A manufacturing execution management system based on big data

Abstract

The application relates to the technical field of manufacturing management, and particularly discloses a manufacturing execution management system based on big data, which comprises an efficiency collection module, an efficiency monitoring module and a device management module. The efficiency collection module sets a recording interval, divides the manufacturing process of a product into a plurality of links, obtains the processing amount of each device in the current link and the previous link in the recording interval, and calculates the processing amount. The efficiency monitoring module obtains the total efficiency of all devices in the current link in the recording interval, fits the current link efficiency curve and the previous link efficiency curve, and obtains a real-time accumulation amount at each end time. The device management module obtains the change rate of the accumulation amount curve, and if the change rate is greater than 0, it indicates that there is an efficiency difference. The device management module obtains the efficiency change curve of each device in the current link, calculates the single-device change rate, compares the single-device change rate of each device in the current link with the change rate of the accumulation amount curve, and selects the device in the current link that needs to be overhauled.

Description

Technical Field

[0001] This invention relates to the field of manufacturing execution management technology, and more specifically to a manufacturing execution management system based on big data. Background Technology

[0002] Manufacturing Execution Management (MES) is an integrated information system used to monitor, control, and optimize manufacturing processes. It includes several key functions to ensure efficient, high-quality, and low-cost production processes; these include production planning and scheduling management, production process monitoring and tracking, quality management, and equipment management.

[0003] In modern manufacturing, production processes are typically divided into multiple stages or segments, each responsible for completing a specific processing task. This segmented production method helps improve specialization and efficiency, while also making the entire manufacturing process more flexible and controllable. Once a stage completes its intended task, the resulting semi-finished product is transferred to the next stage for further or different processing. Ideally, these semi-finished products should flow smoothly between production stages, ensuring the continuity and efficiency of the production line.

[0004] However, in actual operation, due to differences in equipment used in different stages (such as technical specifications and operational complexity) and 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 situation, if the production capacity of the upstream stage exceeds the demand of the downstream stage, semi-finished products will accumulate at the junction, forming a so-called "bottleneck" phenomenon. This not only affects the overall production efficiency but may also negatively impact products with strict shelf-life limitations—such as fresh ingredients in the food industry and certain active ingredients in the pharmaceutical field. Once these materials exceed their optimal use period, even if they are not completely damaged, they may become unusable in the final product due to quality degradation, thereby increasing the company's scrap rate and costs. Summary of the Invention

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

[0006] The objective of this invention can be achieved through the following technical solutions:

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

[0008] Efficiency Acquisition Module: Set duration T, set the recording interval according to duration T; divide the product manufacturing process into several stages, obtain the processing amount N of each device in the current stage and the previous stage within the recording interval, and obtain the processing efficiency f = N / T of the device within the recording interval, where the processing amount is the number of products produced by the device;

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

[0010] Then, the processing efficiency of all the equipment in the previous stage within the recording interval is summed to obtain the total efficiency of the previous stage within the recording interval; based on the recording intervals of each number and the total efficiency of all the equipment in the previous stage within the recording intervals of each number, the efficiency curve Ec′(i) of the previous stage is fitted.

[0011] Obtain the end time of each recording interval, and obtain the real-time backlog at each end time. Where n is the latest number; based on the real-time accumulation amount obtained each time, the accumulation amount curve is fitted;

[0012] Equipment Management Module: Obtain the rate of change of the accumulation curve. If the rate of change is greater than 0, obtain the efficiency change curve of each equipment in the current stage and obtain the rate of change of the efficiency change curve, which is recorded as the single equipment change rate. Compare the single equipment change rate of each equipment in the current stage with the rate of change to select the equipment that needs to be repaired in the current stage.

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

[0014] The time consumed by each device in the current stage to produce one product is recorded as the required time. Based on the required time of all devices in the current stage, the average required time t1 of the devices in the current stage is obtained. The required time of each device in the previous stage is obtained to obtain the average required time t2 of all devices in the previous stage. Finally, the set {t1, t2} is obtained, and the maximum value of the set is recorded as Max{t1, t2}.

