Emergency livelihood material transportation guarantee capability evaluation method based on PP-TOPSIS

Abstract

The invention discloses an emergency livelihood material transportation guarantee capability evaluation method based on PP-TOPSIS, and the method comprises the steps: firstly constructing an emergency livelihood material transportation guarantee capability evaluation index system, and then determining the related data of a to-be-evaluated region in the emergency livelihood material transportation guarantee capability evaluation index system, after a projection pursuit model (PSO-PP) based on a particle swarm optimization algorithm is adopted to determine the weight of each secondary index in the secondary index layer of the emergency livelihood material transportation guarantee capability evaluation index system of each to-be-evaluated region, a TOPSIS comprehensive evaluation model is adopted to evaluate the emergency livelihood material transportation guarantee capability of all the to-be-evaluated regions. The emergency livelihood material transportation guarantee capacities of all the to-be-evaluated regions are subjected to good and bad sorting, and an evaluation result is obtained; the method has the advantage of high evaluation result accuracy.

Description

Technical Field

[0001] The present invention relates to an evaluation method for emergency civilian supplies transportation guarantee capability, and in particular to an evaluation method for emergency civilian supplies transportation guarantee capability based on PP-TOPSIS. Background Art

[0002] In recent years, the frequency of sudden natural disasters such as typhoons, earthquakes, and mudslides has increased, which has had a serious impact on the stable economic development and life safety of residents in many places. Emergency civilian material transportation is one of the key links for a region to effectively respond to sudden natural disasters. The comprehensive improvement of the region's emergency civilian material transportation guarantee capacity can provide guarantee for its emergency civilian material transportation, thereby effectively reducing the losses caused by sudden natural disasters in the region. The emergency civilian material transportation guarantee capacity of multiple regions is reasonably evaluated and ranked, and then the emergency civilian material transportation of multiple regions is reasonably arranged according to the ranking results, so that the emergency civilian material transportation guarantee capacity of multiple regions can be improved. Therefore, the reasonable evaluation and ranking of the emergency civilian material transportation guarantee capacity of multiple regions is the premise for improving the emergency civilian material transportation guarantee capacity of each region.

[0003] In the existing evaluation of emergency and civilian material transportation support capabilities, the evaluation indicators are mostly a combination of qualitative and quantitative indicators, and there are very few indicators that are all quantitative. The indicator weights are mostly a combination of subjective and objective weights. The subjective weights mainly use the analytic hierarchy process and the grey correlation analysis method, and the objective weights mainly use the entropy weight method. The evaluation methods mainly include cloud model, grey evaluation, fuzzy comprehensive evaluation method, etc.; and the existing relevant evaluation indicators are mostly unilateral in terms of macro-emergency material supply capabilities. On the one hand, the weight determination and evaluation methods are greatly affected by subjective factors, and on the other hand, the rationality and accuracy of the weight calculation results and evaluation results that may be caused by the large number of indicator dimensions are not fully considered. These factors all lead to inaccurate final evaluation results. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide an emergency civilian supplies transportation support capability evaluation method based on PP-TOPSIS with high evaluation result accuracy.

[0005] The technical solution adopted by the present invention to solve the above technical problems is: a method for evaluating the transportation guarantee capability of emergency civilian supplies based on PP-TOPSIS, comprising the following steps:

[0006] S1. Construct an evaluation index system for the transport guarantee capability of emergency civilian supplies, which includes a target layer, a first-level index layer and a second-level index layer; the target layer is the evaluation target of the transport guarantee capability of emergency civilian supplies; the first-level index layer includes four first-level indicators: the economic guarantee capability of emergency civilian supplies transportation, the transportation capacity guarantee capability of emergency civilian supplies transportation, the road network guarantee capability of emergency civilian supplies transportation and the demand guarantee capability of emergency civilian supplies; the second-level index layer includes the proportion of regional disaster emergency management expenditure to GDP, the fixed asset investment amount of the logistics industry, the proportion of logistics industry employees to the affected population, the scale of emergency civilian supplies transportation vehicles, the freight volume of highway transportation, the density of the road network for emergency civilian supplies transportation, the accessibility of transportation enterprises to emergency civilian supplies supply points, and the road network for emergency civilian supplies transportation. The ten bipolar indicators are network connectivity, the ratio of the total population affected by disasters in the region to the emergency rescue reserve personnel, and the ratio of the scale of disasters in the region to the scale of emergency material reserves; among them, the ratio of regional disaster emergency management expenditure to GDP and the fixed asset investment in the logistics industry are both subordinate to the economic guarantee capacity for the transportation of emergency civilian materials, the ratio of logistics industry employees to the affected population, the scale of emergency civilian material transportation vehicles and the freight volume of road transportation are all subordinate to the capacity guarantee capacity for the transportation of emergency civilian materials, the density of the road network for the transportation of emergency civilian materials, the accessibility of transportation enterprises to the supply points of emergency civilian materials and the connectivity of the road network for the transportation of emergency civilian materials are all subordinate to the road network guarantee capacity for the transportation of emergency civilian materials, the ratio of the total population affected by disasters in the region to the emergency rescue reserve personnel and the ratio of the scale of disasters in the region to the scale of emergency material reserves are both subordinate to the demand guarantee capacity for emergency civilian materials;

[0007] S2. Determine the data of ten secondary indicators for all regions to be evaluated. All regions to be evaluated are at the same administrative level. For each region to be evaluated, the data of the four secondary indicators in the secondary indicator layer, namely, the proportion of regional disaster emergency management expenditure to GDP, the fixed asset investment in the logistics industry, the scale of emergency civilian material transportation vehicles, and the road transportation freight volume, are directly collected. The data of the three secondary indicators, namely, the proportion of logistics industry employees to the disaster-affected population, the ratio of the total disaster-affected population in the region to the emergency rescue reserve personnel, and the ratio of the regional disaster-affected scale to the scale of the emergency material reserve, are obtained from the national or provincial government statistics bureau if there is data. If not, the passive flooding algorithm or active flooding algorithm is used for simulation calculation. The three secondary indicators in the secondary indicator layer, namely, the density of the emergency civilian material transportation road network, the accessibility of the transportation enterprise to the emergency civilian material supply point, and the connectivity of the emergency civilian material transportation road network, are obtained by quantification.

