A data processing method, computing device, and computer-readable storage medium

Abstract

The application discloses a data processing method, a computing device and a computer readable storage medium. The data processing method is applied to an integrated work platform and comprises the following steps: in response to determining that a target work is executed, determining a data processing rule corresponding to the target work; collecting work data of the target work; and performing data processing on the work data by using the data processing rule. In this way, by uniformly collecting and processing the work data of the target work on the integrated work platform, the information islands among work flows of different systems are broken, and the efficiency of the work flow is improved.

Description

Technical Field

[0001] This application relates to the field of production management, and in particular to a data processing method, computing device, and computer-readable storage medium. Background Technology

[0002] In modern manufacturing, especially in discrete manufacturing industries with complex product structures, deep BOM (Bill of Materials) levels, and high requirements for assembly precision and traceability (such as vehicle and parts production), management systems are set up based on each business to achieve refined management of the production process. These systems include work order management, equipment control, quality inspection, data collection, and problem feedback.

[0003] However, these operations are usually managed by independent systems or manual processes, requiring operators to frequently switch between multiple interfaces, terminals, or paper documents. This results in fragmented production information, cumbersome operations, and consequently, low work efficiency.

[0004] Therefore, breaking down information silos in workflows between different systems and improving the efficiency of workflow execution has become an urgent technical problem to be solved. Summary of the Invention

[0005] The purpose of this application is to provide a data processing method, computing device, and computer-readable storage medium that breaks down information silos between workflows of different systems and improves the efficiency of workflow execution.

[0006] To achieve the above objectives: In a first aspect, embodiments of this application provide a data processing method applied to an integrated work platform, comprising: In response to determining the target task, the corresponding data processing rules are determined. Collect the work data of the target task; The working data is processed using the aforementioned data processing rules.

[0007] In one embodiment, the target task includes at least one of the following: assembly operation, testing, data acquisition, and production. The response to determining the target task and determining the corresponding data processing rules for the target task includes at least one of the following: In response to determining the assembly operation, the data processing rule corresponding to the assembly operation is determined to control the tool corresponding to the assembly operation to perform the operation. In response to determining a test task, the data processing rule corresponding to the test task is determined to be generating a work report corresponding to the test task; In response to determining the data collection task, the data processing rule corresponding to the data collection task is determined to be to perform anomaly detection on the data collection task. In response to determining a production task, the data processing rule corresponding to the production task is determined to be to store abnormal events for the production task.

[0008] In one embodiment, when the target task is an assembly operation, the collection of work data for the target task includes: Establish a connection with at least one tightening gun during the assembly operation, and obtain torque result information of the tightening gun operation based on the connection; The process of processing the working data using the data processing rules includes: The operation of at least one tightening gun is controlled based on the torque result information.

[0009] In one embodiment, controlling the operation of the at least one tightening gun based on the torque result information includes at least one of the following: If the torque result information of the first tightening gun among the at least one tightening guns does not meet the operating standard, then the first tightening gun is controlled to reoperate based on the operating standard; When determining the bolt to be tightened based on the torque result information, the second tightening gun is controlled to tighten the bolt.

[0010] In one embodiment, when the target task is a test task, the collection of the target task's work data includes: Acquire test data during the testing process; The process of processing the working data using the data processing rules includes: The test data is processed based on at least one preset rule; Based on a preset template, the test data and processed data are used to generate a work report for the current test.

[0011] In one embodiment, when the target task is a data acquisition task, the acquisition of the target task's work data includes: Acquire material information during the data collection process; The data processing of the working data using the data processing rules includes: Obtain the list of materials to be collected; The material information is compared with the list of materials to be collected to determine the current collection status; When an abnormality occurs during the data collection process, the corresponding abnormality will be marked.

[0012] In one embodiment, the method for determining if the data collection is abnormal includes at least one of the following: If the materials in the material information are inconsistent with the materials in the list of materials to be collected, it is determined that the collection situation is abnormal; If the quantity collected in the material information is inconsistent with the quantity collected in the list of materials to be collected, it is determined that the collection situation is abnormal. If the material information and the list of materials to be collected do not belong to the same project category, it is determined that the collection situation is abnormal.

[0013] In one embodiment, when the target task is a production task, the collection of work data for the target task includes: During the production process, in response to triggered abnormal alarms, the corresponding abnormal information is obtained; The process of processing the working data using the data processing rules includes: Output the exception information and store the exception information.

[0014] Secondly, embodiments of this application provide a computing device, specifically including: a processor and a memory for storing executable instructions; wherein the processor is configured to execute the instructions for performing the data processing method as described in the first aspect.

[0015] Thirdly, embodiments of this application provide a computer-readable storage medium storing a computer program, wherein when the instructions in the computer-readable storage medium are executed by a processor of a computing device, the computing device is able to implement the data processing method as described in the first aspect.

[0016] This application provides a data processing method, computing device, and computer-readable storage medium. The data processing method is applied to an integrated work platform and includes: in response to determining a target task to be executed, determining data processing rules corresponding to the target task; collecting work data for the target task; and processing the work data using the data processing rules. Thus, on the integrated work platform, by uniformly collecting and processing work data for the target task, information silos between workflows of different systems are broken down, and workflow efficiency is improved. Attached Figure Description

[0017] Figure 1 Flowchart of the data processing method provided in the embodiments of the present invention Figure 1 .

[0018] Figure 2 A schematic diagram of the ATLAS integration principle in the data processing method provided in the embodiments of the present invention.

[0019] Figure 3 This is a schematic diagram of at least one preset rule for the testing work provided in the embodiments of the present invention.

[0020] Figure 4 This is a schematic diagram of a preset template for testing work provided in an embodiment of the present invention.

[0021] Figure 5 This is a schematic diagram illustrating the data types of abnormal events in production operations, provided as an embodiment of the present invention.

