MES-based intelligent transportation equipment production full life cycle quality tracing system

Abstract

The invention relates to the field of quality supervision, and discloses an MES-based intelligent transportation equipment production full-life-cycle quality tracing system, which comprises a material tracing module, a production process monitoring module, a product full-life-cycle file module and an intelligent analysis early warning module. The material tracing module manages raw material related data; the production process monitoring module collects various production data through a sensor network and a personnel operation recording system; the product full-life-cycle file module distributes a unique identification code for a product and associates and stores production link data; the intelligent analysis and early warning module analyzes the collected data, judges quality defects and carries out early warning in time. The system effectively improves the quality control capability of intelligent transportation equipment production through a series of specific analysis methods such as process parameter comparison, equipment state matching, personnel operation specification judgment and the like and by adopting a grading early warning mechanism. According to the invention, a solution is provided for production quality tracing of intelligent transportation equipment.

Description

Technical Field

[0001] The present invention relates to the field of quality supervision, and in particular to a MES-based intelligent transportation equipment production full life cycle quality tracing system. Background Art

[0002] As a key component in ensuring the efficient and safe operation of transportation systems, the quality of intelligent transportation equipment is directly related to traffic safety and operational efficiency. However, traditional production quality control methods have many drawbacks.

[0003] On the one hand, the raw material traceability system is imperfect. During the production process, it is difficult to accurately grasp the batch information of raw materials, the supplier source, and the specific flow of materials throughout the entire production process. This makes it difficult to quickly and accurately determine the specific batch of raw materials if a product quality problem occurs, and it is also difficult to conduct effective quality assessment and management of suppliers. As a result, it is difficult to identify the root cause of quality problems and to take measures to improve quality at the source.

[0004] On the other hand, production process monitoring is inadequate. It's impossible to fully and comprehensively obtain real-time information on the process parameters and operating status of production equipment, and there's a lack of effective record-keeping and standardized oversight of personnel operations. This makes it difficult to promptly identify and address potential quality risks during production, increasing uncertainty in the production process and causing significant fluctuations in product quality. Summary of the Invention

[0005] The purpose of the present invention is to provide a quality traceability system for the entire life cycle of intelligent transportation equipment production based on MES to solve the above technical problems.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A quality traceability system for the entire production life cycle of intelligent transportation equipment based on MES, including: Material traceability module, used to record and manage raw material batch information, supplier data and material flow data; The production process monitoring module connects to the production equipment through a sensor network to obtain process parameters and equipment status data in real time, and collects personnel operation data through the personnel operation recording system; The product life cycle archive module is used to assign a unique identification code to each intelligent transportation device and associate the identification code with data from various stages of the production process; The intelligent analysis and early warning module analyzes the data collected by the production process monitoring module to determine whether there are quality defects and issues early warning information in a timely manner when quality defects exist.

[0007] As a further technical solution, the intelligent analysis and early warning module includes: The analysis subunit compares the collected process parameters with the preset standard process parameter range, matches the equipment status data with the characteristic data of the normal operation of the equipment, and combines the personnel operation data to determine whether the operation complies with the standard process, thereby determining whether there are quality defects; The early warning subunit is used to issue an early warning based on the analysis and judgment results of the analysis unit. When the analysis unit determines that there is a quality defect, an early warning message is issued.

[0008] As a further technical solution, the process of comparing the collected process parameters with the preset standard process parameter range is as follows: The real-time variation curve of each process parameter in unit time is obtained by fitting and placed in a rectangular coordinate system. The upper limit value and lower limit value of the preset standard process parameter range are then made into an upper limit straight line and a lower limit straight line respectively; If the area SA enclosed below the real-time change curve of the current process parameter and above the upper limit straight line exceeds the warning value SA0, or the area SB enclosed above the real-time change curve of the current process parameter and below the lower limit straight line exceeds the warning value SB0, it is judged that the current process parameter is abnormal.

