A welding machine detection task collaborative processing system based on process control

Abstract

This invention relates to a collaborative processing system for welding machine inspection tasks based on process control. The system includes: acquiring basic data offline, combining timestamp synchronization and automatic retransmission mechanisms to obtain well-organized, multi-dimensional raw data; eliminating abnormal data through a process threshold library and statistical models, performing dimensional unification and standardization conversion according to industry standards, parsing feature parameters and matching quality rating labels, and simultaneously constructing a blockchain traceability chain containing equipment, personnel, and operation records; dynamically allocating inspection equipment and personnel based on the real-time status of a resource pool, monitoring task progress and pushing notifications through node thresholds, and linking multiple roles for cross-stage collaboration; automatically capturing data to generate compliant inspection reports, archiving the entire process data based on the unique identifier of the welding machine, generating a visualized traceability map and multi-dimensional statistical results, thus achieving compliant delivery of welding machine inspection tasks and full-lifecycle tracking management of equipment.

Description

Technical Field

[0001] This invention belongs to the field of welding automation inspection and industrial process collaborative management technology, specifically relating to a welding machine inspection task collaborative processing system based on process control. Background Technology

[0002] The current technology and management models in the field of welding machine inspection still have the following areas for improvement: In industrial production, welding machines, as key welding equipment, directly impact product quality and production safety through their performance stability and testing accuracy. Efficient welding machine testing is crucial for industrial production. However, current technologies and management models in the welding machine testing field still have many areas for improvement, severely restricting testing efficiency and quality control levels.

[0003] In terms of data acquisition, testing scenarios often involve complex environments such as outdoor locations and areas without network access. Existing systems lack sufficient offline acquisition capabilities, data transmission is prone to interruption, and there is a lack of robust encrypted fragmented storage and intelligent retransmission mechanisms. The efficiency of multi-dimensional data integration and organization is low, and there are delays in data synchronization between mobile and PC terminals, affecting the timeliness of on-site testing and even leading to data loss or duplicate uploads. Outlier removal methods are simplistic, relying solely on basic statistical rules without considering the welding machine's process characteristics and operational conditions. This makes it difficult to handle multi-source interference data under complex operating conditions, and the accuracy of identifying latent anomalies is insufficient, affecting the reliability of test results.

[0004] The testing scheme and resource allocation processes rely heavily on manual experience, lacking dynamic adaptation algorithms based on real-time task requirements, resource load, and equipment priorities. This results in low collaborative scheduling efficiency, prone to resource idleness or conflicts, and an inability to achieve optimal resource allocation. Multi-role collaborative processes are fragmented, with delays in information transmission across market, testing, review, and archiving stages. Task progress lacks visual tracking and node early warning mechanisms, leading to high communication costs, untimely problem response, and difficulty in achieving efficient linkage between different stages.

[0005] In terms of data traceability and equipment management, the existing system's traceability chain only covers the testing stage, failing to link information across the entire lifecycle of equipment, including registration, maintenance, calibration, and modification. This lack of traceability depth prevents the linkage between test results and the equipment's historical status, hindering problem identification and responsibility determination. Furthermore, the equipment lifecycle management is limited to a single dimension, focusing solely on archiving test data and lacking predictive management functions such as fault warnings, maintenance plan development, and remaining lifespan assessment. This makes it difficult to support preventative maintenance, increasing equipment failure risks and operational costs.

[0006] The report generation and delivery process suffers from limited automation, requiring manual input of substantial amounts of unstructured information. Furthermore, the lack of flexible, customized templates adaptable to different industry standards leads to low efficiency in compliant delivery and a susceptibility to formatting discrepancies. The report review and signing process is cumbersome, relying heavily on offline workflows or single-endpoint operations. Electronic signature authorization and verification mechanisms are complex, resulting in low efficiency for cross-role signing collaboration and a tendency for omissions or process bottlenecks. Simultaneously, mobile testing functionality is inadequate, lacking support for scenarios such as offline data entry, real-time on-site collaboration, and instant data upload. Seamless integration between PC and mobile platforms is not achieved, impacting on-site testing efficiency.

