A database-based automobile instrument assembly line tracking system

Abstract

The present invention specifically relates to a database-based automotive instrument assembly line tracking system, which relates to the fields of industrial automation and intelligent manufacturing. The system includes a data acquisition and interaction module, an RFID tag detection module, a database management and storage module, and a real-time production process tracking module. The system uses a two-byte comparison of RFID reader / writer bytes to eliminate temporary false alarms caused by electromagnetic interference. A true fault is determined only when the two byte reads are abnormally consistent, improving the accuracy of initial anomaly identification. Linear array camera visual analysis quantifies contour loss to identify physical damage. Infrared thermal imaging temperature field correlation analysis captures thermal conduction anomalies such as antenna breakage. RFID tag failure is comprehensively determined based on the missing quantification value and temperature-related deviation values, significantly reducing traceability interruptions or quality risks caused by tag failure during automotive instrument assembly.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial automation and intelligent manufacturing, and in particular to a database-based automobile instrument assembly line tracking system. Background Art

[0002] RFID tags are required in the automotive instrument assembly line to track each auto part in real time. However, traditional RFID tag anomaly detection has the following problems:

[0003] First, there is the risk of misjudgment due to relying solely on a single data read, and second, there is a blind spot in the detection of physical damage and hidden faults. At the data reading level, traditional solutions, because they do not perform multiple byte count comparisons, are susceptible to electromagnetic interference or momentary interruptions in reading, leading to false alarms of non-real faults (such as byte count anomalies caused by momentary signal attenuation). This solution, however, uses two byte counts for consistency judgment to filter out temporary interference and accurately identify true anomalies such as physical damage to the tag storage area or chip failure.

[0004] At the deep fault detection level, traditional methods lack the ability to collaboratively analyze the physical structure and thermal characteristics of tags, making it difficult to detect two key issues:

[0005] There is no obvious damage on the outside, but there is hidden damage to the antenna (such as micro cracks less than 0.5mm);

[0006] Abnormal heat conduction caused by poor contact between the chip or antenna (such as local temperature rise caused by increased resistance).

[0007] Therefore, a database-based automobile instrument assembly line tracking system is needed to address the above-mentioned problems. Summary of the Invention

[0008] The purpose of the present invention is to solve the above problems and to propose a database-based automobile instrument assembly line tracking system.

[0009] In order to achieve the above object, the present invention adopts the following technical solutions:

[0010] A database-based automobile instrument assembly line tracking system includes:

[0011] The data acquisition and interaction module is configured to collect data from all links of the assembly line in real time and realize data interaction with external systems;

[0012] The RFID tag detection module is configured to trigger a status detection of the RFID tag when an abnormality occurs in the RFID tag read by the RFID reader in the data acquisition and interaction module; obtain the status information of the RFID tag, calculate the output tag fault coefficient through the RFID tag information analysis unit, and perform a fault assessment;

[0013] The database management and storage module is configured as the data center of the system and is responsible for data storage, management, maintenance and security;

[0014] The production process real-time tracking module is configured to provide all-round real-time monitoring and dynamic management of the production process of the automobile instrument assembly line.

[0015] Preferably, the data collection and interaction module specifically includes:

[0016] Deploy sensors at key nodes of assembly equipment and conveyor lines to obtain comprehensive data on equipment operating status and production environment;

[0017] Use RFID readers and barcode scanners to uniquely identify automotive instrument components and fixtures;

[0018] Use real-time data transmission protocol to upload collected data to the database server; set priority transmission for key production data;

[0019] Receive production plans and material requirements from the ERP system through the Web Service interface, and feed back work order completion status and actual material consumption data to the ERP system;

[0020] Interact with the MES system to obtain production schedules and process standards, and upload production progress, equipment status, and quality inspection result data.

[0021] Preferably, in the RFID tag detection module, the abnormality of the RFID tag read by the RFID reader includes: when the RFID reader reads the RFID tag and the number of bytes read does not match the preset complete data bytes, recording the number of bytes read;

[0022] The RFID reader reads the RFID tag again and records the number of bytes contained in the read RFID tag; the number of bytes of the RFID tag read twice is compared; if the number of bytes of the RFID tag read twice is the same, the RFID tag is judged to be abnormal.

