Manufacturing execution management system based on big data

Abstract

The invention relates to the technical field of execution manufacturing management, and particularly discloses a manufacturing execution management system based on big data, and the system comprises an efficiency collection module which is used for setting a recording interval, dividing the manufacturing process of a product into a plurality of links, obtaining the machining amount of each device in the recording interval in the current link and the previous link, and calculating the machining amount; the efficiency monitoring module is used for fitting the total efficiency of all equipment in the recording interval of the current link to obtain an efficiency curve of the current link and an efficiency curve of the previous link; the real-time accumulation amount is obtained at each ending moment; the equipment management module is used for acquiring the change rate of the accumulation volume curve, and if the change rate is greater than 0, efficiency difference exists; obtaining an efficiency change curve of each device in the current link, and calculating a single device change rate; and comparing the single equipment change rate of each equipment in the current link with the change rate of the accumulation volume curve, and selecting the equipment needing to be overhauled in the current link.

Description

Technical Field

[0001] The present invention relates to the technical field of manufacturing execution management, and in particular to a manufacturing execution management system based on big data. Background Art

[0002] Manufacturing Execution Management (MES) is an integrated information system used to monitor, control and optimize the manufacturing process. It includes multiple key functions to ensure the efficiency, quality and low cost of the production process; including production planning and scheduling management, production process monitoring and tracking, quality management and equipment management.

[0003] In modern manufacturing, the production process is usually divided into multiple links or stages, each of which is responsible for completing specific processing tasks. This segmented production method helps to improve the level of specialization and efficiency, and also makes the entire manufacturing process more flexible and controllable. When a link completes its scheduled work, the semi-finished products will be transferred to the next link for further or different processing. Ideally, these semi-finished products should be able to flow smoothly between various production links to ensure the continuity and efficiency of the production line.

[0004] However, in actual operations, due to differences in equipment used in different links (such as technical specifications, operational complexity, etc.), as well as the gradual decline in equipment performance over time, the processing speed of subsequent stages often cannot keep up with the pace of the previous stage. In this case, if the production capacity of the upstream link exceeds the demand of the downstream link, semi-finished products will pile up at the handover point, forming the so-called "bottleneck" phenomenon. This not only affects the efficiency of overall production, but may also have a negative impact on products with strict shelf life restrictions-such as fresh ingredients in the food industry, certain active ingredients in the pharmaceutical field, etc. Once the best use period is exceeded, these materials may not be used in the final product due to quality degradation even if they are not completely damaged, thereby increasing the company's scrap rate and cost expenditure. Summary of the invention

[0005] The purpose of the present invention is to provide a manufacturing execution management system based on big data to solve the above technical problems.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A manufacturing execution management system based on big data, comprising:

[0008] Efficiency acquisition module: set the duration T, and set the recording interval according to the duration T; divide the product manufacturing process into several links, obtain the processing volume N of each device in the current link and the previous link in the recording interval, and obtain the processing efficiency f=N / T of the device in the recording interval, where the processing volume is the number of products produced by the device;

[0009] Efficiency monitoring module: accumulate the processing efficiency of all equipment in the current link within the recording interval to obtain the total efficiency of the current link within the recording interval; collect the total efficiency of each recording interval in real time, number each recording interval according to the collection order, and fit the current link efficiency curve Ec(i) according to each numbered recording interval and its corresponding total efficiency, where i is the number;

[0010] The processing efficiencies of all the equipment in the previous link within the recording interval are accumulated to obtain the total efficiency of the previous link within the recording interval; according to the recording intervals of each number and the total efficiency of all the equipment in the previous link within the recording intervals of each number, the efficiency curve Ec′(i) of the previous link is obtained by fitting;

[0011] Get the end time of each recording interval and get the real-time accumulation amount at each end time Where n is the latest number; according to the real-time accumulation amount obtained each time, the accumulation amount curve is fitted;

[0012] Equipment management module: obtain the change rate of the accumulation curve. If the change rate is greater than 0, obtain the efficiency change curve of each device in the current link, and obtain the change rate of the efficiency change curve, which is recorded as the single device change rate; compare the single device change rate of each device in the current link with the change rate, and select the equipment that needs to be repaired in the current link.