[0007] Furthermore, the fragmented management of testing resources, the lack of effective coordination between equipment requisition, repair requests, scrapping, and testing tasks, and the untimely updating of resource status lead to delays in equipment scheduling during the testing process, further impacting task progress. These problems prevent welding machine testing tasks from meeting the high-quality development demands of modern industrial production in terms of efficiency, accuracy, collaboration, and full lifecycle management. Therefore, a collaborative processing system for welding machine testing tasks based on process control is urgently needed to address these technical pain points. Summary of the Invention

[0008] To address the aforementioned problems in the existing technology, this invention provides a collaborative processing system for welding machine inspection tasks based on process control. The objective of this invention can be achieved through the following technical solutions: Data acquisition unit, data processing unit, process scheduling unit, and results delivery and equipment lifecycle management unit; The data acquisition unit is used to acquire basic data for welding machine testing. Through offline acquisition and automatic retransmission mechanisms, it acquires well-organized multi-dimensional data. The data processing unit removes outliers from the multi-dimensional data and standardizes the multi-dimensional data based on the removal results; it also analyzes the feature parameters of the multi-dimensional data using preset detection standards to obtain standardized detection data for quality rating labels and establishes a data traceability chain. Based on the standardized testing data, the process scheduling unit obtains a suitable testing scheme and allocates testing resources; monitors the task progress in real time and triggers node push; dynamically adjusts resource configuration based on data quality assessment results, and coordinates welding machine testing tasks involving multiple roles and multiple stages. The results delivery and equipment lifecycle management unit automatically captures the standardized testing data and generates a compliant testing report. Based on the unique identifier of the welding machine, it archives the testing report, the basic data, and the process records, and simultaneously generates a visual traceability map of the testing data and multi-dimensional statistical analysis results, enabling compliant delivery and full lifecycle management of the testing results.

[0009] As a preferred technical solution of the present invention, the offline acquisition and automatic retransmission mechanism includes: acquiring encrypted offline data packets based on the unique identifier of the welding machine equipment and through offline acquisition via a mobile terminal; establishing data integrity verification rules and filtering fragmented data based on the timestamp and checksum of the encrypted offline data packets; automatically triggering retransmission requests for missing fragmented data based on real-time network status monitoring results; and restoring the original data of multi-dimensional detection based on the complete data packets after retransmission through format parsing.

[0010] Specifically, the outlier removal process includes: establishing a mean-standard difference anomaly identification mechanism based on the statistical distribution characteristics of standardized test data, and initially screening potential outlier data within a preset range; performing secondary verification on the initially screened potential outlier data based on the process parameter thresholds of welding machine testing, and removing data outliers from the process logic; and using the data change trends of adjacent testing cycles, determining data mutation points through a trend fitting algorithm, and processing abnormal fluctuation data.

[0011] Specifically, the standardization process includes: establishing standardized mapping rules for multi-dimensional data; performing dimensional unification processing on the original data after removing outliers; converting non-numerical data into standardized codes; performing logarithmic transformation and standardized scaling on skewed data based on the normalization requirements of data distribution to generate intermediate data that meets the requirements of statistical analysis; and compressing the data through normalization based on the consistency verification results of the intermediate data to obtain standardized detection data.

[0012] Specifically, the process of obtaining standardized testing data for quality rating labels includes: establishing a mapping relationship between feature parameters and quality ratings based on the quality grading indicators of preset testing standards; extracting feature parameters based on the standardized testing data and matching them with the mapping relationship; calculating quality rating scores through fuzzy comprehensive evaluation based on the matching results to obtain quality rating labels; and generating a standardized testing dataset with labels based on the quality rating labels and the mapping relationship.

[0013] Specifically, the process of establishing the data traceability chain includes: obtaining the basic traceability chain based on the unique number of the detection task; binding the operation log with the standardized detection dataset to supplement the traceability nodes; encrypting and storing the data in the traceability chain, generating a data traceability QR code, identifying the data of the entire detection process, and tracing the detection results backward.

[0014] Specifically, the process of obtaining a suitable testing solution and allocating testing resources includes: matching the testing requirements of the welding machine equipment with a preset testing solution library to obtain a target testing solution; querying the real-time status of the resource pool based on the resource requirements of the target testing solution; optimizing resource allocation based on the resource query results; allocating testing tasks to suitable testing teams and equipment; and generating a resource allocation list.

[0015] Specifically, the process of real-time monitoring of task progress and triggering node push includes: setting progress thresholds and time nodes based on the process decomposition results of the detected task; collecting the completion status of nodes and updating the task progress dashboard based on real-time data feedback from mobile and PC terminals; generating push messages based on the progress threshold triggering mechanism; and pushing node notifications through appropriate channels based on the role permissions of the receiving object.

