Moving path optimization system and method for high-precision flexible machining complete equipment

Abstract

The invention relates to the technical field of production control, and discloses a moving path optimization system and method for high-precision flexible machining complete equipment. The method comprises the steps that a semantic map is established, the semantic map comprises a plurality of production areas, transportation channels exist between the production areas, and each production area has a corresponding production process; generating a production scheme based on the production target; taking the shortest path as a target, and generating an initial path diagram based on the production process and the semantic map; calculating the route saturation of a transportation channel in the semantic map based on the processing moving path, positioning a conflict area in the initial path map based on the route saturation, changing part of the processing moving path to reduce the route saturation, and obtaining an optimized path map; and performing priority ranking on the target product on the basis of the optimized path diagram to obtain a final moving scheme. According to the invention, path planning can be rapidly carried out on the flexible production process, and the time cost and the hardware cost are relatively low.

Description

Technical Field

[0001] The present application relates to the technical field of production control, and particularly to a mobile path optimization system and method for a high-precision flexible machining complete set of equipment. Background Technique

[0002] A flexible machining complete set of equipment is a manufacturing unit that realizes the efficient production of multi-variety and small-batch products through highly automated and digital technologies. The flexible machining complete set of equipment includes production equipment and mobile equipment. The production equipment can produce products of different specifications according to requirements, while the mobile equipment can adapt to production requirements for material transfer. The flexible machining complete set of equipment can quickly switch the processing processes of different products on the same production line, while optimizing resource utilization and reducing costs.

[0003] However, there are currently the following problems. In a dense operation scenario, conflicts are likely to occur when multiple mobile devices request the same intersection simultaneously. To solve this problem, the following methods have been proposed in the prior art. For example, the Chinese patent document with the publication number CN118259631A discloses a method, a server, and related devices for controlling the movement of each device according to a production plan. This method plans the movement path based on the current positioning coordinates and target point coordinates of the processing equipment included in the processing plan. When planning, priorities are assigned to different mobile devices. After the server determines that there is a conflict point in the current scheduling plan, it will perform temporal planning on the devices passing through the conflict point according to the target priority sorting, so as to avoid conflicts. Another example is the Chinese patent document with the publication number CN118655841A, which discloses a discrete flexible layout workshop production scheduling and mobile robot scheduling method. This method considers conflict-free path planning and uses a conflict avoidance mechanism to adjust the genetic algorithm population solution. The solution efficiency and quality are better than the method of directly solving the planning formula with a solver and then adjusting, thus greatly reducing the number of path conflict points.

[0004] However, when the production scheduling volume is large, there will inevitably be devices with the same priority in the first method above. Devices with the same priority are prone to deadlocks when arriving at the conflict point simultaneously, and manual intervention is still required. The second method obtains the optimal production plan and path plan through intelligent algorithm solving. When the production scheduling volume is large, it will consume a large amount of time and require high computing power hardware, which requires a large amount of time cost and financial cost in the early stage. Summary of the Invention

[0005] To solve the problems raised in the above background technique, the present application provides a mobile path optimization system and method for a high-precision flexible machining complete set of equipment.

[0006] To achieve the above invention purpose, the present invention proposes a mobile path optimization method for a high-precision flexible machining complete set of equipment, including:

[0007] Build a semantic map based on the distribution of factory equipment. The semantic map includes multiple production areas, there are transportation channels between the production areas, and each production area has a corresponding production process.

[0008] Determine the production target, which includes the target product, target output, and target time limit. Generate a production plan based on the production target. The production plan includes the production processes of each target product.

[0009] With the goal of the shortest path, generate an initial path map based on the production process and the semantic map. The initial path map includes the processing and moving paths of various target products.

[0010] Calculate the route saturation of the transportation channels in the semantic map based on the processing and moving paths. Locate the conflict areas in the initial path map based on the route saturation, and change some of the processing and moving paths to reduce the route saturation to obtain an optimized path map.

[0011] Perform a priority ranking on the target products based on the optimized path map to obtain the final moving plan.

[0012] Further, generating the production plan includes the following steps:

[0013] The production plan includes a leading plan and a relay plan. Calculate the total output based on the types of target products and the corresponding target outputs. Select a part of the output as the first output from the total output. Set the objective function and constraints for the first output, and solve the objective function under the constraints based on an intelligent algorithm to obtain the leading plan for the first output.

