Gearbox production line optimization scheduling control method based on big data

Abstract

The invention discloses a gearbox production line optimization scheduling control method based on big data, and relates to the technical field of production line optimization scheduling. Comprising the steps that damage degree data of a production line is acquired, and a damage score item is obtained and used for representing a daily damage score of a target production line in the using process; and setting scoring contents of the damaged scoring items, wherein the scoring contents comprise a working time score, a working environment score, a maintenance frequency score, an average daily working duration score and an average daily yield score. According to the method, the damage degree score of the production line is set, and the damage degree score comprises the working time score of the production line, the working environment score of the production line, the maintenance frequency score of the production line, the daily average working duration score of the production line and the daily average yield score of the production line, so that the actual damage state data of each production line is obtained; according to the actual damaged state data, work distribution is carried out on each production line, and the intelligent optimization scheduling effect on the production lines is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of production line optimization scheduling, and particularly to an optimization scheduling control method for a gearbox production line based on big data. Background Technique

[0002] The gearbox, also known as the transmission system, is a device in a vehicle used to adjust the relationship between the engine output power and the wheels. Its main function is to change the engine speed and torque according to the vehicle driving conditions and driving requirements, so as to achieve different driving speeds and power demands. When using a production line to produce gearboxes, the production line of conventional gearboxes usually includes multiple processes and links to ensure the high-precision manufacturing and assembly of gearbox components, including raw material preparation, milling, turning, grinding, heat treatment, surface treatment, and assembly, etc. During the production process, it is usually necessary to optimize the production line, and optimizing the production line scheduling can greatly improve efficiency, reduce costs, and improve production flexibility.

[0003] An optimization scheduling method for a printed circuit board surface mounting production line with the patent publication number CN109002903A proposes an optimization scheduling problem for a printed circuit board surface mounting production line with the total weighted delay time as the objective, establishes a disjunctive programming model, and can effectively reduce the risk of falling into a local optimal solution by using an improved sine-cosine algorithm, and obtains a solution of the global optimal PCB mounting sequence to realize the optimization of the mounting process; it can improve the mounting efficiency by 12% - 20%, can reduce the production cost of the factory, can greatly meet the needs of customers, and improve the economic benefits and reputation of the factory.

[0004] When optimizing and adjusting the production line with the above and similar technical solutions, since the conventional scheduling optimization is usually static, tasks are assigned to each production line equipment based on a fixed production plan, and then due to the different health conditions of each production line, such as different working hours of the production line, different environmental states of the production line, etc., it will affect the health of the production line, and the health status directly affects its production capacity. Therefore, it will lead to unreasonable optimization scheduling of the production line and cannot improve the overall utilization efficiency of resources. Summary of the Invention

[0005] The purpose of the present invention is to provide an optimization scheduling control method for a gearbox production line based on big data to solve the problems raised in the above background technique.

[0006] To achieve the above purpose, the present invention provides the following technical solution: An optimization scheduling control method for a gearbox production line based on big data, including: Obtain the damage degree data of the production line to obtain damage scoring items, and the damage scoring items are used to represent the daily damage score of the target production line during use; Set the scoring content of the damaged scoring items, where the scoring content includes working hours scoring, working environment scoring, number of repairs scoring, average daily working hours scoring, and average daily output scoring; Respectively obtain the actual scoring data of the scoring content of the damaged scoring items, set the ratio weights, obtain the working hours item, working environment item, number of repairs item, average daily working hours item, and average daily output item, and then obtain the result combination item, which is used to represent the comprehensive damaged state data of the target production line; Based on the damaged scoring items, obtain the working environment data of the production line, interfere with the working environment item, adjust the specific data of the working environment item, and adjust the damaged degree scoring of the production line according to the dynamic environment data to obtain the updated scoring item; Obtain the order status and uncompleted order data of the target production line to obtain the production status item and the remaining order item; Based on the updated scoring item, distribute the order data ratio for the production line to obtain the target ratio item. Based on the combined result of the target ratio item, production status item, and remaining order item, obtain the target task item. Determine whether the production status item corresponding to the target production line meets the target task item. When the production status item does not meet the target task item, perform task scheduling based on the remaining order item. When the production status item meets the target task item, use the excess part as the scheduling item and combine it with the remaining order item to complete the scheduling optimization of the production line.

