Production execution MES system

Abstract

The invention discloses a production execution MES system, and particularly relates to the technical field of MES systems, which comprises the steps of collecting energy consumption information, historical information and operation information generated by production equipment in the production process of a product, obtaining an energy consumption change condition of the production equipment according to the energy consumption information generated by the production equipment, and calculating the energy consumption change condition of the production equipment; according to historical information of energy consumption information generated by production equipment, a clustering algorithm is used for identifying potential risks of the production equipment, key features of replaceable equipment are extracted, a fuzzy rule base is constructed, defuzzification processing is carried out, operation scores of the production equipment are obtained, and production process information and production quality information are collected. The matching degree of products in the production process is quantified, the genetic algorithm is used, the optimal manufacturing path of the products in the MES system is obtained, clear and intelligent decision support is provided for selectively and preferentially shutting down the equipment poor when multiple pieces of production equipment work at the same time, and the fault risk of production is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of MES systems, and more specifically, to a production execution MES system. Background Art

[0002] In the prior art, a multi-factory capacity coordination method based on the MES system (CN116976653B) mentions the process of factory processing parts and the steps of part transfer between factories. However, in the manufacturing execution system (MES), it is usually faced with the problem of how to select the most suitable equipment among multiple identical production equipment to optimize production efficiency and reduce costs. Traditional selection methods may rely on fixed rules or simple priority sorting, but these methods may not be flexible enough in the face of complex production environments and are difficult to adapt to changes in production conditions. Among them, the synergy between production equipment may be ignored and the working status of the current production equipment cannot be more comprehensively evaluated.

[0003] In order to solve the above defects, a technical solution is now provided. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a production execution MES system to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: A production execution MES system, specifically comprising a data acquisition module, a clustering module, a fuzzy logic module, a collaborative evaluation module and a path selection module, and signal connections between the modules; The data collection module is used to collect energy consumption information, historical information and operation information generated by production equipment during the production process of the product by arranging edge devices; The data analysis module is used to obtain the energy consumption changes of the production equipment based on the energy consumption information generated by the production equipment, and to identify the potential risks of the production equipment using a clustering algorithm based on the historical information of the energy consumption information generated by the production equipment; The fuzzy logic module is used to extract the key features of alternative equipment based on the energy consumption information, historical information and operation information generated by the production equipment, and obtain the operation score of the production equipment by building a fuzzy rule base and performing defuzzification processing; Collaborative evaluation module, which is used to collect production process information and production quality information, and conduct comprehensive analysis on the collected production process information and production quality information to quantify the degree of coordination of products in the production process; The path selection module is used to obtain the optimal manufacturing path of the product in the MES system by setting the fitness function of the genetic algorithm and using the genetic algorithm.

[0006] In a preferred embodiment, obtaining the energy consumption change of the production equipment according to the energy consumption information generated by the production equipment includes: Mark multiple production equipment that meet a certain production step at the same time as replaceable equipment. Obtain the energy consumption variation coefficient through the energy consumption information of the replaceable equipment. The logic for obtaining the energy consumption variation coefficient is: set the monitoring interval, obtain the energy consumption data of the replaceable equipment in the monitoring interval through the edge device, and mark the energy consumption data of the replaceable equipment in the monitoring interval as: , where n=1, 2, 3, ..., N, N is a positive integer, and n is the number of the energy consumption data of the replaceable device sampled by the edge device within the monitoring interval; Calculate the mean and standard deviation of the energy consumption data of the replaceable equipment within the monitoring interval, and mark the mean and standard deviation of the energy consumption data of the replaceable equipment within the monitoring interval as: and ,in, , ; Calculate the coefficient of variation of the energy consumption data of alternative equipment within the monitoring interval. The calculation formula is: ,in, is the coefficient of variation of energy consumption data of alternative equipment within the monitoring interval; Calculate the energy consumption variation coefficient of replaceable equipment within the monitoring interval. The calculation formula is: ,in, is the energy consumption variation coefficient.