[0015] Obtain historical equipment usage data for the current stage, including the lifespan of all historically used equipment, where lifespan is the time elapsed from when the equipment is put into use until it is repaired or replaced; based on the historical equipment usage data for the current stage, obtain the average lifespan L1 of the equipment in the current stage; and obtain historical equipment usage data for the previous stage, and based on the historical equipment usage data for the previous stage, obtain the average lifespan L2 of the equipment in the previous stage; obtain the lifespan set {L1, L2}, and select the minimum value of the lifespan set, denoted as Min{L1, L2};

[0016] The time duration T is set within the range of [Max{t1, t2}, Min{L1, L2} / 2].

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

[0018] Establish a coordinate system with the serial number as the horizontal axis and the total efficiency as the vertical axis; convert the recording interval of each serial number and its corresponding total efficiency into coordinate points of the corresponding positions in the coordinate system, and connect each coordinate point with a smooth curve; record the smooth curve as the efficiency curve of the current stage.

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

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

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

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

[0023] Obtain the absolute value of the individual device change rate for each device, and compare the absolute value of the individual device change rate for each device with the magnitude of the change rate.

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

[0025] Equipment whose absolute value of single-device change rate is greater than the aforementioned change rate is selected and designated as low-efficiency equipment. All low-efficiency equipment is then inspected and repaired.

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

[0027] If the absolute value of the individual device change rate of each device is less than or equal to the change rate, then the absolute values ​​are sorted according to their magnitude to obtain the 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 k smallest absolute values ​​such that the following constraints are satisfied: If e∈[1,m] and is a positive integer, then the devices corresponding to these k absolute values ​​are denoted as inefficient devices.

[0028] The beneficial effects of this invention are:

[0029] This invention, by setting recording intervals and collecting equipment processing efficiency data in real time, enables the system to monitor the efficiency of each stage in real time. This real-time monitoring can promptly identify inefficient or abnormal equipment, allowing for rapid adjustments or maintenance. By fitting the efficiency curves of the current and previous stages, detailed data analysis can be provided, helping managers understand the trends in production efficiency and the accumulation of finished or semi-finished products between connected stages. This data can provide a scientific basis for decisions such as production plan adjustments and resource allocation optimization. The accumulation curve reflects the cumulative effect of efficiency differences between stages; by analyzing the rate of change of the accumulation curve, the overall changes in production efficiency can be visually observed. This helps identify key factors affecting production efficiency, allowing for targeted improvements. By obtaining the rate of change of the accumulation curve and comparing it with the rate of change of each individual piece of equipment, equipment requiring maintenance can be accurately identified. This refined management approach 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 stages, optimize production processes, and ultimately achieve lower production costs and improved product quality. By analyzing equipment efficiency curves, potential equipment problems can be predicted, allowing for preventative maintenance and avoiding sudden failures. This not only extends equipment lifespan but also reduces production interruptions caused by equipment malfunctions. In summary, this invention, through big data technology and real-time monitoring, achieves refined management and optimization of the production process, significantly improving production efficiency and equipment utilization. Attached Figure Description

[0030] The invention will now be further described with reference to the accompanying drawings.

[0031] Figure 1 This is a flowchart illustrating a manufacturing execution management system based on big data according to the present invention. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

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

[0034] Efficiency Acquisition Module: Set duration T, set the recording interval according to duration T; divide the product manufacturing process into several stages, obtain the processing amount N of each device in the current stage and the previous stage within the recording interval, and obtain the processing efficiency f = N / T of the device within the recording interval, where the processing amount is the number of products produced by the device;

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

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

[0037] The time consumed by each device in the current stage to produce one product is recorded as the required time. Based on the required time of all devices in the current stage, the average required time t1 of the devices in the current stage is obtained. The required time of each device in the previous stage is obtained to obtain the average required time t2 of all devices in the previous stage. Finally, the set {t1, t2} is obtained, and the maximum value of the set is recorded as Max{t1, t2}.

[0038] Obtain historical equipment usage data for the current stage, including the lifespan of all historically used equipment, where lifespan is the time elapsed from when the equipment is put into use until it is repaired or replaced; based on the historical equipment usage data for the current stage, obtain the average lifespan L1 of the equipment in the current stage; and obtain historical equipment usage data for the previous stage, and based on the historical equipment usage data for the previous stage, obtain the average lifespan L2 of the equipment in the previous stage; obtain the lifespan set {L1, L2}, and select the minimum value of the lifespan set, denoted as Min{L1, L2};

[0039] The time duration T is set within the range of [Max{t1, t2}, Min{L1, L2} / 2];

[0040] It should be noted that the set range takes into account the average time required for the two stages and the average lifespan 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 should be greater than the time required by the equipment in the current stage and the previous stage, that is, the time required to produce one product; Min{L1, L2} / 2 indicates that the duration of the recording interval should be less than half of the lifespan of the equipment in the current stage and the previous stage.