[0014] The part of the total output except the first output is defined as the second output. Split the second output into multiple production batches, extract one of the production batches as the first batch, generate multiple production path combinations for the first batch, and simulate and calculate the production time of each production path combination under the scheduling of the leading plan, and select the one with the minimum production time as the optimal combination.

[0015] Continue to extract the second batch from the production batches. Under the scheduling of the first batch, simulate and calculate to obtain the optimal combination of the second batch, and repeat this step until the optimal combinations of all production batches are obtained, and synthesize the optimal combinations to obtain the relay plan.

[0016] Further, calculating the production time includes the following steps:

[0017] Establish a production database, where the production database includes multiple processing stages, each of the processing stages includes multiple production processes with the same functions, each of the production processes includes at least one process program, each of the process programs has a corresponding standard processing time, and a switching time for converting its own processing specifications;

[0018] Based on the process flow of the target product, screen the corresponding processing stages, traverse and combine the production processes under different processing stages to generate multiple production path combinations;

[0019] Calculate the processing time based on the process programs and the standard processing time included in the production path combination, calculate the queuing time and the switching time based on the production processes occupied in the production path, and calculate the production time of the production path combination based on the processing time, the queuing time and the switching time.

[0020] Further, generating the initial path map includes the following steps:

[0021] Determine the production starting point in the semantic map, and the return routes from each production area back to the production starting point. Combine the production starting point, the production path combination and the return routes to generate the circuit movement routes of each target product, and obtain the initial path map by integrating all the circuit movement routes.

[0022] Further, locating the conflict area includes the following steps:

[0023] Locate the connection paths connecting the production areas in the transportation channels, split the total duration of the relay plan into multiple sub - time periods, calculate the route saturation of each connection path, where the route saturation is the number of transportation devices on the connection path during the sub - time period, locate the overlapping areas between the connection paths, define the connection paths forming the overlapping areas as intersection paths, accumulate the route saturation of the intersection paths as the intersection congestion degree of the overlapping areas, and define the overlapping areas with the intersection congestion degree greater than the first threshold as the conflict areas.

[0024] Further, optimizing the initial path map includes the following steps:

[0025] Locate the sub - time period when the conflict area appears as the target time period, and the transportation channel connected to the conflict area as the optimization channel. Under the restriction of maintaining the order of the production processes, change the optimization channel to change the circuit movement route of the target product during the target time period to reduce the intersection congestion degree of the conflict area.

[0026] Further, the target products are prioritized based on a dynamic weighted priority scheduling algorithm.

[0027] Further, the objective function is to minimize the production time, and the constraint conditions include the material transfer speed constraint between the production areas, the process assembly speed constraint of the production areas, the maximum production time constraint, the position constraint, the walking path constraint, the collision constraint, the assembly quantity constraint, and the production process constraint.

[0028] Further, the intelligent algorithms include genetic algorithm, simulated annealing algorithm, and particle swarm algorithm.

[0029] This application also provides a mobile path optimization system for a high-precision flexible machining complete set of equipment, which is used to implement the above-mentioned mobile path optimization method for a high-precision flexible machining complete set of equipment. The system includes:

[0030] A map module that creates a semantic map based on the distribution of factory equipment. The semantic map includes multiple production areas, there are transportation channels between the production areas, and each production area has a corresponding production process.

[0031] A scheme generation module that determines production objectives, where the production objectives include target products, target production quantities, and target time limits, and generates a production scheme based on the production objectives. The production scheme includes the production processes of each target product.

[0032] A path generation module that aims to minimize the path length, generates an initial path map based on the production processes and the semantic map. The initial path map includes the processing movement paths of various target products, calculates the route saturation of the transportation channels in the semantic map based on the processing movement paths, locates the conflict areas in the initial path map based on the route saturation, and changes some of the processing movement paths to reduce the route saturation to obtain an optimized path map.

[0033] An optimization module that prioritizes the target products based on the optimized path map to obtain a final movement scheme.