[0023] Preferably, the method further includes:

[0024] The image information of the RFID tag is acquired through a line array camera and pre-processed to obtain the outline of the RFID tag;

[0025] Use a deep learning model to detect discontinuous areas of the RFID tag outline and mark the detected discontinuous areas to obtain marked areas; extract endpoints from the marked areas as marker points;

[0026] Extract the contour center from the RFID tag contour, and use the contour center as the starting point to connect the starting point and the mark point with a straight line, calculate the length of the straight line, and obtain the mark line length;

[0027] Obtain all the marking line lengths of each marking area in turn, sort all the marking line lengths in descending order according to their numerical values, and extract the largest marking line length, which is recorded as the missing quantization value;

[0028] After analyzing the temperature of the RFID tag, the temperature-related deviation is obtained;

[0029] The missing quantization value and the temperature-related deviation value are weighted to obtain the tag failure coefficient;

[0030] A tag failure coefficient threshold is preset, and the tag failure coefficient is compared with the tag failure coefficient threshold. If the tag failure coefficient is greater than the tag failure coefficient threshold, it is determined that the RFID tag is faulty.

[0031] Preferably, the process of obtaining the temperature-related deviation includes:

[0032] The infrared thermal imager is used to scan the temperature field of the RFID tag surface in real time, divide the tag surface into grids, and obtain the coordinates of each grid point. , and its corresponding temperature time series data ;

[0033] Analyze the temperature change correlation coefficient between two grid points. The formula is as follows:

[0034] ;

[0035] in:

[0036] The numerator is the covariance of the deviation of the two temperature points from the mean;

[0037] The denominator is the product of the standard deviations of the two temperature points, which is used to standardize the covariance. The value range is -1 to 1;

[0038] Obtain the temperature change correlation coefficients corresponding to all two arbitrary grid points in sequence, preset a temperature change correlation coefficient threshold, and record the temperature change correlation coefficient greater than the temperature change correlation coefficient threshold as a temperature change anomaly coefficient;

[0039] The temperature-related deviation is obtained by counting the number of all temperature-change anomaly coefficients and dividing it by the number of all temperature-change correlation coefficients.

[0040] Preferably, the database management and storage module specifically includes:

[0041] Choose a relational database to store structured data; by establishing relationships between tables;

[0042] For time series data collected at high frequency, Influx DB is used for storage;

[0043] Use Mongo DB to store unstructured data;

[0044] A multi-level permission control mechanism is adopted to assign different data access permissions to different user roles; sensitive data is encrypted and stored.

[0045] Preferably, the production process real-time tracking module specifically includes:

[0046] Create production work orders based on the production plan issued by ERP and send the work orders to the HMI terminals of each production station;

[0047] Utilize RFID and barcode technology to record the start time, end time, operator, and equipment usage of each work order at each assembly station;

[0048] By connecting the equipment's sensors and control systems, the equipment's operating parameters, working status, and production quantity information can be collected in real time;

[0049] The equipment operation data is analyzed based on machine learning algorithms to establish an equipment failure prediction model; when the equipment operation parameters show abnormal trends or approach the failure threshold, an early warning message is issued to notify maintenance personnel to perform preventive maintenance.

[0050] Preferably, the quality inspection and traceability module specifically includes:

[0051] Establish a complete process specification database to store the quality standards and inspection specifications of each assembly link;

[0052] Flexible configuration of detection solutions based on different models of automotive instruments;

[0053] Deeply integrate with various testing equipment to realize automatic collection and transmission of test data;

[0054] Use statistical process control tools to analyze quality inspection data and generate control charts, histograms, and Pareto charts.

[0055] Preferably, the material management and scheduling module is configured to manage the entire process of materials required for automobile instrument assembly, including procurement, warehousing, storage, issuance, consumption and distribution of materials, to ensure the timeliness and accuracy of material supply and reduce inventory costs.

[0056] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0057] 1. This invention eliminates temporary false alarms caused by electromagnetic interference by comparing the byte counts of two RFID readers. A true fault is determined only when the byte counts read twice are abnormally consistent, improving the accuracy of initial anomaly identification. Linear array camera visual analysis quantifies contour loss to identify physical damage. Infrared thermal imaging temperature field correlation analysis captures thermal conduction anomalies such as antenna breakage. A comprehensive judgment of RFID tag failure is made based on the missing quantization value and temperature-related deviation values, significantly reducing traceability interruptions or quality risks caused by tag failure during automotive instrument assembly.

