A method for evaluating the intelligence level of logistics parks based on GRA-TOPSIS

Abstract

The present invention discloses a method for evaluating the intelligent level of a logistics park based on GRA-TOPSIS, specifically: step 1: establishing an evaluation index; step 2: assigning a value to each secondary evaluation index; step 3: using the range transformation method to normalize the value of the jth secondary evaluation index of the ith logistics park, obtaining the normalized value, and constructing a standard decision matrix from the normalized secondary evaluation index; step 4: using a combined weighting method combining an order relationship method and an entropy weight method to calculate the comprehensive weight of the secondary evaluation index; step 5: establishing an evaluation model based on the calculated comprehensive weight of the secondary evaluation index and the standard decision matrix, and using the evaluation model to evaluate the intelligent level of the logistics park. The present invention can more comprehensively and scientifically and objectively evaluate the intelligent logistics park.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent construction of logistics parks. Background Art

[0002] With the emergence of new technologies such as the Internet of Things, big data, 5G, virtual reality technology, artificial intelligence and new models such as new energy vehicles, drones, and unmanned driving, industrial transformation and economic growth have been greatly affected. Intelligence has become the development direction of innovation-driven, transformation and upgrading of logistics parks. my country attaches great importance to the intelligent upgrading of logistics parks.

[0003] Currently, many regions have launched demonstration pilot projects of smart logistics parks. The selection criteria and indicator details cover all aspects of the performance of logistics parks, providing a basis for judging the level of logistics parks. However, the existing evaluation indicators are large and comprehensive, and cannot specifically reflect the effect of the park's intelligent transformation. Summary of the invention

[0004] Purpose of the invention: In order to solve the problems existing in the above-mentioned prior art, the present invention provides a method for evaluating the intelligence level of a logistics park based on GRA-TOPSIS.

[0005] Technical solution: The present invention provides a method for evaluating the intelligence level of a logistics park based on GRA-TOPSIS, which specifically includes the following steps:

[0006] Step 1: Establish evaluation indicators; the evaluation indicators include primary evaluation indicators, each primary evaluation indicator is further provided with a number of secondary evaluation indicators, and there are n secondary evaluation indicators in total;

[0007] Step 2: Assign values ​​to each secondary evaluation indicator;

[0008] Step 3: Use the range transformation method to calculate the value x of the j-th secondary evaluation index of the i-th logistics park. ij Perform normalization to obtain the normalized value y ij , the standard decision matrix Y is constructed by the normalized secondary evaluation indicators, i = 1, 2, ..., m, j = 1, 2, ..., n, m is the total number of parks, y ij is the element in the i-th row and j-th column of the standard decision matrix Y;

[0009] Step 4: Use the combined weighting method combining the order relationship method and the entropy weight method to calculate the comprehensive weight w of the secondary evaluation index;

[0010] Step 5: Establish an evaluation model based on the calculated comprehensive weights of the secondary evaluation indicators and the standard decision matrix, and use the evaluation model to evaluate the intelligence level of the logistics park.

[0011] Furthermore, the first-level indicators in step 1 include enterprise logistics A1, park management A2, and public services A3;

[0012] The corresponding secondary indicators under enterprise logistics A1 include the application level of logistics technology of enterprises in the park under enterprise logistics B1, application of standardized equipment B2, application of smart warehousing technology B3, application of smart distribution technology B4, evaluation of smart logistics enterprises B5, utilization of network platform B6, and cooperation between industry, academia and research B7;

[0013] The corresponding secondary indicators under park management A2 include smart logistics park planning under management B8, information technology investment B9, realized functions of the park vehicle management system B10, proportion of research and development personnel B11, and individual characteristics and innovation B12;

[0014] The corresponding secondary indicators under public service A3 include the functions realized by the park property management system under public services B13, the functions realized by the public platform B14, the interconnection status of the public platform B15, the standard data format B16, and the information visualization operation status B17.

[0015] Furthermore, in step 3, the following formula is used to calculate x ij Perform normalization:

[0016]

[0017] Among them, x j represents the jth secondary evaluation index, max x j is the maximum value of the jth secondary evaluation index among m parks, minx j It is the minimum value of the jth secondary evaluation index among the m parks.

[0018] Furthermore, in step 4, the order relationship method is used to calculate the subjective weight coefficient w of the j-th secondary evaluation index. 主j , the entropy weight method is used to calculate the objective weight coefficient w of the jth secondary evaluation index 客j ; According to the calculated subjective weight coefficient and objective weight coefficient, calculate the comprehensive weight w of the j-th secondary evaluation index j =α×w 主j +β×w 客j ; where α and β are coefficients.

