Production line production efficiency optimization method and device

Abstract

The invention provides a production line production efficiency optimization method and device, and relates to the technical field of intelligent production scheduling, and the optimization method comprises the steps: obtaining equipment monitoring data; performing abnormity monitoring on the equipment monitoring data; when the abnormal production of the production line is monitored, according to the abnormal production of the production line, analyzing to obtain a trigger process corresponding to the abnormal production of the production line of the processed product; based on the triggering process, obtaining a compensation repair process and a compensation repair parameter corresponding to the processed product; and controlling the production line to continuously perform compensation repair on the processed product through the compensation repair process and the compensation repair parameters so as to optimize the defective rate of production of the production line. According to the optimization method and device provided by the invention, the increase of the total quantity of products produced by the production line in order to reach the preset yield due to the increase of the defective product quantity can be effectively reduced, so that the production efficiency of the production line with the preset yield is improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent production scheduling technology, and in particular to a method and apparatus for optimizing production line efficiency. Background Technology

[0002] Improving production line efficiency is key for companies to increase production capacity, improve product quality, and reduce production costs. Production capacity refers to the quantity of products a production line can produce within a given time frame.

[0003] In existing automated production processes, even before a predicted equipment failure occurs or before the failure actually happens, products may still be produced abnormally before the test data reaches the corresponding threshold. This can lead to products being processed abnormally in the corresponding equipment failure or malfunction process, resulting in an increased defect rate, wasted capacity, and a significant impact on the production efficiency of the preset order quantity. Summary of the Invention

[0004] The production line production efficiency optimization method and apparatus provided by this invention solves the technical problem in the prior art that, before a predicted equipment failure occurs or before the failure actually occurs, the products produced will still be abnormally processed in the corresponding equipment failure or equipment failure process due to abnormal production before the detection data value reaches the corresponding threshold, which leads to an increase in the defect rate, waste of production capacity, and a significant impact on the production efficiency of the preset order quantity.

[0005] To achieve the above and other related objectives, this invention provides a method for optimizing production line efficiency, comprising: acquiring equipment monitoring data; monitoring the equipment monitoring data for anomalies; when a production line anomaly is detected, analyzing the triggering process corresponding to the production line anomaly of the processed products; based on the triggering process, deriving the compensation and repair process and compensation and repair parameters corresponding to the processed products; and controlling the production line to continue to compensate and repair the processed products through the compensation and repair process and compensation and repair parameters, so as to optimize the defect rate of the production line.

[0006] In one embodiment of the present invention, monitoring equipment monitoring data for anomalies includes: comparing the equipment monitoring data with a corresponding production anomaly threshold; when the equipment monitoring data is detected to exceed the production anomaly threshold, a corresponding production line anomaly is obtained based on the equipment monitoring data; when the equipment monitoring data is not detected to exceed the production anomaly threshold, it indicates that there is no production line anomaly.

[0007] In one embodiment of the present invention, the fault induced by production line anomalies includes either a predicted fault or a real-time fault.

[0008] In one embodiment of the present invention, based on production line anomalies, the triggering process corresponding to the production line anomaly of the processed products is analyzed and obtained, including: based on the production line anomaly, the process directly affected by the production line anomaly is obtained; the difference between the equipment monitoring data and the corresponding production anomaly threshold is calculated to obtain the anomaly difference; based on the anomaly difference and the conversion coefficient of the processing item of the process corresponding to the process, the deviation increment of the processing item corresponding to the process is obtained; the deviation increment of the processing item is monitored for the deviation of the downstream process corresponding to the process; when the deviation increment of the processing item is less than the deviation threshold of the downstream process corresponding to the process, the process is designated as the triggering process; when the deviation increment of the processing item is greater than the deviation threshold of the downstream process corresponding to the process, the process is designated as the first triggering process, and the deviation monitoring of the downstream processes is continued according to the deviation increment of the processing item to select the second triggering process from the downstream process set composed of the downstream processes, and the first and second triggering processes are designated as triggering processes.

[0009] In one embodiment of the present invention, the deviation monitoring of downstream processes is continued according to the deviation increment of the action work processing item, so as to select the second triggering process from the set of downstream processes. The first triggering process and the second triggering process are used as triggering processes. This includes: obtaining the deviation increment of the downstream work processing item corresponding to each downstream process according to the deviation increment of the action work processing item and the conversion coefficient of the downstream process processing item corresponding to each downstream process. The calculation formula of the deviation increment of the downstream work processing item is: ,in, This indicates equipment monitoring data. Indicates the threshold for production anomalies. This represents the conversion factor for the processing item in the operational process. This indicates the number of downstream processes of the process that performs the action. Indicates the downstream of the action process. The conversion coefficient of the downstream process processing item corresponding to each downstream process; according to the process execution order, determine whether the deviation increment of the downstream work processing item corresponding to each downstream process is less than the downstream deviation threshold of the corresponding downstream process; take all downstream processes before the target downstream process and after the action process that are determined to be less than the downstream deviation threshold of the corresponding downstream process as the second trigger process, and take the first trigger process and the second trigger process as the trigger process.

[0010] In one embodiment of the present invention, based on the triggering process, the compensation and repair process and compensation and repair parameters corresponding to the processed product are obtained, including: based on the triggering process, performing compensation and repair conflict monitoring on the processed product after the triggering process; when it is detected that there is no compensation and repair conflict in the processed product, predicting the cumulative abnormal action data under the abnormal cumulative action time corresponding to each triggering process according to the triggering process and equipment monitoring data; and based on the cumulative abnormal action data, obtaining the compensation and repair process and compensation and repair parameters of the processed product.

