Methods, devices, electronic equipment, and storage media for handling production bottlenecks

Abstract

This application provides a method, apparatus, electronic device, and storage medium for handling production bottlenecks. This solution identifies bottleneck production equipment based on operational data from various production devices on the production line. It then uses an equipment status map algorithm to determine the critical values ​​for blockage / material shortage duration and overall line downtime for each bottleneck device. Furthermore, it divides all alarm records of the bottleneck device into bottleneck data sets containing alarm records with different processing priorities, enabling alarm event handling based on these priorities. This solution achieves priority-based bottleneck handling, making the process more scientific and significantly improving production efficiency.

Description

Technical Field

[0001] This application relates to the field of automation technology, and more specifically, to a method, apparatus, electronic device, and storage medium for handling production bottlenecks. Background Technology

[0002] Currently, most production scenarios employ a flow-type production method comprised of production equipment. While flow-type production lines are highly efficient, the individual pieces of equipment exhibit significant mutual constraints and a "weakest link" effect, meaning the overall line's maximum efficiency is limited by the performance of each individual piece of equipment. Therefore, identifying and addressing the bottleneck factors affecting the overall production efficiency of flow-type production lines is crucial for improving production efficiency.

[0003] Traditional methods of bottleneck analysis for production efficiency often rely on the number of alarms or cumulative alarm durations to identify bottleneck factors. These traditional methods suffer from incomplete analytical dimensions and insufficient depth, failing to reveal fundamental or primary bottleneck factors. Consequently, during the ramp-up phase of production lines, resources for improvement are not allocated to addressing the truly critical bottlenecks, thus offering little benefit to improving production efficiency. Summary of the Invention

[0004] The purpose of this application includes, for example, providing a method, apparatus, electronic device, and storage medium for handling production bottlenecks, which enables priority-based bottleneck handling and greatly helps to improve production efficiency.

[0005] The embodiments of this application can be implemented as follows:

[0006] Firstly, this application provides a method for handling production bottleneck factors, the method comprising:

[0007] For each piece of production equipment on the production line, obtain the operating data of each piece of production equipment;

[0008] Based on the operating data of each of the aforementioned production equipment, the bottleneck production equipment is identified.

[0009] For each bottleneck production equipment, the critical values ​​for blockage and material shortage duration and the critical value for overall line downtime are calculated using the equipment panoramic status diagram algorithm.

[0010] All alarm records of the bottleneck production equipment are obtained. Combined with the critical values ​​of blockage and material shortage duration and the critical value of overall line downtime, all alarm records are assigned to multiple bottleneck data sets, and corresponding processing priorities are set for each bottleneck data set.

[0011] In an optional implementation, the step of calculating the critical values ​​for blockage and material shortage durations and the critical values ​​for overall line downtime corresponding to the bottleneck production equipment using the equipment panoramic state diagram algorithm includes:

[0012] For the bottleneck production equipment, obtain the time point at which the entire production line stops;

[0013] Search backwards from the point when the entire production line stopped on the timeline to obtain the first alarm trigger point for a single machine.

[0014] Starting from the first alarm trigger point of a single machine, the operating status of each production equipment at each time point is obtained through traversal, until the time point of the entire line shutdown is reached.

[0015] Based on the operating status of each production equipment at each time point obtained through traversal, the critical values ​​for the duration of blockage and material shortage, as well as the critical value for the duration of the entire production line downtime, are obtained.

[0016] In an optional implementation, the step of obtaining the critical values ​​for blockage / material shortage duration and the critical value for overall line downtime based on the operating status of each production device at each time point obtained through traversal includes:

[0017] The production equipment that triggered the alarm at the first single-machine alarm trigger point is used as the benchmark production equipment, and the operating status of each front-end process production equipment and each back-end process production equipment of the benchmark production equipment at each time point is obtained.

[0018] The first time point when the operating status of the production equipment in the upstream process or the production equipment in the downstream process is abnormal, the second time point when the operating status of both the production equipment in the upstream process and the production equipment in the downstream process is abnormal, and the third time point when the operating status of all production equipment on the entire line is abnormal.

[0019] The critical values ​​for the duration of blockage and material shortage, and the critical value for the duration of the entire production line shutdown are obtained based on the first time point, the second time point, and the third time point.

[0020] In an optional implementation, the critical value for the duration of blockage or material shortage includes a first critical value that causes abnormal operation of the production equipment in the upstream process or the production equipment in the downstream process, and a second critical value that causes abnormal operation of the production equipment in the upstream process and the production equipment in the downstream process.

[0021] The steps of obtaining the critical values ​​for blockage and material shortage durations and the critical values ​​for overall line downtime based on the first time point, the second time point, and the third time point include:

[0022] The first duration from the first single-machine alarm trigger point to the first time point is taken as the first duration threshold, the second duration from the first single-machine alarm trigger point to the second time point is taken as the second duration threshold, and the third duration from the first single-machine alarm trigger point to the third time point is taken as the entire line shutdown duration threshold.

