Work order processing method and device based on data analysis, equipment and storage medium

Abstract

The invention relates to the technical field of information processing, in particular to a work order processing method, device and equipment based on data analysis and a storage medium, by identifying the clipboard authority state of a user, the work order input authority of the user can be dynamically managed, the user satisfaction degree is improved, and when the clipboard authority state is authorized, the work order input authority of the user can be dynamically managed. If yes, monitoring the clipboard content in real time by using a monitor, and when the monitor monitors that the clipboard content has work order information, acquiring current work order information, initiating a work order pop-up window, filling the work order information into the work order pop-up window, and analyzing the work order information in the work order pop-up window to obtain work order data; a preset work order type keyword library is utilized to classify work order data, a corresponding structured data model is matched based on the work order type, the method adapts to complex and changeable work order content, the applicability is improved, key information of the work order data is extracted through a regular expression and the structured data model, the manual input cost is reduced, and the work order data processing efficiency is improved. And the work order input processing efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of information processing technology, and in particular to a work order processing method, device, equipment and storage medium based on data analysis. Background Art

[0002] Currently, traditional work order processing relies on manual data entry, which is inefficient and error-prone, with poor data entry accuracy and high manual entry costs. Some systems use fixed templates to match work order information, but this does not support complex work order scenarios. The work order entry types are not scalable and have low applicability, making them unable to adapt to complex and changing work order content (such as mixed-type work orders, multiple waybill numbers, and dynamic user needs). Furthermore, existing solutions lack real-time monitoring and intelligent analysis capabilities for clipboard data and a dynamic management mechanism for user privacy authorization, reducing user satisfaction. Summary of the Invention

[0003] In order to overcome the shortcomings of the existing technology, the purpose of the present invention is to provide a work order processing method, device, equipment and storage medium based on data analysis that reduces manual entry costs, improves work order entry processing efficiency, adapts to complex and changeable work order content, improves applicability, can dynamically manage user work order entry permissions, and improves user satisfaction.

[0004] The first aspect of the present invention provides a work order processing method based on data analysis, including: identifying the user's clipboard permission status, and when the clipboard permission status is authorized, using a listener to monitor the clipboard content in real time; when the listener monitors that the clipboard content contains work order information, obtaining the current work order information and initiating a work order pop-up window; filling the work order information into the work order pop-up window, parsing the work order information in the work order pop-up window to obtain work order data; using a preset BERT model to semantically understand the context of the work order data, and using a preset work order type keyword library to classify the work order data to obtain a work order type; matching the corresponding structured data model based on the work order type, extracting key information of the work order data through regular expressions and the structured data model.

[0005] Optionally, in a first implementation method of the first aspect of the present invention, the user's clipboard permission status is identified, and when the clipboard permission status is authorized, a listener is used to monitor the clipboard content in real time, including: calling a clipboard permission API, and using the clipboard permission API to query the user's clipboard permission status; when the clipboard permission status is authorized, a listener is created; and the clipboard content is monitored in real time using the listener.

[0006] Optionally, in a second implementation method of the first aspect of the present invention, when the listener detects that the clipboard content contains work order information, the current work order information is obtained, and a work order pop-up window is initiated, including: extracting a preset pop-up window template from a template library, and generating a modal pop-up window based on the pop-up window template; when the listener detects that the clipboard content contains work order information, the current work order information is obtained; and the work order pop-up window is triggered in real time according to the modal pop-up window.

[0007] Optionally, in a third implementation method of the first aspect of the present invention, the work order information is filled into the work order pop-up window, and the work order information is parsed in the work order pop-up window to obtain work order data, including: converting the work order information into a standard format for pop-up window parameter input to obtain standard work order information; filling real-time standard work order information into the work order pop-up window; parsing the work order information in the work order pop-up window to obtain work order data, and mapping each attribute of the work order data to the corresponding form field.

[0008] Optionally, in a fourth implementation method of the first aspect of the present invention, the context of the work order data is semantically understood using a preset BERT model, and the work order data is classified using a preset work order type keyword library to obtain the work order type, including: constructing a work order domain knowledge graph, associating specific keywords with knowledge nodes, and the knowledge nodes include business rules, operating procedures, and historical cases; using a preset BERT model to semantically understand the context of the work order data; when the BERT model identifies ambiguous keywords, extracting relevant knowledge corresponding to the ambiguous keywords from the work order domain knowledge graph; performing a comprehensive analysis based on the relevant knowledge and in combination with the context of the work order data to obtain a keyword analysis result; classifying the work order data based on the keyword analysis result and using a preset work order type keyword library to obtain a work order type.