[0041] It is worth noting that, under normal circumstances, the lifespan of equipment is much longer than the required time. Therefore, equipment can often produce a lot of products when it is half of its lifespan.

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

[0043] Then, the processing efficiency of all the equipment in the previous stage within the recording interval is summed to obtain the total efficiency of the previous stage within the recording interval; based on the recording intervals of each number and the total efficiency of all the equipment in the previous stage within the recording intervals of each number, the efficiency curve Ec′(i) of the previous stage is fitted.

[0044] Obtain the end time of each recording interval, and obtain the real-time backlog at each end time. Where n is the latest number; based on the real-time accumulation amount obtained each time, the accumulation amount curve is fitted;

[0045] It is understandable that the actual accumulation amount reflects the difference in processing efficiency between the two stages. If the real-time accumulation amount is greater than 0, there is product accumulation between the previous stage and the current stage, that is, the overall efficiency of the previous stage is greater than that of the current stage. If the real-time accumulation amount is less than 0, there is no product accumulation between the previous stage and the current stage.

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

[0047] Establish a coordinate system with the serial number as the horizontal axis and the total efficiency as the vertical axis; convert the recording interval of each serial number and its corresponding total efficiency into coordinate points of the corresponding positions in the coordinate system, and connect each coordinate point with a smooth curve; record the smooth curve as the efficiency curve of the current stage.

[0048] It is understandable that through this fitting process, a continuous and smooth efficiency curve can be generated, thereby more intuitively showing the efficiency changes of the current link; this invention only lists a commonly used mathematical fitting method. There are many fitting methods in the prior art. The most suitable fitting method can be selected according to the actual situation, which will not be elaborated here.

[0049] Equipment Management Module: Obtain the rate of change of the accumulation curve. If the rate of change is greater than 0, obtain the efficiency change curve of each equipment in the current stage and obtain the rate of change of the efficiency change curve, which is recorded as the single equipment change rate. Compare the single equipment change rate of each equipment in the current stage with the rate of change to select the equipment that needs to be repaired in the current stage.

[0050] It is understandable 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 rate of change of individual equipment reflects the fluctuation of equipment efficiency; by comparing the rate of change of individual equipment of each equipment in the current stage with the rate of change of the accumulation curve, it is possible to identify which equipment efficiency changes have the greatest impact on the overall accumulation.

[0051] It is worth noting that regardless of the current quantity of stockpiled goods or whether it results in product waste, as long as the rate of change is greater than 0, it indicates that the stockpiled quantity 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 rate of change includes:

[0053] Several reference points are selected equally on the accumulation curve, the slope at each reference point is obtained, and the average value of all slopes is obtained, which is recorded as the rate of change.

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

[0055] It is worth noting that if the rate of change is less than or equal to 0, it may be due to the low overall efficiency of the previous stage. In this case, the real-time accumulation between the previous stage and the stage before that can be monitored to determine whether the overall efficiency of the previous stage 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 stage with the change rate includes:

[0057] Obtain the absolute value of the single-device change rate of each device, and compare the absolute value of the single-device change rate of each device with the magnitude of the change rate;

[0058] The process of selecting equipment that needs maintenance in the current stage includes:

[0059] Select equipment whose absolute value of single equipment change rate is greater than the change rate and mark it as low-efficiency equipment. Repair all low-efficiency equipment.

[0060] The process of selecting equipment that needs maintenance in the current stage also includes:

[0061] If the absolute value of the individual device change rate of each device is less than or equal to the change rate, then the absolute values ​​are sorted according to their magnitude to obtain the 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 k smallest absolute values ​​such that the following constraints are satisfied: Where e∈[1,m] and is a positive integer, then the devices corresponding to these k absolute values ​​are denoted as inefficient devices;

[0062] It is understandable that for equipment with declining efficiency, the rate of change of a single device is generally negative, while the rate of change is positive when making comparisons. Therefore, it is necessary to obtain the absolute value of the rate of change of a single device for comparison. If the absolute value of the rate of change of a single device is greater than the rate of change, then the efficiency of the device is problematic and it is one of the devices that causes product accumulation.

