing or future recovery.

[0095] The transient waveform data for each sample is then atomically stored as an independent physical entity file, with a strong association between the filename and the "case ID". Preferably, each waveform data entity file is named with its corresponding "case ID" and stored in a binary format that supports efficient concurrent read and write operations to maximize data loading speed. Meanwhile, to ensure that downstream algorithm modules can directly call the data, the waveform data stored in the entity file library are standardized analysis waveforms that have undergone uniform standardization processing (including but not limited to time window alignment, sampling rate normalization, and numerical normalization), avoiding the need for the algorithm to repeatedly perform preprocessing operations each time it is called, thus improving the overall system operating efficiency.

[0096] B: Transactional online maintenance of the entire sample lifecycle: Establish a standardized execution process for operations such as adding, modifying, and deleting samples, and introduce a state machine-based "soft deletion" mechanism to achieve non-destructive and reversible management of the entire lifecycle of samples from entry into the database and version changes to archiving.

[0097] Specifically, based on the strongly correlated data architecture built in A, the concepts of transactional execution and state machine management are introduced to ensure that each maintenance operation is consistent and reversible in a high-concurrency, multi-person collaborative online environment, fundamentally eliminating the risk of data misalignment and state inconsistency.

[0098] For newly added transient recording event samples of the distribution network Figure 5This is a schematic diagram of the sample addition operation procedure provided by an optional embodiment of the present invention, such as... Figure 5 As shown, this process ensures that the process of adding new samples to the database is safe, reliable, and has complete traceability through a series of mechanisms such as pre-verification, status decision-making, log pre-writing, and transactional execution.

[0099] The system receives all data for the new samples, including standardized waveform data entities and metadata containing various label and management information. After receiving the data, the system directly extracts or generates a "case ID" according to preset rules, and performs a pre-validation on the "case ID." This validation includes at least the following:

[0100] Uniqueness check: Verify its uniqueness in the metadata master index to prevent duplicate entries.

[0101] Integrity verification: Check whether all required tag fields and management fields in the metadata have been provided to ensure the completeness of the information.

[0102] Before physical data import, the system calls the built-in confidence assessment module to automatically evaluate the data quality and information completeness of the sample. Based on the assessment results, the system sets an initial lifecycle state for the sample: if the confidence level is higher than a preset threshold, the initial state is set to "Active," and the sample can be directly used by downstream algorithms; if the confidence level is lower than the threshold, the initial state is set to "Pending_Review," and the sample is isolated and prompted for manual review by experts. After determining the initial state, the system initiates the transactional data import process. This process strictly follows the principles of "log first" and "entity first, index later." First, the system constructs an operation log record of type "CREATE," which encapsulates all information of this new operation, including the operator, timestamp, determined initial state, and all metadata content to be imported. This log record is preferentially written to a separate log database to complete the pre-write operation. Second, after the log pre-write is successful, the system writes the waveform data entity file to the specified physical storage location. Finally, the formal record of the sample is created in the metadata master index database. The system writes all metadata of the sample to the master index database. Furthermore, this execution process incorporates a rollback mechanism: if the final metadata record creation step fails for any reason (such as database connection errors, write conflicts, etc.), the system automatically triggers the rollback mechanism. This mechanism will accurately locate and delete the waveform data entity files and operation log records successfully created in this transaction based on previously written log information. This mechanism ensures the reliability of new operations; that is, the transaction either succeeds completely or fails without leaving any trace, thus guaranteeing the data consistency of the sample library at all times.