[0019] Furthermore, the subjective weight coefficient of the secondary evaluation index calculated by the ordinal relationship method is specifically:

[0020] According to the jth secondary evaluation index, the importance x' of the first-level evaluation index corresponding to the second-level evaluation index j, sort the n secondary evaluation indicators from large to small according to their importance; obtain the secondary evaluation indicator sequence; assign a weight coefficient to each secondary evaluation indicator;

[0021] Calculate the importance between two adjacent secondary evaluation indicators in the secondary evaluation indicator sequence:

[0022]

[0023] w k-1 is the weight coefficient of the k-1th secondary rating evaluation indicator in the secondary evaluation indicator sequence; w k is the weight coefficient of the kth secondary evaluation index in the secondary evaluation index sequence, k = 2,…n;

[0024] The subjective weight coefficient w of the nth secondary evaluation index is calculated according to the following formula: 主n :

[0025]

[0026] The subjective weight coefficients of the 1st to n-1th secondary price indicators are calculated according to the following formula:

[0027] w 主k-1 =r k w k .

[0028] Furthermore, the objective weight coefficient of the secondary evaluation index calculated by the entropy weight method is specifically:

[0029] Calculate the information entropy e of the secondary evaluation index j :

[0030]

[0031] where p ij is the proportion of the j-secondary evaluation indicators of the ith logistics park in all logistics parks, expressed as:

[0032]

[0033] Calculate the redundancy d of the information entropy value of the jth secondary evaluation index j :

[0034] d j =1-e j

[0035] Calculate the objective weight coefficient w of the jth secondary evaluation index 客j :

[0036]

[0037] Furthermore, the step 5 is specifically as follows:

[0038] Step 5.1: Construct the weighted standard matrix Z = (z ij ) m×n , where z ij is the element in the i-th row and j-th column of the matrix Z, z ij The expression is:

[0039] z ij =y ij × j

[0040] Step 5.2: Determine an ideal solution, wherein the ideal solution includes a positive ideal solution and a negative ideal solution. in represents the positive ideal solution value of the j-th secondary evaluation index, Where max(.) is the maximum value function; the negative ideal solution in represents the negative ideal solution value of the j-th secondary evaluation index, Among them, min(.) is the minimum value function;

[0041] Step 5.3: Use the TOPSIS method to calculate the Euclidean distance between the secondary evaluation index of each logistics park and the ideal solution:

[0042] The jth secondary evaluation index and Euclidean distance for:

[0043]

[0044] The jth secondary evaluation index and Euclidean distance for:

[0045]

[0046] Step 5.4: Calculate the grey correlation coefficient of the i-th logistics park based on the positive ideal solution for the j-th index And calculate the grey correlation coefficient of the i-th logistics park based on the negative ideal solution for the j-th index

[0047]

[0048]

[0049] in represents the maximum value of the jth secondary evaluation index among all logistics parks in the matrix z, It represents the minimum value of the jth secondary evaluation index among all logistics parks in the matrix z; is the minimum difference of two positive ideal solutions, It is the maximum difference between the two levels of positive ideal solutions; is the minimum difference between the two negative ideal solutions, is the maximum difference between the two levels of the negative ideal solution, and ρ is the resolution coefficient;

[0050] Step 5.5: Calculate the positive ideal solution V of the i-th logistics park + Grey relational degree And calculate the negative ideal solution V of the i-th logistics park + Grey relational degree

[0051]

[0052]

[0053] Step 5.5: Use the following formula to To standardize:

[0054]

[0055] in, or or

[0056] Step 5.6: Calculate the closeness of the i-th logistics park to the positive ideal solution Calculate the proximity between the i-th logistics park and the negative ideal solution

[0057]

[0058]

[0059] Among them, a is the preset preference coefficient;

[0060] Step 5.7: Establish and calculate the relative proximity C of the i-th logistics park i :

[0061]

[0062] Step 5.8: If C i ∈[0,A1] indicates that the intelligence level of the i-th logistics park is poor, C i∈(A1,A2] indicates that the intelligence level of the i-th logistics park is medium, C i ∈(A2,A3] indicates that the intelligence level of the i-th logistics park is good, C i ∈(A3,1] indicates that the intelligence level of the i-th logistics park is excellent; A1, A2, and A3 are all preset thresholds.

