Workshop production software error response optimization system and method

Abstract

The invention relates to the technical field of industrial automation and informatization, and particularly discloses a workshop production software error response optimization system and method, and the system comprises a data collection and processing module which is used for collecting data from a sensor, a programmable logic controller and an MES system in real time, and generating a structured error log when an abnormality is detected; the knowledge management module is used for maintenance personnel to create, edit and associate standardized processing steps and multimedia attachments for specific error IDs, and the standardized processing steps and the multimedia attachments are structurally stored in a database; the intelligent matching engine is used for matching and returning solutions associated with the error IDs, and the solutions are sorted according to a sorting rule; the user interaction and feedback module is used for receiving and visually displaying the solution and collecting feedback information; a file storage system; through automatic acquisition of error information and manual input of standardized processing steps, intelligent matching of error codes and solutions is realized, and the fault processing efficiency and the production continuity are improved, so that the production efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial automation and information, in particular to a workshop production software error response optimization system and method. BACKGROUND

[0002] In the field of industrial manufacturing, workshop production software is the core support for ensuring stable operation of equipment and accurate execution of process. The response capability of the workshop production software when detecting equipment abnormalities (such as module connection timeout) or process errors (such as RF test failure) directly affects the production continuity and product yield. However, the current error response mechanism of the workshop production software has significant defects, which are specifically manifested as follows: the existing production software only provides basic error prompts (such as "module connection timeout"), lacks specific processing guidelines, and troubleshooting completely depends on the personal experience of maintenance personnel. New employees are difficult to respond quickly due to insufficient problem handling experience, which prolongs the downtime of faults and is also prone to secondary faults due to improper operation. Similar errors lack a unified standardized solution, and different maintenance personnel or different shifts will adopt different processing methods, leading to repeated troubleshooting and trial-and-error, resulting in fragmented processing flow. Error information and corresponding solutions are not systematically stored, and historical processing experience cannot be reused. At the same time, personnel turnover will lead to the loss of implicit technical experience with the departure of personnel, and new replacement personnel need to accumulate experience again, forming an "experience fault", which cannot form an enterprise-level fault handling knowledge asset. The current technology proposes error log classification storage, but does not realize dynamic matching of processing strategies. The existing technology uses machine learning to predict faults, but does not integrate standardized operation processes.

[0003] In summary, the existing technology cannot simultaneously meet the full-link requirements of "automated error collection-structured knowledge deposition-intelligent matching response-closed-loop optimization iteration"; therefore, there is an urgent need for an error response optimization scheme combining automated data collection and artificial knowledge deposition. SUMMARY

[0004] In order to overcome the above technical problems existing in the prior art, the present application provides a workshop production software error response optimization system and method, which automatically collects error information, manually enters standardized processing steps, and stores them into a database, realizes intelligent matching of error codes and solutions, reduces dependence on personnel experience, improves fault handling efficiency and production continuity, and thus improves production efficiency.

[0005] To achieve the above object, the embodiment of the present application provides a workshop production software error response optimization system, comprising: a data acquisition and processing module, deployed on an industrial gateway or an industrial computer, used for acquiring data from sensors, programmable logic controllers and MES systems in real time, and generating a structured error log containing an error ID and context data when an exception is detected; a knowledge management module, providing a graphical knowledge management interface, for a maintenance personnel to create, edit and associate standardized processing steps and multimedia attachments for a specific error ID, and to store them in a database in a structured manner; an intelligent matching engine, deployed on an application server cluster, used for receiving an error ID, querying the database, matching and returning a solution associated with the error ID, the solution being sorted according to a predefined sorting rule, and outputting a sorted solution list; a user interaction and feedback module, integrated into a production software interface, used for receiving and visually displaying the solution, and collecting feedback information of an operator on a solution execution result; a file storage system, used for storing the multimedia attachments, and providing file access services for the knowledge management module and the user interaction and feedback module; the feedback information collected by the user interaction and feedback module is recorded to the database, used for optimizing the sorting rule of the intelligent matching engine and updating the standardized processing steps.