[0023] In an optional implementation, each of the production devices has a sequentially set device number and numerical status information;

[0024] The steps of obtaining the first time point when the operating status of the upstream or downstream production equipment becomes abnormal, the second time point when the operating status of both the upstream and downstream production equipment becomes abnormal, and the third time point when the operating status of all production equipment on the entire production line becomes abnormal include:

[0025] For the numerical status information of each of the front-end process production equipment and the numerical status information of each of the back-end process production equipment, one type is processed into decimal form and the other type is processed into integer form;

[0026] For each point in time, the digitized status information of all production equipment at that point in time is accumulated;

[0027] Based on the decimal and integer information in the accumulated result, obtain the first time point when the production equipment in the front-end process or the production equipment in the back-end process experiences an abnormal operating status, and the second time point when the production equipment in the front-end process or the production equipment in the back-end process experiences an abnormal operating status.

[0028] The decimal part of the accumulated result is restored to integer form, and combined with the integer part of the accumulated result and the total number of production equipment on the production line, the third time point at which all production equipment on the entire line experiences an abnormal operating status is determined.

[0029] In an optional implementation, the step of obtaining the critical values ​​for blockage / material shortage duration and the critical value for overall line downtime based on the operating status of each production device at each traversed time point further includes:

[0030] For each time point, production equipment that is not adjacent to the benchmark production equipment and whose operation status is abnormal at that time point is excluded.

[0031] In an optional implementation, the operational data includes the average time between failures per unit time.

[0032] The step of determining the bottleneck production equipment based on the operating data of each of the production equipment includes:

[0033] The average failure interval time per unit time for each of the aforementioned production equipment was statistically analyzed.

[0034] The production equipment with the shortest average failure interval per unit time is identified as the bottleneck production equipment.

[0035] In an optional implementation, the operation data includes the duration of material shortage and / or the duration of material blockage per unit time.

[0036] The step of determining the bottleneck production equipment based on the operating data of each of the production equipment includes:

[0037] The duration of material shortage and / or material blockage of each of the aforementioned production equipment within multiple unit time periods is statistically analyzed.

[0038] The average material shortage duration and / or material blockage duration per unit time is calculated based on the material shortage duration and / or material blockage duration per unit time.

[0039] The production equipment with the longest average material shortage time and / or blockage time per unit time is identified as the bottleneck production equipment.

[0040] In an optional implementation, the method further includes:

[0041] Identify alarm records with causal relationships among multiple alarm records in the multiple bottleneck data sets;

[0042] Delete all alarm records except the first alarm record from alarm records that have a causal relationship.

[0043] Secondly, this application provides a production bottleneck factor handling device, the device comprising:

[0044] The acquisition module is used to obtain the operation data of each production equipment on the production line;

[0045] The determination module is used to identify the bottleneck production equipment based on the operating data of each of the production equipment.

[0046] The calculation module is used to calculate the critical values ​​of blockage and material shortage duration and the critical value of overall line downtime for each bottleneck production equipment using the equipment panoramic status diagram algorithm.

[0047] The setting module is used to obtain all alarm records of the bottleneck production equipment, combine the blockage and material shortage duration thresholds and the overall line downtime thresholds, classify all alarm records into multiple bottleneck data sets, and set corresponding processing priorities for each bottleneck data set.

[0048] Thirdly, this application provides an electronic device, comprising:

[0049] At least one processor;

[0050] and a memory connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method steps as described in any of the foregoing embodiments.

[0051] Fourthly, this application provides a computer-readable storage medium storing executable instructions, which, when executed by a processor, implement the method steps described in any of the foregoing embodiments.

[0052] The beneficial effects of the embodiments of this application include, for example:

[0053] This application provides a method, apparatus, electronic device, and storage medium for handling production bottlenecks. By identifying the bottleneck production equipment and using an equipment status map algorithm to determine the corresponding critical values ​​for blockage / material shortage duration and overall line downtime, the alarm records of the bottleneck production equipment are then categorized and prioritized. This priority-based bottleneck handling is more scientific and can significantly improve production efficiency. Attached Figure Description

[0054] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 A flowchart of a production bottleneck factor handling method provided in an embodiment of this application;

[0056] Figure 2 for Figure 1 A flowchart of the sub-steps included in step S12;

[0057] Figure 3 for Figure 1 Another flowchart of the sub-steps included in step S12;

[0058] Figure 4 for Figure 1 A flowchart of the sub-steps included in step S13;

[0059] Figure 5 for Figure 4A flowchart of the sub-steps included in step S134;

[0060] Figure 6 This is a schematic diagram of the overall state of the device provided in the embodiments of this application;

[0061] Figure 7 for Figure 5 A flowchart of the sub-steps included in step S1342;

[0062] Figure 8 The flowchart of the deletion method in the production bottleneck factor handling method provided in the embodiments of this application;

[0063] Figure 9 Functional block diagram of the production bottleneck factor handling device provided in the embodiments of this application;

[0064] Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0065] Icons: 100 - Production bottleneck factor handling device; 110 - Acquisition module; 120 - Determination module; 130 - Calculation module; 140 - Setting module; 210 - Processor; 220 - Memory; 230 - Communication bus; 240 - Computer program. Detailed Implementation

[0066] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0067] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0068] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0069] Furthermore, the terms "first," "second," and "third," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance. The term "multiple" as used in the claims and description of this application refers to at least two.

[0070] It should be noted that, where there is no conflict, the features in the embodiments of this application can be combined with each other.

[0071] Please see Figure 1 This application provides a method for handling production bottleneck factors, which includes the following steps:

[0072] S11, For each production equipment on the production line, obtain the operation data of each production equipment.

[0073] S12, Based on the operating data of each of the production equipment, the bottleneck production equipment is determined.