[0007] In a preferred embodiment, based on the historical information of energy consumption information generated by the production equipment, a clustering algorithm is used to identify potential risks of the production equipment, including: The risk concealment coefficient is obtained through the historical information of replaceable equipment. The logic of obtaining the risk concealment coefficient is as follows: set the unit time, collect the fault data of the replaceable equipment within the unit time, use the time when the replaceable equipment fault occurs as a timestamp, and convert the timestamp of the fault into a relative time format, starting from the time when the first fault occurs, so that the fault time point is represented as a continuous time series; By setting The radius and the minimum number of points are used to cluster the fault points of the replaceable equipment within a unit time using the DBSCAN clustering algorithm. The fault points that are close to each other in time are assigned to the same cluster. The average time of all fault points in the cluster is calculated and used as the cluster center. The timestamp of the cluster center of the replaceable equipment within a unit time is marked as: ; Timestamp the current time as: , the time-anonymous value is obtained by the difference between the timestamp of the current time and the timestamp of the cluster center of the replaceable device in unit time, and the time-anonymous value is marked as: ,in, ; Obtain the number of failures that occur in the replaceable equipment per unit time, and mark the number of failures that occur in the replaceable equipment per unit time as: C, and calculate the risk concealment coefficient. The calculation formula is: ,in, is the risk concealment coefficient.

[0008] In a preferred embodiment, obtaining the operation score of the production equipment includes: The energy consumption variation coefficient, the risk concealment coefficient and the operation status data are used as the key features of the alternative equipment, wherein the operation status data includes the current operation temperature of the alternative equipment and the current operation vibration amplitude of the alternative equipment. The energy consumption variation coefficient, the risk concealment coefficient, the current operation temperature of the alternative equipment and the current operation vibration amplitude of the alternative equipment are converted into fuzzy variables, and fuzzy sets and their membership functions are defined to construct a fuzzy rule base, and the fuzzy rules are applied to the key features of the input alternative equipment. After defuzzification, the fuzzy output of the alternative equipment is obtained, and the fuzzy output of the alternative equipment is used as the standard for evaluating the operation status of the alternative equipment, wherein the operation score of the alternative equipment is used as the fuzzy output of the alternative equipment; The fuzzy outputs of alternative equipment are classified into best, suitable, and poor. The alternative equipment with the best fuzzy output is given priority, followed by the alternative equipment with the suitable fuzzy output. If the fuzzy outputs of the alternative equipment are all poor, production is stopped and the alternative production equipment is repaired and inspected. If the fuzzy outputs of the alternative equipment are the same, the operation score of the alternative equipment is obtained through defuzzification processing. By comparing the operation scores of the alternative equipment, the alternative equipment with a higher operation score is retained for production.

[0009] In a preferred embodiment, collecting production process information includes: The production process information is represented by the qualified deviation coefficient. The logic of obtaining the synergy performance coefficient is as follows: obtain the changeover time between production equipment and mark the changeover time between production equipment as: , where i=1, 2, 3, ..., I, I is a positive integer, and i is the number of the product that needs to be replaced each time; , where i=1, 2, 3, ..., I, I is a positive integer, and i is the number of the product that needs to be replaced each time; Get the speed at which the production equipment completes the production of the product, and mark the speed at which the production equipment completes the production of the product as: ; Set the standard value of the changeover time between production equipment and the standard value of the speed at which product production is completed between production equipment, and mark the standard value of the changeover time between production equipment and the standard value of the speed at which product production is completed between production equipment as: and ; Calculate the synergy performance coefficient. The calculation formula of the synergy performance coefficient is: ,in, is the synergy performance coefficient.

[0010] In a preferred embodiment, collecting production quality information includes: The production quality information is represented by the qualified deviation coefficient. The logic of obtaining the qualified deviation coefficient is: according to the historical records of different production equipment, the qualified rate of the products produced by different production equipment is obtained, and the qualified rate of the products produced by different production equipment is marked as: , set the standard value of the qualified rate of products produced by the production equipment, and mark the standard value of the qualified rate of products produced by the production equipment as: , calculate the qualified deviation coefficient, the calculation formula is: ,in, is the qualified deviation coefficient.