[0006] Preferably, the data acquisition and processing module comprises a plurality of sensors, PLCs, industrial gateways, protocol adaptation layers, data collectors and error log generators; the protocol adaptation layer supports Modbus TCP / RTU, OPC UA and MES API protocols, used for communicating with different hardware and industrial communication protocols, and uniformly parsing raw data into structured key-value pair data; the data collector is used for polling or listening to events at a preset frequency, obtaining data from the protocol adaptation layer and generating a context data snapshot; the error log generator is used for receiving the context data snapshot when the system triggers an exception or a rule engine judges an error, and generating the structured error log containing an error ID, a timestamp and a device state; the plurality of sensors comprise temperature sensors, voltage and current sensors and visual sensors.

[0007] Preferably, the knowledge management module comprises: an error code table, used for storing an error ID, a process link to which the error ID belongs, an error description and an error level; a processing step table, used for storing a solution ID, an associated error ID, a processing step text and a storage path of an associated multimedia attachment; a history record table, used for storing event records of each error processing, including a solution ID adopted, a processing time consumption and a flag indicating whether the processing is successful.

[0008] Preferably, the intelligent matching engine comprises: the sorting rule is that solutions are sorted based on historical success rates of each solution ID in the history record table, the historical success rate being a ratio of a number of times that a solution ID corresponds to being marked as "success" to a total number of execution times thereof; the matching algorithm comprises an association recommendation function based on an Apriori algorithm, used to recommend associated alternative solutions when a certain solution ID is marked as "failure"; the intelligent matching engine provides services externally through REST or gRPC API, and a solution list output comprises a processing step text, a multimedia attachment access path and a historical success rate field.

[0009] Preferably, the user interaction and feedback module comprises an embedded UI component, a multimedia guidance renderer and an external tool integrator: the embedded UI component automatically pops up or highlights when an error is triggered; the multimedia guidance renderer is used to obtain and render picture, video or three-dimensional model content from the file storage system according to a multimedia attachment path provided in a solution; and the external tool integrator is used to provide an interface for one-key calling of external calibration software or programming tools.

[0010] Preferably, the file storage system is configured with a RAID array to guarantee data reliability, supports generation of a unique access address of a multimedia attachment through a path manager, and stores the attachment_path field in the processing step table; the file storage system is selected to be a network attached storage (NAS) or a file server, and provides file sharing services through SMB, NFS or FTP protocols.

[0011] Correspondingly, the application also provides a workshop production software error response optimization method applied to the workshop production software error response optimization system provided by the application, and the method comprises the following steps: step S1, real-time collection of production data through a data acquisition and processing module, and generation of a structured error log when an exception is detected; step S2, reception of processing steps and multimedia attachments input by a maintenance personnel through a knowledge management module, association of the processing steps and multimedia attachments with a specific error ID, and storage of the processing steps and multimedia attachments in a database; step S3, query of the database through an intelligent matching engine when a production software triggers the same error ID again, acquisition of associated solutions and display of the solutions according to a sorting rule; step S4, display of the solutions to an operator through a user interaction and feedback module, and collection of feedback of an execution result; and step S5, update of a history record in the database according to the collected feedback information, optimization of the sorting rule and processing step content based on the updated history record, and closed-loop optimization of a knowledge base.

[0012] Preferably, in the step S1, the JSON format of the error log at least includes error_id, the production process to which the error belongs, error description, occurrence time, device number and sensor snapshot data; in the step S2, the processing step is written by a rich text editor, and graphic-text mixed arrangement is supported.

[0013] Preferably, in the step S3, the optimization process of the sorting rule includes: calculating the historical success rate of each solution in a preset time period, the formula of the historical success rate is: success times / total execution times; dynamically adjusting the sorting position of the solution in the next matching according to the historical success rate; in the step S4, the closed-loop optimization further includes: when the operator feedbacks that a processing step is “cumbersome” or “low accuracy”, the maintenance personnel modify the corresponding processing step and multimedia attachment, update the solution list, and record the version information to the historical record table.

