Logistics park digital level evaluation method based on Delphi-entropy weight-TOPSIS

Abstract

The invention provides a digital level evaluation method for a logistics park based on Delphi-entropy weight-TOPSIS (Total Operational Particle Swarm Identity System). An evaluation index system of the digital level of the logistics park is established and comprises four first-level indexes including digital technology application, digital input situation, digital platform construction and digital management service and 16 second-level indexes. And calculating the comprehensive weight of the evaluation index by combining a Delphi method and an entropy weight method, establishing a logistics park digital level evaluation model based on combined weighting and TOPSIS, and calculating to obtain an evaluation sorting result of the digital level of the logistics park. According to the method, expert experience and objective actual data are fully combined, a method can be provided for pertinently evaluating the digital level of the logistics park in each region, and the method meets the demand for pilot application of the intelligent logistics park of the government.

Description

Technical Field

[0001] The present invention relates to the technical field of digital construction and evaluation of logistics parks, and in particular to a method for evaluating the digital level of logistics parks based on Delphi-entropy weight-TOPSIS. Background Art

[0002] With the continuous improvement of information infrastructure and the continuous advancement of technological innovation, the digital economy has risen rapidly. New logistics demands such as e-commerce express delivery and agile manufacturing derived from the digital economy require logistics parks to accelerate the process of digital transformation. my country attaches great importance to the digital transformation of logistics parks. In order to cultivate new quality productivity of logistics and promote the improvement of quality, cost reduction and efficiency of the logistics industry, many policy documents have been issued to promote the digitalization of logistics parks.

[0003] Driven by a series of national policies, the overall digitalization process of my country's logistics parks has accelerated. Many provinces and regions have begun to implement demonstration pilot projects for smart logistics parks, and their selection criteria and specific indicators cover a wide range of dimensions of logistics park operation performance. However, the existing evaluation indicators are too broad and fail to accurately focus on the actual results of the digital transformation and upgrading of the park. Therefore, it is necessary to establish an evaluation system for the digitalization level of logistics parks, to more deeply and specifically evaluate the effectiveness of logistics parks in digital transformation, and then provide a reference for relevant government departments to formulate more accurate and scientific logistics park development strategies and improve the digitalization level of the logistics industry. Summary of the invention

[0004] Technical problem: The purpose of the present invention is to provide an evaluation index system for the digitalization level of logistics parks. Starting from the perspectives of digital technology application, digital investment, digital platform construction, digital management services, etc., the index-related data are collected, and the evaluation indicators are combined and weighted by combining the Delphi method and the entropy weight method. A logistics park digitalization level evaluation model based on Delphi-entropy weight-TOPSIS is established to evaluate and rank the digitalization level of logistics parks.

[0005] Technical solution:

[0006] A method for evaluating the digital level of a logistics park based on Delphi-entropy weight-TOPSIS includes the following steps:

[0007] S1. Establish an evaluation index system for the digital level of logistics parks, including 4 first-level indicators and 16 second-level indicators;

[0008] S2, collect relevant data of indicators to construct indicator matrix D, where qualitative indicators adopt the arithmetic mean of expert scores;

[0009] S3. Use the Delphi method to subjectively weight the evaluation index system of the digital level of the logistics park, and use the Kendall W coefficient for consistency test. If the subjective weight e is calculated, j , if not passed, consult the expert again;

[0010] S4. Use the entropy weight method to objectively weight the evaluation index system of the digital level of the logistics park and obtain the objective weight h j ;

[0011] S5. Use the linear weighted combination weighting method to calculate the comprehensive weight p j ;

[0012] S6. Use the TOPSIS method to evaluate and rank the digitalization level of logistics parks according to comprehensive weights.

[0013] As a further preferred solution, the evaluation index system of the logistics park intelligence level in step S1 includes:

[0014] (1) Application of digital technology A1

[0015] Standardized pallet application B1: This indicator counts the proportion of enterprises that use standardized pallets in the park, reflecting the level of standardized equipment application in the park. Statistical unit: %.

[0016] Application of smart warehousing technology B2: This indicator counts the number of smart warehousing technologies applied in the park, including automated three-dimensional warehouses, shared pallets, unmanned forklifts, automated stackers, automated guided vehicles (AGVs), barcodes, multi-layer shuttle systems, digital picking systems, warehouse management systems, real-time warehouse monitoring, product quality traceability, electronic data interchange (EDI), and radio frequency identification (RFID). The more types of technologies applied, the higher the level of digitalization of the park's warehousing links. Statistical unit: type.

