A system and method for managing data of equipment in a workshop based on MES

Abstract

The application relates to the technical field of equipment data management, in particular to a workshop equipment data management system and method based on MES, the steps of the method comprising the following steps: acquiring equipment collection data and equipment historical collection data, and acquiring equipment operation data; performing data anomaly evaluation on the equipment collection data based on the equipment historical collection data; performing anomaly correlation analysis based on the data anomaly evaluation result and the equipment operation data; and positioning an interference equipment and generating an equipment interference early warning according to the anomaly correlation analysis result. The application realizes interference equipment positioning by correlating and analyzing abnormal data and equipment operation data, significantly improves the reliability of interference equipment positioning, and effectively guarantees the accuracy of equipment data collection.

Description

Technical Field

[0001] This application relates to the field of equipment data management technology, and in particular to a workshop equipment data management system and method based on MES. Background Technology

[0002] MES (Manufacturing Execution System) is a real-time information system for production management in manufacturing workshops. This system achieves real-time monitoring of the entire lifecycle of workshop data through the collection and analysis of workshop data. The equipment data management system is one of the core modules of the MES system. It achieves equipment performance monitoring and maintenance management by collecting equipment operation data in real time and analyzing the equipment operation status.

[0003] In a workshop production environment, production equipment is often densely packed and diverse. This dense layout and varied equipment types lead to frequent interference between devices. For example, electromagnetic radiation interference generated by high-power equipment during operation can severely affect the data acquisition of surrounding equipment, resulting in distorted data acquisition results. However, existing workshop equipment data management methods typically only analyze the data acquisition of individual devices independently, ignoring the impact of mutual interference between devices on the completeness and accuracy of data acquisition. This causes the equipment data acquisition results to deviate from reality, thus failing to provide reliable support for equipment maintenance, fault diagnosis, and production scheduling. Summary of the Invention

[0004] To overcome the defects and shortcomings of existing technologies, this application provides a workshop equipment data management system and method based on MES. By correlating and analyzing abnormal data with equipment operation data, it can locate interfering equipment, significantly improving the reliability and accuracy of interfering equipment location and effectively ensuring the accuracy of equipment data collection.

[0005] To achieve the above objectives, this application adopts the following technical solution:

[0006] In a first aspect, this application provides a workshop equipment data management method based on MES, comprising the following steps:

[0007] Acquire device-collected data and historical data collected by the device; acquire device operation data.

[0008] Based on the historical data collected by the device, an anomaly assessment is performed on the data collected by the device.

[0009] An anomaly correlation analysis is performed based on the data anomaly assessment results and the equipment operation data;

[0010] Based on the results of abnormal correlation analysis, the interfering devices are located and device interference warnings are generated.

[0011] Optionally, the step of evaluating data anomalies in the device's collected data based on the device's historical collected data includes:

[0012] Acquire the data collected by the device and the historical data collected by the device to obtain the device operating cycle;

[0013] The mean and standard deviation of the historical data collected by the device in the sliding window are calculated using the historical data collected by the device, and the size of the sliding window is the operating cycle of the device.

[0014] The normal range for the device's collected data is set based on the mean and standard deviation of the device's historical collected data. The normal range for the device's collected data is: ,in, This represents the average of historical data collected by the device. This indicates the standard deviation of the historical data collected by the device. Indicates the sensitivity coefficient;

[0015] If the data collected by the device exceeds the normal range, the collected data will be marked as abnormal data and added to the abnormal data set.

[0016] Optionally, the anomaly correlation analysis based on the data anomaly assessment results and the equipment operation data includes:

[0017] Obtain the abnormal data set from the data anomaly assessment results and the equipment operation data, and extract the abnormal data from the abnormal data set and the corresponding abnormal data collection time. The equipment operation data includes the equipment operation range, equipment operation event time nodes, and equipment operation status.