[0011] In one embodiment of the present invention, based on a triggering process, compensation and repair conflict monitoring is performed on the processed products after the triggering process, including: obtaining the work execution order of the work processing items according to the triggering process and the work processing items corresponding to the triggering process; querying the conflict execution order in the compensation and repair conflict table; when there is a corresponding conflict execution order in the work execution order, obtaining the remaining execution order in the work execution order according to the conflict execution order; calculating the degree of abnormality according to the work processing item deviation increment, the processing item weight, the weight ratio of the remaining execution order relative to the work execution order, the first indicator score conversion coefficient corresponding to the weight ratio, the second indicator score conversion coefficient corresponding to each work processing item in the work execution order, the first weight corresponding to the work processing item deviation increment, and the second weight corresponding to the weight ratio, to obtain an abnormality index. The calculation formula for the abnormality index is as follows: , This represents the weight of each work item in the remaining execution order. This indicates the weight of each processing item in the work execution sequence. This represents the work item deviation increment for each work item in the remaining execution sequence. Indicates the first weight. Indicates the second weight. Indicates the number of work items in the remaining execution order. Indicates the number of work items in the remaining execution order. Indicates the weight percentage. This represents the conversion coefficient of the first indicator score corresponding to the weighting percentage. This represents the conversion coefficient of the second indicator score for each work processing item in the work execution sequence. When the anomaly index reaches the indicator threshold, it indicates that there is a compensation and repair conflict for the processed product, and the compensation and repair of the processed product is stopped. When the anomaly index does not reach the indicator threshold, it indicates that there is no compensation and repair conflict for the processed product, and the compensation and repair of the processed product continues.

[0012] In one embodiment of the present invention, based on triggering process and equipment monitoring data, the cumulative abnormal effect data under the cumulative abnormal effect time corresponding to each triggering process is predicted, including: dividing the work processing item deviation increment of the processed product when executing the corresponding triggering process into multiple processing batches according to the change range of equipment monitoring data over time, to obtain the work processing item deviation stage increment of the processed product under each processing batch; and predicting the cumulative abnormal effect data under the cumulative abnormal effect time corresponding to each triggering process based on the work processing item deviation stage increment and the corresponding triggering process's cumulative abnormal effect time on the processed product. The calculation formula for the cumulative abnormal effect data is as follows: , This represents the cumulative data of abnormal effects. Indicates the increment of deviation in work processing items. Indicates the number of processing batches in the division. This represents the interval of deviation increment for each processing batch. This indicates the stage increment of the deviation in the work processing item. This indicates the cumulative time that the corresponding triggering process has affected the abnormality of the processed product.

[0013] In one embodiment of the present invention, based on the cumulative data of abnormal effects, a compensation and repair process and compensation and repair parameters for the processed product are derived, including: obtaining a compensation and repair process for the processed product according to the triggering process corresponding to the cumulative data of abnormal effects, wherein the compensation and repair process includes either a backup repair process or a triggering process; obtaining the compensation and repair processing parameter value corresponding to the compensation and repair process by looking up a table according to the cumulative data of abnormal effects; and obtaining the compensation and repair processing time according to the compensation and repair processing parameter value, the cumulative data of abnormal effects, and the processing volume conversion coefficient, wherein the calculation formula for the compensation and repair processing time is: ,in, This represents the cumulative data of abnormal effects. This indicates the parameter value for compensation and repair processing. This represents the processing volume conversion coefficient; the compensation and repair processing parameter values ​​and the compensation and repair processing time are used as compensation and repair parameters.

[0014] To achieve the above and other related objectives, the present invention also provides a production line efficiency optimization device, comprising: a data acquisition unit for acquiring equipment monitoring data; an anomaly monitoring unit for monitoring anomalies in the equipment monitoring data; an anomaly analysis unit for analyzing and determining the triggering process corresponding to the production line anomaly when a production line anomaly is detected; a compensation calculation unit for determining the compensation and repair process and compensation and repair parameters corresponding to the processed product based on the triggering process; and a compensation control unit for controlling the production line to continue to compensate and repair the processed product through the compensation and repair process and compensation and repair parameters, thereby optimizing the defect rate of the production line.

[0015] The beneficial effects of this invention are as follows: The production line efficiency optimization method and apparatus proposed in this invention, by monitoring equipment abnormalities during the production line process, can identify relevant processing steps for product handling abnormalities as trigger steps when production line abnormalities are detected. Based on the trigger steps, compensation and repair steps and compensation and repair parameters for the processed products are analyzed and calculated. Then, based on the compensation and repair steps and compensation and repair parameters, the processed products are further compensated and repaired to reduce the increase in defect rate caused by production line abnormalities. Through further compensation and repair of the processed products in such cases, the increase in the amount of defective products can be effectively reduced, thereby reducing the increase in the total amount of products produced by the production line to achieve the preset output, and thus improving the production line efficiency of the preset output. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0017] In the attached diagram: Figure 1 A flowchart illustrating the production line efficiency optimization method provided in this embodiment of the invention.

[0018] Figure 2 The diagram shown is a structural block diagram of a production line efficiency optimization device provided in an embodiment of the present invention.

[0019] Figure 3 The diagram shown is a structural schematic of an electronic device according to an embodiment of the present invention.