[0103] For modifying the transient waveform event samples of the distribution network. Figure 6This is a schematic diagram of a sample modification operation procedure provided by an optional embodiment of the present invention, such as... Figure 6 As shown, version control, pre-logging, and state migration mechanisms ensure the accuracy and traceability of each modification operation, supporting dynamic assessment of the impact on sample validity based on the importance of the modified content. The system can extract the "case ID" from modification requests received from the online maintenance interface and use it as a unique index to accurately locate the sample in the main metadata index. To prevent data conflicts caused by concurrent modifications in multi-user collaborative scenarios, after successfully locating and reading the latest version of the target sample record, the system immediately applies a temporary edit lock to that record, locking it from other modification requests until the current modification transaction is completed. Then, the system compares the new data from the request with the current record read in the previous step, field by field, to accurately identify the changed data items and generate a structured change set containing field names, the data content before the change, and the data content after the change. Subsequently, based on this change set, the system constructs an "MODIFY" type operation log record, which fully encapsulates the operator, responsible unit, timestamp, and the aforementioned structured change set. This log entry is preferentially written to a separate log repository with anti-tampering mechanisms, completing the "write-ahead" operation. This step ensures that even if the subsequent main index update fails, the intent and content of the modification are permanently recorded, providing a basis for subsequent traceability and troubleshooting. Before executing the main index update, the system calls the built-in confidence assessment module to evaluate the confidence of the generated modification request. Based on the assessment result, the system resets the lifecycle status of the sample: if the confidence is higher than the preset threshold, the status is set to "Online (Active)," and the sample can be directly used by downstream algorithms; if the confidence is lower than the threshold, the status is set to "Pending_Review," the sample is isolated, and experts are prompted for manual review. After completing the log write-ahead and status transition decision, the system executes the update operation on the main index repository. This operation writes the new data in the changeset to the corresponding fields and synchronously updates the following management information fields: incrementing the "Version Number" field by 1, and refreshing the "Last Modified Timestamp" and "Last Modified Person Identifier" fields using the timestamp of the current operation and the operator's identifier, respectively. Once all fields have been successfully updated, the system will release the edit lock applied to the record, and the modification will be complete.

[0104] For deleting transient waveform event samples from the distribution network Figure 7 This is a schematic diagram of a sample deletion operation procedure provided by an optional embodiment of the present invention, such as... Figure 7As shown, "soft deletion," which uses a state machine to migrate the lifecycle state, replaces "hard deletion" by physically removing data, achieving a safe and reversible "archiving" operation and ensuring the integrity of historical data and the traceability of operations. First, an online operation request containing the "case ID" of the sample to be deleted is received. Based on this "case ID," the system accurately locates the sample in the metadata master index and performs a preliminary verification of the target sample's current state. This verification aims to ensure the validity of the operation and includes at least: Existence verification: confirming that the record corresponding to the "case ID" actually exists. Status verification: confirming that the current state of the record is not "Archived" to avoid duplicate deletion operations on already archived samples. Lock verification: confirming that the record is not currently locked by other maintenance transactions. Operation requests that pass all preliminary verifications will initiate the subsequent deletion transaction. After the target sample passes verification, the system constructs an operation log record of type "DELETE." This log encapsulates key information about the deletion operation, including the operation timestamp, operator ID, responsible unit, the "Case ID" of the sample being deleted, and its lifecycle status before deletion (e.g., "online"). This log record is preferentially written to an independent log repository with anti-tampering mechanisms, completing the "write-ahead" operation to ensure that the deletion is permanently and reliably recorded, achieving process traceability. After the deletion operation log is successfully written, the system executes the core step of this deletion transaction, namely, updating the target record in the metadata master index. The core of this operation is to call the state machine model to migrate the "status" field value corresponding to the "Case ID" from the current status (e.g., "online" or "pending review") to "Archived". At the same time, the system also uses the information from the current operation to synchronously refresh the "last modifier ID" and "last modification timestamp" fields of the record. After the state migration operation in the master index is successful, the deletion ends, and the system returns a success message to the operation terminal. It should be noted that this deletion operation does not involve any physical deletion of the waveform data entity file. The entity file will be fully preserved, thus ensuring the reversibility of the entire deletion operation—if needed, the sample status can be restored from "archived" to "online" through the reverse operation procedure, and the sample data can be reactivated.

[0105] C: Establish a mandatory logkeeping mechanism covering all maintenance operations, automatically generating and independently storing logs containing information such as time, personnel, unit, and content changes during the process. Once generated, these logs are immutable, enabling complete traceability and accountability for all changes.

[0106] Specifically, to ensure the mandatory and complete nature of log recording, the log triggering mechanism is designed as a mandatory pre-processing step for all online maintenance operations. At the system architecture level, the log recording function is implemented as a decorator or middleware for core business logic such as adding, modifying, and deleting samples. This means that any external request to call core maintenance functions must first be processed by the log recording module before proceeding with subsequent business operations. This mechanism ensures the automation and mandatory nature of log recording, eliminating the possibility of bypassing or omission, and guaranteeing from a process perspective that "every operation is recorded." To ensure the standardization and usability of log information, each log entry is defined as a standardized, multi-dimensional structured data object. This object describes in detail all elements of a maintenance operation; preferably, its data structure includes at least the following dimensions:

[0107] (1) Event identifier: including globally unique log ID (log id And an operation timestamp accurate to milliseconds, used to uniquely identify and sort each recorded event.