[0018] By analyzing the correlation between the abnormal data collection time and the equipment operation interval, the first time correlation between the abnormal data and the equipment operation is obtained.

[0019] By analyzing the correlation between the abnormal data collection time and the time nodes of the equipment operation events, a second time correlation degree between the abnormal data and the equipment operation is obtained;

[0020] By performing correlation analysis on the abnormal data, the time of collection of the abnormal data, and the operating status of the equipment, the temporal periodic correlation degree between the abnormal data and the equipment operation is obtained.

[0021] The data anomaly correlation degree is calculated using the first time correlation degree, the second time correlation degree, and the time-series periodicity correlation degree. This data anomaly correlation degree is used to quantitatively analyze anomaly correlations. The formula for calculating the data anomaly correlation degree is as follows: ;

[0022] in, Indicates the degree of relevance at the first moment. Indicates the second time correlation. Indicates the degree of periodic correlation in time series. Indicates the weight of the first-time correlation. This indicates the weight of the second time-related correlation. Indicates the weight of time-series periodic correlation. This indicates the degree of abnormal correlation in the data.

[0023] Optionally, obtaining the correlation between the abnormal data and the device's operation at the first moment includes:

[0024] The abnormal data collection time and the device operating range are obtained, and the device operating range includes the device startup time and the device shutdown time.

[0025] The first time correlation degree is calculated based on the abnormal data collection time and the device operating range. The formula for calculating the first time correlation degree is as follows: ;

[0026] in, Indicates the first The device startup time for each device operating range Indicates the first Equipment downtime within each equipment operating range Indicates the number of equipment operating ranges. Indicates the first in the abnormal data set Abnormal data collection time, Indicates the number of outlier data. Indicates an indicator function, if Belongs to the Each device operating range, then ,like Not belonging to the first Each device operating range, then , This indicates the degree of relevance at the first moment.

[0027] Optionally, obtaining the second time correlation between the abnormal data and the device operation includes:

[0028] The abnormal data collection time and the device operation event time node are obtained, and the device operation event time node includes the device operation time, the device failure time, and the device failure recovery time.

[0029] The second time correlation degree is calculated based on the abnormal data acquisition time and the device operation event time node. The formula for calculating the second time correlation degree is as follows: ;

[0030] in, Indicates the first in the abnormal data set Abnormal data collection time, This indicates the time node of the first device operation event. This indicates the time point of the second device operation event. Indicates the first Each device operation event time node This indicates the number of time points in the device's operation. Indicates the number of outlier data. Describes the minimum value function. This indicates the second time correlation.

[0031] Optionally, obtaining the time-series periodic correlation between abnormal data and device operation includes:

[0032] Acquire the abnormal data, the time of abnormal data acquisition, and the device operating status, wherein the device operating status includes the device load status, the device voltage status, and the device current status;

[0033] The main frequency of the abnormal data is extracted by performing a discrete Fourier transform on the abnormal data and the abnormal data acquisition time.

[0034] By performing a discrete Fourier transform on the device's operating state and extracting the main frequencies of different types of operating states;

[0035] The time-series periodicity correlation degree is calculated using the main frequency of the abnormal data and the main frequency of the operating state. The formula for calculating the time-series periodicity correlation degree is as follows: ;

[0036] in, Indicates the main frequency of abnormal data. Indicates the first The main frequency of each type of running state This indicates the number of types of equipment operating status. It represents the degree of periodic correlation in time series.

[0037] Optionally, the step of locating interfering devices and generating device interference warnings based on the abnormal correlation analysis results includes:

[0038] Obtain the degree of abnormal data correlation in the abnormal correlation analysis results;

[0039] Devices whose data anomaly correlation degree is greater than a preset data anomaly correlation degree are marked as interfering devices and device interference warnings are generated.