[0008] S3. Use the projection pursuit model based on particle swarm optimization algorithm (PSO-PP) to determine the weight of each secondary indicator in the secondary indicator layer of the evaluation index system of emergency civilian supplies transportation guarantee capacity for each area to be evaluated;

[0009] S4. Based on the weights of each secondary indicator in the secondary indicator layer of the evaluation index system of emergency civilian supplies transportation guarantee capacity for each area to be evaluated, the TOPSIS comprehensive evaluation model is used to evaluate the emergency civilian supplies transportation guarantee capacity of each area to be evaluated, and the emergency civilian supplies transportation guarantee capacity of all areas to be evaluated are ranked to obtain the evaluation results.

[0010] Further, in the step S2, the three secondary indicators of the emergency livelihood materials transportation road network density, the accessibility of the transportation enterprise to the emergency livelihood materials supply point and the emergency livelihood materials transportation road network connectivity in the secondary indicator layer of the emergency livelihood materials transportation guarantee capacity evaluation index system of the i-th area to be evaluated are quantified, wherein i=1,2,3,…, n, n is the total number of areas to be evaluated, and the specific process of obtaining the three secondary indicators of the emergency livelihood materials transportation road network density, the accessibility of the transportation enterprise to the emergency livelihood materials supply point and the emergency livelihood materials transportation road network connectivity in the secondary indicator layer of the emergency livelihood materials transportation guarantee capacity evaluation index system of the i-th area to be evaluated is:

[0011] Step 2.1: Collect the road network data of the i-th area to be evaluated, the total length of roads closed due to sudden natural disasters, emergency livelihood supply points, transportation companies with a scale of more than 10 vehicles, and road network vector diagrams;

[0012] Step 2.2: Use formula (1) to quantify the “network density of emergency civilian supplies transportation routes” in the secondary indicator layer of the evaluation index system for the emergency civilian supplies transportation guarantee capacity of the i-th area to be evaluated as the ratio of the total length of roads in the i-th area to be evaluated to its total area:

[0013]

[0014] in, represents the network density of emergency supplies transportation routes in the i-th region to be evaluated; L ai -L bi represents the total length of accessible roads in the i-th area to be evaluated, L ai represents the total length of roads in the i-th area to be evaluated, L bi To represent the total length of road closures caused by a sudden natural disaster in the i-th evaluated area. Since all evaluated areas are at the same administrative level, it can be determined by the emergency response plan for sudden incidents and natural disasters issued by the lowest administrative level government of the i-th evaluated area. The unit is km, Ai Represents the total area of ​​the i-th region to be evaluated, in km 2 ;

[0015] Step 2.3: Formula (2) is used to quantify the “accessibility of transport enterprises to emergency livelihood supplies supply points” in the secondary indicator layer of the evaluation index system of emergency livelihood supplies transportation guarantee capacity of the i-th region to be evaluated as the average value of the shortest distance from the emergency supplies supply point to the transport enterprises with a scale of more than 10 vehicles in the i-th region to be evaluated:

[0016]

[0017] in, is the accessibility of the transportation enterprises in the i-th area to be evaluated to the emergency livelihood supplies supply point, is the xth region in the i-th region to be evaluated i Emergency material supply point to y i The shortest distance for a transport enterprise with more than 10 vehicles, x i =1,2,3,…,P i ,y i =1,2,3,…,m i , where p i is the total number of emergency livelihood material supply points in the i-th area to be evaluated, m i is the total number of transport enterprises with a scale of more than 10 vehicles in the i-th region to be evaluated;

[0018] Step 2.4: Formula (3) is used to quantify the "connectivity of the emergency livelihood materials transportation road network" in the secondary indicator layer of the emergency livelihood materials transportation guarantee capacity evaluation index system of the i-th evaluated area as the intensity of the interconnection between the emergency material supply points and the transportation enterprises with a scale of more than 10 vehicles in the emergency material transportation road network relying on road transportation:

[0019]

[0020] Among them C i is the connectivity of the emergency civilian supplies transportation network in the i-th area to be evaluated, L i is the total mileage of accessible roads in the i-th area to be evaluated, L i =L ai -L bi , unit is km; ζ i is the deformation coefficient of the transportation road network in the i-th area to be evaluated, and its value is the ratio of the total actual line length to the total straight-line distance between all emergency civilian material supply points and transportation enterprises with a scale of more than 10 vehicles in the i-th area to be evaluated.

[0021] Furthermore, in step S3, the specific process of using the projection pursuit model based on particle swarm optimization algorithm (PSO-PP) to determine the weight of each secondary indicator in the secondary indicator layer of the emergency livelihood materials transportation guarantee capacity evaluation index system of each area to be evaluated is:

[0022] Step 3.1: The proportion of regional disaster emergency management expenditure to GDP, the amount of fixed asset investment in the logistics industry, the proportion of logistics industry employees to the affected population, the scale of emergency civilian material transportation vehicles, the freight volume of highway transportation, the density of the emergency civilian material transportation road network, the accessibility of transportation enterprises to emergency civilian material supply points, the connectivity of the emergency civilian material transportation road network, the ratio of the total disaster-stricken population in the region to the emergency rescue reserve personnel, and the ratio of the regional disaster-stricken scale to the scale of the emergency material reserve are respectively referred to as the first to the tenth secondary indicators; the 10 secondary indicator data of n regions are used to form the initial evaluation data set, which is expressed as {x ij |i=1,2,…n;j=1,2,…q}, where x ij is the jth secondary indicator data of the i-th area to be evaluated; q represents the total number of secondary indicators, which is 10; formula (4) is used to sum and normalize the initial evaluation data set to eliminate its dimension effect:

[0023]

[0024] Among them, m ij Indicates x ij The data obtained after summing and normalization processing, ∑ is the accumulation symbol;

[0025] Step 3.2: Transform the q-dimensional data {m ij |j=1,2,…q} is synthesized into {a=a (1) ,a (2) ,…,a (q)} is the projection value Z(i) in the projection direction, which is expressed by formula (5):

[0026]

[0027] Among them, a (j) is the projection direction vector of the jth secondary indicator of the i-th area to be evaluated;

[0028] Step 3.3: Construct the projection objective function Q(a) as shown in equation (6):

[0029] Q(a)=S z D z , (6)

[0030] in, S zis the standard deviation of the projection value Z(i); D z is the local density of the projection value Z(i); E(z) is the mean of the sequence {Z(i)|i=1,2,…n}; R is the local density D z The window radius is max{} means taking the maximum value; r ik is the distance between the projection values ​​of the i-th region to be evaluated and the k-th region, r ik =|Z(i)-Z(k)|, where || is the absolute value symbol, k=1,2,…n; u(Rr ik ) is a unit step function and satisfies when R ≥ r ik When u(Rr ik )=1, when R <r ik When u(Rr ik )=0;