[0017] Application of smart transportation technology B3: This indicator counts the number of smart transportation technologies applied in the park, including unmanned delivery, autonomous driving, on-the-way tracking, intelligent scheduling, automatic fee settlement, driver monitoring, and route optimization. The more types of technologies applied, the higher the level of digitalization of the park's warehousing links. Statistical unit: type. Statistical unit: type.

[0018] Enterprise network platform application B4: This indicator counts the number of enterprises in the park that have built platforms with external online transaction functions, reflecting the digitalization level of logistics enterprises in the park. Statistical unit: company.

[0019] Industry-university-research cooperation B5: This indicator counts the number of enterprises in the park that have carried out industry-university-research cooperation, reflecting the digital innovation level of the park. Statistical unit: company.

[0020] (2) Digital investment A2

[0021] Digital capital investment B6: This indicator counts the total investment in digital construction of logistics parks. Statistical unit: 10,000 yuan.

[0022] R&D personnel ratio B7: R&D personnel ratio refers to the ratio of research and development personnel. This indicator counts the proportion of personnel engaged in digital project research and development in the logistics park management team, that is, the ratio of these personnel to the total number of management personnel. Statistical unit: %.

[0023] Park digital construction project B8: This indicator uses an expert scoring method to comprehensively evaluate the implementation results of completed and ongoing digital construction or transformation projects in the park. The statistical results are presented in the form of scores to quantify the progress and results of the park's digital construction. Statistical unit: points.

[0024] Digital development characteristics and innovation B9: This indicator adopts the expert scoring method. Given that each park serves different objects and is committed to exploring a digital development model for logistics parks that matches its own positioning, this indicator focuses on reflecting the park's innovation capabilities and development characteristics. Statistical unit: points.

[0025] (3) Digital platform construction A3

[0026] Park public information platform B10: This indicator counts the number of functions that have been realized by the logistics park public information platform, including information release, data exchange, transportation capacity trading, payment settlement, customs declaration and inspection, port business, solution design, logistics insurance, cargo tracking, warehouse receipt pledge, guarantee business, credit management, and big data analysis. The more types of functions realized by the platform, the higher the degree of perfection of the logistics park digital platform construction. Statistical unit: type.

[0027] Platform interconnection and interoperability B11: This indicator counts the number of park platforms connected with provincial platforms, municipal platforms, other parks and enterprises, reflecting the interconnection and interoperability of the platforms. Statistical unit: individual.

[0028] Data exchange standard B12: This indicator counts whether the park has adopted a unified data exchange standard, thus reflecting the degree of standardization of the park's digitalization. If the park applies the standard, it is assigned a value of 1, otherwise it is assigned a value of 0.

[0029] (4) Digital Management Service A4

[0030] Park vehicle management system B13: This indicator counts the number of functions of the logistics park vehicle management system, including smart access control, smart weighing, cargo inspection, dangerous goods detection equipment, container number recognition, license plate recognition, path navigation within the park, smart parking, and vehicle positioning. The more system functions there are, the more perfect the logistics park vehicle management system is. Statistical unit: type.

[0031] Park property management system B14: This indicator counts the number of functions of the logistics park property management system, including park display, information release, investment management, energy consumption monitoring, park monitoring, one-card system, conference system, business payment, security management, and repair processing. The more system functions there are, the more perfect the logistics park property management system is. Statistical unit: type.

[0032] Park enterprise management system B15: This indicator counts the number of functions of the logistics park property management system, including enterprise operation data submission, industrial and commercial registration, qualification certification, park taxation, market supervision, production safety, quality supervision, and industry standard implementation. The more system functions there are, the more perfect the logistics park enterprise management system is. Statistical unit: type.

[0033] Visual operation status B16: The visual dispatch center of the logistics park has realized the collection, classification analysis, intuitive display and decision support functions of various types of data in the park by integrating and applying cutting-edge technologies such as the Internet of Things and big data analysis, which concentrates on the development level of digitalization and intelligence of the logistics park. This indicator uses expert scoring to qualitatively evaluate the construction results and application status of the visual dispatch center of the logistics park. Statistical unit: points.