[0040] It should be noted that the values ​​of the sensitivity coefficient, first time correlation weight, second time correlation weight, time-series periodic correlation weight, and preset data anomaly correlation threshold are determined as follows: 5000 sets of equipment acquisition data, historical equipment acquisition data, and equipment operation data are obtained. The effectiveness of identifying interfering equipment is differentiated to determine whether it meets production requirements. The equipment acquisition data, historical equipment acquisition data, and equipment operation data are substituted into the data anomaly correlation calculation formula for calculation. The calculated data anomaly correlation and differentiation results are simultaneously imported into the fitting software. The optimal sensitivity coefficient, first time correlation weight, second time correlation weight, time-series periodic correlation weight, and preset data anomaly correlation threshold that match the differentiation accuracy are output.

[0041] Secondly, this application provides a workshop equipment data management system based on MES, including:

[0042] The data acquisition module is used to acquire data collected by the device, historical data collected by the device, and data on device operation.

[0043] The data anomaly assessment module is used to assess data anomalies in the data collected by the device based on the device's historical data.

[0044] An anomaly correlation analysis module is used to perform anomaly correlation analysis based on the data anomaly assessment results and the equipment operation data;

[0045] The operation status adjustment module is used to locate interfering devices and generate device interference warnings based on the results of abnormal correlation analysis.

[0046] Thirdly, this application provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a workshop equipment data management method based on MES by calling the computer program stored in the memory.

[0047] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a workshop equipment data management method based on MES.

[0048] Compared with the prior art, this application has the following advantages and beneficial effects:

[0049] This application first assesses the data anomalies in the equipment's historical data collection, then performs anomaly correlation analysis by combining the data anomaly assessment results with the equipment's operational data to evaluate the relationship between data anomalies and equipment operation. Finally, it locates the interfering equipment by analyzing the anomaly correlation analysis results, significantly improving the reliability and accuracy of interfering equipment location, thereby effectively ensuring the accuracy of equipment data collection. Attached Figure Description

[0050] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0051] Figure 1 This is a schematic diagram of the overall process of a workshop equipment data management method based on MES provided in an embodiment of this application;

[0052] Figure 2 This is a schematic diagram of the abnormal correlation analysis process in a workshop equipment data management method based on MES provided in an embodiment of this application;

[0053] Figure 3 This is a schematic diagram of the structure of a workshop equipment data management system based on MES provided in an embodiment of this application;

[0054] Figure 4 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0055] The technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments and specific features in the embodiments are detailed descriptions of the technical solution of this application, rather than limitations thereof. In the absence of conflict, the embodiments and technical features in the embodiments can be combined with each other.

[0056] Please see Figure 1 , Figure 1 This is a schematic diagram of the overall process of a workshop equipment data management method based on MES provided in an embodiment of this application, which specifically includes the following steps:

[0057] S110: Acquire device-collected data and historical device-collected data, and acquire device operation data.

[0058] S120: Perform data anomaly assessment on the equipment's collected data based on the equipment's historical collected data;

[0059] By statistically analyzing historical data collected from equipment, typical performance during normal operation can be effectively identified, thus establishing a normal range for the collected data. Simultaneously, the mean and standard deviation of historical data are calculated using a sliding window technique, making anomaly assessment more flexible and adaptable to dynamic changes in equipment operation. Furthermore, a sensitivity coefficient is introduced to adjust the leniency of the normal range to accommodate the specific requirements of different equipment. The specific steps for anomaly assessment of equipment collected data include:

[0060] Acquire device-collected data and historical data, and obtain the device's operating cycle;

[0061] The mean and standard deviation of the historical data collected by the equipment are calculated in the sliding window, and the size of the sliding window is the equipment operating cycle.

[0062] The normal range for equipment data collection is defined based on the mean and standard deviation of historical data collected by the equipment. The normal range for equipment data collection is: ,in, This represents the average of historical data collected by the device. This indicates the standard deviation of the historical data collected by the device. Indicates the sensitivity coefficient;

[0063] If the data collected by the device exceeds the normal range, the collected data will be marked as abnormal data and added to the abnormal data set.