[0014] Through the technical scheme provided by the present application, the present application at least has the following technical effects: Through the intelligent matching engine, the standardized solution is directly pushed, the dependence on manual experience is avoided, the average fault processing time is shortened, the processing efficiency is improved, the fault processing efficiency is greatly improved, the operation threshold of the multimedia guidance and the standardized step is reduced, the error processing accuracy of the novice operator is greatly improved, the training cycle of the new employee is shortened, the secondary fault caused by improper operation is reduced, the knowledge assetization and the reusability are improved, the error information and the solution form an enterprise-level structured knowledge base, cross-plant and cross-production-line reuse is supported, the historical experience does not flow away with personnel, the error repetition rate is reduced, and “one-time solution, global reuse” is realized; the system has strong extensibility: the error code and the processing step can be quickly added through the database management interface, the new device type is adapted, the core architecture does not need to be modified, and the system upgrade cost is reduced. BRIEF DESCRIPTION OF DRAWINGS

[0015] The accompanying drawings are included to provide a further understanding of the embodiments of the present application, and constitute a part of the specification, and are used together with the following specific embodiments to explain the embodiments of the present application, but do not constitute a limitation on the embodiments of the present application. In the drawings: Figure 1 is a framework diagram of a workshop production software error response optimization system provided by an embodiment of the present application; Figure 2 is a flowchart of a workshop production software error response optimization method provided by an embodiment of the present application; Figure 3 is a new abnormality processing flowchart in an embodiment of the present application; Figure 4 is an existing abnormality processing flowchart in an embodiment of the present application.

[0016] Icon: data acquisition and processing module 100, knowledge management module 200, intelligent matching engine 300, user interaction and feedback module 400, file storage system 500. DETAILED DESCRIPTION

[0017] The specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely intended to illustrate and explain the present application, and are not intended to limit the present application.

[0018] The terms "system" and "network" in the embodiments of the present application can be used interchangeably. "Multiple" means two or more, and in view of this, "multiple" in the embodiments of the present application can also be understood as "at least two". "And / or" describes the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can represent the following three cases: A exists alone, A and B exist together, and B exists alone. In addition, the character " / ", if not specially stated, generally represents that the associated objects before and after it are in an "or" relationship. In addition, it should be understood that in the description of the embodiments of the present application, "first", "second", etc. are used only for the purpose of distinguishing the description, and cannot be understood as indicating or implying relative importance, nor can it be understood as indicating or implying order.

[0019] Please refer to Figure 1The embodiment of the present application provides a workshop production software error response optimization system, the system includes five core modules of data acquisition and processing module 100, knowledge management module 200, intelligent matching engine 300, user interaction and feedback module 400 and file storage system 500, the functions of each module are independent and synergistic linkage, form a complete error response and optimization closed loop, the detailed configuration and functions of each module are as follows, for the data acquisition and processing module 100, the module is deployed in industrial gateway or industrial computer, the module is the "perception layer" of the system, responsible for real-time acquisition of production field original data from sensors, programmable logic controller and MES system, and generates structured error log containing error ID and converted context data when detecting abnormality, the core components and working logic are as follows: hardware components include temperature sensor, voltage and current sensor, visual sensor (for sensing physical signals), PLC (programmable logic controller, device control core, generating status code and error code), industrial gateway (carrying software components, supporting multi-protocol communication); software components include protocol adaptation layer, data collector and error log generator, wherein the protocol adaptation layer supports Modbus TCP / RTU, OPC UA, MESAPI and other industrial protocols, for communication with different hardware and industrial communication protocols, the original analog signals (such as voltage value) output by sensors, PLC and MES system are parsed into unified structured key-value pair data; the data collector is used for polling protocol adaptation layer data at preset frequency (such as 1 time / s) or listening to abnormal event push of MES system, obtaining data from protocol adaptation layer, such as aggregated device number, timestamp, sensor reading, PLC status code and other information, generating "context data snapshot"; the error log generator is used for receiving "context data snapshot" when production software triggers abnormality or rule engine determines error, embedding unique error ID (such as "FUNC-PINR-002"), generating JSON structured error log containing error ID, error belonging production process, error description, occurrence time (timestamp), device status, device number and sensor snapshot, synchronously uploading to database and marking as "to be processed" state.