[0034] As a further preferred solution, assuming that there are m evaluation parks and n evaluation indicators in step S2, the value of the jth indicator of park i can be expressed as d ij (i=1,2,…,m;j=1,2,…,n), the indicator matrix D=(d ij ) m×n ; Among them, qualitative indicators include the park’s digital construction projects, digital development characteristics and innovations, and visual operation conditions. The arithmetic mean of the experts’ scores is used to assign values, ranging from 1 to 3 points. The larger the score, the better the logistics park’s performance in that indicator.

[0035] As a further preferred solution, in step S3, the Delphi method is used to subjectively weight the evaluation index system of the digital level of the logistics park. The specific steps are as follows:

[0036] (1) Consult an expert

[0037] Assuming that the number of experts is m, the expert set can be expressed as Z = {z1,z2,…,z m}, z i represents the i-th expert; assuming that the number of indicators is n, the Likert five-level scoring method is adopted, and expert z i The weighted opinion is A i =(a i,1 ,a i,2 ,…,a i,n ).

[0038] (2) Calculation of expert opinion consistency

[0039] The consistency of the expert consultation results was determined by calculating the Kendall W coordination coefficient. The weight vector is A ki =(a ki,1 ,a ki,2 ,…,a ki,n ), construct the corresponding sorting vector as:

[0040] B ki =(b ki,1 ,b ki,2 ,…,b ki,n )

[0041] Where b ki,j (1≤j≤n) is the corresponding a ki,j In A ki The specific value method is: when a ki,j A ki When all dimensional components in are minimum, let b ki,j =1; when a ki,j A ki When all the dimensions in are of the smallest value, let b ki,j =2; and so on, we can get the corresponding sort number vector value.

[0042] Kendall W coordination coefficient calculation formula is:

[0043]

[0044] (3) Determine whether further consultation is needed

[0045] When n<7, the conclusion can be drawn based on the Kendall coordination coefficient W value table; when n>7, the large sample approximation method is used to calculate the test statistic χ 2 , and according to the degree of freedom υ=n-1, compare χ 2 Critical value table. Test statistic χ 2 The calculation formula is:

[0046] χ2 =m×(n-1)×W

[0047] If W or χ 2 If it is greater than the critical value L in the table, the significance level is reached and the next step of calculation is performed; otherwise, the expert is consulted again until W or χ 2 Until it is greater than L.

[0048] (4) Calculate the normalized subjective weight E

[0049] First, according to the expert's score on the indicator, calculate the score weight r of the jth indicator j :

[0050]

[0051] Then, according to the importance ranking of the indicators by the experts, the ranking weight t of the jth indicator is calculated. j Assume that expert i believes that the importance of indicator j among all n indicators is ranked as i,j , then 1≤rank i,j ≤n, the smaller the value, the more important it is. At this time, the ranking weight t of the jth indicator can be calculated j :

[0052]

[0053] Finally, the normalized subjective weight e of the jth indicator is calculated j :

[0054]

[0055] α and β are optional parameters. If the scoring and ranking weights are considered to have equal value, α = β = 1 can be used. Based on the expert consultation, the normalized subjective weight value of each indicator is calculated as E = {e1, e2, …, e n}.

[0056] As a further preferred solution, in step S4, the entropy weight method is used to objectively weight the evaluation index system of the digital level of the logistics park. The specific steps are as follows:

[0057] (1) Dimensionless processing of indicator matrix

[0058] Since the established evaluation index system for the digital level of logistics parks only involves positive indicators, the standardized operation is:

[0059]

[0060] The dimensionless normalized matrix after processing is X = (x ij ) m×n .

[0061] (2) Calculate the objective weight of indicators

[0062] According to the definition of entropy, calculate the entropy value f of the jth indicator j for:

[0063]

[0064] In the formula, q ij represents the contribution of the i-th solution under the j-th indicator, then the weight of the j-th indicator is:

[0065]

[0066] The index weight value H = {h1,h2,…,h n}, where h i Represents the objective weight of the i-th indicator.