[0064] S130: Conduct anomaly correlation analysis based on data anomaly assessment results and equipment operation data;

[0065] Please see Figure 2 , Figure 2 This is a flowchart illustrating the anomaly correlation analysis in a workshop equipment data management method based on MES provided in this application embodiment, including:

[0066] Obtain the abnormal data set and equipment operation data from the data anomaly assessment results, and extract the abnormal data and corresponding abnormal data collection time from the abnormal data set. The equipment operation data includes the equipment operation range, equipment operation event time nodes, and equipment operation status.

[0067] By analyzing the correlation between the time of abnormal data collection and the equipment operating range, the first-time correlation between abnormal data and equipment operation is obtained.

[0068] By analyzing the correlation between the time of abnormal data collection and the time nodes of equipment operation events, a second time correlation degree between abnormal data and equipment operation is obtained;

[0069] By performing correlation analysis on abnormal data, the time of abnormal data collection, and the operating status of equipment, the temporal periodic correlation degree between abnormal data and equipment operation is obtained.

[0070] The data anomaly correlation degree is calculated using the first-time correlation degree, the second-time correlation degree, and the time-series periodicity correlation degree. This anomaly correlation degree is used to quantitatively analyze anomaly correlations. The formula for calculating the data anomaly correlation degree is as follows: ;

[0071] in, Indicates the degree of relevance at the first moment. Indicates the second time correlation. Indicates the degree of periodic correlation in time series. Indicates the weight of the first-time correlation. This indicates the weight of the second time-related degree. Indicates the weight of time-series periodic correlation. This indicates the degree of abnormal correlation in the data.

[0072] By comparing the collection time of abnormal data with the operating range of equipment in the workshop, it is possible to effectively distinguish the operating status of different equipment when data collection anomalies occur, thereby revealing the mutual influence between equipment. Furthermore, by determining when specific equipment might interfere with data collection, the interference patterns and causal relationships between different equipment can be further understood. The specific steps to obtain the first-time correlation between abnormal data and equipment operation include:

[0073] Obtain the abnormal data collection time and the equipment operating range, which includes the equipment startup time and equipment downtime.

[0074] The first-time correlation degree is calculated based on the abnormal data collection time and the equipment operating range. The formula for calculating the first-time correlation degree is: ;

[0075] in, Indicates the first The device startup time for each device operating range Indicates the first Equipment downtime for each equipment operating range Indicates the number of equipment operating ranges. Indicates the first in the abnormal data set Abnormal data collection time, Indicates the number of outlier data. Indicates an indicator function, if Belongs to the Each device operating range, then ,like Not belonging to the first Each device operating range, then , This indicates the degree of relevance at the first moment.

[0076] By analyzing the differences between the data collection time and equipment operation time, fault occurrence time, and fault recovery time of abnormal data, the correlation between the data collection anomaly occurrence time and the time nodes of equipment operation events can be revealed. This allows for the identification of the impact of different equipment operations or faults on data collection, thereby clarifying the mutual influence relationships between equipment. The specific steps to obtain the second time correlation between abnormal data and equipment operation include:

[0077] Obtain the abnormal data collection time and the equipment operation event time nodes. The equipment operation event time nodes include the equipment operation time, the equipment failure time, and the equipment failure recovery time.

[0078] The second time correlation is calculated based on the abnormal data collection time and the device operation event time node. The formula for calculating the second time correlation is: ;

[0079] in, Indicates the first in the abnormal data set Abnormal data collection time, This indicates the time node of the first device operation event. This indicates the time point of the second device operation event. Indicates the first Each device operation event time node This indicates the number of time points in the device's operation. Indicates the number of outlier data. Describes the minimum value function. This indicates the second time correlation.

