Multi-dimensional industry chain evaluation method and device, equipment, storage medium and product

Abstract

The invention discloses a multi-dimensional industrial chain evaluation method and device, equipment, a storage medium and a product. The method comprises the steps of obtaining multi-dimensional original evaluation indexes of an industrial chain of a target area; wherein the multiple dimensions comprise an innovation chain, a value chain and a supply chain; performing factor fitness analysis on the original evaluation index to obtain an evaluation index of the industrial chain; performing factor extraction on the evaluation index to obtain a principal component characteristic value and a variance contribution rate of the evaluation index, and calculating a weight of the evaluation index according to the principal component characteristic value and the variance contribution rate; constructing a three-chain collaborative evaluation model of the industrial chain according to the evaluation indexes and the corresponding weights; and evaluating the industrial chain by adopting the three-chain collaborative evaluation model to obtain the collaborative degree and collaborative efficiency of the industrial chain. And a high-technology industry chain level and a lifting path thereof can be discussed.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial chains, and in particular, to a multi-dimensional industrial chain evaluation method, device, equipment, storage medium and product. Background Art

[0002] High-tech industries refer to industrial clusters that produce high-tech products using contemporary cutting-edge technologies (mainly in the fields of information technology, bioengineering, and new materials, etc.). High-tech industries develop rapidly and have strong penetration capabilities into other industries. Their main characteristics are: 1. Knowledge and technology intensive, with a large proportion of scientific and technological personnel and high cultural and technical levels of employees; 2. Less consumption of resources and energy, with diversified and software-based products, small batch sizes, rapid replacement, and high added value; 3. Large investment in research and development; 4. High industrial growth rate.

[0003] The advantages of high-tech industries such as intelligence, innovation, strategy, and less environmental pollution are of extremely important significance to the development of society and economy. As technology- and knowledge-intensive high-tech industries, their development determines the strength of a country's competitiveness and the speed of high-quality development. Therefore, exploring the level of high-tech industrial chains and their improvement paths is of great significance to national development. Summary of the Invention

[0004] The present invention provides a multi-dimensional industrial chain evaluation method, device, equipment, storage medium and product, which obtains evaluation indicators of the industrial chain from multiple dimensions of the supply chain, innovation chain, and value chain, and constructs a three-chain collaborative evaluation model of the industrial chain to evaluate the industrial chain, so as to explore the level of high-tech industrial chains and their improvement paths.

[0005] To achieve the above object, an embodiment of the present invention provides a multi-dimensional industrial chain evaluation method, including:

[0006] Obtain the original evaluation indicators of the industrial chain in the target area from multiple dimensions; wherein, the multiple dimensions include the innovation chain, value chain, and supply chain;

[0007] Conduct a factor fitness analysis on the original evaluation indicators to obtain the evaluation indicators of the industrial chain;

[0008] Extract factors from the evaluation indicators to obtain the principal component eigenvalues and variance contribution rates of the evaluation indicators, and calculate the weights of the evaluation indicators according to the principal component eigenvalues and variance contribution rates;

[0009] Construct a three-chain collaborative evaluation model of the industrial chain according to the evaluation indicators and the corresponding weights; use the three-chain collaborative evaluation model to evaluate the industrial chain to obtain the collaboration degree and collaboration efficiency of the industrial chain.

[0010] As an improvement to the above solution, the factor fitness analysis of the original evaluation indicators is performed to obtain the evaluation indicators of the industrial chain, including:

[0011] The original evaluation indicators are standardized using min-max standardization to obtain standardized evaluation indicators;

[0012] The factor fitness analysis is performed on the standardized evaluation indicators, and based on the results of the factor fitness analysis, the evaluation indicators of the industrial chain are determined.

[0013] As an improvement to the above solution, the factor extraction of the evaluation indicators is performed to obtain the principal component eigenvalues and variance contribution rates of the evaluation indicators, and based on the principal component eigenvalues and variance contribution rates, the weights of the evaluation indicators are calculated, including:

[0014] The factor extraction of the evaluation indicators is performed using the principal component analysis method to obtain the principal component eigenvalues and variance contribution rates of the evaluation indicators;

[0015] Based on the principal component eigenvalues and variance contribution rates, the triangular fuzzy AHP method is used to calculate the weights of the evaluation indicators.

[0016] As an improvement to the above solution, the three-chain collaborative evaluation model of the industrial chain is constructed based on the evaluation indicators and the corresponding weights, including:

[0017] Based on the evaluation indicators and the corresponding weights, the weighted average evaluation value and collaborative efficiency of the industrial chain are calculated;

[0018] Based on the weighted average evaluation value and collaborative efficiency, the three-chain collaborative evaluation model of the industrial chain is constructed.

[0019] As an improvement to the above solution, the expression of the three-chain collaborative evaluation model is:

[0020]

[0021] In the formula, XT is the collaborative degree of the industrial chain, Sum avg is the weighted average evaluation value of the industrial chain; XL is the collaborative efficiency of the industrial chain; n is the number of evaluation indicators of the industrial chain; Y y is the index value of the y-th evaluation indicator in the industrial chain; m y is the weight value of the y-th evaluation indicator in the industrial chain.

[0022] As an improvement to the above solution, after obtaining the collaborative degree and collaborative efficiency of the industrial chain, the method further includes:

[0023] Based on the collaborative degree and collaborative efficiency of the industrial chain, the development of high-tech industries in the target area is adjusted.