[0009] Optionally, in a fifth implementation of the first aspect of the present invention, the structured data model corresponding to the work order type is matched, and the key information of the work order data is extracted through regular expressions and the structured data model, including: constructing multiple structured data models in JSON or XML format, defining data fields and hierarchical structures of different structured data models according to the logical relationship of the work order; storing multiple structured data models in a model library; matching the corresponding structured data model from the model library based on the work order type; and extracting the key information of the work order data through regular expressions and the structured data model.

[0010] Optionally, in a sixth implementation of the first aspect of the present invention, after matching the corresponding structured data model based on the work order type and extracting the key information of the work order data through regular expressions and the structured data model, it also includes: judging whether the key information cannot be accurately identified or is missing, and obtaining a judgment result; when the judgment result is that the key information can be accurately identified and there is no missing, entering the key information into the work order system; when the judgment result is that the key information cannot be accurately identified or is missing, generating supplementary prompt information; sending the supplementary prompt information to the user end, so that the user end generates and displays a supplementary prompt page based on the supplementary prompt information.

[0011] The second aspect of the present invention provides a work order processing device based on data analysis, including: an identification and monitoring module, which is used to identify the user's clipboard permission status, and when the clipboard permission status is authorized, the listener is used to monitor the clipboard content in real time; an acquisition and initiation module, which is used to obtain the current work order information and initiate a work order pop-up window when the listener monitors that the clipboard content contains work order information; a filling and parsing module, which is used to fill the work order information into the work order pop-up window, parse the work order information in the work order pop-up window, and obtain work order data; an understanding and classification module, which is used to use a preset BERT model to perform semantic understanding on the context of the work order data, and use a preset work order type keyword library to classify the work order data to obtain the work order type; a matching and extraction module, which is used to match the corresponding structured data model based on the work order type, and extract key information of the work order data through regular expressions and the structured data model.

[0012] Optionally, in a first implementation method of the second aspect of the present invention, the identification and monitoring module includes: a calling query unit, used to call the clipboard permission API, and use the clipboard permission API to query the user's clipboard permission status; a creation unit, used to create a listener when the clipboard permission status is authorized; and a monitoring unit, used to use the listener to monitor the clipboard content in real time.

[0013] Optionally, in a second implementation of the second aspect of the present invention, the acquisition initiation module includes: an extraction and generation unit, used to extract a preset pop-up template from a template library, and generate a modal pop-up window based on the pop-up template; an acquisition unit, used to obtain the current work order information when the listener detects that the clipboard content contains work order information; and a triggering unit, used to trigger the work order pop-up window in real time according to the modal pop-up window.

[0014] Optionally, in a third implementation of the second aspect of the present invention, the filling and parsing module includes: a conversion unit, used to convert the work order information into a standard format for pop-up window parameter input to obtain standard work order information; a filling unit, used to fill real-time standard work order information into the work order pop-up window; a parsing and mapping unit, used to parse the work order information in the work order pop-up window to obtain work order data, and map each attribute of the work order data to the corresponding form field.

[0015] Optionally, in a fourth implementation of the second aspect of the present invention, the understanding and classification module includes: a construction unit, which is used to construct a work order domain knowledge graph and associate specific keywords with knowledge nodes, and the knowledge nodes include business rules, operating procedures and historical cases; an understanding unit, which is used to use a preset BERT model to perform semantic understanding on the context of the work order data; a first extraction unit, which is used to extract relevant knowledge corresponding to the ambiguous keyword from the work order domain knowledge graph when the BERT model identifies an ambiguous keyword; an analysis unit, which is used to perform a comprehensive analysis based on the relevant knowledge and in combination with the context of the work order data to obtain a keyword analysis result; and a classification unit, which is used to classify the work order data based on the keyword analysis result and using a preset work order type keyword library to obtain a work order type.

[0016] Optionally, in a fifth implementation of the second aspect of the present invention, the matching and extraction module includes: a construction definition unit, used to construct multiple structured data models in JSON or XML format, and define data fields and hierarchical structures of different structured data models according to the logical relationship of the work order; a storage unit, used to store multiple structured data models in a model library; a matching unit, used to match the corresponding structured data model from the model library based on the work order type; and a second extraction unit, used to extract key information of the work order data through regular expressions and the structured data model.

[0017] Optionally, in the sixth implementation method of the second aspect of the present invention, it also includes: a judgment module, used to judge whether the key information cannot be accurately identified or is missing, and obtain a judgment result; an entry module, used to enter the key information into the work order system when the judgment result is that the key information can be accurately identified and there is no missing; a generation module, used to generate supplementary prompt information when the judgment result is that the key information cannot be accurately identified or is missing; a sending module, used to send the supplementary prompt information to the user end, so that the user end generates and displays a supplementary prompt page based on the supplementary prompt information.

[0018] The third aspect of the present invention provides a work order processing device based on data analysis, which includes: a memory and at least one processor, wherein instructions are stored in the memory; at least one of the processors calls the instructions in the memory to enable the work order processing device based on data analysis to execute each step of the work order processing method based on data analysis described above.