[0020] The attached figures are labeled as follows: Electronic device 1; Production line efficiency optimization system 11; Memory 12; Processor 13; Data acquisition unit 111; Anomaly monitoring unit 112; Anomaly analysis unit 113; Compensation calculation unit 114; Compensation control unit 115. Detailed Implementation

[0021] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0022] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. The drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0023] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0024] This invention provides a method for optimizing production line efficiency. By monitoring equipment malfunctions during the production process, it can identify relevant processing steps as trigger steps when production malfunctions occur. Based on these trigger steps, it analyzes and calculates compensation and repair steps and parameters for the processed products. Then, based on these compensation and repair steps and parameters, it further compensates and repairs the processed products to reduce the increase in defect rate caused by production line malfunctions. By further compensating and repairing the processed products in such cases, it can effectively reduce the increase in the total number of products produced by the production line to achieve the preset output due to the increase in defective products, thereby improving the production line efficiency for the preset output.

[0025] Figure 1 A flowchart of a production line efficiency optimization method according to an exemplary embodiment of this application is shown, applied to a production line efficiency optimization system, including steps S10-S50. The following will be combined with... Figure 1 The technical solution of this application will be described in detail below.

[0026] First, execute step S10 to obtain device monitoring data.

[0027] The monitoring equipment used in acquiring equipment monitoring data can be temperature sensors, vibration sensors, etc. By acquiring equipment monitoring data for each process in different workflows, the data is then uploaded to the production line efficiency optimization system. Alternatively, the production line efficiency optimization system can proactively acquire equipment monitoring data from each monitoring device to monitor abnormal production conditions on the production line. This allows for timely responses to these abnormalities, identification of abnormal batches, and determination of corresponding compensation and repair parameters. Dynamic compensation and repair of processed products can then be implemented to reduce the defect rate.

[0028] Next, step S20 is executed to monitor the equipment monitoring data for anomalies.

[0029] After acquiring equipment monitoring data, the production line efficiency optimization system will further monitor production line anomalies based on the equipment monitoring data. In order to enable timely feedback of compensation and repair parameters for processed products when anomalies occur, so as to compensate and repair the processing and production anomalies of processed products.

[0030] In step S20, anomaly monitoring is performed on the equipment monitoring data, including: Compare equipment monitoring data with the corresponding production anomaly thresholds; When the equipment monitoring data is detected to exceed the production anomaly threshold, the corresponding production line anomaly is obtained based on the equipment monitoring data. If no equipment monitoring data exceeds the production anomaly threshold, it indicates that there is no production anomaly on the production line.

[0031] During the anomaly monitoring of equipment monitoring data, anomaly monitoring can be based on the production anomaly threshold corresponding to each type of equipment monitoring data. That is, each type of equipment monitoring data is compared with its corresponding production anomaly threshold. When the equipment monitoring data is within the production anomaly threshold range, it indicates that there is no production line anomaly, and production of each product order continues according to the production line's scheduling sequence. However, when the equipment monitoring data exceeds the production anomaly threshold, it indicates that the existing equipment monitoring data has caused a chain reaction, resulting in abnormal processing of the product at the corresponding abnormal process step, thus causing a production line anomaly.

[0032] Specifically, faults induced by production line anomalies include either predicted faults or real-time faults. In other words, the production anomaly thresholds corresponding to equipment monitoring data can be manually set based on monitoring objectives. For example, the production anomaly thresholds corresponding to equipment monitoring data can be trigger thresholds used to predict faults, or they can be fault thresholds used to determine when a fault occurs.

[0033] Therefore, when the failure induced by production line anomalies is a predicted failure, when the monitored equipment data exceeds the trigger threshold, not only can the failure time of the corresponding equipment in the production line be predicted, but also, based on the changing trend of the detected monitored equipment data and the corresponding triggering process, different degrees of compensation can be selected for the processed products to make up for the impact of the abnormal situation on the processed products.

[0034] Similarly, when the equipment monitoring data is detected to exceed the fault threshold, not only can the fault information be reported in a timely manner, but also, based on the detected trend of the equipment monitoring data and the corresponding triggering process, different levels of compensation can be selected for the processed products to make up for the impact of the abnormal situation on the processed products.

[0035] Next, step S30 is executed. When a production line abnormality is detected, the triggering process corresponding to the production line abnormality of the processed products is analyzed based on the production line abnormality.

[0036] When a production line production efficiency optimization system identifies a production line anomaly, it further analyzes and determines the triggering process corresponding to the anomaly in the processed products. Based on the triggering process and the corresponding abnormal effect, it determines the amount of compensation and repair to be done on the processed products. This allows for precise compensation and repair of the processed products, ensuring the pass rate of the processed products and optimizing the production efficiency of the production line to produce the preset output.

[0037] In step S30, based on the production line anomaly, the triggering process corresponding to the production line anomaly of the processed products is analyzed and obtained, including: Based on the production line abnormality, identify the process steps that directly affect the production line abnormality. The difference between the equipment monitoring data and the corresponding production anomaly threshold is calculated to obtain the anomaly difference. Based on the abnormal difference and the conversion coefficient of the corresponding processing item of the processing procedure, the deviation increment of the processing item of the processing procedure is obtained. For the deviation increment of the action work processing item, monitor the deviation of the downstream process corresponding to the action process; When the deviation increment of the action work item is less than the action deviation threshold of the downstream process corresponding to the action process, the action process is used as the trigger process. When the increment of the deviation of the action work item is greater than the action deviation threshold of the downstream process corresponding to the action process, the action process is designated as the first trigger process, and the deviation monitoring of the downstream processes is carried out sequentially according to the increment of the action work item deviation. The second trigger process is then selected from the downstream process set composed of the downstream processes, and the first and second trigger processes are designated as trigger processes.