[0031] Step 3.4: Set the constraints as follows: And -1 (j) <1, the particle swarm optimization algorithm (PSO) is used to optimize the projection objective function Q(a) and obtain the a that maximizes the projection objective function Q(a) (j) As the optimal projection direction vector, denoted as

[0032] Step 3.5: Let the weight of the j-th secondary indicator be w j , using formula (7) to calculate w j :

[0033]

[0034] Furthermore, in step S4, based on the weights of each secondary indicator in the secondary indicator layer of the emergency livelihood materials transportation guarantee capability evaluation index system, the TOPSIS comprehensive evaluation model is used to rank the emergency livelihood materials transportation guarantee capabilities of all the areas to be evaluated and the specific process of obtaining the evaluation results is as follows:

[0035] Step 4.1: Construct the secondary indicator weight matrix of all evaluation areas, denoted as W, as shown in formula (8):

[0036]

[0037] Step 4.2: Use the data obtained by summing and normalizing in step 3.1 to construct a normalized matrix M, M = (m ij ) n×q , m ik is the element in the i-th row and j-th column of the normalized matrix M, and then the weighted normalized matrix V is obtained using formula (9): ​

[0038]

[0039] Among them, v ij =w j m ij , v ij The element in the i-th row and j-th column of the weighted normalized matrix V represents the weighted value of the j-th evaluation indicator of the i-th area to be evaluated;

[0040] Step 4.2: Use formula (10) and formula (11) to determine the positive ideal solution of the j-th secondary index and negative ideal solution The details are as follows:

[0041]

[0042] Among them, min{} means taking the minimum value. The second-level indicators from the 1st to the 8th are all benefit indicators, and the larger the value, the better, that is, their indicator type is positive; the 9th and 10th second-level indicators are both cost indicators, and the smaller the value, the better, that is, their indicator type is negative;

[0043] Step 4.3: Use formulas (12) and (13) to calculate the Euclidean space distance from the i-th area to be evaluated to the positive ideal solution. and the Euclidean distance to the negative ideal solution

[0044]

[0045] Step 4.4: Use formula (14) to calculate the relative closeness S between the i-th area to be evaluated and the ideal solution i :

[0046]

[0047] Step 4.5: S i As the emergency civilian supplies transportation guarantee capability of the i-th region to be evaluated, S i The larger the value is, the better the emergency civilian supplies transportation guarantee capacity of the ith area to be evaluated, and vice versa. The n areas are sorted from large to small according to their emergency civilian supplies transportation guarantee capacity to obtain the evaluation results.

[0048] Compared with the prior art, the advantages of the present invention are: by comprehensively considering the supply and demand of emergency civilian materials transportation in the selection of emergency civilian materials transportation guarantee capacity evaluation indicators, the weights of various indicators calculated by using the projection pursuit model (PSO-PP) based on the particle swarm optimization algorithm have moderate differences, which overcomes the homogenization disadvantages of subjective weighting methods such as the grey correlation analysis method, and the defect of objective weighting methods such as the entropy weight method that the weight is set too large due to the relatively discrete distribution of the values ​​of a certain type of indicators, and can more reasonably reflect the influence of a certain indicator on the evaluation target; in addition, in the process of iteratively searching for the best projection, the flexibility and adaptability are stronger, so that the final evaluation result is more accurate and true, and combining it with the TOPSIS method not only overcomes the shortcomings of the traditional TOPSIS method in evaluation subjectivity and low accuracy, but also can reasonably judge the advantages and disadvantages of the emergency civilian materials transportation guarantee capacity of each area to be evaluated, so that the final evaluation result is more objective and comprehensive. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a flow chart of the emergency civilian supplies transportation guarantee capability evaluation method based on PP-TOPSIS of the present invention;

[0050] Figure 2 It is a regional map of the evaluation area in the method for evaluating the transportation guarantee capability of emergency civilian supplies based on PP-TOPSIS of the present invention in Example 2;

[0051] Figure 3 The distribution map of emergency livelihood material supply points and transportation enterprises in the emergency livelihood material transportation guarantee capability evaluation method based on PP-TOPSIS of the present invention in Example 2;

[0052] Figure 4 In the method for evaluating the transportation security capability of emergency civilian supplies based on PP-TOPSIS of the present invention in Example 2, the distribution map of Ningbo road network;

[0053] Figure 5 This is a diagram of the simulated flood inundation range of Ningbo under different return periods in the PP-TOPSIS-based emergency livelihood material transportation support capability evaluation method of the present invention in Example 2;

[0054] Figure 6 This is a diagram showing the numerical distribution of various indicators in different regions in the method for evaluating the transportation security capability of emergency civilian supplies based on PP-TOPSIS of the present invention in Example 2;

[0055] Figure 7 It is an optimization process diagram of the projection index function in the emergency livelihood material transportation guarantee capability evaluation method based on PP-TOPSIS of the present invention in Example 2;

[0056] Figure 8 This is a result diagram of different weight calculation methods in the PP-TOPSIS-based emergency livelihood material transportation support capability evaluation method of the present invention in Example 2;

[0057] Fig. 9 This is a distribution diagram of the emergency livelihood supplies transportation guarantee capability levels of the ten major districts, counties and cities in Ningbo in the emergency livelihood supplies transportation guarantee capability evaluation method based on PP-TOPSIS of the present invention in Example 2. DETAILED DESCRIPTION

[0058] The present invention is further described in detail below with reference to the accompanying drawings.

[0059] Embodiment 1: Figure 1 As shown, a method for evaluating the transportation support capability of emergency civilian supplies based on PP-TOPSIS includes the following steps:

[0060] S1. Construct an evaluation index system for the transport guarantee capability of emergency civilian supplies, which includes a target layer, a first-level index layer and a second-level index layer; the target layer is the evaluation target of the transport guarantee capability of emergency civilian supplies; the first-level index layer includes four first-level indicators: the economic guarantee capability of emergency civilian supplies transportation, the transportation capacity guarantee capability of emergency civilian supplies transportation, the road network guarantee capability of emergency civilian supplies transportation and the demand guarantee capability of emergency civilian supplies; the second-level index layer includes the proportion of regional disaster emergency management expenditure to GDP, the fixed asset investment amount of the logistics industry, the proportion of logistics industry employees to the affected population, the scale of emergency civilian supplies transportation vehicles, the freight volume of highway transportation, the density of the road network for emergency civilian supplies transportation, the accessibility of transportation enterprises to emergency civilian supplies supply points, and the road network for emergency civilian supplies transportation. The ten bipolar indicators are network connectivity, the ratio of the total population affected by disasters in the region to the emergency rescue reserve personnel, and the ratio of the scale of disasters in the region to the scale of emergency material reserves; among them, the ratio of regional disaster emergency management expenditure to GDP and the fixed asset investment in the logistics industry are both subordinate to the economic guarantee capacity for the transportation of emergency civilian materials, the ratio of logistics industry employees to the affected population, the scale of emergency civilian material transportation vehicles and the freight volume of road transportation are all subordinate to the capacity guarantee capacity for the transportation of emergency civilian materials, the density of the road network for the transportation of emergency civilian materials, the accessibility of transportation enterprises to the supply points of emergency civilian materials and the connectivity of the road network for the transportation of emergency civilian materials are all subordinate to the road network guarantee capacity for the transportation of emergency civilian materials, the ratio of the total population affected by disasters in the region to the emergency rescue reserve personnel and the ratio of the scale of disasters in the region to the scale of emergency material reserves are both subordinate to the demand guarantee capacity for emergency civilian materials;

[0061] S2. Determine the data of ten secondary indicators for all regions to be evaluated. All regions to be evaluated are at the same administrative level. For each region to be evaluated, the data of the four secondary indicators in the secondary indicator layer, namely, the proportion of regional disaster emergency management expenditure to GDP, the fixed asset investment in the logistics industry, the scale of emergency civilian material transportation vehicles, and the road transportation freight volume, are directly collected. The data of the three secondary indicators, namely, the proportion of logistics industry employees to the disaster-affected population, the ratio of the total disaster-affected population in the region to the emergency rescue reserve personnel, and the ratio of the regional disaster-affected scale to the scale of the emergency material reserve, are obtained from the national or provincial government statistics bureau if there is data. If not, the passive flooding algorithm or active flooding algorithm is used for simulation calculation. The three secondary indicators in the secondary indicator layer, namely, the density of the emergency civilian material transportation road network, the accessibility of the transportation enterprise to the emergency civilian material supply point, and the connectivity of the emergency civilian material transportation road network, are obtained by quantification.

[0062] S3. Use the projection pursuit model based on particle swarm optimization algorithm (PSO-PP) to determine the weight of each secondary indicator in the secondary indicator layer of the evaluation index system of emergency civilian supplies transportation guarantee capacity for each area to be evaluated;

[0063] S4. Based on the weights of each secondary indicator in the secondary indicator layer of the evaluation index system of emergency civilian supplies transportation guarantee capacity for each area to be evaluated, the TOPSIS comprehensive evaluation model is used to evaluate the emergency civilian supplies transportation guarantee capacity of all areas to be evaluated, and the emergency civilian supplies transportation guarantee capacity of all areas to be evaluated is ranked according to their merits to obtain the evaluation results.

[0064] In this embodiment, a relatively reasonable evaluation index system for the transportation guarantee capability of emergency civilian supplies is constructed, consisting of four first-level indicators and ten second-level indicators. The index data is obtained by collecting and calculating. The system is concise, feasible and close to reality. The TOPSIS method based on projection pursuit weighting is used for comprehensive evaluation, which can reasonably judge the quality of the transportation guarantee capability of emergency civilian supplies in each area to be evaluated, making the final evaluation result more objective and comprehensive.

[0065] Embodiment 2: This embodiment is basically the same as Embodiment 1, except that: in this embodiment, in step S2, the three secondary indicators of emergency livelihood material transportation road network density, accessibility from transportation enterprises to emergency livelihood material supply points, and emergency livelihood material transportation road network connectivity in the secondary indicator layer of the emergency livelihood material transportation guarantee capacity evaluation index system of the i-th area to be evaluated are quantified, wherein i=1,2,3,…, n, n is the total number of areas to be evaluated, and the specific process of obtaining the three secondary indicators of emergency livelihood material transportation road network density, accessibility from transportation enterprises to emergency livelihood material supply points, and emergency livelihood material transportation road network connectivity in the secondary indicator layer of the emergency livelihood material transportation guarantee capacity evaluation index system of the i-th area to be evaluated is:

[0066] Step 2.1: Collect the road network data of the i-th area to be evaluated, the total length of roads closed due to sudden natural disasters, emergency livelihood supply points, transportation companies with a scale of more than 10 vehicles, and road network vector diagrams;

[0067] Step 2.2: Use formula (1) to quantify the “network density of emergency civilian supplies transportation routes” in the secondary indicator layer of the evaluation index system for the emergency civilian supplies transportation guarantee capacity of the i-th area to be evaluated as the ratio of the total length of roads in the i-th area to be evaluated to its total area:

[0068]

[0069] in, represents the network density of emergency supplies transportation routes in the i-th region to be evaluated; L ai -L bi represents the total length of accessible roads in the i-th area to be evaluated, L ai represents the total length of roads in the i-th area to be evaluated, L bi To represent the total length of road closures caused by a sudden natural disaster in the i-th evaluated area. Since all evaluated areas are at the same administrative level, it can be determined by the emergency response plan for sudden incidents and natural disasters issued by the lowest administrative level government of the i-th evaluated area. The unit is km, A i Represents the total area of ​​the i-th region to be evaluated, in km 2 ;

[0070] Step 2.3: Formula (2) is used to quantify the “accessibility of transport enterprises to emergency livelihood supplies supply points” in the secondary indicator layer of the evaluation index system of emergency livelihood supplies transportation guarantee capacity of the i-th region to be evaluated as the average value of the shortest distance from the emergency supplies supply point to the transport enterprises with a scale of more than 10 vehicles in the i-th region to be evaluated:

[0071]

[0072] in, is the accessibility of the transportation enterprises in the i-th area to be evaluated to the emergency livelihood supplies supply point, is the xth value in the i-th region to be evaluated i Emergency material supply point to y i The shortest distance for a transport enterprise with more than 10 vehicles, x i =1,2,3,…,p i ,y i =1,2,3,…,m i , where p i is the total number of emergency livelihood material supply points in the i-th area to be evaluated, m i is the total number of transport enterprises with a scale of more than 10 vehicles in the i-th region to be evaluated;

[0073] Step 2.4: Formula (3) is used to quantify the "connectivity of the emergency livelihood materials transportation road network" in the secondary indicator layer of the emergency livelihood materials transportation guarantee capacity evaluation index system of the i-th evaluated area as the intensity of the interconnection between the emergency material supply points and the transportation enterprises with a scale of more than 10 vehicles in the emergency material transportation road network relying on road transportation:

[0074]

[0075] Among them C i is the connectivity of the emergency civilian supplies transportation network in the i-th area to be evaluated, L i is the total mileage of accessible roads in the i-th area to be evaluated, L i =L ai -L bi , unit is km; ζ i is the deformation coefficient of the transportation road network in the i-th area to be evaluated, and its value is the ratio of the total actual line length to the total straight-line distance between all emergency civilian material supply points and transportation enterprises with a scale of more than 10 vehicles in the i-th area to be evaluated.