[0019] A fourth aspect of the present invention provides a computer-readable storage medium having instructions stored thereon, which, when executed by a processor, implement the various steps of any of the above-mentioned work order processing methods based on data analysis.

[0020] In the technical solution of the present invention, by identifying the user's clipboard permission status, the user's work order entry permission can be dynamically managed to improve user satisfaction. When the clipboard permission status is authorized, the listener is used to monitor the clipboard content in real time. When the listener detects that the clipboard content contains work order information, the current work order information is obtained, and a work order pop-up window is initiated to fill the work order information into the work order pop-up window. The work order information is parsed in the work order pop-up window to obtain work order data, and the preset BERT model is used to semantically understand the context of the work order data. The work order data is classified using a preset work order type keyword library to obtain the work order type. The corresponding structured data model is matched based on the work order type to adapt to complex and changeable work order content and improve applicability. The key information of the work order data is extracted through regular expressions and structured data models to reduce manual entry costs and improve work order entry processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A first flow chart of a work order processing method based on data analysis provided by an embodiment of the present invention;

[0022] Figure 2 A second flow chart of the work order processing method based on data analysis provided in an embodiment of the present invention;

[0023] Figure 3 A third flow chart of the work order processing method based on data analysis provided in an embodiment of the present invention;

[0024] Figure 4 A fourth flow chart of the work order processing method based on data analysis provided by an embodiment of the present invention;

[0025] Figure 5 A schematic structural diagram of a work order processing device based on data analysis provided by an embodiment of the present invention;

[0026] Figure 6Another structural diagram of a work order processing device based on data analysis provided by an embodiment of the present invention;

[0027] Figure 7 A schematic structural diagram of a work order processing device based on data analysis provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The present invention provides a work order processing method, device, equipment and storage medium based on data analysis, which reduces manual entry costs, improves work order entry processing efficiency, adapts to complex and changeable work order content, improves applicability, can dynamically manage user work order entry permissions, and improves user satisfaction.

[0029] The terms "first," "second," "third," "fourth," and the like (if any) in the description and claims of the present invention and in the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to these processes, methods, products, or apparatus.

[0030] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 In one embodiment of the present invention, a method for processing a work order based on data analysis includes:

[0031] 101. Identify the user's clipboard permission status. When the clipboard permission status is authorized, use a listener to monitor the clipboard content in real time.

[0032] In this embodiment, by calling the clipboard permission API provided by the system, it is queried whether the current user has authorized the application to access the clipboard. If the API returns that the clipboard access permission is authorized, the listener is used to monitor the clipboard content in real time.

[0033] 102. When the listener detects that the clipboard contains work order information, it obtains the current work order information and initiates a work order pop-up window;

[0034] In this embodiment, the listener monitors whether the clipboard content changes. Once the clipboard content changes, the listener will be triggered and check whether the clipboard data contains work order information. If the clipboard data contains work order information, the listener will call the callback function to process the clipboard data and initiate a work order pop-up window. The work order pop-up window contains various fields of the work order and allows users to view or edit related content. The pop-up window can provide functions such as a "close" button and an edit button, allowing users to perform operations during the work order processing process.

[0035] 103. Fill the work order information into the work order pop-up window, parse the work order information in the work order pop-up window, and obtain the work order data;

[0036] In this embodiment, the work order information is filled into the work order pop-up window, and the work order information obtained from the clipboard is displayed in the work order pop-up window through JavaScript or a framework (such as React, Vue). The work order information will be stored in the pop-up window in the form of structured data. The work order information is parsed in the work order pop-up window to extract the value of each field.

[0037] 104. Use the preset BERT model to perform semantic understanding on the context of the work order data, and use the preset work order type keyword library to classify the work order data to obtain the work order type;

[0038] In this embodiment, the work order data is converted into an input format suitable for BERT model processing, and the work order data is input into the BERT model. The BERT model will understand the contextual information through its bidirectional attention mechanism and capture the semantic features in the text. The result output by the BERT model contains the context embedding vector of each word. These vectors can be used to represent the features of the work order text. After obtaining the semantic representation of the work order, by matching the semantic features extracted by the BERT model with the work order type keyword library, it is possible to determine which keywords are contained in the work order, classify it according to the keywords, and obtain the work order type.

[0039] 105. Based on the work order type matching corresponding structured data model, extract key information of the work order data through regular expressions and structured data model;

[0040] In this embodiment, the corresponding structured data model is matched based on the work order type. Each type of structured data model has different fields to ensure that the key information of the work order of that type can be captured. The key information of the work order data is extracted using regular expressions and structured data models.