[0024] To achieve the above object, an embodiment of the present invention provides a multi-dimensional industrial chain evaluation device, which is characterized by including:

[0025] An original index acquisition module, configured to acquire original evaluation indexes of multiple dimensions of the industrial chain in the target area; wherein, the multiple dimensions include an innovation chain, a value chain, and a supply chain;

[0026] An evaluation index determination module, configured to perform factor fitness analysis on the original evaluation indexes to obtain the evaluation indexes of the industrial chain;

[0027] An index weight determination module, configured to perform factor extraction on the evaluation indexes to obtain the main component eigenvalues and variance contribution rates of the evaluation indexes, and calculate the weights of the evaluation indexes according to the main component eigenvalues and variance contribution rates;

[0028] An evaluation model construction module, configured to construct a three-chain collaborative evaluation model of the industrial chain according to the evaluation indexes and the corresponding weights; and use the three-chain collaborative evaluation model to evaluate the industrial chain to obtain the collaboration degree and collaboration efficiency of the industrial chain.

[0029] To achieve the above object, an embodiment of the present invention correspondingly provides a multi-dimensional industrial chain evaluation device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the above multi-dimensional industrial chain evaluation method is implemented.

[0030] To achieve the above object, an embodiment of the present invention further provides a computer-readable storage medium, which includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute the above multi-dimensional industrial chain evaluation method.

[0031] To achieve the above object, an embodiment of the present invention further provides a computer program product, which is stored in a storage medium, and the program product is executed by at least one processor to implement the steps of the above multi-dimensional industrial chain evaluation method.

[0032] Compared with the prior art, a multi-dimensional industrial chain evaluation method, device, equipment, storage medium and product disclosed in an embodiment of the present invention obtain original evaluation indicators of multiple dimensions of the industrial chain in a target area; wherein, the multiple dimensions include an innovation chain, a value chain and a supply chain; perform factor fitness analysis on the original evaluation indicators to obtain the evaluation indicators of the industrial chain; perform factor extraction on the evaluation indicators to obtain the principal component eigenvalues and variance contribution rates of the evaluation indicators, and calculate the weights of the evaluation indicators according to the principal component eigenvalues and variance contribution rates; construct a three-chain collaborative evaluation model of the industrial chain according to the evaluation indicators and the corresponding weights; use the three-chain collaborative evaluation model to evaluate the industrial chain to obtain the collaboration degree and collaboration efficiency of the industrial chain. It is possible to obtain the evaluation indicators of the industrial chain based on multiple dimensions of the supply chain, innovation chain and value chain, and construct a three-chain collaborative evaluation model of the industrial chain to evaluate the industrial chain, so as to explore the level and improvement path of the high-tech industrial chain according to the collaboration degree of the industrial chain, and provide data support and suggestions for the differential role positioning and strategic measures of the government-market, the support of the city for the high-tech industrial chain and the optimization of space supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 is a schematic flowchart of a multi-dimensional industrial chain evaluation method provided by an embodiment of the present invention;

[0034] Figure 2 The "three-chain" collaboration degree - collaboration efficiency quadrant diagram provided by an embodiment of the present invention;

[0035] Figure 3 is a schematic structural diagram of a multi-dimensional industrial chain evaluation device provided by an embodiment of the present invention;

[0036] Figure 4 is a structural block diagram of a multi-dimensional industrial chain evaluation device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0038] It should be noted that the terms "including" and "specific" in the present invention and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0039] Please refer to Figure 1 , Figure 1 which is a schematic flow chart of a multi-dimensional industrial chain evaluation method provided by an embodiment of the present invention. The multi-dimensional industrial chain evaluation method includes:

[0040] S1. Obtain the original evaluation indicators of the multi-dimensions of the industrial chain in the target area; wherein, the multi-dimensions include an innovation chain, a value chain, and a supply chain;

[0041] S2. Conduct a factor fitness analysis on the original evaluation indicators to obtain the evaluation indicators of the industrial chain;

[0042] S3. Extract factors from the evaluation indicators to obtain the principal component eigenvalues and variance contribution rates of the evaluation indicators, and calculate the weights of the evaluation indicators according to the principal component eigenvalues and variance contribution rates;

[0043] S4. Construct a three-chain collaborative evaluation model of the industrial chain according to the evaluation indicators and the corresponding weights; use the three-chain collaborative evaluation model to evaluate the industrial chain to obtain the collaboration degree and collaboration efficiency of the industrial chain.

[0044] Exemplarily, the multi-dimensional industrial chain evaluation method described in the embodiment of the present invention is implemented by an industrial chain management server, and the industrial chain management server can interact with the target user. The industrial chain management server obtains the original evaluation indicators of the multi-dimensions (such as, innovation chain, value chain, and supply chain, etc.) of the industrial chain in the target area; conducts a factor fitness analysis on the original evaluation indicators to find the evaluation indicators with relevant relationships in the industrial chain; extracts factors from the evaluation indicators to obtain the principal component eigenvalues and variance contribution rates of the evaluation indicators, and calculates the weights of the evaluation indicators according to the principal component eigenvalues and variance contribution rates; constructs a three-chain collaborative evaluation model of the industrial chain according to the evaluation indicators and the corresponding weights; uses the three-chain collaborative evaluation model to evaluate the industrial chain to obtain the collaboration degree and collaboration efficiency of the industrial chain. The embodiment of the present invention can obtain the evaluation indicators of the industrial chain based on multiple dimensions of the supply chain, innovation chain, and value chain, and construct a three-chain collaborative evaluation model of the industrial chain to evaluate the industrial chain, so as to explore the high-tech industrial chain level and its improvement path according to the collaboration degree of the industrial chain.

