A method for optimizing the distribution path of emergency materials for public health emergencies

Abstract

The present invention discloses a method for optimizing the distribution path of emergency materials for public health emergencies, which belongs to the field of path planning technology, and includes the steps of initializing relevant parameters of a path optimization algorithm; obtaining information of each cell; obtaining the relative distance length between each cell, calculating the actual shortest distance between cell i and cell j, considering infection risk factors, determining influencing factors, and then determining a heuristic information function; introducing the infection density of a road section, improving the transfer probability function of the path optimization algorithm; searching for the optimal path, using pheromone concentration, heuristic information function and road section infection density function together, and using the improved path optimization algorithm for path planning. The present invention fully considers the impact of the situation of public health emergencies and improves the efficiency of safe path search.

Description

Technical Field

[0001] The present invention relates to the technical field of path planning, and in particular to a method for optimizing the distribution path of emergency materials for public health emergencies. Background Art

[0002] During the outbreak of public health emergencies, many communities with confirmed cases were placed under key prevention and control. The distribution of emergency supplies is crucial. During the distribution process, the development of the public health emergency directly affects the life safety of the delivery personnel. Therefore, based on the development of public health emergencies, providing a reasonable route planning scheme for the distribution of emergency supplies, while ensuring the timely delivery of emergency supplies and minimizing the risk of infection for relevant staff, is of great significance for improving the prevention and control effect.

[0003] In the existing technology, the number of emergency supplies in n controlled communities is counted, and the distribution center dispatches vehicles to distribute the supplies. Each time the supplies are distributed, only one truck is responsible for the distribution, and it delivers to n communities one by one and passes through each community only once, and finally returns to the distribution center. The purpose is to solve an optimal distribution path.

[0004] In order to better respond to the prevention and control of public health emergencies and ensure the material needs of the controlled communities, it is necessary to develop an optimization method for the distribution routes of emergency materials for public health emergencies. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a method for optimizing the distribution path of emergency materials for public health emergencies, which takes into account the impact of the development trend of public health emergencies on the basis of a path optimization algorithm and improves the efficiency of safe path search.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A method for optimizing the distribution path of emergency materials for public health emergencies, comprising the following steps:

[0008] Step 1: Initialize the relevant parameters of the path optimization algorithm;

[0009] Step 2: Get information about each cell;

[0010] Step 3: Obtain the relative distance between each cell and use the Floyd algorithm to calculate the actual shortest distance d between cell i and cell j ij , determine the impact factor ε j , and then determine the improved heuristic information function so that it is affected by the shortest path d ij and impact factor ε j The dual inspiration effect;

[0011] Step 4: Introducing the road segment infection density αij , improve the transition probability function of the path optimization algorithm;

[0012] Step 5: Optimal path search, pheromone concentration, heuristic information function and road section infection density function work together, and the improved path optimization algorithm is used for path planning.

[0013] The further improvement of the technical solution of the present invention is that in step 1, the relevant parameters of the initialization path optimization algorithm include the number of drivers, the number of cycles, the heuristic information parameter, and the pheromone concentration, and the initial transfer probability function is:

[0014]

[0015] In the formula, τ ij is the pheromone concentration between cell i and cell j; n ij is the heuristic function; a is the importance of pheromone, b is the importance of heuristic information; allowk is the set of cells that the k-th driver can transfer to next. As time goes by, the number of elements stored in allowk will decrease and eventually become an empty set;

[0016] in,

[0017] The driver completes a cycle and updates the pheromone concentration of each path using the following formula:

[0018]

[0019] in,

[0020] Where: m is the number of drivers, 0<ρ≤1 is the evaporation rate of pheromone, is the pheromone left by the kth driver on the path from i to j, Q is the pheromone constant, and l is the total path length obtained after the kth driver completes the path.

[0021] A further improvement of the technical solution of the present invention is that in step 2, the cell information includes the geographical location coordinates (x, y) of each cell, the current total number of people in the cell N, the number of confirmed cases U, and the area S of the cell.