[0041] In an embodiment of the present invention, by identifying the user's clipboard permission status, the user's work order entry permission can be dynamically managed to improve user satisfaction. When the clipboard permission status is authorized, a listener is used to monitor the clipboard content in real time. When the listener detects that the clipboard content contains work order information, the current work order information is obtained, and a work order pop-up window is initiated to fill the work order information into the work order pop-up window. The work order information is parsed in the work order pop-up window to obtain work order data, and a preset BERT model is used to semantically understand the context of the work order data. The work order data is classified using a preset work order type keyword library to obtain the work order type. The corresponding structured data model is matched based on the work order type to adapt to complex and changeable work order content and improve applicability. The key information of the work order data is extracted through regular expressions and structured data models to reduce manual entry costs and improve work order entry processing efficiency.

[0042] See also Figure 2 A second embodiment of the work order processing method based on data analysis in the embodiment of the present invention includes:

[0043] 201. Call the clipboard permission API and use the clipboard permission API to query the user's clipboard permission status;

[0044] In this embodiment, by calling the clipboard permission API provided by the system, it is queried whether the current user has authorized the application to access the clipboard.

[0045] 202. When the clipboard permission status is authorized, create a listener;

[0046] In this embodiment, if the API returns that the clipboard access permission is authorized, a listener is created. The function of the listener is to monitor the user's paste operation on the clipboard.

[0047] 203. Use the listener to monitor the clipboard content in real time;

[0048] In this embodiment, a listener is used to monitor the clipboard content in real time, and the clipboard content is checked periodically to further process the pasted content. For example, certain text content may be restricted, or the content may be automatically formatted when the user pastes. The content pasted by the user may also be restricted or modified according to actual conditions.

[0049] 204. Extracting a preset pop-up window template from a template library, and generating a modal pop-up window based on the pop-up window template;

[0050] In this embodiment, a template library is defined, which contains various preset pop-up templates, such as warning pop-ups, confirmation pop-ups, information pop-ups, etc. The preset pop-up templates are extracted from the template library. Once the preset pop-up templates are obtained from the template library, the template contents need to be parsed. If the template is a dynamic template (such as one with placeholders), the placeholders need to be replaced with dynamic data, and a modal pop-up is generated after the template is extracted and parsed.

[0051] 205. When the listener detects that the clipboard content contains work order information, the current work order information is obtained;

[0052] In this embodiment, the work order information has a specific format or keywords, such as work order number, status, description, etc. When the listener obtains the clipboard content, it first needs to analyze whether the content meets the standards of the work order information. If the work order information has a fixed format (such as JSON, specific tags, etc.), regular expressions or other text analysis methods can be used to match the work order information. Once the clipboard content is confirmed to be work order information, the next step is to obtain this information and store or further process it.

[0053] 206. Trigger the work order pop-up window in real time according to the modal pop-up window;

[0054] In this embodiment, a work order pop-up window is triggered in real time according to the modal pop-up window when the user submits a work order, updates the work order status, or obtains work order information from the clipboard.

[0055] In an embodiment of the present invention, by calling the clipboard permission API and monitoring the clipboard content in real time, it is possible to quickly respond to user operations and trigger corresponding work order pop-ups. The steps reduce manual intervention through automated permission checks and real-time monitoring, improve the smoothness and reliability of operations, and combine the template design of modal pop-ups to dynamically generate and display work order information according to different clipboard contents, thereby improving user experience. The entire process can respond instantly when the user's clipboard content changes, ensuring that information is entered in a timely and accurate manner.

[0056] See also Figure 3 A third embodiment of the work order processing method based on data analysis in the embodiments of the present invention includes:

[0057] 301. Convert the work order information into a standard format for pop-up window parameter input to obtain standard work order information;

[0058] In this embodiment, key information in the work order information is extracted, such as the work order number, description, creation time, priority, etc., and the time is formatted according to the display requirements of the pop-up window to ensure that each field of the work order conforms to the standardized format. For example, the priority field can be converted into Chinese or an icon, the status field can be converted into a different label or color, and the attachment field can be converted into a clickable link or thumbnail.

[0059] 302. Fill the real-time standard work order information into the work order pop-up window;

[0060] In this embodiment, once the data processing is completed, the next step is to fill this data into the pop-up window, use the work order number or a brief description to fill the pop-up window title, fill the work order description, creation time, priority, status, etc. into the corresponding HTML elements, and the attachment field needs to be displayed in the pop-up window in the form of a link or thumbnail.

[0061] 303. Parse the work order information in the work order pop-up window to obtain the work order data, and map each attribute of the work order data to the corresponding form field;

[0062] In this embodiment, the work order information is parsed in the work order pop-up window using JavaScript or other front-end technologies, converted into a usable format, and the work order data is obtained. The various attributes of the work order are mapped to the form fields in the pop-up window, and the work order number is filled in. This is usually displayed as a title at the top of the pop-up window for user identification. The description field displays the detailed content of the work order, which is displayed in a text box or paragraph in the pop-up window to help users understand the specific problems of the work order. The priority field usually needs to be displayed in different styles according to different priority values ​​(such as "high", "medium", and "low"). The status of the work order may include "pending", "processing", "completed", etc., and needs to be displayed with different background colors or font colors to facilitate users to quickly view the processing status of the current work order. The attachment field can be displayed as a link or thumbnail, and users can click the link to download the attachment file.