[0034] Beneficial effects:

[0035] Through the present invention, there is no need to use intelligent algorithms to solve the objective function for path planning, thereby reducing the computational complexity and having high interpretability. And before the priority ranking, the path is optimized to reduce the passing frequency of each intersection area, thereby greatly reducing the probability of conflicts between devices with the same priority. Description of the Drawings

[0036] Figure 1Flowchart of the mobile path optimization method for the high-precision flexible manufacturing complete equipment in this application;

[0037] Figure 2 Structural schematic diagram of the production database in this application;

[0038] Figure 3 Principle schematic diagram of optimizing the initial path map in this application;

[0039] Figure 4 Structural schematic diagram of the mobile path optimization system for the high-precision flexible manufacturing complete equipment in this application. Detailed implementation manners

[0040] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0041] It can be understood that the terms "first", "second", etc. used in this application can be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish the first element from another element. For example, without departing from the scope of this application, the first xx script can be called the second xx script, and similarly, the second xx script can be called the first xx script.

[0042] As Figure 1 shown, a mobile path optimization method for a high-precision flexible manufacturing complete equipment includes:

[0043] S1: Establish a semantic map based on the distribution of factory equipment. The semantic map includes multiple production areas. There are transportation channels between the production areas, and each production area has a corresponding production process.

[0044] S2: Determine the production target. The production target includes the target product, target output and target time limit. Generate a production plan based on the production target. The production plan includes the production processes of each target product.

[0045] The production functions of each production area and the locations of the production areas in the factory are marked in the semantic map. For example, production area 1 is used for milling and production area 2 is used for cutting; the transportation channels are marked in the semantic map. The transportation channels are used to restrict the moving areas of transportation equipment, and the transportation equipment is used to transfer materials between production areas.

[0046] For production goals, there may be multiple target products included. For example, the target products include split air conditioner A and split air conditioner B, with target production volumes of 9000 units and 6000 units respectively, and the target time limit for both is 30 days. Then, production scheduling can be carried out according to these requirements to ensure that production is completed within the target time limit. The method for generating the specific production plan will be introduced later.

[0047] The production plan of this embodiment includes the planning of the production processes for each target product. For 9000 split air conditioners A, the production plan includes that the first air conditioner needs to be processed in production process 1 on processing equipment 1, and the second air conditioner needs to be processed in production process 1 on processing equipment 2. Processing equipment 1 and 2 have the same function, that is, the production plan includes the production process specific to each air conditioner.

[0048] S3: With the goal of the shortest path, generate an initial path map based on the production processes and the semantic map. The initial path map includes the processing and moving paths of various target products.

[0049] Since the production plan includes the production processes that each target product has to go through, and each production area corresponds to a production process, after determining the production plan, based on the transportation channels marked in the semantic map, with the goal of the shortest path, the processing and moving paths of each target product can be generated. A more detailed generation method will be described later. After generating the processing and moving paths of all target products, the initial path map is obtained.

[0050] S4: Calculate the route saturation of the transportation channels in the semantic map based on the processing and moving paths, locate the conflict areas in the initial path map based on the route saturation, and change some of the processing and moving paths to reduce the route saturation to obtain an optimized path map.

[0051] The production plan also includes the start time and end time of each target product in each production process. For example, the first air conditioner starts production at 10:00, is processed in production process 1 between 10:01 - 10:02, and is processed in production process 2 between 10:03 - 10:04. Combining this information with the processing and moving paths of the target products, it can be calculated that the transportation equipment is located in the transportation channel from the starting point to production process 1 between 10:00 - 10:01, and is located in the transportation channel from production process 1 to production process 2 between 10:02 - 10:03. After determining the number of transportation equipment existing in each transportation channel at each time period, the corresponding route saturation can be calculated.

[0052] There must be overlapping areas among multiple transportation channels. If the overlapping area has a high route saturation, then this intersection area is defined as a conflict area. To reduce the conflict probability in the intersection area, first, by changing the processing and moving paths of some target products, they are transported through transportation channels with a longer path but a lower route saturation.

[0053] S5: Based on the optimized path map, prioritize the target products to obtain the final movement plan.

[0054] Specifically, prioritize the target products based on the dynamic weighted priority scheduling algorithm.