[0016] Specifically, the process of coordinating and linking welding machine inspection tasks involving multiple roles and multiple stages includes: based on task information pushed by nodes, roles complete corresponding operations and provide feedback on operation results; based on the verification feedback of operation results, tasks are assigned to the next stage roles; based on the collaborative logs of multi-stage operations, communication and interaction between roles are established, and feedback and collaboration are provided for cross-role issues.

[0017] Specifically, the process of generating a compliant test report includes: automatically extracting the required information for the report based on standardized test data with quality rating labels; filling in the information according to industry standards for test reports to generate an initial report; automatically verifying the data consistency of the initial report based on the report's logical verification rules and correcting format and data deviations; and generating a compliant formal test report based on the verified draft report, combined with supplementary notes and anomaly analysis by the testing personnel.

[0018] Specifically, the archiving process includes: storing associated data into the corresponding data archive based on the classification rules of the archive index; encrypting and backing up the archived data based on data security storage standards; and generating an archive list and retrieval catalog based on the completed archived dataset.

[0019] Specifically, the process of compliant delivery and full lifecycle management includes: conducting compliance review of the test reports based on archived test reports and compliance verification rules; delivering the test reports in compliance with customer requirements and delivery agreements, and recording the delivery status; establishing equipment test archives based on the full lifecycle test records of the welding equipment, and tracking the status changes of the welding equipment test cycle; and managing the full lifecycle of the welding equipment through testing based on the historical data of the test archives.

[0020] The beneficial effects of this invention are as follows: by using an offline acquisition and automatic retransmission mechanism, it achieves complete capture of multi-dimensional basic data such as welding machine voltage and defect images in a network-free environment. Combined with timestamp synchronization and data verification technology, it avoids the risk of data loss, ensures the regularity and integrity of the acquired data, and significantly improves the acquisition efficiency compared to traditional methods.

[0021] Employing a three-tiered processing model of outlier removal, standardization transformation, and quality label mapping, and integrating a dedicated process threshold library for welding machine testing with industry standard adaptation logic, this system accurately filters invalid data and generates standardized data with quality rating labels. Simultaneously, a blockchain + QR code dual traceability chain is constructed, significantly enhancing data credibility and traceability, providing high-quality data support for subsequent decision-making. Based on standardized testing data, the system automatically matches the optimal testing solution and dynamically allocates testing equipment and personnel according to the real-time status of the resource pool. Through node threshold monitoring and multi-channel push mechanisms, it achieves cross-stage collaboration among multiple roles such as the marketing department, testing department, and auditing department, effectively reducing communication and waiting costs.

[0022] Integrated report generation and signing shortens delivery cycle: It automatically captures standardized data to generate compliant test reports, integrates the Fadada electronic signature system to support remote hierarchical signing, and leaves a complete record of the signing process. The cycle from report generation to signing completion is shortened compared to the traditional offline model, greatly improving delivery efficiency.

[0023] From welding equipment registration, testing task initiation, process monitoring to report archiving, compliance delivery, and re-inspection reminders, a closed-loop management system is built to cover the entire process. Combined with a visual traceability map and multi-dimensional statistical analysis, it supports the prediction of equipment testing trends and the automatic triggering of re-inspection tasks, realizing proactive and refined management of testing services, while meeting customers' core needs for reverse traceability of testing results.

[0024] It supports seamless collaboration between PC and mobile devices. The mobile device is adapted for core scenarios such as on-site offline data collection, task progress viewing, and node approval, while the PC device focuses on data processing, report editing, and statistical analysis. Data is synchronized in real time, meeting the operational needs of different roles in different scenarios and reducing the learning and usage costs of the system. Attached Figure Description

[0025] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0026] Figure 1 This is a flowchart illustrating a collaborative processing system for welding machine inspection tasks based on process control, according to the present invention. Figure 2 This is a structural block diagram of the process scheduling process in this invention. Detailed Implementation