[0108] (2) Operating entity: including the user identifier (operator) performing the operation. id ), and its responsible unit (operator) unit ) and the network address from which the operation originated (source) ip ), used to accurately trace the initiator of the operation.

[0109] (3) Operation object: Clearly record the "case ID" of the sample being operated on. idref Establish the association between logs and specific samples.

[0110] (4) Operation behavior: The specific category of the operation is recorded using a standardized enumeration type. type ), such as CREATE (add), MODIFY (modify), ARCHIVE (archive / delete).

[0111] (5) Change Details: Records specific changes to the data content in a structured format. For MODIFY operations, this field will also record the previous value (old value) of the modified field. value ) and the changed value (new) value For the CREATE operation, all initial metadata of the sample when it is added to the database is recorded; for the ARCHIVE operation, the final state of the sample before it is archived is recorded.

[0112] To ensure the objectivity and authority of log records, log data is physically and logically isolated from the sample database's metadata master index and waveform entity file database. Preferably, it is stored in an independent database table or a dedicated log service. In terms of access control, application-layer programs are only granted "append-write" permissions to the log database, strictly prohibiting any modification or deletion of existing log records. Preferably, database triggers or blockchain-based data digest chains can be further employed to perform chain-like encryption or verification protection of log records, thereby technically ensuring that once log data is generated, it cannot be tampered with or deleted, guaranteeing its absolute reliability as a basis for traceability and auditing.

[0113] D: Regularly perform bidirectional cross-validation between the "Case ID" main index library and the waveform data entity file library to proactively detect and warn of data inconsistencies such as "broken links" and "orphans," ensuring the long-term health and integrity of the sample library. Figure 8 This is a schematic diagram of routine inspection of bidirectional consistency between index and entity provided by an optional embodiment of the present invention, such as... Figure 8 The mechanism operates independently of user-triggered online maintenance operations, functioning routinely as a backend service. Its core objective is to proactively detect and warn of data inconsistencies caused by unexpected events (such as disk failures, manual errors, or abnormal data migration) through periodic bidirectional cross-validation between the metadata index and waveform data entities. This forms a closed-loop management process integrating detection, warning, and automatic handling, ensuring the long-term health and integrity of the sample database. Specifically, the inspection task can be divided into "index-entity" integrity verification and "entity-index" validity verification. The "index-entity" integrity verification aims to detect "broken link" data inconsistencies, where there is an index but no entity. Specifically, firstly, after starting at a preset period (e.g., every morning), the backend inspection service retrieves a list of all "case IDs" recorded in the metadata master index. Secondly, the service iterates through each "case ID" in this list and, according to a preset naming convention, queries the waveform data entity file database to see if a corresponding physical file exists. Finally, if no corresponding entity file is found, the system marks the "Case ID" as having a "broken link" risk and adds it to the anomaly list for this round of inspection. The "Entity-Index" validity inspection aims to identify "orphan" data inconsistencies where "entities exist but indexes are missing." Specifically, first, the inspection service scans the physical storage directory of the waveform data entity file library to obtain a list of all existing entity file names. Second, it iterates through this file list, parsing the "Case ID" represented by each file name and using this ID to query the metadata master index. Finally, if no corresponding record is found, the system marks the entity file as an "orphan" file and adds it to the anomaly list for this round of inspection.

[0114] After completing two-way inspections and compiling the anomaly list, the system will initiate differentiated and automated handling and early warning processes based on the risk level of the problem. For automated handling of high-risk issues such as "disconnection," the system will iterate through the list of all "case IDs" identified as "disconnections" and perform the following automated repair tasks for each record:

[0115] (1) Forced log generation: The system first calls the logging mechanism to generate an operation log of type "MODIFY". To clarify the responsible party, the "operator identifier" field of the log will be assigned a special system identifier (such as System_Inspection_Service), and the reason for the action will be clearly stated in the "change details", for example: "Due to the loss of the associated waveform entity file, the system automatically performs an archiving operation to ensure data consistency".