[0058] 2. The present invention reduces storage costs through partitioned storage and hot and cold separation of structured, time-series, and unstructured data. The real-time tracking module of the production process reduces equipment downtime and improves OEE through RFID work order tracing and equipment OEE analysis, combined with a machine learning fault prediction model. The material management module improves inventory turnover and shortens downtime caused by material shortages through batch tracing and AGV intelligent distribution, thereby achieving efficiency optimization and cost control for the entire automotive instrument assembly process. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Further details, features and advantages of the present application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0060] Figure 1 This is a system structure diagram of the present invention. DETAILED DESCRIPTION

[0061] Several embodiments of the present application will be described in more detail below with reference to the accompanying drawings so that those skilled in the art can implement the present application. The present application can be embodied in many different forms and for many different purposes and should not be limited to the embodiments described herein. These embodiments are provided to make the present application comprehensive and complete and to fully convey the scope of the present application to those skilled in the art. The embodiments do not limit the present application.

[0062] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the relevant art and / or the context of this specification, and will not be interpreted in an idealized or overly formal sense unless expressly defined as such herein.

[0063] Example 1

[0064] The specific implementation method is combined with the attached Figure 1 Provide detailed explanation.

[0065] Attachment Figure 1 This is a block diagram of the structure of a database-based automotive instrument assembly line tracking system provided by an embodiment of the present invention. It shows the connection relationship between the data acquisition and interaction module, RFID tag detection module, database management and storage module, and real-time production process tracking module, and annotates the main functional interaction process of each module.

[0066] In this embodiment, it includes:

[0067] The data acquisition and interaction module is configured to collect data from all aspects of the assembly line in real time and implement data interaction with external systems to provide raw data support for subsequent modules;

[0068] Specifically include:

[0069] Deploy sensors at key nodes such as assembly equipment and conveyor lines. For example, photoelectric sensors are used to detect the position of workpieces, torque sensors monitor bolt tightening force, and temperature and humidity sensors record environmental parameters to comprehensively obtain equipment operating status and production environment data.

[0070] RFID readers and barcode scanners are used to uniquely identify automotive instrument components and fixtures. When components enter the assembly station, the reader automatically reads the RFID tag information and associates it with data such as model, batch, and supplier. The barcode scanner is used to scan the traceability code on the finished instrument to quickly enter production information.

[0071] Field devices are connected to the edge computing gateway via industrial bus protocols (such as PROFINET and EtherCAT) to achieve centralized collection and preliminary processing of device data;

[0072] Use real-time data transmission protocol to upload collected data to the database server to ensure the real-time and stability of the data; set priority transmission for key production data to avoid data loss or delay;

[0073] Receive production plans, material requirements, and other information from the ERP system through Web Services or APIs, and feed back work order completion status and actual material consumption data to the ERP system, enabling collaboration between production and the supply chain.

[0074] Interact with the MES system to obtain production schedules and process standards, and upload production progress, equipment status, and quality inspection results to provide the MES system with real-time information on the production site to assist in production decision-making;

[0075] The RFID tag detection module is configured to trigger a status detection of the RFID tag when an abnormality occurs in the RFID tag read by the RFID reader in the data acquisition and interaction module; obtain the status information of the RFID tag, calculate the output tag fault coefficient through the RFID tag information analysis unit, and perform a fault assessment;

[0076] When the RFID reader reads the RFID tag and the number of bytes read does not match the preset complete data bytes, the number of bytes read is recorded;

[0077] The RFID reader reads the RFID tag again and records the number of bytes contained in the read RFID tag; the number of bytes of the RFID tag read twice is compared. If the number of bytes of the RFID tag read twice is the same, the RFID tag is judged to be abnormal:

[0078] Abnormal byte counts during the first read may be caused by temporary factors such as electromagnetic interference or tag position deviation (e.g., signal reflection from metal equipment on the assembly line causing a momentary interruption in reading). By performing a second read and comparing the byte counts, such incidental anomalies can be eliminated. Only when the same number of bytes are missing in both reads is it determined to be a true fault, such as physical damage to the tag storage area or chip failure, to avoid false rejection due to interference (e.g., a normal tag being mistakenly identified as faulty).