[0027] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0028] Please see Figure 1-2 A collaborative processing system for welding machine inspection tasks based on process control includes: a data acquisition unit, a data processing unit, a process scheduling unit, and a results delivery and equipment lifecycle management unit. The data acquisition unit is used to acquire basic data for welding machine testing. Through offline acquisition and automatic retransmission mechanisms, it acquires well-organized multi-dimensional data. The data processing unit removes outliers from the multi-dimensional data and standardizes the multi-dimensional data based on the removal results; it also analyzes the feature parameters of the multi-dimensional data using preset detection standards to obtain standardized detection data for quality rating labels and establishes a data traceability chain. Based on the standardized testing data, the process scheduling unit obtains a suitable testing scheme and allocates testing resources; monitors the task progress in real time and triggers node push; dynamically adjusts resource configuration based on data quality assessment results, and coordinates welding machine testing tasks involving multiple roles and multiple stages. The results delivery and equipment lifecycle management unit automatically captures the standardized testing data and generates a compliant testing report. Based on the unique identifier of the welding machine, it archives the testing report, the basic data, and the process records, and simultaneously generates a visual traceability map of the testing data and multi-dimensional statistical analysis results, enabling compliant delivery and full lifecycle management of the testing results.

[0029] As a preferred technical solution of the present invention, the offline acquisition and automatic retransmission mechanism includes: acquiring encrypted offline data packets based on the unique identifier of the welding machine equipment and through offline acquisition via a mobile terminal; establishing data integrity verification rules and filtering fragmented data based on the timestamp and checksum of the encrypted offline data packets; automatically triggering retransmission requests for missing fragmented data based on real-time network status monitoring results; and restoring the original data of multi-dimensional detection based on the complete data packets after retransmission through format parsing.

[0030] In this embodiment, when inspectors are testing batch welding machines at an outdoor construction site without network access, the mobile device first reads the unique device identifier of each welding machine (such as a QR code or RFID chip information corresponding to the factory serial number). Then, it collects multi-dimensional basic parameters such as the welding machine's output voltage, current, welding temperature, weld image, and equipment operating noise. Simultaneously, it records auxiliary data such as the temperature, humidity, and altitude of the testing environment. All data is integrated to generate an encrypted offline data packet with a unique timestamp and checksum. The system splits and stores the data packet according to preset fragmentation rules to avoid storage anomalies caused by excessively large single packets. When the inspectors move to an area with signal coverage, the mobile device monitors network recovery in real time and immediately initiates a data integrity verification process. By comparing the locally stored fragmented data checksum with preset rules, it quickly identifies missing or damaged fragmented data and automatically sends a retransmission request to the server. During the retransmission process, breakpoint resume technology is used. If network interruption occurs again, transmission can resume from the point of interruption after recovery, without needing to re-upload all data. After all the fragmented data has been retransmitted, the system uses a format parsing algorithm to restore the complete multi-dimensional original detection data and automatically synchronizes it to the PC system, ensuring that there are no omissions or duplications in the entire data collection process, and adapting to the actual needs of complex detection scenarios.

[0031] Specifically, the outlier removal process includes: establishing a mean-standard difference anomaly identification mechanism based on the statistical distribution characteristics of standardized test data, and initially screening potential outlier data within a preset range; performing secondary verification on the initially screened potential outlier data based on the process parameter thresholds of welding machine testing, and removing data outliers from the process logic; and using the data change trends of adjacent testing cycles, determining data mutation points through a trend fitting algorithm, and processing abnormal fluctuation data.

[0032] Specifically, the standardization process includes: establishing standardized mapping rules for multi-dimensional data; performing dimensional unification processing on the original data after removing outliers; converting non-numerical data into standardized codes; performing logarithmic transformation and standardized scaling on skewed data based on the normalization requirements of data distribution to generate intermediate data that meets the requirements of statistical analysis; and compressing the data through normalization based on the consistency verification results of the intermediate data to obtain standardized detection data.

[0033] In this embodiment, when removing outliers from welding machine inspection data, a mean-standard deviation anomaly identification mechanism is first constructed based on the principle of normal distribution. The mean and standard deviation of the inspection data are calculated, and data exceeding the mean ± 3 times the standard deviation are marked as potential outliers. The formula is as follows: , Screen for potentially outlier data that exceeds the mean by ±3 standard deviations. iFor a single detection data point, μ is the dataset mean, and σ is the dataset standard deviation, which can quickly filter out data with extreme deviations.

[0034] Subsequently, the system's built-in welding machine detection process parameter threshold library is retrieved. This library contains professional parameters such as voltage fluctuation range, current stability range, and temperature control thresholds for different models and welding materials, and a second verification is performed on potentially abnormal data. For example, if the welding current detection value of a welding machine exceeds the statistical range, but considering the process requirements for welding thick-walled pipes, the current value is within a reasonable range, and the equipment operation log shows that the process parameters were adjusted at that time, then the data is retained. If a detection data point exceeds the statistical range, does not conform to the process logic under any operating condition, and has no corresponding operation record to support it, then it is judged as an outlier and removed. Finally, the data change curves of adjacent detection cycles are analyzed using a linear trend fitting algorithm. If the data shows a large abrupt change without process adjustment or equipment operation, and the magnitude of the change exceeds the maximum fluctuation range under normal operating conditions, such as a sudden increase in welding temperature without a corresponding power adjustment record, then it is judged as abnormal fluctuation data and removed.