[0045] It should be noted that the original data used in the original evaluation indicators in step S1 are all sourced from the national and provincial / municipal statistical bulletins on national economic and social development over the years, the "National Statistical Bulletin on Science and Technology Expenditure" over the years, the "China High-Tech Industry Statistical Yearbook", the "High-Tech Industry Statistical Yearbook", and so on. At present, independent innovation in high-tech enterprises in China has not yet taken the lead. The innovation of most high-tech enterprises still mainly focuses on improving existing technologies and imitating and learning the technologies of advanced enterprises in the industry. The expenditures on technology introduction, technological transformation, digestion and absorption, and the purchase of domestic technologies are regarded as technology acquisition funds and are used as the capital investment in the stage of achievement transformation, that is, industrial achievement investment. The original evaluation indicators are shown in Table 1.

[0046] Table 1 Original evaluation indicators for the multi-dimensions of the industrial chain in the target area

[0047]

[0048] Specifically, step S2 includes:

[0049] S21, perform standardization processing on the original evaluation indicators using min-max standardization to obtain standardized evaluation indicators;

[0050] S22, conduct factor fitness analysis on the standardized evaluation indicators, and determine the evaluation indicators of the industrial chain according to the results of the factor fitness analysis.

[0051] Exemplarily, the min-max standardization method performs a linear transformation on the original data. For example, let minC and maxC be the minimum and maximum values of the third-level indicator C respectively. A raw value x of C is mapped to a value x' in the interval [0,1] through min-max standardization. The formula for min-max standardization is: new data = (original data - minimum value) / (maximum value - minimum value).

[0052] In step S22, use the fuzzy comprehensive evaluation algorithm to conduct factor fitness analysis on the standardized evaluation indicators, and determine the evaluation indicators of the industrial chain according to the results of the factor fitness analysis. For example, establish a factor set U = {u1, u2,..., u N} and a language evaluation set V = {v1, v2,..., v β}, where the factors in the factor set U are the corresponding standardized evaluation indicators, a total of N; the language evaluation set V is the evaluation remarks for each standardized evaluation indicator, a total of β. Use the set-valued statistical iteration method to allocate weights to the factors (standardized evaluation indicators) in the factor set U to obtain the weight values of each factor. Assume that h people participate in the determination of the weight distribution A. At the beginning, an initial value q needs to be selected: 1 ≤ q << N. Subsequently, the α-th person (α = 1, 2,..., h) completes the statistical experiment in the following way successively:

[0053] In the first-step operation, the α-th person selects q factors from U to obtain a subset of U (these q factors are the ones he deems the most important). Among them, is the q-th factor selected by the α-th person (standardized evaluation index); in the second-step operation, the α-th person selects 2q factors from U to obtain a second subset of U (these 2q factors are the ones he deems the most important); it can be seen that if this factor is selected as important in the first selection, it will always be considered important later. Therefore, each time q more factors always need to be selected on the basis of the previous time. In this way, the cumulative number of important factors is 2q, 3q, and so on... Each operation increases the number of factors by q on average. In the s-th operation, the α-th person selects sq factors from U to obtain the s-th subset of U (these sq factors are the ones he deems the most important): Among them, is the sq-th factor selected by the α-th person; if N = pq + γ and satisfies in the (p + 1)-th step, the iterative process will terminate, and take Then, calculate the coverage frequency f(u i ) of the i-th factor (standardized evaluation index) u i , where i = 1, 2,..., N, and f(u i ) represents the number of times the factor u i is selected in all iterative steps. Among them, C is a characteristic function used to calculate the coverage frequency of each factor; after normalization, the coverage frequency f(u i ) is converted into a weight value a i , Finally, the weight distribution A = (a1, a2,..., a N ) is obtained; using the pre-established fuzzy comprehensive evaluation model, each factor is evaluated to obtain the evaluation result of the fuzzy comprehensive evaluation, and the factors (evaluation indicators) suitable for factor analysis are output.

[0054] It should be noted that the basic idea of the fuzzy comprehensive evaluation algorithm is to construct a model using the F linear transformation principle and the maximum membership degree principle to make a comprehensive evaluation of fuzzy things after considering various factors related to the thing to be evaluated.

[0055] In another specific implementation, the Kaiser-Meyer-Olkin (KMO) test or Bartlett's spherical test is used to analyze the factor fitness of the standardized evaluation indicators. According to the results of the factor fitness analysis, the evaluation indicators of the industrial chain are determined. If the KMO test result of the standardized evaluation indicator is greater than 0.5, it indicates that there is a correlation between the variables (standardized evaluation indicators), and factor analysis is suitable; the KMO test result of the standardized evaluation indicator can be obtained through the calculation formula of the KMO test; the range of the KMO test result is from 0 to 1, and the closer the value is to 1, the more common factors there are between the variables, and the more suitable the standardized evaluation indicator is for factor analysis. When the KMO test result is less than 0.5, it is generally considered not suitable for factor analysis; among them, the calculation formula of the KMO test is as follows:

[0056]

[0057] where r ij is the simple correlation coefficient between the i-th variable (the i-th standardized evaluation indicator) and the j-th variable (the j-th standardized evaluation indicator), and p ij is the partial correlation coefficient between the i-th variable and the j-th variable, and N is the total number of variables, that is, the number of standardized evaluation indicators. When the partial correlation coefficient is close to 0, the KMO value is close to 1; when the partial correlation coefficient is close to 1, the KMO value is close to 0.