[0020] In the embodiment of the present application, the knowledge management module 200 of the inter-vehicle production software error response optimization system is deployed on the web server and the client browser. The module is the "knowledge sedimentation layer" of the system, responsible for converting the experience of maintenance personnel into structured digital knowledge. The module provides a graphical knowledge management interface for maintenance personnel to create, edit and associate standardized processing steps and multimedia attachments for a specific error ID, and stores them in the database in a structured manner. Specifically, the core configuration of the module includes core data tables and human-computer interaction functions. The core data tables are stored in the SQL database, and include error code table, processing step table and history record table. The fields of the error code table include error ID (error_id, primary key), process link (such as "RF test"), error description (such as "function test resistance value exceeds the set range") and error level (such as "emergency / general"). The fields of the processing step table (Solutions table) include solution ID (solution_id, primary key), associated error ID (error_id, foreign key, associated error code table), processing step text (standardized operation guide) and associated multimedia attachment storage path (attachment_path). The fields of the history record table include record_id (primary key), error ID (error_id), solution ID (solution_id), processing timestamp, processing time, processing success flag (Boolean value) and operator feedback. The human-computer interaction function is used to provide a graphical knowledge management interface (single-page application SPA), which supports a rich text editor, a file upload component and error code association. The rich text editor is used to write the processing steps in a mixed arrangement of text and pictures (such as "step 1: check the module and tool connection line, replace the worn connector"). The file upload component supports drag-and-drop upload of multimedia attachments such as videos, PDF manuals and 3D models. The error code association selects the error ID from the error code table, binds the processing steps and multimedia attachments with the error ID, and ensures flexible adaptation of "one error with multiple solutions" or "multiple errors with one solution".

[0021] In the embodiments of the present application, the intelligent matching engine 300 of the inter-vehicle production software error response optimization system is the "decision core layer" of the system, responsible for efficient matching and sorting of errors and solutions; this module is deployed on the application server cluster, used to receive error IDs, query the database, match and return the solutions associated with the error ID, and the solutions are sorted according to the pre-defined sorting rules, and the sorted solution list is output; wherein the matching algorithm of this module can realize accurate or fuzzy matching, specifically receiving the error ID sent by the user interaction module, querying the processing step table, and obtaining all associated solutions; the matching algorithm of this module can also realize the associated recommendation function, specifically based on the Apriori algorithm (association rule mining), when a solution is marked as "failed", automatically recommend alternative solutions with high correlation to the error in the historical data (example: if "restart PLC" fails, recommend "check switch port configuration"); further, the sorting rule of this module is: based on the "historical success rate" of each solution ID (solution_id) in the historical record table, each solution is sorted, wherein the calculation formula of "historical success rate" is: historical success rate = the number of times that the solution is marked as "success" / the total number of times that the solution is executed (example: solution A success rate 90%, solution B success rate 85%, then solution A is sorted first); the service interface of the intelligent matching engine specifically includes: providing services to the outside through REST or gRPC API, the output solution list includes processing step text, multimedia attachment access path (from attachment_path), historical success rate field, so that the production software can be quickly called.

[0022] In the embodiment of the present application, the user interaction and feedback module 400 in the system is the "human-computer interaction layer" of the system, responsible for showing the solution to the operator and collecting feedback. The module is integrated into the production software interface, used to receive and visually display the solution, and collect feedback information from the operator on the execution result of the solution. Specifically, the module includes an embedded UI component, a multimedia guidance renderer, and an external tool integrator. The embedded UI component is integrated into the existing production software interface in the form of a pop-up window or panel, and automatically pops up or highlights when an error is triggered, intuitively displaying the sorted solution list. The multimedia guidance renderer obtains and renders picture, video, or three-dimensional model content according to the path pointed to by the multimedia attachment path (attachment_path) field in the solution, from the file storage system (example: playing "PLC restart operation video"), reducing the operation understanding threshold. The external tool integrator provides an interface for one-key calling of external calibration software or programming tools, supporting direct start of PLC programming software, multimeter calibration software, etc., without the need for manual application switching. The module also includes a feedback collector, which provides "solved / not solved" buttons and text input boxes. After the operator submits the processing result, the system automatically writes the error ID, solution ID, processing time consumption, and feedback content into the history record table.