[0011] In a preferred embodiment, setting the fitness function of the genetic algorithm includes: The synergy performance coefficient and the qualified deviation coefficient are weighted to obtain the synergy evaluation coefficient. The calculation formula of the synergy evaluation coefficient is: ,in, is the synergy evaluation coefficient, , is the proportional coefficient of the synergistic performance coefficient and the qualified deviation coefficient, , They are both greater than 0; Set the fitness function of the genetic algorithm and mark the fitness function of the genetic algorithm as: , where q=1, 2, 3, ..., Q, Q is a positive integer, and q is the serial number of the replaceable device. is the adjustment factor to ensure that the denominator of the fitness function is not zero.

[0012] Technical effects and advantages of the present invention: The present invention mines the potential operating risks of production equipment by analyzing the energy consumption information, historical information and operating information of production equipment, and evaluates the potential hidden dangers of production equipment by using the DBSCAN clustering algorithm. By building a fuzzy rule base and using fuzzy logic to link the energy consumption information, historical information and operating information with the current operating status of production equipment, the present invention helps to provide clear and intelligent decision support for selectively shutting down the poorer equipment in priority when multiple production equipment are working at the same time, and helps to optimize resource allocation, improve production efficiency, reduce operating costs, and reduce the risk of production failures. The present invention solves the selection problem of multiple identical production equipment in the MES system by using a genetic algorithm, and in the process of selecting the optimal manufacturing path, gives priority to the coordination between equipment and the current operating status of the equipment, which helps to improve production efficiency and reduce operating costs, and realizes intelligent selection of production equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings; Figure 1 It is a structural schematic diagram of a production execution MES system of the present invention. DETAILED DESCRIPTION

[0014] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0015] Example 1

[0016] Figure 1 A structural diagram of a production execution MES system is given, which includes data acquisition module, clustering module, fuzzy logic module, collaborative evaluation module and path selection module, and signal connections between modules; The data collection module is used to collect energy consumption information, historical information and operation information generated by production equipment during the production process of the product by arranging edge devices; The data analysis module is used to obtain the energy consumption changes of the production equipment based on the energy consumption information generated by the production equipment, and to identify the potential risks of the production equipment using a clustering algorithm based on the historical information of the energy consumption information generated by the production equipment; The fuzzy logic module is used to extract the key features of alternative equipment based on the energy consumption information, historical information and operation information generated by the production equipment, and obtain the operation score of the production equipment by building a fuzzy rule base and performing defuzzification processing; Collaborative evaluation module, which is used to collect production process information and production quality information, and conduct comprehensive analysis on the collected production process information and production quality information to quantify the degree of coordination of products in the production process; The path selection module is used to obtain the optimal manufacturing path of the product in the MES system by setting the fitness function of the genetic algorithm and using the genetic algorithm.

[0017] A production execution system includes multiple production equipment and supports the processing of multiple products. Each product has its own processing flow. When the production speed of the product is not the primary consideration, it is usually necessary to use fewer production equipment to meet production needs. The MES system arranges and dispatches appropriate production equipment to produce the product, ensuring the redundancy of the production equipment and optimizing the production process.

[0018] According to the production requirements of the product and the equipment library information, the MES system matches each production step with the equipment in the equipment library to determine the equipment required for each step. When there are multiple production equipment in the equipment library that simultaneously meet a certain production step, the multiple production equipment that simultaneously meet a certain production step will be marked as alternative equipment.

[0019] Edge devices are arranged at the replaceable devices, and the operating status of the replaceable devices is monitored by using sensors, data collectors, edge computing devices, etc., and the recent operating data of the replaceable devices is saved as the historical operating records of the monitoring devices.