[0007] Furthermore, the method for obtaining the damaged scoring items includes: Based on the ratio weights, obtain the distribution weight item. Based on the combined result of the distribution weight item and the scoring content, obtain the working hours weight, working environment weight, number of repairs weight, average daily working hours weight, and average daily output weight; Obtain the actual data of each scoring content to obtain the actual data item. Based on the combined result of the actual data item and the working hours weight, working environment weight, number of repairs weight, average daily working hours weight, and average daily output weight, obtain the damaged scoring item.

[0008] Furthermore, the target production line stores basic information. The method for obtaining the working hours item includes: Based on the basic information, obtain the actual working hours of the target production line to obtain the working hours data; Based on the basic information, obtain the maximum working hours limit of the target production line to obtain the working limit item; Obtain the ratio result of the working hours data and the working limit item to obtain the working information item; Based on the combined result of the working information item and the ratio weights, obtain the working hours item.

[0009] Furthermore, the method for obtaining the working environment item includes: Obtain the position information of the production line to get production position items, and use the production position items as feature points to obtain a position feature set; Obtain the air dust particle content data of the position feature set to get feature data items, and obtain the average value data of the feature data items as benchmark data to get benchmark items; Obtain the difference between the feature data items and the benchmark items to get feature difference items, and obtain the ratio of the feature difference items to the benchmark items to get data information items; Based on the combined result of the data information items and the proportion weights, obtain working environment items.

[0010] Furthermore, the method for obtaining the maintenance times item includes: Obtain the maintenance times information of the target production line to get a maintenance information set, and the maintenance information set includes at least the maintenance times information of two production lines; Based on the average times information of the maintenance times set, obtain the average times item, and obtain the ratio relationship between the maintenance times information of the target production line and the average times item to get a maintenance result item; Based on the combined result of the maintenance result item and the proportion weights, obtain the maintenance times item.

[0011] Furthermore, the method for obtaining the average daily working hours item includes: Set a first acquisition time threshold, and based on the first acquisition time threshold, obtain the working hours information of the target production line to get a target duration set, and the target duration set includes at least the working hours information of two production lines; Obtain the average duration information of the target duration set to get the average duration item; Based on the difference result between the working hours information of the target production line and the average duration item, obtain a duration difference item, and based on the ratio result of the duration difference item to the average duration item, obtain the average daily information item; Based on the combined result of the average daily information item and the proportion weights, obtain the average daily working hours item.

[0012] Furthermore, basic information is stored on the target production line, and the method for obtaining the average daily output item includes: Based on the basic information, obtain the actual working time of the target production line to get working time data; Obtain the output information of the target production line to get a target output item, and obtain the ratio relationship between the target output item and the working time data to get the average output data of the target production line; Based on the average output data of the target production line, obtain the average output information of the average output data to get the average output item; Obtain the difference between the average output data of the target production line and the average output item to get the output difference item, and based on the ratio result of the output difference item and the average output item, obtain the output result item; Based on the combined result of the daily average output item and the ratio weight, obtain the daily average output item.

[0013] Furthermore, the method for obtaining the updated score item includes: Set an acquisition threshold, and based on the acquisition threshold, obtain the air dust particle content data of the target production line through the target device to get the real-time environment item; Obtain the average value data of the real-time environment item as the target data to get the target item, obtain the difference between the real-time environment item and the target item to get the environment difference item, and obtain the ratio of the environment difference item to the target item to get the data update item; Based on the combined result of the data update item and the ratio weight, obtain the updated environment item; Based on the combined result of the updated environment item and the working environment item, obtain the environment change item, and based on the combined result of the working time item, the number of maintenance times item, the daily average working duration item, and the daily average output item, obtain the updated score item.

