Community epidemic prevention and control-oriented disinfection operation path optimization method and system

Abstract

This invention discloses a method and system for optimizing disinfection operation routes in community epidemic prevention and control, belonging to the field of route optimization. It includes: acquiring road data of the disinfection area; preprocessing the collected road data to form disinfection arcs; numbering the preprocessed disinfection arcs and obtaining the required quantity; generating an initial solution as the first current solution; generating a new solution based on the current solution; using the Metropolis criterion to determine whether to accept the new solution; after reaching the required number of iterations, determining the algorithm termination condition, and outputting the optimal solution if satisfied; after obtaining the optimal solution, synthesizing the results to obtain a complete optimized disinfection route plan and forming a task set, and assigning tasks in the task set to various disinfection vehicles. This achieves optimization of disinfection operation routes for community epidemic prevention and control.

Description

Technical Field

[0001] This invention belongs to the field of path optimization, and more specifically, relates to a method and system for optimizing disinfection operation paths for community epidemic prevention and control. Background Technology

[0002] Disinfection and sterilization are important means of cutting off transmission routes and controlling the spread of infectious diseases. For disinfection in communities, there are not only linear disinfection tasks such as main roads, but also surface disinfection tasks in special areas such as parks and parking lots next to main roads, which are difficult to handle with general path optimization methods. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a method and system for optimizing disinfection operation paths in community epidemic prevention and control. By introducing the concept of single disinfection width, it enables the processing of disinfection tasks in special areas.

[0004] To achieve the above objectives, in a first aspect, the present invention provides a method for optimizing disinfection operation routes for community epidemic prevention and control, comprising the following steps:

[0005] S1, Obtain road data for the disinfection area; where each road includes one main road and N special areas adjacent to the main road;

[0006] S2, for any road, if N=0, then the road is classified as a first-class disinfection arc; if N=1, then the road is divided into segments, and the segments containing special areas are classified into second-class and third-class disinfection arcs with the same starting point, the same length, and different amounts of disinfectant required for a single disinfection. The remaining continuous road segments are classified as first-class disinfection arcs. Among them, the third-class disinfection arcs only disinfect the special areas or disinfect the special areas and part of the main road, and the second-class disinfection arcs only disinfect the main road.

[0007] The number of disinfection cycles required for the first type of disinfection arc is:

[0008] like If the number is even, the number of disinfection attempts required for the second type of disinfection arc is [number missing].

[0009] like It is an odd number, and If the number is even, the number of disinfection attempts required for the second type of disinfection arc is [number missing].

[0010] like The number of disinfection cycles required for the second type of disinfection arc is 1.

[0011] like It is an odd number, and For odd numbers greater than 1, the number of disinfection attempts required for the second type of disinfection arc is...

[0012] The third type of disinfection arc requires 1 disinfection cycle;

[0013] Where d represents the width of the main road, b represents the width of a single disinfection operation, t represents the width of the special area, and h represents the length of the special area. Indicates rounding up;

[0014] S3, number all the disinfection arcs, and obtain the number of disinfection attempts required for each disinfection arc, the amount of disinfectant required for a single disinfection, and the shortest distance between each node, where the node is the endpoint of the disinfection arc;

[0015] S4. Under the condition of satisfying the constraints, with the goal of minimizing the total invalid distance of each disinfection vehicle, determine the disinfection arc number and disinfection order for each disinfection vehicle to be disinfected. The invalid distance refers to the distance that the disinfection vehicle travels from the current disinfection arc to the next disinfection arc.

[0016] Further, S4 includes:

[0017] S41, generate a K×N two-dimensional empty array. Each row of the array corresponds to a disinfection vehicle, and each element of the array corresponds to the disinfection arc number of the disinfection vehicle in that row. Here, K is the number of disinfection vehicles, and N is the maximum number of disinfection arcs that a disinfection vehicle can disinfect without exceeding the capacity constraint.

[0018] S42, randomly select a elimination arc that has not met the elimination count, randomly fill it into the two-dimensional array, and determine whether the initial solution obtained after filling meets the capacity constraint. If it does, the filling is successful, and the elimination count required for the elimination arc is reduced by 1; otherwise, the filling is unsuccessful, and another elimination arc is selected for filling until the elimination count required for all elimination arcs is met.

[0019] S43: Fill the unfilled spaces in the two-dimensional array with 0 elements to obtain the initial solution, which is then used as the current solution.