[0074] S13, for each bottleneck production equipment, use the equipment panoramic status diagram algorithm to calculate the critical values ​​of the blockage and material shortage duration and the critical value of the entire line downtime corresponding to the bottleneck production equipment.

[0075] S14: Obtain all alarm records of the bottleneck production equipment, and combine the blockage and material shortage duration thresholds and the overall line downtime thresholds to assign all alarm records to multiple bottleneck data sets, and set corresponding processing priorities for each bottleneck data set.

[0076] In this embodiment, a flow-type production line has multiple workstations, and each workstation may have corresponding production equipment. Therefore, a flow-type production line has multiple production devices. These multiple production devices are arranged sequentially on the flow-type production line according to the work sequence.

[0077] During production, each piece of equipment generates a significant amount of operational data. In this embodiment, each piece of equipment can connect to a gateway device, which can then acquire this operational data. To ensure accurate identification of bottleneck factors, this embodiment acquires the operational data of each piece of equipment under certain constraints. These constraints are defined as the normal ramp-up phase of the entire production line, ensuring sufficient material supply to the upstream processes and smooth material discharge to the downstream processes. This avoids adverse effects on bottleneck identification due to inaccurate data acquisition during non-stop rectification or no-load test runs.

[0078] The operational data obtained from each production device can include, but is not limited to, real-time output, equipment status, alarm records, actual minute-by-minute capacity, and cycle time data. Real-time output refers to the output of the current shift. Equipment status can be categorized as normal production, shutdown alarm, material blockage in the next process, and material shortage in the previous process. Alarm records can include shutdown alarm prompts and handling records. Actual minute-by-minute capacity refers to the number of effective products produced per minute, while cycle time data refers to the time consumed for each action cycle.

[0079] In the production process, all the production actions that complete a process step are usually called a cycle. Each cycle may consist of one or more action groups, and each action group consists of multiple production actions.

[0080] In a flow production line, the main factors affecting production efficiency are blockages, material shortages, and malfunctions. For multiple production machines, blockages, material shortages, and malfunctions can occur on any of them. However, production machines experiencing more severe blockages, material shortages, or malfunctions have a greater impact on overall production efficiency. In this embodiment, such production machines are referred to as bottleneck production machines.

[0081] In this embodiment, the focus is on analyzing the relevant information of bottleneck production equipment, thereby realizing the analysis and processing of overall production bottleneck factors.

[0082] The equipment panoramic status map algorithm can analyze and process information about each production device in a flow production line and the operating status of each device at various points in time. This analysis yields corresponding critical values ​​for the duration of blockages and material shortages for each bottleneck production device, as well as the critical value for the overall line downtime. Specifically, the critical value for the duration of blockages and material shortages represents the minimum duration that causes blockages or material shortages in adjacent production devices, while the critical value for the overall line downtime represents the minimum duration that a particular production device causes a complete line downtime.

[0083] Bottleneck production equipment may trigger multiple alarms during production. Some alarms may be prolonged, potentially affecting the operational status of upstream or downstream equipment, or even causing abnormal operation of the entire production line. Alternatively, multiple alarms may occur but recover quickly, possibly without causing production stoppages for other equipment.

[0084] Based on the aforementioned determination of the critical values ​​for blockage, material shortage duration, and overall line downtime, each alarm record from the bottleneck production equipment can be assigned to a corresponding bottleneck data set to determine which type each alarm record belongs to. It is evident that the alarm records contained in the multiple bottleneck data sets have varying degrees of importance; therefore, corresponding processing priorities can be set for each bottleneck data set.

[0085] For example, the highest processing priority can be set for the bottleneck data set that may cause abnormal operation of all production equipment on the entire production line, the second highest processing priority can be set for the bottleneck data set that may affect both upstream and downstream production equipment, and the lowest processing priority can be set for the bottleneck data set that may affect either upstream or downstream production equipment.

[0086] In this way, by identifying the bottleneck production equipment and combining it with the equipment panoramic status map algorithm to determine the corresponding critical values ​​for blockage, material shortage duration and overall line downtime, the alarm records of the bottleneck production equipment can be divided and prioritized. This allows for priority-based bottleneck factor handling, making the handling more scientific and greatly helping to improve production efficiency.

[0087] As mentioned above, one of the main factors hindering improvements in production efficiency is the occurrence of malfunctions, including their frequency and duration. Therefore, please refer to [further details]. Figure 2 In one implementation of this embodiment, the bottleneck production equipment can be determined from the perspective of the fault occurrence, which can be done in the following way:

[0088] S121A, Calculate the average failure interval time per unit time for each of the aforementioned production equipment.

[0089] S122A identifies the production equipment with the minimum average failure interval time per unit time as the bottleneck production equipment.

[0090] In this embodiment, the obtained operational data for each production device includes the average failure interval time per unit time, which can be, for example, an hour. For each production device, its average failure interval time can be calculated by averaging the obtained average failure interval times over multiple unit time periods. The production device with the smallest average failure interval time indicates that it experiences failures most frequently and for the longest duration. Therefore, this production device can be identified as the bottleneck production device.

[0091] In addition, blockages and material shortages are also factors that significantly impact production efficiency. Therefore, in this embodiment, please refer to... Figure 3 In one possible implementation, the bottleneck production equipment can also be identified based on the dimensions of blockage and material shortage, which can be achieved in the following way:

[0092] S121B, Statistically calculate the material shortage duration and / or material blockage duration of each of the aforementioned production equipment within multiple unit time periods.

