Dynamic path planning method and device based on scene prediction

Abstract

The invention provides a dynamic path planning method and device based on scene prediction. The method comprises the following steps: constructing a static vehicle path planning model; establishing an objective function of dynamic vehicle path planning; calculating a potential customer influence degree; according to the potential customer influence degree, constructing a prediction customer set; and based on the predicted customer set, solving a dynamic vehicle path problem. The method comprises the following steps: calculating the influence degree of each potential customer according to the change degree of the potential customer to a known customer path; generating a prediction scene through a roulette by using the influence degree; path information is updated on a fixed time slice according to a prediction scene so as to improve the planning efficiency and the quality of a solution; adjusting the scene state based on a periodic re-optimization and continuous re-optimization method; a taboo list is set for a to-be-accessed route and an accessed route, so that an established route is prevented from being influenced in a local search and greedy insertion process. The method effectively solves the problem of path optimization in dynamic path planning.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle path planning, and in particular to a dynamic path planning method and device based on scene prediction. Background Art

[0002] With the rapid development of the logistics industry, the dynamic vehicle routing problem (DVRP) has become an important research direction for solving intra-city distribution and real-time transportation scheduling. The core of DVRP is to dynamically plan the optimal vehicle route to meet real-time customer needs with the lowest driving cost. However, the dynamic and uncertain nature of new customer requests poses a severe challenge to route optimization, and it is necessary to frequently adjust the established driving route to complete the delivery task.

[0003] Existing methods mostly focus on coping with dynamic changes by improving the global search capability of the algorithm or enhancing the diversity of solutions, but there are relatively few studies on the prediction of new customer requests. Traditional prediction methods usually rely on historical data, which is often difficult to obtain in practical applications, limiting their scope of application. Summary of the invention

[0004] The present invention proposes a dynamic path planning method, device and storage medium based on scene prediction, which can solve at least one of the technical problems in the background technology.

[0005] To achieve the above object, the present invention adopts the following technical solutions: A dynamic path planning method based on scenario prediction, wherein a computer device performs the following steps: S1. Construct a static vehicle path planning model; S2. Establishing the objective function of dynamic vehicle path planning; S3. Calculate the impact of potential customers; S4. Construct a predicted customer set based on the impact of potential customers; S5. Solve the dynamic vehicle routing problem based on the predicted customer set.

[0006] Further, applied to 1 warehouse , n customers , m transport vehicles, the warehouse and n customers Node Set , ,in, The customers in the database are divided into known customers and potential customers, thus obtaining the known customer set. and potential customer sets ; Node Set The straight line paths between nodes in the ;in, Indicates any Nodes and Nodes The straight line path between The straight-line distance is recorded as ; Let the maximum load of each transport vehicle be ; Assume that all vehicles are based on warehouse for the starting point and the end point; Step S1 specifically includes: Step 1.1: Given a path planning duration of T, set T to time slices of equal length, define the sequence number of any time slice as q, and initialize =1; Step 1.2: All customers appearing in the time slice are regarded as the known customer set , the current All customers who appear in the time slice are regarded as the new customer set ; Step 1.3: Use formula (1) to establish the current The objective function of the static vehicle path planning model under time slices : (1) Formula (1) represents the total distance traveled by all vehicles. Indicates Time slice Transport vehicle Nodes and The straight-line distance between nodes, Indicates Whether the u-th vehicle passes the Nodes and If the straight line path between nodes passes through 1, otherwise, let ; Step 1.2: Use equations (2) to (7) to construct the current Constraints of the static vehicle path planning model under time slices: (2) (3) (4) (5) (6) (7) (2) and (3) indicate that each client must and can only access it once; In formula (4), m is the total number of vehicles in the warehouse. represents the vehicles departing from the warehouse, constraint (4) ensures that the number of dispatched vehicles should not be greater than the total number of vehicles provided in the warehouse; Constraint (5) is a subring elimination constraint. Representing a collection The number of customers in In formula (6) and (7) Indicates the customer Constraint (6) ensures that the total demand on each route cannot exceed the vehicle's capacity.

[0007] Furthermore, in step 2, equation (8) is used to establish the objective function of dynamic vehicle path planning: : (8).