[0020] S44, under the constraint of capacity, swap any two different elements in the current solution to generate a new solution;

[0021] S45, calculate the difference in objective function between the new solution and the current solution, and determine whether to accept the new solution according to preset rules;

[0022] S46. Repeat S44 and S45 until the iteration stopping condition is met, and determine the optimal solution.

[0023] Furthermore, the capacity constraint means that the sum of the disinfection requirements corresponding to the disinfection arc numbers contained in each row of the two-dimensional array cannot exceed the upper limit of the amount of medicine carried by the disinfection vehicle.

[0024] Furthermore, after obtaining the initial solution, the simulated annealing algorithm is used to find the optimal solution.

[0025] Furthermore, in S45, if the objective function difference Δf between the new solution and the current solution is less than 0, the new solution is accepted with a 100% probability; if Δf ≥ 0, the probability exp(-Δf / T) is calculated, and then a number c between 0 and 1 is randomly generated. If exp(-Δf / T) > c, the new solution is accepted; otherwise, the current solution is retained. Here, T represents the current temperature of the simulated annealing algorithm.

[0026] Furthermore, in step S46, after the number of iterations at the current temperature is completed, it is determined whether the termination condition of the simulated annealing algorithm is met. If it is met, the optimal solution result is output; if it is not met, the temperature is slowly reduced, and the process returns to step S44 to continue execution.

[0027] Secondly, the present invention provides a disinfection operation path optimization system for community epidemic prevention and control, comprising: a computer-readable storage medium and a processor;

[0028] The computer-readable storage medium is used to store executable instructions;

[0029] The processor is used to read executable instructions stored in the computer-readable storage medium and execute the disinfection operation path optimization method for community epidemic prevention and control as described in the first aspect.

[0030] In summary, the above-described technical solutions conceived in this invention can achieve the following beneficial effects:

[0031] The key to this path optimization problem lies in the fact that most road network maps have multiple entrances and exits. Path optimization requires constant consideration of the choice of the starting and ending points of the path. Furthermore, the areas requiring disinfection not only include linear roads but also special area types. It is necessary to combine disinfection activities from both dimensions, which is difficult to solve using only precise or heuristic algorithms. This invention acquires road data for the disinfection area; preprocesses the collected road data; numbers the roads and obtains the required quantity; generates an initial solution as the first current solution; generates a new solution based on the current solution; uses the Metropolis criterion to determine whether to accept the new solution; after reaching the required number of iterations, determines the algorithm's termination condition; if the condition is met, the optimal solution is output; otherwise, the condition is reduced; after obtaining the optimal solution, the results are synthesized to obtain a complete optimized disinfection path scheme and form a task set. The tasks in the task set are then assigned to various disinfection vehicles, thus realizing the optimization of disinfection operation paths for community epidemic prevention and control. Attached Figure Description

[0032] Figure 1This is a flowchart of a disinfection operation path optimization method for community epidemic prevention and control provided by an embodiment of the present invention;

[0033] Figure 2 This is a schematic diagram illustrating an example of ordinary road processing provided in an embodiment of the present invention;

[0034] Figure 3 This is a schematic diagram illustrating an example of a road with a special area provided in an embodiment of the present invention;

[0035] Figure 4 This is a schematic diagram illustrating an example of road processing in special areas provided by an embodiment of the present invention;

[0036] Figure 5 This is a schematic diagram illustrating an example of disinfection process 1 provided in an embodiment of the present invention;

[0037] Figure 6 This is a schematic diagram illustrating an example of redundant disinfection phenomena provided in an embodiment of the present invention;

[0038] Figure 7 This is a schematic diagram illustrating an example of disinfection process 2 provided in an embodiment of the present invention;

[0039] Figure 8 This is a schematic diagram illustrating an example of disinfection process 3 provided in an embodiment of the present invention;

[0040] Figure 9 This is a schematic diagram illustrating an example of disinfection process 4 provided in an embodiment of the present invention;

[0041] Figure 10 This is a schematic diagram illustrating an example of an initial solution provided in an embodiment of the present invention;

[0042] Figure 11 This is a schematic diagram illustrating an example of a new solution generated by swapping two non-zero elements in different rows, as provided in an embodiment of the present invention.

[0043] Figure 12 This is a schematic diagram illustrating an example of a new solution generated by swapping two non-zero elements in the same row, as provided in an embodiment of the present invention.