[0022] A further improvement of the technical solution of the present invention is that in step 3, the Euclidean distance between cell i and cell j is changed to the actual shortest distance d between the two obtained by the Floyd algorithm according to the actual map. ij According to the actual confirmed cases in each community, the impact of infection risk factors on the psychological factors of drivers is considered and the influencing factor ε is determined. j , and then determine the edge weight calculation model between cells i and j, and then determine the heuristic information function so that it is affected by the shortest path dij and impact factor ε j The double heuristic effect of is as follows:

[0023] 3.1 Floyd algorithm to solve the actual shortest path d ij ;

[0024] 3.2 Determine the impact factor:

[0025] The community is located in a low-risk area, with a small number of confirmed cases and no new cases within 14 days, so the psychological fluctuations of drivers are relatively small;

[0026] The community is located in a medium-risk area, with a large number of confirmed cases and the possibility of new confirmed cases within 14 days, and the driver's psychological fluctuations are obvious;

[0027] The community is located in a high-risk area, with a large number of confirmed cases and new confirmed cases within 14 days, and the psychological fluctuations of drivers are very obvious;

[0028] According to the impact of infection risk factors on the driver's psychological factors, the influencing factors are determined:

[0029]

[0030] Where N j is the total population of community j; U j is the total number of confirmed cases in community j; ε j is the influencing factor; A, B, and C are the cell sets located in low-risk areas, medium-risk areas, and high-risk areas, respectively;

[0031] 3.3 Determine the edge weight W between cell i and cell j ij The expression is:

[0032]

[0033] Where, d ij is the shortest distance between cells i and j after solving by Floyd algorithm; 0≤k j ≤1 is a parameter adjusted according to actual needs;

[0034] 3.4 Determine the heuristic function in the improved path optimization algorithm:

[0035]

[0036] The further improvement of the technical solution of the present invention is that in step 4, the road section infection density is introduced to improve the transition probability function of the path optimization algorithm:

[0037] 4.1 Introducing road section infection density:

[0038] The infection density of a community refers to the number of infected people per unit area of ​​the community; suppose the infection density of a road section is proportional to the infection density of the community, with a proportionality coefficient of η; suppose the total number of close contacts in the community is a multiple of ζ of the total number of confirmed cases, and determine the infection density of the road section between community i and community j:

[0039]

[0040] Where U j is the number of confirmed cases in the jth community; S j is the area of ​​the jth cell;

[0041] 4.2 The transfer probability function of the improved path optimization algorithm is:

[0042]

[0043] In the formula, τ ij is the pheromone concentration between cell i and cell j; n ij is the heuristic function; α ij The infection density of the road section; a is the importance of pheromone, b is the importance of heuristic information, and c is the importance of the infection density of the road section; allowk is the set of cells that the k-th driver can transfer to next. As time goes by, the number of elements stored in allowk will decrease and eventually become an empty set.

[0044] A further improvement of the technical solution of the present invention is that in step 5, during the optimal path search process, pheromone concentration, heuristic information function and road section infection density function work together, and an improved path optimization algorithm is used for path planning.

[0045] Due to the adoption of the above technical solution, the technical progress achieved by the present invention is:

[0046] 1. The present invention changes the Euclidean distance between cell i and cell j into the actual shortest distance d between the two obtained by the Floyd algorithm based on the actual map. ij , which improves the accuracy of path search during control.

[0047] 2. The present invention determines the influencing factor ε based on the actual confirmed cases in each community and the impact of infection risk factors on the psychological factors of drivers. j , achieving a quantitative evaluation of the impact of infection risk on material distribution.

[0048] 3. The present invention introduces the road section infection density, improves the transfer probability function of the path optimization algorithm, and improves the safety of the search path.

[0049] 4. The present invention is applicable to the optimization of distribution routes of emergency materials for public health emergencies. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is a flow chart of the present invention;

[0051] Figure 2 This is a flow chart of path search using the improved path optimization algorithm in the present invention;

[0052] Figure 3 is the path planning result graph in the present invention;

[0053] Figure 4 is the sum of the minimum edge weights and the average weight in each iteration of the present invention;

[0054] Figure 5 It is the emergency material distribution community information in Table 1 in the embodiment of the present invention;

[0055] Figure 6 is the relative distance between the emergency material distribution communities in Table 2 in the embodiment of the present invention;

[0056] Figure 7 is the actual shortest distance between the emergency material distribution communities in Table 3 in the embodiment of the present invention;

[0057] Figure 8 is the shortest path matrix between the emergency material distribution communities in Table 4 in the embodiment of the present invention;

[0058] Fig. 9 is the edge weight between the emergency material distribution communities in Table 5 in the embodiment of the present invention;