[0080] Regular fluctuations during equipment operation typically refer to the periodic fluctuations in various operating states that occur during normal operation. For example, equipment may have different load demands at different times or under different operating modes, leading to periodic fluctuations in equipment load. Additionally, during equipment startup, shutdown, or load changes, the equipment voltage or current may suddenly rise or fall, resulting in instantaneous fluctuations. These fluctuations are normal phenomena within a certain range of equipment operation, but they may also become potential causes or interference factors for data acquisition anomalies. The specific steps for obtaining the time-series periodic correlation between abnormal data and equipment operation include:

[0081] Acquire abnormal data, abnormal data acquisition time, and equipment operating status. Equipment operating status includes equipment load status, equipment voltage status, and equipment current status.

[0082] The main frequency of the abnormal data was extracted by performing a discrete Fourier transform on the abnormal data and the abnormal data acquisition time.

[0083] By performing a discrete Fourier transform on the equipment's operating status and extracting the main frequencies of different types of operating status;

[0084] The time-series periodicity correlation degree is calculated using the main frequency of abnormal data and the main frequency of operating status. The formula for calculating the time-series periodicity correlation degree is as follows: ;

[0085] in, Indicates the main frequency of abnormal data. Indicates the first The main frequency of each type of running state This indicates the number of types of equipment operating status. It represents the degree of periodic correlation in time series.

[0086] S140: Locate interfering devices and generate device interference warnings based on the results of abnormal correlation analysis;

[0087] By identifying interfering devices that negatively impact equipment data acquisition, managers can effectively improve the timeliness and accuracy of locating these devices. Furthermore, by implementing specific anti-interference measures, the occurrence of equipment interference can be reduced. The specific steps for locating interfering devices and generating equipment interference warnings include:

[0088] Obtain the degree of abnormal correlation in the data from the abnormal correlation analysis results;

[0089] Devices with an abnormal data correlation greater than a preset abnormal data correlation threshold are marked as interfering devices and an interfering device warning is generated. The interfering device warning is used to remind managers to install anti-interference shielding facilities on the interfering devices to avoid continuous output of interfering devices.

[0090] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of a workshop equipment data management system 200 based on MES provided in an embodiment of this application, including:

[0091] Data acquisition module 210 is used to acquire device-collected data and historical data collected by the device, and to acquire device operation data;

[0092] The data anomaly assessment module 220 is used to assess data anomalies in the data collected by the device based on the device's historical data.

[0093] The anomaly correlation analysis module 230 is used to perform anomaly correlation analysis based on data anomaly assessment results and equipment operation data;

[0094] The operation status adjustment module 240 is used to locate interfering devices and generate device interference warnings based on the abnormal correlation analysis results.

[0095] In this embodiment, the data anomaly assessment module 220 is used to assess data anomalies in the device's collected data based on the device's historical collected data, including:

[0096] Acquire device-collected data and historical data, and obtain the device's operating cycle;

[0097] The mean and standard deviation of the historical data collected by the equipment are calculated in the sliding window, and the size of the sliding window is the equipment operating cycle.

[0098] The normal range for equipment data collection is defined based on the mean and standard deviation of historical data collected by the equipment. The normal range for equipment data collection is: ,in, This represents the average of historical data collected by the device. This indicates the standard deviation of the historical data collected by the device. Indicates the sensitivity coefficient;

[0099] If the data collected by the device exceeds the normal range, the collected data will be marked as abnormal data and added to the abnormal data set.

[0100] In this embodiment, the anomaly correlation analysis module 230 is used to perform anomaly correlation analysis based on data anomaly assessment results and equipment operation data, including:

[0101] Obtain the abnormal data set and equipment operation data from the data anomaly assessment results, and extract the abnormal data and corresponding abnormal data collection time from the abnormal data set. The equipment operation data includes the equipment operation range, equipment operation event time nodes, and equipment operation status.

[0102] By analyzing the correlation between the time of abnormal data collection and the equipment operating range, the first-time correlation between abnormal data and equipment operation is obtained.