[0022] Figure 6 A flowchart illustrating the specific workflow of the assembly operation provided in this embodiment of the invention.

[0023] Figure 7 A flowchart illustrating the specific workflow of data acquisition provided in this embodiment of the invention.

[0024] Figure 8 This is a schematic diagram of the structure of a computing device provided in an embodiment of the present invention.

[0025] Processor 410, memory 411, network interface 412, bus system 413. Detailed Implementation

[0026] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0027] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, components, features, and elements with the same names in different embodiments of this application may have the same meaning or different meanings, the specific meaning of which must be determined by its interpretation in that specific embodiment or further in conjunction with the context of that specific embodiment.

[0028] It should be understood that although the terms first, second, third, etc., may be used herein to describe various information, such information should not be limited to these terms. These terms are used only to distinguish information of the same type from one another. For example, without departing from the scope of this document, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if," as used herein, can be interpreted as "when," "when," or "in response to determination." Furthermore, as used herein, the singular forms "a," "an," and "the" are intended to also include the plural forms unless the context indicates otherwise. It should be further understood that the terms "comprising," "including," indicate the presence of the stated feature, step, operation, element, component, item, kind, and / or group, but do not exclude the presence, occurrence, or addition of one or more other features, steps, operations, elements, components, items, kinds, and / or groups. The terms "or" and "and / or" as used herein are to be interpreted as inclusive, or mean any one or any combination thereof. Therefore, "A, B, or C" or "A, B, and / or C" means "any one of the following: A; B; C; A and B; A and C; B and C; A, B, and C". Exceptions to this definition will only occur if the combination of elements, functions, steps, or operations is inherently mutually exclusive in some way.

[0029] It should be understood that although the steps in the flowcharts of this application's embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0030] It should be noted that step designations such as S101 and S102 are used in this document for the purpose of more clearly and concisely describing the corresponding content, and do not constitute a substantial limitation on the order. In specific implementation, those skilled in the art may execute S102 first and then S101, etc., but these should all be within the protection scope of this application.

[0031] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0032] In the following description, the use of suffixes such as "module," "part," or "unit" to denote elements is solely for the purpose of illustrative purposes and has no specific meaning in itself. Therefore, "module," "part," or "unit" may be used interchangeably.

[0033] In modern manufacturing, especially in discrete manufacturing industries with complex product structures, deep BOM levels, and high requirements for assembly precision and traceability (such as vehicle and parts production), achieving refined, transparent, and efficient management of the production process has become crucial for enterprises to enhance their core competitiveness in order to meet the challenges of product complexity and diversified customer demands. Against this backdrop, refined control of the production process has become the core of management. However, this management goal faces severe challenges at the front-line execution level. Taking the vehicle final assembly workshop as an example, 80% to 90% of the workload on the front line consists of complex assembly tasks. Simultaneously, to meet the stringent requirements for manufacturing process recording and traceability, frequent switching between multiple modes, systems, and pages is necessary to complete various tasks, including: Material assembly: Users need to find, check and assemble materials themselves according to paper drawings or independent bills of materials, which can easily lead to incorrect assembly or omissions.

[0034] Data acquisition: It requires manual recording or entering key information such as test data, torque value, and serial number into multiple different interfaces, which is inefficient and prone to errors.

[0035] Assembly error prevention relies on user experience and responsibility, or uses separate, expensive error prevention hardware, and lacks deep integration with the software process.

[0036] Problem entry: When quality or process issues are discovered, it is necessary to fill out complicated paper reports or switch to another quality management system, which can easily lead to process interruptions and delayed responses.

[0037] Record maintenance: Production records are scattered across paper documents, Excel spreadsheets, or different systems, making them difficult to retrieve and hard to create a complete and traceable digital archive.

[0038] Here, existing technologies typically provide independent solutions for each of the aforementioned functionalities, leading users to face the dilemma of "information silos" and "operational fragmentation." For example, the MES system is responsible for work order dispatch, the QMS system is responsible for problem recording, and the WMS system is responsible for material management. Operators need to frequently switch between multiple systems, which not only increases cognitive load and operation time, but more seriously, the fragmented information flow makes it difficult to achieve real-time, closed-loop error prevention and process control, severely impacting product quality and production efficiency.

[0039] In response, this application proposes a data processing method that, on an integrated work platform, uniformly collects and processes work data for the target task, breaking down information silos between workflows of different systems and improving the efficiency of workflow execution.

[0040] Optionally, the integrated work platform integrates core business flows in the production process and also covers functions that frontline operators frequently maintain daily. To provide frontline operators with a better user experience and a more user-friendly interface, the design of the integrated work platform's pages takes into account the execution sequence of business processes and the information that needs to be frequently queried during operations. Simultaneously, to reduce user maintenance workload and minimize errors caused by manual maintenance, it is integrated with field equipment to control and verify parameters, thereby improving production quality.

[0041] Optionally, the first part of the integrated work platform interface is for selecting production tasks. Users can select work orders according to their actual needs, such as querying the basic information and progress information of the current work order or executing work order-related business.

[0042] Optionally, in the second part of the integrated work platform interface, detailed work order information, configuration sheet printing, and execution progress information can be displayed according to the selected work order. Optionally, to ensure normal production of work orders and quickly handle on-site anomalies, a manual check-in backup solution is also provided here to ensure the normal progress of subsequent business. Integration with the AGV (Automated Guided Vehicle) system is also provided to ensure the normal operation of the AGV. The manual check-in and AGV release functions here do not contradict the system's automatic check-in and AGV system functions; it is an extended functional interface used for anomaly recovery.