[0058] It should be noted that the KMO test statistic is a method for comparing the simple correlation coefficient and the partial correlation coefficient between variables, and is mainly applied to the factor analysis of multivariate statistics. The KMO statistic takes values between 0 and 1. When the sum of the squares of the simple correlation coefficients between all variables is much larger than the sum of the squares of the partial correlation coefficients, the KMO value is close to 1. The closer the KMO value is to 1, the higher the degree of correlation between variables is considered, the better the effect that can be obtained by factor analysis is, and the more suitable it is for factor analysis; when the sum of the squares of the simple correlation coefficients between all variables is close to 0, the KMO value is also close to 0. When the KMO value is closer to 0, the lower the degree of correlation between variables is considered, the lower the degree of correlation between the original variables is, and the actual problem cannot be reflected through factor analysis. Therefore, it is not suitable to use factor analysis. The measurement standard of KMO is: more than 0.9 is considered very suitable, 0.8 means suitable, 0.7 means general, 0.6 means less suitable, and less than 0.5 means very unsuitable.

[0059] If the significance probability of Bartlett's test of sphericity is < 0.05, it indicates that the variables have a good fitting effect and are very suitable for factor analysis. The principal component analysis method can be used to extract factors and calculate the weights of each index. It should be noted that Bartlett's test of sphericity is a statistical test method mainly used to test whether the correlation matrix is an identity matrix. An identity matrix has all 1s on the main diagonal and all 0s elsewhere. In the case of an identity matrix, there is a high degree of independence among variables, and the information reflected by independent variables has a very low correlation, so it is considered not suitable for factor analysis. In multivariate statistical analysis methods such as factor analysis and principal component analysis, this test plays an important role. It can help determine whether there is a correlation between variables and then determine whether it is suitable for factor analysis. Because if variables are independent of each other (i.e., the correlation matrix is an identity matrix), then performing dimension reduction operations such as factor analysis is meaningless because no common factors can be extracted. The correlation matrix can be tested whether it is an identity matrix through the statistic of Bartlett's test of sphericity. The statistic of Bartlett's test of sphericity is obtained from the determinant of the correlation coefficient matrix. If this value is large and its corresponding associated probability value is less than the given significance level, then the null hypothesis is rejected, that is, it is considered that the correlation matrix is not an identity matrix, there is a significant correlation between variables, and it is suitable for factor analysis. On the contrary, it is not suitable for factor analysis.

[0060] Specifically, step S3 includes:

[0061] S31, using the principal component analysis method to extract factors from the evaluation indicators, and obtaining the principal component eigenvalues and variance contribution rates of the evaluation indicators;

[0062] S32, according to the principal component eigenvalues and variance contribution rates, using the triangular fuzzy AHP method to calculate the weights of the evaluation indicators.

[0063] Exemplarily, using the principal component analysis method, factors are extracted from the evaluation indicators according to the principle that the eigenvalue is greater than 1, and the principal component eigenvalues and variance contribution rates of the evaluation indicators are obtained. For example, the main factor components with eigenvalues greater than 1 in the multi-dimensional evaluation indicators of the industrial chain include C1, C4, C6, C8, and C9. They are respectively the evaluation indicators of the R & D expenditure / operating income of high-tech industries, the sales revenue of high-tech industrial technology products / operating income, the export complexity index, the added value of high-tech industries / number of employees, and the total profit / operating income. The corresponding principal component eigenvalues are 1.204, 1.186, 1.521, 1.257, and 1.104.