[0063] 304. Build a knowledge graph for the work order domain and associate specific keywords with knowledge nodes. Knowledge nodes include business rules, operating procedures, and historical cases.

[0064] In this embodiment, when determining the knowledge scope of the work order field and constructing the knowledge graph, it is necessary to identify each knowledge node and determine the relationship between them, extract keywords from the work order text data, and then map these keywords to relevant nodes in the knowledge graph. Each keyword should point to a specific knowledge node or concept, and define the relationship between nodes, such as dependency relationships, category relationships, and historical case reference relationships.

[0065] 305. Use the preset BERT model to semantically understand the context of the work order data;

[0066] In this embodiment, before using the BERT model, the work order data text needs to be preprocessed to ensure that the model can effectively process the text and decompose the sentences or paragraphs in the work order into processable words. The BERT model usually uses WordPiece for word segmentation, removes irrelevant characters (such as extra spaces, punctuation marks, etc.), unifies synonyms or common phrases, and inputs the processed text into the BERT model. The BERT model will encode the text to obtain the embedded representation of each word or phrase in its context, perform semantic understanding of the context of the work order data, and identify entity information in the work order, such as equipment name, fault type, customer name, etc.

[0067] 306. When the BERT model identifies an ambiguous keyword, it extracts relevant knowledge corresponding to the ambiguous keyword from the work order domain knowledge graph;

[0068] In this embodiment, ambiguous keywords refer to words or phrases that may have different meanings in different contexts. The BERT model can identify these ambiguous keywords through its bidirectional understanding feature. When the BERT model identifies ambiguous keywords, it extracts relevant knowledge corresponding to the ambiguous keywords from the work order domain knowledge graph.

[0069] 307. Perform comprehensive analysis based on relevant knowledge and the context of the work order data to obtain keyword analysis results;

[0070] In this embodiment, the BERT model first analyzes the input work order text and marks any potentially ambiguous keywords. The BERT model then extracts the contextual information of the ambiguous keywords and, leveraging the characteristics of bidirectional encoding, analyzes the sentences, phrases, and even entire paragraphs surrounding the word to perform preliminary reasoning. If the BERT model's understanding of the ambiguous keywords is still unclear, it will query the knowledge graph of the work order domain to obtain more relevant knowledge. Based on the relevant knowledge extracted from the knowledge graph, the BERT model will adjust its understanding of the ambiguous keywords and obtain keyword analysis results.

[0071] 308. Based on the keyword analysis results and using a preset work order type keyword library, the work order data is classified to obtain the work order type;

[0072] In this embodiment, the preset work order type keyword library contains mapping relationships between work order types and corresponding keywords. Using these mapping relationships, the keyword analysis results can be matched with specific work order types to obtain the work order type corresponding to the work order data.

[0073] In the embodiment of the present invention, the entire process ensures the standardization and consistency of work order processing by standardizing work order information and gradually filling it into pop-up windows. It introduces knowledge graphs and BERT models for semantic understanding and context analysis, improves the intelligence level of work order processing, and can automatically identify and resolve ambiguities to improve accuracy. Through mapping, classification and analysis, it ensures that each work order can be accurately judged and processed, providing a reliable basis for subsequent operations. Automated knowledge extraction and semantic analysis reduce manual intervention, speed up work order processing, and reduce errors and ambiguities through intelligent analysis.

[0074] See also Figure 4 The fourth embodiment of the work order processing method based on data analysis in the embodiments of the present invention includes:

[0075] 401. Use JSON or XML format to build multiple structured data models, and define the data fields and hierarchical structures of different structured data models based on the logical relationship of the work order;

[0076] In this embodiment, a structured data model suitable for work order data is designed. The JSON format is easy to read and supports nested structures. The XML format is self-describing and can clearly express data content through tags. When designing the work order data model, it is necessary to define corresponding data fields and hierarchical structures according to the different contents of the work order. Each data field is defined according to the content of the work order and business requirements. Based on the different logical relationships of the work order, the structured data model should support multi-level data storage. Each work order type has a different processing flow and data that needs to be recorded. Therefore, different structured data models can be defined according to different work order types.

[0077] 402. Storing multiple structured data models in a model library;

[0078] In this embodiment, all structured data models that need to be stored are designed, appropriate storage media are selected based on the scale, access frequency and volatility of the data, data tables / data storage structures are established, and appropriate tools or programs are used to import multiple defined structured data models from the source (such as the input interface, API or import file) into the model library.