[0009] As a further technical solution, the process of matching the device status data with the characteristic data of the normal operating state of the device is as follows: Compare the real-time parameter value of each device with the reference parameter value when the device is in normal operation. If the absolute value of the difference between the real-time parameter value and the reference parameter value exceeds the preset tolerance threshold, it is determined that the current device parameter is abnormal. If at least one device parameter is abnormal, the parameter abnormality ratio is recorded as the ratio of the number of abnormal device parameters to the total number of device parameters in each unit time. Get the number of abnormal device parameters that occur abnormally per unit time, using the formula After the cumulative sum calculation is performed, it is recorded as the parameter abnormality; The device abnormality index is calculated by the parameter abnormality ratio × parameter abnormality amount. If the device abnormality index exceeds the device abnormality warning value, it is determined that the current device status is abnormal.

[0010] As a further technical solution, the process of judging whether the operation complies with the standard process based on the personnel operation data is as follows: The analysis unit pre-stores standard operating procedures and corresponding operating time intervals for each link in the production of intelligent transportation equipment; Compare the personnel operation sequence collected by the personnel operation record system with the standard operation process step by step, and determine whether the execution time of each operation step is within the corresponding operation time range; If the operation sequence does not conform to the standard process, or the operation time exceeds the corresponding range, it is determined that the personnel operation does not comply with the standard process.

[0011] As a further technical solution, the early warning subunit implements a hierarchical early warning mechanism: When a single parameter abnormality is detected, a first-level warning is triggered and a prompt notification is issued; When the area SA, SB or equipment abnormality index reaches 80% of the corresponding warning value, the second-level warning is triggered and an on-site warning is issued; When quality defects are detected, a level 3 warning is triggered and the machine is shut down for inspection.

[0012] As a further technical solution, the sensor network of the production process monitoring module includes: Temperature sensor group, monitoring the temperature distribution of key process points; Vibration sensor array, collecting equipment operation vibration spectrum; Visual inspection unit, which uses industrial cameras to obtain product appearance images; Environmental monitoring unit records the temperature, humidity and cleanliness data of the production environment.

[0013] As a further technical solution, if at least one process parameter is abnormal, the current equipment status is abnormal, and the personnel operation does not comply with the standard process, it is determined that there is a quality defect.

[0014] Beneficial effects of the present invention: (1) The material traceability module records raw material batch information, supplier data, and material flow data. When a product has quality problems, the company can quickly locate the batch and supplier of the problematic raw materials, providing an accurate basis for root cause analysis of the quality problem, thereby taking targeted measures, such as communicating with suppliers for improvements, adjusting the raw material inspection process, etc., effectively reducing product quality risks caused by raw material problems; (2) The production process monitoring module uses the sensor network to obtain process parameters and equipment status data in real time. Combined with the operation data collected by the personnel operation record system, the intelligent analysis and early warning module can promptly detect problems such as abnormal process parameters, abnormal equipment status, and irregular personnel operation. At the same time, the graded early warning mechanism can issue different levels of early warnings according to the severity of the problem, such as first-level prompt notifications, second-level on-site warnings, and third-level shutdown inspections, so that enterprises can take timely measures to adjust and deal with quality problems in the early stages of their occurrence, avoid the expansion of quality problems, reduce production costs, and improve production efficiency. (3) The product life cycle archive module assigns a unique identification code to each intelligent transportation device and stores data from all stages of the production process. This not only facilitates enterprises to track and manage product quality throughout the production, sales, and after-sales stages, meeting customers' needs for product quality traceability and improving customer satisfaction, but also helps enterprises collect product quality data at different stages, conduct big data analysis, explore potential quality improvement points, continuously optimize production processes and management processes, and improve product quality and overall competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The present invention will be further described below with reference to the accompanying drawings.

[0016] Figure 1 Schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0018] See also Figure 1 As shown, the present invention is a quality traceability system for the entire life cycle of intelligent transportation equipment production based on MES, including: The material traceability module is used to record and manage raw material batch information, supplier data, and material flow data. For example, using the relational database MySQL, separate tables are created for raw material batch information, supplier data, and material flow data. The raw material batch information table can include fields such as batch number, production date, and quality inspection report number; the supplier data table can record supplier name, contact information, and credit rating; and the material flow data table can record detailed information on the flow of materials from warehousing, through various production stages, and finally to finished product shipment. The database's query function allows for quick and easy access to required data.

[0019] The production process monitoring module connects to production equipment via a sensor network to obtain real-time process parameters and equipment status data. It also collects human operation data through a human operation recording system. The sensor network can be composed of various sensor types, such as temperature, pressure, and speed sensors, each used to collect different process parameters. These sensors are connected to key parts of the production equipment and transmit the collected data to the monitoring system via wired or wireless communication. The human operation recording system can utilize an operator terminal device, through which operators enter information such as operation instructions, operation time, and operation content during various operations.