[0067] As a further preferred solution, in step S5, a linear weighted combined weighting method is used to calculate the comprehensive weight, and the specific steps are as follows:

[0068] Assume that α and β represent the decision maker's preference for subjective and objective weighting, respectively, and α+β=1. j With objective weight h j Perform linear weighted operation to obtain the comprehensive weight p of the evaluation index of the Delphi-entropy weight combination empowerment method j :

[0069] p j =αe j +βh j

[0070] The corresponding weight matrix P is:

[0071] P=diag(p1,p2,…,p n )

[0072] As a further preferred solution, in step S6, the TOPSIS method is used to evaluate and rank the digitalization levels of the logistics parks according to the comprehensive weights. The specific steps are as follows:

[0073] (1) Calculate the weighted normalized decision matrix V

[0074]

[0075] (2) Determine the positive ideal solution G + , negative ideal solution G -

[0076]

[0077] (3) Calculate the Euclidean distance between the evaluation index of each logistics park and the positive and negative ideal solutions

[0078] Optimal distance

[0079] Worst distance

[0080] (4) Calculate the closeness

[0081]

[0082] Each logistics park is ranked according to its closeness to the positive ideal solution. The larger the value, the closer the logistics park is to the positive ideal solution and the farther it is from the negative ideal solution, and the higher the level of digitization.

[0083] Beneficial effects: The present invention responds to the government's plan for a pilot project for smart logistics parks, and proposes an evaluation system that meets the digitalization level of logistics parks, which is targeted and feasible. It improves the traditional expert scoring method and combines it with the entropy weight method to obtain the weights of evaluation indicators, thus avoiding the subjectivity of experts and the objectivity of actual data, and can conduct a more comprehensive and scientific evaluation of smart logistics parks. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1 It is the specific process for evaluating the digitalization level of logistics parks. DETAILED DESCRIPTION

[0085] In order to make the purpose, technical solution and advantages of the present invention more obvious and easy to understand, the technical solution protected by the present invention will be clearly and completely described below using specific embodiments and drawings. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of them. Based on the embodiments in this patent, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this patent.

[0086] 1. Establish an evaluation index system for the digital level of logistics parks

[0087] (1) Evaluation index screening ideas

[0088] The screening of evaluation indicators is divided into three steps:

[0089] First, determine the primary indicators of the evaluation index system and establish the basic framework;

[0090] Second, based on literature materials, policy documents, industry standards and reports, extract indicators that can be used for reference, summarize and classify them, and establish an initial set of evaluation indicators;

[0091] Third, according to the establishment principles of the indicator system, the initial set of evaluation indicators is screened, the evaluation indicators with high relevance are merged and modified, and the unreasonable and too subjective evaluation indicators are deleted;

[0092] (2) Establishment of evaluation index system

[0093] After screening, the present invention selects 4 primary indicators to evaluate the digitalization level of logistics parks from four aspects: digital technology application, digital investment, digital platform construction, and digital management services. The digital technology application includes standardized pallet application, smart warehousing technology application, smart transportation technology application, enterprise network platform application, and industry-university-research cooperation; the digital investment includes digital capital investment, R&D personnel ratio, park digital construction projects, and digital development characteristics and innovation; the digital platform construction includes the park public information platform, platform interconnection, and data exchange standards; the digital management service includes the park vehicle management system, park property management system, park enterprise management system, and visualized operation. Combining quantitative and qualitative indicators, a total of 16 indicators are included. The specific indicator system is shown in Table 1.

[0094] Table 1 Evaluation index system of digital level of logistics park

[0095]

[0096] 2. Collection of indicator-related data

[0097] The present invention uses 12 logistics parks as samples for example verification. The sample parks are named from A to L, and the index-related data are collected. The qualitative indicators are assigned the arithmetic mean of the expert scores, as shown in Table 2. The data summary of the sample logistics parks is shown in Table 3.

[0098] Table 2 Qualitative index scoring results of sample logistics parks

[0099]

[0100] Table 3 Sample logistics park index data table

[0101]

[0102]

[0103] 3. Calculate subjective weight using the Delphi method

[0104] (1) Questionnaires issued to experts

[0105] In this case, the number of consulting experts is 6, and the Likert five-level scoring method is used to assign weights to each indicator in the evaluation index system, as shown in Tables 4 and 5.