[0023] In the embodiment of the present application, the file storage system 500 of the system is the "unstructured data storage layer" of the system, responsible for storing multimedia attachments and providing access services. The module is used to store the multimedia attachments and provide file access services for the knowledge management module and the user interaction and feedback module. Specifically, the file storage system uses NAS (Network Attached Storage) or file server on hardware selection, configures RAID array (such as RAID5) to ensure data reliability and avoid attachment loss. It supports SMB, NFS, or FTP protocols on service protocols, provides file read-write and sharing services, and can be selected as network attached storage NAS or file server. On the path management, the "path manager" generates a unique access address for the uploaded multimedia attachments, synchronously stores it to the attachment_path field of the processing step table, and ensures that the user interaction module can accurately call.

[0024] In the embodiment of the present application, the modules work cooperatively through the following four data streams to ensure the integrity and real-time performance of data flow: 1. Acquisition stream (real-time): production field sensor / PLC / MES system → protocol adaptation layer (parse data) → data collector (generate snapshot) → error log generator (generate error log) → database (store log); 2. Knowledge flow (manual): Maintenance personnel -> Knowledge management interface (input steps, upload attachments) -> Web server (process requests) -> Branch 1: File storage system (store attachments, generate attachment_path); Branch 2: Database (update error code table, process steps table); 3. Matching flow (request-response): Production software (trigger error) -> User interaction module (initiate matching request) -> Intelligent matching engine (query database, sort solutions) -> User interaction module (receive and display solutions); 4. Optimization flow (closed loop): Operator (submit feedback) -> Feedback collector (record results) -> Database (update history table) -> Maintenance personnel (analyze data, optimize steps) -> Database (update process steps table) -> Intelligent matching engine (adjust sorting rules).

[0025] By using the workshop production software error response optimization system provided by the embodiments of the application, the application process of the system is described in detail below in combination with two actual production scenarios: Specific embodiment one: automatic line PLC communication error handling: 1. Error triggering: The automatic line PLC reports an error "communication timeout (RF-PLC_COM-001)", and the system automatically collects current network state, device load rate, and power stability data.

[0026] 2. Knowledge matching: The database returns three solutions: solution A: restart the PLC controller (historical success rate 88%); solution B: check the switch port configuration (historical success rate 75%); solution C: update the PLC firmware (historical success rate 62%).

[0027] 3. Execution and feedback: The operator selects solution A, and the interface displays a restart operation animation. After execution, the fault is resolved.

[0028] Specific embodiment two: manual line RF calibration failure 1. Error triggering: The manual line detects "RF calibration failure (RF-CAL_FAIL-003)", and the system pushes the following solutions: Step 1: Use the standard sample to calibrate the test fixture line loss (attach calibration steps); Step 2: Check if the wireless network tester is dead, and try to restart the tester.

[0029] 2. AR assistance: The operator scans the device two-dimensional code through the tablet, and automatically plays the calibration video, synchronously recording the execution time and results.

[0030] Please refer to Figure 2Based on the same inventive concept, this invention provides a method for optimizing error response in workshop production software, applied to the error response optimization system for workshop production software provided in this invention. This method forms a closed loop of "collection-retention-response-feedback-optimization," and specifically includes the following steps: Step S1: Collect production data in real time through the data acquisition and processing module, and generate a structured error log when an anomaly is detected; Step S2: Through the knowledge management module, receive the processing steps and multimedia attachments input by maintenance personnel, associate them with specific error IDs, and store them in the database; Step S3: When the production software triggers the same error ID again, the database is queried through the intelligent matching engine to obtain and display the associated solutions according to the sorting rules; Step S4: Show the solution to the operator through the user interaction and feedback module, and collect their feedback on the execution results; Step S5: Update the historical records in the database based on the collected feedback information, and optimize the sorting rules and processing steps based on the updated historical records to achieve closed-loop optimization of the knowledge base.