[0116] (2) Lifecycle state migration: After the log is successfully written, the system will automatically perform an update operation on the record of the "Case ID" in the metadata master index, forcibly changing the value of its "status" field from the current status to "Archived". This automated process ensures that all known high-risk inconsistent data is proactively and promptly isolated from the "online" sample set, eliminating the risk of downstream algorithms crashing due to calling invalid data.

[0117] Warnings are issued for risk issues related to "orphan" files. For identified "orphan" entity files, since they do not directly affect the operation of the online algorithm, the system will not automatically delete them, but will only issue a warning. A list of all "orphan" files, along with the automatic handling results of the aforementioned "broken link" issue, will be integrated into a structured data health inspection report. This report will be pushed to the system maintenance management interface as warning information for operations and maintenance personnel to review and make subsequent manual decisions.

[0118] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0119] Through the above description of the embodiments, those skilled in the art can clearly understand that the dynamic maintenance method for the distribution network transient waveform event sample library according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0120] According to embodiments of the present invention, a dynamic maintenance device for a distribution network transient recording event sample library is also provided for implementing the above-described dynamic maintenance method for the distribution network transient recording event sample library. Figure 9 This is a structural block diagram of a dynamic maintenance device for a distribution network transient recording event sample library provided according to an embodiment of the present invention, such as... Figure 9 As shown, the dynamic maintenance device for the distribution network transient waveform event sample library includes: an acquisition module 902, a receiving module 904, a generation module 906, a detection module 908, and a determination module 910. The following is a description of the dynamic maintenance device for the distribution network transient waveform event sample library.

[0121] The acquisition module 902 is used to acquire a first preset database, wherein the first preset database stores multiple power distribution network transient recording event samples and the corresponding identifiers of the multiple power distribution network transient recording event samples.

[0122] The receiving module 904, connected to the obtaining module 902, is used to receive maintenance operations on a first preset database performed based on a target account, wherein the maintenance operations include at least one of the following: adding, deleting, and modifying.

[0123] The generation module 906, connected to the receiving module 904, is used to generate logs based on maintenance operations and store the logs in a second preset database. The logs include the operation time corresponding to the maintenance operation and the identifier corresponding to the target account.

[0124] The detection module 908, connected to the generation module 906, is used to detect whether there are abnormal power distribution network transient waveform event samples without corresponding identifiers in the first preset database based on a preset interval time.

[0125] The determination module 910, connected to the detection module 908, is used to determine that the maintenance operation was successfully executed if no abnormal power distribution network transient waveform event sample exists in the first preset database.

[0126] It should be noted that the acquisition module 902, receiving module 904, generation module 906, detection module 908, and determination module 910 mentioned above correspond to steps S202 to S210 in the embodiments. Multiple modules implement the same instances and application scenarios as their corresponding steps, but are not limited to the content disclosed in the above embodiments. It should also be noted that the above modules, as part of the device, can run on the computer terminal 10 provided in the embodiments.

[0127] Embodiments of the present invention may provide a computer device. Optionally, in this embodiment, the computer device may be located in at least one of a plurality of network devices in a computer network. The computer device includes a memory and a processor.

[0128] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the dynamic maintenance method and apparatus for the distribution network transient waveform event sample library in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby realizing the aforementioned dynamic maintenance method for the distribution network transient waveform event sample library. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to a computer terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0129] The processor can invoke information and application programs stored in the memory via a transmission device to perform the following steps: acquiring a first preset database, wherein the first preset database stores multiple distribution network transient waveform recording event samples and their corresponding identifiers; receiving maintenance operations on the first preset database based on a target account, wherein the maintenance operations include at least one of the following: adding, deleting, and modifying; generating logs based on the maintenance operations and storing the logs in a second preset database, wherein the logs include the operation time corresponding to the maintenance operation, the identifier corresponding to the distribution network transient waveform recording event sample corresponding to the maintenance operation, and the identifier corresponding to the target account; detecting whether there are abnormal identifiers in the first preset database based on a preset interval, wherein abnormal identifiers indicate that there is no corresponding distribution network transient waveform recording event sample; and determining that the maintenance operation was successfully executed if no abnormal identifiers are found in the first preset database.

