A method and system for ticket checking based on a ticket checking robot

Abstract

This invention discloses a method and system for checking operation tickets based on a ticket checking robot, comprising: collecting first feature information during the operation process by installing a plug-in; sorting the first feature information according to a time sequence to obtain a feature sequence of the first feature information; performing information checks on the feature sequence of the first feature information; the information check includes a matching check of a first rule and a custom check of a second rule; and outputting the result of the information check. This achieves a high degree of automation in operation ticket checking, quickly completing the standardization and accuracy checks of operation tickets with a one-click operation, significantly improving work efficiency.

Description

Technical Field

[0001] This invention relates to the field of ticket checking technology, specifically to a ticket checking method and system based on a ticket checking robot. Background Technology

[0002] In power grid dispatching, operation tickets are important documents that ensure the safe operation of power equipment and the standardization of dispatching operations. They typically contain a series of specific operation instructions and execution steps.

[0003] However, most existing technologies rely on manual review of the content and standardization of operation tickets. This method is not only time-consuming and labor-intensive, but also prone to errors due to human negligence, thereby affecting the safe and stable operation of the power system.

[0004] In addition, existing operation ticket inspection tools often have shortcomings in terms of flexibility, automation and integration capabilities, such as limited rule base customization capabilities, simple inspection logic, and difficulty in integrating with existing systems. Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is that the existing ticket checking methods have problems such as high error rate, time consumption and high labor cost.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a ticket checking method based on a ticket checking robot, comprising:

[0008] By installing a plugin, the first characteristic information during the operation process can be collected;

[0009] The first feature information is sorted according to the time series to obtain the feature sequence of the first feature information;

[0010] For the feature sequence, perform information checking on the first feature information; the information checking includes matching check of the first rule and custom check of the second rule;

[0011] The results of the information check are output.

[0012] As a preferred embodiment of the ticket checking method based on the ticket checking robot described in this invention, the plugin is installed on the client to collect page data and obtain the first feature information during the operation process.

[0013] As a preferred embodiment of the ticket checking method based on the ticket checking robot described in this invention, the feature sequence includes: using the plugin to sort the collected first feature information to obtain the first feature information sequence in the time series.

[0014] As a preferred embodiment of the ticket checking method based on the ticket checking robot described in this invention, the matching check of the first rule includes, but is not limited to, determining whether the feature sequence of the current operation process is correct through predetermined rules.

[0015] As a preferred embodiment of the ticket checking method based on the ticket checking robot described in this invention, the custom check of the second rule includes, but is not limited to, using custom specific content to determine whether the single first feature information is correct.

[0016] As a preferred embodiment of the ticket checking method based on a ticket checking robot according to the present invention, wherein: the matching check of the first rule further includes storing the correct feature sequence in each operation instruction in a sequence library;

[0017] Obtain the operation instruction, and search the sequence library for the feature sequence corresponding to the currently executed operation instruction as a comparison sequence;

[0018] The comparison sequence is compared with the feature sequence acquired in real time using a plugin;

[0019] Determine whether the feature sequence in the current operation process is correct;

[0020] If incorrect, mark it in the output report.

[0021] As a preferred embodiment of the ticket checking method based on the ticket checking robot described in this invention, the custom check of the second rule further includes: constructing a constraint-algorithm pairing library, pre-setting standard content for custom detection, and pairing each standard content with the corresponding recognition algorithm;

[0022] Based on the specific content of the customization, identify the customized constraint content;

[0023] The constraint content is compared with the standard content of the custom detection in the constraint-algorithm pairing library to calculate the similarity. Based on the similarity calculation result, the most similar standard content is matched.

[0024] Using the matched standard content of the custom detection, it is paired with the recognition algorithm in the constraint-algorithm pairing library, and the recognition algorithm obtained by the pairing is used to detect whether the constraints in the operation item are satisfied;

[0025] If the conditions are not met, mark the error in the output report.

[0026] A ticket checking system based on a ticket checking robot, employing the method described in this invention, is characterized by:

[0027] The acquisition unit acquires first feature information during the operation process by installing a plugin; and sorts the first feature information according to the time sequence to obtain the feature sequence of the first feature information.

[0028] The analysis unit performs information checks on the feature sequence based on the first feature information; the information checks include matching checks of the first rule and custom checks of the second rule.