[0055] Since there will still be intersection areas after optimization, based on the previous step, by prioritizing each target product, the collision probability in the intersection area is reduced. After the optimization in the previous step, the computational amount and complexity of conflict optimization for the intersection area are greatly reduced, so that a better conflict optimization effect can be obtained. The method for setting priorities can refer to the existing technology and will not be introduced here.

[0056] The present invention first establishes a semantic map based on the distribution of factory equipment and generates a production plan for specific production goals. Then, based on the production plan, it generates the processing and moving paths for each target product to obtain an initial path map, and the generation aims at the shortest path length, so that the generated initial path map has the highest transportation efficiency. Then, based on the initial path map, calculate the route saturation of the transportation channels and locate the conflict areas. By adjusting the moving paths of some materials, the load of the high-saturation conflict areas can be reduced. On this basis, prioritize the target products to significantly reduce the collision probability of materials during transportation, thereby reducing the risk of production interruption and delay.

[0057] Through the present invention, there is no need to use intelligent algorithms to solve the objective function for path planning, thereby reducing the computational complexity and having high interpretability. And before priority sorting, by optimizing the paths, the passing frequency of each intersection area is reduced, and further, the occurrence probability of conflicts among devices with the same priority is greatly reduced.

[0058] It should be particularly noted that through the present invention, the path planning for the flexible production process can be quickly carried out, and it has low time cost and hardware cost.

[0059] In this embodiment, generating the production plan includes the following steps:

[0060] The production plan includes a pilot plan and a relay plan. Calculate the total output based on the types of target products and their corresponding target outputs. Select a part of the total output as the first output, set the objective function and constraints for the first output, and solve the objective function under the constraints based on an intelligent algorithm to obtain the pilot plan for the first output.

[0061] The intelligent algorithms include genetic algorithm, simulated annealing algorithm, and particle swarm optimization algorithm.

[0062] For example, there are target product A and target product B, and their corresponding target outputs are 5000 units and 6000 units respectively. Then the total output is 11000 units. In this embodiment, the extraction value is set to 500, and 500 units are extracted from the total output of 11000 units as the first output. Then set the objective function and constraints, and solve the objective function under the limitation of the constraints to obtain a pilot plan that can complete the production of the first output in the shortest time, and perform actual production according to the pilot plan. Since only a small part is extracted for solution, the result can be obtained quickly.

[0063] The part of the total output except the first output is defined as the second output. Split the second output into multiple production batches, extract one production batch as the first batch, generate multiple production path combinations for the first batch, simulate and calculate the production time of each production path combination under the scheduling of the pilot plan, and select the one with the minimum production time as the optimal combination.

[0064] In this embodiment, the second output is 10500 units, and each target product is regarded as a production batch, so there are 10500 batches in total. In other embodiments, according to the actual production capacity, 2 or 3 can also be regarded as one batch. For the first batch, when extracting, the more urgent target products are extracted first according to the production time limit requirements. The production path combination is all the process combinations to complete the target production, which can be obtained by the exhaustive method. Considering that there is already a pilot plan in production, during the production process of the pilot plan, a relay plan after the end of the pilot plan is generated through simulation calculation.

[0065] Specifically, first simulate the production status of each production area when the pilot plan is about to end, and use it as the status when the first batch is put into production. Calculate the production time of each process combination based on this, and select the one with the shortest generation time as the optimal combination. During actual production, schedule the first batch according to the optimal combination.

[0066] Continue to extract the second batch from the production batches. Under the condition of scheduling the first batch, simulate and calculate to obtain the optimal combination of the second batch, and repeat this step until the optimal combinations of all production batches are obtained, and obtain the relay plan by integrating the optimal combinations.

[0067] Subsequently, continue to simulate the production status of each production area in the case of scheduling the first batch, and then conduct the simulation of the second batch to obtain the optimal combination of the second batch. Repeat this process until the simulation of the last 10,500 batches is completed. The present invention continuously generates production plans for each batch through the exhaustive method. The exhaustive process does not involve complex constraint solving problems, and the calculation of production time is also a linear calculation process, enabling the computer to quickly give the calculation results in an extremely short time, so that the production plan can be obtained in a short time.

[0068] The calculation of production time in this embodiment includes the following steps:

[0069] Establish a production database. The production database includes multiple processing stages. Each processing stage includes multiple production processes with the same function. Each production process includes at least one process program. Each process program has a corresponding standard processing time and a switching time for converting its own processing specifications.