[0035] Specifically, the process of obtaining standardized testing data for quality rating labels includes: establishing a mapping relationship between feature parameters and quality ratings based on the quality grading indicators of preset testing standards; extracting feature parameters based on the standardized testing data and matching them with the mapping relationship; calculating quality rating scores through fuzzy comprehensive evaluation based on the matching results to obtain quality rating labels; and generating a standardized testing dataset with labels based on the quality rating labels and the mapping relationship.

[0036] In this embodiment, the standardization operation stage first constructs a standardized mapping rule system for multi-dimensional data. For raw data with different dimensions such as voltage (V), current (A), and temperature (°C), the Z-score standardization method is used to unify the dimensions, converting the data into dimensionless standardized values. For non-numerical data such as equipment model, detection type, and welding material, they are converted into standardized digital codes according to preset coding rules, such as encoding electrofusion welding machines as 01 and hot melt welding machines as 02, ensuring data format uniformity. For data with skewed distributions, such as welding time and the number of weld defects, logarithmic transformation is used to adjust them to a form close to a normal distribution, and then the Min-Max standardization method is used to scale and compress the data to the [0,1] interval, generating intermediate data that meets the requirements of statistical analysis. Subsequently, the intermediate data is subjected to consistency verification, comparing the logical correlation of data from different dimensions in the same detection task, such as whether the welding temperature and welding power data match, and whether the detection time and equipment running time are consistent. After eliminating data inconsistencies, the data distribution is further optimized through a normalization algorithm to obtain standardized detection data.

[0037] Specifically, the process of establishing the data traceability chain includes: obtaining the basic traceability chain based on the unique number of the detection task; binding the operation log with the standardized detection dataset to supplement the traceability nodes; encrypting and storing the data in the traceability chain, generating a data traceability QR code, identifying the data of the entire detection process, and tracing the detection results backward.

[0038] In this embodiment, firstly, based on industry-preset testing standards, the quality grading indicators for welding machine testing are defined, including key indicators such as equipment output stability, weld pass rate, and safety protection device sensitivity. A mapping relationship is established between characteristic parameters and three quality rating levels: excellent, qualified, and unqualified. After extracting key characteristic parameters such as output voltage stability, current fluctuation amplitude, weld defect density, and safety device response time from standardized testing data, they are matched one by one with the preset mapping relationship. Fuzzy comprehensive evaluation is used, combining the weight coefficients of each characteristic parameter to calculate the comprehensive quality rating; the fuzzy comprehensive evaluation formula is: , Among them, w k For the weight of the k-th feature parameter, s k The k-th feature parameter is scored, and n is the total number of feature parameters.

[0039] By binding quality rating labels to standardized testing data one by one, a labeled standardized testing dataset is generated.

[0040] Specifically, the process of obtaining a suitable testing solution and allocating testing resources includes: matching the testing requirements of the welding machine equipment with a preset testing solution library to obtain a target testing solution; querying the real-time status of the resource pool based on the resource requirements of the target testing solution; optimizing resource allocation based on the resource query results; allocating testing tasks to suitable testing teams and equipment; and generating a resource allocation list.

[0041] In this embodiment, after receiving a testing request from a welding machine, the system automatically parses the key information in the request, including the welding machine type, service life, testing purpose (such as periodic inspection, troubleshooting, factory testing), and testing standard requirements. This information is then intelligently matched with a pre-set testing solution library. The library stores standardized solutions for different application scenarios. For example, a troubleshooting solution for older welding machines includes comprehensive performance testing, faulty component location, and safety hazard assessment, while a testing solution for newly manufactured welding machines focuses on factory parameter calibration and performance compliance verification. The system then selects the most suitable target testing solution through matching. Subsequently, the system queries the real-time status of the resource pool, including the professional qualifications, current workload, and geographical location of the testing personnel, as well as the model, specifications, operating status, and idle status of the testing equipment. Based on a genetic algorithm, resources are optimally allocated. For example, tasks requiring high-precision testing are assigned to qualified testing teams with low current workloads; portable testing equipment is assigned to outdoor testing tasks; and laboratory-specific equipment is assigned to indoor calibration tasks. A detailed resource allocation list is generated, clearly specifying the person in charge, participants, equipment used, and testing time nodes for each testing task.