[0043] Optionally, the third part of the integrated work platform interface is process quality management, which achieves precise control over the product manufacturing process through process assembly guidance, binding of key materials, process parameter control, and process problem recording. Here, the production process is controlled using the work steps in the production schedule as the control dimension. First, the process flow card for the current work step is displayed, showing how many steps are included in the current work step, how many materials need to be assembled in each step, and how to assemble them, with auxiliary guidance in PDF or animation format. Generally, for some key materials, a one-item-one-code control method is used. However, in the actual assembly process, materials are prepared using the PTL method, making it impossible to confirm the physical barcode batch of the delivered items to the line. To achieve more precise control, a manual data collection function is provided. Simultaneously, the tightening values ​​generated during bolt tightening operations are displayed and collected on the page. The tightening operation is deeply integrated with the on-site ATLAS equipment.

[0044] Optionally, the integrated work platform also features a sidebar navigation function, primarily including commonly used business functions, quick query pages, and on-site Andon functions. Commonly used business functions include material preparation details, delivery status, abnormal material requisition, one-item-one-code information, material master data, and interaction logs with external systems. Quick queries include execution information, material collection status, torque detection information, production issues, and product reports, supporting work orders and process dimensions. The Andon function mainly integrates with the on-site hardware Andon to display production issues and also allows for manual problem maintenance.

[0045] The above integrated work platform embodies a unified human-computer interaction interface, allowing users to perform tasks in one stop. This solves the problems caused by cross-page, cross-system, and cross-terminal operations. While integrating functions, it also provides a clear understanding of the tasks being executed and their status, enhancing the user's sense of control and transparency. Furthermore, through quick navigation, users can quickly access relevant information to assist in production.

[0046] This application provides a data processing method, specifically as follows: Figure 1 As shown, the data processing method provided in this application embodiment can be implemented using software and / or hardware. In this embodiment, the data processing method is applied to the aforementioned integrated working platform. The data processing method provided in this application embodiment includes the following steps: Step S101: In response to determining the target task, determine the data processing rules corresponding to the target task.

[0047] Optionally, the target task refers to the work tasks that need to be performed during the manufacturing process, used to match the complex assembly tasks in the front-line workshops. Optionally, multiple tasks that need to be performed and the corresponding data processing rules for each task can be predetermined. In this way, when the target task is being performed, the corresponding data processing rules are determined. Here, different types of tasks correspond to different data processing rules.

[0048] In one embodiment, the target task includes at least one of the following: assembly operation, testing, data acquisition, and production. In response to determining the target task, the corresponding data processing rules for the target task are determined, including at least one of the following: In response to determining the assembly operation, the data processing rules corresponding to the assembly operation are used to control the tools corresponding to the assembly operation to perform the operation. In response to determining the test tasks, the data processing rules corresponding to the test tasks are determined to generate the corresponding test task reports; In response to determining the data collection task, the corresponding data processing rule for the data collection task is to perform anomaly detection for the data collection task. In response to the determination of production tasks, the corresponding data processing rules for production tasks are to store abnormal events for those tasks.

[0049] Optionally, assembly operations refer to the assembly of parts into finished or semi-finished products. Tools used in assembly operations can include tightening guns, screwdrivers, and robotic arms. When performing an assembly operation, assembly data can be acquired, or the tools used can be controlled, such as the screwdriver's rotation speed, the robotic arm's assembly accuracy, and its movement path, to ensure the assembly process meets standards.

[0050] Optionally, testing refers to operations that inspect the performance, quality, and function of a product or component, such as insulation resistance testing, withstand voltage testing, and current testing. During testing, the equipment acquires test data and integrates and processes this data (e.g., test results, pass / fail determination, abnormal data), generating a structured test report for easy review and analysis later.

[0051] Optionally, data collection refers to the operation of collecting various types of data during the production process, such as collecting equipment operating parameters, product size data, and production environment temperature and humidity. When data collection is performed, the data processing rules will monitor the collected data in real time to determine whether there are any abnormal values ​​that exceed the normal range (such as equipment temperature suddenly being too high or product size deviation being too large). Once an abnormality is detected, an alert or subsequent processing procedure will be triggered.

[0052] Optionally, production work refers to the core operations directly involved in product manufacturing, such as processing, injection molding, welding, and other processes that form the physical product. When production operations are performed, if an abnormal event occurs during the production process (such as equipment failure and shutdown, or production interruption caused by unqualified raw materials), the data processing rules will record and store the detailed information of the abnormal event (such as the time of occurrence, the type of abnormality, and the scope of impact) to facilitate subsequent traceability and problem analysis.

[0053] Step S102: Collect working data for the target task.

[0054] Optionally, the work data includes operational process data, result data, and environmentally relevant data acquired during the production process. Operational process data records dynamic information during work execution, such as the tightening gun's operating speed during assembly and the real-time parameters of testing equipment during testing. Result data reflects the final output information, such as product dimensions acquired during data collection and the number of qualified products in a batch during production. Environmentally relevant data includes external environmental information affecting work execution, such as the temperature and humidity of the production workshop and the stability of the power supply voltage in the assembly area.

[0055] Alternatively, data can be acquired directly through sensors or smart devices without human intervention. In one embodiment, some information can be confirmed or entered manually. For example, during testing, after the equipment automatically detects the data, the test product number can be manually entered and confirmed for submission.

[0056] Step S103: Process the working data using data processing rules.

[0057] In one embodiment, when the target task is an assembly operation, the work data of the target task is collected, including: Establish a connection with at least one tightening gun during the assembly operation, and obtain torque result information of the tightening gun operation based on the connection; Data processing rules are used to process the working data, including: Control at least one tightening gun operation based on the torque result information.

[0058] Optionally, the torque result information is the actual torque (tightening force) data and related status information generated by the tightening gun after completing one screw / bolt tightening operation, which is the core indicator for measuring tightening quality.