[0031] In this embodiment of the invention, step S1 mainly completes error acquisition and log generation; specifically, this step is as follows: First, the protocol adaptation layer of the data acquisition and processing module receives raw data from the sensor, PLC, and MES system in real time and parses it into structured key-value pairs; then, the data collector aggregates the data at a preset frequency to generate a "context data snapshot" containing the device number, timestamp, and sensor readings; finally, when the production software detects an anomaly (such as "resistance value exceeds the set range"), the error log generator is activated, embedding error_id (such as FUNC-PINR-002), and generating a JSON-formatted structured error log (example: {"error_id":"FUNC-PINR-002","process":"RF test","description":"functional test resistance value exceeds the set range","timestamp":"2024-05-10 09:30:00","device_id": The error log ("RF-Line-03", "sensor_data":{"resistance":150Ω, "standard_range":100-120Ω}}) is uploaded to the database and marked as "pending processing". In this step, the generated error log in JSON format must include at least: error_id, the production process to which the error belongs, error description, occurrence time, equipment number, and sensor snapshot data.

[0032] Further, step S2 specifically completes knowledge entry and structured storage, the processing steps in this step are written through a rich text editor, supporting picture-text mixed layout; this step specifically includes: first, the maintenance personnel log in the knowledge management interface through the client browser, query the error log in the "to be processed" state, analyze the error root cause (such as "the resistance value exceeds the error is caused by the loose connection between the module and the tooling"); then use the rich text editor to write the standardized processing steps (example: step 1: turn off the power of the test equipment, check the connection line between the module to be tested and the tooling, replace the worn connector; step 2: start the power supply, calibrate the tooling with a standard sample machine; step 3: re-execute the test); then drag and upload the operation video (such as "module connection line replacement tutorial.mp4") matched with the steps, select error_id ("FUNC-PINR-002") through the error code associated component, bind the processing steps and the video; finally, the web server writes the processing steps into the processing step table, stores the video in the NAS and generates attachment_path (such as "NAS / RF-Line / Solutions / FUNC-PINR-002 / connect_replace.mp4"), and synchronously updates the error log state to "archived".

[0033] Further, step S3 specifically completes intelligent matching and solution display, the optimization process of the sorting rule in this step includes: calculating the historical success rate of each solution in a preset time period, the calculation formula of the historical success rate is: number of successes / total number of executions, and then dynamically adjusting the sorting position of the solution in the next matching according to the historical success rate; this step specifically includes: first, when the production software detects the error with error_id "FUNC-PINR-002" again, the user interaction module automatically calls the REST API of the intelligent matching engine (request example: GET / api / solutions Error_id=FUNC-PINR-002); then the intelligent matching engine queries the processing step table to obtain all solutions associated with "FUNC-PINR-002", combines the historical record table to calculate the historical success rate of each solution (such as the success rate of solution 1 is 92%, the success rate of solution 2 is 85%), and sorts in descending order of success rate; finally, the embedded UI component receives the sorted solution list, renders and displays the processing step text and video link, and the operator can watch the operation video through the multimedia guidance renderer by clicking the link.

[0034] In the embodiment of the present application, step S4 mainly performs feedback and result recording. In this step, the closed-loop optimization further includes that when the operator feeds back that a certain processing step is "cumbersome" or "low accuracy", the maintenance personnel modify the corresponding processing step and multimedia attachment, update the solution list, and record the version information to the historical record table. This step specifically includes: first, the operator performs the operation according to the solution guide (such as replacing the connecting line, calibrating the tooling); then, if the problem is solved, click the "Solved" button; if the problem is not solved, click "Not Solved" and input the feedback (such as "still out of tolerance after step 2 calibration") in the text box. The system automatically starts the "expert assistance" process (supports remote access by maintenance personnel); finally, the feedback collector writes error_id, solution_id, processing time (such as 5 minutes), processing result (success / failure), and feedback content into the historical record table.

[0035] In the embodiment of the present application, step S5 mainly completes closed-loop optimization and knowledge iteration. This step specifically includes: first, the maintenance personnel regularly check the historical record table to analyze the execution effect of the solution (such as multiple operators feeding back that "step 2 calibration operation is cumbersome and time-consuming"); then, based on the feedback, the processing step is optimized (such as optimizing step 2 to "use a multimeter to directly detect the resistance value of the tooling, and if it is out of the standard range, replace the new tooling"), and the optimized operation video is re-uploaded to update the processing step table; then, the intelligent matching engine re-calculates the historical success rate of each solution based on the updated historical record table, and dynamically adjusts the sorting position (such as the success rate of the optimized solution being improved to 95%, and being sorted in priority to the original solution); finally, the system generates an error processing efficiency report (including average fault processing time, occurrence rate of each error_id, and processing accuracy of new users) every month to guide the optimization of equipment maintenance plan (such as purchasing spare connecting lines in advance for the high-frequency error "FUNC-PINR-002").