[0079] 403. Match the corresponding structured data model from the model library based on the work order type;

[0080] In this embodiment, when a new work order is received, the system needs to identify the specific category of the work order through the work order type. Once the work order type is determined, the system should be able to automatically query the model library to find the structured data model corresponding to the work order type.

[0081] 404. Extract key information from work order data using regular expressions and structured data models;

[0082] In this embodiment, regular expressions are bound to fields of a structured data model, and key information of the work order data is extracted through regular expressions and the structured data model, ensuring that the key information extracted from the work order data can be accurately mapped to the corresponding fields.

[0083] 405. Determine whether key information cannot be accurately identified or is missing, and obtain a determination result;

[0084] In this embodiment, it is checked whether the key information fields are empty. If some fields should have values ​​(such as work order ID, work order date, etc.) but are empty or missing, it means that the data may be missing or cannot be accurately identified. Then, it is determined whether the key information cannot be accurately identified or is missing, and the judgment result is obtained.

[0085] 406. When the judgment result is that the key information can be accurately identified and there is no missing information, the key information is entered into the work order system;

[0086] In this embodiment, when the judgment result is that the key information can be accurately identified and there is no missing, it is confirmed again whether the format of the extracted key information meets the system requirements, ensuring that all key information fields have been filled in completely, and then ensuring that the data has been formatted according to the requirements of the target system. Finally, the key information is entered into the work order system. After the key information is entered, the system should generate feedback and logs.

[0087] 407. When the judgment result is that the key information cannot be accurately identified or is missing, a supplementary prompt information is generated;

[0088] In this embodiment, when the judgment result is that the key information cannot be accurately identified or is missing, supplementary prompt information is generated. The supplementary prompt information clearly points out which key information is missing or cannot be identified. In addition to pointing out the problem, the supplementary prompt information also includes correction suggestions.

[0089] 408. Send the supplementary prompt information to the user terminal, so that the user terminal generates and displays a supplementary prompt page based on the supplementary prompt information;

[0090] In this embodiment, the supplementary prompt information is sent to the user terminal through an API or other communication mechanism. Once the user terminal successfully receives the supplementary prompt information, the user terminal will generate and display a supplementary prompt page based on the supplementary prompt information.

[0091] In an embodiment of the present invention, by constructing a structured data model and storing it in a model library, it is possible to quickly match a suitable data model from the library, effectively simplify the data processing process, support multiple structured data models, and be able to flexibly adapt to different work order types to meet different needs. Regular expressions and intelligent judgment mechanisms are introduced to automatically extract and verify key information of work orders, reduce manual intervention, and when key information cannot be accurately identified, guide users through supplementary prompt information to ensure a smooth data supplement process and improve user experience.

[0092] The above describes the work order processing method based on data analysis in the embodiment of the present invention. The following describes the work order processing device based on data analysis in the embodiment of the present invention. Figure 5 In one embodiment of the present invention, a work order processing device based on data analysis includes:

[0093] Identification and monitoring module 501, used to identify the user's clipboard permission status, when the clipboard permission status is authorized, the clipboard content is monitored in real time using a monitor;

[0094] The acquisition initiation module 502 is used to obtain the current work order information and initiate a work order pop-up window when the listener detects that the clipboard content contains work order information;

[0095] Filling and parsing module 503, used to fill the work order information into the work order pop-up window, parse the work order information in the work order pop-up window, and obtain the work order data;

[0096] Understanding and classification module 504, for using a preset BERT model to perform semantic understanding on the context of the work order data, and using a preset work order type keyword library to classify the work order data to obtain the work order type;

[0097] The matching and extraction module 505 is used to match the corresponding structured data model based on the work order type and extract key information of the work order data through regular expressions and the structured data model.

[0098] In this embodiment, by identifying the user's clipboard permission status, the user's work order entry permission can be dynamically managed to improve user satisfaction. When the clipboard permission status is authorized, the listener is used to monitor the clipboard content in real time. When the listener detects that the clipboard content contains work order information, the current work order information is obtained, and a work order pop-up window is initiated to fill the work order information into the work order pop-up window. The work order information is parsed in the work order pop-up window to obtain work order data. The preset BERT model is used to semantically understand the context of the work order data, and the preset work order type keyword library is used to classify the work order data to obtain the work order type. The corresponding structured data model is matched based on the work order type to adapt to complex and changeable work order content and improve applicability. The key information of the work order data is extracted through regular expressions and structured data models to reduce manual entry costs and improve work order entry processing efficiency.