[0014] Furthermore, the method for obtaining the target ratio item includes: Based on the updated score item, obtain the total data of the production line to get the total score item; Based on the ratio result of the updated score item and the total score item, obtain the target allocation rate, and the target allocation rate corresponds to the target production line respectively; Based on the target allocation rate and the updated score item, sort them in descending order and ascending order respectively to get the allocation sorting item and the score sorting item; Pair the allocation sorting item and the score sorting item respectively, and then obtain the target ratio item.

[0015] Compared with the prior art, the beneficial effects of the present invention are: This optimization scheduling method for the gearbox production line based on big data determines the damage degree score of the production line, and the damage degree score includes the working time score, the working environment score, the number of maintenance times score, the daily average working duration score, and the daily average output score of the production line. By obtaining the working time score, the working environment score, the number of maintenance times score, the daily average working duration score, and the daily average output score, obtain the working time item, the working environment item, the number of maintenance times item, the daily average working duration item, and the daily average output item. Then, according to the combined result, obtain the actual damage state data of each production line, and according to the actual damage state data, use the allocation method to allocate work for each production line, realizing the intelligent optimization scheduling effect of the production line.

[0016] Meanwhile, based on the damage degree score, the working environment of the production line is obtained in real time to get real-time environment items. The working environment items are interfered based on the real-time environment items, so as to adjust the specific values of the working environment items, and thus adjust the damage degree score of the production line according to the dynamic environment data, making the intelligent optimization scheduling effect of the production line more targeted. Brief Description of the Drawings

[0017] Figure 1 It is a schematic diagram of the overall process of the present invention; Figure 2 It is a schematic diagram of the relationship between the ratio weight and the combined result items of the present invention; Figure 3 It is a schematic diagram of the characteristic data items and the working environment items of the present invention; Figure 4 It is a schematic diagram of the relationship between the real-time environment items and the environment change items of the present invention; Figure 5 It is a schematic diagram of the relationship between the score ranking items and the allocation ranking items of the present invention. Detailed Embodiment

[0018] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0019] The optimization and adjustment of the production line is an important part of improving production efficiency and resource utilization. However, traditional scheduling optimization methods are often static and rely on fixed production plans to assign tasks to each production line equipment. Although this method can meet production needs to a certain extent, it cannot fully consider the health of the production line and its impact on production capacity. The health of the production line is not only related to the failure rate of the equipment and the timeliness of maintenance, but also closely related to factors such as the time the production line is put into operation and the environmental conditions in which it is located. For example, if a production line works in a high temperature and high humidity environment, it may cause increased wear of the equipment, thereby affecting its operating efficiency. In addition, the frequency of use of the production line will also affect the health of the equipment. Frequent starts and stops may cause fatigue of mechanical components, thereby affecting production capacity. Therefore, the health of the production line is a dynamically changing process that needs to be evaluated through real-time monitoring and data analysis. However, the traditional static scheduling optimization method fails to take these dynamic factors into account, resulting in the rationality of the optimization scheduling being questioned. The application provides The technical solution sets a damage score for the production line, wherein the damage score includes a working time score for the production line, a working environment score for the production line, a maintenance number score for the production line, an average daily working time score for the production line, and an average daily output score for the production line. By obtaining the working time score, the working environment score, the maintenance number score, the average daily working time score, and the average daily output score, the working time item, the working environment item, the maintenance number item, the average daily working time item, and the average daily output item are obtained. Then, based on the combination result, the actual damage state data of each production line is obtained. Based on the actual damage state data, work is allocated to each production line through an allocation method, thereby realizing an intelligent optimization scheduling effect of the production line. At the same time, based on the damage score, the working environment of the production line is obtained in real time to obtain a real-time environment item. Based on the real-time environment item, the working environment item is interfered with, thereby adjusting the specific value of the working environment item, thereby adjusting the damage score of the production line according to the dynamic environmental data, so that the intelligent optimization scheduling effect of the production line is more targeted. Specifically, in the present application, Figure 1 As shown, steps S100-S500 are included.