[0079] A line scan camera, coupled with an LED strip light source, is installed directly above the production line, with its scanning speed synchronized with the conveyor belt. The line scan camera captures image information from RFID tags and pre-processes the image information, including grayscale conversion, noise reduction, and edge enhancement, to obtain the RFID tag outline.

[0080] The hardware layout is installed directly above the assembly line, without interrupting the production process, and can be directly embedded in the existing RFID tag packaging production line. The detection efficiency is fully matched with the production speed, and it is suitable for mass production scenarios with an average of millions of tags per day.

[0081] Use a deep learning model to detect discontinuous areas of the RFID tag outline and mark the detected discontinuous areas to obtain marked areas; extract endpoints from the marked areas as marker points;

[0082] Extract the contour center from the RFID tag contour, and use the contour center as the starting point to connect the starting point and the mark point with a straight line, calculate the length of the straight line, and obtain the mark line length;

[0083] Obtain all the marking line lengths of each marking area in turn, sort all the marking line lengths in descending order according to their numerical values, and extract the largest marking line length, which is recorded as the missing quantization value;

[0084] Physical damage with discontinuous contours was converted into comparable numerical indicators (unit: pixels or millimeters) by extracting the maximum marker line length as the missing quantification value;

[0085] For example, the missing quantization value of a normal label is 0 (contour is continuous); the quantization value of a label with a tear length of 5 mm corresponds to approximately 10 pixels (based on a resolution of 0.5 mm / pixel), which can be directly used to set the classification threshold (for example, >3 pixels is considered severely damaged).

[0086] The quantification value can unify the damage evaluation criteria for different batches and different models of labels, avoiding the subjectivity of manual visual inspection. For example, in the detection of RFID labels on food packaging, the missing quantification value can be set to > 2mm to trigger rejection, ensuring that the physical integrity of the label meets the waterproof and tear-proof requirements.

[0087] After analyzing the temperature of the RFID tag, the temperature-related deviation is obtained;

[0088] include:

[0089] The infrared thermal imager is used to scan the temperature field of the RFID tag surface in real time, divide the tag surface into grids, and obtain the coordinates of each grid point. , and its corresponding temperature time series data ;

[0090] After gridding the tag surface, the temperature correlation of all point pairs is calculated to achieve a full-area scan of the entire tag's temperature field. Compared to single-point temperature measurement or local area analysis, this method can capture more subtle temperature distribution anomalies (such as antenna branch breakage, chip solder joint defects, and other local faults), avoiding misjudgments caused by missing sampling points.

[0091] For example, if a section of an RFID antenna breaks, the temperature correlation between the grid points near the break and the distant points will be significantly reduced. Traditional single-point temperature measurement may only detect a local temperature rise at the break and cannot be associated with the overall thermal conduction anomaly.

[0092] Analyze the temperature change correlation coefficient between two grid points. The formula is as follows:

[0093] ;

[0094] in:

[0095] The numerator is the covariance of the temperature deviations of the two points from the mean, reflecting the synchronization of temperature changes;

[0096] The denominator is the product of the standard deviations of the two temperature points, which is used to standardize the covariance. The value range is -1 to 1;

[0097] The closer it is to 1, the more consistent the temperature change trends at the two points are (e.g., the heat conduction path of a normal antenna is continuous); If it is close to 0 or negative, it means that the temperature change is irrelevant (for example, the antenna is broken and the heat conduction is interrupted);

[0098] Obtain the temperature change correlation coefficients corresponding to all two arbitrary grid points in sequence, preset a temperature change correlation coefficient threshold, and record the temperature change correlation coefficient greater than the temperature change correlation coefficient threshold as a temperature change anomaly coefficient;

[0099] Count all the temperature change anomaly coefficients and divide them by all the temperature change correlation coefficients to get the temperature correlation deviation;

[0100] Convert the correlation anomaly of the temperature field into a quantitative index between 0 and 1 to avoid the ambiguity of subjective threshold judgment;

[0101] For example, the temperature correlation deviation of normal labels is usually < 0.2 (most point pair correlations > threshold);

[0102] When the antenna is broken and the local heat conduction is interrupted, the deviation may be greater than 0.5 (a large number of point pair correlations are less than the threshold);

[0103] This temperature-related deviation can be directly embedded in the decision logic of the automated detection system (e.g., triggering an alarm when the deviation is > 0.3), facilitating standardized quality control in industrial mass production.