[0020] According to the historical operation records of replaceable equipment, a fuzzy rule base is constructed. The historical operation records of replaceable equipment are helpful to comprehensively evaluate the status and performance of the equipment. Among them, the historical operation records of replaceable equipment are of the following types: Energy consumption information: The amount of energy consumed by the equipment over a period of time, such as electricity and water. Higher energy consumption may indicate that the equipment is operating inefficiently or is wasting energy. When energy consumption increases or decreases over time, it reveals changes in equipment performance and potential problems. Historical information: the total operating time of the equipment since it was put into use. A longer cumulative operating time may mean that the equipment is at risk of wear or aging. The number of failures that occurred during operation. A high failure rate may indicate that the equipment has low reliability. Operation information: Real-time recording of the equipment's operating status obtained through sensors and other monitoring devices during operation, including the equipment's operating temperature, vibration amplitude, etc.

[0021] By analyzing the historical operation records of the replaceable equipment, the key features of the replaceable equipment are extracted. The key features of the replaceable equipment can reflect the current health status of the equipment. Based on the key features of the replaceable equipment, the corresponding fuzzy variables are defined. Each fuzzy variable uses different language terms to describe the different degrees of the status. Among them, the energy consumption variation coefficient is obtained through the energy consumption information of the replaceable equipment, the risk concealment coefficient is obtained through the historical information of the replaceable equipment, and the real-time operation status data of the replaceable equipment is obtained through the operation information of the replaceable equipment. The energy consumption variation coefficient and the risk concealment coefficient are marked as: and , and takes the energy consumption variation coefficient, risk concealment coefficient and operation status data as the key characteristics of alternative equipment.

[0022] The logic for obtaining the energy consumption variation coefficient is as follows: set a monitoring interval, obtain the energy consumption data of replaceable equipment within the monitoring interval through the edge device, and mark the energy consumption data of replaceable equipment within the monitoring interval as: , where n=1, 2, 3, ..., N, N is a positive integer, and n is the number of the energy consumption data of the replaceable device sampled by the edge device within the monitoring interval; It should be noted that the energy consumption data of alternative equipment is usually expressed through the power consumption data. When the energy consumption of alternative equipment changes, it means that the equipment may be idling, inefficient or have hidden faults. The monitoring interval represents a specific time period. The monitoring period can reflect the energy consumption of the alternative equipment during operation. The monitoring interval is set by professional staff.

[0023] Calculate the mean and standard deviation of the energy consumption data of the replaceable equipment within the monitoring interval, and mark the mean and standard deviation of the energy consumption data of the replaceable equipment within the monitoring interval as: and ,in, , ; Calculate the coefficient of variation of the energy consumption data of alternative equipment within the monitoring interval. The calculation formula is: ,in, is the coefficient of variation of energy consumption data of alternative equipment within the monitoring interval; Calculate the energy consumption variation coefficient of replaceable equipment within the monitoring interval. The calculation formula is: ,in, is the energy consumption variation coefficient.

[0024] It can be seen from the formula that the larger the energy consumption variation coefficient is, the more unstable the equipment operation is, which may indicate that the equipment has potential health problems or potential failures, such as: Wear or aging: As equipment ages or parts wear out, equipment operating efficiency decreases and energy consumption becomes unstable; Precursor of failure: Abnormal fluctuations in energy consumption may be a precursor to equipment failure, such as motor overheating, bearing damage, or transmission system failure.

[0025] The logic for obtaining the risk concealment coefficient is as follows: set a unit time, collect the failure data of replaceable equipment within the unit time, use the time when the failure of the replaceable equipment occurs as a timestamp, and convert the timestamp of the failure into a relative time format, such as in minutes or hours, starting from the time when the first failure occurs, so that the failure time point is represented as a continuous time series; By setting The radius and the minimum number of points are used to cluster the fault points of the replaceable equipment within a unit time using the DBSCAN clustering algorithm. The fault points that are close to each other in time are assigned to the same cluster. The average time of all fault points in the cluster is calculated and used as the cluster center. The timestamp of the cluster center of the replaceable equipment within a unit time is marked as: ; It should be noted that the unit time is a specific length of time. Usually, the unit time is a longer period of time, which is used to record the failures of replaceable equipment within the unit time. The unit time is recorded by professional staff and equipment. The radius and the minimum number of points are set by professional staff based on actual conditions and will not be elaborated here. The cluster center reflects the historical high-incidence period of failure of replaceable equipment.