[0103] By analyzing the correlation between the time of abnormal data collection and the time nodes of equipment operation events, a second time correlation degree between abnormal data and equipment operation is obtained;

[0104] By performing correlation analysis on abnormal data, the time of abnormal data collection, and the operating status of equipment, the temporal periodic correlation degree between abnormal data and equipment operation is obtained.

[0105] The data anomaly correlation degree is calculated using the first-time correlation degree, the second-time correlation degree, and the time-series periodicity correlation degree. This anomaly correlation degree is used to quantitatively analyze anomaly correlations. The formula for calculating the data anomaly correlation degree is as follows: ;

[0106] in, Indicates the degree of relevance at the first moment. Indicates the second time correlation. Indicates the degree of periodic correlation in time series. Indicates the weight of the first-time correlation. This indicates the weight of the second time-related degree. Indicates the weight of time-series periodic correlation. This indicates the degree of abnormal correlation in the data.

[0107] Obtain the immediate correlation between abnormal data and device operation, including:

[0108] Obtain the abnormal data collection time and the equipment operating range, which includes the equipment startup time and equipment downtime.

[0109] The first-time correlation degree is calculated based on the abnormal data collection time and the equipment operating range. The formula for calculating the first-time correlation degree is: ;

[0110] in, Indicates the first The device startup time for each device operating range Indicates the first Equipment downtime for each equipment operating range Indicates the number of equipment operating ranges. Indicates the first in the abnormal data set Abnormal data collection time, Indicates the number of outlier data. Indicates an indicator function, if Belongs to the Each device operating range, then ,like Not belonging to the first Each device operating range, then , This indicates the degree of relevance at the first moment.

[0111] Obtain the second time correlation between abnormal data and device operation, including:

[0112] Obtain the abnormal data collection time and the equipment operation event time nodes. The equipment operation event time nodes include the equipment operation time, the equipment failure time, and the equipment failure recovery time.

[0113] The second time correlation is calculated based on the abnormal data collection time and the device operation event time node. The formula for calculating the second time correlation is: ;

[0114] in, Indicates the first in the abnormal data set Abnormal data collection time, This indicates the time node of the first device operation event. This indicates the time point of the second device operation event. Indicates the first Each device operation event time node This indicates the number of time points in the device's operation. Indicates the number of outlier data. Describes the minimum value function. This indicates the second time correlation.

[0115] Obtain the time-series periodic correlation between abnormal data and equipment operation, including:

[0116] Acquire abnormal data, abnormal data acquisition time, and equipment operating status. Equipment operating status includes equipment load status, equipment voltage status, and equipment current status.

[0117] The main frequency of the abnormal data was extracted by performing a discrete Fourier transform on the abnormal data and the abnormal data acquisition time.

[0118] By performing a discrete Fourier transform on the equipment's operating status and extracting the main frequencies of different types of operating status;

[0119] The time-series periodicity correlation degree is calculated using the main frequency of abnormal data and the main frequency of operating status. The formula for calculating the time-series periodicity correlation degree is as follows: ;

[0120] in, Indicates the main frequency of abnormal data. Indicates the first The main frequency of each type of running state This indicates the number of types of equipment operating status. It represents the degree of periodic correlation in time series.

[0121] In this embodiment, the operation status adjustment module 240 is used to locate interfering devices and generate device interference warnings based on the abnormal correlation analysis results, including:

[0122] Obtain the degree of abnormal correlation in the data from the abnormal correlation analysis results;

[0123] Devices with an abnormal data correlation greater than a preset abnormal data correlation threshold are marked as interfering devices and device interference warnings are generated.

[0124] The parameters and steps for implementing the corresponding functions of each unit module in the MES-based workshop equipment data management system of this application can be referred to the parameters and steps in the above embodiment of the MES-based workshop equipment data management method, and will not be repeated here.