[0099] See also Figure 6 Another embodiment of the work order processing device based on data analysis in the embodiment of the present invention includes:

[0100] Identification and monitoring module 501, used to identify the user's clipboard permission status, when the clipboard permission status is authorized, the clipboard content is monitored in real time using a monitor;

[0101] The acquisition initiation module 502 is used to obtain the current work order information and initiate a work order pop-up window when the listener detects that the clipboard content contains work order information;

[0102] Filling and parsing module 503, used to fill the work order information into the work order pop-up window, parse the work order information in the work order pop-up window, and obtain the work order data;

[0103] Understanding and classification module 504, for using a preset BERT model to perform semantic understanding on the context of the work order data, and using a preset work order type keyword library to classify the work order data to obtain the work order type;

[0104] Matching and extraction module 505, used to match the corresponding structured data model based on the work order type, and extract key information of the work order data through regular expressions and the structured data model;

[0105] In this embodiment, the identification monitoring module 501 includes: a calling query unit 5011, which is used to call the clipboard permission API and use the clipboard permission API to query the user's clipboard permission status; a creation unit 5012, which is used to create a listener when the clipboard permission status is authorized; and a monitoring unit 5013, which is used to use the listener to monitor the clipboard content in real time.

[0106] In this embodiment, the acquisition initiation module 502 includes: an extraction and generation unit 5021, which is used to extract a preset pop-up template from a template library and generate a modal pop-up window based on the pop-up template; an acquisition unit 5022, which is used to obtain the current work order information when the listener detects that the clipboard content contains work order information; and a triggering unit 5023, which is used to trigger the work order pop-up window in real time according to the modal pop-up window.

[0107] In this embodiment, the filling and parsing module 503 includes: a conversion unit 5031, which is used to convert the work order information into a standard format for pop-up window parameter input to obtain standard work order information; a filling unit 5032, which is used to fill real-time standard work order information into the work order pop-up window; a parsing and mapping unit 5033, which is used to parse the work order information in the work order pop-up window to obtain work order data, and map each attribute of the work order data to the corresponding form field.

[0108] In this embodiment, the understanding classification module 504 includes: a construction unit 5041, which is used to construct a work order domain knowledge graph and associate specific keywords with knowledge nodes, and the knowledge nodes include business rules, operating procedures and historical cases; an understanding unit 5042, which is used to use a preset BERT model to perform semantic understanding on the context of the work order data; a first extraction unit 5043, which is used to extract relevant knowledge corresponding to the ambiguous keyword from the work order domain knowledge graph when the BERT model identifies an ambiguous keyword; an analysis unit 5044, which is used to perform a comprehensive analysis based on the relevant knowledge and in combination with the context of the work order data to obtain a keyword analysis result; a classification unit 5045, which is used to classify the work order data based on the keyword analysis result and using a preset work order type keyword library to obtain a work order type.

[0109] In this embodiment, the matching extraction module 505 includes: a construction definition unit 5051, which is used to construct multiple structured data models in JSON or XML format, and define the data fields and hierarchical structures of different structured data models according to the logical relationship of the work order; a storage unit 5052, which is used to store multiple structured data models in a model library; a matching unit 5053, which is used to match the corresponding structured data model from the model library based on the work order type; a second extraction unit 5054, which is used to extract key information of the work order data through regular expressions and structured data models.

[0110] In this embodiment, it also includes: a judgment module 506, which is used to judge whether the key information cannot be accurately identified or is missing, and obtain a judgment result; an entry module 507, which is used to enter the key information into the work order system when the judgment result is that the key information can be accurately identified and there is no missing; a generation module 508, which is used to generate supplementary prompt information when the judgment result is that the key information cannot be accurately identified or is missing; a sending module 509, which is used to send the supplementary prompt information to the user end, so that the user end generates and displays a supplementary prompt page based on the supplementary prompt information.

[0111] above Figure 5 and Figure 6 The work order processing device based on data analysis in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The work order processing device based on data analysis in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0112] Figure 7 This is a schematic structural diagram of a work order processing device based on data analysis provided by an embodiment of the present invention. The work order processing device 600 based on data analysis may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 610 (for example, one or more processors) and a memory 620, and one or more storage media 630 (for example, one or more massive storage devices) for storing application programs 633 or data 632. Among them, the memory 620 and the storage medium 630 can be temporary storage or permanent storage. The program stored in the storage medium 630 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the work order processing device 600 based on data analysis. Furthermore, the processor 610 can be configured to communicate with the storage medium 630, and execute a series of instruction operations in the storage medium 630 on the work order processing device 600 based on data analysis to implement the steps of the work order processing method based on data analysis provided by the above-mentioned method embodiments.

[0113] The work order processing device 600 based on data analysis may further include one or more power supplies 640, one or more wired or wireless network interfaces 650, one or more input and output interfaces 660, and / or one or more operating systems 631, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. It will be understood by those skilled in the art that Figure 7The structure of the work order processing device based on data analysis shown does not constitute a limitation on the work order processing device based on data analysis, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0114] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the steps of a work order processing method based on data analysis.

[0115] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0116] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially 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. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc., various media that can store program code.