[0020] Step S100: Acquire damage degree data of the production line to obtain a damage score item, where the damage score item is used to represent the daily damage score suffered by the target production line during use.

[0021] It should be noted that during the normal use of a production line, the health of the production line will decline with the increase in usage time, the increase in the number of maintenance times and other factors, which will lead to a decrease in the production capacity of the production line or the quality of the products produced. Therefore, it is necessary to obtain the damage data of the production line and score the damage degree of the production line.

[0022] Step S200: Set the scoring content of the damaged scoring items. The scoring content includes working time scoring, working environment scoring, repair times scoring, average daily working hours scoring, and average daily output scoring. Obtain the actual scoring data of the scoring content of the damaged scoring items respectively.

[0023] It should be noted that, as Figure 2 shown, according to the set ratio weights, since the scoring content includes working time scoring, working environment scoring, repair times scoring, average daily working hours scoring, and average daily output scoring, the set ratio weights are all 20%. Obtain the working time item, working environment item, repair times item, average daily working hours item, and average daily output item, and then obtain the result combination item. The result combination item is used to represent the comprehensive damaged state data of the target production line.

[0024] It should be noted that the method for obtaining the damaged scoring items includes: based on the ratio weights, obtain the allocated weight items, and based on the combined results of the allocated weight items and the scoring content, obtain the working time weight, working environment weight, repair times weight, average daily working hours weight, and average daily output weight; obtain the actual data of each scoring content to obtain the actual data items, and based on the combined results of the actual data items and the working time weight, working environment weight, repair times weight, average daily working hours weight, and average daily output weight, obtain the damaged scoring items.

[0025] It should be noted that the target production line stores basic information. The method for obtaining the working time item includes: based on the basic information, obtain the actual working time of the target production line to obtain the working time data; based on the basic information, obtain the maximum working time limit of the target production line to obtain the working limit item; obtain the ratio result of the working time data and the working limit item to obtain the working information item; based on the combined result of the working information item and the ratio weight, obtain the working time item.

[0026] In a specific implementation process, it is obtained that there are two production lines in a production workshop, namely production line A and production line B. The production lines both store basic information, that is, the actual startup time of the production line. The actual working time of production line A is obtained as 10 years, and the actual working time of production line B is obtained as 8 years. At the same time, the scrap years of production line A and production line B are obtained as 20 years, that is, the working limit item is 20 years. At this time, the ratios of the working time data of production line A and production line B to the working limit item are obtained as 0.5 and 0.4 respectively. And since the ratio weight item is 20%, the working time items of production line A and production line B are 0.1 and 0.08 respectively.

[0027] It should be noted that the method for obtaining the working environment item includes: obtaining the location information of the production line to get the production location item, using the production location item as a feature point to obtain the location feature set; obtaining the air dust particle content data of the location feature set to get the feature data item, obtaining the average value data of the feature data item as the benchmark data to get the benchmark item; obtaining the difference between the feature data item and the benchmark item to get the feature difference item, obtaining the ratio of the feature difference item to the benchmark item to get the data information item; obtaining the working environment item based on the combined result of the data information item and the proportion weight.