[0013] As a further solution of the present invention: the setting process of the duration T includes:

[0014] Obtain the time consumed by each device in the current link to produce a product, record it as the required time, and obtain the average required time t1 of the devices in the current link based on the required time of all devices in the current link; and obtain the required time of each device in the previous link, and obtain the average required time t2 of all devices in the previous link; finally obtain the set {t1, t2}, and the maximum value in the set is recorded as Max{t1, t2};

[0015] Obtain the historical equipment usage data of the current link, the historical equipment usage data includes the service life of all equipment used in the past, and the service life is the time from when the equipment is put into use to when it is repaired or replaced; obtain the average life L1 of the equipment in the current link based on the historical equipment usage data of the current link; and obtain the historical equipment usage data of the previous link, and obtain the average life L2 of the equipment in the previous link based on the historical equipment usage data of the previous link; obtain the life set {L1, L2}, select the minimum value of the life set, and record it as Min{L1, L2};

[0016] Then the setting range of the time length T is [Max{t1, t2}, Min{L1, L2} / 2].

[0017] As a further solution of the present invention: the fitting process of the current link efficiency curve includes:

[0018] A coordinate system is established with the number as the horizontal axis and the total efficiency as the vertical axis; the recording intervals of each number and its corresponding total efficiency are converted into coordinate points of corresponding positions in the coordinate system, and the coordinate points are connected with a smooth curve; the smooth curve is recorded as the efficiency curve of the current link.

[0019] As a further solution of the present invention: the process of obtaining the change rate includes:

[0020] A number of reference points are equally selected on the accumulation amount curve, the slope at each reference point is obtained, and the average value of all the slopes is obtained and recorded as the rate of change.

[0021] As a further solution of the present invention: if the change rate is less than or equal to 0, then there is no product accumulation between the previous link and the current link.

[0022] As a further solution of the present invention: the process of comparing the single device change rate of each device in the current link with the change rate includes:

[0023] The absolute value of the single device change rate of each device is obtained, and the absolute value of the single device change rate of each device is compared with the change rate.

[0024] As a further solution of the present invention: the process of selecting the equipment that needs to be repaired in the current link includes:

[0025] The equipment whose absolute value of the change rate of a single equipment is greater than the change rate is selected and recorded as low-efficiency equipment, and all low-efficiency equipment is repaired.

[0026] As a further solution of the present invention: the process of selecting the equipment that needs to be repaired in the current link also includes:

[0027] If the absolute values ​​of the single device change rates of all devices are less than or equal to the change rate, the absolute values ​​are sorted according to their size to obtain a sequence {V1, V2, ..., V m}, where V1 is the first absolute value after sorting, and m is the total number of absolute values; select the smallest k absolute values ​​to satisfy the constraint: Where e∈[1, m] is a positive integer, and the devices corresponding to these k absolute values ​​are recorded as inefficient devices.

[0028] Beneficial effects of the present invention:

[0029] The present invention sets a recording interval and collects equipment processing efficiency data in real time, so that the system can monitor the efficiency of each link in real time. This real-time monitoring can timely discover inefficient or abnormal equipment, so that measures can be taken quickly to adjust or maintain it. By fitting the efficiency curves of the current link and the previous link, detailed data analysis can be provided to help managers understand the changing trend of production efficiency and the accumulation of finished products or semi-finished products produced between connected links. These data can provide a scientific basis for decisions such as production plan adjustment and resource allocation optimization. The accumulation curve reflects the cumulative effect of efficiency differences in each link. By analyzing the rate of change of the accumulation curve, the overall change of production efficiency can be intuitively seen. This helps to identify the key factors affecting production efficiency, so as to make targeted improvements. By obtaining the rate of change of the accumulation curve and comparing it with the single device change rate of each device, the equipment that needs to be repaired can be accurately identified. This refined management method can reduce unnecessary downtime and improve equipment utilization and production efficiency. Through real-time monitoring, data analysis and refined management, the system can effectively improve overall production efficiency. Reduce the impact of inefficient links, optimize production processes, and ultimately achieve reduced production costs and improved product quality. By analyzing the equipment efficiency change curve, it is possible to predict possible problems with the equipment, perform preventive maintenance in advance, and avoid sudden failures. This can not only extend the service life of the equipment, but also reduce production interruptions caused by equipment failures. In summary, the present invention realizes the refined management and optimization of the production process through big data technology and real-time monitoring means, and significantly improves production efficiency and equipment utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The present invention will be further described below in conjunction with the accompanying drawings.