[0029] The output unit outputs the results of the information check.

[0030] A computer device includes: a memory and a processor; the memory stores a computer program, wherein: when the processor executes the computer program, it implements the steps of the method described in any one of the present invention.

[0031] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method described in any one of the present invention.

[0032] The beneficial effects of this invention are as follows: The operation ticket checking method based on the ticket checking robot provided by this invention achieves a high degree of automation in operation ticket checking. It quickly completes the standardization and accuracy checks of operation tickets through one-click operation, significantly improving work efficiency. It supports flexible customization of the rule base to meet the needs of different power grid structures and enterprises. Adopting a non-intrusive architecture, it achieves seamless integration without modifying existing systems, ensuring system integrity and stability. It provides a simple and intuitive user interface and detailed inspection reports, including error prompts and improvement suggestions, facilitating user management and auditing. Furthermore, the system enhances data transmission security through encryption technology, comprehensively improving the reliability and intelligence level of power grid dispatching. Attached Figure Description

[0033] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is an overall flowchart of a ticket checking method based on a ticket checking robot, provided in the first embodiment of the present invention.

[0035] Figure 2 This is a script recording diagram of the rule recognition process in a ticket checking method based on a ticket checking robot, provided in the second embodiment of the present invention. Detailed Implementation

[0036] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0037] Example 1, referring to Figure 1 As an embodiment of the present invention, a method for checking tickets based on a ticket checking robot is provided, comprising:

[0038] S1: Collect the first feature information during the operation process by installing a plugin.

[0039] The plugin, installed on the client side, is responsible for collecting page data and acquiring the first feature information during the operation process. In this embodiment, the first feature information is the operation ticket in the power grid dispatch operation ticket management system. The system consists of a client and a server. The client is responsible for executing specific inspection tasks, while the server is used to manage inspection rules, rule templates, and generate inspection reports. This system utilizes NET8 for server-side development, supports custom rule bases, and can perform comprehensive and detailed inspections of operation tickets for both the main grid and distribution network. It also provides a one-click inspection function, thereby improving the efficiency and accuracy of operation ticket management.

[0040] Client-side: Based on a Chrome extension, users can use the extension on any device with the Chrome browser installed. The client is responsible for collecting page data and checking operation tickets according to a pre-defined rule base.

[0041] Server-side: Built using the .NET 8 framework, providing a RESTful API interface for communication with clients. The server is primarily responsible for storing and managing the rule base and rule templates, as well as generating and storing inspection reports.

[0042] After opening the operation ticket page in the browser, users can click the plugin icon to start a one-click check.

[0043] The plugin automatically captures the operation ticket data on the page and sends it to the server for processing.

[0044] The server checks the data according to the rule base and returns the results to the client.

[0045] The client receives the results and displays the inspection report on the interface, highlighting the parts that do not meet the specifications.

[0046] In an optional embodiment, the first feature information may also be real-time electrical parameter data collected by a real-time monitoring device. The system determines whether the real-time electrical parameter feature sequence during the current operation conforms to the power grid operation specifications based on predetermined rules. When a real-time electrical parameter that does not conform to the rules is found in the feature sequence, the system marks the specific abnormal time point, parameter value, and suggested operational adjustments in the inspection report.

[0047] S2: Sort the first feature information according to the time series to obtain the feature sequence of the first feature information.

[0048] The feature sequence includes, using the plugin, inserting a timestamp into each piece of the first feature information while collecting the first feature information; and sorting the collected first feature information according to the timestamps to obtain a sequence of the first feature information under the time series.

[0049] It's important to note that organizing data by time series accurately reconstructs the chronological order of operation ticket execution, facilitating checks to ensure operations conform to predetermined procedures and sequences. In operation ticket checks, many rules rely on specific time sequences; for example, closing operations must be performed after opening operations. Time series ensures that the checking logic is based on the correct temporal context, improving the accuracy of the checks. Time-sorted feature sequences can intuitively reflect the time of the problem's occurrence and the related operational items, providing a reliable basis for subsequent anomaly localization and problem tracing.

[0050] In this embodiment, the feature sequence refers to the order of operations in the operation process. In an alternative embodiment, the feature sequence can also be other time-related variables, such as the fluctuation curves of current and voltage over time.