[0044] Figure 13 This is a schematic diagram illustrating an example of a new solution generated by swapping non-zero elements and zero elements in different rows, as provided in an embodiment of the present invention.

[0045] Figure 14 This is a schematic diagram illustrating an example of a new solution generated by swapping non-zero elements and zero elements in the same row, with non-zero elements in between, provided by an embodiment of the present invention.

[0046] Figure 15 This is a schematic diagram illustrating an example of a new solution provided by an embodiment of the present invention, where non-zero elements and zero elements in the same row are interchanged and no non-zero elements are generated in between.

[0047] Figure 16 This is a schematic diagram illustrating an example of calculating the objective function value provided in an embodiment of the present invention. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this invention clearer, the 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 merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0049] In the research conducted in this invention, the problem of optimizing the disinfection operation route is regarded as an open-ended MDCRP (Multiple Depot Capacitated Arc Routing Problem) or a derivative problem. That is, the area to be served contains multiple depots, which can be used as the starting point for service vehicles to start their service. The service vehicles start their service from the starting point and return to the nearest depot after completing the service tasks assigned to them.

[0050] like Figure 1 As shown, this invention provides a method for optimizing disinfection operation routes for community epidemic prevention and control, comprising the following steps:

[0051] S1, obtain road data of the disinfection area; where each road includes a main road and N special areas adjacent to the main road.

[0052] In this embodiment, special areas include parks and parking lots next to main roads.

[0053] S2 uses the collected road data to calculate the required number of disinfection cycles for the road.

[0054] Specifically, after obtaining relevant data such as road length, main road width, and special area width in the area where disinfection activities need to be carried out, the required number of disinfection times for the road is calculated based on the single disinfection width set for the disinfection vehicle. At the same time, four disinfection processes are compiled for the disinfection of special areas, and the appropriate process is selected based on the width of the special area.

[0055] S2 involves the following parameters:

[0056] s0 — Number of disinfection operations required for ordinary roads;

[0057] s1 — Number of disinfection cycles required for disinfection process 1 on the road;

[0058] s2 — Number of disinfection cycles required for road disinfection process 2;

[0059] S3 – Number of disinfection cycles required for road disinfection process 3;

[0060] S4 – Number of disinfection cycles required for road disinfection process 4;

[0061] d — width of the main road;

[0062] b—Width of a single disinfection operation;

[0063] t — Width of the special area;

[0064] h — Length of the special region;

[0065] The single-pass disinfection width is the maximum width that a disinfection vehicle can disinfect in a single pass. If the disinfection vehicle does not need to reach the single-pass disinfection width to complete the disinfection task during the actual disinfection process, it can be adjusted.

[0066] For example Figure 2 The example shown is a regular road excluding special areas. The number of disinfection sessions required is the ratio of the main road width to the width of a single disinfection session, rounded up.

[0067] For example Figure 3 The example shown is a road in a special planar area including parks and parking lots (the area below the dashed line is the main road used for disinfection, and the area above the dashed line is the special area that needs to be disinfected). The treatment method for this type of road is as follows: Figure 4 The example illustrates separating the aforementioned road containing special areas and treating it as an independent road segment. For the disinfection of such roads, this invention employs a method of separately disinfecting the main road and the special areas, and has developed the following four disinfection processes for special areas of various sizes:

[0068] (1) Disinfection process 1: If the ratio of the width of the special area to the width of a single disinfection is rounded up to an even number, then as follows: Figure 5 The example disinfection path shows that, in this case, disinfecting a specific area is equivalent to disinfecting the entire road once. However, this process can result in redundant disinfection, such as… Figure 6 As shown in the example, if the width of a special area is 7 and the width of a single disinfection is 2, then during the final horizontal disinfection, units with a width of 1 in the main road will be disinfected. Subsequent disinfection of the main road will then require one less unit to be disinfected. Therefore, this process reduces the number of disinfection cycles.

[0069] (2) Disinfection process 2: If the ratio of the width of the special area to the width of a single disinfection is rounded up to an odd number, but the ratio of the length to the width of a single disinfection is rounded up to an even number, then the following applies: Figure 7 The example disinfection path shows that, in this case, disinfecting a specific area is equivalent to disinfecting the entire road once.

[0070] Therefore, the number of disinfection cycles for this process is...

[0071] (3) Disinfection process 3: If the ratio of the length of the special area to the width of a single disinfection is rounded up to 1, then... Figure 8 The example disinfection path illustrates that, in this case, disinfecting a specific area is equivalent to disinfecting the entire road once. Therefore, this process requires [number of disinfection cycles].