[0130] Optionally, the processor may also execute program code for the following steps: obtaining a first preset database, including: assigning identifiers to multiple distribution network transient waveform recording event samples respectively; constructing a tag index corresponding to each of the multiple distribution network transient waveform recording event samples based on the identifiers corresponding to each of the multiple distribution network transient waveform recording event samples; establishing a sample tag index library based on the tag indexes corresponding to each of the multiple distribution network transient waveform recording event samples; establishing a physical entity file library for storing waveform data entity files corresponding to each of the multiple distribution network transient waveform recording event samples; and determining the first preset database based on the sample tag index library and the physical entity file library.

[0131] Optionally, the processor may also execute program code with the following steps: when the maintenance operation is new, including: receiving new distribution network transient waveform recording event samples; assigning a new identifier to the new distribution network transient waveform recording event sample, wherein the new identifier is different from the identifiers corresponding to each of the multiple distribution network transient waveform recording event samples in the first preset database; determining the tag index corresponding to the new distribution network transient waveform recording event sample based on the new identifier corresponding to the new distribution network transient waveform recording event sample; storing the waveform data entity file corresponding to the new distribution network transient waveform recording event sample into the physical entity file library; and storing the tag index corresponding to the new distribution network transient waveform recording event sample into the sample tag index library.

[0132] Optionally, the processor may also execute program code that performs the following steps: setting up a state machine model, wherein the state machine model is used to update the state in the tag index corresponding to each of the multiple distribution network transient waveform event samples in the sample tag index library, and the state includes online, pending review and archived.

[0133] Optionally, the processor may also execute program code with the following steps: when the maintenance operation is deletion, including: receiving a deletion identifier based on the target account input; searching for a distribution network transient waveform event sample to be deleted corresponding to the deletion identifier in a first preset database; and setting the state corresponding to the distribution network transient waveform event sample to be deleted to archived based on a state machine model.

[0134] Optionally, the processor may also execute program code for the following steps: when the maintenance operation is new, it further includes: receiving new distribution network transient waveform recording event samples; determining the confidence level corresponding to the new distribution network transient waveform recording event samples; determining whether the confidence level is higher than a preset threshold; if the confidence level is higher than the preset threshold, setting the status of the new distribution network transient waveform recording event samples to online; if the confidence level is not higher than the preset threshold, setting the status of the new distribution network transient waveform recording event samples to pending review.

[0135] Optionally, the processor may also execute program code that performs the following steps: determining whether a corresponding waveform data entity file exists in the physical entity file library based on the tag index in the sample tag index library; determining whether a corresponding tag index exists in the sample tag index library based on the waveform data entity file in the physical entity file library; and generating an early warning message if a corresponding waveform data entity file does not exist in the physical entity file library and / or a corresponding tag index does not exist in the sample tag index library.

[0136] This invention provides a method for dynamically maintaining a distribution network transient waveform recording event sample library. The method involves: acquiring a first preset database containing multiple distribution network transient waveform recording event samples and their corresponding identifiers; receiving maintenance operations on the first preset database based on a target account, where the maintenance operations include at least one of the following: adding, deleting, and modifying; generating logs based on the maintenance operations and storing them in a second preset database, where the logs include the operation time corresponding to the maintenance operation, the identifier of the corresponding distribution network transient waveform recording event sample, and the identifier of the target account; detecting whether there are abnormal identifiers in the first preset database based on a preset interval, where abnormal identifiers indicate that no corresponding distribution network transient waveform recording event sample exists; and determining that the maintenance operation was successfully executed if no abnormal identifiers exist in the first preset database. This achieves the goal of storing distribution network transient waveform recording event samples with explicit association relationships, thereby reducing the risk of data misalignment and solving the technical problem that currently, when maintaining a distribution network transient waveform recording event sample library, there is a certain risk of data errors due to the implicit association between waveform data and tag information.

[0137] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a non-volatile storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0138] Embodiments of the present invention also provide a non-volatile storage medium. Optionally, in this embodiment, the aforementioned non-volatile storage medium can be used to store the program code executed by the dynamic maintenance method for the distribution network transient waveform event sample library provided in the above embodiments.

[0139] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0140] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: obtaining a first preset database, including: assigning identifiers to multiple distribution network transient waveform recording event samples respectively; constructing a tag index corresponding to each of the multiple distribution network transient waveform recording event samples based on the identifiers corresponding to each of the multiple distribution network transient waveform recording event samples; establishing a sample tag index library based on the tag index corresponding to each of the multiple distribution network transient waveform recording event samples; establishing a physical entity file library for storing waveform data entity files corresponding to each of the multiple distribution network transient waveform recording event samples; and determining the first preset database based on the sample tag index library and the physical entity file library.

