Mobile path optimization system and method for high-precision flexible processing equipment

Abstract

The present application relates to the field of production control technology, and discloses a mobile path optimization system and method for high-precision flexible processing equipment. The method includes: establishing a semantic map, the semantic map includes multiple production areas, there are transportation channels between the production areas, and each production area has a corresponding production process; generating a production plan based on the production target; taking the shortest path as the goal, generating an initial path map based on the production process and the semantic map; calculating the route saturation of the transportation 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; prioritizing the target products based on the optimized path map to obtain the final movement plan. The present invention can quickly plan the path of the flexible production process with low time and hardware costs.

Description

Technical Field

[0001] The present application relates to the field of production control technology, and in particular to a moving path optimization system and method for high-precision flexible processing equipment. Background Art

[0002] A flexible processing unit is a manufacturing unit that uses highly automated and digital technologies to efficiently produce a wide variety of products in small batches. It consists of production equipment that can produce products of varying specifications based on demand, while the mobile equipment can adaptively transfer materials based on production needs. This unit enables rapid switching between different product processing steps on the same production line, while optimizing resource utilization and reducing costs.

[0003] However, there is currently a problem: in densely populated work scenarios, conflicts are likely to occur when multiple mobile devices simultaneously request the same intersection. To address this problem, the following methods have been proposed in the prior art. For example, Chinese patent document No. CN118259631A discloses a method, server, and related equipment for controlling the movement of various devices according to a production plan. This method plans the movement path for the current positioning coordinates and target point coordinates of the processing equipment contained in the processing plan, assigning priorities to different mobile devices during planning. After the server determines that there are conflict points in the current scheduling plan, it will perform time-series planning for devices passing through the conflict points based on the target priority, thereby avoiding conflicts. Another example is Chinese patent document No. 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 superior to the method of directly adjusting the planning formula after the solver solves it, thereby greatly reducing the number of path conflict points.

[0004] However, when production volumes are high, the first method inevitably encounters devices with the same priority. These devices, when simultaneously reaching a conflict point, can easily deadlock, requiring manual intervention. The second method, which uses intelligent algorithms to determine the optimal production plan and routing, is time-consuming and requires high-performance hardware for large production volumes, requiring significant upfront investment in both time and financial resources. Summary of the Invention

[0005] In order to solve the problems raised in the above background technology, the present application provides a moving path optimization system and method for high-precision flexible processing equipment.

[0006] In order to achieve the above-mentioned object of the invention, the present invention proposes a moving path optimization method for high-precision flexible processing equipment, comprising:

[0007] Establishing a semantic map based on the distribution of factory equipment, wherein the semantic map includes multiple production areas, transportation channels between the production areas, and each production area has a corresponding production process;

[0008] Determining production targets, including target products, target output, and target time limits, and generating a production plan based on the production targets, the production plan including the production process for each target product;

[0009] 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;

[0010] 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, and modifying part of the processing movement path to reduce the route saturation to obtain an optimized path map;

[0011] The target products are prioritized based on the optimization path map to obtain a final movement plan.

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

[0013] 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 portion of the output is selected from the total output as a first output. An objective function and constraints are set for the first output. The objective function is solved under the constraints based on an intelligent algorithm to obtain a pilot plan for the first output.

[0014] 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 which is selected as the first batch. Multiple production path combinations for 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 combination with the shortest production time is selected as the optimal combination.

[0015] 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.

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

[0017] Establishing a production database, wherein 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 procedure, and each process procedure 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, the corresponding processing stage is selected, and the production processes under different processing stages are traversed and combined to generate multiple production path combinations;

[0019] 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.

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

[0021] Determine the production starting point in the semantic map, as well as the return route from each production area back to the production starting point, combine the production starting point, the production path combination and the return route to generate a tour movement route for each target product, and integrate all the tour movement routes to obtain the initial path map.

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

[0023] Locate the connection path connecting the production area in the transportation 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 transportation 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.

[0024] Furthermore, optimizing the initial path graph includes the following steps:

[0025] 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.

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

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

[0028] Furthermore, the intelligent algorithm includes a genetic algorithm, a simulated annealing algorithm and a particle swarm algorithm.