[0038] After confirming a production line anomaly, the triggering process is determined by first identifying the directly affected production processes based on the anomaly. Then, the anomaly quantity (anomaly difference) is calculated by comparing equipment monitoring data with the corresponding production anomaly threshold. This anomaly difference is then combined with the conversion coefficient of the corresponding processing item for the affected process to further calculate the deviation increment of the processing item. The deviation increment of the processing item can be calculated using the formula... It was calculated accurately, where, This indicates equipment monitoring data. Indicates the threshold for production anomalies. This represents the conversion coefficient for the processing item in the affected process. Production anomaly threshold. The conversion coefficient for the processing items in the work process is pre-calibrated according to manual requirements. The conversion relationship between the manual equipment monitoring data and the corresponding work processing item deviation increment is obtained in advance by calibration.

[0039] After calculating the deviation increment of the action work item, a threshold analysis can be performed on this deviation increment to determine whether only the action process can act as the triggering process, or whether several downstream processes downstream of the action process act as triggering processes in conjunction with the action process. Specifically, by comparing the deviation increment of the action work item with the action deviation threshold of the corresponding downstream process, if the deviation increment is less than the action deviation threshold of the corresponding downstream process, the impact of the deviation increment on the downstream process is small, and the action process can be used as the triggering process alone. However, if the deviation increment is greater than the action deviation threshold of the corresponding downstream process, it indicates that the deviation increment has already affected the downstream process. Further analysis is then performed to identify which downstream processes are affected sequentially. The affected downstream processes are designated as the second triggering processes, and combined with the action process as the first triggering process, all triggering processes are determined, thus accurately identifying all production processes affected by the deviation increment of the action work item.

[0040] For example, when equipment monitoring data exceeds production anomaly thresholds, it could be due to abnormal vibration or temperature data from the stamping motor, the painting motor, or even abnormal monitoring data from other equipment in other production processes. Furthermore, an anomaly in the stamping process might further affect the painting process, causing abnormalities in the painting position or amount, or in the assembly process, such as abnormal assembly clearances. Similarly, an anomaly in the painting process can lead to assembly anomalies and anomalies in other processes. Moreover, anomalies in other processes can create a chain reaction, causing anomalies in downstream processes as well.

[0041] Specifically, the deviation monitoring of downstream processes continues sequentially based on the deviation increment of the action work processing item, so as to select the second triggering process from the downstream process set composed of downstream processes. The first triggering process and the second triggering process are used as triggering processes, including: The deviation increment of the downstream work processing item for each downstream process is obtained by using the deviation increment of the work processing item and the conversion coefficient of the downstream process processing item for each downstream process. According to the process execution sequence, determine in turn whether the deviation increment of the downstream work processing item corresponding to each downstream process is less than the downstream deviation threshold of the corresponding downstream process; All downstream processes that are determined to be less than the downstream deviation threshold of the corresponding downstream process, before the target downstream process and after the action process, are designated as the second triggering process, and the first and second triggering processes are designated as the triggering processes.

[0042] In the process of determining the second triggering step, after determining that the incremental deviation of the action work processing item is greater than the action deviation threshold of the downstream process corresponding to the action step, the incremental deviation of the action work processing item can be further combined with the conversion coefficient of the downstream process processing item corresponding to each downstream process to calculate the incremental deviation of the downstream work processing item corresponding to each downstream process. Then, according to the upstream and downstream relationship of the process, that is, the execution order of the process, the calculated incremental deviation of the downstream work processing item corresponding to each downstream process is compared with the downstream deviation threshold of the corresponding downstream process to determine whether the corresponding downstream process can be used as the second triggering step. That is, when the incremental deviation of the downstream work processing item corresponding to the downstream process is greater than the downstream deviation threshold of the corresponding downstream process, the downstream process will be used as the second triggering step. This continues until the incremental deviation of the downstream work processing item corresponding to the target downstream process is less than the downstream deviation threshold of the corresponding downstream process. This indicates that the target downstream process is relatively less affected, and all downstream processes upstream of the target downstream process and downstream of the action step can be used as the second triggering steps. These are then combined with the action steps of the first triggering step to obtain all the triggering steps.

[0043] Preferably, the formula for calculating the deviation increment of downstream work processing items is: , in, This indicates equipment monitoring data. Indicates the threshold for production anomalies. This represents the conversion factor for the processing item in the operational process. This indicates the number of downstream processes of the process that performs the action. Indicates the downstream of the action process. The conversion coefficient of the downstream process processing item corresponding to each downstream process.

[0044] In calculating the deviation increment of downstream work processing items using the formula, the process first involves using equipment monitoring data, production anomaly thresholds, and the conversion coefficient of the processing item in the affected process, through the formula... The deviation increment of the action operation processing item is calculated. Then, since each subsequent downstream operation has a corresponding downstream operation processing item conversion coefficient, a conversion factor exists for each downstream operation's processing item. The conversion coefficient for the downstream process processing item is also manually calibrated based on the actual situation of each downstream process. Finally, the total number of second triggering processes that satisfy the condition that the incremental deviation of the downstream work processing item corresponding to the downstream process is greater than the downstream deviation threshold of the corresponding downstream process is determined, which is the number of downstream processes affecting the downstream process. The product is then performed to obtain the deviation increment of the work item corresponding to each downstream process, which is calculated using the formula. The number of the second triggering process is calculated as follows: The downstream work processing item deviation increment at that time. Furthermore, upon reaching the maximum number of second trigger processes. Previously, the deviation increment of the downstream work processing item of the corresponding downstream process could be obtained by multiplying the conversion coefficients of the downstream process processing items of different downstream processes in sequence. This deviation increment could then be compared with the downstream deviation threshold of the corresponding downstream process to accurately complete the division of the number of the second triggering process composed of all downstream processes.

[0045] Next, step S40 is executed to obtain the compensation and repair process and compensation and repair parameters corresponding to the processed product based on the triggering process.