[0028] In a specific implementation process, as Figure 3 shown, it is obtained that there are two production lines in a certain production workshop, namely production line A and production line B. The location information of production line A and production line B is obtained respectively and set as feature points to get feature point A and feature point B. By installing a laser particle counter, the dust particle data in the air is obtained. The air dust particle content data of feature point A and feature point B is obtained respectively. The number of particles with a size of ≥ 0.5 µm at feature point A is 2,500,032 particles / m³, and the number of particles with a size of ≥ 0.5 µm at feature point B is 2,800,122 particles / m³. At this time, the benchmark data is 2,650,077. By obtaining the difference between the feature data items of feature point A and feature point B and the benchmark data, the results are -150,045 and 150,045 respectively. Then, the ratio of this result to the benchmark data is obtained again, and the results are -0.057 and 0.057 respectively. At this time, the combined result of this data and the proportion weight is obtained again to get the working environment item. Since the proportion weight item is 20%, the working environment items of production line A and production line B are -0.0114 and 0.0114 respectively.

[0029] It should be noted that the method for obtaining the maintenance times item includes: obtaining the maintenance times information of the target production line to get the maintenance information set, and the maintenance information set includes at least the maintenance times information of two production lines; obtaining the average times item based on the average times information of the maintenance times set, obtaining the ratio relationship between the maintenance times information of the target production line and the average times item to get the maintenance result item; obtaining the maintenance times item based on the combined result of the maintenance result item and the proportion weight.

[0030] In a specific implementation process, it is obtained that there are two production lines in a certain production workshop, namely production line A and production line B. The maintenance times information of production line A and production line B is obtained respectively, and the obtained maintenance times information is 2 times for both. At this time, the average times information of production line A and production line B is also 2 times, and the ratio of the maintenance times information of production line A and production line B to the average times information is 1 for both, getting the maintenance result item. When the proportion weight is 20%, the combined result of the maintenance result item and the proportion weight is 0.2 for both, that is, the maintenance times items of production line A and production line B are both 0.2.

[0031] It should be noted that the method for obtaining the average daily working hours item includes: setting a first acquisition time threshold, where the first acquisition time threshold is 30 days. Based on the first acquisition time threshold, obtain the working hours information of the target production line to obtain a target duration set, and the target duration set includes at least the working hours information of two production lines; obtain the average duration information of the target duration set to obtain an average duration item; based on the difference result between the working hours information of the target production line and the average duration item, obtain a duration difference item, and based on the ratio result of the duration difference item to the average duration item, obtain an average daily information item; based on the combined result of the average daily information item and the ratio weight, obtain the average daily working hours item.

[0032] In a specific implementation process, it is obtained that there are two production lines in a certain production workshop, namely production line A and production line B. It is obtained that within the first acquisition time threshold of 30 days, the working hours information of production line A and production line B are 359h and 340h respectively. At this time, the average duration information is 349.5. At this time, the difference results between the working hours information of production line A and production line B and the average duration item are 9.5 and -9.5 respectively. Obtain the ratio result of this data to the average duration item again to obtain the average daily information item. The results show that the average daily information items of production line A and production line B are 0.027 and -0.027 respectively. At this time, the combined results of the average daily information item and the ratio weight are 0.0054 and -0.0054, that is, the average daily working hours items of production line A and production line B are 0.0054 and -0.0054 respectively.

[0033] It should be noted that the target production line stores basic information. The method for obtaining the average daily output item includes: obtaining the actual working time of the target production line based on the basic information to obtain working time data; obtaining the output information of the target production line to obtain a target output item, obtaining the ratio relationship between the target output item and the working time data to obtain the average output data of the target production line; based on the average output data of the target production line, obtaining the average output information of the average output data to obtain an average output item; obtaining the difference between the average output data of the target production line and the average output item to obtain an output difference item, and based on the ratio result of the output difference item to the average output item, obtaining an output result item; based on the combined result of the average daily output item and the ratio weight, obtaining the average daily output item.