[0036] In the embodiment of the present application, as shown in Figure 3 When a new exception is processed, the processing flow steps are: after the software or sensor detects the exception, the software automatically collects the exception data, inputs it into the system through manual input, then generates an error code table, sets the exception to a "to-be-processed" state, then inputs the processing step for the exception, can also add the video recorded in the processing process, and updates the processing state of the exception to "completed"; as shown in Figure 4As shown, when the exception to be processed is an existing exception, the processing flow steps are: when the exception is detected, first throw an error code, and query whether the error code exists in the error code table, and check whether the processing state is "to be processed", if it is "to be processed", then give the processing steps combined with the error code, if there is processing video, it can also be given together, if a better processing step than the existing processing step is found during processing, the processing step is updated, and the processing result is recorded as "complete exception processing".

[0037] By using the workshop production software error response optimization system and method provided by the embodiment of the application, the beneficial effects are: through the intelligent matching engine, the standardized solution is directly pushed, the dependence on manual experience is avoided, the average fault processing time is shortened, the processing efficiency is improved, the fault processing efficiency is greatly improved; novice operation standardization and accuracy improvement: multimedia guidance and standardized steps reduce the operation threshold, greatly improve the error processing accuracy of novice operators, also shorten the training cycle of new employees, reduce the secondary faults caused by improper operation; knowledge assetization and reusability improvement, error information and solutions form an enterprise-level structured knowledge base, support cross-plant and cross-production line reuse, historical experience no longer flows away with personnel, thereby reducing the error repetition rate and realizing "one-time solution, global reuse"; the system has strong extensibility: through the database management interface, error codes and processing steps can be quickly added, new equipment types can be adapted, the core architecture does not need to be modified, and the system upgrade cost is reduced.

[0038] The above describes the optional implementation of the embodiment of the application in detail in combination with the drawings, but the embodiment of the application is not limited to the specific details in the above implementation, and various simple modifications can be made to the technical solutions of the embodiment of the application within the technical concept range of the embodiment of the application, and these simple modifications all belong to the protection range of the embodiment of the application.

[0039] In addition, it should be noted that each specific technical feature described in the above specific implementation can be combined in any appropriate manner without contradiction. In order to avoid unnecessary repetition, various possible combinations are not described again by the embodiment of the application.

[0040] Those skilled in the art can understand that all or part of the steps of the method in the above-mentioned embodiments can be completed by programs instructing the relevant hardware, the programs are stored in a storage medium, and the programs include a plurality of instructions for enabling a single-chip microcomputer, a chip or a processor to execute all or part of the steps of the method described in various embodiments of the present application. The foregoing storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk and various storage media capable of storing program codes.

[0041] In addition, various different embodiments of the embodiments of the present application can also be combined arbitrarily, as long as they do not deviate from the idea of the embodiments of the present application, and should also be considered as disclosed by the embodiments of the present application.

Claims

1. A plant production software error response optimization system, characterized by, The application relates to a system for intelligent error handling in industrial production, comprising: a data acquisition and processing module deployed on an industrial gateway or industrial computer, for real-time acquisition of data from sensors, programmable logic controllers and MES systems, and generation of a structured error log containing an error ID and context data when an anomaly is detected; a knowledge management module providing a graphical knowledge management interface for maintenance personnel to create, edit and associate standardized processing steps and multimedia attachments for a specific error ID, and to store them in a database in a structured manner; an intelligent matching engine deployed on an application server cluster, for receiving an error ID, querying the database, matching and returning solutions associated with the error ID, the solutions being sorted according to a predefined sorting rule, and outputting a sorted solution list; a user interaction and feedback module integrated into a production software interface, for receiving and visually displaying the solutions, and collecting feedback information from operators on the execution results of the solutions; a file storage system for storing the multimedia attachments and providing file access services for the knowledge management module and the user interaction and feedback module; the feedback information collected by the user interaction and feedback module is recorded in the database, for optimizing the sorting rule of the intelligent matching engine and updating the standardized processing steps.