[0046] After the triggering process is determined through the production line efficiency optimization system, the compensation and repair process and parameters corresponding to the processed products can be analyzed and calculated to achieve compensation and repair of the processed products.

[0047] In step S40, based on the triggering process, the compensation and repair process and compensation and repair parameters corresponding to the processed product are obtained, including: Based on the triggering process, compensation and repair conflict monitoring is performed on the processed products after the triggering process. When it is detected that there is no compensation or repair conflict for the processed products, the cumulative abnormal action data under the cumulative abnormal action time corresponding to each triggering process is predicted based on the triggering process and equipment monitoring data. Based on the cumulative data of abnormal effects, the compensation and repair procedures and parameters for the processed products are derived.

[0048] In calculating the compensation and repair procedures and parameters for processed products, the compensation and repair conflict can be monitored first by using the triggering procedure. If the triggering procedure only includes the action procedure, there is no conflict, and the corresponding compensation and repair procedures and parameters can be directly applied. However, if the triggering procedure includes both the action procedure and downstream procedures, compensation and repair conflict monitoring is required to determine whether there will be any conflicts when each procedure compensates for the processed product. If no conflicts are detected, the cumulative abnormal action time for each triggering procedure can be predicted and calculated based on the triggering procedure and equipment monitoring data. Then, based on the cumulative abnormal action data, the compensation and repair procedures for each processed product can be determined according to the corresponding compensation and repair parameters.

[0049] Among these, based on the triggering process, compensation and repair conflict monitoring is performed on the processed products after the triggering process, including: Based on the triggering process and the corresponding work processing item, the work execution order of the work processing item is obtained; The order of work execution is queried in the conflict resolution and repair conflict table; When there are conflicting execution orders in the work execution order, the remaining execution order in the work execution order is obtained according to the conflicting execution order; Based on the work item deviation increment, work item weight, remaining execution order weight ratio relative to work execution order, first indicator score conversion coefficient corresponding to weight ratio, first weight corresponding to work item deviation increment, and second weight corresponding to weight ratio in the remaining execution order, the degree of abnormality is calculated to obtain the degree of abnormality index. When the anomaly index reaches the index threshold, it indicates that there is a compensation and repair conflict in the processed products, and the compensation and repair of the processed products will be stopped. If the anomaly level indicator does not reach the indicator threshold, it means that there is no compensation and repair conflict for the processed products, and the compensation and repair of the processed products will continue.

[0050] In the process of monitoring compensation and repair conflicts, the triggering process includes the action process and the downstream processes sequentially downstream of the triggering process. By identifying the work items corresponding to the action process and each downstream process based on the order of the triggering process, the work items can be sorted to obtain the execution order of the work items. Before production, a compensation and repair conflict table can be pre-established, storing the execution order of conflicts with compensation conflicts. Then, when querying the conflict execution order in the compensation and repair conflict table, if a conflicting execution order exists, it can be removed, finally determining the remaining execution order without compensation and repair conflicts. To verify the importance of the remaining execution order, it is necessary to further calculate the degree of abnormality affecting the overall abnormality of the processed product based on the work item deviation increment, the work item weight, the weight ratio of the remaining execution order relative to the work execution order, the first indicator score conversion coefficient corresponding to the weight ratio, the second indicator score conversion coefficient corresponding to each work item in the work execution order, the first weight corresponding to the work item deviation increment, and the second weight corresponding to the weight ratio. Furthermore, the anomaly severity index is compared with a manually set threshold. When the anomaly severity index reaches the threshold, it indicates that the process anomaly corresponding to the conflicting execution sequence of the processed product has a significant impact, thus indicating a compensation and repair conflict. In this case, the compensation and repair process for the processed product can be stopped. Conversely, when the anomaly severity index does not reach the threshold, it indicates that the process anomaly corresponding to the conflicting execution sequence of the processed product has a minor impact, which can be ignored as no compensation and repair conflict. Therefore, the compensation and repair process for the processed product can continue. This ensures the reliability of the compensation and repair selection, allowing the determination of whether a compensation and repair conflict can be identified based on the impact index of each process (i.e., downstream process) on the overall anomaly severity of the product even when a conflicting execution sequence occurs.

[0051] Preferably, the formula for calculating the anomaly degree index can be expressed as: , in, This represents the weight of each work item in the remaining execution order. This indicates the weight of each processing item in the work execution sequence. This represents the work item deviation increment for each work item in the remaining execution sequence. Indicates the first weight. Indicates the second weight. Indicates the number of work items in the remaining execution order. Indicates the number of work items in the remaining execution order. Indicates the weight percentage. This represents the conversion coefficient of the first indicator score corresponding to the weighting percentage. This represents the conversion coefficient of the second indicator score for each work processing item in the work execution sequence.

[0052] Through the calculation formula In calculating the anomaly index, the score can be based on the weighting of the remaining execution order relative to the work execution order. The index score of the deviation increment of each work item in the remaining execution sequence. The results are calculated in detail by combining the corresponding first and second weights. The first and second weights can be manually determined based on the importance of their scores. The conversion coefficients of the first indicator scores corresponding to the weight percentages and the conversion coefficients of the second indicator scores corresponding to each work item in the work execution sequence are also manually pre-calibrated.