[0029] The present application also provides a mobile path optimization system for high-precision flexible processing equipment, which is used to implement the mobile path optimization method for high-precision flexible processing equipment described above. The system includes:

[0030] The map module 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.

[0031] The plan generation module determines the production target, which includes the target product, target output and target time limit, and generates a production plan based on the production target, which includes the production process of each target product.

[0032] 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, wherein the initial path map includes the processing and movement paths of various target products, calculates the route saturation of the transportation channel in the semantic map based on the processing and movement paths, locates the conflict area in the initial path map based on the route saturation, modifies part of the processing and movement paths to reduce the route saturation, and obtains an optimized path map.

[0033] The optimization module prioritizes the target products based on the optimization path map to obtain a final movement plan.

[0034] Beneficial effects:

[0035] This invention eliminates the need to use intelligent algorithms to solve the objective function for path planning, thereby reducing computational complexity and enhancing interpretability. Furthermore, before prioritization, path optimization reduces the frequency of traffic in each intersection, significantly reducing the probability of conflicts between devices of the same priority. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1This is a flow chart of the mobile path optimization method for high-precision flexible processing equipment in this application;

[0037] Figure 2 A schematic diagram of the structure of the production database for this application;

[0038] Figure 3 Schematic diagram of the principle of optimizing the initial path map for this application;

[0039] Figure 4 This is a structural diagram of the mobile path optimization system used in this application for high-precision flexible processing equipment. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, 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 intended to limit the present invention.

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

[0042] like Figure 1 As shown, a moving path optimization method for high-precision flexible processing 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. Each production area has a corresponding production process.

[0044] S2: Determine production targets, which include target products, target output, and target time limits. Generate a production plan based on the production targets, which includes the production process for each target product.

[0045] The semantic map marks the production function of each production area and its location in the factory, such as production area 1 for milling and production area 2 for cutting; the semantic map also marks transportation channels, which are used to limit the movement area of ​​transportation equipment, and transportation equipment is used to transfer materials between production areas.

[0046] A production target might include multiple target products. For example, for split air conditioners A and B, with target production volumes of 9,000 and 6,000 units, respectively, and a target deadline of 30 days, production can be scheduled based on these requirements to ensure completion within the target deadlines. The specific method for generating a production plan is described later.

[0047] The production plan of this embodiment includes planning for the production process of each target product. For 9,000 split-type air conditioners A, the production plan includes that the first air conditioner needs to be processed in production process 1 by processing equipment 1, and the second air conditioner needs to be processed in production process 1 by processing equipment 2. Processing equipment 1 and 2 have the same functions, that is, the production plan includes specific production processes for each air conditioner.

[0048] S3: With the shortest path as the goal, an initial path map is generated based on the production process and semantic map. The initial path map includes the processing movement paths of various target products.

[0049] Since the production plan includes the production steps that each target product must go through, and each production area corresponds to a production step, once the production plan is determined, the shortest possible processing path can be generated for each target product based on the transportation channels marked in the semantic map. A more detailed generation method is described later. Once the processing paths for all target products are generated, the initial path map is obtained.

[0050] S4: Calculate the route saturation of the transport channel in the semantic map based on the processing movement path, locate the conflict area in the initial path map based on the route saturation, change part of the processing movement path to reduce the route saturation, and obtain the optimized path map.

[0051] The production plan also includes the start and end times of each target product in each production process. For example, if the first air conditioner begins production at 10:00, is processed in Production Process 1 between 10:01 and 10:02, and then in Production Process 2 between 10:03 and 10:04. Combining this information with the target product's processing path, it can be calculated that the transport equipment is located in the transport channel from the starting point to Production Process 1 between 10:00 and 10:01, and in the transport channel from Production Process 1 to Production Process 2 between 10:02 and 10:03. After determining the number of transport equipment in each transport channel during each time period, the corresponding route saturation can be calculated.

[0052] There will inevitably be overlapping areas between multiple transport channels. If the overlapping area has a high route saturation, this intersection is defined as a conflict area. To reduce the probability of conflict in the intersection area, the processing and movement paths of some target products are first changed, so that they are transported through transport channels with longer routes but lower route saturation.