[0008] Further, step 3 includes, knowing the customer set Add a potential customer , thus forming the l Temporary customer set , according to the current Static vehicle path planning model under time slices Solve it and get Corresponding customer access path sequence , thus according to Calculate potential customers The degree of influence ; Step 3.1: Calculate the capacity of the delivery vehicle using formula (9) : (9) In formula (9), Represents a known customer set The number of known customers in Step 3.2: According to Using ant colony algorithm The customer set Solve and obtain the customer access sequence , and calculate the length of the customer access sequence as ;Use ant colony algorithm to Solve and obtain the customer access sequence , and calculate the length of the customer access sequence as ; Step 3.3, calculate the influence degree of the potential customer according to the obtained customer access sequence and the length of the customer access sequence according to formulas (10)-(14); (10) (11) (12) (13) (14) The function calculation method is: from After deleting, get the Customer access sequence, compared to and Different numbers of customers at the same location, Function Representation The number of customers included in the solution; α and β are control parameters that adjust the importance of disturbances to travel costs and routes. In formula (14), Represents the potential customers obtained by calculation The extent of the impact.

[0009] Further, step S4 includes, step 4.2, calculating the number of customers that need to be added to the predicted customer set: (15) In formula (15) express The number of customers in; Step 4.3: According to the degree of impact To potential customers Sort the customers in descending order; Step 4.4: From potential customers Before the selection customers are added to the known customers set In the prediction customer set , while from potential customers Delete the corresponding customer; Step 4.5: Optimize the access route of the prediction scenario based on the static path problem model .

[0010] Furthermore, step S5 specifically includes: Step 5.2, At the beginning of each time slice, the newly appeared customers Add to the current known customer collection ; Step 5.3: Determine the size of the predicted customer set Whether the expected quantity is reached: (16) In formula (16) express The number of customers in; Step 5.4: If , according to the degree of impact From potential customers Select the customer with the greatest impact Add to prediction customer set , and merge it into the new customer set in, and from Delete from; like , according to the degree of impact From the predicted customer set Select a potential customer with the greatest impact , and from the predicted customer set Delete Add to potential customer set ,exist Medium connection The previous customer and the next customer; Step 5.5, repeat step 5.4 until ; Step 5.5: Use the greedy insertion method to insert the new customer set Insert into path superior; Step 5.6, local search: Step 5.7: Repeat steps 5.2 to 5.6 until the time slice ends and return the solution of dynamic path planning.

[0011] Further, step 5.6, local search, specifically includes, Step 5.6.1: Define five local search operators Execute the operation: 1. Randomly select a route A customer of , randomly move its position; 2. Randomly select a route Two customers in and , exchange the customer's position; 3. Randomly select a route Two routes in and ,from Randomly select a customer ,from Randomly select a customer ,exchange from after, from The route afterwards; 4. Randomly select a route A route in ,from Randomly select two customers and ,Will and The order of customers between them is reversed; 5. Randomly select a route The routes in ,from Randomly select two customers and ,Will and Customers in between are randomly inserted into other positions; Step 5.6.2: Randomly select a search operator for optimization and continue optimizing until the time reaches the current scene allocation time .

[0012] On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0013] On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0014] From the above technical solution, it can be seen that the present invention proposes a scenario prediction method based on the degree of influence, which roughly estimates the location and demand quantity of potential customers, generates a complete customer network within the dynamic planning time range, quickly processes new customer requests and reduces driving costs. This method does not need to rely on detailed historical data, is suitable for rapid response needs in dynamic environments, and improves the efficiency and accuracy of path planning while reducing computational complexity.

[0015] The dynamic path planning method and system based on scenario prediction of the present invention include calculating the influence of each potential customer according to the degree of change of the potential customer on the path formed by the known customers; using the influence degree to generate prediction scenarios according to roulette; designing a multi-scenario prediction strategy, updating the path information by selecting the current optimal solution and synchronizing it to other scenarios; performing multi-scenario synchronization on a fixed time slice to improve the efficiency and solution quality in the planning process; adjusting the scene state based on the path planning method of periodic re-optimization and continuous re-optimization; setting a taboo list for the routes to be visited and visited to avoid affecting the determined routes in local search and greedy insertion. This method effectively solves the problem of multi-scenario synchronization in dynamic path planning, and can continuously optimize the path execution effect of the vehicle in complex scenarios.