[0076] In this embodiment, in step S3, the specific process of using the projection pursuit model based on particle swarm optimization algorithm (PSO-PP) to determine the weight of each secondary indicator in the secondary indicator layer of the emergency livelihood materials transportation guarantee capacity evaluation index system of each area to be evaluated is:

[0077] Step 3.1: The proportion of regional disaster emergency management expenditure to GDP, the amount of fixed asset investment in the logistics industry, the proportion of logistics industry employees to the affected population, the scale of emergency civilian material transportation vehicles, the freight volume of highway transportation, the density of the emergency civilian material transportation road network, the accessibility of transportation enterprises to emergency civilian material supply points, the connectivity of the emergency civilian material transportation road network, the ratio of the total disaster-stricken population in the region to the emergency rescue reserve personnel, and the ratio of the regional disaster-stricken scale to the scale of the emergency material reserve are respectively referred to as the first to the tenth secondary indicators; the 10 secondary indicator data of n regions are used to form the initial evaluation data set, which is expressed as {x ij |i=1,2,…n;j=1,2,…q}, where x ij is the jth secondary indicator data of the i-th area to be evaluated; q represents the total number of secondary indicators, which is 10; formula (4) is used to sum and normalize the initial evaluation data set to eliminate its dimension effect:

[0078]

[0079] Among them, m ij Indicates x ij The data obtained after summing and normalization processing, ∑ is the accumulation symbol;

[0080] Step 3.2: Transform the q-dimensional data {m ij |j=1,2,…q} is synthesized into {a=a (1) ,a (2) ,…,a (q)} is the projection value Z(i) in the projection direction, which is expressed by formula (5):

[0081]

[0082] Among them, a (j) is the projection direction vector of the jth secondary indicator of the i-th area to be evaluated;

[0083] Step 3.3: Construct the projection objective function Q(a) as shown in equation (6):

[0084] Q(a)=S z D z , (6)

[0085] in, S z is the standard deviation of the projection value Z(i); D z is the local density of the projection value Z(i); E(z) is the mean of the sequence {Z(i)|i=1,2,…n}; R is the local density D z The window radius is max{} means taking the maximum value; r ik is the distance between the projection values ​​of the i-th region to be evaluated and the k-th region, r ik =|Z(i)-Z(k)|, where || is the absolute value symbol, k=1,2,…n; u(Rr ik ) is a unit step function and satisfies when R ≥ r ik When u(Rr ik )=1, when R <r ik When u(Rr ik )=0;

[0086] Step 3.4: Set the constraints as follows: And -1 (j) <1, the particle swarm optimization algorithm (PSO) is used to optimize the projection objective function Q(a) and obtain the a that maximizes the projection objective function Q(a) (j) As the optimal projection direction vector, denoted as

[0087] Step 3.5: Let the weight of the j-th secondary indicator be w j , using formula (7) to calculate w j :

[0088]

[0089] In this embodiment, in step S4, based on the weights of each secondary indicator in the secondary indicator layer of the emergency livelihood materials transportation guarantee capability evaluation index system, the TOPSIS comprehensive evaluation model is used to rank the emergency livelihood materials transportation guarantee capabilities of all the areas to be evaluated and the specific process of obtaining the evaluation results is as follows:

[0090] Step 4.1: Construct the secondary indicator weight matrix of all evaluation areas, denoted as W, as shown in formula (8):

[0091]

[0092] Step 4.2: Use the data obtained by summing and normalizing in step 3.1 to construct a normalized matrix M, M = (m ij ) n×q , m ij is the element in the i-th row and j-th column of the normalized matrix M, and then the weighted normalized matrix V is obtained using formula (9):

[0093]

[0094] Among them, v ij =w j m ij , v ij ​The element in the i-th row and j-th column of the weighted normalized matrix V represents the weighted value of the j-th evaluation indicator of the i-th area to be evaluated;

[0095] Step 4.2: Use formula (10) and formula (11) to determine the positive ideal solution of the j-th secondary index and negative ideal solution The details are as follows:

[0096]

[0097] Among them, min{} means taking the minimum value. The second-level indicators from the 1st to the 8th are all benefit indicators, and the larger the value, the better, that is, their indicator type is positive; the 9th and 10th second-level indicators are both cost indicators, and the smaller the value, the better, that is, their indicator type is negative;

[0098] Step 4.3: Use formulas (12) and (13) to calculate the Euclidean space distance from the i-th area to be evaluated to the positive ideal solution. and the Euclidean distance to the negative ideal solution

[0099]

[0100] Step 4.4: Use formula (14) to calculate the relative closeness S between the i-th area to be evaluated and the ideal solution i :

[0101]

[0102] Step 4.5: S i As the emergency civilian supplies transportation guarantee capability of the i-th region to be evaluated, S i The larger the value is, the better the emergency civilian supplies transportation guarantee capacity of the ith area to be evaluated, and vice versa. The n areas are sorted from large to small according to their emergency civilian supplies transportation guarantee capacity to obtain the evaluation results.

[0103] In this embodiment, the value of n is 10, and the n regions are the ten districts, counties and cities under the jurisdiction of Ningbo City: Haishu District, Jiangbei District, Beilun District, Zhenhai District, Yinzhou District, Fenghua District, Xiangshan County, Ninghai County, Yuyao City and Cixi City. This embodiment uses Ningbo Haishu, Yinzhou, Beilun, Jiangbei, Zhenhai, Cixi, Yuyao, Fenghua, Ninghai and Xiangshan as the evaluation subjects to conduct a strength evaluation and analysis of their emergency livelihood material transportation guarantee capabilities under typhoon natural disasters. The research scope is as follows: Figure 2 shown.