[0106] Table 4 Summary of expert weighting scores

[0107] index Expert 1 Expert 2 Expert 3 Expert 4 Expert 5 Expert 6 A1 5 5 5 4 5 4 A2 5 5 3 5 5 5 A3 5 5 4 5 5 5 A4 4 5 3 3 5 3 B1 3 4 3 2 4 3 B2 5 5 5 4 5 4 B3 5 5 3 5 5 5 B4 5 5 4 5 5 5 B5 4 5 3 3 5 3 B6 3 4 3 2 4 3 B7 5 5 5 5 5 3 B8 4 5 3 3 4 4 B9 5 5 5 3 5 5 B10 4 5 3 3 3 3 B11 4 5 5 5 5 5 B12 5 5 4 3 5 4 B13 5 5 4 4 5 5 B14 4 5 4 4 4 4 B15 3 4 5 3 3 5 B16 3 5 4 4 4 5

[0108] Table 5: Ranking of first-level indicators

[0109]

[0110]

[0111] (2) Calculation of expert opinion consistency

[0112] SPSS software was used to calculate the Kendall W coordination coefficient to check the consistency of expert opinions, and the asymptotic significance P value was 0.000 < 0.05, indicating that the consistency of this case was statistically significant. The calculation showed that the W coefficient value was 0.522. It is generally believed that W between 0.4 and 0.6 indicates a moderate degree of consistency.

[0113] (3) Determine whether further consultation is needed

[0114] In this case, n>7, calculate the test statistic χ 2 The value of is 62.64, which is larger than the χ value when the degree of freedom is 15. 2 The critical value is 24.96, which means that the weight scores of the six experts are somewhat consistent and the data is credible, so the next step of calculation is carried out.

[0115] (4) Calculate the normalized subjective weight E

[0116] First, according to the expert's score on the indicator, calculate the score weight r of the jth indicator j :

[0117]

[0118] Calculate the ranking weight t of the jth indicator j :

[0119]

[0120] Finally, the normalized subjective weight e of the jth indicator is calculated j :

[0121]

[0122] In this case, α = β = 1. Based on the indicator weight scoring and ranking of expert consultation, the normalized subjective weight values ​​of each indicator are calculated as shown in Table 6.

[0123] Table 6 Subjective weight values ​​of evaluation indicators

[0124]

[0125]

[0126] 4. Calculate objective weights using entropy weight method

[0127] (1) Dimensionless processing of indicator matrix

[0128] The processed dimensionless sample logistics park evaluation index data is shown in Table 7.

[0129] Table 7 Dimensionless sample logistics park evaluation index data

[0130]

[0131] (2) Calculate the objective weight of indicators

[0132] According to the definition of entropy, calculate the entropy value f of the jth indicator j for:

[0133]

[0134] In the formula, q ij represents the contribution of the i-th solution under the j-th indicator, then the weight of the j-th indicator is:

[0135]

[0136] The indicator weight values ​​calculated based on the entropy weight method are shown in Table 8.

[0137] Table 8 Summary of objective weights of evaluation indicators

[0138]

[0139] 5. Use the linear weighted combination weighting method to calculate the comprehensive weight

[0140] This case believes that expert experience can better reflect the reality. At the same time, combined with expert opinions, it is believed that the results of the entropy weight method only reflect the distribution of sample data, which cannot fully reflect the importance of the essence of the indicator and has little correlation with the meaning of the indicator itself, resulting in a certain discrepancy between the results of the entropy weight method and expert opinions. Therefore, it is believed that subjective weights have higher importance. When the linear weighting method is used for combined weighting, α is assigned a value of 0.75 and β is assigned a value of 0.25, and the comprehensive weights of each evaluation indicator are finally obtained, as shown in Table 9.

[0141] Table 9 Summary of comprehensive weights of evaluation indicators

[0142]

[0143]

[0144] 6. Use TOPSIS method to evaluate and rank

[0145] (1) Calculate the weighted normalized decision matrix

[0146] According to the normalized indicator data matrix in Table 7 and the comprehensive weight matrix of each indicator in Table 9, a weighted normalized evaluation indicator matrix is ​​constructed, as shown in Table 10.

[0147] Table 10 Summary of evaluation indicators of weighted normalized sample logistics parks