[0064] In step S32, according to the principal component eigenvalue and variance contribution rate, the Analytic Hierarchy Process (AHP) method of triangular fuzzy numbers is used to calculate the weights of the evaluation indicators. For example, experts score each factor (evaluation indicator) in the form of triangular fuzzy numbers, and based on the scoring results, a judgment matrix R is constructed by pairwise comparison of the importance of each factor n , the judgment matrix R n is an n×n matrix, where n is the number of the evaluation indicators (the evaluation indicator refers to the multi-dimensional evaluation indicator of the industrial chain obtained after factor fitness analysis of the original evaluation indicator). The element r zy in the judgment matrix represents the importance of the x-th evaluation indicator relative to the y-th evaluation indicator; according to the average value of each column in the judgment matrix R n , the average value matrix B of the judgment matrix R n is calculated. The z-th element (evaluation indicator) of the average value matrix B According to the formula calculate the comprehensive fuzzy value of the z-th element in the K-th layer and use the formula to pairwise compare the comprehensive importance degree values of the elements. Among them, is the triangular fuzzy number of the z-th evaluation indicator in the K-th layer relative to the y-th evaluation indicator, z = 1, 2, 3, …, n, y = 1, 2, 3, …, n; represents the multiplication operation of triangular fuzzy numbers; represents all The inverse of the sum; D B1 and D B2 are the comprehensive fuzzy values of element B1 and element B2; l1, m1, u1 are the lower bound, modulus and upper bound of D B1 respectively; l2, m2, u2 are the lower bound, modulus and upper bound of D B2 respectively; ∧ represents taking the minimum value. According to the formula d(B z ) = V(D Bz ≥D B1 , …, D Bz-1 , D Bz+1 , …, D Bn ) = min{V(D Bz ≥D Bk )}, the weight fuzzy vector of the index W = (d(B1), d(B2), …, d(B n )) T , then normalize the weight values to obtain the weight vector W B of the index, where D Bz is the comprehensive fuzzy value of the z-th evaluation indicator, D Bkis the comprehensive fuzzy value of the k-th evaluation index, where k = 1, 2, …, n and k ≠ z; V(D Bz ≥ D Bk ) is the importance degree value for comparing D Bz and D Bk ; min{V(D Bz ≥ D Bk )} represents taking the minimum value among all comparison results; according to the formula W Czy = W Bz W′ Czy , the total weight vector of the evaluation index is obtained; where W Czy is the total weight vector of the evaluation index y with respect to the upper-level evaluation index z; W Bz is the weight vector of the upper-level evaluation index z; W′ Czy is the weight vector of the evaluation index y.

[0065] It should be noted that the basic idea of the triangular fuzzy number analytic hierarchy process is: the elements (evaluation indexes) C yk , k = 1, 2, 3, …, n are pairwise compared with respect to the upper-level elements (secondary evaluation indexes) B y , y = 1, 2, 3, …, n′, and the comparison results are quantitatively represented by triangular fuzzy numbers, from which a fuzzy judgment matrix composed of triangular fuzzy numbers can be obtained. Definition: Let be a triangular fuzzy number, and its membership function can be expressed as:

[0066]

[0067] In the formula, l and u are its upper and lower bounds; m is its median value; u - l represents the degree of fuzziness, and the larger u - l is, the greater the degree of fuzziness.

[0068] Specifically, in step S4, the construction of the three-chain collaborative evaluation model of the industrial chain according to the evaluation index and the corresponding weight includes:

[0069] S41, calculating the weighted average evaluation value and collaborative efficiency of the industrial chain according to the evaluation index and the corresponding weight;

[0070] S42, constructing the three-chain collaborative evaluation model of the industrial chain according to the weighted average evaluation value and collaborative efficiency.

[0071] More specifically, the expression of the three-chain collaborative evaluation model is:

[0072]

[0073] In the formula, XT is the collaborative degree of the industrial chain, Sum avgis the weighted average evaluation value of the industrial chain; XL is the collaborative efficiency of the industrial chain; n is the number of evaluation indicators of the industrial chain; Y y is the index value of the y-th evaluation indicator in the industrial chain; m y is the weight value of the y-th evaluation indicator in the industrial chain.

[0074] In specific implementation, according to the evaluation indicators and corresponding weights, calculate the weighted average evaluation value of each link of the industrial chain;

[0075] Among them, the weighted average evaluation value I of the supply chain avg : w kI is the weight value of the k-th evaluation indicator (tertiary indicator) in the supply chain, I k is the index value of the k-th evaluation indicator in the supply chain,

[0076] n1 is the number of evaluation indicators in the supply chain.

[0077] The weighted average evaluation value V of the innovation chain avg : w yV is the weight value of the y-th evaluation indicator in the innovation chain, V y is the index value of the y-th evaluation indicator in the innovation chain, n2 is the number of evaluation indicators in the innovation chain.

[0078] The weighted average evaluation value S of the value chain avg : w zS is the weight value of the z-th evaluation indicator in the value chain, S z is the index value of the z-th evaluation indicator in the value chain, n3 is the number of evaluation indicators in the value chain.

[0079] According to the weighted average evaluation values of the innovation chain, value chain and supply chain, calculate the weighted average evaluation value of the industrial chain:

[0080] Sum avg =I avg +V avg +S avg

[0081] According to the weighted average evaluation value, calculate the coordination degree XT of the industrial chain; according to the evaluation indicators and corresponding weights, calculate the collaborative efficiency XL of the industrial chain:

[0082]

[0083] In the formula, XT is the coordination degree of the industrial chain, Sum avgis the weighted average evaluation value of the industrial chain; XL is the collaborative efficiency of the industrial chain; n is the number of evaluation indicators of the industrial chain; Y y is the index value of the y-th evaluation indicator in the industrial chain; m y is the weight value of the y-th evaluation indicator in the industrial chain.

[0084] Further, after obtaining the synergy degree and collaborative efficiency of the industrial chain, the method further includes:

[0085] S5. Adjust the development of the high-tech industry in the target area according to the synergy degree and collaborative efficiency of the industrial chain.

[0086] Specifically, step S5 includes:

[0087] S51. Determine the collaborative development status of the industrial chain according to the synergy degree and collaborative efficiency of the industrial chain, and obtain the collaborative development strategy of the industrial chain according to the collaborative development status and the preset collaborative development strategy; wherein, the preset collaborative development strategy sets the collaborative development strategies under several collaborative development statuses.

[0088] S52. Adjust the development of the high-tech industry in the target area according to the collaborative development strategy.