[0072] (4) Disinfection process 4: If the ratio of the width of the special area to the width of a single disinfection is rounded up to an odd number, and the ratio of the length to the width of a single disinfection is rounded up to an odd number greater than 1, then the following applies: Figure 9 The example disinfection path, in this case, disinfecting the special area is equivalent to disinfecting the entire road 0 times. Therefore, the total number of disinfection attempts for this road should be one more than the number of attempts for the main road. Furthermore, this process, like disinfection process 1, involves redundant disinfection. Therefore, the number of disinfection attempts for this process is...

[0073] The remaining main roads will be disinfected using the same methods as ordinary roads.

[0074] To facilitate calculation, the concept of extinguishing arc is introduced, specifically:

[0075] For any road, if N=0, the road is classified as a first-class disinfection arc; if N=1, the road is divided into segments. The segments containing special areas are classified into second-class and third-class disinfection arcs with the same starting point, the same length, and different amounts of disinfectant required for a single disinfection. All other continuous road segments are classified as first-class disinfection arcs. Among them, the third-class disinfection arcs only disinfect the special areas or disinfect the special areas and part of the main road, while the second-class disinfection arcs only disinfect the main road.

[0076] Understandably, for roads containing special areas, the amount of disinfection required for a single operation on the main road is often less than the amount required to disinfect the special area. Therefore, to facilitate subsequent algorithm calculations, roads containing special areas are divided into two arcs with the same starting point, the same length, but different amounts of disinfection required per operation. One arc only disinfects the main road, with a single disinfection requirement equal to the amount required to disinfect the main road, and the required number of disinfection operations is (s). ij -1), where s ij Let represent the number of disinfection attempts required for road (i,j), where i and j represent the two endpoints of one road (i ≠ j). Another road requires disinfection of a special area and part of the main road; the number of disinfection attempts required for a single attempt is the same as that required for the special area and part of the main road, and the number of attempts required is 1. Therefore:

[0077] The number of disinfection cycles required for the first type of disinfection arc is:

[0078] like If the number is even, the number of disinfection attempts required for the second type of disinfection arc is [number missing].

[0079] like It is an odd number, and If the number is even, the number of disinfection attempts required for the second type of disinfection arc is [number missing].

[0080] like The number of disinfection cycles required for the second type of disinfection arc is 1.

[0081] like It is an odd number, and For odd numbers greater than 1, the number of disinfection attempts required for the second type of disinfection arc is:

[0082] The third type of disinfection arc requires 1 disinfection cycle.

[0083] S3, number all disinfection arcs, and obtain the number of disinfection attempts required for each disinfection arc, the amount of medicine required for a single disinfection, and the shortest distance between each node, where the node is the endpoint of the disinfection arc.

[0084] In this embodiment, different directions of a single disinfection arc are assigned the same number, i.e., (i,j) and (j,i) are numbered the same. However, the second and third types of disinfection arcs containing the same endpoints are assigned different numbers. Then, based on the collected road data and actual conditions, the required amount of pesticide for each disinfection arc per application is determined as the demand for that arc.

[0085] Furthermore, the Floyd algorithm is used to obtain the shortest distance between nodes in the road network diagram and the nodes required to traverse the shortest distance.

[0086] S4. Under the condition of satisfying the constraints, with the goal of minimizing the total invalid distance of each disinfection vehicle, determine the disinfection arc number and disinfection order for each disinfection vehicle to be disinfected. The invalid distance refers to the distance that the disinfection vehicle travels from the current disinfection arc to the next disinfection arc.

[0087] In this embodiment, the simulated annealing algorithm is used as an example to explain the optimization process in detail.

[0088] S41: Generate an initial solution based on capacity constraint rules and elimination count constraint rules;

[0089] (1) Capacity constraint rule: The sum of the disinfection requirements corresponding to the disinfection arc numbers contained in each row cannot exceed the maximum amount of disinfectant that the disinfection vehicle can carry.

[0090] (2) Constraint rule for the number of times of disinfection: The total number of numbers corresponding to each disinfection arc in the array is equal to the number of times the disinfection arc needs to be disinfected.