[0093] S122B, based on the material shortage duration and / or blockage duration within the plurality of unit time periods, calculate the average material shortage duration and / or blockage duration within a unit time period.

[0094] S123B defines the production equipment with the longest average material shortage time and / or longest material blockage time per unit time as the bottleneck production equipment.

[0095] In this embodiment, the operational data obtained for each production device includes the duration of material shortage and / or material blockage within a unit of time, which can be in the unit of hour. When a production device experiences an excessively long duration of material shortage, it may be due to an abnormal operating status of the upstream production equipment, where the material feeding conditions are not met, resulting in the upstream production equipment being in a standby state before completing its processing. When a production device experiences an excessively long duration of material blockage, the conditions for the next material discharge are not met, which may lead to an abnormal operating status of the downstream production equipment.

[0096] It is evident that prolonged material shortages or blockages can impact other production equipment. In this embodiment, after obtaining the blockage or material shortage durations for each piece of equipment over multiple unit time periods, an average value can be calculated. The production equipment with the highest average material shortage or blockage duration is likely to significantly affect the operation of other production equipment, and this type of equipment is identified as a bottleneck production equipment.

[0097] Based on this, the critical values ​​for blockage / material shortage duration and overall line downtime can be determined specifically for bottleneck production equipment. Please refer to [link / reference]. Figure 4 In this embodiment, in one possible implementation, the critical values ​​for blockage / material shortage duration and the critical value for overall line downtime can be obtained as follows:

[0098] S131, for the bottleneck production equipment, obtain the time point when the entire production line stops.

[0099] S132, Search backwards on the timeline from the point when the entire line stopped to obtain the first alarm trigger point for a single machine.

[0100] S133, starting from the first alarm trigger point of a single machine, traverse the time points to obtain the operating status of each production equipment at each time point, until the time point when the entire line stops.

[0101] S134, based on the operating status of each production equipment at each time point obtained through traversal, obtain the critical values ​​for the duration of blockage and material shortage and the critical value for the duration of the entire production line downtime.

[0102] In this embodiment, production status can be analyzed within a statistical time period, such as within 24 hours or 48 hours, etc. The gateway device can record the operating status of each production device at each point in time within this statistical time period. The point in time can be a sampling point, and the interval between sampling points can be set as needed, without specific restrictions.

[0103] During the statistical period, one or more instances of complete line shutdown may occur, allowing us to obtain the exact time point of each shutdown. The complete line shutdown time point is the point at which all production equipment on the production line experiences an abnormal operating state, such as a shutdown. For each complete line shutdown time point, there is a prior initial alarm trigger point for a single machine. This initial alarm trigger point is the time point when the first production equipment issues an alarm; in this embodiment, the production equipment that first issues an alarm is the bottleneck production equipment. There should be a time period between the initial alarm trigger point for a single machine and the complete line shutdown time point.

[0104] Starting from the initial alarm trigger point of a single machine and traversing backwards, the operating status of the production equipment at each point in time can be obtained up to the point when the entire production line is shut down. These points in time include the initial alarm trigger point of a single machine, the point when the entire production line is shut down, and all points in between.

[0105] Using the above method, based on the operating status of all production equipment at all points in time from the first alarm to the complete line shutdown, the critical values ​​for the duration of blockage and material shortage caused by the bottleneck production equipment to cause the first abnormal operating status of the upstream and downstream production equipment and the entire production line, as well as the critical value for the entire line shutdown, can be calculated.

[0106] In this embodiment, please refer to Figure 5 In one possible implementation, the critical values ​​for blockage / material shortage duration and overall line downtime duration can be obtained based on the operating status of each production device at each time point through the following methods:

[0107] S1341, take the production equipment that alarms at the first single-machine alarm trigger point as the reference production equipment, and obtain the operating status of each front-end process production equipment and each back-end process production equipment of the reference production equipment at each time point.

[0108] S1342, obtain the first time point when the operating status of the production equipment in the front-end process or the production equipment in the back-end process is abnormal, the second time point when the operating status of both the production equipment in the front-end process and the production equipment in the back-end process is abnormal, and the third time point when the operating status of all production equipment on the entire line is abnormal.

[0109] S1343, obtain the critical values ​​for the duration of blockage and material shortage and the critical value for the duration of the entire production line shutdown based on the first time point, the second time point and the third time point.

[0110] Please refer to the following: Figure 6 , Figure 6The horizontal axis can be the time axis, and the vertical axis can be the number of each production device on the production line. The information in the boxes represents the operating status of the corresponding production device. Blank boxes indicate normal operating status, while other boxes indicate abnormal operating status.

[0111] like Figure 6 As shown, the production equipment that triggered the alarm for the first time when a single machine alarm was set was production equipment numbered M14 (marked as J when the operating status is abnormal). This means that this production equipment can be used as the baseline production equipment (essentially the bottleneck production equipment). Therefore, production equipment numbered M1 to M13 are the upstream process production equipment of the baseline production equipment (marked as X when the operating status is abnormal), and production equipment numbered M15 to M26 are the downstream process production equipment of the baseline production equipment (marked as H when the operating status is abnormal).

[0112] When the baseline production equipment triggers an alarm, it will subsequently cause abnormal operating conditions in both upstream and downstream production equipment. It's possible that the upstream equipment will malfunction first, or vice versa. For example, Figure 6 As shown, at time point 9, the baseline production equipment M14 will first cause an operational anomaly in its downstream production equipment, and then, moving forward, at time point 13, cause an operational anomaly in its upstream production equipment. From time point 13 onwards, both the upstream and downstream production equipment will experience operational anomalies. And by time point 29, all production equipment on the production line will be experiencing operational anomalies.