[0053] S5: Prioritize the target products based on the optimization path map to obtain the final mobile plan.

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

[0055] Since intersections still exist after optimization, building on the previous step, we prioritize each target product to reduce the probability of collisions in these intersections. This optimization significantly reduces the computational complexity and computational effort required for conflict optimization in these intersections, resulting in better conflict optimization results. Priority setting methods can be found in existing technologies and will not be discussed here.

[0056] The present invention first establishes a semantic map based on the distribution of factory equipment and generates a production plan tailored to specific production objectives. Based on the production plan, a processing and movement path for each target product is generated, thereby obtaining an initial path map. This initial path map is generated with the goal of minimizing path length, ensuring the highest transportation efficiency. The route saturation of the transport corridor is then calculated based on the initial path map, and conflict areas are located. By adjusting the movement paths of some materials, the load in highly saturated conflict areas can be reduced. This prioritization of target products significantly reduces the probability of collisions during transport, thereby minimizing the risk of production interruptions and delays.

[0057] This invention eliminates the need to use intelligent algorithms to solve the objective function for path planning, thereby reducing computational complexity and enhancing interpretability. Furthermore, before prioritization, path optimization reduces the frequency of traffic in each intersection, significantly reducing the probability of conflicts between devices of the same priority.

[0058] It is particularly noteworthy that the present invention can quickly plan the path of a flexible production process with low time and hardware costs.

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

[0060] The production plan includes a pilot plan and a relay plan. The total output is calculated based on the type of target product and the corresponding target output. Part of the output is selected from the total output as the first output. The objective function and constraints of the first output are set. The objective function is solved under the constraints based on an intelligent algorithm to obtain the pilot plan for the first output.

[0061] Intelligent algorithms include genetic algorithms, simulated annealing algorithms and particle swarm algorithms.

[0062] For example, if there are target products A and B, with target production volumes of 5,000 and 6,000 units, respectively, for a total production volume of 11,000 units, this example sets the sampling value to 500, sampling 500 units from the total production volume of 11,000 units as the first production volume. The objective function and constraints are then set, and the objective function is solved within the constraints to obtain a pilot solution that can achieve the first production volume in the shortest possible time. Actual production is then carried out based on the pilot solution. Since only a small portion is sampled for solution, results can be obtained quickly.

[0063] The part of the total output excluding the first output is defined as the second output. The second output is divided into multiple production batches, and one production batch is extracted 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.

[0064] In this embodiment, the second production volume is 10,500 units, and each target product is considered a production batch, resulting in a total of 10,500 batches. In other embodiments, two or three batches can be considered based on actual production capacity. For the first batch, the more urgent target products are selected first, based on production deadlines. The production path combination is the complete process combination that can complete the target production. This can be obtained through exhaustive enumeration. Considering that a pilot solution is already in production, a follow-up solution is generated during the pilot solution's production process through simulation calculations.

[0065] Specifically, we 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. We calculate the production time of each process combination and select the one with the shortest generation time as the optimal combination. During actual production, the first batch will be scheduled according to the optimal combination.

[0066] 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 production batches is obtained, and obtain the relay plan by combining the optimal combination.

[0067] The simulation then continues to simulate the production status of each production area under the first batch of production scheduling, and then simulates the second batch to obtain the optimal combination for the second batch. This process is repeated until the final 10,500 batches are simulated. The present invention continuously generates production plans for each batch through an exhaustive method. The exhaustive process does not involve complex constraint solving problems, and the calculation of production time is also a linear calculation process, allowing the computer to quickly provide calculation results in a very short time, thereby obtaining a production plan in a short time.

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

[0069] A production database is established, which includes multiple processing stages. Each processing stage includes multiple production processes with the same functions. Each production process includes at least one process procedure. Each process procedure has a corresponding standard processing time and a switching time for converting its own processing specifications.

[0070] like Figure 2 As shown, the production database includes multiple processing stages, such as milling in processing stage 1 and cutting in processing stage 2. In processing stage 1, there are multiple production processes with the same function, such as 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 mold adjustment, batch processing, and online testing. The corresponding standard processing time is, for example, 1 minute for mold adjustment, 5 minutes for batch processing, and 2 minutes for online testing. In addition, when the specifications of the current target product are different from those of the subsequent target product, there is also a switching time. For example, for mold adjustment, when the specifications of the current and subsequent target products are different, a parameter switching time of 1 minute is required.