[0089] Exemplarily, on the basis of the Cartesian coordinate plane, the plane coordinate system is divided into multiple quadrants by using the demarcation points, and the "three-chain" collaborative development types and characteristics represented by each quadrant are determined in combination with the value ranges and meanings of the horizontal and vertical coordinates. The comprehensive method is used to determine the demarcation points of the quadrant diagram. The comprehensive method (also known as the comprehensive geometry method) is a method of solving problems by integrating multiple geometric properties. When determining the demarcation points of the quadrant diagram, it usually refers to determining the quadrant boundaries on the coordinate plane, and these boundaries are defined by the coordinate axes.

[0090] For example, if the collaborative development status is completely non-collaborative (synergy degree [-1, 0) and collaborative efficiency < 1), at this stage, all links of the industrial chain are seriously disjointed, the enterprise benefits are poor, and the industry lacks competitiveness. The collaborative development strategy should be to strengthen government guidance, formulate an industrial development plan, clarify the development direction, such as setting up an industrial guidance fund to attract enterprises and capital to enter. Establish a cross-departmental coordination mechanism to break down industry barriers and promote communication and cooperation between upstream and downstream enterprises in the industrial chain. At the same time, build a cooperation platform for industry, university and research, organize the docking of universities, scientific research institutions and enterprises, accelerate the transformation and application of innovation achievements, and improve the technical level of enterprises.

[0091] If the collaborative development situation is low in both collaborative degree and collaborative efficiency (collaborative degree ∈ [0, 0.3) and collaborative efficiency < 1): In this stage, prominent problems exist in industrial development, the "three chains" are loosely connected, and resource waste is serious. The collaborative development strategy is to strengthen government guidance, set up special funds for industrial development, encourage enterprises to increase R & D investment, and enhance innovation capabilities. Build an industrial public service platform to provide enterprises with resource sharing services such as technology, talents, and information, and reduce the operating costs of enterprises. Promote in-depth cooperation between industry, universities, and research institutions, establish a joint R & D center, accelerate the transformation of scientific research results, and promote the integration of the "three chains".

[0092] If the collaborative development situation is low in collaborative degree but high in collaborative efficiency (collaborative degree ∈ [0, 0.3) and collaborative efficiency ≥ 1): Although resources are utilized more reasonably, the "three chains" lack coordination. Collaborative development strategy: Optimize the industrial chain layout, introduce upstream and downstream supporting enterprises around the leading industries, and improve the industrial chain. Cultivate industrial clusters, strengthen collaboration and communication among enterprises, and form an industrial synergy effect. Support enterprise technological innovation, encourage enterprises to participate in the formulation of industry standards, and enhance the industry's right to speak.

[0093] If the collaborative development situation is medium in collaborative degree but low in collaborative efficiency (collaborative degree ∈ [0.3, 0.7) and collaborative efficiency < 1): The "three chains" have been initially coordinated, but there is resource waste. Collaborative development strategy: Strengthen management innovation, guide enterprises to optimize internal management processes, and improve operating efficiency. Increase the intensity of talent cultivation, cooperate with universities and vocational colleges to carry out customized talent cultivation projects to meet the needs of industrial development. Promote digital transformation, and use technologies such as big data and artificial intelligence to improve production efficiency and resource allocation efficiency.

[0094] If the collaborative development situation is high in collaborative degree but low in collaborative efficiency (collaborative degree ∈ [0.7, 1] and collaborative efficiency < 1): The "three chains" are well coordinated, but there is room for improvement in resource utilization. Collaborative development strategy: Promote the high-end development of the industry, encourage enterprises to increase R & D and production investment in high-end products, and increase product added value. Strengthen brand building, and enhance the brand awareness and influence of high-tech industries in the region. Optimize the industrial development environment, simplify the administrative approval process, and reduce the institutional transaction costs of enterprises.

[0095] If the collaborative development situation is high in both collaborative degree and collaborative efficiency (collaborative degree ∈ [0.7, 1] and collaborative efficiency ≥ 1): The industrial development trend is good; Collaborative development strategy: Continuously drive innovation, encourage enterprises to increase investment in cutting-edge technology research, and maintain a technological leading edge. Expand the international market, support enterprises to carry out cross-border business, and participate in international competition and cooperation. Strengthen industrial ecosystem construction, cultivate an innovation and entrepreneurship culture, attract more high-quality enterprises and projects to settle in, and consolidate and enhance industrial competitiveness.

[0096] As Figure 2 shown, Figure 2The "Three Chains" synergy degree - synergy efficiency quadrant diagram provided by the embodiments of the present invention. Among them, the synergy degree (synergy effect) of the "Three Chains" is divided into 4 levels, and the synergy efficiency includes 2 levels. The plane coordinate system is divided into 8 quadrants, forming a "Three Chains" synergy degree - synergy efficiency quadrant diagram with 8 quadrants. In the quadrant diagram, the division of the synergy efficiency coordinate axis is based on 1 as the boundary. If the efficiency value is lower than 1, the "Three Chains" synergy is invalid, indicating that there is a large amount of resource waste or the technical level and management system need to be improved during the implementation of the "Three Chains" synergy, and the "Three Chains" synergy development is in an unsustainable state; if it is greater than or equal to 1, the "Three Chains" synergy is effective, and the resources invested in realizing the "Three Chains" synergy are effectively utilized, and a relatively healthy market environment is formed, and the technical level matches the production scale. In the first quadrant, both the synergy degree and the synergy efficiency reach a relatively high level, presenting an optimal development state; in the second and third quadrants, the synergy degree and the synergy efficiency have reached effectiveness, but the "Three Chains" synergy development effect is relatively low and needs to be further improved; in the fourth quadrant, the synergy efficiency has reached effectiveness, and the synergy degree is negative. It is necessary to take strengthening the synergy degree as the top priority to achieve sustainable development; in the fifth quadrant, the synergy degree and the synergy efficiency of the "Three Chains" are invalid values, and it is urgent to improve both the effect and the efficiency; in the sixth, seventh, and eighth quadrants, the goal should be to improve the synergy efficiency, strengthen the technical and management levels, and improve the current situation of ineffective efficiency.