2. The system of claim 1, wherein, The data acquisition and processing module comprises a plurality of sensors, PLCs, industrial gateways, protocol adaptation layers, data collectors and error log generators; the protocol adaptation layer supports Modbus TCP / RTU, OPC UA and MES API protocols, and is used for communication with different hardware and industrial communication protocols, and unified parsing of raw data into structured key-value pair data; the data collector is used for polling or listening to events at a preset frequency, acquiring data from the protocol adaptation layer and generating a context data snapshot; the error log generator is used for receiving the context data snapshot when the system triggers an anomaly or a rule engine judges an error, and generating the structured error log containing an error ID, a timestamp and a device state; the plurality of sensors comprise temperature sensors, voltage and current sensors and visual sensors.

3. The system of claim 1, wherein, The knowledge management module comprises: an error code table for storing error IDs, belonging process links, error descriptions and error levels; a processing step table for storing solution IDs, associated error IDs, processing step texts and storage paths of associated multimedia attachments; a history record table for storing event records of each error processing, including adopted solution IDs, processing time consumption and a flag indicating whether the processing is successful.

4. The system of claim 3, wherein, The intelligent matching engine comprises: the sorting rule is that the solutions are sorted based on historical success rates of the solution IDs in the history record table, and the historical success rate is a ratio of the number of times that a solution ID is marked as "successful" to the total execution times of the solution ID; the matching algorithm comprises an association recommendation function based on an Apriori algorithm, for recommending associated alternative solutions when a certain solution ID is marked as "failed". The intelligent matching engine provides services through REST or gRPC API, and the output solution list includes processing step text, multimedia attachment access path and historical success rate field.

5. The system of claim 1, wherein, The user interaction and feedback module includes embedded UI components, multimedia guidance renderers and external tool integrators: The embedded UI components automatically pop up or highlight when errors are triggered; The multimedia guidance renderers are used to obtain and render picture, video or three-dimensional model content from the file storage system according to the multimedia attachment path provided in the solution; The external tool integrator provides an interface for one-key calling of external calibration software or programming tools.

6. The system of claim 1, wherein, The file storage system configures a RAID array to ensure data reliability, supports generation of unique access addresses of multimedia attachments by the path manager, and stores the attachment_path field in the processing step table; The file storage system is selected as a network attached storage (NAS) or a file server, which provides file sharing services through SMB, NFS or FTP protocols.

7. A method for optimizing response to software errors in a production plant, applied to a system according to any one of claims 1-6, characterized in that, The method comprises the following steps: Step S1: Real-time acquisition of production data by the data acquisition and processing module, and generation of structured error logs when an exception is detected; Step S2: The knowledge management module receives the processing steps and multimedia attachments input by the maintenance personnel, associates them with a specific error ID, and stores them in the database; Step S3: When the same error ID is triggered again by the production software, the intelligent matching engine queries the database to obtain and display the associated solutions according to the sorting rules; Step S4: The user interaction and feedback module displays the solutions to the operator and collects feedback on the execution results; Step S5: Update the historical records in the database according to the collected feedback information, and optimize the sorting rules and processing step content based on the updated historical records to realize closed-loop optimization of the knowledge base.

8. The method of claim 7, wherein, In step S1, the JSON format of the error log at least includes: error_id, error belonging to production process, error description, occurrence time, device number and sensor snapshot data; In step S2, the processing steps are written by a rich text editor, supporting picture-text mixed layout.

9. The method of claim 7, wherein, The optimization process of the sorting rules in step S3 includes: Calculate the historical success rate of each solution in a preset time period, and the calculation formula of the historical success rate is: success times / total execution times; Adjust the sorting position of the solution in the next matching according to the historical success rate; In step S4, the closed-loop optimization also includes: when the operator feedbacks that a processing step is "cumbersome" or "low accuracy", the maintenance personnel modify the corresponding processing step and multimedia attachment, update the solution list, and record the version information to the historical record table.

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