[0091] S41 involves the following parameters:

[0092] K – Number of vehicles disinfected;

[0093] N – The maximum number of disinfection arcs that a single disinfection vehicle can disinfect without exceeding the capacity constraint;

[0094] First, generate a K×N two-dimensional empty array. Each row of the array corresponds to a disinfection vehicle, and each element of the array corresponds to the disinfection arc number of the disinfection vehicle in that row. The disinfection order of the vehicle is determined from front to back.

[0095] Then, randomly select a disinfection arc number that has not met the required number of disinfection attempts, and randomly fill it into a unit in the two-dimensional array that is still waiting to be filled. Check whether the initial solution obtained after filling satisfies the capacity constraint rule. If it does, the filling is successful, and the required number of disinfection attempts for that arc is reduced by 1; otherwise, the filling is unsuccessful, and a new disinfection arc number is selected for filling. This process is repeated until the required number of disinfection attempts for all disinfection arcs is met.

[0096] Finally, fill the unfilled spaces in the array with 0 elements. These 0 elements have no practical meaning and are only used to facilitate the generation of new solutions later.

[0097] If filling any elimination arc number that has not met the elimination count into the array would cause the capacity constraint rule to be unsatisfactory, then the initial solution generation will restart.

[0098] Example diagram of initial solution as follows Figure 10 As shown, vehicle 1 disinfects the disinfection arcs numbered 1, 2, and 4 in sequence, while vehicle 2 disinfects the disinfection arcs numbered 5, 1, and 3 in sequence.

[0099] S42: Determine a sufficiently large initial temperature and a sufficiently small termination temperature based on the scale of the road network map, the number of iterations at each temperature, the cooling coefficient, and the initial solution as the current solution;

[0100] S43: Swap any two distinct elements in the current solution while satisfying the capacity constraint rules to generate a new solution;

[0101] During the process of swapping to create a new solution, if both elements being swapped are non-zero, two scenarios can occur:

[0102] (1) If the two elements to be swapped are in different rows, such as Figure 11 As shown in the example, this is equivalent to swapping the disinfection arcs required by the two disinfection vehicles;

[0103] (2) If the two elements to be swapped are in the same row, such as Figure 12As shown in the example, this is equivalent to changing the order of the disinfection arcs required by the same disinfection vehicle.

[0104] During the process of swapping to create a new solution, if one of the two elements being swapped is a non-zero element and the other is a zero element, three situations will arise:

[0105] (1) If the two elements to be swapped are in different rows, such as Figure 13 As shown in the example, this is equivalent to assigning the disinfection arc that one vehicle needs to disinfect to another vehicle;

[0106] (2) If the two elements to be swapped are in the same row and contain a non-zero element between them, such as Figure 14 As shown in the example, this is equivalent to changing the disinfection order of a disinfection arc of a certain disinfection vehicle, and may also create new filling space at the same time.

[0107] (3) If the two elements to be swapped are in the same row and there are no non-zero elements between them, such as Figure 15 As shown in the example, this is equivalent to creating a new filling space between two adjacent elimination arcs to facilitate the generation of new solutions and increase the probability of finding a better solution.

[0108] In the process of generating a new solution, two conditions must be met:

[0109] (1) The two elements to be swapped cannot be the same, that is, there will be no two non-zero elements with the same value or two zero elements to be swapped;

[0110] (2) The new solution generated must satisfy the capacity constraint rule, that is, the sum of the disinfection demand corresponding to the disinfection arc number contained in each row cannot exceed the disinfection limit of the disinfection vehicle;

[0111] If conditions (1) and (2) cannot be met, the swap is invalid. In order to make each iteration valid, the swap needs to be performed again based on the current solution until the conditions are met.

[0112] S44: Calculate the objective function values ​​for the current solution and the new solution;

[0113] In this invention, the objective function value of the solution is expressed by the total non-elimination distance consumed by the task set corresponding to the solution. The non-elimination distance represents the distance consumed by the elimination vehicle from the current elimination arc to the next elimination arc, which is also the invalid distance.