[0031] Figure 1 It is a flow chart of a manufacturing execution management system based on big data of the present invention. DETAILED DESCRIPTION

[0032] 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.

[0033] See also Figure 1 As shown, the present invention is a manufacturing execution management system based on big data, comprising:

[0034] Efficiency acquisition module: set the duration T, and set the recording interval according to the duration T; divide the product manufacturing process into several links, obtain the processing volume N of each device in the current link and the previous link in the recording interval, and obtain the processing efficiency f=N / T of the device in the recording interval, where the processing volume is the number of products produced by the device;

[0035] It is understandable that the manufacturing process of a product is divided into several links; these links may include different production steps such as raw material processing, component assembly, and finished product inspection; the processing efficiency is the processing volume of the equipment per unit time, which is used to monitor the equipment status of each equipment;

[0036] In a preferred embodiment of the present invention, the process of setting the duration T includes:

[0037] Obtain the time consumed by each device in the current link to produce a product, record it as the required time, and obtain the average required time t1 of the devices in the current link based on the required time of all devices in the current link; and obtain the required time of each device in the previous link, and obtain the average required time t2 of all devices in the previous link; finally obtain the set {t1, t2}, and the maximum value in the set is recorded as Max{t1, t2};

[0038] Obtain the historical equipment usage data of the current link, the historical equipment usage data includes the service life of all equipment used in the past, and the service life is the time from when the equipment is put into use to when it is repaired or replaced; obtain the average life L1 of the equipment in the current link based on the historical equipment usage data of the current link; and obtain the historical equipment usage data of the previous link, and obtain the average life L2 of the equipment in the previous link based on the historical equipment usage data of the previous link; obtain the life set {L1, L2}, select the minimum value of the life set, and record it as Min{L1, L2};

[0039] Then the setting range of the time length T is [Max{t1, t2}, Min{L1, L2} / 2];

[0040] It should be noted that the setting range takes into account the average time required for the two links and the average life of the equipment to ensure that the duration is neither too long nor too short, thereby ensuring a balance between production efficiency and equipment maintenance; Max{t1, t2} indicates that the duration of the recording interval must be greater than the time required for the equipment in the current link and the previous link, that is, the time required to produce a product; Min{L1, L2} / 2 indicates that the duration of the recording interval must be less than half of the service life of the equipment in the current link and the previous link;

[0041] It is worth noting that in general, the service life of the equipment is much longer than the time required for the equipment, so the equipment can often produce many products when it is half of its service life;

[0042] Efficiency monitoring module: accumulate the processing efficiency of all equipment in the current link within the recording interval to obtain the total efficiency of the current link within the recording interval; collect the total efficiency of each recording interval in real time, number each recording interval according to the collection order, and fit the current link efficiency curve Ec(i) according to each numbered recording interval and its corresponding total efficiency, where i is the number;

[0043] The processing efficiencies of all the equipment in the previous link within the recording interval are accumulated to obtain the total efficiency of the previous link within the recording interval; according to the recording intervals of each number and the total efficiency of all the equipment in the previous link within the recording intervals of each number, the efficiency curve Ec′(i) of the previous link is obtained by fitting;

[0044] Get the end time of each recording interval and get the real-time accumulation amount at each end time Where n is the latest number; according to the real-time accumulation amount obtained each time, the accumulation amount curve is fitted;

[0045] It can be understood that the implemented accumulation volume reflects the difference in processing efficiency between the two links. If the real-time accumulation volume is greater than 0, there is an accumulation of products from the previous link to the current link, that is, the total efficiency of the previous link is generally greater than that of the current link; if the real-time accumulation volume is less than 0, there is no accumulation of products from the previous link to the current link;