[0042] Specifically, the process of real-time monitoring of task progress and triggering node push includes: setting progress thresholds and time nodes based on the process decomposition results of the detected task; collecting the completion status of nodes and updating the task progress dashboard based on real-time data feedback from mobile and PC terminals; generating push messages based on the progress threshold triggering mechanism; and pushing node notifications through appropriate channels based on the role permissions of the receiving object.

[0043] In this embodiment, the testing task is broken down into several key nodes, including data collection, data processing, testing implementation, report preparation, review and approval, and delivery and archiving. Clear progress thresholds and timeframes are set for each node; for example, the data collection node must be completed within a preset hour after the task is initiated, and the report preparation node must be started within a preset hour after data processing is completed. Real-time data feedback from mobile and PC terminals collects the completion status of each node in real time, such as the completion percentage of the data collection node, the current progress of the testing implementation node, and the processing status of the review node. The task progress dashboard is dynamically updated and displayed in a visual chart format, allowing managers to monitor the overall progress of the task in real time. When a node's progress reaches a preset threshold or approaches a timeframe, the system automatically generates a push message. The push channel is selected based on the recipient's role and permissions: a task progress reminder is sent to testing personnel via internal system messages; a node completion report is sent to managers via a combination of SMS and system messages; and a notification of pending review tasks is sent to reviewers.

[0044] Specifically, the process of coordinating and linking welding machine inspection tasks involving multiple roles and multiple stages includes: based on task information pushed by nodes, roles complete corresponding operations and provide feedback on operation results; based on the verification feedback of operation results, tasks are assigned to the next stage roles; based on the collaborative logs of multi-stage operations, communication and interaction between roles are established, and feedback and collaboration are provided for cross-role issues.

[0045] In this embodiment, the system accurately pushes task information to the corresponding roles based on the workflow nodes of the testing task. After receiving the data acquisition task notification, the testing personnel complete the on-site testing and report the testing data and on-site conditions via mobile terminal. After receiving the data processing task notification, the data processing personnel complete outlier removal and standardization processing and report the processing results. The system automatically verifies the operation results, such as verifying the integrity and format standardization of the testing data. After the verification is passed, the system assigns tasks to the next role. For example, after the data processing is completed and verified to be error-free, the system automatically assigns a solution matching task to the testing solution adaptation personnel and pushes the data to be reviewed to the review personnel. At the same time, a collaborative log for multi-stage operations is established, which records in detail the operation time, operation content, feedback information and data flow trajectory of each role. When cross-role issues occur, such as discrepancies between testing data and processing results, or conflicts between testing solutions and resource configurations, the relevant roles can trace the source of the problem based on the collaborative log, communicate in real time through the system's built-in communication module, clarify solutions and track the processing progress, and achieve efficient collaborative linkage among multiple roles and stages.

[0046] Specifically, the process of generating a compliant test report includes: automatically extracting the required information for the report based on standardized test data with quality rating labels; filling in the information according to industry standards for test reports to generate an initial report; automatically verifying the data consistency of the initial report based on the report's logical verification rules and correcting format and data deviations; and generating a compliant formal test report based on the verified draft report, combined with supplementary notes and anomaly analysis by the testing personnel.

[0047] In this embodiment, the system automatically extracts the core information required for the report based on standardized test data with quality rating labels. This includes basic welding machine information (model, serial number, and service life), test item details, test data for each item, quality rating results, and instructions for handling abnormal data. Following industry standards for test report format, content structure, and terminology, the system fills the extracted information into the corresponding report template to generate an initial report. Subsequently, based on preset report logic verification rules, the initial report undergoes multi-dimensional verification, including logical consistency between test data and quality rating results, cross-validation of data in different sections of the report, and compliance with format specifications (such as unit labeling, table styles, and signature column placement). Any format deviations are automatically corrected, and data inconsistencies are marked and prompted for manual review. Finally, incorporating supplementary notes from the testing personnel, such as temporary equipment malfunctions during testing, adjustments under special operating conditions, and detailed handling procedures for abnormal data, the initial report draft is refined to generate a formal test report that meets industry standards and customer requirements. The report supports export in multiple formats, including PDF and Word.