[0034] In a specific implementation process, it is obtained that there are two production lines in a certain production workshop, namely production line A and production line B. Basic information is stored on the production lines. The basic information shows that the actual working hours of production line A and production line B are 10 years and 8 years respectively. The output information of production line A and production line B is obtained to get the target output items. The output information of production line A and production line B is 51,210 units and 48,756 units respectively. The ratio relationship between the target output item and the working time data is obtained to get the average output data of the production line. That is, the average output data of production line A and production line B are 5,121 and 6,094.5 respectively. At this time, the average output information of the average output data is 5,607.75, that is, the average output item is 5,607.75. The difference between the average output data of the production line and the average output item is obtained to get the output difference item. That is, the output difference items of production line A and production line B are -486.75 and 486.75 respectively. The ratio result of the output difference item and the average output item is obtained. That is, the output result items of production line A and production line B are -0.087 and 0.087 respectively. Based on the combined result of the daily average output item and the ratio weight, the daily average output item is obtained. The daily average output items of production line A and production line B are -0.0174 and 0.0174 respectively.

[0035] Step S300: Based on the damaged score item, obtain the working environment data of the production line, interfere with the working environment item, adjust the specific data of the working environment item, and adjust the damaged degree score of the production line according to the dynamic environment data to obtain the updated score item.

[0036] It should be noted that the method for obtaining the updated score item includes: setting an acquisition threshold, the acquisition threshold is 1 day, based on the acquisition threshold, obtaining the air dust particle content data of the target production line through the target device, the target device is a laser particle counter, to obtain the real-time environment item; obtaining the average value data of the real-time environment item as the target data to obtain the target item, obtaining the difference between the real-time environment item and the target item to obtain the environment difference item, obtaining the ratio of the environment difference item and the target item to obtain the data update item; based on the combined result of the data update item and the ratio weight, obtaining the updated environment item; based on the combined result of the updated environment item and the working environment item, obtaining the environment change item, and based on the combined result of the working time item, the number of maintenance times item, the daily average working hours item, and the daily average output item, obtaining the updated score item.

[0037] In a specific implementation process, such as Figure 4As shown, it is obtained that there are two production lines in a production workshop, namely production line A and production line B. Through a laser particle counter, dust particle data in the air is obtained, and the air dust particle content data of production line A and production line B are respectively obtained. It is obtained that the number of particles with a size of ≥ 0.5 µm in production line A is 2,410,032 particles / m³, and the number of particles with a size of ≥ 0.5 µm at production line B is 2,401,122 particles / m³. At this time, the target item is 2,405,577. By obtaining the environmental differences between production line A and production line B, which are 4,455 and -4,455 respectively, and obtaining the ratios of these results to the target item again, the results are 0.0018 and -0.0018 respectively. At this time, the updated environmental items are 0.00036 and -0.00036 respectively. Since the working environmental items of production line A and production line B are -0.0114 and 0.0114 respectively, the environmental change items are -0.01104 and 0.01104 respectively.

[0038] Step S400: Obtain the order status of the target production line and the uncompleted order data to obtain the production status item and the remaining order item.

[0039] In a specific implementation process, it is obtained that there are two production lines in a production workshop, namely production line A and production line B. The order statuses of production line A and production line B are 300 units and 280 units respectively, and the uncompleted data is 1,000 units.

[0040] Step S500: Based on the updated scoring item, obtain the proportion of the distributed order data of the production line to obtain the target proportion item. Based on the combined result of the target proportion item, the production status item, and the remaining order item, obtain the target task item.

[0041] It should be noted that it is necessary to determine whether the production status item corresponding to the target production line meets the target task item. When the production status item does not meet the target task item, task scheduling is performed based on the remaining order item. When the production status item meets the target task item, the excess part is used as the scheduling item and combined with the remaining order item to complete the scheduling optimization of the production line.

[0042] It should be noted that the method for obtaining the target proportion item includes: based on the updated scoring item, obtain the total data of the production line to obtain the total scoring item; based on the ratio result of the updated scoring item and the total scoring item, obtain the target allocation rate, and the target allocation rate corresponds to the target production line respectively; based on the target allocation rate and the updated scoring item, sort them in descending order and ascending order respectively to obtain the allocation sorting item and the scoring sorting item; pair the allocation sorting item and the scoring sorting item respectively, and then obtain the target proportion item.