[0051] S3: Perform information checks on the feature sequence based on the first feature information; the information checks include matching checks of the first rule and custom checks of the second rule.

[0052] Furthermore, the matching check of the first rule includes determining whether the feature sequence of the current operation process is correct according to established rules. Specifically, the sequence library stores the correct feature sequence (the order of operation items) in each operation instruction.

[0053] Obtain the operation instruction, and search the sequence library for the feature sequence corresponding to the currently executed operation instruction as a comparison sequence.

[0054] The comparison sequence is compared with the feature sequence acquired in real time using the plugin; it is then determined whether the feature sequence in the current operation process is correct. If incorrect, it is marked in the output report.

[0055] It's worth noting that the rule base contains various types of inspection rules, such as the correctness of operation items, the rationality of operation order, and the compliance of key parameters. Rules can range from simple pattern matching based on regular expressions to complex business logic judgments. Users can customize new rules or modify existing rules through the interface provided by the server.

[0056] The second rule's custom check includes using custom specific content to determine whether a single operation item is correct.

[0057] Specifically, a constraint-algorithm pairing library is constructed, pre-defined standard content for custom detection is set, and each standard content (i.e. standard constraint content, used as an example) is paired with the corresponding recognition algorithm.

[0058] The recognition algorithm can be selected from various options, such as: machine learning classification algorithms: using pre-trained models to classify feature information and determine whether the features meet preset classification criteria; image object detection algorithms: detecting specific objects in an image and accurately labeling their locations; and convolutional neural networks: used to detect and classify key features in images, widely applied in image classification and object detection, etc. Example structure:

[0059]

[0060]

[0061] Based on the specific custom content, identify the custom constraint content (specific constraints on the operation items). The identification of the constraint content can rely on large language models (such as GPT, BERT) combined with language models and rule generation tools, giving full play to their advantages in text understanding, rule reasoning and context analysis.

[0062] The constraint content is compared with the standard content of the custom detection in the constraint-algorithm pairing library to calculate similarity. Based on the similarity calculation result, the most similar standard content is matched.

[0063] Using the matched standard content of the custom detection, a matching algorithm is performed with the constraint-algorithm pairing library. The matching algorithm is then used to check whether the constraints in the operation item are satisfied. If not, a mark is made in the output report.

[0064] In an embodiment of the present invention, the matching check of the first rule is to check whether the operation flow composed of the order of operation items satisfies the instruction, and to check the order of operation items; the custom check of the second rule is to check the specific requirements of each operation item.

[0065] In an optional embodiment, the matching check of the first rule can also be a parallel operation conflict check to determine whether a specific step conflicts with other steps. Alternatively, it can be a device association check to verify whether the devices involved in the operation ticket match the process requirements. The custom check of the second rule can also be a customization of the threshold range or a customization of the device objects in the process.

[0066] The matching check of the first rule and the custom check of the second rule can be not only parallel as used in this application, but also dependent. The custom check of the second rule can be used to customize the device object, determining specific data within it; the device object data filtered by the custom check determines the input for the matching analysis of the first rule. Then, the matching check of the first rule is performed. If the custom check result is empty, the first rule cannot complete the matching analysis. If it is not empty, the matching mechanism is analyzed on these specific data, and anomalies are matched based on these characteristic data to check if the device object has any anomalies. Alternatively, the device object's operating status can be matched based on these characteristic data.

[0067] S4: Output the results of the information check.

[0068] The system automatically saves the inspection record after each inspection is completed.

[0069] Users can view all historical inspection records on the server and download detailed inspection reports.

[0070] The report will list all the problems found, along with corresponding recommendations and solutions.

[0071] If the above functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a 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 this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0072] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0073] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0074] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0075] Example 2, refer to Figure 2 This invention provides a method for checking tickets based on a ticket checking robot, as one embodiment of the present invention.

[0076] Here is an example based on this method:

[0077] Example 1: Operation item correctness check

[0078] Scenario: A power grid company needs to ensure that every operation in the operation ticket is carried out in accordance with standard procedures.

[0079] Rule settings:

[0080] The operation item must contain the correct device name.

[0081] The actions of the operation items (such as "closing" or "opening") must be consistent with the equipment type.

[0082] The operating sequence should follow established safety procedures.