[0048] Specifically, the archiving process includes: storing associated data into the corresponding data archive based on the classification rules of the archive index; encrypting and backing up the archived data based on data security storage standards; and generating an archive list and retrieval catalog based on the completed archived dataset.

[0049] In this embodiment, following the classification rules of the archive index, related data such as test reports, basic test data, process records, operation logs, resource allocation lists, and anomaly handling instructions are categorized and organized according to key fields such as test task number, welding machine unique identifier, test time, and test type, and stored in the corresponding database archive module. In accordance with national data security storage standards and industry confidentiality requirements, the archived data is encrypted using the AES encryption algorithm to prevent data leakage and unauthorized tampering. Simultaneously, a dual backup mechanism of local and cloud storage is established. Local backup is stored on the enterprise's private server, while cloud backup uses encrypted cloud storage services, with regular data synchronization and backup verification to ensure secure and reliable data storage. After archiving is completed, a detailed archive list and search directory are automatically generated. The archive list clearly specifies the storage location, data type, file size, and archiving time of each archived data item. The search directory supports multi-dimensional searching by test task number, welding machine number, test time period, etc., facilitating rapid subsequent querying, retrieval, and reuse of archived data.

[0050] Specifically, the process of compliant delivery and full lifecycle management includes: conducting compliance review of the test reports based on archived test reports and compliance verification rules; delivering the test reports in compliance with customer requirements and delivery agreements, and recording the delivery status; establishing equipment test archives based on the full lifecycle test records of the welding equipment, and tracking the status changes of the welding equipment test cycle; and managing the full lifecycle of the welding equipment through testing based on the historical data of the test archives.

[0051] In this embodiment, multi-dimensional compliance verification rules are established to conduct a comprehensive compliance review of archived test reports. This includes the completeness of the report content (e.g., whether it includes all test results and whether signatures and seals are complete), the accuracy of the data (e.g., whether the test data is consistent with the original records), the standardization of the format (e.g., whether it meets industry report template requirements), and the applicability of the standards (e.g., whether the agreed-upon testing standards are used). After the review is passed, the test report is delivered to the customer in compliance with the delivery method agreed upon in the delivery agreement, such as online platform transmission, encrypted email delivery, or mailing of paper reports. The delivery status is recorded in real time, including delivered, pending delivery, and delivery failure. A retry mechanism is automatically triggered for delivery failures, and the reason is recorded. Based on the unique identifier of the welding machine equipment, all test records throughout its entire lifecycle, from factory testing, periodic inspections, fault repair to scrap testing, are integrated to establish a complete equipment test archive. This archive tracks in detail the performance status changes of the equipment in different testing cycles, such as output stability trends, fault frequency, and defect repair status. By combining historical data from the testing archives, trend analysis algorithms are used to predict the performance degradation patterns of equipment, and targeted full lifecycle testing management plans are developed. These plans include optimizing regular testing cycles, identifying special testing opportunities, and planning maintenance programs in advance. This enables refined and proactive management of equipment testing, reducing equipment failure risks and maintenance costs.

[0052] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A collaborative processing system for welding machine inspection tasks based on process control, characterized in that, include: Data acquisition unit, data processing unit, process scheduling unit, and results delivery and equipment lifecycle management unit; The data acquisition unit is used to acquire basic data for welding machine testing. Through offline acquisition and automatic retransmission mechanisms, it acquires well-organized multi-dimensional data. The data processing unit performs outlier removal on the multi-dimensional data and standardizes the multi-dimensional data based on the removal results. By analyzing the feature parameters of the multi-dimensional data through preset testing standards, standardized testing data for quality rating labels are obtained, and a data traceability chain is established. Based on the standardized testing data, the process scheduling unit obtains a suitable testing scheme and allocates testing resources; monitors the task progress in real time and triggers node push; dynamically adjusts resource configuration based on data quality assessment results, and coordinates welding machine testing tasks involving multiple roles and multiple stages. The deliverables and equipment lifecycle management unit automatically captures the standardized testing data and generates a testing report that conforms to the specifications. Based on the unique identifier of the welding machine, the test report, the basic data and process records are archived and processed, and a visual traceability map of the test data and multi-dimensional statistical analysis results are generated simultaneously to ensure compliant delivery and full life cycle management of the test results.