[0020] The product lifecycle archive module assigns a unique identification code to each intelligent transportation device and associates and stores this code with data from each stage of the production process. This unique identification code can be in the form of a QR code or RFID tag. At each stage of the production process, a scanner or RFID reader / writer links the device's identification code to the corresponding data and stores it in a database. This data covers raw material usage, production process parameters, inspection and testing results, and more, forming a complete production history.

[0021] The intelligent analysis and early warning module analyzes the data collected by the production process monitoring module to determine whether there are quality defects and issues early warning information in a timely manner when quality defects exist.

[0022] This implementation enables quality traceability throughout the entire production lifecycle of intelligent transportation equipment. Data from raw material procurement to product production and inspection is effectively recorded and managed. If a quality issue arises, the problem can be quickly located, providing a basis for quality improvement. Furthermore, real-time production process monitoring and intelligent analysis and early warning can promptly identify potential quality risks, prevent the mass production of substandard products, and improve production efficiency and product quality.

[0023] The intelligent analysis and early warning module includes: The analysis sub-unit compares the collected process parameters with the preset standard process parameter range, matches the equipment status data with the characteristic data of the normal operation of the equipment, and combines the personnel operation data to determine whether the operation complies with the standard process, thereby determining whether there are quality defects; The early warning subunit is used to issue an early warning based on the analysis and judgment results of the analysis unit. When the analysis unit determines that there is a quality defect, an early warning message is issued.

[0024] In this embodiment, the quality defect judgment and early warning functions are subdivided, which improves the professionalism and pertinence of the system analysis and problem handling; precise analysis can more accurately judge quality defects, and timely early warnings allow relevant personnel to take quick measures to avoid the expansion of quality problems and ensure the continuity and stability of production.

[0025] The process of comparing the collected process parameters with the preset standard process parameter range is as follows: The real-time change curve of each process parameter in unit time is obtained by fitting and placed in a rectangular coordinate system, and then the upper and lower limits of the preset standard process parameter range are made into upper and lower limit straight lines respectively; the real-time change curve of the process parameters can be obtained by fitting using a curve fitting algorithm such as the least squares fitting method, and the sampling data of the process parameters in unit time can be fitted into a curve through programming; the construction of the rectangular coordinate system and the drawing of straight lines can be completed with the help of a data visualization library such as Python's Matplotlib library.

[0026] If the area SA enclosed by the current process parameter's real-time change curve below and the upper limit line exceeds the warning value SA0, or the area SB enclosed by the current process parameter's real-time change curve above and the lower limit line below exceeds the warning value SB0, then it is determined that the current process parameter is abnormal. The setting of the warning values ​​SA0 and SB0 needs to be determined based on the actual requirements of the production process and statistical analysis of historical data. Reasonable thresholds can be set by studying the normal fluctuation range of process parameters in previous production and combining production experience. In this embodiment, whether the process parameters are abnormal is determined in a quantitative manner. Compared with simple upper and lower limit judgments, it takes into account the changing trend of the process parameters over a period of time, which is more scientific and accurate. Potential abnormalities in process parameters can be discovered in a timely manner, and adjustment measures can be taken in advance to ensure the stability of the production process and improve the consistency of product quality.

[0027] The process of matching device status data with the characteristic data of the normal operating status of the device is as follows: The real-time parameter value of each device is compared one by one with the reference parameter value when the device is in normal operation. If the absolute value of the difference between the real-time parameter value and the reference parameter value exceeds the preset tolerance threshold, it is judged that the current device parameter is abnormal. The setting of the tolerance threshold should be determined based on the design specifications and production experience of the equipment, and the tolerance threshold of different types of equipment is different.

[0028] If at least one device parameter is abnormal, the parameter abnormality ratio is recorded as the ratio of the number of abnormal device parameters to the total number of device parameters in each unit time. Get the number of abnormal device parameters that occur abnormally per unit time, using the formula After the cumulative summation calculation, it is recorded as the parameter abnormality quantity; wherein, represents the number of times of abnormality of the m-th abnormal device parameter in a unit time, represents the total number of abnormal device parameters; represents the total number of abnormal device parameters; The device abnormality index is calculated by the parameter abnormality ratio and the parameter abnormality quantity, and if the device abnormality index exceeds the device abnormality early warning value, it is determined that the current device state is abnormal.