[0104] Obtain the data of various secondary indicators in the evaluation index system of emergency civilian material transportation guarantee capacity as shown in Table 1, collect relevant economic and traffic data through Ningbo Statistical Yearbook and the website of the Statistics Bureau, and the statistical yearbooks of Ningbo districts and counties; the vehicle scale data of transportation enterprises and the POI data of supply points are from the Zhejiang Provincial Road Freight Transportation Special Survey and the AutoNavi API Open Platform respectively; Ningbo road network data, elevation data and population raster data are obtained through http: / / srtm.csi.cgiar.org / srtmdata / , Open Street Map and https: / / landscan.ornl.gov / respectively. Use ArcGIS to import Ningbo's relevant emergency civilian material supply points, transportation enterprises with a scale of more than 10 vehicles, and road network vector diagrams, such as Figure 3 and Figure 4 shown.

[0105] Table 1 Evaluation index system for emergency civilian supplies transportation support capability

[0106]

[0107] The data of three secondary indicators, namely the proportion of logistics industry employees to the affected population, the ratio of the total affected population in the region to the emergency rescue reserve personnel, and the ratio of the regional disaster scale to the scale of the emergency material reserve, were used. Based on the passive inundation algorithm principle and ArcGIS, and based on the Ningbo DEM data, the flood control standards of Ningbo's 5-year flood control design high water level of 2.60m, 10-year flood control design high water level of 2.85m, 20-year flood control design high water level of 3.10m, 50-year flood control design high water level of 3.43m, and 100-year flood control design high water level of 3.67m were used to simulate the flood inundation range of Ningbo under different return periods, such as Figure 5 As shown in Figure 2, the affected areas of each district and county within different inundation ranges were calculated and statistically analyzed, and the affected population of each district and county was statistically analyzed in combination with the Ningbo population grid data to obtain the data of these three secondary indicators. The data values ​​of other indicators were calculated using formulas (1)-(3). The original data of each indicator are shown in Table 2.

[0108] Table 2 Original data of evaluation indicators of emergency livelihood materials transportation support capability in Ningbo's ten major districts and counties

[0109]

[0110]

[0111] Through step 3.1, the normalization results of each index are obtained, as shown in Table 3.

[0112] Table 3 Normalized results of evaluation attribute values ​​of emergency livelihood material transportation support capabilities of Ningbo’s ten major districts, counties and cities

[0113]

[0114] In order to more clearly see the changes in the values ​​of each indicator, the normalized results are converted into a scatter plot, such as Figure 6 Then, the projection pursuit model based on particle swarm optimization algorithm (PSO-PP) is implemented through Python software, and the projection pursuit model based on particle swarm optimization algorithm (PSO-PP) is used to find the optimal solution. The optimal projection direction vector is finally obtained as The parameters of the projection pursuit model based on the particle swarm optimization algorithm are: population size N = 50, number of iterations M = 200, learning factor c1 = c2 = 2, maximum inertia weight W max =0.9, minimum inertia weight W min =0.4, the solution process is as follows Figure 7 The weights of each indicator can be obtained by squaring each component of the optimal projection direction vector, as shown in Table 4.

[0115] Table 4 Comparison of the results of determining index weights using projection pursuit model, grey correlation and entropy weight method

[0116]

[0117]

[0118] Table 4 also compares the weights calculated by the PSO-PP model with the weights determined by the grey correlation method and the entropy weight method. Figure 8 As shown in Table 4 and Figure 8 Comprehensive analysis shows that the weight differences of various indicators of emergency civilian material transportation support capacity calculated by projection pursuit model are more moderate than the results of grey correlation method and entropy weight method, and the indicator weight values ​​are neither too homogenized as the weight results of grey correlation method, nor too concentrated on certain indicators as the weight results of entropy weight method. Therefore, the present invention uses PSO-PP model to calculate and analyze the weights of various indicators. On the one hand, it can make up for the fact that grey correlation is relatively rough when reflecting multi-level and multi-criteria practical problems, and the calculated indicator weights are relatively close and tend to be homogenized; on the other hand, it overcomes the problem that the entropy weight method requires a relatively uniform distribution of indicator data when calculating weights, and the weight calculation is not accurate enough if the data variation is large or small, and has certain feasibility and effectiveness.

[0119] According to formula (9), the weighted normalization matrix is ​​obtained, and the results are shown in Table 5:

[0120] Table 5 Weighted normalization results of the evaluation attribute values ​​of the emergency livelihood material transportation guarantee capacity of the ten major districts, counties and cities in Ningbo

[0121]

[0122] The ranking of emergency livelihood material transportation support capabilities of the ten major districts and counties in Ningbo is obtained by calculation and analysis using formulas (10)-(14). The results are shown in Table 6:

[0123] Table 6 Evaluation results of emergency livelihood material transportation support capabilities in Ningbo's top ten districts and counties

[0124]

[0125]

[0126] In order to further verify the rationality of the evaluation results of the emergency civilian supplies transportation security capability evaluation method based on PP-TOPSIS of the present invention, the evaluation results of the emergency civilian supplies transportation security capability evaluation method based on PP-TOPSIS are compared with those of the traditional TOPSIS and entropy weight TOPSIS evaluation methods, as shown in Table 7.

[0127] Table 7 Comparison of evaluation results of different methods for emergency livelihood material transportation support capabilities in various districts, counties and cities in Ningbo

[0128] Districts and counties Projection pursuit weighted TOPSIS Sorting Traditional TOPSIS Sorting Entropy Weight TOPSIS Sorting Haishu District 0.213 4 0.231 8 0.205 3 Yinzhou District 0.272 2 0.316 3 0.264 2 Zhenhai District 0.158 6 0.305 5 0.189 4 Jiangbei District 0.230 3 0.221 9 0.184 5 Beilun District 0.825 1 0.698 1 0.800 1 Fenghua District 0.169 5 0.268 6 0.163 6 Cixi City 0.131 8 0.307 4 0.137 8 Yuyao City 0.146 7 0.216 10 0.153 7 Ninghai County 0.088 9 0.247 7 0.117 9 Xiangshan County 0.076 10 0.321 2 0.110 10

[0129] The final evaluation results of TOPSIS weighted by projection pursuit are divided into 5 levels using the ArcGIS natural breakpoint method. According to the data distribution, the natural breakpoints are found by minimizing the differences within the category and maximizing the differences between categories, and the data are divided into a group of relatively uniform categories, where 0.088 and below are poor, 0.088-0.146 are fair, 0.146-0.169 are medium, 0.169-0.272 are good, and 0.272 and above are excellent. Fig. 9 shown.