[0070] As Figure 2 shown, the production database includes multiple processing stages. For example, processing stage 1 is milling, and processing stage 2 is cutting. There are multiple production processes with the same function in processing stage 1. For example, production processes 11, 12, and 13. In this embodiment, multiple production processes with the same function are production equipment with the same function. For example, there are production equipment 1 - 3 in processing stage 1, and production equipment 1 - 3 can all complete the milling function. For production equipment 1, the milling process specifically includes die adjustment, batch processing, and on - line inspection, and their corresponding standard processing times are, for example, 1 minute for die adjustment, 5 minutes for batch processing, and 2 minutes for on - line inspection. In addition, when the specifications of the previous target product are different from those of the subsequent target product, there is also a switching time. For example, for die adjustment, when the specifications of the target products produced in two consecutive times are different, a parameter switching time of 1 minute is also required.

[0071] Screen the corresponding processing stages based on the process flow of the target product, traverse and combine the production processes under different processing stages to generate multiple production path combinations.

[0072] Calculate the processing time based on the process programs and standard processing times included in the production path combination, calculate the queuing time and switching time based on the occupied production processes in the production path, and calculate the production time of the production path combination based on the processing time, queuing time, and switching time.

[0073] If the production path combination of the target product can be either production processes 11, 25, 48 or production processes 12, 26, 47. By traversing the combinations, all possible production path combinations for completing the target production can be obtained. Then, in combination with the production database, the processing time is calculated. Specifically, the standard processing times of the process programs included in the production path combination are accumulated to obtain the processing time. For example, if the calculation result is 20 minutes, and after simulation, it is determined that when reaching production process 25, the previous target product is still being processed and still needs 3 minutes to complete, then 3 minutes is used as the queuing time. Since the current target product has a different specification from the subsequent production target products, and production process 5 also requires a 2-minute switching time, the value of the production time of the production path combination is 20 + 3 + 2 = 25 minutes.

[0074] The steps for generating the initial path map in this embodiment include the following:

[0075] Determine the production starting point in the semantic map and the return routes from each production area back to the production starting point. Combine the production starting point, production path combination, and return routes to generate the tour movement route for each target product, and obtain the initial path map by integrating all the tour movement routes.

[0076] As Figure 3 shown, the production starting point is determined to be G. For target product A, in the relay plan, it needs to pass through production processes 5, 4, 3 in sequence. The above production programs respectively correspond to Figure 3 production areas 5, 4, 3 in. Then, according to the shortest path rule, the tour movement route is determined to be Y1 - Y2 - Y3 - Y4 - Y6 - Y5 - Y6 - Y3 - Y1.

[0077] The steps for locating the conflict area in this embodiment include the following:

[0078] Locate the connection paths connecting the production areas in the transportation channels. Split the total duration of the relay plan into multiple sub - time periods, calculate the route saturation of each connection path. The route saturation is the number of transportation devices on the connection path during the sub - time period. Locate the overlapping areas between the connection paths, define the connection paths forming the overlapping areas as the intersection paths, accumulate the route saturations of the intersection paths as the intersection congestion degree of the overlapping areas, and define the overlapping areas with an intersection congestion degree greater than the first threshold as the conflict areas.

[0079] For example, take Figure 3The transportation channels Y8, Y9, Y6, and Y4 between production areas 1 and 4 serve as connection path 1, and the transportation channels between the transportation channels Y7, Y9, and Y5 of production areas 1 and 3 serve as connection path 2. If the relay plan takes a total of 300 minutes to complete production, it is split into 30 sub-time periods, each with a length of 10 minutes. Then, calculate the route saturation of each connection path during each sub-time period in the production process. It can be determined by the number of mobile devices present on the connection path. As before, it is possible to infer during what time period and between which two production areas each transportation device moves through the production plan.

[0080] For example, within 0 - 10 minutes, if the transportation device is moving from production area 1 to production area 3, it is inferred that the transportation device is located in connection path 1. If there are 6 and 7 transportation devices on connection path 1 and connection path 2 respectively within 0 - 10 minutes, then there are 6 + 7 = 13 transportation devices in Y9, which is the overlapping area, and the intersection congestion degree is 13. The first threshold is set to 10. Since the intersection congestion degree of the overlapping area Y9 is 13, it is determined as a conflict area within 0 - 10 minutes.