[0125] like Figure 4 As shown, embodiments of the present invention also provide an electronic device 300, including a memory 310, a processor 320, and a communication bus 330; the memory 310 and the processor 320 are connected via the communication bus 330. The memory 310 stores a workshop equipment data management method based on MES, which can be loaded and executed by the processor 320 as provided in the above embodiments.

[0126] The memory 310 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 310 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the MES-based workshop equipment data management method provided in the above embodiments. The data storage area may store data involved in the MES-based workshop equipment data management method provided in the above embodiments.

[0127] Processor 320 may include one or more processing cores. Processor 320 executes instructions, programs, code sets, or instruction sets stored in memory 310, and calls data stored in memory 310 to perform various functions and process data as described in this application. Processor 320 may be at least one of the following: Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), Central Processing Unit (CPU), Controller, Microcontroller, and Microprocessor. It is understood that, for different devices, the electronic devices used to implement the functions of processor 320 may also be other types, and this application embodiment does not specifically limit the specific devices used.

[0128] The communication bus 330 may include a path for transmitting information between the aforementioned components. The communication bus 330 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 330 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 The symbol is represented by a single double arrow, but this does not mean that there is only one bus or one type of bus.

[0129] This application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as described in the above embodiments, which is a workshop equipment data management method based on MES.

[0130] In this embodiment, the computer-readable storage medium can be a tangible device that holds and stores instructions used by an instruction execution device. The computer-readable storage medium can be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, the computer-readable storage medium can be a portable computer disk, a hard disk, a USB flash drive, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), staging random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory stick, floppy disk, optical disk, magnetic disk, mechanical encoding device, or any combination thereof.

[0131] 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 process, method, article, or apparatus.

[0132] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing application concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. A method for managing workshop equipment data based on MES, characterized in that, Includes the following steps: Acquire device-collected data and historical data collected by the device; acquire device operation data. Based on the historical data collected by the device, an anomaly assessment is performed on the data collected by the device. An anomaly correlation analysis is performed based on the data anomaly assessment results and the equipment operation data; Based on the results of abnormal correlation analysis, locate interfering devices and generate device interference warnings; The anomaly correlation analysis based on the data anomaly assessment results and the equipment operation data includes: Obtain the abnormal data set from the data anomaly assessment results and the equipment operation data, and extract the abnormal data from the abnormal data set and the corresponding abnormal data collection time. The equipment operation data includes the equipment operation range, equipment operation event time nodes, and equipment operation status. By analyzing the correlation between the abnormal data collection time and the equipment operation interval, the first time correlation between the abnormal data and the equipment operation is obtained. By analyzing the correlation between the abnormal data collection time and the time nodes of the equipment operation events, a second time correlation degree between the abnormal data and the equipment operation is obtained; By performing correlation analysis on the abnormal data, the time of collection of the abnormal data, and the operating status of the equipment, the temporal periodic correlation degree between the abnormal data and the equipment operation is obtained. The data anomaly correlation degree is calculated using the first time correlation degree, the second time correlation degree, and the time-series periodicity correlation degree. This data anomaly correlation degree is used to quantitatively analyze anomaly correlations. The formula for calculating the data anomaly correlation degree is as follows: ； in, Indicates the degree of relevance at the first moment. Indicates the second time correlation. Indicates the degree of periodic correlation in time series. Indicates the weight of the first-time correlation. This indicates the weight of the second time-related degree. Indicates the weight of time-series periodic correlation. This indicates the degree of abnormal correlation in the data.

2. The workshop equipment data management method based on MES according to claim 1, characterized in that, The process of evaluating data anomalies in the device's collected data based on the device's historical collected data includes: Acquire the data collected by the device and the historical data collected by the device to obtain the device operating cycle; The mean and standard deviation of the historical data collected by the device in the sliding window are calculated using the historical data collected by the device, and the size of the sliding window is the operating cycle of the device. The normal range for the device's collected data is set based on the mean and standard deviation of the device's historical collected data. The normal range for the device's collected data is: ,in, This represents the average of historical data collected by the device. This indicates the standard deviation of the historical data collected by the device. Indicates the sensitivity coefficient; If the data collected by the device exceeds the normal range, the collected data will be marked as abnormal data and added to the abnormal data set.