[0029] In this embodiment, the device state is determined by calculating the device abnormality index by comprehensively considering multiple factors of device parameter abnormality, which can more comprehensively and accurately evaluate the device running condition; potential fault hidden dangers of the device are found in time, device maintenance is arranged in advance, device failure downtime is reduced, and device utilization and production efficiency are improved.

[0030] The process of determining whether the operation conforms to the standard process according to the personnel operation data is as follows: The analysis unit pre-stores standard operation processes for each link of intelligent transportation device production and corresponding operation time intervals; The personnel operation sequence collected by the personnel operation recording system is compared with the standard operation process step by step, and it is simultaneously determined whether the execution time of each operation step is within the corresponding operation time interval; If the operation sequence does not conform to the standard process or the operation time exceeds the corresponding interval, it is determined that the personnel operation does not conform to the standard process.

[0031] In this embodiment, the standard operation process can be stored in the system in the form of digital operation manual, and be organized in structured data such as XML and JSON format for easy query and comparison; the setting of the operation time interval needs to be determined according to the production process standard and actual production experience, and can be familiarized by the employees during new employee training and continuously optimized in the production process; the personnel operation recording system can collect the operation sequence and time by setting a time stamp recording function on the production terminal corresponding to each operation step, and the system automatically records the time and operation content after the operation personnel complete each operation; the standard personnel operation process ensures that each production link is executed according to the standard, and reduces quality problems caused by non-standard personnel operation; it helps to improve the standardization degree of the production process, facilitates new employees to quickly get started, and improves the overall production efficiency and product quality stability As a further technical solution, the early warning subunit implements a hierarchical early warning mechanism: When a single parameter abnormality is detected, a first-level early warning is triggered, and a prompt notification is made; When the area SA, SB or device abnormality index reaches 80% of the corresponding early warning value, a second-level early warning is triggered, and an on-site warning is made; When quality defects are detected, a level 3 warning is triggered and the machine is shut down for inspection.

[0032] In this embodiment, the implementation of the graded warning mechanism can be completed by writing a logical judgment program; when a single parameter abnormality is detected, or the area or equipment abnormality index reaches a corresponding proportion, the program triggers different warning actions. Prompt notifications can be set as pop-up messages, containing abnormal parameter information; on-site warnings can be achieved through sound and light alarms in the workshop, emitting different colors of light and sound to distinguish warning levels; shutdown inspections can send control instructions to production equipment through the system to stop the equipment from running, and at the same time display the shutdown reason and related prompt information on the equipment operation interface; graded warnings are issued according to the severity of the abnormal situation, so that relevant personnel can quickly understand the urgency and importance of the problem and take appropriate measures; avoid overreaction or underreaction, reasonably allocate resources, effectively respond to different degrees of quality risks, and ensure production safety and product quality.

[0033] The sensor network of the production process monitoring module includes: Temperature sensor group, monitoring the temperature distribution of key process points; Vibration sensor array, collecting equipment operation vibration spectrum; Visual inspection unit, which uses industrial cameras to obtain product appearance images; Environmental monitoring unit records the temperature, humidity and cleanliness data of the production environment.

[0034] In this embodiment, multiple types of sensors work together to comprehensively collect various data in the production process, providing rich data support for production process monitoring and quality analysis; timely discover abnormal changes in the production environment and equipment operating status, ensure that production is carried out in a suitable environment, and improve the reliability of product quality.

[0035] If at least one process parameter is abnormal, the current equipment status is abnormal, and the personnel operation does not comply with the standard process, it is determined that there is a quality defect.

[0036] In this embodiment, quality defects are determined by comprehensively considering abnormal conditions in multiple production links, thereby improving the accuracy and reliability of quality defect judgments; avoiding erroneous processing caused by misjudgment of a single factor, ensuring timely handling of real quality problems, and improving product quality and production management levels.