[0046] In a preferred embodiment of the present invention, the fitting process of the current link efficiency curve includes:

[0047] A coordinate system is established with the number as the horizontal coordinate and the total efficiency as the vertical coordinate; the recording intervals of each number and the total efficiency corresponding thereto are converted into coordinate points of corresponding positions in the coordinate system, and each coordinate point is connected with a smooth curve; the smooth curve is recorded as the efficiency curve of the current link;

[0048] It can be understood that through this fitting process, a continuous and smooth efficiency curve can be generated, thereby more intuitively showing the efficiency change of the current link; the present invention only lists a commonly used mathematical fitting method, there are many fitting methods in the prior art, and the most appropriate fitting method can be selected according to the actual situation, which will not be described in detail here;

[0049] Equipment management module: obtaining the change rate of the accumulation amount curve, if the change rate is greater than 0, obtaining the efficiency change curve of each device in the current link, and obtaining the change rate of the efficiency change curve, recorded as the single device change rate; comparing the single device change rate of each device in the current link with the change rate, and selecting the equipment that needs to be repaired in the current link;

[0050] It can be understood that if the rate of change is greater than 0, it means that the accumulation is increasing, indicating that the efficiency of some equipment is decreasing or malfunctioning; the single equipment change rate reflects the fluctuation of equipment efficiency; the single equipment change rate of each equipment in the current link is compared with the change rate of the accumulation curve; through this comparison, it can be identified which equipment's efficiency change has the greatest impact on the overall accumulation;

[0051] It is worth noting that no matter how much the current accumulation is and whether it causes product waste, as long as the change rate is greater than 0, it indicates that the accumulation is on an increasing trend and product waste will eventually occur;

[0052] In a preferred embodiment of the present invention, the process of obtaining the change rate includes:

[0053] Selecting a number of reference points equally on the accumulation curve, obtaining the slope at each reference point, and obtaining the average value of all the slopes, which is recorded as the rate of change;

[0054] In a preferred embodiment of the present invention, if the change rate is less than or equal to 0, then there is no product accumulation between the previous link and the current link;

[0055] It is worth noting that if the change rate is less than or equal to 0, it may also be caused by the low total efficiency of the previous link; at this time, the real-time accumulation amount between the previous link and the previous link can be monitored to determine whether the total efficiency of the previous link is normal;

[0056] In a preferred embodiment of the present invention, the process of comparing the single device change rate of each device in the current link with the change rate includes:

[0057] Obtaining the absolute value of the single device change rate of each device, and comparing the absolute value of the single device change rate of each device with the change rate;

[0058] The process of selecting the equipment that needs to be repaired in the current link includes:

[0059] Select the equipment whose absolute value of the change rate of a single equipment is greater than the change rate, record it as a low-efficiency equipment, and repair all the low-efficiency equipment;

[0060] The process of selecting the equipment that needs to be repaired in the current link also includes:

[0061] If the absolute values ​​of the single device change rates of all devices are less than or equal to the change rate, the absolute values ​​are sorted according to their size to obtain a sequence {V1, V2, ..., V m}, where V1 is the first absolute value after sorting, and m is the total number of absolute values; select the smallest k absolute values ​​to satisfy the constraint: Where e∈[1, m] is a positive integer, then the devices corresponding to these k absolute values ​​are recorded as inefficient devices;

[0062] It is understandable that for equipment with reduced efficiency, its single-device change rate is generally a negative value, while the change rate is a positive value during comparison, so it is necessary to obtain the absolute value of the single-device change rate for comparison; if the absolute value of the single-device change rate of a device is greater than the change rate, then there is a problem with the efficiency of the device, and it is one of the devices that causes product accumulation.