[0026] Timestamp the current time as: , the time-anonymous value is obtained by the difference between the timestamp of the current time and the timestamp of the cluster center of the replaceable device in unit time, and the time-anonymous value is marked as: ,in, ; It should be noted that the time-hidden value reflects that the replaceable device has a higher probability of failure in the next work, which means that the failures recently experienced by the device are concentrated in a shorter time period. The closer the current time is to the time period of frequent failures, the higher the possibility of failure of the replaceable device may be.

[0027] Obtain the number of failures that occur in the replaceable equipment per unit time, and mark the number of failures that occur in the replaceable equipment per unit time as: C, and calculate the risk concealment coefficient. The calculation formula is: ,in, is the risk concealment coefficient.

[0028] It can be seen from the formula that the larger the risk concealment coefficient, the higher the potential risk of the alternative equipment, which may indicate that the equipment has potential health problems or hidden dangers of failure.

[0029] The energy consumption variation coefficient, risk concealment coefficient and operating status data are taken as key features of alternative equipment, wherein the operating status data include the current operating temperature of the alternative equipment and the current operating vibration amplitude of the alternative equipment. The energy consumption variation coefficient, risk concealment coefficient, the current operating temperature of the alternative equipment and the current operating vibration amplitude of the alternative equipment are converted into fuzzy variables, and fuzzy sets and their membership functions are defined. A fuzzy rule base is constructed, and the fuzzy rules are applied to the input key features of the alternative equipment. After defuzzification processing, the fuzzy output of the alternative equipment is obtained. The fuzzy output of the alternative equipment is used as a standard for evaluating the operating status of the alternative equipment, wherein the operating score of the alternative equipment is taken as the fuzzy output of the alternative equipment.

[0030] Among them, the fuzzy rule base can be expressed as: For example, Rule 1: If (energy consumption variation coefficient IS is low) and (risk concealment coefficient IS is low) and (operation status data IS is good), then (the operation status of the alternative equipment IS is optimal); Rule 2: If (energy consumption variation coefficient IS medium) and (risk concealment coefficient IS medium) and (operation status data IS normal), then (alternative equipment operation status IS appropriate); Rule 3: If (the energy consumption variation coefficient IS is high) and (the risk concealment coefficient IS is high) and (the operating status data IS is poor), then (the operating status of the alternative equipment IS is poor).

[0031] It should be noted that the operating status data is specifically set by professional staff, and parameters that can evaluate the operating status of the equipment need to be set according to the specific conditions of the production equipment. This embodiment uses the current operating temperature and the current operating vibration amplitude of the replaceable equipment as examples. The construction of the fuzzy rule base is based on the historical data of the production equipment and the work experience of professional staff. The fuzzy rule base contains multiple rules, which will not be repeated here.

[0032] By defining the fuzzy output of alternative equipment, the fuzzy output of alternative equipment is divided into best, suitable and poor. The alternative equipment with the best fuzzy output is given priority, and the alternative equipment with the suitable fuzzy output is secondly selected. If the fuzzy outputs of the alternative equipment are all poor, the production is stopped and the alternative production equipment is repaired and inspected. If the fuzzy outputs of the alternative equipment are the same, the operation score of the alternative equipment is obtained through defuzzification processing, usually using the post-center of gravity method and weighted average method. By comparing the operation scores of the alternative equipment, the alternative equipment with a higher operation score is retained for production first.

[0033] This embodiment analyzes the energy consumption information, historical information and operation information of the production equipment to explore the potential operation risks of the production equipment, and evaluates the potential hidden dangers of the production equipment by using the DBSCAN clustering algorithm. It also builds a fuzzy rule base and uses fuzzy logic to link the energy consumption information, historical information and operation information with the current operation status of the production equipment. This helps to provide clear and intelligent decision support for selectively shutting down the worse equipment in priority when multiple production equipment are working at the same time, and helps to optimize resource allocation, improve production efficiency, reduce operating costs, and reduce the risk of production failures.