[0097] A multi - dimensional industrial chain evaluation method disclosed by the embodiments of the present invention. By obtaining the original evaluation indicators of the multi - dimensions of the industrial chain in the target area; wherein, the multi - dimensions include the innovation chain, the value chain, and the supply chain; performing factor fitness analysis on the original evaluation indicators to obtain the evaluation indicators of the industrial chain; performing factor extraction on the evaluation indicators to obtain the principal component eigenvalues and variance contribution rates of the evaluation indicators, and calculating the weights of the evaluation indicators according to the principal component eigenvalues and variance contribution rates; constructing a three - chain synergy evaluation model of the industrial chain according to the evaluation indicators and the corresponding weights; and using the three - chain synergy evaluation model to evaluate the industrial chain to obtain the synergy degree and synergy efficiency of the industrial chain. It can obtain the evaluation indicators of the industrial chain based on multiple dimensions of the supply chain, innovation chain, and value chain, and construct a three - chain synergy evaluation model of the industrial chain to evaluate the industrial chain, so as to explore the high - tech industrial chain level and its improvement path according to the synergy degree of the industrial chain, and provide data support and suggestions for the differential role positioning and strategic measures of the government - market, the support of the city for the high - tech industrial chain, and the optimization of space supply.

[0098] See Figure 3 , Figure 3 is a schematic structural diagram of a multi - dimensional industrial chain evaluation device 10 provided by the embodiments of the present invention. The multi - dimensional industrial chain evaluation device 10 includes:

[0099] The original index acquisition module 11 is used to acquire the original evaluation indexes of multiple dimensions of the industrial chain in the target area; among them, the multiple dimensions include the innovation chain, the value chain, and the supply chain;

[0100] The evaluation index determination module 12 is used to perform factor fitness analysis on the original evaluation indexes to obtain the evaluation indexes of the industrial chain;

[0101] The index weight determination module 13 is used to extract factors from the evaluation indexes to obtain the principal component eigenvalues and variance contribution rates of the evaluation indexes, and calculate the weights of the evaluation indexes according to the principal component eigenvalues and variance contribution rates;

[0102] The evaluation model construction module 14 is used to construct a three-chain collaborative evaluation model of the industrial chain according to the evaluation indexes and the corresponding weights; use the three-chain collaborative evaluation model to evaluate the industrial chain to obtain the collaboration degree and collaboration efficiency of the industrial chain.

[0103] Further, the multi-dimensional industrial chain evaluation device 10 further includes:

[0104] The high-tech industry adjustment module 15 is used to adjust the development of high-tech industries in the target area according to the collaboration degree and collaboration efficiency of the industrial chain.

[0105] The multi-dimensional industrial chain evaluation device 10 provided by the embodiment of the present invention can implement all the processes of the multi-dimensional industrial chain evaluation method in the above embodiment. The functions of each module in the device and the achieved technical effects are respectively the same as the functions and achieved technical effects of the multi-dimensional industrial chain evaluation method in the above embodiment, and will not be elaborated here.

[0106] See Figure 4 , Figure 4 is a schematic structural diagram of a multi-dimensional industrial chain evaluation device 20 provided by an embodiment of the present invention. The multi-dimensional industrial chain evaluation device 20 in this embodiment includes: a processor 21, a memory 22, and a computer program stored in the memory 22 and executable on the processor 21. When the processor 21 executes the computer program, the steps in the embodiment of the multi-dimensional industrial chain evaluation method are implemented. Alternatively, when the processor 21 executes the computer program, the functions of each module in the embodiment of the multi-dimensional industrial chain evaluation device are implemented.

[0107] Exemplarily, the computer program can be divided into one or more modules. The one or more modules are stored in the memory 22 and executed by the processor 21 to complete the present invention. The one or more modules can be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the multi-dimensional industrial chain evaluation device 20.

[0108] The multi-dimensional industrial chain evaluation device 20 may be a computing device such as a desktop computer, a notebook, a handheld computer, and a cloud server. The multi-dimensional industrial chain evaluation device 20 may include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art can understand that the schematic diagram is only an example of the multi-dimensional industrial chain evaluation device 20, and does not constitute a limitation on the multi-dimensional industrial chain evaluation device 20. It may include more or fewer components than those shown in the figure, or combine certain components, or different components. For example, the multi-dimensional industrial chain evaluation device 20 may further include input / output devices, network access devices, buses, etc.

[0109] The so-called processor 21 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor 21 is the control center of the multi-dimensional industrial chain evaluation device 20, and connects various parts of the entire multi-dimensional industrial chain evaluation device 20 through various interfaces and lines.