[0104] The missing quantization value and the temperature-related deviation value are weighted to obtain the tag failure coefficient;

[0105] Preset weight factors for missing quantization values ​​and temperature-related deviation values, multiply the missing quantization values ​​and temperature-related deviation values ​​by their corresponding weight factors, and sum them to obtain a tag failure coefficient;

[0106] A tag failure coefficient threshold is preset, and the tag failure coefficient is compared with the tag failure coefficient threshold. If the tag failure coefficient is greater than the tag failure coefficient threshold, it is determined that the RFID tag is faulty;

[0107] The database management and storage module is configured as the data center of the system, responsible for data storage, management, maintenance and security, and providing efficient data access and query services for other modules;

[0108] Specifically include:

[0109] Use relational databases such as MySQL or SQL Server to store structured data, such as production order information, process standard parameters, material ledgers, personnel permissions, etc.; by establishing relationships between tables, standardize data management and ensure data consistency and integrity;

[0110] InfluxDB is used to store frequently collected time series data, such as equipment operating parameters and quality inspection data. Time series databases are optimized for time series data and can efficiently store and query large amounts of timestamp data, meeting the needs of real-time monitoring and historical data analysis.

[0111] Use MongoDB to store unstructured data, such as equipment log files, quality inspection photos, and assembly process videos. The flexible architecture of unstructured databases can easily accommodate data storage requirements in different formats and structures.

[0112] Data is partitioned and stored based on dimensions such as production time, production line, and work order. For example, daily production data can be stored in independent partitioned tables to improve data query and retrieval efficiency. Historical data can also be archived regularly, and infrequently used old data can be migrated to low-cost storage media to free up space in the main database.

[0113] Store recently frequently accessed active data (hot data) in high-performance SSD disk arrays to ensure fast read and write speeds; migrate historical data (cold data) to mechanical hard drives or cloud storage to reduce storage costs; and further accelerate access to hot data through data caching mechanisms.

[0114] Develop a comprehensive data backup strategy that combines full and incremental backups. Perform a full backup daily and incremental backups hourly, and store the backup data in an off-site disaster recovery center. In the event of a system failure or data loss, data can be quickly restored to ensure business continuity.

[0115] Before data is stored in the database, the data cleaning program automatically filters out abnormal values, duplicate data, and incomplete data. For example, torque data that exceeds the process range and unreasonable production timestamps are marked and processed. At the same time, the data integrity is checked to ensure data accuracy and availability.

[0116] A multi-level permission control mechanism is used to assign different data access permissions to different user roles, such as read-only, read-write, and delete. Sensitive data (such as supplier information and financial data) is encrypted and stored, and the SSL / TLS protocol is used to ensure data transmission security and prevent data leakage and illegal access.

[0117] The real-time production process tracking module is configured to provide all-round real-time monitoring and dynamic management of the production process of the automotive instrument assembly line, ensuring the smooth execution of the production plan and timely detection and resolution of production anomalies.

[0118] Specifically include:

[0119] Create production work orders based on the production plan issued by the ERP system, and define in detail the product model, production quantity, delivery date, and process requirements of the work order; automatically send the work order to the HMI terminal of each production station to clarify the production tasks;

[0120] Utilizing RFID and barcode technology, the system records the start and end time, operator, and equipment usage of each work order at each assembly station. A visual interface displays the real-time location and production progress of each work order, allowing managers to clearly understand the production status of each work order, such as whether it is being processed, completed, or awaiting materials.

[0121] When an abnormality occurs during the production process (such as equipment failure, material shortage, or quality issues), the system automatically suspends the work order and notifies relevant personnel through pop-up windows, sound and light alarms, etc. Managers can adjust the work order based on the actual situation, such as reallocating workstations, extending delivery times, or terminating the work order.

[0122] By connecting the equipment's sensors and control systems, the system collects real-time information on the equipment's operating parameters (such as speed, temperature, current, and voltage), working status (operating, standby, and fault), and production quantity. The system displays the equipment status on the large monitoring screen using intuitive charts and color codes, such as green for normal operation and red for fault shutdown.