[0034] Example 2 The above embodiment provides a criterion for selectively shutting down inferior equipment. In the actual production process, the coordination between production equipment should be considered. This embodiment determines the optimal production path of products in the production execution system by using a genetic algorithm.

[0035] The operation scores of alternative equipment after defuzzification are collected through edge devices and sent to the cloud computing platform. The cloud computing platform stores the operation scores of different alternative equipment in the production process of the product, and integrates the synergy information between the production equipment, and uses genetic algorithms to determine the optimal production path for the product.

[0036] Among them, the operation score of the replaceable device is sent to the cloud computing platform, usually using MQTT, HTTP or other protocols, and the edge device uploads the score data to the cloud.

[0037] Based on the production quality information and production process information of the product during the production process, the synergy information of the product production process is determined, wherein the production quality information is represented by the qualified deviation coefficient, which is obtained by analyzing the qualified rate of the products produced between the two production equipments, and the production process information is represented by the synergy performance coefficient, which is obtained by analyzing the speed and changeover time of the products produced between the two production equipments, and the synergy information of the product production process is represented by the synergy evaluation coefficient.

[0038] The logic for obtaining the synergy performance coefficient is as follows: obtain the changeover time between production equipment and mark the changeover time between production equipment as: , where i=1, 2, 3, ..., I, I is a positive integer, and i is the number of the product that needs to be replaced each time; It should be noted that usually a product needs to be processed by multiple different production equipment before it can be processed. Therefore, there will be a period of non-production between the previous production equipment and the next production equipment. For example, when the distance between two production equipment is far, the product will have a distance to travel when it is transported on the conveyor belt. In lean production, shortening the changeover time is regarded as an important means to improve production efficiency and reduce costs. By reducing the changeover time, the response speed and resource utilization of the production line can be improved.

[0039] Get the speed at which the production equipment completes the production of the product, and mark the speed at which the production equipment completes the production of the product as: ; Set the standard value of the changeover time between production equipment and the standard value of the speed at which product production is completed between production equipment, and mark the standard value of the changeover time between production equipment and the standard value of the speed at which product production is completed between production equipment as: and ; It should be noted that the standard value of the changeover time between production equipment and the standard value of the speed at which product production is completed between production equipment are set by staff in professional fields.

[0040] Calculate the synergy performance coefficient. The calculation formula of the synergy performance coefficient is: ,in, is the synergy performance coefficient.

[0041] It should be noted that the shorter the changeover time between production equipment, the higher the production utilization rate and the lower the production cost; the faster the speed at which product production is completed between production equipment, the higher the performance of the equipment and the higher the compatibility between the equipment.

[0042] It can be seen from the formula that the larger the synergy performance coefficient is, the better the performance of the product's production chain is, and the higher the degree of coordination between different production equipment may be.

[0043] The logic for obtaining the qualified deviation coefficient is: according to the historical records of different production equipment, the qualified rate of the products produced by different production equipment is obtained, and the qualified rate of the products produced by different production equipment is marked as: , calculate the qualified deviation coefficient, the calculation formula is: ,in, is the qualified deviation coefficient.

[0044] It can be seen from the formula that the larger the qualified deviation coefficient is, the poorer the coordination between production equipment may be, and the higher the possibility that the products produced will be of unqualified quality.

[0045] The synergy performance coefficient and the qualified deviation coefficient are weighted to obtain the synergy evaluation coefficient. The calculation formula of the synergy evaluation coefficient is: ,in, is the synergy evaluation coefficient, , is the proportional coefficient of the synergistic performance coefficient and the qualified deviation coefficient, , They are both greater than 0; Set the fitness function of the genetic algorithm and mark the fitness function of the genetic algorithm as: , where q=1, 2, 3, ..., Q, Q is a positive integer, and q is the serial number of the replaceable device. is the adjustment factor to ensure that the denominator of the fitness function is not zero.