[0063] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A manufacturing execution management system based on big data, characterized in that: include: Efficiency collection module: set the time length T, and set the recording interval according to the time length T; The manufacturing process of the product is divided into several links, and the processing volume N of each device in the current link and the previous link within the recording interval is obtained to obtain the processing efficiency f=N / T of the device within the recording interval, where the processing volume is the number of products produced by the device; Efficiency monitoring module: accumulate the processing efficiency of all equipment in the current link within the recording interval to obtain the total efficiency of the current link within the recording interval; collect the total efficiency of each recording interval in real time, number each recording interval according to the collection order, and fit the current link efficiency curve Ec(i) according to each numbered recording interval and its corresponding total efficiency, where i is the number; The processing efficiencies of all the equipment in the previous link within the recording interval are accumulated to obtain the total efficiency of the previous link within the recording interval; according to the recording intervals of each number and the total efficiency of all the equipment in the previous link within the recording intervals of each number, the efficiency curve Ec′(i) of the previous link is obtained by fitting; Get the end time of each recording interval and get the real-time accumulation amount at each end time Where n is the latest number; according to the real-time accumulation amount obtained each time, the accumulation amount curve is fitted; Equipment management module: obtain the change rate of the accumulation curve. If the change rate is greater than 0, obtain the efficiency change curve of each device in the current link, and obtain the change rate of the efficiency change curve, which is recorded as the single device change rate; compare the single device change rate of each device in the current link with the change rate, and select the equipment that needs to be repaired in the current link.

2. A manufacturing execution management system based on big data according to claim 1, characterized in that: The process of setting the duration T includes: Obtain the time consumed by each device in the current link to produce a product, record it as the required time, and obtain the average required time t1 of the devices in the current link based on the required time of all devices in the current link; and obtain the required time of each device in the previous link, and obtain the average required time t2 of all devices in the previous link; finally obtain the set {t1, t2}, and the maximum value in the set is recorded as Max{t1, t2}; Obtain the historical equipment usage data of the current link, the historical equipment usage data includes the service life of all equipment used in the past, and the service life is the time from when the equipment is put into use to when it is repaired or replaced; obtain the average life L1 of the equipment in the current link based on the historical equipment usage data of the current link; and obtain the historical equipment usage data of the previous link, and obtain the average life L2 of the equipment in the previous link based on the historical equipment usage data of the previous link; obtain the life set {L1, L2}, select the minimum value of the life set, and record it as Min{L1, L2}; Then the setting range of the time length T is [Max{t1, t2}, Min{L1, L2} / 2].

3. The manufacturing execution management system based on big data according to claim 1, characterized in that: The fitting process of the current link efficiency curve includes: A coordinate system is established with the number as the horizontal axis and the total efficiency as the vertical axis; the recording intervals of each number and its corresponding total efficiency are converted into coordinate points of corresponding positions in the coordinate system, and the coordinate points are connected with a smooth curve; the smooth curve is recorded as the efficiency curve of the current link.

4. The manufacturing execution management system based on big data according to claim 1, characterized in that: The process of obtaining the change rate includes: A number of reference points are equally selected on the accumulation amount curve, the slope at each reference point is obtained, and the average value of all the slopes is obtained and recorded as the rate of change.

5. The manufacturing execution management system based on big data according to claim 1, characterized in that: If the change rate is less than or equal to 0, then there is no product accumulation between the previous link and the current link.

6. The manufacturing execution management system based on big data according to claim 1, characterized in that: The process of comparing the single device change rate of each device in the current link with the change rate includes: The absolute value of the single device change rate of each device is obtained, and the absolute value of the single device change rate of each device is compared with the change rate.

7. The manufacturing execution management system based on big data according to claim 6, characterized in that: The process of selecting the equipment that needs to be repaired in the current link includes: The equipment whose absolute value of the change rate of a single equipment is greater than the change rate is selected and recorded as low-efficiency equipment, and all low-efficiency equipment is repaired.

8. The manufacturing execution management system based on big data according to claim 7, characterized in that: The process of selecting the equipment that needs to be repaired in the current link also includes: If the absolute values ​​of the single device change rates of all devices are less than or equal to the change rate, the absolute values ​​are sorted according to their size to obtain a sequence {V1, V2, ..., V m }, where V1 is the first absolute value after sorting, and m is the total number of absolute values; select the smallest k absolute values ​​to satisfy the constraint: Where e∈[1, m] is a positive integer, and the devices corresponding to these k absolute values ​​are recorded as inefficient devices.

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