[0141] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: when the maintenance operation is new, the steps include: receiving new distribution network transient waveform recording event samples; assigning a new identifier to the new distribution network transient waveform recording event sample, wherein the new identifier is different from the identifiers corresponding to each of the multiple distribution network transient waveform recording event samples in the first preset database; determining the tag index corresponding to the new distribution network transient waveform recording event sample based on the new identifier corresponding to the new distribution network transient waveform recording event sample; storing the waveform data entity file corresponding to the new distribution network transient waveform recording event sample into a physical entity file library; and storing the tag index corresponding to the new distribution network transient waveform recording event sample into a sample tag index library.

[0142] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: setting a state machine model, wherein the state machine model is used to update the state in the tag index corresponding to each of the multiple distribution network transient waveform event samples in the sample tag index library, and the state includes online, pending review, and archived.

[0143] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: when the maintenance operation is deletion, the steps include: receiving a deletion identifier based on the target account input; searching for a distribution network transient waveform event sample to be deleted corresponding to the deletion identifier in a first preset database; and setting the state corresponding to the distribution network transient waveform event sample to be deleted to archived based on a state machine model.

[0144] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: when the maintenance operation is new, it further includes: receiving new distribution network transient waveform recording event samples; determining the confidence level corresponding to the new distribution network transient waveform recording event samples; determining whether the confidence level is higher than a preset threshold; if the confidence level is higher than the preset threshold, setting the status of the new distribution network transient waveform recording event samples to online; if the confidence level is not higher than the preset threshold, setting the status of the new distribution network transient waveform recording event samples to pending review.

[0145] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining whether a corresponding waveform data entity file exists in the physical entity file library based on the tag index in the sample tag index library; determining whether a corresponding tag index exists in the sample tag index library based on the waveform data entity file in the physical entity file library; and generating an early warning prompt if a corresponding waveform data entity file does not exist in the physical entity file library and / or a corresponding tag index does not exist in the sample tag index library.

[0146] Embodiments of the present invention also provide a computer program product, including a computer program. Optionally, in this embodiment, when the computer program is executed by a processor, it can: acquire a first preset database, wherein the first preset database stores multiple distribution network transient waveform recording event samples and identifiers corresponding to each of the multiple distribution network transient waveform recording event samples; receive maintenance operations on the first preset database based on a target account, wherein the maintenance operations include at least one of the following: adding, deleting, and modifying; generate logs based on the maintenance operations and store the logs in a second preset database, wherein the logs include the operation time corresponding to the maintenance operation, the identifiers corresponding to the distribution network transient waveform recording event samples corresponding to the maintenance operation, and the identifiers corresponding to the target account; detect whether there are abnormal identifiers in the first preset database based on a preset interval, wherein the abnormal identifiers represent identifiers where no corresponding distribution network transient waveform recording event sample exists; and determine that the maintenance operation was successfully executed if no abnormal identifiers exist in the first preset database.

[0147] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0148] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0149] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0150] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0151] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0152] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0153] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for dynamically maintaining a sample library of transient waveform events in a distribution network, characterized in that, include: Obtain a first preset database, wherein the first preset database stores multiple power distribution network transient recording event samples and the identifiers corresponding to each of the multiple power distribution network transient recording event samples; Receive maintenance operations on the first preset database based on the target account, wherein the maintenance operations include at least one of the following: adding, deleting, and modifying; Based on the maintenance operation, a log is generated and stored in a second preset database. The log includes the operation time corresponding to the maintenance operation, the identifier corresponding to the distribution network transient waveform event sample corresponding to the maintenance operation, and the identifier corresponding to the target account. Based on a preset interval, it is detected whether there is an anomaly marker in the first preset database, wherein the anomaly marker represents an identifier that does not have a corresponding power distribution network transient waveform event sample; If the anomaly identifier does not exist in the first preset database, the maintenance operation is determined to have been successfully executed.