[0059] Fig.10 It is the infection density of the road sections around each community in Table 6 in the embodiment of the present invention. DETAILED DESCRIPTION

[0060] The present invention is further described in detail below with reference to the accompanying drawings and embodiments:

[0061] like Figure 1-4 As shown, a method for optimizing the distribution path of emergency materials for public health emergencies includes the following steps:

[0062] Step 1: Initialize the relevant parameters of the path optimization algorithm;

[0063] The relevant parameters of the initialization path optimization algorithm include the number of drivers, the number of cycles, the heuristic information parameters, the pheromone concentration, etc. The initial transfer probability function is:

[0064]

[0065] In the formula, τ ij is the pheromone concentration between cell i and cell j; nij is the heuristic function; a is the importance of pheromone, b is the importance of heuristic information; allowk is the set of cells that the k-th driver can transfer to next. As time goes by, the number of elements stored in allowk will decrease and eventually become an empty set;

[0066] in,

[0067] The driver completes a cycle and updates the pheromone concentration of each path using the following formula:

[0068]

[0069] in,

[0070] Where: m is the number of drivers, 0<ρ≤1 is the evaporation rate of pheromone, is the pheromone left by the kth driver on the path from i to j, Q is the pheromone constant, and l is the total path length obtained after the kth driver completes the path.

[0071] Step 2: Obtain information about each cell; the cell information includes the geographic location coordinates (x, y) of each cell, the current total population N of the cell, the number of confirmed cases U, the area S of the cell, etc.

[0072] Step 3: Obtain the relative distance between each cell and use the Floyd algorithm to calculate the actual shortest distance d between cell i and cell j ij , determine the impact factor ε j , and then determine the improved heuristic information function so that it is affected by the shortest path d ij and impact factor ε j The dual heuristic effect improves the efficiency of safe path search;

[0073] The Euclidean distance between cell i and cell j is changed to the actual shortest distance d between the two after the Floyd algorithm is used to solve the actual map. ij According to the actual confirmed cases in each community, the impact of infection risk factors on the psychological factors of drivers is considered and the influencing factor ε is determined. j , and then determine the edge weight calculation model between cells i and j, and then determine the heuristic information function so that it is affected by the shortest path d ij and impact factor ε j The double heuristic effect of is as follows:

[0074] 3.1 Floyd algorithm to solve the actual shortest path d ij ;

[0075] 3.2 Determine the impact factor:

[0076] The community is located in a low-risk area, with a small number of confirmed cases and no new cases within 14 days, so the psychological fluctuations of drivers are relatively small;

[0077] The community is located in a medium-risk area, with a large number of confirmed cases and the possibility of new confirmed cases within 14 days, and the driver's psychological fluctuations are obvious;

[0078] The community is located in a high-risk area, with a large number of confirmed cases and new confirmed cases within 14 days, and the driver's psychological fluctuations are very obvious;

[0079] According to the impact of infection risk factors on the driver's psychological factors, the influencing factors are determined:

[0080]

[0081] Where N j is the total population of community j; U j is the total number of confirmed cases in community j; ε j is the influencing factor; A, B, and C are the cell sets located in low-risk areas, medium-risk areas, and high-risk areas, respectively.

[0082] 3.3 Determine the edge weight W between cell i and cell j ij The expression is:

[0083]

[0084] Where, d ij The shortest distance between cells i and j after solving by Floyd algorithm; 0<k j ≤1 is a parameter that is adjusted according to actual needs;

[0085] 3.4 Determine the heuristic function in the improved path optimization algorithm:

[0086]

[0087] Step 4: Introducing the road segment infection density α ij , the transition probability function of the path optimization algorithm is improved, which further improves the efficiency of safe path search;

[0088] Introducing the infection density of the road section, the transition probability function of the path optimization algorithm is improved:

[0089] 4.1 Introducing road section infection density:

[0090] The infection density of a community refers to the number of infected people per unit area of ​​the community; suppose the infection density of a road section is proportional to the infection density of the community, with a proportionality coefficient of η; suppose the total number of close contacts in the community is a multiple of ζ of the total number of confirmed cases, and determine the infection density of the road section between community i and community j:

[0091]

[0092] Where U j is the number of confirmed cases in the jth community; S j is the area of ​​the jth cell;

[0093] 4.2 The transfer probability function of the improved path optimization algorithm is:

[0094]

[0095] In the formula, τ ij is the pheromone concentration between cell i and cell j; n ij is the heuristic function; α ij is the infection density function of the road section; a is the importance of pheromone, b is the importance of heuristic information, and c is the importance of infection density; allowk is the set of cells that the k-th driver can transfer to next. As time goes by, the number of elements stored in allowk will decrease and eventually become an empty set.