[0130] The comparison data of the evaluation and analysis results of the above examples show that, among the ten selected quantitative evaluation indicators, the weights of each indicator calculated by the PP-TOPSIS-based emergency civilian supplies transportation guarantee capability evaluation method of the present invention are quite different, which to a certain extent makes up for the deficiency of the homogenization of the results obtained by the grey correlation degree. The PP-TOPSIS-based emergency civilian supplies transportation guarantee capability evaluation method of the present invention overcomes the shortcoming of the traditional TOPSIS that the subjectivity is strong, and is more in line with reality than the entropy weight TOPSIS, and can effectively judge the advantages and disadvantages of the emergency civilian supplies transportation guarantee capabilities of the ten major districts, counties and cities in Ningbo.

[0131] In summary, the PP-TOPSIS-based evaluation method for emergency civilian supplies transportation security capability has high evaluation accuracy and can be effectively applied to the evaluation of emergency civilian supplies transportation security capability of multiple evaluated areas at the same administrative level.

Claims

1. A method for evaluating the transportation support capability of emergency civilian supplies based on PP-TOPSIS, characterized by The following steps are involved: S1. Construct an evaluation index system for the transport guarantee capability of emergency civilian supplies, which includes a target layer, a first-level index layer and a second-level index layer; the target layer is the evaluation target of the transport guarantee capability of emergency civilian supplies; the first-level index layer includes four first-level indicators: the economic guarantee capability of emergency civilian supplies transportation, the transportation capacity guarantee capability of emergency civilian supplies transportation, the road network guarantee capability of emergency civilian supplies transportation and the demand guarantee capability of emergency civilian supplies; the second-level index layer includes the proportion of regional disaster emergency management expenditure to GDP, the fixed asset investment amount of the logistics industry, the proportion of logistics industry employees to the affected population, the scale of emergency civilian supplies transportation vehicles, the freight volume of highway transportation, the density of the road network for emergency civilian supplies transportation, the accessibility of transportation enterprises to emergency civilian supplies supply points, and the road network for emergency civilian supplies transportation. The ten bipolar indicators are network connectivity, the ratio of the total population affected by disasters in the region to the emergency rescue reserve personnel, and the ratio of the scale of disasters in the region to the scale of emergency material reserves; among them, the ratio of regional disaster emergency management expenditure to GDP and the fixed asset investment in the logistics industry are both subordinate to the economic guarantee capacity for the transportation of emergency civilian materials, the ratio of logistics industry employees to the affected population, the scale of emergency civilian material transportation vehicles and the freight volume of road transportation are all subordinate to the capacity guarantee capacity for the transportation of emergency civilian materials, the density of the road network for the transportation of emergency civilian materials, the accessibility of transportation enterprises to the supply points of emergency civilian materials and the connectivity of the road network for the transportation of emergency civilian materials are all subordinate to the road network guarantee capacity for the transportation of emergency civilian materials, the ratio of the total population affected by disasters in the region to the emergency rescue reserve personnel and the ratio of the scale of disasters in the region to the scale of emergency material reserves are both subordinate to the demand guarantee capacity for emergency civilian materials; S2. Determine the data of ten secondary indicators for all regions to be evaluated. All regions to be evaluated are at the same administrative level. For each region to be evaluated, the data of the four secondary indicators in the secondary indicator layer, namely, the proportion of regional disaster emergency management expenditure to GDP, the fixed asset investment in the logistics industry, the scale of emergency civilian material transportation vehicles, and the road transportation freight volume, are directly collected. The data of the three secondary indicators, namely, the proportion of logistics industry employees to the disaster-affected population, the ratio of the total disaster-affected population in the region to the emergency rescue reserve personnel, and the ratio of the regional disaster-affected scale to the scale of the emergency material reserve, are obtained from the national or provincial government statistics bureau if there is data. If not, the passive flooding algorithm or active flooding algorithm is used for simulation calculation. The three secondary indicators in the secondary indicator layer, namely, the density of the emergency civilian material transportation road network, the accessibility of the transportation enterprise to the emergency civilian material supply point, and the connectivity of the emergency civilian material transportation road network, are obtained by quantification. S3. Use the projection pursuit model based on particle swarm optimization algorithm (PSO-PP) to determine the weight of each secondary indicator in the secondary indicator layer of the evaluation index system of emergency civilian supplies transportation guarantee capacity for each area to be evaluated; S4. Based on the weights of each secondary indicator in the secondary indicator layer of the evaluation index system of emergency civilian supplies transportation guarantee capacity for each area to be evaluated, the TOPSIS comprehensive evaluation model is used to evaluate the emergency civilian supplies transportation guarantee capacity of all areas to be evaluated, and the emergency civilian supplies transportation guarantee capacity of all areas to be evaluated is ranked according to their merits to obtain the evaluation results.

2. The PP-TOPSIS-based emergency civilian supplies transportation support capability evaluation method according to claim 1 is characterized in that In the step S2, the three secondary indicators of the emergency livelihood materials transportation road network density, the accessibility of the transportation enterprise to the emergency livelihood materials supply point and the emergency livelihood materials transportation road network connectivity in the secondary indicator layer of the emergency livelihood materials transportation guarantee capacity evaluation index system of the i-th area to be evaluated are quantified, wherein i=1,2,3,…,n,n is the total number of areas to be evaluated, and the specific process of obtaining the three secondary indicators of the emergency livelihood materials transportation road network density, the accessibility of the transportation enterprise to the emergency livelihood materials supply point and the emergency livelihood materials transportation road network connectivity in the secondary indicator layer of the emergency livelihood materials transportation guarantee capacity evaluation index system of the i-th area to be evaluated is: Step 2.1: Collect the road network data of the i-th area to be evaluated, the total length of roads closed due to sudden natural disasters, emergency livelihood supply points, transportation companies with a scale of more than 10 vehicles, and road network vector diagrams; Step 2.2: Use formula (1) to quantify the "emergency civilian supplies transportation route network density" in the secondary indicator layer of the emergency civilian supplies transportation guarantee capacity evaluation index system of the i-th evaluated area as the ratio of the total length of roads in the i-th evaluated area to its total area: in, represents the network density of emergency supplies transportation routes in the i-th region to be evaluated; L ai -L bi represents the total length of accessible roads in the i-th area to be evaluated, L ai represents the total length of roads in the i-th area to be evaluated, L bi To represent the total length of road closures caused by a sudden natural disaster in the i-th evaluated area. Since all evaluated areas are at the same administrative level, it can be determined by the emergency response plan for sudden incidents and natural disasters issued by the lowest administrative level government of the i-th evaluated area. The unit is km, A i Represents the total area of ​​the i-th region to be evaluated, in km 2 ; Step 2.3: Formula (2) is used to quantify the "accessibility of transport enterprises to emergency livelihood supplies supply points" in the secondary indicator layer of the evaluation index system of emergency livelihood supplies transportation guarantee capacity of the i-th evaluated area as the average value of the shortest distance from the emergency supplies supply point to the transport enterprises with a scale of more than 10 vehicles in the i-th evaluated area: in, is the accessibility of the transportation enterprises in the i-th area to be evaluated to the emergency livelihood supplies supply point, is the xth region in the i-th region to be evaluated i Emergency material supply point to y i The shortest distance for a transport enterprise with more than 10 vehicles, x i =1,2,3,…,p i ,y i =1,2,3,…,m i , where p i is the total number of emergency livelihood material supply points in the i-th area to be evaluated, m i is the total number of transport enterprises with a scale of more than 10 vehicles in the i-th region to be evaluated; Step 2.4: Formula (3) is used to quantify the "connectivity of the emergency livelihood materials transportation road network" in the secondary indicator layer of the evaluation index system of the emergency livelihood materials transportation guarantee capacity of the i-th evaluated area as the intensity of the interconnection between the emergency material supply points and the transportation enterprises with a scale of more than 10 vehicles in the emergency material transportation road network relying on road transportation: Among them C i is the connectivity of the emergency civilian supplies transportation network in the i-th area to be evaluated, L i is the total mileage of accessible roads in the i-th area to be evaluated, L i =L ai -L bi , unit is km; ζ i is the deformation coefficient of the transportation road network in the i-th area to be evaluated, and its value is the ratio of the total actual line length to the total straight-line distance between all emergency civilian material supply points and transportation enterprises with a scale of more than 10 vehicles in the i-th area to be evaluated.