[0063] Beneficial effects: The present invention proposes an evaluation system that is in line with the degree of intelligent construction of logistics parks, which is targeted and feasible. It improves the traditional expert scoring method and adopts a combination of the order relationship method and the entropy weight method to obtain the weights of the evaluation indicators, avoiding the subjectivity of experts and the objectivity of actual data, and can conduct a more comprehensive, scientific and objective evaluation of smart logistics parks. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is the screening idea for the evaluation system of the intelligent level of logistics parks.

[0065] Figure 2 It is a flow chart of the method of the present invention.

[0066] Figure 3 It is the evaluation index of the intelligence level of the logistics park proposed in the present invention. DETAILED DESCRIPTION

[0067] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0068] Establish as Figure 1 Evaluation index screening ideas shown

[0069] The screening of evaluation indicators is divided into four steps:

[0070] Step ①: Based on the development path, determine the six dimensions of strategic planning, infrastructure, logistics operations, public services, and sustainable development as first-level indicators and establish a basic framework.

[0071] Step ②: Based on literature, industry reports, etc., select appropriate secondary indicators as the selection indicator set for the smart logistics park evaluation system.

[0072] Step ③: Summarize and classify the relevant influencing factors, and select the set of selected indicators into a preliminary, streamlined and effective smart logistics park evaluation system.

[0073] Step ④: Questionnaires were distributed to experts, scholars and logistics parks, and corresponding evaluation indicators were extracted based on the concentration, dispersion and coefficient of variation of each indicator fed back by the survey subjects.

[0074] like Figure 2As shown, the method flow of the present invention is as follows Figure 2 As shown:

[0075] 1. Establishment of evaluation index system

[0076] After screening, the present invention selects three primary indicators to evaluate the intelligence level of logistics parks from three aspects: enterprise logistics A1, park management A2, and public service A3. The specific indicator system is as follows: Figure 3 As shown. The corresponding secondary indicators under enterprise logistics A1 include the application level of enterprise logistics technology in the park B1, standardized equipment application B2, smart warehousing technology application B3, smart distribution technology application B4, smart logistics enterprise rating B5, network platform utilization B6, industry-university-research cooperation B7; the corresponding secondary indicators under park management A2 include the planning of smart logistics parks under management B8, information technology investment B9, the functions of the park vehicle management system B10, the proportion of research and development personnel B11, and individual characteristics and innovation B12; the corresponding secondary indicators under public services A3 include the functions of the park property management system B13, the functions of the public platform B14, the interconnection of the public platform B15, the standard data format B16, and the operation of information visualization B17, a total of 17 indicators combining quantitative and qualitative methods.

[0077] 2. Collection of indicator-related data

[0078] The present invention uses logistics parks L1, L2, L3, L4, and L5 as samples for example verification. The relevant data of sample logistics park indicators are collected, and the qualitative indicators are scored by experts (1-10 points, the qualitative indicators are B8, B12, and B17;), and the values ​​of other secondary indicators are the number of corresponding equipment or the corresponding investment funds under the secondary indicator; the equipment corresponding to each secondary indicator is shown in Table 1 below:

[0079] Table 1:

[0080]

[0081]

[0082] The secondary evaluation index values ​​of the sample logistics parks are summarized in Table 2.

[0083] Table 2

[0084]

[0085]

[0086] 3. Normalization of evaluation matrix

[0087] Construct a decision matrix X = (x ij) m×n , m represents the logistics park to be evaluated, n represents the number of secondary evaluation indicators, x ij It represents the specific values ​​of the j secondary evaluation indicators of the ith logistics park, where i = 1, 2, ..., m; j = 1, 2, ..., n.

[0088] Use the following formula for x ij Perform normalization:

[0089]

[0090] Among them, x j represents the jth secondary evaluation index, max x j is the maximum value of the jth secondary evaluation index among m parks, mianx j is the minimum value of the jth secondary evaluation index among the m parks. ij Construct the standard decision matrix Y, y ij is the element in the i-th row and j-th column of the standard decision matrix Y; Y = (y ij ) m×n .