[0113] like Figure 6 As shown, the first time point obtained can be time point 9, the second time point can be time point 13, and the third time point can be time point 29. By obtaining the critical time points that cause changes in the state of production equipment on the production line, the critical values ​​for blockage / material shortage duration and the critical value for overall line downtime can be obtained.

[0114] In this embodiment, specifically, the critical values ​​for the duration of blockage and material shortage include a first critical value that causes abnormal operation of the production equipment in the upstream process or the production equipment in the downstream process, and a second critical value that causes abnormal operation of the production equipment in the upstream process and the production equipment in the downstream process.

[0115] The first duration from the first single-unit alarm trigger point to the first time point can be used as the first duration threshold, the second duration from the first single-unit alarm trigger point to the second time point can be used as the second duration threshold, and the third duration from the first single-unit alarm trigger point to the third time point can be used as the overall line shutdown duration threshold.

[0116] In the process of electronic devices processing data, numerical information processing is more convenient and efficient. Therefore, in this embodiment, each production device on the production line has a sequentially set device number and numerical status information, which represents the operating status. For example, assuming there are 5 production devices on the production line, the device numbers can be 1-5, and the numerical status information can be 0 or 1, where 1 represents an abnormal operating status and 0 represents a normal operating status. At a certain point in time, the relevant information of these 5 production devices can be recorded as {(1,1),(2,0),(3,0),(4,0),(5,0)}, indicating that except for the first production device which is in an abnormal state, the other production devices are in a normal state.

[0117] Thus, the first, second, and third time points can be obtained based on numerical information. For details, please refer to [link to relevant documentation]. Figure 7 This can be achieved in the following ways:

[0118] S13421, for the numerical status information of each of the front-end process production equipment and the numerical status information of each of the back-end process production equipment, one type is processed into decimal form and the other type is processed into integer form.

[0119] S13422, for each time point, the numerical status information of all production equipment except the reference production equipment at the time point is accumulated.

[0120] S13423, based on the decimal and integer information in the accumulated result, obtain the first time point when the production equipment in the upstream process or the production equipment in the downstream process experiences an abnormal operating state, and the second time point when the production equipment in the upstream process or the production equipment in the downstream process experiences an abnormal operating state.

[0121] S13424, restore the decimal part of the accumulated result to integer form, and combine the integer part of the accumulated result with the total number of production equipment on the production line to determine the third time point when all production equipment on the entire line experiences an abnormal operating status.

[0122] In this embodiment, to distinguish the state changes of upstream and downstream production equipment, one type of numerical state information can be processed into decimal form, while the other type can be retained as an integer. For example, the numerical state information of each upstream production equipment can be divided by 100; if its numerical state information is 1, the result is 0.01. For downstream production equipment, if its numerical state information is 1, it is retained as the integer 1. Alternatively, the numerical state information of each downstream production equipment can be processed into decimal form, while the numerical state information of each upstream production equipment can be retained as an integer; this embodiment does not impose specific limitations on this approach.

[0123] Thus, by accumulating the numerical status information of all production equipment except the base production equipment at a certain point in time, the fractional part of the accumulated result can represent the overall status of the production equipment in the preceding process, and the integer part can represent the overall status of the production equipment in the following process. Alternatively, the integer part of the accumulated result can represent the overall status of the production equipment in the following process, and the fractional part can represent the overall status of the production equipment in the preceding process.

[0124] Thus, if any abnormal operation occurs in the production equipment of the upstream or downstream processes, the decimal or integer part of the information will change accordingly to represent it, thereby determining the first and second time points.

[0125] The status of each production device can be characterized by the fractional and integer parts of the information. By combining the total number of production devices, it can be determined whether all production devices are malfunctioning, and thus the third time point can be determined.

[0126] In one possible implementation, if there are 5 production machines on the production line, numbered 1-5, 1 represents an abnormal operating status and 0 represents a normal operating status. Among them, the 3rd production machine is the base production machine. The numerical status information of the production machines in the preceding process is processed in decimal form, while the numerical status information of the production machines in the following process is retained in integer form.

[0127] If at a certain moment, the integer part or the decimal part of the accumulated result is greater than 0, it indicates that the production equipment in the downstream process or the production equipment in the upstream process is in an abnormal state. This moment can be recorded as the first moment.

[0128] If the first time point has been recorded, we can then determine whether the integer part of the accumulated result is greater than 0 and the decimal part is greater than 0. If so, it means that both the production equipment in the downstream process and the production equipment in the upstream process have experienced abnormalities, and the time point can be recorded as the second time point.

[0129] If the second time point has been recorded, the decimal part of the accumulated result can be restored to integer form. The restored integer and the integer part of the accumulated result are added together. If the result equals the total number of production equipment (5), it indicates that all production equipment on the production line is malfunctioning. This time point can be recorded as the third time point.

[0130] In this embodiment, it is considered that during the production process, in addition to anomalies caused by adjacent production equipment, there may also be alarms and shutdowns caused by other reasons. These alarms and shutdowns are referred to as noise points. At a certain point in time, these noise points are often not adjacent to production equipment with abnormal operating conditions, but are separated by production equipment with normal operating conditions.