[0046] It should be noted that the fitness function of the genetic algorithm is based on the selection of shutting down identical equipment and retaining the only production equipment when there are alternative equipment.

[0047] In order to avoid search failure caused by excessive fitness differences, the linear scaling formula is used to scale the fitness, and the optimal manufacturing path of the product in the MES system is obtained by minimizing the fitness function.

[0048] The present invention solves the selection problem of multiple identical production equipment in the MES system by using a genetic algorithm, and in the process of selecting the optimal manufacturing path, gives priority to the coordination between equipment and the current operating status of the equipment, which helps to improve production efficiency and reduce operating costs, and realizes intelligent selection of production equipment.

[0049] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0050] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by 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 programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is 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. 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 site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0051] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0052] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0053] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0054] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0055] 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 can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0056] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A production execution MES system, characterized in that: Specifically, it includes data acquisition module, clustering module, fuzzy logic module, collaborative evaluation module and path selection module, and signal connections between modules; The data collection module is used to collect energy consumption information, historical information and operation information generated by production equipment during the production process of the product by arranging edge devices; The data analysis module is used to obtain the energy consumption changes of the production equipment based on the energy consumption information generated by the production equipment, and to identify the potential risks of the production equipment using a clustering algorithm based on the historical information of the energy consumption information generated by the production equipment; The fuzzy logic module is used to extract the key features of alternative equipment based on the energy consumption information, historical information and operation information generated by the production equipment, and obtain the operation score of the production equipment by building a fuzzy rule base and performing defuzzification processing; Collaborative evaluation module, which is used to collect production process information and production quality information, and conduct comprehensive analysis on the collected production process information and production quality information to quantify the degree of coordination of products in the production process; The path selection module is used to obtain the optimal manufacturing path of the product in the MES system by setting the fitness function of the genetic algorithm and using the genetic algorithm.

2. A production execution MES system according to claim 1, characterized in that: According to the energy consumption information generated by the production equipment, the energy consumption changes of the production equipment are obtained, including: Mark multiple production equipment that meet a certain production step at the same time as replaceable equipment. Obtain the energy consumption variation coefficient through the energy consumption information of the replaceable equipment. The logic for obtaining the energy consumption variation coefficient is: set the monitoring interval, obtain the energy consumption data of the replaceable equipment in the monitoring interval through the edge device, and mark the energy consumption data of the replaceable equipment in the monitoring interval as: , where n=1, 2, 3, ..., N, N is a positive integer, and n is the number of the energy consumption data of the replaceable device sampled by the edge device within the monitoring interval; Calculate the mean and standard deviation of the energy consumption data of the replaceable equipment within the monitoring interval, and mark the mean and standard deviation of the energy consumption data of the replaceable equipment within the monitoring interval as: and ,in, , ; Calculate the coefficient of variation of the energy consumption data of alternative equipment within the monitoring interval. The calculation formula is: ,in, is the coefficient of variation of energy consumption data of alternative equipment within the monitoring interval; Calculate the energy consumption variation coefficient of replaceable equipment within the monitoring interval. The calculation formula is: ,in, is the energy consumption variation coefficient.

3. A production execution MES system according to claim 1, characterized in that: Based on the historical information of energy consumption generated by production equipment, clustering algorithms are used to identify potential risks of production equipment, including: The risk concealment coefficient is obtained through the historical information of replaceable equipment. The logic of obtaining the risk concealment coefficient is as follows: set the unit time, collect the fault data of the replaceable equipment within the unit time, use the time when the replaceable equipment fault occurs as a timestamp, and convert the timestamp of the fault into a relative time format, starting from the time when the first fault occurs, so that the fault time point is represented as a continuous time series; By setting The radius and the minimum number of points are used to cluster the fault points of the replaceable equipment within a unit time using the DBSCAN clustering algorithm. The fault points that are close to each other in time are assigned to the same cluster. The average time of all fault points in the cluster is calculated and used as the cluster center. The timestamp of the cluster center of the replaceable equipment within a unit time is marked as: ; Timestamp the current time as: , the time-anonymous value is obtained by the difference between the timestamp of the current time and the timestamp of the cluster center of the replaceable device in unit time, and the time-anonymous value is marked as: ,in, ; Obtain the number of failures that occur in the replaceable equipment per unit time, and mark the number of failures that occur in the replaceable equipment per unit time as: C, and calculate the risk concealment coefficient. The calculation formula is: ,in, is the risk concealment coefficient.