[0123] Analyze equipment operating data based on machine learning algorithms to establish equipment failure prediction models. When equipment operating parameters show abnormal trends or approach failure thresholds, early warning information is issued to notify maintenance personnel to perform preventive maintenance, reducing equipment downtime.

[0124] Calculate the overall equipment efficiency and evaluate the equipment's operating efficiency from three dimensions: time utilization rate, performance utilization rate, and yield rate. By analyzing the overall equipment efficiency data, identify the causes of low equipment efficiency, such as long changeover time and equipment idling, and formulate improvement measures.

[0125] By embedding process documents and operating specifications into the system, the HMI terminal at each workstation displays the process requirements and operating instructions for the current process. During the worker's operation, the system monitors the execution of key process parameters in real time, such as welding temperature, tightening torque, and software flash version, to ensure that the production process meets process standards.

[0126] Error-proofing technologies (such as RFID verification, sensor detection, and visual recognition) are used to prevent worker errors. For example, if a worker installs a component of the wrong model, the RFID reader will detect the discrepancy and the system will immediately issue an alarm and lock subsequent operations. If the visual inspection system detects a deviation in the component installation position, it will automatically prompt the worker to make adjustments.

[0127] The quality inspection and traceability module is configured to run through the entire process of automotive instrument assembly, achieving automation, standardization and traceability of quality inspection, ensuring that product quality meets requirements and reducing quality risks;

[0128] Specifically include:

[0129] Establish a comprehensive process specification database to store the quality standards and inspection specifications for each assembly link; including the dimensional tolerances, performance indicators, appearance requirements of parts and components, as well as the process parameter ranges during the assembly process;

[0130] Flexible configuration of testing solutions based on different models of automotive instruments; determination of mandatory inspection items (such as hardware function testing, software compatibility testing, and sealing testing), sampling ratios, and testing equipment to meet diverse quality inspection needs;

[0131] Deeply integrated with various testing equipment (such as functional testers, air tightness testers, and optical testers) to achieve automatic collection and transmission of test data; after the testing equipment completes the test, the results (such as Pass / Fail, specific parameter values) are uploaded to the system database in real time to avoid manual entry errors;

[0132] Use statistical process control tools to analyze quality inspection data and generate control charts, histograms, and Pareto charts; by analyzing quality data, identify quality fluctuation trends and potential problems, calculate process capability indices, and evaluate the stability and quality assurance capabilities of the production process;

[0133] When non-conforming products are detected, the system automatically classifies and labels them (e.g., fatal defects, serious defects, minor defects) and triggers the non-conforming product handling process. Quality management personnel are notified to conduct re-inspection and make a decision. Based on the decision, they can choose a handling method such as rework, repair, scrapping, or concession acceptance.

[0134] Quality traceability: Based on the production data and quality inspection records in the database, full-process traceability of unqualified products is achieved. By entering the product traceability code or batch number, information such as the source of components, production time, operators, assembly process, and inspection results can be queried to quickly locate the root cause of quality issues and take targeted improvement measures.

[0135] The material management and scheduling module is configured to manage the entire process of materials required for automobile instrument assembly, including procurement, warehousing, storage, issuance, consumption and distribution, ensuring the timeliness and accuracy of material supply and reducing inventory costs;

[0136] Specifically including material life cycle management, material collection and distribution, material loss and surplus management;

[0137] Management of the entire material life cycle: Establish a material master data management system to record the basic information of the material in detail, such as material code, name, specification model, unit, supplier, safety stock, shelf life, etc. Use material coding to achieve unique identification and full-process tracking of materials; strictly manage each batch of materials, and record batch number, storage time, quantity, inspection status and other information; during the material collection and use process, use RFID or barcode scanning to associate material batches with production work orders to achieve forward and reverse traceability of material batches; monitor material inventory quantities in real time, and calculate indicators such as inventory turnover rate and stagnant inventory. When the inventory is lower than the safety stock threshold, the system automatically generates a replenishment application and notifies the purchasing department to purchase; for materials that have exceeded the shelf life or have been stagnant for too long, early warning prompts and processing will be issued;

[0138] Material collection and distribution: Workers submit material collection applications through the HMI terminal, and electronic material collection orders are automatically generated based on work order requirements, specifying information such as material name, specifications, quantity, and collection location. Warehouse managers prepare and distribute materials based on the material collection orders and update inventory data. Integration with intelligent warehousing systems and AGVs enables automatic material distribution.