[0148] index A B C D E F G H I J K L B1 0.000 0.021 0.033 0.042 0.012 0.001 0.045 0.024 0.028 0.000 0.000 0.000 B2 0.064 0.055 0.058 0.064 0.004 0.007 0.029 0.050 0.039 0.086 0.040 0.000 B3 0.060 0.028 0.017 0.000 0.000 0.001 0.011 0.038 0.027 0.000 0.000 0.000 B4 0.032 0.014 0.000 0.021 0.004 0.035 0.011 0.059 0.045 0.038 0.079 0.088 B5 0.030 0.014 0.025 0.000 0.000 0.002 0.059 0.024 0.022 0.038 0.040 0.088 B6 0.000 0.021 0.000 0.000 0.000 0.002 0.000 0.045 0.031 0.000 0.000 0.000 B7 0.000 0.007 0.017 0.000 0.071 0.092 0.053 0.050 0.033 0.057 0.040 0.088 B8 0.064 0.028 0.017 0.021 0.000 0.000 0.000 0.031 0.036 0.000 0.000 0.000 B9 0.000 0.000 0.017 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 B10 0.004 0.021 0.033 0.064 0.012 0.007 0.053 0.047 0.037 0.000 0.000 0.000 B11 0.028 0.007 0.041 0.000 0.008 0.003 0.016 0.038 0.027 0.029 0.040 0.088 B12 0.047 0.014 0.041 0.021 0.000 0.004 0.024 0.059 0.045 0.048 0.040 0.088 B13 0.000 0.021 0.033 0.042 0.012 0.001 0.045 0.024 0.028 0.000 0.000 0.000 B14 0.064 0.055 0.058 0.064 0.004 0.007 0.029 0.050 0.039 0.086 0.040 0.000 B15 0.060 0.028 0.017 0.000 0.000 0.001 0.011 0.038 0.027 0.000 0.000 0.000 B16 0.032 0.014 0.000 0.021 0.004 0.035 0.011 0.059 0.045 0.038 0.079 0.088

[0149] (2) Determine the positive ideal solution G + , negative ideal solution G -

[0150] Positive ideal solution

[0151] Negative ideal solution G - ={0,0,0,0,0,0,0,0,0,0,0,0,0,0,0}

[0152] (3) Calculate the optimal and worst distances and proximity of each logistics park

[0153] The calculation results are shown in Table 11.

[0154] Table 11 Calculation results of the Delphi-entropy weight-TOPSIS model for sample logistics parks

[0155]

[0156] 6. Ranking of logistics park intelligence levels

[0157] According to the calculation results of relative closeness, the digitalization level of logistics parks is ranked as G>B>D>L>E>K>J>A>C>H>F>I.

[0158] 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 digital level of logistics parks based on Delphi-entropy weight-TOPSIS, characterized in that: The method comprises the following steps: S1. Establish an evaluation index system for the digital level of logistics parks, including 4 first-level indicators and 16 second-level indicators; S2, collect relevant data of indicators to construct indicator matrix D, where qualitative indicators adopt the arithmetic mean of expert scores; S3. Use the Delphi method to subjectively weight the evaluation index system of the digital level of the logistics park, and use the KendallW coefficient for consistency test. If the subjective weight e is calculated, j , if not passed, consult the expert again; S4. Use the entropy weight method to objectively weight the evaluation index system of the digital level of the logistics park and obtain the objective weight h j ; S5. Use the linear weighted combination weighting method to calculate the comprehensive weight p j ; S6. Use the TOPSIS method to evaluate and rank the digitalization level of logistics parks according to comprehensive weights.

2. The method for evaluating the digital level of a logistics park based on Delphi-entropy weight-TOPSIS according to claim 1 is characterized in that: The evaluation index system of the intelligent level of the logistics park in S1 includes: First-level indicators: digital technology application, digital investment, digital platform construction, digital management services Secondary indicators: Digital technology application includes standardized pallet application, smart warehousing technology application, smart transportation technology application, enterprise network platform application, and industry-university-research cooperation; In the case of digital investment, it includes digital capital investment, R&D personnel ratio, park digital construction projects, digital development characteristics and innovation; The construction of the digital platform includes the park's public information platform, platform interconnection and interoperability, and data exchange standards; The digital management service includes the park vehicle management system, park property management system, park enterprise management system, and visualized operation status, combining both quantitative and qualitative indicators for a total of 16 indicators.

3. The method for evaluating the digital level of a logistics park based on Delphi-entropy weight-TOPSIS according to claim 1 is characterized in that: Assuming that there are m evaluation parks and n evaluation indicators in S2, the value of the jth indicator of park i can be expressed as d ij (i=1,2,…,m;j=1,2,…,n), the indicator matrix D=(d ij ) m×n ; Among them, qualitative indicators include the park’s digital construction projects, digital development characteristics and innovations, and visual operation conditions. The arithmetic mean of the experts’ scores is used to assign values, ranging from 1 to 3 points. The larger the score, the better the logistics park’s performance in that indicator.