[0043] In a specific implementation process, such as Figure 5As shown, it is obtained that there are two production lines in a certain production workshop, namely production line A and production line B. The order statuses of production line A and production line B are 300 units and 280 units respectively, and the uncompleted data is 1000 units. The working time items of production line A and production line B are 0.1 and 0.08 respectively, the working environment items are -0.0114 and 0.0114 respectively, the number of maintenance times items are both 0.2, the average daily working hours items are 0.0054 and -0.0054 respectively, the average daily output items are -0.0174 and 0.0174 respectively, and the environmental change items are -0.01104 and 0.01104 respectively. Therefore, the updated scoring items of production line A and production line B are 0.27696 and 0.30304 respectively. At this time, the total scoring item is 0.58. The target allocation rates of production line A and production line B are 47.75% and 52.25% respectively. Based on the target allocation rate and the updated scoring item, they are sorted in descending and ascending order respectively to obtain the allocation sorting item and the scoring sorting item, and the allocation sorting item and the scoring sorting item are paired respectively, and then the target proportion item is obtained. That is, the target proportion item corresponding to production line A is 52.25%, and the target proportion item corresponding to production line B is 47.75%. Based on the combined result of the target proportion item, the production status item and the remaining order item, the target task item is obtained. That is, the target task items of production line A and production line B are 825.55 and 754.45 respectively. Since the production of the transmission is in whole units, the target task items of production line A and production line B are 826 and 754 or 825 and 755 respectively.

[0044] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirits of the present invention. The scope of the present invention is defined by the appended embodiments and their equivalents.

Claims

1. A gearbox production line optimization scheduling control method based on big data, characterized in that: include: Acquire the damage degree data of the production line and obtain the damage score item, which is used to represent the daily damage score of the target production line during use; Set the scoring content of the damaged scoring items, including working time score, working environment score, maintenance number score, daily average working time score and daily average output score; The actual scoring data of the scoring contents of the damaged scoring items are obtained respectively, and the matching weights are set to obtain the working time item, the working environment item, the number of maintenance items, the average daily working time item, and the average daily output item, and then the result combination item is obtained. The result combination item is used to represent the comprehensive damage status data of the target production line; Based on the damaged score item, the working environment data of the production line is obtained, the working environment item is interfered with, the specific data of the working environment item is adjusted, and the damage score of the production line is adjusted according to the dynamic environment data to obtain an updated score item; Obtain the order status and unfinished order data of the target production line, and obtain the production status item and remaining order items; Based on the updated scoring items, the proportion of order data is distributed for the production line to obtain the target proportion item. Based on the combination of the target proportion item, the production status item and the remaining order items, the target task item is obtained. It is judged whether the production status item corresponding to the target production line meets the target task item. When the production status item does not meet the target task item, task scheduling is performed based on the remaining order items. When the production status item meets the target task item, the excess part is used as the scheduling item and combined with the remaining order items to complete the scheduling optimization of the production line.

2. The gearbox production line optimization scheduling control method based on big data according to claim 1 is characterized by: The method for obtaining the damaged scoring item includes: Based on the weight ratio, the allocation weight item is obtained. Based on the combination of the allocation weight item and the scoring content, the working time weight, the working environment weight, the maintenance number weight, the average daily working time weight and the average daily output weight are obtained. The actual data of each scoring content is obtained to obtain the actual data item, and the damaged scoring item is obtained based on the combination result of the actual data item and the working time weight, working environment weight, maintenance number weight, average daily working time weight and average daily output weight.

3. The gearbox production line optimization scheduling control method based on big data according to claim 1 is characterized by: The target production line stores basic information, and the method for obtaining the working time item includes: Based on the basic information, the actual working time of the target production line is obtained to obtain working time data; Based on the basic information, the maximum working time limit of the target production line is obtained to obtain the working limit item; Obtain the ratio result of the working time data and the working restriction item to obtain the working information item; Based on the combination result of the work information item and the matching weight, the work time item is obtained.