3. The PP-TOPSIS-based emergency civilian supplies transportation support capability evaluation method according to claim 1 is characterized in that In step S3, the specific process of using the projection pursuit model based on particle swarm optimization algorithm (PSO-PP) to determine the weight of each secondary indicator in the secondary indicator layer of the emergency livelihood materials transportation guarantee capacity evaluation index system of each area to be evaluated is: Step 3.1: The proportion of regional disaster emergency management expenditure to GDP, the amount of fixed asset investment in the logistics industry, the proportion of logistics industry employees to the affected population, the scale of emergency civilian material transportation vehicles, the freight volume of highway transportation, the density of the emergency civilian material transportation road network, the accessibility of transportation enterprises to emergency civilian material supply points, the connectivity of the emergency civilian material transportation road network, the ratio of the total disaster-stricken population in the region to the emergency rescue reserve personnel, and the ratio of the regional disaster-stricken scale to the scale of the emergency material reserve are respectively referred to as the first to the tenth secondary indicators; the 10 secondary indicator data of n regions are used to form the initial evaluation data set, which is expressed as {x ij |i=1,2,…n;j=1,2,…q}, where x ij is the data of the jth secondary indicator of the i-th area to be evaluated; q represents the total number of secondary indicators, and its value is 10; Formula (4) is used to sum and normalize the initial evaluation data set to eliminate its dimension effect: Among them, m ij Indicates x ij The data obtained after summing and normalization processing, ∑ is the accumulation symbol; Step 3.2: Transform the q-dimensional data {m ij |j=1,2,…q} is synthesized into {a=a (1) ,a (2) ,…,a (q) } is the projection value Z(i) in the projection direction, which is expressed by formula (5): Among them, a (j) is the projection direction vector of the jth secondary indicator of the i-th area to be evaluated; Step 3.3: Construct the projection objective function Q(a) as shown in equation (6): Q(a)=S z D z , (6) in, S z is the standard deviation of the projection value Z(i); D z is the local density of the projection value Z(i); E(z) is the mean of the sequence {Z(i)|i=1,2,…n}; R is the local density D z The window radius is max{} means taking the maximum value; r ik is the distance between the projection values ​​of the i-th region to be evaluated and the k-th region, r ik =|Z(i)-Z(k)|, where || is the absolute value symbol, k=1,2,…n; u(Rr ik ) is a unit step function and satisfies when R ≥ r ik When u(Rr ik )=1, when R <r ik When u(Rr ik )=0; Step 3.4: Set the constraints as follows: And -1 (j) <1, the particle swarm optimization algorithm (PSO) is used to optimize the projection objective function Q(a) and obtain the a that maximizes the projection objective function Q(a) (j) As the optimal projection direction vector, denoted as ​ Step 3.5: Let the weight of the j-th secondary indicator be w j , using formula (7) to calculate w j :

4. The PP-TOPSIS-based emergency civilian supplies transportation support capability evaluation method according to claim 1 is characterized in that In step S4, based on the weights of the secondary indicators in the secondary indicator layer of the emergency livelihood materials transportation guarantee capability evaluation index system, the TOPSIS comprehensive evaluation model is used to rank the emergency livelihood materials transportation guarantee capabilities of all the areas to be evaluated and the specific process of obtaining the evaluation results is as follows: Step 4.1: Construct the secondary indicator weight matrix of all evaluation areas, denoted as W, as shown in formula (8): Step 4.2: Use the data obtained by summing and normalizing in step 3.1 to construct a normalized matrix M, M = (m ij ) n×q , m ij is the element in the i-th row and j-th column of the normalized matrix M, and then the weighted normalized matrix V is obtained using formula (9): Among them, v ij =w j m ij , v ij The element in the i-th row and j-th column of the weighted normalized matrix V represents the weighted value of the j-th evaluation indicator of the i-th area to be evaluated; Step 4.2: Use formula (10) and formula (11) to determine the positive ideal solution of the j-th secondary index and negative ideal solution The details are as follows: Among them, min{} means taking the minimum value. The second-level indicators from the 1st to the 8th are all benefit indicators, and the larger the value, the better, that is, their indicator type is positive; the 9th and 10th second-level indicators are both cost indicators, and the smaller the value, the better, that is, their indicator type is negative; Step 4.3: Use formulas (12) and (13) to calculate the Euclidean space distance from the i-th area to be evaluated to the positive ideal solution. and the Euclidean distance to the negative ideal solution Step 4.4: Use formula (14) to calculate the relative closeness S between the i-th area to be evaluated and the ideal solution i : Step 4.5: S i As the emergency civilian supplies transportation guarantee capability of the i-th region to be evaluated, S i The larger the value is, the better the emergency civilian supplies transportation guarantee capacity of the ith area to be evaluated, and vice versa. The n areas are sorted from large to small according to their emergency civilian supplies transportation guarantee capacity to obtain the evaluation results.

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