[0081] The optimization of the initial path diagram in this embodiment includes the following steps:

[0082] Locate the sub-time period when the conflict area appears as the target time period, and the transportation channel connected to the conflict area as the optimization channel. Under the limitation of maintaining the production process sequence, change the optimization channel to change the circuit movement route of the target product during the target time period to reduce the intersection congestion degree of the conflict area.

[0083] Continue to refer to Figure 3 , the current conflict area is Y9, and the transportation channel connected to Y9 is Y10. If the current transportation device needs to move from production area 1 to production area 4, then after optimization, the transportation device bypasses to production area 4 through transportation channel Y10, thereby reducing the intersection congestion degree of the conflict area Y9.

[0084] In this embodiment, the objective function is the shortest production time, and the constraint conditions include the material transfer speed constraint between production areas, the process assembly speed constraint of the production area, the maximum production time constraint, the position constraint, and the production process constraint of the target product.

[0085] The objective function is specifically Min where, T 生 is the production time to complete the first production volume, N is the quantity of the first production volume, t iThe production time of the i-th target product, Min() is the minimization function, the material transfer speed constraint is the maximum transportation speed of the transportation equipment, and the process assembly speed constraint is the production time limit of each production process. For example, if the process assembly speed of production process 1 is 10s, then the corresponding process assembly speed constraint is T 配 ≥ 10, T 配 is the process assembly speed constraint of production process 1, and the maximum time constraint is the maximum production limit time of the target product. For example, T 总 ≤ 1000, T 总 The maximum limit time for the production of target product 1 from start to end is 1000s.

[0086] The collision constraint is to avoid collisions when transportation equipment arrives at the intersection area simultaneously. The assembly quantity constraint is the maximum processing quantity that can be processed simultaneously in each production area. The production process constraint is that the target product must be produced according to a fixed process. The position constraint is the position of the production area, and the walking path constraint is the position of the transportation channel. The positions of the production area and the transportation channel can be encoded in advance, and the corresponding constraint functions can be set according to the encoding results. The specific form will not be introduced.

[0087] For example Figure 4 As shown, a mobile path optimization system for a high-precision flexible machining complete set of equipment is used to implement the above-mentioned mobile path optimization method for a high-precision flexible machining complete set of equipment. The system includes:

[0088] A map module that establishes a semantic map based on the distribution of factory equipment. The semantic map includes multiple production areas, there are transportation channels between the production areas, and each production area has a corresponding production process.

[0089] A scheme generation module that determines the production target, which includes the target product, target output, and target time limit, and generates a production scheme based on the production target. The production scheme includes the production processes of each target product.

[0090] A path generation module that aims to minimize the path, generates an initial path map based on the production process and the semantic map. The initial path map includes the processing movement paths of various target products, calculates the route saturation of the transportation channels in the semantic map based on the processing movement paths, locates the conflict areas in the initial path map, and changes some processing movement paths to reduce the route saturation to obtain an optimized path map.

[0091] An optimization module that prioritizes the target products based on the optimized path map to obtain the final movement scheme.

[0092] It should be understood that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0093] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention should be included outside the protection scope of the present invention.

Claims

1. A method for optimizing the moving path of a high-precision flexible processing equipment set, characterized in that: Establishing a semantic map based on the distribution of factory equipment, wherein the semantic map includes a plurality of production areas, transportation channels exist between the production areas, and each of the production areas has a corresponding production process; Determine a production target, wherein the production target includes a target product, a target output, and a target time limit, and generate a production plan based on the production target, wherein the production plan includes the production process of each of the target products; With the shortest path as the goal, an initial path map is generated based on the production process and the semantic map, wherein the initial path map includes processing movement paths of various target products; Calculating the route saturation of the transport channel in the semantic map based on the processing movement path, locating the conflict area in the initial path map based on the route saturation, changing part of the processing movement path to reduce the route saturation, and obtaining an optimized path map; The target products are prioritized based on the optimization path map to obtain a final movement plan.