[0091] 4. Determination of evaluation index weights

[0092] 4.1 Subjective weighting method based on order relationship method

[0093] Step ①: Determine the order of each secondary evaluation indicator. According to the importance x' of the jth secondary evaluation indicator to the primary evaluation indicator corresponding to the secondary evaluation indicator j , sort the n secondary evaluation indicators from large to small according to their importance; obtain the secondary evaluation indicator sequence; assign a weight coefficient to each secondary evaluation indicator;

[0094] Step ②: Determine the relative importance of each indicator: Calculate the importance between two adjacent secondary evaluation indicators in the secondary evaluation indicator sequence:

[0095]

[0096] where w k Represents the secondary evaluation index x k The weight coefficient of x k The weight coefficients of the weight coefficients are preset according to the following table 3:

[0097] Table 3

[0098] <![CDATA[r k ]]> illustrate 1.0 <![CDATA[Indicator x k-1 is equally important as Indicator x k equally important]]> 1.2 <![CDATA[Indicator x k-1 Compared with indicator x k is slightly more important]]> 1.4 <![CDATA[Indicator x k-1 is more important than Indicator x k significantly]]> 1.6 <![CDATA[Indicator x k-1 Compared to Indicator x k Strongly important]]> 1.8 <![CDATA[Indicator x k-1 Compared with indicator x k Extremely important]]>

[0099] Step ③: Calculation of weight coefficient. The sum of all indicator weights is equal to 1, and the last indicator w is obtained by combining relevant calculations. n The weight w主n for:

[0100]

[0101] The weights of other indicators are calculated as follows:

[0102] w 主k-1 =r k w k ,(k=n,n-1,n-2,…,3,2)

[0103] 4.2 Objective weighting method based on entropy weight method

[0104] Step ①: Calculate the information entropy e of the secondary evaluation index j . Let p ij is the proportion of the j-th secondary indicator of the ith logistics park in all logistics parks, then it is as follows

[0105]

[0106]

[0107] Among them, when p ij =0, ln(p ij ) is meaningless in mathematics and inconvenient to calculate, so we assign ln(p ij )=0.

[0108] Step ②: Calculate the information entropy redundancy d of the evaluation index j The larger the entropy value, the greater the disorder and the smaller the information contained; conversely, the greater the weight.

[0109] d j =1-e j ,j=1,2,…,n

[0110] Step ③: Calculate the weight w of the evaluation index 客j .

[0111]

[0112] 4.3 Combined weighting method based on linear weighting method

[0113] The linear weighted combination weighting method is used to assign the comprehensive weight w of the j-th secondary evaluation index. j Assume that α and β represent the decision maker’s preference for subjective weighting and objective weighting, respectively, and α+β=1, then w j The calculation is as follows:

[0114] w j =α×w 主j +β×w 客j

[0115] The ordinal relationship method is determined based on the experience of experts and is more authentic and representative. Therefore, α is assigned a value of 0.6 and β is assigned a value of 0.4 to obtain the weights of the evaluation indicators.

[0116] 5. Calculation of evaluation results based on the GRA-TOPSIS model

[0117] Step ①: Construct a weighted standard matrix. The comprehensive weight of each indicator and the dimensionless decision matrix Y = (y ij ) m×n Multiply them together to get the weighted decision matrix Z = (z ij ) m×n :

[0118] z ij =w×y ij

[0119] z ij is the element in the i-th row and j-th column of the matrix Z;

[0120] Step ②: Determine the positive ideal solution V + With negative ideal solution V - .

[0121]

[0122]

[0123] in represents the positive ideal solution value of the j-th secondary evaluation index, Among them, max(.) is the maximum value function, represents the negative ideal solution value of the j-th secondary evaluation index, Where min(.) is the minimum value function.

[0124] Step ③: Use the TOPSIS method to calculate the Euclidean distance between the indicators of each logistics park and the ideal solution.

[0125]

[0126]

[0127] is the jth secondary evaluation index and The Euclidean distance of is the jth secondary evaluation index and The Euclidean distance of .

[0128] Step ④: Calculate the relationship between each logistics park and the positive ideal solution V + With negative ideal solution V- Grey correlation coefficient δ ij . With the weighted normalization matrix Z = (z ij ) m×n Based on, calculate the i-th logistics park and the optimal solution (positive ideal solution V + ) and the worst solution (negative ideal solution V - ) is the grey relational coefficient of the jth index. The resolution coefficient ρ takes values ​​in [0,1] and is generally 0.5.