[0037] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A quality traceability system for the entire production life cycle of intelligent transportation equipment based on MES, characterized by: include: Material traceability module, used to record and manage raw material batch information, supplier data and material flow data; The production process monitoring module connects to the production equipment through a sensor network to obtain process parameters and equipment status data in real time, and collects personnel operation data through the personnel operation recording system; The product life cycle archive module is used to assign a unique identification code to each intelligent transportation device and associate the identification code with data from various stages of the production process; The intelligent analysis and early warning module analyzes the data collected by the production process monitoring module to determine whether there are quality defects and issues early warning information in a timely manner when quality defects exist.

2. The MES-based intelligent transportation equipment production life cycle quality traceability system according to claim 1 is characterized in that: The intelligent analysis and early warning module includes: The analysis sub-unit compares the collected process parameters with the preset standard process parameter range, matches the equipment status data with the characteristic data of the normal operation of the equipment, and combines the personnel operation data to determine whether the operation complies with the standard process, thereby determining whether there are quality defects; The early warning subunit is used to issue an early warning based on the analysis and judgment results of the analysis unit. When the analysis unit determines that there is a quality defect, an early warning message is issued.

3. The MES-based intelligent transportation equipment production life cycle quality traceability system according to claim 2 is characterized in that: The process of comparing the collected process parameters with the preset standard process parameter range is as follows: The real-time variation curve of each process parameter in unit time is obtained by fitting and placed in a rectangular coordinate system. The upper limit value and lower limit value of the preset standard process parameter range are then made into an upper limit straight line and a lower limit straight line respectively; If the area SA enclosed by the real-time change curve of the current process parameters and the upper limit straight line exceeds the warning value SA0, or the area SB enclosed by the real-time change curve of the current process parameters and the lower limit straight line exceeds the warning value SB0, it is judged that the current process parameters are abnormal.

4. The MES-based intelligent transportation equipment production life cycle quality traceability system according to claim 2 is characterized in that: The process of matching device status data with the characteristic data of the normal operating status of the device is as follows: Compare the real-time parameter value of each device with the reference parameter value when the device is in normal operation. If the absolute value of the difference between the real-time parameter value and the reference parameter value exceeds the preset tolerance threshold, it is determined that the current device parameter is abnormal. If at least one device parameter is abnormal, the parameter abnormality ratio is recorded as the ratio of the number of abnormal device parameters to the total number of device parameters in each unit time. Get the number of abnormal device parameters that occur abnormally per unit time, using the formula After the cumulative sum calculation is performed, it is recorded as the parameter abnormality; The device abnormality index is calculated by the parameter abnormality ratio × parameter abnormality amount. If the device abnormality index exceeds the device abnormality warning value, it is determined that the current device status is abnormal.

5. The MES-based intelligent transportation equipment production life cycle quality traceability system according to claim 2 is characterized in that: The process of judging whether the operation complies with the standard process based on the personnel operation data is as follows: The analysis unit pre-stores standard operating procedures and corresponding operating time intervals for each link in the production of intelligent transportation equipment; Compare the personnel operation sequence collected by the personnel operation record system with the standard operation process step by step, and determine whether the execution time of each operation step is within the corresponding operation time range; If the operation sequence does not conform to the standard process, or the operation time exceeds the corresponding range, it is determined that the personnel operation does not comply with the standard process.

6. The MES-based intelligent transportation equipment production life cycle quality traceability system according to claim 3 or 4 is characterized in that: The early warning subunit implements a hierarchical early warning mechanism: When a single parameter abnormality is detected, a first-level warning is triggered and a prompt notification is issued; When the area SA, SB or equipment abnormality index reaches 80% of the corresponding warning value, the second-level warning is triggered and an on-site warning is issued; When quality defects are detected, a level 3 warning is triggered and the machine is shut down for inspection.

7. The MES-based intelligent transportation equipment production life cycle quality traceability system according to claim 1 is characterized in that: The sensor network of the production process monitoring module includes: Temperature sensor group, monitoring the temperature distribution of key process points; Vibration sensor array, collecting equipment operation vibration spectrum; Visual inspection unit, which uses industrial cameras to obtain product appearance images; Environmental monitoring unit records the temperature, humidity and cleanliness data of the production environment.

8. The MES-based intelligent transportation equipment production life cycle quality traceability system according to claim 2 or 5 is characterized in that: If at least one process parameter is abnormal, the current equipment status is abnormal, and the personnel operation does not comply with the standard process, it is determined that there is a quality defect.

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