[0059] Optionally, such as Figure 2 As shown, deep integration with ATLAS devices and "EOL devices" (End of Line detection devices) enables data processing during assembly operations, improving production efficiency and quality. Specifically, it utilizes WebSocket long connections and ATLAS's open protocol technology; the interaction principle is as follows: First, within the integrated work platform, at least one tightening gun to be connected is selected. The platform's backend invokes the open protocol MID 0001 command to establish a connection with the ATLAS server. Here, the frontend uses a WebSocket persistent connection. Understandably, an ATLAS server can connect to multiple tightening guns simultaneously. Then, after the ATLAS server successfully establishes the connection, it responds by sending the MID 0002 command. Next, the integrated work platform sends the MID 9999 command to initiate a heartbeat request, verifying normal connection communication. Correspondingly, the ATLAS server, upon receiving the command, responds with the same command. In this way, the integrated work platform can begin subscribing to torque result information; here, it subscribes to the most recent torque result and sends the corresponding MID 0060 command. Upon receiving the subscription message, the ATLAS server responds by sending the MID 0005 command to indicate a successful subscription. Optionally, during assembly operations, the tightening gun performs tightening operations. According to the process parameters, the integrated work platform sends the corresponding Pset program block to control the tightening gun to perform the operation, sending the command MID 0018. Correspondingly, after receiving the Pset, the ATLAS server sends the command MID0005 in response, indicating that the program block was successfully sent.

[0060] ATLAS is a tool management system commonly used in industrial assembly operations, typically referring to a tightening tool management system. It can connect multiple tightening guns, centrally monitoring, recording, and managing the working status of each gun (such as current tightening parameters, fault status, and historical tightening data). Optionally, the execution workbench establishes a connection with a designated tightening gun via a WebSocket long connection and ATLAS's open protocol (MID 0001 command) to obtain real-time tightening gun working data (such as current tightening progress and torque value) and send control commands to the tightening gun (such as starting tightening and adjusting parameters), ensuring the tightening process is traceable (data is synchronized to the system archive).

[0061] In one embodiment, controlling the operation of at least one tightening gun based on torque result information includes at least one of the following: If the torque result information of the first tightening gun in at least one tightening gun does not meet the operating standard, then control the first tightening gun to reoperate based on the operating standard; When determining the bolts to be tightened based on the torque result information, control the second tightening gun to tighten the bolts.

[0062] Optionally, the operating standard is a set qualification standard for the tightening gun's operation, including qualification thresholds for the tightening gun's torque, angle, and time. For example, it can be set to include: torque range (e.g., 18-22N). The tightening angle (e.g., 90°±5°) and torque rise time (e.g., reaching the target torque within 2-3 seconds to avoid instantaneous impact) are specified. Optionally, the operating standards can be preset by the user or determined based on the bolt design requirements (e.g., load-bearing capacity, anti-loosening grade).

[0063] Optionally, the torque data of the first tightening gun can be compared with the operating standard in real time to monitor the working status of at least one tightening gun. Optionally, common non-compliance cases include: a final torque value exceeding the range, for example, a torque value of 25N in the torque result information. m > Upper limit of work standards 22N m, which may cause bolt breakage; the torque value in the torque result information is 15N. m < lower limit of work standard 18N m may lead to loosening; abnormal torque curves are unacceptable, such as a sudden drop in torque, which may be due to stripped bolt threads; or failure to reach the target torque for a long time, which may be due to thread jamming; torque results that do not meet angle requirements are unacceptable, such as only being tightened to 70°, which does not reach the 90° standard, even if the torque is qualified, it is still considered unacceptable.

[0064] Optionally, if it is determined that the torque result information of the first tightening gun does not meet the operating standard, that is, if it is determined that the working result of the first tightening gun is unqualified, the first tightening gun can be controlled to re-operate based on the operating standard until the operating standard is met.

[0065] Optionally, if a bolt to be tightened is detected in the torque result information, the system can automatically jump to that bolt and control the second tightening gun to perform the operation based on the work standards. Optionally, the second tightening gun can be the tightening gun corresponding to the bolt to be tightened, or it can be a tightening gun that is currently idle. Optionally, during the assembly operation performed by the second tightening gun, the torque result information of the second tightening gun is acquired and compared with the work standards. If the work standards are not met, the second tightening gun can be controlled to re-operate based on the work standards to ensure the compliance of each tightening point.

[0066] In one implementation, when the target task is a test task, the working data of the target task is collected, including: Acquire test data during the testing process; Data processing rules are used to process the working data, including: The test data is processed based on at least one pre-defined rule; Based on a preset template, a work report for the current test task is generated by matching the test data with the processed data.

[0067] Optionally, multiple layers can be set up in the system architecture. The IoT layer (Internet of Things layer) is generally responsible for connecting various devices and performing initial data collection and transmission. Utilizing the API methods provided by EOL devices to bypass the IoT layer and directly interact with the integration workbench backend can improve data interaction efficiency, simplify the interaction process, or address specific data interaction needs that the IoT layer cannot meet. Optionally, during testing, test results can be uploaded to the integration workbench via a RESTful interface for data storage.

[0068] Optionally, at least one preset rule is a predefined processing logic used to perform calculations, format conversions, data analysis, and other processing on the acquired test data. Specifically, for example... Figure 3 As shown, the type of at least one preset rule may include: calculation logic, maximum and minimum value determination logic, mapping, retention of decimal places, similar accumulation, similar reading, hexadecimal to decimal conversion, etc.

[0069] Optionally, the preset template is a pre-designed report format containing fixed modules and dynamic data population areas. Generating work reports based on the preset template helps improve the consistency and standardization of report generation. Specifically, as shown... Figure 4 As shown, the preset template can include product name, device model, part number, product code, production line name, final judgment result, number, test items, test content, specified standards, test results, etc.