[0016] Specifically, the present invention provides a dynamic path planning method and system based on scenario prediction, which has the following advantages: The present invention solves the problem of lack of historical data in actual dynamic vehicle path planning by using an innovative disturbance level scenario prediction method, without relying on detailed historical data of customer requests, and only using rough information of potential customer locations and expected number of requests. By analyzing the disturbance level of customers offline, giving priority to high-disturbance customers, and integrating their pre-calculation into the initial customer network, the complexity of online calculation is effectively reduced, and rapid response to dynamic changes is achieved. The system designs an efficient multi-scenario collaboration mechanism, which improves the efficiency of path planning and the quality of solutions through solution synchronization and information sharing between scenarios. Combining periodic re-optimization and continuous re-optimization methods, it can continuously optimize path planning in a dynamic environment to ensure stability and adaptability in complex scenarios. In addition, the present invention sets a taboo list in the planning process to avoid the influence of local search and greedy insertion on the established route, thereby further ensuring the effectiveness of the planning results. Experimental results show that the present invention shows significant superiority in multiple dynamic path planning instances, provides a low-cost and high-efficiency path planning solution, and has significant optimization effect and practical value compared with the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flowchart of the present invention; Figure 2 The figure is a comparison chart of the results of the present invention and other methods. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0019] like Figure 1As shown, the multi-scenario collaborative dynamic path planning method based on population evolution algorithm described in this embodiment is applied to a warehouse , n customers , m transport vehicles, the warehouse and n customers Node Set , ,in, The customers in the database are divided into known customers and potential customers, thus obtaining the known customer set. and potential customer sets ; Node Set The straight line paths between nodes in the ;in, Indicates any Nodes and Nodes The straight line path between The straight-line distance is recorded as ; Let the maximum load of each transport vehicle be ; Assume that all vehicles are based on warehouse is the starting point and the end point; the multi-scenario collaborative dynamic path planning method is performed according to the following steps: Step 1: Build a static vehicle path planning model: Step 1.1: Given a path planning duration of T, set T to time slices of equal length, define the sequence number of any time slice as q, and initialize =1; Step 1.2: All customers appearing in the time slice are regarded as the known customer set , the current All customers who appear in the time slice are regarded as the new customer set ; Step 1.3: Use formula (1) to establish the current The objective function of the static vehicle path planning model under time slices : (1) Formula (1) represents the total distance traveled by all vehicles. Indicates Time slice Transport vehicle Nodes and The straight-line distance between nodes, Indicates Whether the u-th vehicle passes the Nodes and If the straight line path between nodes passes through 1, otherwise, let ; Step 1.2: Use equations (2) to (7) to construct the current Constraints of the static vehicle path planning model under time slices: (2) (3) (4) (5) (6) (7) (2) and (3) indicate that each client must and can only access it once; In formula (4), m is the total number of vehicles in the warehouse. represents the vehicles departing from the warehouse, constraint (4) ensures that the number of dispatched vehicles should not be greater than the total number of vehicles provided in the warehouse; Constraint (5) is a subring elimination constraint. Representing a collection The number of customers in In formula (6) and (7) Indicates the customer Constraint (6) ensures that the total demand on each route cannot exceed the vehicle's capacity.

[0020] Step 2: Use formula (8) to establish the objective function of dynamic vehicle path planning : (8) Step 3: Known Customer Set Add a potential customer , thus forming the l Temporary customer set , according to the current Static vehicle path planning model under time slices Solve it and get Corresponding customer access path sequence , thus according to Calculate potential customers The degree of influence ; Step 3.1: Calculate the capacity of the delivery vehicle using formula (9) : (9) In formula (10), Represents a known customer set The number of known customers in Step 3.2: According to Using ant colony algorithm The customer set Solve and obtain the customer access sequence , and calculate the length of the customer access sequence as ;Use ant colony algorithm to Solve and obtain the customer access sequence , and calculate the length of the customer access sequence as ; Step 3.3, calculate the influence degree of the potential customer according to the obtained customer access sequence and the length of the customer access sequence according to formulas (10)-(14); (10) (11) (12) (13) (14) The function calculation method is: from After deleting, get the Customer access sequence, compared to and Different numbers of customers at the same location, Function Representation The number of customers included in the solution; α and β are control parameters that adjust the importance of disturbances to travel costs and routes. In formula (14), Represents the potential customers obtained by calculation the extent of the impact; Step 4: Build the predicted customer set: Step 4.2, calculate the number of customers that need to be added to the predicted customer set: (15) In formula (15) express The number of customers in; Step 4.3: According to the degree of impact To potential customers Sort the customers in descending order; Step 4.4: From potential customers Before the selection customers are added to the known customers set In the prediction customer set , while from potential customers Delete the corresponding customer; Step 4.5: Optimize the access route of the prediction scenario based on the static path problem model ; Step 5: Solve the dynamic vehicle routing problem based on the predicted customer set: Step 5.1, initialize the online prediction scene; Step 5.2, At the beginning of each time slice, the newly appeared customers Add to the current known customer collection ; Step 5.3: Determine the size of the predicted customer set Whether the expected quantity is reached: (16) In formula (16) express The number of customers in; Step 5.4: If , according to the degree of impact From potential customers Select the customer with the greatest impact Add to prediction customer set , and merge it into the new customer set in, and from Delete from; like , according to the degree of impact From the predicted customer set Select a potential customer with the greatest impact , and from the predicted customer set Delete Add to potential customer set ,exist Medium connection The previous customer and the next customer; Step 5.5, repeat step 5.4 until ; Step 5.5: Use the greedy insertion method to insert the new customer set Insert into path superior; Step 5.6, local search: Step 5.6.1: Define five local search operators Execute the operation: 1. Randomly select a route A customer of , randomly move its position; 2. Randomly select a route Two customers in and , exchange the customer's position; 3. Randomly select a route Two routes in and ,from Randomly select a customer ,from Randomly select a customer ,exchange from after, from The route afterwards; 4. Randomly select a route A route in ,from Randomly select two customers and ,Will and The order of customers between them is reversed; 5. Randomly select a route The routes in ,from Randomly select two customers and ,Will and Customers in between are randomly inserted into other positions; Step 5.6.2: Randomly select a search operator for optimization and continue optimizing until the time reaches the current scene allocation time ; Step 5.7: Repeat steps 5.2 to 5.6 until the time slice ends and return the solution of dynamic path planning.