[0096] Step 5: Optimal path search, pheromone concentration and heuristic information function work together, and the improved path optimization algorithm is used for path planning; During the optimal path search process, pheromone concentration, heuristic information function and road section infection density function work together, and the improved path optimization algorithm is used for path planning.

[0097] Example

[0098] A method for optimizing the distribution path of emergency materials for public health emergencies, comprising the following steps:

[0099] Step 1: Initialize the relevant parameters of the path optimization algorithm; including the number of drivers, number of cycles, heuristic information parameters, pheromone concentration, etc.

[0100] Step 2: Get the information of each community, including the geographical coordinates (x, y) of each community, the total number of people in the community N, the number of confirmed cases U, the area S, etc., and label each community. The specific information is as follows: Figure 5 (Table 1 Emergency material distribution community information) as shown.

[0101] Step 3: Obtain the relative distance between each cell and calculate the actual shortest distance d between cell i and cell j ij , considering infection risk factors, determine the heuristic information function, including:

[0102] 3.1 Obtain the relative distance between each cell, such as Figure 6 (Table 2 Relative distances between emergency material distribution communities);

[0103] 3.2 Using the Floyd algorithm to calculate the actual shortest distance d between cell i and cell j ij The specific calculation results are as follows: Figure 7 (Table 3 Actual shortest distance between emergency material distribution communities) and Figure 8 (Table 4 The shortest path matrix between each emergency material distribution area) is shown;

[0104] 3.3 Considering the impact factor ε of regions with different risk levels j , calculate the model W based on the edge weight between the determined cells i and j ij , the edge weight matrix is ​​calculated as Fig. 9 (Table 5 Edge weights between emergency material distribution communities);

[0105] 3.4 Heuristic information function:

[0106]

[0107] Step 4: Introduce the infection density of the road section and improve the transition probability function of the path optimization algorithm, including:

[0108] 4.1 Introducing the infection density of the road section. The infection density of the community refers to the number of infected people per unit area of ​​the community; suppose the infection density of the road section is proportional to the infection density of the community, and the proportional coefficient η is taken as η = 0.5; suppose the total number of close contacts in the community is ζ times the total number of confirmed cases, and ζ = 3, according to the infection density formula α of the road section between community i and community j ij Calculate the infection density of the road sections around community j. The calculation results are as follows: Fig.10 (Table 6 Infection density of road sections around each community) as shown;

[0109] Among them, the number 0 is the material transportation center, with no confirmed cases or new confirmed cases. Strict disinfection and control work is carried out in the transportation center area. The personnel infection density in the transportation center is 0, and the infection density in the surrounding roads is 0.

[0110] (2) Determine the transition probability function of the improved path optimization algorithm The number of drivers is m = 30, and the maximum number of iterations is N max =200, pheromone evaporation rate ρ = 0.5, pheromone importance a = 1.5, heuristic information importance b = 2.2, road section infection density importance c = 5, pheromone constant Q = 1000000;

[0111] Step 5: Optimal path search, pheromone concentration, heuristic information function and road section infection density function work together, according to Figure 2 The path search flow chart of the path optimization algorithm is shown in Figure 1. The improved path optimization algorithm is used for path planning. After code operation, the optimal route is obtained: 1→5→3→4→9→2→6→7→8→13→10→12→11→1. The path planning result is as follows: Figure 3 As shown in the figure, the sum of the minimum edge weights and the average weight in each iteration are as follows Figure 4 shown.

[0112] In summary, according to the specific situation under the control of public health emergencies, the present invention changes the Euclidean distance between cell i and cell j into the actual shortest distance d between the two after the actual map is solved by the Floyd algorithm. ij ; According to the actual confirmed cases in each community, considering the infection risk factors, the edge weight calculation model between communities i and j is determined, and the double heuristic information function is determined; the road section infection density is introduced, the transfer probability function of the path optimization algorithm is improved, and the safety of the search path is improved.