[0070] In one embodiment, when the target task is a data acquisition task, the data acquisition of the target task includes: Acquire material information during the data collection process; Data processing using data processing rules includes: Obtain the list of materials to be collected; Compare the material information with the list of materials to be collected to determine the current collection status; When an anomaly occurs during data collection, the corresponding anomaly will be marked.

[0071] Optionally, common methods for acquiring material information should be selected based on the level of automation in the data collection scenario, with the core objective of minimizing errors from manual recording. These methods include: barcode scanning, system-linked acquisition, and manual data entry. In barcode scanning, information such as material code, name, and specifications can be read and entered based on the barcode or QR code attached to the material. In system-linked acquisition, if the data collection process is integrated with the warehousing system, materials can be automatically identified via RFID (Radio Frequency Identification) or sensors when they are moved to the collection area, simultaneously acquiring material information and collection time without manual intervention, making it suitable for automated data collection scenarios. For materials without labels or with damaged labels, manual data entry is required, involving manual selection of the material name, specifications, and quantity.

[0072] Optionally, the list of materials to be collected is an important document or dataset used to clarify the detailed information of the materials to be collected during the collection process. It plays a key reference role in the entire material collection and data processing flow.

[0073] Optionally, this data collection process is applicable to both the immature early-stage manufacturing and the standardized late-stage manufacturing. During the pre-mass production trial assembly phase, when the bill of materials (BOM) is in the verification period, there may be discrepancies between the actual assembly and the manufacturing BOM. To better and more intuitively display these differences, the data collection status can be determined by comparing the material information with the BOM to be collected. Different colors can be used for marking to more clearly show the collection status. Based on the collection status, further analysis is conducted to determine if any anomalies exist, such as which data is still missing, or which data is more or less than the BOM in the actual assembly, and corresponding notes are made. The use of replacement materials is also marked and displayed.

[0074] Optionally, when an anomaly occurs during data collection, the anomaly type and details can be automatically marked next to the material information, and a reminder window can pop up, for example, displaying "An error has been found in the collected materials, please verify."

[0075] In one embodiment, the method for determining an anomaly in the data collection process includes at least one of the following: When the materials in the material information do not match the materials in the list of materials to be collected, it is determined that an abnormality has occurred in the collection process. When the quantity collected in the material information is inconsistent with the quantity collected in the list of materials to be collected, it is determined that there is an anomaly in the collection situation. An anomaly is identified when the material information and the list of materials to be collected do not belong to the same project category.

[0076] Optionally, material information includes material category, material data, and the project to which it belongs.

[0077] Optionally, before collecting materials, the project category corresponding to the current material collection step is determined. Then, a pre-defined list of materials to be collected is obtained based on this project category. Understandably, the list of materials to be collected will differ for different project categories.

[0078] Optionally, if the material category in the material information is inconsistent with the material category in the list to be collected, or if at least one material in the material information has a category that cannot be found in the list to be collected, a material inconsistency is determined, the collection situation is abnormal, and it is necessary to determine whether there is a procurement error. In one embodiment, if the material actually collected is an upgraded version of the material in the list (e.g., with better performance or compatible parameters), or other replaceable materials with similar functions, the material can be marked for user verification. Optionally, if it is determined that the material information in the current collection process is missing at least one material from the list of materials to be collected, and the material in the current material information is inconsistent with the material in the list of materials to be collected, an abnormal collection situation is determined.

[0079] Optionally, when detecting whether the collected data is abnormal, it can be specified whether there is an "allowable deviation range". If there is no description of the deviation range, the default is "zero deviation", that is, if it exceeds the allowable deviation range, it is judged as abnormal. Optionally, after comparing and confirming the materials in the material information with the materials in the list to be collected, the collected data of the materials is further compared to determine whether there is any abnormality in the collection based on the collected quantity.

[0080] Optionally, the current target project category can be determined based on the materials currently being collected in the material information. If the target project category is different from the project category in the material list to be collected, it is determined that the current collection situation is abnormal. Understandably, a standard material collection list, i.e., the collection list mentioned above, will be set for different project categories.

[0081] In one embodiment, when the target task is a production task, the work data of the target task is collected, including: During the production process, in response to triggered abnormal alarms, the corresponding abnormal information is obtained; Data processing rules are used to process the working data, including: Output the exception information and store it.

[0082] Optionally, the triggering method for abnormal alarms is not limited to manual recording. It can also involve problem feedback based on the on-site operational status, generating problem work orders. Afterwards, users can provide a detailed description of the problem work order or close it. The triggering mechanism for abnormal alarms can include three types: automatic triggering by on-site equipment, manual triggering by controlled LED lights, and online problem maintenance.

[0083] Optionally, for automatic triggering of field devices, the status of the devices is monitored in real time through the Internet of Things (IoT) layer. When a device is in an abnormal state, an abnormal problem is automatically created. The problem type is generally a device failure. A red bubble will appear on the device menu in the quick navigation bar of the integrated workbench, indicating the number of problems. Clicking on it will allow you to view the specific problem list, thus realizing the hardware and system software integration.

[0084] Optionally, for manually controlled hardware triggering, i.e., the Andon method, in case of an emergency offline, a manual emergency light is activated, and an issue will also be generated on the integrated workbench. Unlike equipment failure, users can identify the issue type, which can be a quality problem, a process problem, a material-related problem, a safety problem, etc.

[0085] Here, both on-site equipment triggering and manual control of the light triggering are automatically created by the system. Problems can also be recorded manually, such as assembly issues and discrepancies in process guidance documents.

[0086] Optionally, when outputting exception information, it can be based on Figure 5 The template shown corresponds to the storage of this exception information.

[0087] In summary, the data processing method provided in the above embodiments, by uniformly collecting and processing the work data of the target work on the integrated work platform, breaks down the information silos between workflows of different systems and improves the efficiency of executing workflows.