4. The method for evaluating the digital level of a logistics park based on Delphi-entropy weight-TOPSIS according to claim 1 is characterized in that: The specific steps of S3 are: (1) Consult an expert Assuming that the number of experts is m, the expert set can be expressed as Z = {z1,z2,…,z m }, z i represents the i-th expert; Assuming that the number of indicators is n, the Likert five-level scoring method is used, and expert z i The weighted opinion is A i =(a i,1 ,a i,2 ,…,a i,n ); (2) Calculation of expert opinion consistency The consistency of the expert consultation results was determined by calculating the Kendall W coordination coefficient. The weight vector is A ki =(a ki,1 ,a ki,2 ,…,a ki,n ), construct the corresponding sorting vector as: B ki =(b ki,1 ,b ki,2 ,…,b ki,n ) Where b ki,j (1≤j≤n) is the corresponding a ki,j In A ki The specific value method is: when a ki,j A ki When all dimensional components in are minimum, let b ki,j =1; when a ki,j A ki When all the dimensions in are of the smallest value, let b ki,j =2; and so on, we can get the corresponding sorting number vector value; Kendall W coordination coefficient calculation formula is: (3) Determine whether further consultation is needed When n<7, the conclusion can be drawn based on the Kendall coordination coefficient W value table; when n>7, the large sample approximation method is used to calculate the test statistic χ 2 , and according to the degree of freedom υ=n-1, compare χ 2 Critical value table; test statistic χ 2 The calculation formula is: x 2 =m×(n-1)×W If W or χ 2 If it is greater than the critical value L in the table, the significance level is reached and the next step of calculation is performed; otherwise, the expert is consulted again until W or χ 2 Until it is greater than L; (4) Calculate the normalized subjective weight E First, according to the expert's score on the indicator, calculate the score weight r of the jth indicator j : Then, according to the importance ranking of the indicators by experts, the ranking weight t of the jth indicator is calculated. j ; Assume that expert i believes that the importance of indicator j among all n indicators is ranked i,j , then 1≤rank i,j ≤n, the smaller the value, the more important it is; at this time, the ranking weight t of the jth indicator can be calculated j : Finally, the normalized subjective weight e of the jth indicator is calculated j : Among them, α and β are optional parameters. If the scoring and ranking weights are considered to have equal value, α = β = 1 can be taken. Based on the expert consultation, the weighted scoring and ranking of the indicators are calculated to obtain the normalized subjective weight value of each indicator E = {e1, e2, …, e n }.

5. The method for evaluating the digital level of a logistics park based on Delphi-entropy weight-TOPSIS according to claim 1 is characterized in that: The specific steps of S4 are: (1) Dimensionless processing of indicator matrix Since the established evaluation index system for the digital level of logistics parks only involves positive indicators, the standardized operation is: The dimensionless normalized matrix after processing is X = (x ij ) m × n ; (2) Calculate the objective weight of indicators According to the definition of entropy, calculate the entropy value f of the jth indicator j for: In the formula, q ij represents the contribution of the i-th solution under the j-th indicator, then the weight of the j-th indicator is: The index weight value H = {h1,h2,…,h n }, where h i Represents the objective weight of the i-th indicator.

6. The method for evaluating the digital level of a logistics park based on Delphi-entropy weight-TOPSIS according to claim 1 is characterized in that: The specific calculation steps of the S5 comprehensive weight are: Assume that α and β represent the decision maker’s preference for subjective and objective weighting, respectively, and α+β=1; j With objective weight h j Perform linear weighted operation to obtain the comprehensive weight p of the evaluation index of the Delphi-entropy weight combination empowerment method j : p j =αe j +βh j The corresponding weight matrix P is: P=diag(p1,p2,…,p n )。 7. The method for evaluating the digital level of a logistics park based on Delphi-entropy weight-TOPSIS according to claim 1 is characterized in that: The specific calculation steps of S6 are: (1) Calculate the weighted normalized decision matrix V (2) Determine the positive ideal solution G + , negative ideal solution G - (3) Calculate the Euclidean distance between the evaluation index of each logistics park and the positive and negative ideal solutions Optimal distance Worst distance (4) Calculate the closeness Each logistics park is ranked according to its closeness to the positive ideal solution; the larger the value, the closer the logistics park is to the positive ideal solution and the farther it is from the negative ideal solution, and the higher the level of digitization.

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