[0071] Based on the process flow of the target product, the corresponding processing stages are screened, the production procedures under different processing stages are traversed and combined to generate multiple production path combinations.

[0072] The processing time is calculated based on the process procedures and standard processing time included in the production path combination, the queuing time and switching time are calculated based on the occupied production processes in the production path, and the production time of the production path combination is calculated based on the processing time, queuing time and switching time.

[0073] For example, the production path combination for the target product can be either production processes 11, 25, and 48, or production processes 12, 26, and 47. By traversing the combination, all possible production path combinations that can complete the target production can be obtained. The processing time is then calculated in conjunction with the production database. Specifically, the standard processing time of the process included in the production path combination is accumulated to obtain the processing time. For example, if the calculated result is 20 minutes, if after simulation, it is determined that when reaching production process 25, the previous target product is still being processed and it will take another 3 minutes to complete, then 3 minutes will be used as the queue time. The current target product has different specifications from the subsequent target product, and production process 5 still requires 2 minutes of switching time. In this case, the production time value of the production path combination is 20+3+2=25 minutes.

[0074] This embodiment generates an initial path map including the following steps:

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

[0076] like Figure 3 As shown, the production starting point is determined to be G. For the target product A, the relay plan needs to go through production processes 5, 4, and 3 in sequence. The above production procedures correspond to Figure 3 In production areas 5, 4, and 3, the patrol movement route is determined to be Y1-Y2-Y3-Y4-Y6-Y5-Y6-Y3-Y1 based on the shortest path rule.

[0077] In this embodiment, locating the conflicting area includes the following steps:

[0078] The connection paths connecting the production areas are located in the transportation channel, the total duration of the relay plan is divided into multiple sub-time periods, and the route saturation of each connection path is calculated. The route saturation is the number of transportation equipment on the connection path within the sub-time period. The overlapping areas between the connection paths are located, and the connection paths forming the overlapping areas are defined as intersection paths. The route saturations of the intersection paths are accumulated as the intersection congestion of the overlapping areas. The overlapping areas with intersection congestion greater than a first threshold are defined as conflict areas.

[0079] For example, Figure 3Transport paths Y8, Y9, Y6, and Y4 between production areas 1 and 4 serve as connection path 1, and transport paths Y7, Y9, and Y5 between production areas 1 and 3 serve as connection path 2. The relay plan requires a total of 300 minutes to complete production, which is divided into 30 sub-time periods, each 10 minutes long. The route saturation of each connection path within each sub-time period during the relay plan's production process is then calculated. This can be determined by the number of mobile devices on the connection path. As mentioned above, the production plan can be used to infer the time periods and between which two production areas each transport device moves.

[0080] If a transport device is traveling from production area 1 to production area 3 between minutes 0 and 10, it is inferred that the transport device is located on connection path 1. If connection paths 1 and 2 have 6 and 7 transport devices, respectively, between minutes 0 and 10, then overlapping area Y9 has 6 + 7 = 13 transport devices, resulting in an intersection congestion of 13. The first threshold is set to 10, and overlapping area Y9 is determined to be a conflict area between minutes 0 and 10 due to its intersection congestion of 13.

[0081] In this embodiment, optimizing the initial path graph includes the following steps:

[0082] The sub-time period where the conflict area appears is identified as the target time period, and the transportation channel connected to the conflict area is identified as the optimized channel. Under the constraint of maintaining the order of the production process, the optimized channel is changed to change the target product's movement route in the target time period to reduce the intersection congestion 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. The current transportation equipment needs to go from production area 1 to production area 4. After optimization, the transportation equipment detours to production area 4 through transportation channel Y10, thereby reducing the intersection congestion of the conflict area Y9.

[0084] In this embodiment, the objective function is to minimize production time, and the constraints include material transfer speed constraints between production areas, process assembly speed constraints in production areas, maximum production time constraints, location constraints, and production process constraints for target products.