[0053] For example, when coating a specific surface of a workpiece, that coating area is not restricted by other areas, and a secondary coating process can be directly performed to compensate for any coating abnormalities. However, if an abnormality occurs during the stamping process, and the downstream process is coating, which in turn is encapsulation or welding, the encapsulation or welding process can affect the coating process. Therefore, a compensation / repair conflict exists. If the abnormality level of the encapsulation or welding process reaches a threshold, it will affect the quality of the compensated / repaired product, rendering it scrap and halting the compensation / repair process. Conversely, if the abnormality level of the encapsulation or welding process does not reach the threshold, it indicates a minimal impact on the product's quality. In this case, stamping and coating compensation can be performed on the already processed product. Therefore, by retrieving the compensation stamping and coating processes, the compensation / repair of the already processed product can be achieved. Furthermore, the compensating stamping process and compensating coating process can be used as compensation and repair processes after the processed product has been abnormally processed, to continue the corresponding compensation and repair parameters. Alternatively, after the processed product has been abnormally processed, it can be compensated again through other spare or compensation and repair-specific compensating stamping processes and compensating coating processes.

[0054] Furthermore, based on the monitoring data of the triggering process and equipment, the cumulative abnormal action data under the cumulative abnormal action time corresponding to each triggering process is predicted, including: Based on the change range of equipment monitoring data over time, the incremental deviation of work processing items of processed products when executing the corresponding triggering process is divided into multiple processing batches to obtain the stage increment of work processing item deviation of processed products under each processing batch. Based on the incremental deviation of the work processing item and the cumulative effect time of the corresponding triggering process on the processed products, the cumulative effect data of the abnormality under the cumulative effect time of the abnormality for each triggering process is predicted.

[0055] During the compensation and repair process for processed products, the products continued to be produced for a period of time before the collected equipment monitoring data exceeded the production anomaly threshold. During this period, changes in the equipment monitoring data resulted in different cumulative values ​​of anomalies at different times. Therefore, based on the variation of equipment monitoring data over time, the processed products can be divided into multiple processing batches according to the increment of work item deviation during the execution of the corresponding triggering process. This yields the stage increment of work item deviation for each processed product in each batch. Then, based on the stage increment of work item deviation for each processed product in each batch and the cumulative anomaly effect time of the corresponding triggering process, the cumulative anomaly effect data for each triggering process is predicted. It is worth noting that in this embodiment, the triggering process can be used to predict the cumulative anomaly effect data of each downstream process and the triggering process in the remaining execution order excluding conflicting execution orders. Alternatively, the cumulative anomaly effect data of the downstream processes corresponding to the conflicting execution order can be directly set to zero.

[0056] Preferably, the formula for calculating the cumulative data of abnormal effects is: , in, This represents the cumulative data of abnormal effects. Indicates the increment of deviation in work processing items. Indicates the number of processing batches in the division. This represents the interval of deviation increment for each processing batch. This indicates the stage increment of the deviation in the work processing item. This indicates the cumulative time that the corresponding triggering process has affected the abnormality of the processed product.

[0057] Among them, the work item deviation increment interval corresponding to each processing batch This can be achieved through pre-calibration and setting to realize the incremental deviation of work processing items at different stages. The deviation increment interval of the work item corresponding to each processing batch The process is divided into multiple segments, and then the cumulative effect time of each triggered process on the processed products is combined. Accumulated data of abnormal effects are obtained through integral calculation. Among them, the cumulative effect time of the anomaly. It can also be pre-set manually based on the production needs of each production process.

[0058] In addition, based on the cumulative data of abnormal effects, the compensation and repair procedures and parameters for the processed products are derived, including: Based on the triggering process corresponding to the cumulative data of abnormal effects, the compensation and repair process for the processed product is obtained. The compensation and repair process includes either the backup repair process or the triggering process. Based on the cumulative data of abnormal effects, the corresponding compensation and repair processing parameter values ​​for the compensation and repair procedures are obtained by looking up the table. The compensation and repair processing time is obtained based on the compensation and repair processing parameter values, the cumulative data of abnormal effects, and the processing volume conversion coefficient. The compensation and repair processing parameter values ​​and the compensation and repair processing time are used as the compensation and repair parameters.

[0059] In determining the compensation and repair procedures and parameters for processed products based on accumulated abnormal action data, the compensation and repair procedure can be the original processing procedure corresponding to the triggering procedure. When compensation and repair are needed, the processed product is directly recycled back to the corresponding processing procedure for reprocessing based on the compensation and repair parameters. Alternatively, it can be a backup repair procedure running on the production line, where the processed product is sent to the corresponding backup repair procedure for reprocessing based on the compensation and repair parameters. After obtaining the accumulated abnormal action data, the corresponding compensation and repair parameter values ​​for the compensation and repair procedure can be obtained by looking up the corresponding values ​​in the table corresponding to the accumulated abnormal action data and the compensation and repair parameter values. Then, based on the compensation and repair parameter values, combined with the accumulated abnormal action data and the processing volume conversion coefficient, the compensation and repair processing time for the processed product in the corresponding compensation and repair procedure is calculated. The compensation and repair processing parameters and processing time are used to achieve the appropriate degree of compensation processing for the processed product.

[0060] Preferably, the formula for calculating the compensation and repair processing time is: , in, This represents the cumulative data of abnormal effects. This indicates the parameter value for compensation and repair processing. This represents the processing volume conversion factor. The compensation and repair processing parameter values ​​can be obtained by looking up a table, and the processing volume conversion factor can be manually calibrated in advance based on the conversion relationship between the compensation and repair processing parameter values ​​and the processing volume.

[0061] Next, step S50 is executed, and the production line is controlled to continue to compensate and repair the processed products through the compensation and repair process and compensation and repair parameters, so as to optimize the defect rate of the production line.

[0062] Please see Figure 2 The present invention also provides a production line production efficiency optimization device 11, comprising: a data acquisition unit 111 for acquiring equipment monitoring data; an anomaly monitoring unit 112 for monitoring anomalies in the equipment monitoring data; an anomaly analysis unit 113 for analyzing and determining the triggering process corresponding to the production line anomaly when a production line anomaly is detected; a compensation calculation unit 114 for determining the compensation and repair process and compensation and repair parameters corresponding to the processed product based on the triggering process; and a compensation control unit 115 for controlling the production line to continue to compensate and repair the processed product through the compensation and repair process and compensation and repair parameters, so as to optimize the defect rate of the production line.