[0139] Material loss and surplus material management: This system records material scrap and loss during the production process, including the cause of loss (e.g., assembly errors, defective incoming materials, equipment failure), the amount of loss, and other information. Through statistical analysis of loss data, the main causes of material loss are identified, and corrective measures are implemented to reduce the loss rate. For unused surplus materials during production, workers initiate a waste recycling request through the HMI terminal. After receiving the surplus materials, warehouse managers conduct inspection and inventory, update inventory data, and mark and re-enter the warehouse with reusable surplus materials.

[0140] The system management and data analysis module is configured to be responsible for the system's user management, permission control, log audit and security management functions, while also conducting in-depth analysis of production data to provide data support for enterprise decision-making;

[0141] Specifically include:

[0142] Define different user roles based on the enterprise organizational structure and business needs, such as system administrator, production manager, quality engineer, workshop operator, etc. Each role is assigned different system function permissions and data access rights to ensure the security and standardization of system operations; adopt a role-based access control model to flexibly allocate user permissions;

[0143] Record all user operations, including login time, logout time, accessed functional modules, performed operations (such as adding work orders, modifying process parameters, deleting data), operation objects, etc. Operation logs are stored in an encrypted manner to ensure the integrity and non-tamperability of log data.

[0144] Auditors can use the log query function to search operation logs by user, time, operation type and other conditions to audit and trace system operations;

[0145] Comprehensively analyze various data in the production process (such as production progress, equipment efficiency, quality indicators, and material consumption) to mine the data value. Through comparative analysis, trend analysis, and other methods, potential problems and optimization opportunities in the production process can be discovered, providing data support for production decision-making;

[0146] Provide report design tools, users can customize report templates according to their own needs, such as daily production report, weekly quality report, monthly equipment analysis report, etc.

[0147] Based on large-screen display technology, a visual dashboard is created for the production command center; key production indicators (such as real-time production capacity, yield rate, equipment status, and material inventory) are displayed in the form of intuitive charts, maps, dashboards, etc., making it easier for managers to grasp the overall production situation in real time and make quick decisions.

[0148] The above formulas are obtained by collecting a large amount of data and performing software simulation, and a formula close to the actual value is selected. The influencing weight factors and specific coefficient values ​​in the formula are set by technical personnel in this field according to actual conditions, and can be adjusted and modified later.

[0149] The above embodiments can be implemented in whole or in part through software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0150] The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0151] The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that contains one or more available media. The available medium can be magnetic media, optical media, or semiconductor media. The semiconductor medium can be a solid-state drive.

[0152] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0153] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways.

[0154] For example, the device embodiments described above are merely illustrative. For example, the division of units described herein is merely a logical functional division. Actual implementations may employ alternative divisions, such as combining or integrating multiple units or components into another system, or omitting or disabling certain features. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through an interface, or indirect coupling or communication connection between devices or units, which may be electrical, mechanical, or other.

[0155] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0156] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0157] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application.

[0158] The aforementioned storage media include: USB flash drives, mobile hard disks, read-only memories, random access memories, magnetic disks, optical disks, and other media that can store program codes.