Claims

1. A method for optimizing the distribution path of emergency materials for public health emergencies, characterized by: The following steps are involved: Step 1: Initialize the relevant parameters of the path optimization algorithm; Step 2: Get information about each cell; Step 3: Obtain the relative distance between each cell and use the Floyd algorithm to calculate the actual shortest distance d between cell i and cell j ij , determine the impact factor ε j , and then determine the improved heuristic information function so that it is affected by the shortest path d ij and impact factor ε j The dual inspiration effect; The Euclidean distance between cell i and cell j is changed to the actual shortest distance d between the two after the Floyd algorithm is used to solve the actual map. ij According to the actual confirmed cases in each community, the impact of infection risk factors on the psychological factors of drivers is considered and the influencing factor ε is determined. j , and then determine the edge weight calculation model between cells i and j, and then determine the heuristic information function so that it is affected by the shortest path d ij and impact factor ε j The double heuristic effect of is as follows: 3.1 Floyd algorithm to solve the actual shortest path d ij ; 3.2 Determine the impact factor: The community is located in a low-risk area, with a small number of confirmed cases and no new cases within 14 days, so the psychological fluctuations of drivers are relatively small; The community is located in a medium-risk area, with a large number of confirmed cases and the possibility of new confirmed cases within 14 days, and the driver's psychological fluctuations are obvious; The community is located in a high-risk area, with a large number of confirmed cases and new confirmed cases within 14 days, and the psychological fluctuations of drivers are very obvious; According to the impact of infection risk factors on the driver's psychological factors, the influencing factors are determined: Where N j is the total population of community j; U j is the total number of confirmed cases in community j; ε j is the influencing factor; A, B, and C are the cell sets located in low-risk areas, medium-risk areas, and high-risk areas, respectively; 3.3 Determine the edge weight W between cell i and cell j ij The expression is: Where, d ij is the shortest distance between cells i and j after the Floyd algorithm solves it; 0<k j ≤1 is a parameter adjusted according to actual needs; 3.4 Determine the heuristic function in the improved path optimization algorithm: Step 4: Introducing the road segment infection density α ij , improve the transition probability function of the path optimization algorithm; 4.1 Introducing road section infection density: The infection density of a community refers to the number of infected people per unit area of ​​the community. Assume that the infection density of a road section is proportional to the infection density of the community, and the proportionality coefficient is η; Assume that the total number of close contacts in the community is ζ times the total number of confirmed cases, and determine the infection density of the road section between community i and community j: Where U j is the number of confirmed cases in the jth community; S j is the area of ​​the jth cell; 4.2 The transfer probability function of the improved path optimization algorithm is: In the formula, τ ij is the pheromone concentration between cell i and cell j; n ij is the heuristic function; α ij is the infection density function of the road section; a is the importance of pheromone, b is the importance of heuristic information, and c is the importance of infection density; allowk is the set of cells that the k-th driver can transfer to next. As time goes by, the number of elements stored in allowk will decrease and eventually become an empty set; Step 5: Optimal path search, pheromone concentration, heuristic information function and road section infection density function work together, and the improved path optimization algorithm is used for path planning.

2. The method for optimizing the distribution path of emergency materials for public health emergencies according to claim 1, characterized in that: In step 1, the relevant parameters of the initialization path optimization algorithm include the number of drivers, the number of cycles, the heuristic information parameter, and the pheromone concentration. The initial transfer probability function is: In the formula, τ ij is the pheromone concentration between cell i and cell j; n ij is the heuristic function; a is the importance of pheromone, b is the importance of heuristic information; allowk is the set of cells that the k-th driver can transfer to next. As time goes by, the number of elements stored in allowk will decrease and eventually become an empty set; in, The driver completes a cycle and updates the pheromone concentration of each path using the following formula: in, Where: m is the number of drivers, 0<ρ≤1 is the evaporation rate of pheromone, is the pheromone left by the kth driver on the path from i to j, Q is the pheromone constant, and l is the total path length obtained after the kth driver completes the path.

3. The method for optimizing the distribution path of emergency materials for public health emergencies according to claim 1, characterized in that: In step 2, the cell information includes the geographical location coordinates (x, y) of each cell, the current total population N of the cell, the number of confirmed cases U, and the area S of the cell.

4. The method for optimizing the distribution path of emergency materials for public health emergencies according to claim 1, characterized in that: In step 5, during the optimal path search process, pheromone concentration, heuristic information function and road section infection density function work together, and the improved path optimization algorithm is used for path planning.

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