[0063] It should be noted that the production line efficiency optimization device 11 provided in the above embodiments and the production line efficiency optimization method provided in the above embodiments belong to the same concept. The specific operation methods of each module and unit have been described in detail in the method embodiments and will not be repeated here. In practical applications, the production line efficiency optimization device 11 provided in the above embodiments can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. This is not a limitation here.

[0064] Please see Figure 3 The electronic device 1 may include a memory 12, a processor 13, and a bus, and may also include a computer program stored in the memory 12 and executable on the processor 13, such as a production line efficiency optimization program.

[0065] The memory 12 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 12 can be an internal storage unit of the electronic device 1, such as a portable hard drive of the electronic device 1. In other embodiments, the memory 12 can be an external storage device of the electronic device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the electronic device 1. Furthermore, the memory 12 can include both internal and external storage units of the electronic device 1. The memory 12 can be used not only to store application software and various types of data installed on the electronic device 1, such as production line efficiency optimization code, but also to temporarily store data that has been output or will be output.

[0066] In some embodiments, the processor 13 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits packaged with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 13 is the control unit of the electronic device 1, connecting various components of the electronic device 1 through various interfaces and lines. It executes programs or modules (such as production line efficiency optimization programs) stored in the memory 12, and calls data stored in the memory 12 to perform various functions of the electronic device 1 and process data.

[0067] The processor 13 executes the operating system of the electronic device 1 and various installed applications. The processor 13 executes the applications to implement the steps in the above-described production line efficiency optimization method.

[0068] For example, the computer program may be divided into one or more modules, which are stored in the memory 12 and executed by the processor 13 to complete this application. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the electronic device 1. For example, the computer program may be divided into units in a production line efficiency optimization device.

[0069] The integrated unit implemented as a software functional module described above can be stored in a computer-readable storage medium, which can be non-volatile or volatile. The software functional module stored in the storage medium includes several instructions to cause a computer device (which may be a personal computer, a computer device, or a network device, etc.) or a processor to execute some functions of the production line efficiency optimization method described in the various embodiments of this application.

[0070] In summary, the production line efficiency optimization method and apparatus disclosed in this invention, by monitoring equipment anomalies during the production line process, can identify relevant processing steps as trigger steps when an anomaly is detected. Based on these trigger steps, compensation and repair steps and parameters for the processed products are analyzed and calculated. Furthermore, based on these compensation and repair steps and parameters, the processed products are further compensated and repaired to reduce the increase in defect rate caused by the production line anomaly. Through further compensation and repair of the processed products in such cases, the increase in the number of defective products can be effectively reduced, thereby reducing the increase in the total number of products produced by the production line to achieve the preset output, and thus improving the production line efficiency for the preset output. Therefore, this invention effectively overcomes the various shortcomings of the prior art and has high industrial application value.

[0071] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for optimizing production line efficiency, characterized in that, include: Obtain device monitoring data; Perform anomaly monitoring on the equipment monitoring data; When a production line abnormality is detected, the triggering process corresponding to the abnormality in the production line of the processed products is analyzed based on the abnormality. Based on the triggering process, the compensation and repair process and compensation and repair parameters corresponding to the processed products are obtained; By using the compensation and repair process and parameters, the production line is controlled to continue to compensate and repair the processed products in order to optimize the defect rate of the production line.

2. The production line efficiency optimization method according to claim 1, characterized in that, Anomaly monitoring of the device monitoring data includes: Compare the equipment monitoring data with the corresponding production anomaly thresholds; When the equipment monitoring data is detected to exceed the production anomaly threshold, the corresponding production line anomaly is obtained based on the equipment monitoring data. If no equipment monitoring data is detected exceeding the production anomaly threshold, it indicates that there is no production line anomaly.

3. The production line efficiency optimization method according to claim 1, characterized in that, Faults induced by production anomalies on the production line include either predicted faults or real-time faults.

4. The production line efficiency optimization method according to claim 1, characterized in that, Based on the production line anomaly, the triggering processes corresponding to the production line anomalies of the processed products are analyzed and identified, including: Based on the production line abnormality, the process directly affecting the production line abnormality is identified. The difference between the equipment monitoring data and the corresponding production anomaly threshold is calculated to obtain the anomaly difference. Based on the abnormal difference and the conversion coefficient of the processing item corresponding to the processing step, the deviation increment of the processing item corresponding to the processing step is obtained; For the deviation increment of the action work processing item, the deviation monitoring is performed on the downstream process corresponding to the action process; When the deviation increment of the action work item is less than the action deviation threshold of the downstream process corresponding to the action process, the action process is used as the trigger process. When the deviation increment of the action work processing item is greater than the action deviation threshold of the downstream process corresponding to the action process, the action process is designated as the first trigger process, and the deviation monitoring of the downstream processes is continued according to the deviation increment of the action work processing item in order to select the second trigger process from the downstream process set composed of the downstream processes, and the first trigger process and the second trigger process are designated as the trigger process.