[0114] Specifically, for communities that need to carry out disinfection activities, there are multiple entry and exit points. A set of starting points is set in the road network map, whose elements are the entry and exit points of the communities that need to carry out disinfection activities. Each disinfection vehicle starts from one point in the set of starting points according to its assigned disinfection task, and returns to one point in the set of starting points after completing the disinfection task. For each task, based on the shortest distance between points calculated using the Floyd algorithm in S3, first determine which starting point in the set of starting points is closest to which endpoint of the first elimination arc in the task. Then, take that starting point as the starting point, the endpoint of the nearest elimination arc as the starting point for eliminating that arc, and the other endpoint as the ending point. Add the distance from the starting point to the nearest endpoint to the objective function value. Next, determine which endpoint of the elimination arc is closest to the next elimination arc. Again, take the endpoint of the next elimination arc as the starting point for eliminating that arc, and add the distance between the two endpoints to the objective function value. Continue this process until the last elimination arc. Then, determine which starting point in the set of starting points is closest to its ending point, and take that starting point as the ending point for the task. Add the distance between the two endpoints to the objective function value. Repeat this process for all tasks, and the resulting objective function value is the desired objective function value.

[0115] S44 involves the following parameters:

[0116] d ij —The shortest distance between point i and point j;

[0117] As an example, such as Figure 16 As shown in the example, assuming the starting point set is {1,2}, a disinfection vehicle needs to sequentially disinfect the disinfection arcs with endpoints (1,5), (3,4), and (6,7). First, determine d 11 d 15 d 21 d 25 Which is the smallest, if d 11 If the minimum value is found, then the disinfection vehicle starts from node 1 and begins the first disinfection arc from node 1. The objective function is then increased by d. 11 Next, determine the shortest distance between node 5 and the second elimination arc, i.e., determine d. 53 d 54 The size of d 53 <d 54 Then the disinfection vehicle starts disinfection from node 3, performing the second disinfection arc, and the objective function is increased by d. 53 And so on, until the last elimination arc. If it starts elimination from node 6 and ends at node 7, then determine which endpoint in the set of starting points node 7 is closest to, i.e., determine d. 71 d 72 The size of d 72 <d 71Then the disinfection vehicle eventually returns to point 2, and the objective function is increased by d. 72 By processing all disinfection vehicles in this way, the objective function value of the corresponding solution can be obtained.

[0118] S45: Calculate the difference in objective functions between the current solution and the new solution, and then choose whether to accept the new solution based on the Metropolis criterion;

[0119] S45 involves the following parameters:

[0120] x old —Current solution;

[0121] x new —A new interpretation;

[0122] f(x) — the objective function value corresponding to the solution x;

[0123] T — Current temperature of the algorithm;

[0124] Calculate the objective function difference Δf = f(x) between the new solution and the current solution. new )-f(x old If Δf < 0, then the new solution is accepted with 100% probability, let x old =x new If Δf ≥ 0, calculate the probability exp(-Δf / T), then randomly generate a number c between 0 and 1. If exp(-Δf / T) > c, accept the new solution and let x old =x new Otherwise, keep the current solution.

[0125] Then, decrease the number of iterations required at the current temperature by 1, and proceed with the next iteration.

[0126] S46: If the remaining iterations for the current temperature are 0, determine whether the algorithm termination condition has been met. If it has, stop the calculation and output the optimal solution; otherwise, cool down the temperature and reset the iteration count.

[0127] The algorithm terminates under any of the following conditions:

[0128] (1) The current temperature has dropped to the termination temperature set by the algorithm;

[0129] (2) A number of new solutions are not accepted consecutively beyond a certain threshold.

[0130] The algorithm will terminate if either condition (1) or (2) is satisfied. After the algorithm terminates, S47 will be executed.

[0131] If the algorithm's termination condition is not met, the temperature will be reduced and the iteration count will be reset.

[0132] S46 involves the following parameters:

[0133] T — Current temperature of the algorithm;

[0134] k—the cooling coefficient, is a number close to 1 but not greater than 1;

[0135] L—The number of iterations for each temperature set in the algorithm;

[0136] —The number of iterations remaining for the current temperature;

[0137] like Then let T = kT to cool down, and at the same time let Reset the iteration count, then return to S43 to continue the iteration.

[0138] S47: After the algorithm terminates, the results of the combined solution are used to obtain a complete and optimized disinfection path scheme and form a task set. The tasks in the task set are then assigned to each disinfection vehicle.

[0139] Each disinfection vehicle receives a task that includes the disinfection arcs to be disinfected and the disinfection sequence, the starting point and ending point of the disinfection task, the endpoint of each disinfection arc to start disinfection from, and the nodes to be passed from the current disinfection arc to the next disinfection arc.

[0140] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is 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 within the scope of protection of the present invention.