[0129]

[0130]

[0131] represents the maximum value of the jth secondary evaluation index among all logistics parks in the matrix z, It represents the minimum value of the jth secondary evaluation index among all logistics parks in the matrix z; is the minimum difference of two positive ideal solutions, It is the maximum difference between the two levels of positive ideal solutions; is the minimum difference between the two negative ideal solutions, It is the maximum difference between the two levels of negative ideal solution;

[0132] Step ⑤: Calculate the positive ideal solution V of the i-th logistics park + Grey relational degree And calculate the negative ideal solution V of the i-th logistics park + Grey relational degree

[0133]

[0134]

[0135] Step ⑥: Calculate the comprehensive closeness. Standardize the Euclidean distance and grey correlation degree:

[0136]

[0137] in, or or when hour, when hour, when hour, when hour,

[0138] Calculate the comprehensive value of the standardized Euclidean distance and the grey correlation degree. Assume that the preference coefficient a reflects the decision maker's preference for distance and similarity, and generally a=0.5. and The larger the value of is, the farther the evaluation object is from the negative ideal solution and the greater the correlation with the positive ideal solution. The evaluation object is better and closer to the ideal solution. and The larger the value of , the farther the evaluation object is from the positive ideal solution and the greater the correlation with the negative ideal solution, and the more the evaluation object deviates from the ideal solution.

[0139] The closeness between the evaluated object (logistics park) and the positive ideal solution:

[0140]

[0141] The closeness of the evaluated object to the negative ideal solution:

[0142]

[0143] Step 7: Calculate relative closeness.

[0144]

[0145] If C i ∈[0,A1] indicates that the intelligence level of the i-th logistics park is poor, C i ∈(A1,A2] indicates that the intelligence level of the i-th logistics park is medium, C i ∈(A2,A3] indicates that the intelligence level of the i-th logistics park is good, C i ∈(A3,1] indicates that the intelligence level of the i-th logistics park is excellent. A1, A2, and A3 are all preset thresholds; in this embodiment, A1 = 0.3, A2 = 0.6, and A3 = 0.8;

[0146] After normalizing the initial indicator data of the sample logistics parks, determining the weights of the evaluation indicators, and calculating the evaluation results based on the GRA-TOPSIS model, the relative closeness of the five sample parks is calculated as follows:

[0147]

[0148]

[0149]

[0150]

[0151]

[0152] 6. Ranking of logistics park intelligence levels

[0153] According to the calculation results of relative closeness, C4>C3>C1>C5>C2, so the intelligence level of logistics parks is ranked as L4>L3>L1>L5>L2.

[0154] The above contents are merely examples and explanations of the concept of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A method for evaluating the intelligent level of logistics parks based on GRA-TOPSIS, characterized in that: The specific steps include: Step 1: Establish evaluation indicators; the evaluation indicators include primary evaluation indicators, each primary evaluation indicator is further provided with a number of secondary evaluation indicators, and there are n secondary evaluation indicators in total; Step 2: Assign values ​​to each secondary evaluation indicator; Step 3: Use the range transformation method to calculate the value x of the j-th secondary evaluation index of the i-th logistics park. ij Perform normalization to obtain the normalized value y ij , the standard decision matrix Y is constructed by the normalized secondary evaluation indicators, i = 1, 2, ..., m, j = 1, 2, ..., n, m is the total number of parks, y ij is the element in the i-th row and j-th column of the standard decision matrix Y; Step 4: Use the combined weighting method combining the order relationship method and the entropy weight method to calculate the comprehensive weight w of the secondary evaluation index; Step 5: Establish an evaluation model based on the calculated comprehensive weights of the secondary evaluation indicators and the standard decision matrix, and use the evaluation model to evaluate the intelligence level of the logistics park; The step 5 is specifically as follows: Step 5.1: Construct the weighted standard matrix Z = (z ij ) m×n , where z ij is the element in the i-th row and j-th column of the matrix Z, z ij The expression is: z ij =y ij ×w j Step 5.2: Determine an ideal solution, wherein the ideal solution includes a positive ideal solution and a negative ideal solution. in represents the positive ideal solution value of the j-th secondary evaluation index, Where max(.) is the maximum value function; the negative ideal solution in represents the negative ideal solution value of the j-th secondary evaluation index, Among them, min(.) is the minimum value function; Step 5.3: Use the TOPSIS method to calculate the Euclidean distance between the secondary evaluation index of each logistics park and the ideal solution: The jth secondary evaluation index and Euclidean distance for: The jth secondary evaluation index and Euclidean distance for: Step 5.4: Calculate the grey correlation coefficient of the i-th logistics park based on the positive ideal solution for the j-th secondary evaluation index And calculate the grey correlation coefficient of the i-th logistics park based on the negative ideal solution for the j-th secondary evaluation index in represents the maximum value of the jth secondary evaluation index among all logistics parks in the matrix z, It represents the minimum value of the jth secondary evaluation index among all logistics parks in the matrix z; is the minimum difference of two positive ideal solutions, It is the maximum difference between the two levels of positive ideal solutions; is the minimum difference between the two negative ideal solutions, is the maximum difference between the two levels of the negative ideal solution, and ρ is the resolution coefficient; Step 5.5: Calculate the positive ideal solution V of the i-th logistics park + Grey relational degree And calculate the negative ideal solution V of the i-th logistics park + Grey relational degree Step 5.5: Use the following formula to To standardize: in, or or Step 5.6: Calculate the closeness of the i-th logistics park to the positive ideal solution Calculate the proximity between the i-th logistics park and the negative ideal solution Among them, a is the preset preference coefficient; Step 5.7: Establish and calculate the relative proximity C of the i-th logistics park i : Step 5.8: If C i ∈[0, A1] indicates that the intelligence level of the i-th logistics park is poor. If C i ∈(A1, A2] indicates that the intelligence level of the i-th logistics park is medium. If C i ∈(A2, A3] indicates that the intelligence level of the i-th logistics park is good. If C i ∈(A3, 1] indicates that the intelligence level of the i-th logistics park is excellent; A1, A2, and A3 are all preset thresholds.