[0083] Inspection process:

[0084] The user opens the operation ticket page to be checked in the browser.

[0085] Activate the plugin, and it will automatically capture the operation ticket data on the page.

[0086] The data is sent to the server, where it is checked using preset rules.

[0087] The inspection results show that if the device name is incorrect or the action is inconsistent in the operation item, the system will mark it in the report and provide correction suggestions.

[0088] The user corrects the operation ticket based on the report.

[0089] Example 2: Custom rule inspection

[0090] Scenario: A power grid company needs to conduct additional checks on some special operational items in a specific project.

[0091] Rule settings:

[0092] New rule: Operation tickets must include specific safety warnings.

[0093] New rule: The operation of a specific device must be performed by designated personnel.

[0094] Inspection process:

[0095] Users can log in to the server management interface and add custom rules.

[0096] Open the operation ticket page in your browser and activate the plugin for one-click inspection.

[0097] In addition to performing the default rule checks, the server will also apply newly added custom rules.

[0098] The inspection results show that if a safety warning is missing or the person performing the action is not the designated individual, the system will clearly point out the problem in the report.

[0099] Users should make corresponding adjustments based on the report.

[0100] pass Figure 2As can be seen, this invention can obtain the operation script based on the plugin and obtain a report on whether the rules are followed by comparison with the rules.

[0101] Example 3, an embodiment of the present invention, provides a ticket checking system based on a ticket checking robot, comprising:

[0102] The acquisition unit acquires first feature information during the operation process by installing a plugin; and sorts the first feature information according to the time sequence to obtain the feature sequence of the first feature information.

[0103] The analysis unit performs information checks on the feature sequence based on the first feature information; the information checks include matching checks of the first rule and custom checks of the second rule.

[0104] The output unit outputs the results of the information check.

[0105] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method of ticket inspection based on an inspection robot, characterized by, The method comprises the following steps: collecting first feature information in an operation process through an installed plug-in; sorting the first feature information according to a time sequence to obtain a feature sequence of the first feature information; performing information checking of the first feature information on the feature sequence; the information checking comprises matching checking of a first rule and custom checking of a second rule; outputting a result of the information checking; the plug-in is installed on a client to collect page data and obtain the first feature information in the operation process; the feature sequence comprises the first feature information sequence in the time sequence obtained by sorting the collected first feature information by using the plug-in; the matching checking of the first rule comprises but is not limited to judging whether the feature sequence in the current operation process is correct through a predetermined rule; the custom checking of the second rule comprises but is not limited to judging whether the single first feature information is correct by using a custom specific content; the matching checking of the first rule further comprises storing a correct feature sequence in each operation instruction in a sequence library; obtaining an operation instruction, searching for a feature sequence corresponding to the operation instruction being executed as a comparison sequence in the sequence library; comparing the comparison sequence with the feature sequence collected in real time by using the plug-in; judging whether the feature sequence in the current operation process is correct; if not, marking in an output report; the custom checking of the second rule further comprises constructing a constraint-algorithm pairing library, presetting a standard content of custom detection, and pairing each standard content with a corresponding recognition algorithm; recognizing a custom constraint content according to the custom specific content; performing similarity calculation of the constraint content in the constraint-algorithm pairing library with the standard content of the custom detection, and matching the closest standard content according to a calculation result of the similarity; pairing the matched standard content of the custom detection with a recognition algorithm in the constraint-algorithm pairing library, and detecting whether a constraint in an operation item is satisfied by using the recognition algorithm obtained through the pairing; if not, marking in an output report.

2. An operation ticket checking system based on a ticket checking robot adopting the method of claim 1, and the system comprises: a collecting unit that collects first feature information in an operation process through an installed plug-in, and sorts the first feature information according to a time sequence to obtain a feature sequence of the first feature information; an analysis unit that performs information checking of the first feature information on the feature sequence; the information checking comprises matching checking of a first rule and custom checking of a second rule; an output unit that outputs a result of the information checking.

3. A computer device comprising: a memory and a processor; the memory stores a computer program, and the processor implements the steps of the method of claim 1 when executing the computer program.

4. A computer readable storage medium having stored thereon a computer program, characterized in that: the computer program is executed by the processor to implement the steps of the method of claim 1.

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