Claims

1. A method for optimizing disinfection and sterilization operation path for community epidemic prevention and control, characterized in that, Includes the following steps: S1, Obtain road data for the disinfection area; where each road includes one main road and N special areas adjacent to the main road; S2, for any road, if N=0, then the road is classified as a first-class disinfection arc; if N=1, then the road is divided into segments, and the segments containing special areas are classified into second-class and third-class disinfection arcs with the same starting point, the same length, and different amounts of disinfectant required for a single disinfection. All other continuous road segments are classified as first-class disinfection arcs. Among them, the third-class disinfection arcs only disinfect the special areas or disinfect the special areas and part of the main road, while the second-class disinfection arcs only disinfect the main road. The number of disinfection cycles required for the first type of disinfection arc is: ; like If the number is even, the number of disinfection cycles required for the second type of disinfection arc is [number missing]. ; like It is an odd number, and If the number is even, the number of disinfection cycles required for the second type of disinfection arc is [number missing]. ; like The number of disinfection cycles required for the second type of disinfection arc is 1. ; like It is an odd number, and For odd numbers greater than 1, the number of disinfection attempts required for the second type of disinfection arc is: ; The third type of disinfection arc requires 1 disinfection cycle; in, Indicates the width of the main road. Indicates the width of a single disinfection operation. Indicates the width of a special area. Indicates the length of a special region. Indicates rounding up; S3, number all the disinfection arcs, and obtain the number of disinfection attempts required for each disinfection arc, the amount of disinfectant required for a single disinfection, and the shortest distance between each node, where the node is the endpoint of the disinfection arc; S4. Under the condition of satisfying the constraints, with the goal of minimizing the total invalid distance of each disinfection vehicle, determine the disinfection arc number and disinfection order for each disinfection vehicle to be disinfected. The invalid distance refers to the distance that the disinfection vehicle travels from the current disinfection arc to the next disinfection arc. S4 includes: S41, generate a A two-dimensional empty array, where each row of the array corresponds to a disinfection vehicle, and each element of the array corresponds to the disinfection arc number of the vehicle in that row; where... To determine the number of vehicles to be disinfected, The maximum number of disinfection arcs that a single disinfection vehicle can disinfect without exceeding the capacity constraint; S42, randomly select a elimination arc that has not met the elimination count, randomly fill it into the two-dimensional array, and determine whether the initial solution obtained after filling meets the capacity constraint. If it does, the filling is successful, and the elimination count required for the elimination arc is reduced by 1; otherwise, the filling is unsuccessful, and another elimination arc is selected for filling until the elimination count required for all elimination arcs is met. S43: Fill the unfilled spaces in the two-dimensional array with 0 elements to obtain the initial solution, which is then used as the current solution. S44, under the constraint of capacity, swap any two different elements in the current solution to generate a new solution; S45, calculate the difference in objective function between the new solution and the current solution, and determine whether to accept the new solution according to preset rules; S46. Repeat S44 and S45 until the iteration stopping condition is met, and determine the optimal solution.

2. The method for optimizing disinfection operation routes for community epidemic prevention and control according to claim 1, characterized in that, The capacity constraint means that the sum of the disinfection requirements corresponding to the disinfection arc numbers contained in each row of the two-dimensional array cannot exceed the upper limit of the amount of medicine carried by the disinfection vehicle.

3. The method for optimizing disinfection operation routes for community epidemic prevention and control according to claim 1, characterized in that, After obtaining the initial solution, the simulated annealing algorithm is used to find the optimal solution.

4. The method for optimizing disinfection operation routes for community epidemic prevention and control according to claim 3, characterized in that, In S45, if the objective function difference between the new solution and the current solution is... If so, the new solution will be accepted with 100% probability; if Then calculate the probability. Then, randomly generate a number between 0 and 1. ,like If the new solution is found, then accept the new solution; otherwise, retain the current solution. This indicates the current temperature of the simulated annealing algorithm.

5. The method for optimizing disinfection operation routes for community epidemic prevention and control according to claim 4, characterized in that, In step S46, after the number of iterations at the current temperature is completed, it is determined whether the termination condition of the simulated annealing algorithm is met. If it is met, the optimal solution result is output; if it is not met, the temperature is slowly reduced, and the process returns to step S44 to continue execution.

6. A disinfection operation path optimization system for community epidemic prevention and control, characterized in that, include: Computer-readable storage media and processors; The computer-readable storage medium is used to store executable instructions; The processor is used to read executable instructions stored in the computer-readable storage medium and execute the disinfection operation path optimization method for community epidemic prevention and control as described in any one of claims 1-5.

Images