[0021] As shown in Table 1, the embodiment of the present invention generates ten data sets GoldenD containing potential customers based on the Golden data set, and compares the algorithm proposed by the present invention with the three most advanced algorithms for solving dynamic vehicle path planning. The results show that the algorithm proposed by the present invention achieves better results on the ten data sets. In Table 1, PB-ILS is Population-Based Iterated Local Search Approach for Dynamic Vehicle Routing Problems; RDMOVR is Robust Dynamic Multi-Objective Vehicle Routing Optimization Method; AGA-ES-TW is Elastic Strategy-Based Adaptive Genetic Algorithm for Solving Dynamic Vehicle Routing Problem With Time Windows.

[0022] Table 1

[0023] To verify the effectiveness of the method of the present invention, the scene prediction module was removed while keeping other parts unchanged, and experiments were conducted based on the GoldenD dataset under the same operating environment. Figure 2 As shown in Figure 3, the results show that the scene prediction module can significantly reduce the length of the generated path, verifying its effectiveness.

[0024] On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0025] On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0026] In another embodiment provided in the present application, a computer program product comprising instructions is also provided, which, when executed on a computer, enables the computer to execute any of the multi-scenario collaborative dynamic path planning methods based on the population evolution algorithm in the above-mentioned embodiments.

[0027] It is understandable that the system, device and storage medium provided in the embodiments of the present invention correspond to the method provided in the embodiments of the present invention, and the explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the above methods.

[0028] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive Solid State Disk (SSD)), etc.

[0029] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0030] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0031] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A dynamic path planning method based on scene prediction, characterized in that: The following steps are included: S1. Construct a static vehicle path planning model; S2. Establishing the objective function of dynamic vehicle path planning; S3. Calculate the impact of potential customers; S4. Build a predicted customer set based on the impact of potential customers; S5. Solve the dynamic vehicle routing problem based on the predicted customer set.

2. The dynamic path planning method based on scenario prediction according to claim 1, characterized in that: Applicable to 1 warehouse , n customers , m transport vehicles, the warehouse and n customers Node Set , ,in, The customers in the database are divided into known customers and potential customers, thus obtaining the known customer set. and potential customer sets ; Node Set The straight line paths between nodes in the ;in, Indicates any Nodes and Nodes The straight line path between The straight-line distance is recorded as ; Let the maximum load of each transport vehicle be ; Assume that all vehicles are based on warehouse for the starting point and the end point; Step S1 specifically includes: Step 1.1: Given a path planning duration of T, set T to time slices of equal length, define the sequence number of any time slice as q, and initialize =1; Step 1.2: All customers appearing in the time slice are regarded as the known customer set , the current All customers who appear in the time slice are regarded as the new customer set ; Step 1.3: Use formula (1) to establish the current The objective function of the static vehicle path planning model under time slices : (1) Formula (1) represents the total distance traveled by all vehicles. Indicates Time slice Transport vehicle Nodes and The straight-line distance between nodes, Indicates Whether the u-th vehicle passes the Nodes and If the straight line path between nodes passes through 1, otherwise, let ; Step 1.2: Use equations (2) to (7) to construct the current Constraints of the static vehicle path planning model under time slices: (2) (3) (4) (5) (6) (7) (2) and (3) indicate that each client must and can only access it once; In formula (4), m is the total number of vehicles in the warehouse. represents the vehicles departing from the warehouse, and constraint (4) ensures that the number of dispatched vehicles should not be greater than the total number of vehicles provided in the warehouse; Constraint (5) is a subring elimination constraint. Representing a collection The number of customers in In formula (6) and (7) Indicates the customer Constraint (6) ensures that the total demand on each route cannot exceed the vehicle's capacity.