4. A production execution MES system according to claim 1, characterized in that: Through defuzzification processing, the operation score of the production equipment is obtained, including: The energy consumption variation coefficient, the risk concealment coefficient and the operation status data are used as the key features of the alternative equipment, wherein the operation status data includes the current operation temperature of the alternative equipment and the current operation vibration amplitude of the alternative equipment. The energy consumption variation coefficient, the risk concealment coefficient, the current operation temperature of the alternative equipment and the current operation vibration amplitude of the alternative equipment are converted into fuzzy variables, and fuzzy sets and their membership functions are defined to construct a fuzzy rule base, and the fuzzy rules are applied to the key features of the input alternative equipment. After defuzzification, the fuzzy output of the alternative equipment is obtained, and the fuzzy output of the alternative equipment is used as the standard for evaluating the operation status of the alternative equipment, wherein the operation score of the alternative equipment is used as the fuzzy output of the alternative equipment; The fuzzy outputs of alternative equipment are classified into best, suitable, and poor. The alternative equipment with the best fuzzy output is given priority, followed by the alternative equipment with the suitable fuzzy output. If the fuzzy outputs of the alternative equipment are all poor, production is stopped and the alternative production equipment is repaired and inspected. If the fuzzy outputs of the alternative equipment are the same, the operation score of the alternative equipment is obtained through defuzzification processing. By comparing the operation scores of the alternative equipment, the alternative equipment with a higher operation score is retained for production.

5. A production execution MES system according to claim 1, characterized in that: Collect production process information, including: The production process information is represented by the qualified deviation coefficient. The logic of obtaining the synergy performance coefficient is as follows: obtain the changeover time between production equipment and mark the changeover time between production equipment as: , where i=1, 2, 3, ..., I, I is a positive integer, and i is the number of the product that needs to be replaced each time; Get the speed at which the production equipment completes the production of the product, and mark the speed at which the production equipment completes the production of the product as: ; Set the standard value of the changeover time between production equipment and the standard value of the speed at which product production is completed between production equipment, and mark the standard value of the changeover time between production equipment and the standard value of the speed at which product production is completed between production equipment as: and ; Calculate the synergy performance coefficient. The calculation formula of the synergy performance coefficient is: ,in, is the synergy performance coefficient.

6. A production execution MES system according to claim 1, characterized in that: Collect production quality information, including: The production quality information is represented by the qualified deviation coefficient. The logic of obtaining the qualified deviation coefficient is: according to the historical records of different production equipment, the qualified rate of the products produced by different production equipment is obtained, and the qualified rate of the products produced by different production equipment is marked as: , set the standard value of the qualified rate of products produced by the production equipment, and mark the standard value of the qualified rate of products produced by the production equipment as: , calculate the qualified deviation coefficient, the calculation formula is: ,in, is the qualified deviation coefficient.

7. A production execution MES system according to claim 1, characterized in that: Set the fitness function of the genetic algorithm, including: The synergy performance coefficient and the qualified deviation coefficient are weighted to obtain the synergy evaluation coefficient. The calculation formula of the synergy evaluation coefficient is: ,in, is the synergy evaluation coefficient, , is the proportional coefficient of the synergistic performance coefficient and the qualified deviation coefficient, , They are both greater than 0; Set the fitness function of the genetic algorithm and mark the fitness function of the genetic algorithm as: , where q=1, 2, 3, ..., Q, Q is a positive integer, and q is the serial number of the replaceable device. is the adjustment factor to ensure that the denominator of the fitness function is not zero.

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