2. The method according to claim 1, characterized in that, The step of obtaining the first preset database includes: Assign identifiers to the various power distribution network transient waveform recording event samples respectively; Based on the identifiers corresponding to the multiple distribution network transient recording event samples, a tag index corresponding to each of the multiple distribution network transient recording event samples is constructed; A sample tag index library is established based on the tag index corresponding to each of the multiple power distribution network transient waveform recording event samples. Establish a physical entity file library for storing the waveform data entity files corresponding to the multiple transient waveform recording event samples of the power distribution network; Based on the sample tag index library and the physical entity file library, the first preset database is determined.

3. The method according to claim 2, characterized in that, When the maintenance operation is new, it includes: Receive newly added transient waveform recording event samples of the distribution network; Assign a new identifier to the newly added distribution network transient waveform recording event sample, wherein the new identifier is different from the identifiers corresponding to the plurality of distribution network transient waveform recording event samples in the first preset database; determine the tag index corresponding to the newly added distribution network transient waveform recording event sample based on the new identifier corresponding to the newly added distribution network transient waveform recording event sample. Store the waveform data entity files corresponding to the newly added distribution network transient recording event samples into the physical entity file library; The tag index corresponding to the newly added power distribution network transient waveform event sample is stored in the sample tag index library.

4. The method according to claim 2, characterized in that, Also includes: A state machine model is set up, wherein the state machine model is used to update the state in the tag index corresponding to each of the multiple distribution network transient waveform event samples in the sample tag index library, and the state includes online, pending review and archived.

5. The method according to claim 4, characterized in that, In the case where the maintenance operation is deletion, it includes: Receive the deletion identifier based on the target account input; In the first preset database, search for the distribution network transient waveform event sample to be deleted that corresponds to the identifier to be deleted; Based on the state machine model, the state corresponding to the transient waveform event sample of the distribution network to be deleted is set to archived.

6. The method according to claim 4, characterized in that, In the case that the maintenance operation is new, it also includes: Receive newly added transient waveform recording event samples of the distribution network; Determine the confidence level corresponding to the newly added transient waveform recording event sample of the distribution network; Determine whether the confidence level is higher than a preset threshold; If the confidence level is higher than a preset threshold, the status of the newly added power distribution network transient waveform recording event sample will be set to online. If the confidence level is not higher than the preset threshold, the status of the newly added power distribution network transient waveform recording event sample is set to pending review.

7. The method according to claim 2, characterized in that, Also includes: Based on the tag index in the sample tag index library, determine whether the physical entity file library contains a corresponding waveform data entity file; Based on the waveform data entity files in the physical entity file library, determine whether a corresponding tag index exists in the sample tag index library; If the corresponding waveform data entity file does not exist in the physical entity file library and / or the corresponding tag index does not exist in the sample tag index library, an early warning message will be generated.

8. A dynamic maintenance device for a distribution network transient waveform event sample library, characterized in that, include: The acquisition module is used to acquire a first preset database, wherein the first preset database stores multiple power distribution network transient recording event samples and the identifiers corresponding to each of the multiple power distribution network transient recording event samples; The receiving module is configured to receive maintenance operations on the first preset database performed based on the target account, wherein the maintenance operations include at least one of the following: adding, deleting, and modifying; The generation module is used to generate logs based on the maintenance operation and store the logs in a second preset database. The logs include the operation time corresponding to the maintenance operation, the identifier corresponding to the distribution network transient waveform event sample corresponding to the maintenance operation, and the identifier corresponding to the target account. The detection module is used to detect whether there is an abnormal identifier in the first preset database based on a preset interval time, wherein the abnormal identifier represents an identifier that does not have a corresponding power distribution network transient waveform recording event sample; The determination module is used to determine that the maintenance operation was successfully executed if the anomaly identifier does not exist in the first preset database.

9. A non-volatile storage medium, characterized in that, The non-volatile storage medium includes a stored program, wherein, when the program is executed, it controls the device where the non-volatile storage medium is located to execute the dynamic maintenance method for the distribution network transient waveform event sample library as described in any one of claims 1 to 7.

10. A computer device, characterized in that, include: Memory and processor The memory stores computer programs; The processor is configured to execute a computer program stored in the memory, wherein when the computer program is executed, the processor performs the dynamic maintenance method for the distribution network transient waveform event sample library as described in any one of claims 1 to 7.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the dynamic maintenance method for the distribution network transient recording event sample library as described in any one of claims 1 to 7.

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