4. The gearbox production line optimization scheduling control method based on big data according to claim 1 is characterized by: The method for obtaining the working environment item includes: Obtain the location information of the production line, obtain the production location item, use the production location item as a feature point, and obtain the location feature set; Obtaining air dust particle content data of a position feature set to obtain a feature data item, obtaining average value data of the feature data item as benchmark data to obtain a benchmark item; Obtaining a difference between a characteristic data item and a reference item to obtain a characteristic difference item, obtaining a ratio of the characteristic difference item to the reference item to obtain a data information item; Based on the combination result of the data information item and the matching weight, the working environment item is obtained.

5. The gearbox production line optimization scheduling control method based on big data according to claim 1 is characterized by: The method for obtaining the maintenance frequency item includes: Obtain maintenance frequency information of the target production line to obtain a maintenance information set, wherein the maintenance information set includes maintenance frequency information of at least two production lines; Based on the average number of times information of the maintenance number set, the average number of times item is obtained, and the ratio relationship between the maintenance number information of the target production line and the average number of times item is obtained to obtain the maintenance result item; Based on the combination of the maintenance result item and the matching weight, the maintenance number item is obtained.

6. The gearbox production line optimization scheduling control method based on big data according to claim 1 is characterized by: The method for obtaining the average daily working hours item includes: A first acquisition time threshold is set, and based on the first acquisition time threshold, working time information of a target production line is acquired to obtain a target time set, where the target time set includes working time information of at least two production lines; Obtain the average duration information of the target duration set and obtain the average duration item; Based on the difference between the working time information of the target production line and the average time item, a time difference item is obtained, and based on the ratio between the time difference item and the average time item, a daily average information item is obtained; Based on the combination of the daily average information item and the matching weight, the daily average working time item is obtained.

7. The gearbox production line optimization scheduling control method based on big data according to claim 1 is characterized by: The target production line stores basic information, and the method for obtaining the average daily output item includes: Based on the basic information, the actual working time of the target production line is obtained to obtain working time data; Obtaining the output information of the target production line, obtaining the target output item, obtaining the ratio relationship between the target output item and the working time data, and obtaining the average output data of the target production line; Based on the average production data of the target production line, average production information of the average production data is obtained to obtain an average production item; Obtain the difference between the average output data of the target production line and the average output item to obtain the output difference item, and obtain the output result item based on the ratio result of the output difference item and the average output item; Based on the combination of the daily average output item and the allocation weight, the daily average output item is obtained.

8. The gearbox production line optimization scheduling control method based on big data according to claim 1 is characterized by: The method for obtaining the update scoring item includes: An acquisition threshold is set, and based on the acquisition threshold, air dust particle content data of a target production line is acquired through a target device to obtain a real-time environmental item; Obtaining average value data of the real-time environment item as target data to obtain the target item, obtaining the difference between the real-time environment item and the target item to obtain the environment difference item, obtaining the ratio of the environment difference item to the target item to obtain the data update item; Based on the combination result of the data update item and the matching weight, an update environment item is obtained; Based on the combination of the update environment item and the work environment item, the environment change item is obtained, and based on the combination of the working time item, the number of maintenance items, the average daily working time item and the average daily output item, the update score item is obtained.

9. The gearbox production line optimization scheduling control method based on big data according to claim 1 is characterized by: The method for obtaining the target proportion item includes: Based on the updated scoring items, the total data of the production line is obtained to obtain the total scoring items; Based on the ratio of the updated scoring item to the total scoring item, the target allocation rate is obtained, and the target allocation rate corresponds to the target production line respectively; Based on the target allocation rate and the updated scoring items, the items are sorted from high to low and from low to high respectively to obtain allocation sorting items and scoring sorting items; Pair the allocation ranking items and the score ranking items respectively to obtain the target proportion items.

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