[0088] Based on the same inventive concept as the foregoing embodiments, this application proposes a specific workflow for the assembly operation in the data processing method, such as... Figure 6 As shown, it includes: Through deep integration with ATLAS, users can freely choose tightening guns (wired or wireless) at their workstations for operation. To ensure accuracy, process parameters are automatically and manually sent to control the tightening gun. If the actual tightening value differs from the parameters, the result is NG (Not Acceptable Error), requiring re-operation. Subscribing to results enables automatic data entry, reducing on-site operations and avoiding potential errors from manual entry. The interface uses color to automatically label the current task item on the same or across pages, making it clearer and more intuitive for users. Specifically, the following steps are included: Step S201: Select the tightening gun for connection.

[0089] Optionally, on the workstation operation interface, users will see a list of selectable tightening guns, clearly indicating the type (wired or wireless) and related information for each gun. Users can select the appropriate tightening gun based on actual work requirements through interactive methods such as clicking or touching. This selection of the tightening gun initiates a connection request, establishes a communication link, and ensures that subsequent commands are accurately transmitted to the tightening gun. During this process, connection status information can be provided in real time, such as successful connection or connection failure requiring retry, allowing for timely understanding of the situation and appropriate action.

[0090] Step S202: Edit the job content and select the Pset program to distribute it.

[0091] Optionally, after connecting the tightening gun, the operating standards for the tightening gun can be determined on the integrated work platform, such as key process parameters like tightening torque, tightening angle, and tightening speed. Simultaneously, multiple predefined Pset programs can be set (which may be customized sets of parameters for different products or tightening parts), allowing for the selection of the appropriate Pset program based on the actual scenario. Optionally, instructions containing the Pset program and set parameters can be sent to the tightening gun to guide it to operate according to predetermined process requirements.

[0092] Step S203: View the torque, angle, and results on the page.

[0093] Optionally, during the assembly process, key data such as real-time torque values ​​and tightening angle changes can be viewed in real time on the integrated work platform. This data is presented intuitively in the form of dynamic charts and numerical displays. Once the tightening operation is complete, the result (pass or fail) is displayed on the integrated work platform, allowing users to quickly obtain feedback.

[0094] Step S204: Judge the tightening result. If it is qualified, proceed to step S206; if it is not qualified, proceed to step S205.

[0095] Optionally, based on the established operating standards for the tightening gun, the torque result of the tightening gun operation is precisely compared with the operating standards. If the actual value is within the allowable error range, the tightening result is deemed qualified, and the process proceeds normally through the recording and workflow stages. If the actual value exceeds the standard range, the result is deemed unqualified, and the tightening gun is controlled to perform the tightening operation again to ensure that the product quality meets the requirements.

[0096] Step S205: Tighten again.

[0097] Optionally, if the torque result information of the tightening gun is unqualified, the tightening gun needs to be prepared for reoperation. This may include checking the condition of the tightening gun, confirming that the workpiece position is correct, etc. Then, control the tightening gun to reoperate based on the operating standards.

[0098] Step S206: Automatically display the torque, angle, and result in the record.

[0099] Optionally, once the torque result information is deemed satisfactory, the key data of this operation—namely, the torque value, angle value, and the final satisfactory result—is recorded in the background database and automatically displayed in the relevant record area of ​​the integrated work platform interface. These records not only provide operators with an intuitive display of the work results for easy access at any time, but also provide important data support for subsequent production statistics, quality analysis, and traceability.

[0100] Step S207: Determine if there is still work to be done. If yes, proceed to step S208; otherwise, end the work.

[0101] Optionally, after completing a tightening operation and recording the result, the current task list can be checked to determine if there are any other bolts or tasks to be tightened. If there are tasks to be tightened, the tightening gun will continue to advance the tightening process on the bolt to be tightened; if all tasks have been completed, the current work will end.

[0102] Step S208: Automatically jump to the next bolt to be tightened and send the Pset program.

[0103] Optionally, when the system determines that there are still tasks to be done, the integrated work platform interface will automatically jump to the relevant operation interface for the next bolt to be tightened. Simultaneously, it can automatically issue the corresponding Pset program based on the bolt's process requirements, ensuring that operators can quickly and accurately begin the next tightening operation. This automated process jump and program issuance function further improves work efficiency, reduces the time operators spend manually searching and setting, and makes the entire tightening process more seamless and smooth.

[0104] Based on the same inventive concept as the first embodiment described above, this application proposes a specific workflow for the data acquisition process in the data processing method corresponding to the acquisition work, such as... Figure 7 As shown, it includes: The specific workflow for this data collection is applicable to both the immature early-stage manufacturing and the standardized late-stage manufacturing. During the pre-mass production trial assembly phase, the BOM (Bill of Materials) is in the verification period, and there are certain discrepancies between the actual assembly and the manufacturing BOM. To better and more intuitively display these differences, the material information during the data collection process is compared with the BOM to determine the current collection status. Simultaneously, the materials that need to be collected according to the BOM, as well as the materials that have already been collected, are displayed using different colors to clearly show the results. For example, it shows which materials are still missing from the data entry, which materials are more or less than the BOM's specifications, and provides corresponding notes. The use of replacement materials is also marked and displayed. The specific steps include: Step S301: Load the list of information to be collected according to the process.

[0105] Optionally, before starting the data collection process, a list of materials to be collected for the current production process can be retrieved from a database or relevant storage medium. This list includes key data such as the material name, number, specifications, and expected collection quantity, providing clear guidance and reference standards for subsequent data collection.

[0106] Step S302: Scan the barcode to collect data.

[0107] Optionally, the barcode affixed to the material to be collected can be scanned. As the barcode serves as a unique identifier for the material, it contains descriptive information. Thus, scanning the barcode allows for quick and accurate acquisition of relevant material data, improving collection efficiency and accuracy.