[0131] To avoid such noise affecting the analysis, it can be excluded during the calculation of critical values ​​for blockage and material shortage durations and critical values ​​for overall line downtime.

[0132] Specifically, for each time point, production equipment with abnormal operation status at that time point, or production equipment with abnormal operation status that is adjacent to the benchmark production equipment but not adjacent to the benchmark production equipment, is removed.

[0133] For example, such as Figure 6 As shown, at time point 11, production equipment numbered M6 experienced an anomaly. However, this anomaly was caused by other reasons, not by the reference production equipment. This production equipment, which is not adjacent to the reference production equipment and is also not adjacent to other production equipment with abnormal operating conditions, can be identified as a noise point and can be eliminated.

[0134] For example, Figure 6 As shown, at time point 16, production equipment M12 experienced an anomaly. However, production equipment M12 is adjacent to production equipment M13, and production equipment M13 is in an abnormal state and is adjacent to the baseline production equipment. Therefore, it can be seen that the anomaly of production equipment M12 may be caused by the anomaly of other production equipment, rather than so-called noise, and does not need to be removed.

[0135] After noise removal, the first time point, the second time point, and the third time point are determined based on the above numerical processing method. Then, the first time point and the second time point, which are included in the blockage and material shortage time thresholds, as well as the overall line downtime threshold, are determined.

[0136] Based on this, when multiple alarm records of bottleneck production equipment are obtained over a period of time, these alarm records can be divided into multiple bottleneck data sets.

[0137] Assuming the data can be divided into three bottleneck sets, for each alarm record, if the alarm duration falls between the first and second duration thresholds, it is assigned to the first bottleneck set. If the alarm duration falls between the second duration threshold and the overall line downtime threshold, it is assigned to the second bottleneck set. If the alarm duration exceeds the overall line downtime threshold, it is assigned to the third bottleneck set. If the alarm duration is less than the first duration threshold, no action is taken.

[0138] In this way, the processing priority of the third bottleneck data set to the first bottleneck data set can be decreased sequentially. When processing alarms, alarms in the third bottleneck data set can be processed first, followed by alarms in the second bottleneck data set and then the first bottleneck data set.

[0139] In this embodiment, considering the potential correlation between alarm events—for example, alarm event A may cause alarm event B to occur—if alarm event A is processed, alarm event B will be automatically deactivated. Therefore, to avoid unnecessary processing, after dividing the data into multiple bottleneck sets, deletion can be performed using the following method (please refer to the relevant documentation). Figure 8 :

[0140] S15, determine the alarm records that have a causal relationship among the multiple alarm records in the multiple bottleneck data sets.

[0141] S16, delete all alarm records except the first alarm record from the alarm records that have a causal relationship.

[0142] In this embodiment, alarm records with causal relationships can be determined by manual calibration, or a processing model can be trained by using alarm records with causal relationships as samples. When applying the model, alarm records with causal relationships can be directly selected. This embodiment does not impose any specific restrictions on this.

[0143] For alarm records with a causal relationship, each alarm record has a corresponding alarm time point. Alarm records whose alarm time point is not the earliest can be deleted. These types of alarm records do not require processing; once the earliest alarm record with a causal relationship is processed and resolved, the alarm event will be automatically cleared, saving processing workload.

[0144] Please see Figure 9 This application also provides a production bottleneck factor processing device 100, which includes an acquisition module 110, a determination module 120, a calculation module 130, and a setting module 140. The functions of each functional module of the production bottleneck factor processing device 100 will be described in detail below.

[0145] The acquisition module 110 is used to obtain the operation data of each production equipment on the production line;

[0146] It is understood that the acquisition module 110 can be used to perform the above step S11. For details on the implementation of the acquisition module 110, please refer to the above content related to step S11.

[0147] The determination module 120 is used to determine the bottleneck production equipment based on the operating data of each of the production equipment.

[0148] It is understood that the determining module 120 can be used to perform the above step S12. For details on the implementation of the determining module 120, please refer to the above content related to step S12.

[0149] The calculation module 130 is used to calculate the critical values ​​of the blockage and material shortage duration and the critical value of the entire line downtime for each bottleneck production equipment using the equipment panoramic status diagram algorithm.

[0150] It is understood that the calculation module 130 can be used to perform the above step S13. For details on the implementation of the calculation module 130, please refer to the above content related to step S13.

[0151] The setting module 140 is used to obtain all alarm records of the bottleneck production equipment, and combine the blockage and material shortage duration thresholds and the overall line downtime thresholds to classify all alarm records into multiple bottleneck data sets, and set corresponding processing priorities for each bottleneck data set.

[0152] It is understood that the setting module 140 can be used to perform the above step S14. For details on the implementation of the setting module 140, please refer to the above content related to step S14.

[0153] In one possible implementation, the operational data includes the average fault interval time per unit time, and the determination module 120 described above can be used to:

[0154] The average failure interval time of each production equipment is calculated per unit time; the production equipment with the smallest average failure interval time per unit time is identified as the bottleneck production equipment.

[0155] In one possible implementation, the operation data includes the duration of material shortage and / or the duration of material blockage per unit time, and the determination module 120 can be used for:

[0156] The material shortage duration and / or blockage duration of each production equipment are statistically analyzed over multiple unit time periods. Based on the material shortage duration and / or blockage duration over multiple unit time periods, the average material shortage duration and / or blockage duration per unit time is calculated. The production equipment with the largest average material shortage duration and / or blockage duration per unit time is identified as the bottleneck production equipment.