[0159] The above description of the embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A database-based automobile instrument assembly line tracking system, characterized in that: include: The data acquisition and interaction module is configured to collect data from all links of the assembly line in real time and realize data interaction with external systems; The RFID tag detection module is configured to trigger a status detection of the RFID tag when an abnormality occurs in the RFID tag read by the RFID reader in the data acquisition and interaction module; obtain the status information of the RFID tag, calculate the output tag fault coefficient through the RFID tag information analysis unit, and perform a fault assessment; The abnormality of the RFID tag read by the RFID reader includes: when the RFID reader reads the RFID tag and the number of bytes read does not match the preset complete data bytes, the number of bytes read is recorded; The RFID reader reads the RFID tag again and records the number of bytes contained in the read RFID tag; the number of bytes of the RFID tag read twice is compared; if the number of bytes of the RFID tag read twice is the same, the RFID tag is judged to be abnormal; Also includes: The image information of the RFID tag is acquired through a line array camera and pre-processed to obtain the outline of the RFID tag; Use a deep learning model to detect discontinuous areas of the RFID tag outline and mark the detected discontinuous areas to obtain marked areas; extract endpoints from the marked areas as marker points; Extract the contour center from the RFID tag contour, and use the contour center as the starting point to connect the starting point and the mark point with a straight line, calculate the length of the straight line, and obtain the mark line length; Obtain all the marking line lengths of each marking area in turn, sort all the marking line lengths in descending order according to their numerical values, and extract the largest marking line length, which is recorded as the missing quantization value; After analyzing the temperature of the RFID tag, the temperature-related deviation is obtained; The missing quantization value and the temperature-related deviation value are weighted to obtain the tag failure coefficient; A tag failure coefficient threshold is preset, and the tag failure coefficient is compared with the tag failure coefficient threshold. If the tag failure coefficient is greater than the tag failure coefficient threshold, it is determined that the RFID tag is faulty; The process of obtaining temperature-related deviation includes: The infrared thermal imager is used to scan the temperature field of the RFID tag surface in real time, divide the tag surface into grids, and obtain the coordinates of each grid point. , and its corresponding temperature time series data ; Analyze the temperature change correlation coefficient between two grid points. The formula is as follows: ； in: The numerator is the covariance of the deviation of the two temperature points from the mean; The denominator is the product of the standard deviations of the two temperature points, which is used to standardize the covariance. The value range is -1 to 1; Obtain the temperature change correlation coefficients corresponding to all two arbitrary grid points in sequence, preset a temperature change correlation coefficient threshold, and record the temperature change correlation coefficient greater than the temperature change correlation coefficient threshold as a temperature change anomaly coefficient; Count all the temperature change anomaly coefficients and divide them by all the temperature change correlation coefficients to get the temperature correlation deviation; The database management and storage module is configured as the data center of the system and is responsible for data storage, management, maintenance and security; The production process real-time tracking module is configured to provide all-round real-time monitoring and dynamic management of the production process of the automobile instrument assembly line.

2. The database-based automobile instrument assembly line tracking system according to claim 1 is characterized in that: Data collection and interaction module, specifically including: Deploy sensors at key nodes of assembly equipment and conveyor lines to obtain comprehensive data on equipment operating status and production environment; Use RFID readers and barcode scanners to uniquely identify automotive instrument components and fixtures; Use real-time data transmission protocol to upload collected data to the database server; set priority transmission for key production data; Receive production plans and material requirements from the ERP system through the Web Service interface, and feed back work order completion status and actual material consumption data to the ERP system; Interact with the MES system to obtain production schedules and process standards, and upload production progress, equipment status, and quality inspection result data.

3. The database-based automobile instrument assembly line tracking system according to claim 2 is characterized in that: Database management and storage module, including: Choose a relational database to store structured data; by establishing relationships between tables; For time series data collected at high frequency, Influx DB is used for storage; Use Mongo DB to store unstructured data; A multi-level permission control mechanism is adopted to assign different data access permissions to different user roles; sensitive data is encrypted and stored.

4. The database-based automobile instrument assembly line tracking system according to claim 3 is characterized in that: The real-time tracking module of the production process includes: Create production work orders based on the production plan issued by ERP and send the work orders to the HMI terminals of each production station; Utilize RFID and barcode technology to record the start time, end time, operator, and equipment usage of each work order at each assembly station; By connecting the equipment's sensors and control systems, the equipment's operating parameters, working status, and production quantity information can be collected in real time; The equipment operation data is analyzed based on machine learning algorithms to establish an equipment failure prediction model; when the equipment operation parameters show abnormal trends or approach the failure threshold, an early warning message is issued to notify maintenance personnel to perform preventive maintenance.

5. The database-based automobile instrument assembly line tracking system according to claim 4 is characterized in that: Quality inspection and traceability module, specifically including: Establish a complete process specification database to store the quality standards and inspection specifications of each assembly link; Flexible configuration of detection solutions based on different models of automotive instruments; Deeply integrate with various testing equipment to realize automatic collection and transmission of test data; Use statistical process control tools to analyze quality inspection data and generate control charts, histograms, and Pareto charts.

6. The database-based automobile instrument assembly line tracking system according to claim 5 is characterized in that: The material management and scheduling module is configured to manage the entire process of materials required for automobile instrument assembly, including material procurement, warehousing, storage, issuance, consumption and distribution, to ensure the timeliness and accuracy of material supply and reduce inventory costs.

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