[0117] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A work order processing method based on data analysis, characterized in that: include: Identify the user's clipboard permission status, and when the clipboard permission status is authorized, use a listener to monitor the clipboard content in real time; When the listener detects that the clipboard content contains work order information, it obtains the current work order information and initiates a work order pop-up window; Filling the work order information into the work order pop-up window, parsing the work order information in the work order pop-up window to obtain work order data; Using a preset BERT model to perform semantic understanding on the context of the work order data, and using a preset work order type keyword library to classify the work order data to obtain the work order type; Based on the work order type matching corresponding structured data model, key information of the work order data is extracted through regular expressions and the structured data model.

2. The work order processing method based on data analysis according to claim 1, characterized in that: The identifying user's clipboard permission status, when the clipboard permission status is authorized, using a listener to monitor the clipboard content in real time, includes: Calling a clipboard permission API to query the user's clipboard permission status using the clipboard permission API; When the clipboard permission status is authorized, a listener is created; The listener is used to monitor the clipboard content in real time.

3. The work order processing method based on data analysis according to claim 1, characterized in that: When the listener detects that the clipboard content contains work order information, it obtains the current work order information and initiates a work order pop-up window, including: Extracting a preset pop-up window template from a template library, and generating a modal pop-up window based on the pop-up window template; When the listener detects that the clipboard content contains work order information, the current work order information is obtained; The work order pop-up window is triggered in real time according to the modal pop-up window.

4. The work order processing method based on data analysis according to claim 1, characterized in that: Filling the work order information into the work order pop-up window, parsing the work order information in the work order pop-up window to obtain work order data, includes: Convert the work order information into a standard format for pop-up window parameter input to obtain standard work order information; Filling the real-time standard work order information into the work order pop-up window; The work order information is parsed in the work order pop-up window to obtain the work order data, and each attribute of the work order data is mapped to a corresponding form field.

5. The work order processing method based on data analysis according to claim 1, characterized in that: The preset BERT model is used to perform semantic understanding on the context of the work order data, and the work order data is classified using a preset work order type keyword library to obtain the work order type, including: Build a knowledge graph for the work order domain, associating specific keywords with knowledge nodes, including business rules, operational procedures, and historical cases; Use the preset BERT model to perform semantic understanding on the context of the work order data; When the BERT model identifies an ambiguous keyword, relevant knowledge corresponding to the ambiguous keyword is extracted from the work order domain knowledge graph; Performing a comprehensive analysis based on the relevant knowledge and in combination with the context of the work order data to obtain a keyword analysis result; Based on the keyword analysis result and using a preset work order type keyword library, the work order data is classified to obtain the work order type.

6. The work order processing method based on data analysis according to claim 1, characterized in that: The step of matching the work order type with the corresponding structured data model and extracting key information of the work order data using a regular expression and the structured data model includes: Use JSON or XML format to build multiple structured data models, and define the data fields and hierarchical structures of different structured data models according to the logical relationship of the work order; Storing the plurality of structured data models in a model library; Matching the corresponding structured data model from the model library based on the work order type; Key information of the work order data is extracted using regular expressions and the structured data model.

7. The work order processing method based on data analysis according to claim 1, characterized in that: After matching the work order type to a corresponding structured data model and extracting key information of the work order data using a regular expression and the structured data model, the method further includes: Determine whether the key information cannot be accurately identified or is missing, and obtain a determination result; When the judgment result is that the key information can be accurately identified and there is no missing, the key information is entered into the work order system; When the judgment result is that the key information cannot be accurately identified or is missing, supplementary prompt information is generated; The supplementary prompt information is sent to the user terminal, so that the user terminal generates and displays a supplementary prompt page based on the supplementary prompt information.

8. A work order processing device based on data analysis, characterized in that: include: An identification and monitoring module is used to identify the user's clipboard permission status. When the clipboard permission status is authorized, the listener is used to monitor the clipboard content in real time; An acquisition initiation module is used to obtain the current work order information and initiate a work order pop-up window when the listener detects that the clipboard content contains work order information; A filling and parsing module is used to fill the work order information into the work order pop-up window, and parse the work order information in the work order pop-up window to obtain work order data; An understanding and classification module is used to perform semantic understanding of the context of the work order data using a preset BERT model, and classify the work order data using a preset work order type keyword library to obtain the work order type; The matching and extraction module is used to match the corresponding structured data model based on the work order type, and extract key information of the work order data through regular expressions and the structured data model.

9. A work order processing device based on data analysis, characterized in that: The work order processing device based on data analysis includes: a memory and at least one processor, wherein the memory stores instructions; At least one of the processors calls the instructions in the memory to enable the work order processing device based on data analysis to execute each step of the work order processing method based on data analysis as described in any one of claims 1 to 7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the various steps of the work order processing method based on data analysis as described in any one of claims 1 to 7 are implemented.

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