3. The dynamic path planning method based on scenario prediction according to claim 2, characterized in that: In step 2, equation (8) is used to establish the objective function of dynamic vehicle path planning: : (8)。 4. The dynamic path planning method based on scenario prediction according to claim 3, characterized in that: Step 3 includes, knowing the customer set Add a potential customer , thus forming the l Temporary customer set , according to the current Static vehicle path planning model under time slices Solve it and get Corresponding customer access path sequence , thus according to Calculate potential customers The degree of influence ; Step 3.1: Calculate the capacity of the delivery vehicle using formula (9) : (9) In formula (9), Represents a known customer set The number of known customers in Step 3.2: According to Using ant colony algorithm The customer set Solve and obtain the customer access sequence , and calculate the length of the customer access sequence as ;Use ant colony algorithm to Solve and obtain the customer access sequence , and calculate the length of the customer access sequence as ; Step 3.3, calculate the influence degree of the potential customer according to the obtained customer access sequence and the length of the customer access sequence according to formulas (10)-(14); (10) (11) (12) (13) (14) The function calculation method is: from After deleting, get the Customer access sequence, compared to and Different numbers of customers at the same location, Function Representation The number of customers included in the solution; α and β are control parameters that adjust the importance of disturbances to travel costs and routes. In formula (14), Represents the potential customers obtained by calculation The extent of the impact.

5. The dynamic path planning method based on scenario prediction according to claim 4, characterized in that: Step S4 includes, step 4.2, calculating the number of customers that need to be added to the predicted customer set: (15) In formula (15), express The number of customers in Step 4.3: According to the degree of impact To potential customers Sort the customers in descending order; Step 4.4: From potential customers Before the selection customers are added to the known customers set The predicted customer set , while from potential customers Delete the corresponding customer; Step 4.5: Optimize the access route of the prediction scenario based on the static path problem model .

6. The dynamic path planning method based on scenario prediction according to claim 5, characterized in that: Step S5 specifically includes: Step 5.2, At the beginning of each time slice, the newly appeared customers Add to the current known customer collection ; Step 5.3: Determine the size of the predicted customer set Whether the expected quantity is reached: (16) In formula (16) express The number of customers in Step 5.4: If , according to the degree of impact From potential customers Select the customer with the greatest impact Add to prediction customer set , and merge it into the new customer set in, and from Delete from; like , according to the degree of impact From the predicted customer set Select a potential customer with the greatest impact , and from the predicted customer set Delete Add to potential customer set ,exist Medium connection The previous customer and the next customer; Step 5.5, repeat step 5.4 until ; Step 5.5: Use the greedy insertion method to insert the new customer set Insert into path superior; Step 5.6, local search: Step 5.7: Repeat steps 5.2 to 5.6 until the time slice ends and return the solution of dynamic path planning.

7. The dynamic path planning method based on scenario prediction according to claim 6, characterized in that: Step 5.6: Local search, specifically including: Step 5.6.1: Define five local search operators Execute the operation: 1) Randomly select a route A customer of , randomly move its position; 2) Randomly select a route Two customers in and , exchange the customer's position; 3) Randomly select a route Two routes in and ,from Randomly select a customer ,from Randomly select a customer ,exchange from after, from The route afterwards; 4) Randomly select a route A route in ,from Randomly select two customers and ,Will and The order of customers between them is reversed; 5) Randomly select a route The routes in ,from Randomly select two customers and ,Will and Customers in between are randomly inserted into other positions; Step 5.6.2: Randomly select a search operator for optimization and continue optimizing until the time reaches the current scene allocation time .

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the computer program is executed by the processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 7.

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