3. The workshop equipment data management method based on MES according to claim 1, characterized in that, The correlation between the obtained abnormal data and the device's operation at the first moment includes: The abnormal data collection time and the device operating range are obtained, and the device operating range includes the device startup time and the device shutdown time. The first time correlation degree is calculated based on the abnormal data collection time and the device operating range. The formula for calculating the first time correlation degree is as follows: ； in, Indicates the first The device startup time for each device operating range Indicates the first Equipment downtime for each equipment operating range Indicates the number of equipment operating ranges. Indicates the first in the abnormal data set Abnormal data collection time, Indicates the number of outlier data. Indicates an indicator function, if Belongs to the Each device operating range, then ,like Not belonging to the first Each device operating range, then , This indicates the degree of relevance at the first moment.

4. The workshop equipment data management method based on MES according to claim 1, characterized in that, The obtained second time correlation between abnormal data and device operation includes: The abnormal data collection time and the device operation event time node are obtained, and the device operation event time node includes the device operation time, the device failure time, and the device failure recovery time. The second time correlation degree is calculated based on the abnormal data acquisition time and the device operation event time node. The formula for calculating the second time correlation degree is as follows: ； in, Indicates the first in the abnormal data set Abnormal data collection time, This indicates the time node of the first device operation event. This indicates the time point of the second device operation event. Indicates the first Each device operation event time node This indicates the number of time points in the device's operation. Indicates the number of outlier data. Describes the minimum value function. This indicates the second time correlation.

5. The workshop equipment data management method based on MES according to claim 1, characterized in that, The method of obtaining the time-series periodic correlation between abnormal data and device operation includes: Acquire the abnormal data, the time of abnormal data acquisition, and the device operating status, wherein the device operating status includes the device load status, the device voltage status, and the device current status; The main frequency of the abnormal data is extracted by performing a discrete Fourier transform on the abnormal data and the abnormal data acquisition time. By performing a discrete Fourier transform on the device's operating state and extracting the main frequencies of different types of operating states; The time-series periodicity correlation degree is calculated using the main frequency of the abnormal data and the main frequency of the operating state. The formula for calculating the time-series periodicity correlation degree is as follows: ； in, Indicates the main frequency of abnormal data. Indicates the first The main frequency of each type of running state Indicates the number of types of equipment operating status. It represents the degree of periodic correlation in time series.

6. The workshop equipment data management method based on MES according to claim 1, characterized in that, The step of locating interfering devices and generating device interference warnings based on abnormal correlation analysis results includes: Obtain the degree of abnormal data correlation in the abnormal correlation analysis results; Devices whose data anomaly correlation degree is greater than a preset data anomaly correlation degree are marked as interfering devices and device interference warnings are generated.

7. A workshop equipment data management system based on MES, applied to the workshop equipment data management method based on MES according to any one of claims 1-6, characterized in that, The system includes: The data acquisition module is used to acquire data collected by the device, historical data collected by the device, and data on device operation. The data anomaly assessment module is used to assess data anomalies in the data collected by the device based on the device's historical data. An anomaly correlation analysis module is used to perform anomaly correlation analysis based on the data anomaly assessment results and the equipment operation data; The operation status adjustment module is used to locate interfering devices and generate device interference warnings based on the results of abnormal correlation analysis.

8. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes a workshop equipment data management method based on MES as described in any one of claims 1-6 by calling the computer program stored in the memory.

9. A computer-readable storage medium, characterized in that, The system stores instructions that, when executed on a computer, cause the computer to perform a workshop equipment data management method based on MES as described in any one of claims 1-6.

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