[0085] The specific objective function is: Min Among them, T 生 is the production time to complete the first output, N is the quantity of the first output, 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, such as T 总 ≤1000, T 总 The maximum time limit for generating target product 1 from start to finish is 1000s.

[0086] The collision constraint is to avoid collisions caused by transportation equipment arriving at the intersection area at the same time. The assembly quantity constraint is the maximum processing quantity that each production area can handle simultaneously. The production process constraint is that the target product must be produced according to a fixed process. The position constraint is the location of the production area. The walking path constraint is the location of the transportation channel. The location of the production area and the transportation channel can be coded in advance, and the corresponding constraint function can be set according to the coding results. The specific form will not be introduced here.

[0087] like Figure 4 As shown, a mobile path optimization system for high-precision flexible processing equipment is used to implement the above-mentioned mobile path optimization method for high-precision flexible processing equipment. The system includes:

[0088] The map module 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] The plan generation module determines the production goals, which include target products, target output and target time limit, and generates a production plan based on the production goals. The production plan includes the production process of each target product.

[0090] The path generation module aims to achieve the shortest path and generates an initial path map based on the production process and semantic map. The initial path map includes the processing and movement paths of various target products. The route saturation of the transportation channel in the semantic map is calculated based on the processing and movement paths. The conflict area in the initial path map is located based on the route saturation, and some processing and movement paths are changed to reduce the route saturation to obtain the optimized path map.

[0091] The optimization module prioritizes the target products based on the optimization path map and obtains the final movement plan.

[0092] It should be understood that the various technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the various technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0093] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included outside the scope of protection 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 multiple production areas, transportation channels between the production areas, and each production area has a corresponding production process; Determining production targets, including target products, target output, and target time limits, and generating a production plan based on the production targets, the production plan including the production process for each target product; 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, and modifying part of the processing movement path to reduce the route saturation to obtain an optimized path map; Prioritizing the target products based on the optimization path map to obtain a final movement plan; 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 portion of the output is selected from the total output as a first output. An objective function and constraints are set for the first output. The objective function is solved under the constraints based on an intelligent algorithm to obtain a pilot plan for the first output. 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 which is selected as the first batch. Multiple production path combinations for 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 combination with the shortest production time is selected as the optimal combination. Continue to extract a 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 combinations of all the production batches are obtained, and obtain the relay plan by combining the optimal combinations; Generating the initial path map comprises the following steps: Determining a production starting point in the semantic map and a return route from each production area back to the production starting point, generating a tour movement route for each target product by combining the production starting point, the production path combination, and the return route, and obtaining the initial path map by combining all the tour movement routes; Locating the conflict area comprises the following steps: Locating connection paths connecting the production areas in the transportation channel, dividing the total duration of the relay plan into multiple sub-time periods, calculating the route saturation of each connection path, where the route saturation is the number of transportation equipment on the connection path within the sub-time period, locating overlapping areas between the connection paths, defining the connection paths forming the overlapping areas as intersection paths, accumulating the route saturations of the intersection paths as the intersection congestion of the overlapping areas, and defining the overlapping areas where the intersection congestion is greater than a first threshold as the conflict area; 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.

2. The method according to claim 1, 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 function, 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 processes under different processing stages are traversed and combined to generate multiple 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.

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

4. The method according to claim 1, wherein The objective function is to minimize production time, and the constraints include material transfer speed constraints between the production areas, process assembly speed constraints in the production areas, maximum production time constraints, position constraints, walking path constraints, collision constraints, assembly quantity constraints, and production process constraints.

5. The method according to claim 1, wherein The intelligent algorithms include genetic algorithm, simulated annealing algorithm and particle swarm algorithm.

6. 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 according to any one of claims 1 to 5, 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, transportation channels between the production areas, and each production area has a corresponding production process; A plan generation module determines production targets, including target products, target output, and target time limits, and generates a production plan based on the production targets, including the production process for each target product; a path generation module, aiming to minimize the path, generating an initial path map based on the production process and the semantic map, the initial path map including processing and movement paths of various target products, calculating the route saturation of the transportation channel in the semantic map based on the processing and movement paths, locating conflicting areas in the initial path map based on the route saturation, modifying portions of the processing and movement paths to reduce the route saturation, and obtaining 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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