5. The production line efficiency optimization method according to claim 4, characterized in that, Continue monitoring the deviation of downstream processes sequentially based on the deviation increment of the action work processing item, so as to select the second triggering process from the set of downstream processes. The first triggering process and the second triggering process are then used as the triggering process, including: Based on the deviation increment of the action work processing item and the conversion coefficient of the downstream process processing item corresponding to each downstream process, the deviation increment of the downstream work processing item corresponding to each downstream process is obtained. The calculation formula for the deviation increment of the downstream work processing item is as follows: ,in, This indicates equipment monitoring data. Indicates the threshold for production anomalies. This represents the conversion factor for the processing item in the operational process. This indicates the number of downstream processes of the process that performs the action. Indicates the downstream of the action process. The conversion coefficient of the downstream process processing item corresponding to each downstream process; According to the process execution sequence, determine in turn whether the deviation increment of the downstream work processing item corresponding to each downstream process is less than the downstream deviation threshold of the corresponding downstream process; All downstream processes that are determined to be less than the downstream deviation threshold of the corresponding downstream process, before the target downstream process and after the action process, are designated as the second triggering process, and the first triggering process and the second triggering process are designated as the triggering process.

6. The production line efficiency optimization method according to claim 1, characterized in that, Based on the triggering process, the corresponding compensation and repair process and parameters for the processed product are derived, including: Based on the triggering process, compensation and repair conflict monitoring are performed on the processed products after the triggering process. When it is detected that there is no compensation or repair conflict for the processed product, the cumulative abnormal action data under the cumulative abnormal action time corresponding to each of the triggering processes is predicted based on the triggering process and the equipment monitoring data. Based on the accumulated data of the abnormal effects, the compensation and repair procedures and parameters for the processed products are derived.

7. The production line efficiency optimization method according to claim 6, characterized in that, Based on the triggering process, compensation and repair conflict monitoring is performed on the processed products after the triggering process, including: Based on the triggering procedure and the corresponding work processing item, the work execution order of the work processing item is obtained; The execution order of the tasks is queried in the conflict repair conflict table; When there is a conflicting execution order in the work execution order, the remaining execution order in the work execution order is obtained according to the conflicting execution order; Based on the work item deviation increment corresponding to each work item in the remaining execution order, the work item weight corresponding to the work item, the weight ratio of the remaining execution order relative to the work execution order, the first indicator score conversion coefficient corresponding to the weight ratio, the second indicator score conversion coefficient corresponding to each work item in the work execution order, the first weight corresponding to the work item deviation increment, and the second weight corresponding to the weight ratio, the degree of anomaly is calculated to obtain an anomaly degree index. The calculation formula for the anomaly degree index is as follows: , This represents the weight of each work item in the remaining execution order. This indicates the weight of each processing item in the work execution sequence. This represents the work item deviation increment for each work item in the remaining execution sequence. Indicates the first weight. Indicates the second weight. Indicates the number of work items in the remaining execution order. Indicates the number of work items in the remaining execution order. Indicates the weight percentage. This represents the conversion coefficient of the first indicator score corresponding to the weighting percentage. This represents the conversion coefficient of the second indicator score for each work processing item in the work execution sequence. When the anomaly index reaches the index threshold, it indicates that there is a compensation and repair conflict in the processed product, and the compensation and repair of the processed product is stopped. If the anomaly level indicator does not reach the indicator threshold, it indicates that there is no compensation and repair conflict for the processed product, and the compensation and repair of the processed product will continue.

8. The production line efficiency optimization method according to claim 6, characterized in that, Based on the triggering process and the equipment monitoring data, the cumulative abnormal action data for each triggering process under the cumulative abnormal action time is predicted, including: Based on the change range of the equipment monitoring data over time, the work processing item deviation increment of the processed product when executing the corresponding triggering process is divided into multiple processing batches to obtain the stage increment of the work processing item deviation of the processed product under each processing batch. Based on the deviation stage increment of the work processing item and the corresponding cumulative abnormal effect time of the triggering process on the processed product, the cumulative abnormal effect data under the cumulative abnormal effect time corresponding to each triggering process is predicted. The calculation formula for the cumulative abnormal effect data is as follows: , This represents the cumulative data of abnormal effects. Indicates the increment of deviation in work processing items. Indicates the number of processing batches in the division. This represents the interval of deviation increment for each processing batch. This indicates the stage increment of the deviation in the work processing item. This indicates the cumulative time that the corresponding triggering process has affected the abnormality of the processed product.

9. The production line efficiency optimization method according to claim 6, characterized in that, Based on the accumulated data of the abnormal effects, the compensation and repair procedures and parameters for the processed products are derived, including: Based on the triggering process corresponding to the accumulated abnormal action data, the compensation and repair process for the processed product is obtained, and the compensation and repair process includes either a backup repair process or a triggering process. Based on the cumulative data of the abnormal effects, the compensation and repair processing parameter values ​​corresponding to the compensation and repair process are obtained by looking up the table. The compensation and repair processing time is obtained based on the compensation and repair processing parameter values, the cumulative data of the abnormal effects, and the processing volume conversion coefficient. The calculation formula for the compensation and repair processing time is as follows: ,in, This represents the cumulative data of abnormal effects. This indicates the parameter value for compensation and repair processing. Indicates the conversion factor for processing volume; The compensation and repair processing parameter values ​​and the compensation and repair processing time are used as the compensation and repair parameters.

10. A production line efficiency optimization device, characterized in that, include: The data acquisition unit is used to acquire equipment monitoring data; An anomaly monitoring unit is used to monitor the device monitoring data for anomalies. The anomaly analysis unit is used to analyze the production line anomaly when it is detected, and to obtain the triggering process corresponding to the production line anomaly of the processed products. The compensation calculation unit is used to derive the compensation and repair process and compensation and repair parameters corresponding to the processed product based on the triggering process. as well as The compensation control unit is used to control the production line to continue to compensate and repair the processed products through the compensation and repair process and compensation and repair parameters, so as to optimize the defect rate of the production line.

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