[0063] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A manufacturing execution management system based on big data, characterized in that, include: Efficiency acquisition module: Set duration T, and set recording intervals based on duration T; The product manufacturing process is divided into several stages. The processing amount N of each device in the current stage and the previous stage within the recorded interval is obtained. The processing efficiency f=N / T of the device within the recorded interval is obtained. The processing amount is the number of products produced by the device. Efficiency monitoring module: The processing efficiency of all equipment in the current stage within the recording interval is accumulated to obtain the total efficiency of the current stage within the recording interval; the total efficiency of each recording interval is collected in real time, each recording interval is numbered according to the collection order, and the efficiency curve Ec(i) of the current stage is fitted according to each numbered recording interval and its corresponding total efficiency, where i is the number; Then, the processing efficiency of all the equipment in the previous stage within the recording interval is summed to obtain the total efficiency of the previous stage within the recording interval; based on the recording intervals of each number and the total efficiency of all the equipment in the previous stage within the recording intervals of each number, the efficiency curve Ec´(i) of the previous stage is fitted. Obtain the end time of each recording interval, and obtain the real-time backlog at each end time. , where n is the latest number; based on the real-time accumulation amount obtained each time, the accumulation amount curve is fitted; Equipment Management Module: Obtain the rate of change of the accumulation curve. If the rate of change is greater than 0, obtain the efficiency change curve of each equipment in the current stage and obtain the rate of change of the efficiency change curve, which is recorded as the single equipment change rate. Compare the single equipment change rate of each equipment in the current stage with the rate of change to select the equipment that needs to be repaired in the current stage.

2. The manufacturing execution management system based on big data according to claim 1, characterized in that, The process of setting the duration T includes: The time consumed by each device in the current stage to produce one product is recorded as the required time. Based on the required time of all devices in the current stage, the average required time t1 of the devices in the current stage is obtained. The required time of each device in the previous stage is obtained to obtain the average required time t2 of all devices in the previous stage. Finally, the set {t1, t2} is obtained, and the maximum value of the set is recorded as Max{t1, t2}. Obtain historical equipment usage data for the current stage, including the lifespan of all historically used equipment, where lifespan is the time elapsed from when the equipment is put into use until it is repaired or replaced; based on the historical equipment usage data for the current stage, obtain the average lifespan L1 of the equipment in the current stage; and obtain historical equipment usage data for the previous stage, and based on the historical equipment usage data for the previous stage, obtain the average lifespan L2 of the equipment in the previous stage; obtain the lifespan set {L1, L2}, and select the minimum value of the lifespan set, denoted as Min{L1, L2}; The time duration T is set within the range of [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 efficiency curve of the current stage includes: Establish a coordinate system with the serial number as the horizontal axis and the total efficiency as the vertical axis; convert the recording interval of each serial number and its corresponding total efficiency into coordinate points of the corresponding positions in the coordinate system, and connect each coordinate point with a smooth curve; record the smooth curve as the efficiency curve of the current stage.

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

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

6. The manufacturing execution management system based on big data according to claim 1, characterized in that, The process of comparing the individual device change rate of each device in the current stage with the change rate includes: Obtain the absolute value of the individual device change rate for each device, and compare the absolute value of the individual device change rate for each device with the magnitude of the change rate.

7. A manufacturing execution management system based on big data according to claim 6, characterized in that, The process of selecting equipment that needs maintenance in the current stage includes: Equipment whose absolute value of single-device change rate is greater than the aforementioned change rate is selected and designated as low-efficiency equipment. All low-efficiency equipment is then inspected and repaired.

8. A manufacturing execution management system based on big data according to claim 7, characterized in that, The process of selecting equipment that needs maintenance in the current stage also includes: If the absolute value of the individual device change rate of each device is less than or equal to the change rate, then the absolute values ​​are sorted according to their magnitude to obtain a sequence. Where V1 is the first absolute value after sorting, and m is the total number of absolute values; select the k smallest absolute values ​​such that the following constraints are satisfied: If e∈[1,m] and is a positive integer, then the devices corresponding to these k absolute values ​​are denoted as inefficient devices.

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