[0108] Step S303: Parse the barcode and obtain barcode-related information.

[0109] Optionally, after scanning the barcode information, the barcode is parsed to extract specific information such as the material number, batch number, and production date. This information will be used for subsequent comparison and processing with the information in the list to be collected.

[0110] Step S304: Determine whether the material corresponding to the barcode is in the list to be collected. If yes, proceed to step S306; otherwise, proceed to step S305.

[0111] Optionally, the parsed barcode information is compared with the loaded list of materials to be collected to determine whether the scanned material is the material that needs to be collected in the current process. Here, if the material corresponding to the barcode is in the list of materials to be collected, it means that it is a material that is planned to be collected; if it is not in the list of materials to be collected, it means that an unplanned material has appeared.

[0112] Step S305: Currently, forced data collection is in progress.

[0113] Optionally, if the material corresponding to the barcode is not in the list to be collected, the collection operation will be marked as forced collection. This may mean that the material was added temporarily, substituted, or there is a special case such as an entry error.

[0114] Step S306: Determine whether the number of samples collected is less than the number of samples to be collected. If yes, proceed to step S308; otherwise, proceed to step S307.

[0115] Optionally, if the material corresponding to the barcode is in the list to be collected, determine whether the quantity of the material being collected is less than the quantity specified in the list. If it is less than the quantity to be collected, it means that the material still needs to be collected; if the quantity collected has reached or exceeded the quantity to be collected, mark this collection as forced collection.

[0116] Step S307: Currently, forced data collection is in progress.

[0117] Optionally, when the number of samples collected reaches or exceeds the number to be collected, this collection will be marked as a forced collection. This may be due to operational errors, changes in material supply, or other reasons. Such unplanned collections need to be specially recorded and processed for subsequent analysis and traceability.

[0118] Step S308: Determine whether the barcode item information belongs to the same item as this product. If yes, proceed to step S310; otherwise, proceed to step S309.

[0119] Optionally, after determining that the quantity to be collected is less than the quantity to be collected, it is further determined whether the material item information corresponding to the scanned barcode belongs to the same item category as the product. If they belong to the same item category, it means that although the quantity of the material collected is insufficient, it still meets the current product production requirements; if they do not belong to the same item category, that is, materials unrelated to the product item have appeared, then this collection is marked as forced collection.

[0120] Step S309: Currently, forced data collection is in progress.

[0121] Optionally, if the barcode item information does not belong to the same item category as this product, this data collection will be marked as mandatory. This may be due to material confusion or misuse, requiring timely handling by operators to ensure the accuracy of the production process and product quality.

[0122] Step S310: Mark the currently collected material in the collection list, insert a record in the collected list, and perform color marking according to forced and normal methods.

[0123] Optionally, if the barcode item information belongs to the same item category as this product and the collected quantity is less than the quantity to be collected, the material currently being collected is marked in the collection list to indicate that the material has been partially collected. Simultaneously, a new record is inserted into the collected records to record detailed information about this collection, including material information, collection time, and collection quantity. Furthermore, different colors are used to mark the records according to the collection status (forced collection or normal collection) to distinguish different types of collection records.

[0124] Step S311: Determine whether to continue data collection. If yes, return to step S302; otherwise, proceed to step S312.

[0125] Optionally, after completing one data collection, a decision is made as to whether to continue the data collection process. If continued collection is required, the barcode scanning and data collection operation continues; if no further collection is desired, the data collection results are summarized and analyzed.

[0126] Step S312: Compare the data to be collected with the actual situation on both sides.

[0127] Optionally, after the data collection is completed, a comprehensive and detailed comparison should be made between the material information collected during the process and the list of materials to be collected. This comparison can clearly show the differences between the actual data collection and the planned data collection, providing a basis for subsequent judgment and processing.

[0128] Step S313: Determine if there are any uncollected materials. If yes, proceed to step S414; otherwise, proceed to step S316.

[0129] Optionally, after comparing the materials to be collected with the actual situation, it can be determined whether there are any materials that have not been collected. If there are any materials that have not been collected, it means that the actual quantity of materials collected has not met the requirements of the list to be collected; if there are no materials that have not been collected, it means that the collection work has met the planned requirements in terms of quantity.

[0130] Step S314: Determine if the actual material list collected is too large. If yes, proceed to step S316; otherwise, proceed to step S315.

[0131] Optionally, when there are uncollected materials, it is determined whether the actual collected material information exceeds the list of materials to be collected. If the actual collected material list exceeds the list, it indicates that although there are uncollected materials, there are also materials exceeding the planned collection. If the actual collected material list does not increase, that is, there are only uncollected materials, the reason for no longer collecting those materials can be noted.

[0132] Step S315, Note the reason for no longer collecting data.

[0133] Optionally, when the actual amount of material information collected does not increase but there are materials that have not been collected, the operator should obtain the reason why the material is no longer being collected. This may include reasons such as material damage, plan changes, or the use of alternative materials. Detailed notes can help with subsequent tracing and analysis of problems in the production process.

[0134] Step S316, Submit Operation: 1. Implement material deduction at the line-side warehouse; 2. Bind record storage; 3. Do not collect record storage.

[0135] Optionally, the submission operation includes the following three aspects: Implementing material deduction at the production line edge: Based on the actual collected materials, the quantity of materials in the production line edge warehouse is deducted accordingly to ensure the accuracy of the material quantity at the production line edge warehouse and provide accurate material inventory information for subsequent production. Binding record storage: Various information during the collection process, such as material information, collection time, collection quantity, and collection type (mandatory or normal), is bound to the product production record and stored in the database. These bound records provide detailed data support for product quality traceability and production process analysis. Storage of non-collected records: Relevant information for materials not collected, including material information and reasons for not collecting, is also stored. These records help analyze problems in the material collection process during production and provide a reference for subsequent improvements.