2. The method according to claim 1, characterized in that Generating the production plan includes the following steps: The production plan includes a pilot plan and a relay plan, the total output is calculated based on the type of the target product and the corresponding target output, a part of the output is selected from the total output as the first output, the objective function and constraint conditions of the first output are set, the objective function is solved under the constraint conditions based on an intelligent algorithm, and the pilot plan of the first output is obtained; The portion of the total output excluding the first output is defined as a second output, the second output is divided into multiple production batches, one of the production batches is selected as the first batch, multiple production path combinations of the first batch are generated, the production time of each production path combination is simulated and calculated under the production scheduling of the pilot plan, and the one with the shortest production time is selected as the optimal combination; Continue to extract the second batch from the production batch, and when scheduling the first batch, simulate and calculate to obtain the optimal combination of the second batch, and repeat this step until the optimal combination of all the production batches is obtained, and obtain the relay plan by combining the optimal combination.

3. The method according to claim 2, characterized in that Calculating the production time includes the following steps: Establishing a production database, wherein the production database includes multiple processing stages, each processing stage includes multiple production processes with the same functions, each production process includes at least one process procedure, and each process procedure has a corresponding standard processing time and a switching time for converting its own processing specifications; Based on the process flow of the target product, the corresponding processing stage is selected, and the production procedures under different processing stages are traversed and combined to generate a plurality of production path combinations; The processing time is calculated based on the process procedures and the standard processing time included in the production path combination, the queuing time and the switching time are calculated based on the production processes occupied in the production path, and the production time of the production path combination is calculated based on the processing time, the queuing time and the switching time.

4. The method according to claim 3, characterized in that Generating the initial path map comprises the following steps: Determine the production starting point in the semantic map, and the return route from each of the production areas back to the production starting point, combine the production starting point, the production path combination and the return route to generate a patrol movement route for each of the target products, and combine all the patrol movement routes to obtain the initial path map.

5. The method according to claim 4, characterized in that Locating the conflict area comprises the following steps: Locate a connection path connecting the production area in the transport channel, divide the total duration of the relay plan into multiple sub-time periods, calculate the route saturation of each connection path, the route saturation is the number of transport equipment on the connection path within the sub-time period, locate the overlapping area between the connection paths, define the connection paths forming the overlapping area as intersection paths, accumulate the route saturations of the intersection paths as the intersection congestion of the overlapping area, and define the overlapping area where the intersection congestion is greater than a first threshold as the conflict area.

6. The method according to claim 5, characterized in that Optimizing the initial path graph comprises the following steps: The sub-time period in which the conflict area appears is located as the target time period, and the transportation channel connected to the conflict area is the optimized channel. Under the constraint of maintaining the order of the production process, the optimized channel is changed to change the patrol movement route of the target product in the target time period to reduce the intersection congestion of the conflict area.

7. The method according to claim 1, characterized in that The target products are prioritized based on a dynamic weighted priority scheduling algorithm.

8. The method according to claim 2, characterized in that: The objective function is to minimize the production time, and the constraints include the material transfer speed constraint between the production areas, the process assembly speed constraint of the production area, the maximum production time constraint, the position constraint, the walking path constraint, the collision constraint, the assembly quantity constraint, and the production process constraint.

9. The method according to claim 2, characterized in that: The intelligent algorithms include genetic algorithm, simulated annealing algorithm and particle swarm algorithm.

10. A mobile path optimization system for high-precision flexible processing equipment, used to implement a mobile path optimization method for high-precision flexible processing equipment as described in any one of claims 1 to 9, characterized in that: A map module, which establishes a semantic map based on the distribution of factory equipment, wherein the semantic map includes multiple production areas, there are transportation channels between the production areas, and each of the production areas has a corresponding production process; A plan generation module determines a production target, wherein the production target includes a target product, a target output, and a target time limit, and generates a production plan based on the production target, wherein the production plan includes the production process of each target product; A path generation module, with the shortest path as the goal, generates an initial path map based on the production process and the semantic map, the initial path map includes processing movement paths of various target products, calculates the route saturation of the transportation channel in the semantic map based on the processing movement paths, locates the conflict area in the initial path map based on the route saturation, changes part of the processing movement paths to reduce the route saturation, and obtains an optimized path map; The optimization module prioritizes the target products based on the optimization path map to obtain a final movement plan.

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