[0110] The memory 22 can be used to store the computer programs and / or modules. The processor 21 realizes various functions of the multi-dimensional industrial chain evaluation device 20 by running or executing the computer programs and / or modules stored in the memory 22, and by calling the data stored in the memory 22. The memory 22 may mainly include a program storage area and a data storage area. Among them, the program storage area may store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phone book, etc.), etc. In addition, the memory 22 may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0111] Among them, if the modules integrated in the multi-dimensional industrial chain evaluation device 20 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor 21, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0112] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the attached drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that they have a communication connection, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0113] The embodiment of the present invention also provides a computer-readable storage medium, and the computer-readable storage medium includes a stored computer program. Among them, when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the multi-dimensional industrial chain evaluation method as described in the above embodiment.

[0114] In addition, the embodiment of the present invention also provides a computer program product. The computer program product is stored in a storage medium, and the program product is executed by at least one processor to implement the steps of the multi-dimensional industrial chain evaluation method as described in the above embodiment.

[0115] The above are the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements are also regarded as the protection scope of the present invention.

Claims

1. A multi-dimensional industrial chain evaluation method, characterized in that, Including: Obtain the original evaluation indicators of the industrial chain in the target area in multiple dimensions; where the multiple dimensions include the innovation chain, value chain, and supply chain; Conduct factor fitness analysis on the original evaluation indicators to obtain the evaluation indicators of the industrial chain; Extract factors from the evaluation indicators to obtain the principal component eigenvalues and variance contribution rates of the evaluation indicators, and calculate the weights of the evaluation indicators based on the principal component eigenvalues and variance contribution rates; Construct a triple-chain collaborative evaluation model of the industrial chain according to the evaluation indicators and the corresponding weights; use the triple-chain collaborative evaluation model to evaluate the industrial chain to obtain the collaboration degree and collaboration efficiency of the industrial chain.

2. The multi-dimensional industrial chain evaluation method according to claim 1, wherein, The conducting factor fitness analysis on the original evaluation indicators to obtain the evaluation indicators of the industrial chain includes: Perform standardization processing on the original evaluation indicators using min-max standardization to obtain standardized evaluation indicators; Conduct factor fitness analysis on the standardized evaluation indicators, and determine the evaluation indicators of the industrial chain according to the results of the factor fitness analysis.

3. The multi-dimensional industrial chain evaluation method according to claim 1, characterized in that The extracting factors from the evaluation indicators to obtain the principal component eigenvalues and variance contribution rates of the evaluation indicators, and calculating the weights of the evaluation indicators based on the principal component eigenvalues and variance contribution rates includes: Extract factors from the evaluation indicators using the principal component analysis method to obtain the principal component eigenvalues and variance contribution rates of the evaluation indicators; Calculate the weights of the evaluation indicators using the triangular fuzzy AHP method based on the principal component eigenvalues and variance contribution rates.

4. The multi-dimensional industrial chain evaluation method according to claim 1, wherein The constructing a triple-chain collaborative evaluation model of the industrial chain according to the evaluation indicators and the corresponding weights includes: Calculate the weighted average evaluation value and collaboration efficiency of the industrial chain according to the evaluation indicators and the corresponding weights; Construct a triple-chain collaborative evaluation model of the industrial chain based on the weighted average evaluation value and collaboration efficiency.

5. The multi-dimensional industrial chain evaluation method according to claim 4, wherein The expression of the triple-chain collaborative evaluation model is: where XT is the synergy degree of the industrial chain, and Sum avg is the weighted average evaluation value of the industrial chain; XL is the synergy efficiency of the industrial chain; n is the number of evaluation indicators of the industrial chain; Y y is the index value of the y-th evaluation indicator in the industrial chain; m y is the weight value of the y-th evaluation indicator in the industrial chain.

6. The multi-dimensional industrial chain evaluation method according to claim 1, wherein After obtaining the collaboration degree and collaboration efficiency of the industrial chain, the method further includes: Adjust the development of high-tech industries in the target area according to the collaboration degree and collaboration efficiency of the industrial chain.

7. A multi-dimensional industrial chain evaluation device, characterized in that, Including: An original index acquisition module for obtaining the original evaluation indicators of the industrial chain in the target area in multiple dimensions; where the multiple dimensions include the innovation chain, value chain, and supply chain; An evaluation index determination module for conducting factor fitness analysis on the original evaluation indicators to obtain the evaluation indicators of the industrial chain; An index weight determination module for extracting factors from the evaluation indicators to obtain the principal component eigenvalues and variance contribution rates of the evaluation indicators, and calculating the weights of the evaluation indicators based on the principal component eigenvalues and variance contribution rates; An evaluation model construction module for constructing a triple-chain collaborative evaluation model of the industrial chain according to the evaluation indicators and the corresponding weights; using the triple-chain collaborative evaluation model to evaluate the industrial chain to obtain the collaboration degree and collaboration efficiency of the industrial chain.

8. A multi-dimensional industrial chain evaluation device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the multi-dimensional industrial chain evaluation method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute the multi-dimensional industrial chain evaluation method according to any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product is stored in a storage medium. The program product is executed by at least one processor to implement the steps of the multi-dimensional industrial chain evaluation method according to any one of claims 1-6.

Images