2. The system according to claim 1, characterized in that, The offline acquisition and automatic retransmission mechanism includes: acquiring encrypted offline data packets based on the unique identifier of the welding machine and through offline acquisition via a mobile terminal; establishing data integrity verification rules and filtering fragmented data based on the timestamp and checksum of the encrypted offline data packets; automatically triggering retransmission requests for missing fragmented data based on real-time network status monitoring results; and restoring the original data from multi-dimensional detection based on the completed retransmission data packets through format parsing.

3. The system according to claim 1, characterized in that, The specific process for outlier removal includes: establishing a mean-standard difference anomaly identification mechanism based on the statistical distribution characteristics of standardized test data, and initially screening potential outlier data within a preset range; performing secondary verification on the initially screened potential outlier data based on the process parameter thresholds of welding machine testing, and removing data outliers from the process logic; and using the data change trends of adjacent testing cycles, determining data mutation points through a trend fitting algorithm, and processing abnormal fluctuation data.

4. The system according to claim 1, characterized in that, The specific process of standardization includes: establishing standardized mapping rules for multi-dimensional data; performing dimensional unification processing on the original data after removing outliers; converting non-numerical data into standardized codes; performing logarithmic transformation and standardized scaling on skewed data based on the normalization requirements of data distribution to generate intermediate data that meets the requirements of statistical analysis; and compressing the data through normalization based on the consistency verification results of the intermediate data to obtain standardized detection data.

5. The system according to claim 1, characterized in that, The specific process for obtaining standardized testing data for quality rating labels includes: establishing a mapping relationship between feature parameters and quality ratings based on the quality grading indicators of preset testing standards; extracting feature parameters based on the standardized testing data and matching them with the mapping relationship; calculating quality rating scores through fuzzy comprehensive evaluation based on the matching results to obtain quality rating labels; and generating a standardized testing dataset with labels based on the quality rating labels and the mapping relationship.

6. The system according to claim 1, characterized in that, The specific process of establishing the data traceability chain includes: obtaining the basic traceability chain based on the unique number of the detection task; binding the operation log with the standardized detection dataset to supplement the traceability nodes; encrypting and storing the data in the traceability chain, generating a data traceability QR code, identifying the data of the entire detection process, and tracing the detection results backward.

7. The system according to claim 1, characterized in that, The specific process of obtaining a suitable testing solution and allocating testing resources includes: matching the testing requirements of the welding machine equipment with a preset testing solution library to obtain a target testing solution; querying the real-time status of the resource pool based on the resource requirements of the target testing solution; optimizing resource allocation based on the resource query results; assigning testing tasks to suitable testing teams and equipment; and generating a resource allocation list.

8. The system according to claim 1, characterized in that, The specific process of real-time monitoring of task progress and triggering node push includes: setting progress thresholds and time nodes based on the process decomposition results of the detected task; collecting the completion status of nodes and updating the task progress dashboard based on real-time data feedback from mobile and PC terminals; generating push messages based on the progress threshold triggering mechanism; and pushing node notifications through appropriate channels based on the role permissions of the receiving objects.

9. The system according to claim 1, characterized in that, The specific process of coordinating welding machine inspection tasks involving multiple roles and stages includes: based on task information pushed by nodes, roles complete corresponding operations and provide feedback on operation results; based on the verification feedback of operation results, tasks are assigned to the next stage roles; based on the collaborative logs of multi-stage operations, communication and interaction between roles are established, and feedback and collaboration are provided for cross-role issues.

10. The system according to claim 1, characterized in that, The specific process for generating a compliant test report includes: automatically extracting the required information for the report based on standardized test data with quality rating labels; filling in the information according to industry standards for test reports to generate an initial report; automatically verifying the data consistency of the initial report based on the report's logical verification rules and correcting format and data deviations; and generating a compliant formal test report based on the verified draft report, combined with supplementary notes and anomaly analysis by the testing personnel.

11. The system according to claim 1, characterized in that, The specific process of archiving includes: storing associated data into the corresponding data archive based on the classification rules of the archive index; encrypting and backing up the archived data based on data security storage standards; and generating an archive list and retrieval catalog based on the completed archived dataset.

12. The system according to claim 1, characterized in that, The specific process of compliant delivery and full lifecycle management includes: conducting compliance review of the test reports based on archived test reports and compliance verification rules; delivering the test reports in compliance with customer requirements and delivery agreements, and recording the delivery status; establishing equipment test archives based on the full lifecycle test records of the welding equipment, and tracking the status changes of the welding equipment test cycle; and managing the full lifecycle of the welding equipment through testing based on the historical data of the test archives.

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