[0157] In one possible implementation, the production bottleneck factor handling device 100 further includes a removal module, which can be used for:

[0158] Identify alarm records with causal relationships among multiple alarm records in the multiple bottleneck data sets; delete the alarm records with causal relationships except for the first alarm record.

[0159] In one possible implementation, the computing module 130 described above can be used for:

[0160] For the bottleneck production equipment, the time point at which the entire production line stops is obtained; searching backwards from the time point on the time axis, the first alarm trigger point for a single machine is obtained; starting from the first alarm trigger point for a single machine, the operating status of each production equipment at each time point is obtained, until the time point of the entire production line stops; based on the operating status of each production equipment at each time point obtained, the critical values ​​for blockage and material shortage durations and the critical value for the entire production line stoppage duration are obtained.

[0161] In one possible implementation, the computing module 130 described above can be used for:

[0162] The production equipment that triggered the alarm at the first single-machine alarm trigger point is taken as the benchmark production equipment. The operating status of each upstream and downstream production equipment of the benchmark production equipment at each time point is obtained. The first time point when the operating status of the upstream or downstream production equipment is abnormal, the second time point when the operating status of both upstream and downstream production equipment is abnormal, and the third time point when the operating status of all production equipment on the entire line is abnormal are obtained. The critical values ​​for blockage and material shortage duration and the critical value for the entire line downtime are obtained based on the first, second, and third time points.

[0163] In one possible implementation, the critical values ​​for the duration of blockage or material shortage include a first critical value that causes abnormal operation of the upstream or downstream production equipment, and a second critical value that causes abnormal operation of both upstream and downstream production equipment. Specifically, the calculation module 130 can be used for:

[0164] The first duration from the first single-machine alarm trigger point to the first time point is taken as the first duration threshold, the second duration from the first single-machine alarm trigger point to the second time point is taken as the second duration threshold, and the third duration from the first single-machine alarm trigger point to the third time point is taken as the entire line shutdown duration threshold.

[0165] In one possible implementation, each of the production devices has a sequentially assigned device number and numerical status information, and the aforementioned calculation module 130 can be used for:

[0166] For the numerical status information of each of the aforementioned front-end process production equipment and each of the aforementioned back-end process production equipment, one type is processed into decimal form and the other into integer form. For each time point, the numerical status information of all production equipment except the reference production equipment at that time point is accumulated. Based on the decimal and integer parts of the accumulated result, the first time point when the front-end or back-end process production equipment experiences an abnormal operating status, and the second time point when both the front-end and back-end process production equipment experience an abnormal operating status are obtained. The decimal part of the accumulated result is restored to integer form, and combined with the integer part of the accumulated result and the total number of production equipment on the production line, the third time point when all production equipment on the entire line experiences an abnormal operating status is determined.

[0167] In one possible implementation, the computing module 130 described above can also be used for:

[0168] For each time point, production equipment that is not adjacent to the benchmark production equipment and whose operation status is abnormal at that time point is excluded.

[0169] Please see Figure 10 This application also provides an electronic device. The electronic device includes: at least one processor 210; and a memory 220 connected to the at least one processor 210. The processor 210 and the memory 220 are connected via a communication bus 230.

[0170] The memory 220 stores instructions, i.e., computer program 240, that can be executed by at least one processor 210. The instructions are executed by the at least one processor 210 to enable the at least one processor 210 to implement the steps of the production bottleneck factor handling method as described in any of the above embodiments.

[0171] This application also provides a computer storage medium storing executable instructions, which, when executed by a processor, implement the steps of the production bottleneck factor handling method as described in any of the above embodiments.

[0172] In summary, the production bottleneck factor processing method, apparatus, electronic device, and storage medium provided in this application obtain the operating data of each production device on the production line, and determine the bottleneck production device based on the operating data of each production device. For each bottleneck production device, the critical values ​​for blockage and material shortage duration and the critical value for overall line downtime are calculated using the equipment panoramic status map algorithm. Then, all alarm records of the bottleneck production device are obtained, and combined with the critical values ​​for blockage and material shortage duration and the critical values ​​for overall line downtime, all alarm records are divided into multiple bottleneck data sets, and corresponding processing priorities are set for each bottleneck data set.

[0173] This solution identifies bottleneck production equipment and uses an equipment status map algorithm to determine the corresponding critical values ​​for blockage, material shortage, and overall line downtime. It then categorizes the alarm records of the bottleneck equipment and prioritizes them accordingly. This priority-based approach to bottleneck handling is more scientific and can significantly improve production efficiency.

[0174] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for handling production bottleneck factors, characterized in that, The method includes: For each piece of production equipment on the production line, obtain the operating data of each piece of production equipment; Based on the operating data of each of the aforementioned production equipment, the bottleneck production equipment is identified. For the bottleneck production equipment, the time point when the entire production line stopped is obtained; the first alarm trigger point for a single machine is obtained by searching backward from the time point of the entire production line stop on the time axis; Starting from the first alarm trigger point of a single machine, the operating status of each production equipment at each time point is obtained through traversal, until the time point of the entire line shutdown is reached. Based on the operating status of each production equipment at each time point obtained through traversal, the critical values ​​for blockage and material shortage durations and the critical values ​​for overall line downtime are obtained. All alarm records of the bottleneck production equipment are obtained. Combined with the critical values ​​of blockage and material shortage duration and the critical value of overall line downtime, all alarm records are assigned to multiple bottleneck data sets, and corresponding processing priorities are set for each bottleneck data set.