[0136] Based on the same inventive concept as the foregoing embodiments, this embodiment of the invention provides a computing device, such as... Figure 8 As shown, the computing device includes: a processor 410 and a memory 411 storing computer programs; wherein, Figure 8 The processor 410 shown in the diagram does not indicate that there is only one processor 410, but only indicates the positional relationship of the processor 410 relative to other devices. In practical applications, there can be one or more processors 410; similarly, Figure 8 The memory 411 shown in the diagram has the same meaning, that is, it is only used to indicate the positional relationship of memory 411 relative to other devices. In practical applications, there can be one or more memories 411. When the processor 410 runs the computer program, the above-described data processing method is implemented.

[0137] The computing device may also include at least one network interface 412. The various components of the computing device are coupled together via a bus system 413. It is understood that the bus system 413 is used to implement communication between these components. In addition to a data bus, the bus system 413 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in... Figure 8 The general designated all buses as Bus System 413.

[0138] The memory 411 can be volatile or non-volatile, or both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), ferromagnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM); magnetic surface memory can be disk storage or magnetic tape storage. Volatile memory can be random access memory (RAM), used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), SyncLink Dynamic Random Access Memory (SLDRAM), and Direct Rambus Random Access Memory (DRRAM).The memory 411 described in the embodiments of the present invention is intended to include, but is not limited to, these and any other suitable types of memory.

[0139] The memory 411 in this embodiment of the invention is used to store various types of data to support the operation of the computing device. Examples of this data include: any computer programs used to operate on the computing device, such as operating systems and applications; contact data; phonebook data; messages; pictures; videos, etc. The operating system includes various system programs, such as the framework layer, core library layer, driver layer, etc., used to implement various basic services and handle hardware-based tasks. Applications can include various applications, such as media players, browsers, etc., used to implement various application services. Here, the program implementing the method of this embodiment of the invention can be included in the application.

[0140] Based on the same inventive concept as the foregoing embodiments, this embodiment also provides a computer-readable storage medium storing a computer program. The computer-readable storage medium can be a magnetic random access memory (FRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM), etc.; it can also be various devices including one or any combination of the above-mentioned memories, such as mobile phones, computers, tablet devices, personal digital assistants, etc. When the computer program stored in the computer-readable storage medium is executed by a processor, it implements the data processing method applied to the aforementioned computing device. For the specific steps implemented when the computer program is executed by the processor, please refer to [link to relevant documentation]. Figure 1 The description of the illustrated embodiments will not be repeated here.

[0141] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0142] In this document, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, which includes not only the elements listed but also other elements not expressly listed.

[0143] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A data processing method applied to an integrated work platform, characterized in that, include: In response to determining the target task, the corresponding data processing rules are determined. Collect the work data of the target task; The working data is processed using the aforementioned data processing rules.

2. The method according to claim 1, characterized in that, The target work includes at least one of the following: assembly operation, testing, data acquisition, and production. The response to determining the target task and determining the corresponding data processing rules for the target task includes at least one of the following: In response to determining the assembly operation, the data processing rule corresponding to the assembly operation is determined to control the tool corresponding to the assembly operation to perform the operation. In response to determining a test task, the data processing rule corresponding to the test task is determined to be generating a work report corresponding to the test task; In response to determining the data collection task, the data processing rule corresponding to the data collection task is determined to be to perform anomaly detection on the data collection task. In response to determining a production task, the data processing rule corresponding to the production task is determined to be to store abnormal events for the production task.

3. The method according to claim 2, characterized in that, When the target task is an assembly operation, the collection of work data for the target task includes: Establish a connection with at least one tightening gun, and obtain torque result information of the tightening gun operation based on the connection; The process of processing the working data using the data processing rules includes: The operation of at least one tightening gun is controlled based on the torque result information.

4. The method according to claim 3, characterized in that, The control of the operation of the at least one tightening gun based on the torque result information includes at least one of the following: If the torque result information of the first tightening gun among the at least one tightening guns does not meet the operating standard, then the first tightening gun is controlled to reoperate based on the operating standard; When determining the bolt to be tightened based on the torque result information, the second tightening gun is controlled to tighten the bolt.

5. The method according to claim 2, characterized in that, When the target task is a test task, the collection of the target task's work data includes: Acquire test data during the testing process; The process of processing the working data using the data processing rules includes: The test data is processed based on at least one preset rule; Based on a preset template, the test data and processed data are used to generate a work report for the current test.

6. The method according to claim 2, characterized in that, When the target task is a data collection task, the data collection for the target task includes: Acquire material information during the data collection process; The data processing of the working data using the data processing rules includes: Obtain the list of materials to be collected; The material information is compared with the list of materials to be collected to determine the current collection status; When an abnormality occurs during the data collection process, the corresponding abnormality will be marked.

7. The method according to claim 6, characterized in that, The method for determining abnormalities in the data collection includes at least one of the following: If the materials in the material information are inconsistent with the materials in the list of materials to be collected, it is determined that the collection situation is abnormal; If the quantity collected in the material information is inconsistent with the quantity collected in the list of materials to be collected, it is determined that the collection situation is abnormal. If the material information and the list of materials to be collected do not belong to the same project category, it is determined that the collection situation is abnormal.

8. The method according to claim 2, characterized in that, When the target task is a production task, the collection of work data for the target task includes: During the production process, in response to triggered abnormal alarms, the corresponding abnormal information is obtained; The process of processing the working data using the data processing rules includes: Output the exception information and store the exception information.

9. A computing device, characterized in that, include: A processor and a memory for storing executable instructions; wherein the processor is configured to execute the instructions to implement the data processing method as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by a processor, the data processing method as described in any one of claims 1-8 is implemented.

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