2. According to claim 1, a method for evaluating the intelligent level of a logistics park based on GRA-TOPSIS is characterized in that: The first-level indicators in step 1 include enterprise logistics A1, park management A2, and public services A3; The corresponding secondary indicators under enterprise logistics A1 include the application level of logistics technology of enterprises in the park under enterprise logistics B1, application of standardized equipment B2, application of smart warehousing technology B3, application of smart distribution technology B4, evaluation of smart logistics enterprises B5, utilization of network platform B6, and cooperation between industry, academia and research B7; The corresponding secondary indicators under park management A2 include smart logistics park planning under management B8, information technology investment B9, realized functions of the park vehicle management system B10, proportion of research and development personnel B11, and individual characteristics and innovation B12; The corresponding secondary indicators under public service A3 include the functions realized by the park property management system under public services B13, the functions realized by the public platform B14, the interconnection status of the public platform B15, the standard data format B16, and the information visualization operation status B17.

3. According to claim 1, a method for evaluating the intelligent level of a logistics park based on GRA-TOPSIS is characterized in that: In step 3, the following formula is used to calculate x ij Perform normalization: Among them, x j represents the jth secondary evaluation index, max x j is the maximum value of the jth secondary evaluation index among m parks, minx j It is the minimum value of the jth secondary evaluation index among the m parks.

4. According to claim 1, a method for evaluating the intelligent level of a logistics park based on GRA-TOPSIS is characterized in that: In step 4, the order relationship method is used to calculate the subjective weight coefficient w of the j-th secondary evaluation index. 主j , the entropy weight method is used to calculate the objective weight coefficient w of the jth secondary evaluation index 客j ; According to the calculated subjective weight coefficient and objective weight coefficient, calculate the comprehensive weight w of the j-th secondary evaluation index j =α×w 主j +β×w 客j ; where α and β are coefficients.

5. According to claim 4, a method for evaluating the intelligent level of a logistics park based on GRA-TOPSIS is characterized in that: The subjective weight coefficient of the secondary evaluation index calculated by the order relationship method is specifically: According to the jth secondary evaluation index, the importance x' of the first-level evaluation index corresponding to the second-level evaluation index j , sort the n secondary evaluation indicators from high to low according to their importance; Obtain a secondary evaluation index sequence; assign a weight coefficient to each secondary evaluation index; Calculate the importance between two adjacent secondary evaluation indicators in the secondary evaluation indicator sequence: w k-1 is the weight coefficient of the k-1th secondary rating evaluation indicator in the secondary evaluation indicator sequence; w k is the weight coefficient of the kth secondary evaluation index in the secondary evaluation index sequence, k = 2, ... n; The subjective weight coefficient w of the nth secondary evaluation index is calculated according to the following formula: 主n : The subjective weight coefficients of the 1st to n-1th secondary price indicators are calculated according to the following formula: w 主k-1 =r k w k 。 6. According to claim 4, a method for evaluating the intelligent level of a logistics park based on GRA-TOPSIS is characterized in that: The objective weight coefficient of the secondary evaluation index calculated by the entropy weight method is specifically: Calculate the information entropy e of the secondary evaluation index j : where p ij is the proportion of the j-secondary evaluation indicators of the ith logistics park in all logistics parks, expressed as: Calculate the redundancy d of the information entropy value of the jth secondary evaluation index j : d j =1-e j Calculate the objective weight coefficient w of the jth secondary evaluation index 客j :

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