2. The method for handling production bottleneck factors according to claim 1, characterized in that, The step of obtaining the critical values ​​for blockage / material shortage duration and the critical value for overall line downtime based on the operating status of each production equipment at each time point obtained through traversal includes: The production equipment that triggered the alarm at the first single-machine alarm trigger point is used as the benchmark production equipment, and the operating status of each front-end process production equipment and each back-end process production equipment of the benchmark production equipment at each time point is obtained. The first time point when the operating status of the production equipment in the upstream process or the production equipment in the downstream process is abnormal, the second time point when the operating status of both the production equipment in the upstream process and the production equipment in the downstream process is abnormal, and the third time point when the operating status of all production equipment on the entire line is abnormal. The critical values ​​for the duration of blockage and material shortage, and the critical value for the duration of the entire production line shutdown are obtained based on the first time point, the second time point, and the third time point.

3. The method for handling production bottleneck factors according to claim 2, characterized in that, The critical values ​​for the duration of blockage and material shortage include a first critical value that causes abnormal operation of the production equipment in the upstream process or the production equipment in the downstream process, and a second critical value that causes abnormal operation of the production equipment in the upstream process and the production equipment in the downstream process. The steps of obtaining the critical values ​​for blockage and material shortage durations and the critical values ​​for overall line downtime based on the first time point, the second time point, and the third time point include: The first duration from the first single-machine alarm trigger point to the first time point is taken as the first duration threshold, the second duration from the first single-machine alarm trigger point to the second time point is taken as the second duration threshold, and the third duration from the first single-machine alarm trigger point to the third time point is taken as the entire line shutdown duration threshold.

4. The method for handling production bottleneck factors according to claim 2, characterized in that, Each of the aforementioned production equipment has a sequentially set equipment number and numerical status information; The steps of obtaining the first time point when the operating status of the upstream or downstream production equipment becomes abnormal, the second time point when the operating status of both the upstream and downstream production equipment becomes abnormal, and the third time point when the operating status of all production equipment on the entire production line becomes abnormal include: For the numerical status information of each of the front-end process production equipment and the numerical status information of each of the back-end process production equipment, one type is processed into decimal form and the other type is processed into integer form; For each point in time, the digitized status information of all production equipment at that point in time is accumulated; Based on the decimal and integer information in the accumulated result, obtain the first time point when the production equipment in the front-end process or the production equipment in the back-end process experiences an abnormal operating status, and the second time point when the production equipment in the front-end process or the production equipment in the back-end process experiences an abnormal operating status. The decimal part of the accumulated result is restored to integer form, and combined with the integer part of the accumulated result and the total number of production equipment on the production line, the third time point at which all production equipment on the entire line experiences an abnormal operating status is determined.

5. The method for handling production bottleneck factors according to claim 2, characterized in that, The step of obtaining the critical values ​​for blockage and material shortage durations and the critical values ​​for overall line downtime based on the operating status of each production equipment at each time point obtained through traversal further includes: For each time point, production equipment that is not adjacent to the benchmark production equipment and whose operation status is abnormal at that time point is excluded.

6. The method for handling production bottleneck factors according to claim 1, characterized in that, The operational data includes the average interval between failures per unit time. The step of determining the bottleneck production equipment based on the operating data of each of the production equipment includes: The average failure interval time per unit time for each of the aforementioned production equipment was statistically analyzed. The production equipment with the shortest average failure interval per unit time is identified as the bottleneck production equipment.

7. The method for handling production bottleneck factors according to claim 1, characterized in that, The operational data includes the duration of material shortage and / or the duration of material blockage per unit time; The step of determining the bottleneck production equipment based on the operating data of each of the production equipment includes: The duration of material shortage and / or material blockage of each of the aforementioned production equipment within multiple unit time periods is statistically analyzed. Based on the material shortage duration and / or blockage duration within the multiple unit time periods, the average material shortage duration and / or blockage duration within a unit time period is calculated. The production equipment with the longest average material shortage time and / or blockage time per unit time is identified as the bottleneck production equipment.

8. The method for handling production bottleneck factors according to claim 1, characterized in that, The method further includes: Identify alarm records with causal relationships among multiple alarm records in the multiple bottleneck data sets; Delete all alarm records except the first alarm record from alarm records that have a causal relationship.

9. A device for handling production bottleneck factors, characterized in that, The apparatus for implementing the production bottleneck factor handling method according to any one of claims 1-8, the apparatus comprising: The acquisition module is used to obtain the operation data of each production equipment on the production line; The determination module is used to identify the bottleneck production equipment based on the operating data of each of the production equipment. The calculation module is used to calculate the critical values ​​of blockage and material shortage duration and the critical value of overall line downtime for each bottleneck production equipment using the equipment panoramic status diagram algorithm. The setting module is used to obtain all alarm records of the bottleneck production equipment, combine the blockage and material shortage duration thresholds and the overall line downtime thresholds, classify all alarm records into multiple bottleneck data sets, and set corresponding processing priorities for each bottleneck data set.

10. An electronic device, characterized in that, include: At least one processor; as well as, A memory connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor, when executed, to perform the steps of the method as described in any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores executable instructions that, when executed by a processor, implement the steps of the method as described in any one of claims 1 to 8.

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