Blueberry industry regional competitiveness evaluation model construction method, system, equipment and medium

Abstract

The invention provides a blueberry industry regional competitiveness evaluation model construction method, system and device and a medium, and belongs to the technical field of agricultural industry big data, and the method comprises the following steps: determining a blueberry industry regional competitiveness evaluation index system according to comparative advantages and competitive advantages in industrial competitiveness evaluation; calculating the weight of each evaluation index by using a weighting method, and determining the final weight of the evaluation index according to the Lagrange condition extremum principle; calculating the competitive power comprehensive index of the blueberry industry area; a data collection platform, a data calculation platform and a data release platform are built in advance, and collection of data corresponding to blueberry industry regional competitiveness evaluation indexes and calculation, analysis and release of the evaluation indexes are carried out. According to the method, the regional competitiveness of the blueberry industry is quantified, so that the development conditions of the blueberry industry in different regions can be directly compared; automatic data acquisition, index calculation, data storage and sharing are realized through a software platform, and the working efficiency is improved.

Description

Background Art

[0002] As a fruit rich in nutritional value, blueberry can also prevent diseases and promote health. Therefore, as an important part of modern agriculture, the blueberry industry can drive the development of regional agricultural economy. The blueberry industry region refers to the collection of a large number of blueberry enterprises in a certain local area, which is an important part of promoting economic development. In the current regional management of the blueberry industry, it is necessary to optimize the resource allocation of the blueberry industry, improve the production efficiency of the blueberry industry, and ultimately enhance the competitive advantage of the blueberry industry. Therefore, in order to guide the comprehensive development of the blueberry industry, a method of evaluating the competitiveness of the regional blueberry industry is needed.

[0003] The existing evaluation methods for the competitiveness of the blueberry industry are relatively abstract and lack quantitative methods, which makes it impossible to directly compare the development of the blueberry industry in various regions. In addition, the amount of data involved is large, resulting in a large amount of data collection, a large amount of calculation workload, and low overall work efficiency.

[0004] In summary, an effective way to evaluate the regional blueberry industry is of great significance to the development of the blueberry industry. The existing regional blueberry industry evaluation method cannot be quantified, which makes it difficult and the overall evaluation work is inefficient.

[0005] This is a shortcoming of the prior art. Therefore, in view of the above-mentioned defects in the prior art, it is very necessary to provide a method, system, equipment and medium for constructing a blueberry industry regional competitiveness evaluation model. Summary of the invention

[0006] The above-mentioned effective method for evaluating the regional blueberry industry in the prior art is of great significance to the development of the blueberry industry. The existing regional blueberry industry evaluation method cannot be quantified, which leads to difficulties and low overall evaluation efficiency. The present invention provides a method, system, equipment and medium for constructing a blueberry industry regional competitiveness evaluation model to solve the above-mentioned technical problems.

[0007] In a first aspect, the present invention provides a method for constructing a blueberry industry regional competitiveness evaluation model, comprising the following steps:

[0008] S1. Determine the evaluation index system of regional competitiveness of blueberry industry based on comparative advantages and competitive advantages in industrial competitiveness evaluation;

[0009] S2. Use the weighting method to calculate the weight of each evaluation index, and determine the final weight of the evaluation index according to the Lagrangian condition extreme value principle;

[0010] S3. Construct a standardized decision matrix through the TOPSIS model, weight the standardized decision matrix using the final weights of the indicators to obtain a standardized matrix, and then calculate the closest evaluation scheme according to the grey correlation model, and use the closest evaluation scheme to calculate the comprehensive index of regional competitiveness of the blueberry industry;

[0011] S4. Pre-build data collection platform, data calculation platform and data release platform to collect data corresponding to the evaluation indicators of regional competitiveness of blueberry industry, as well as calculate, analyze and release the evaluation indicators of regional competitiveness of blueberry industry.

[0012] Furthermore, the specific steps of step S1 are as follows:

[0013] S11. Obtaining evaluation dimensions for regional competitiveness evaluation of the blueberry industry; the evaluation dimensions include product quality, price level, market share and consumer satisfaction;

[0014] S12. According to the comparative advantages and competitive advantages in the industrial competitiveness evaluation, and according to the evaluation dimensions, determine the visible indicators and analytical indicators of the regional competitiveness evaluation of blueberry products; the visible indicators include the quality upgrade index, market share and price index; the analytical indicators include the quality competitiveness index, production price index, market competitiveness index, comparative advantage index and competitive advantage index;

[0015] S13. Determine the calculation formula for each evaluation index.

[0016] Furthermore, in step S13, the calculation formulas of the evaluation indicators in each direction specifically include:

[0017] Quality upgrade index = (blueberry sales in the reporting period / blueberry sales volume in the reporting period) / (blueberry sales in the base period / blueberry sales volume in the base period);

[0018] Quality competitiveness index = regional blueberry quality upgrade index / national blueberry quality upgrade index;

[0019] Price index = regional blueberry sales / regional blueberry sales volume;

[0020] Producer price index = blueberry producer price index in the reporting period / blueberry producer price index in the base period;

[0021] Market share = regional blueberry sales / national blueberry sales;

[0022] Market competitiveness index = (regional blueberry production - regional blueberry consumption) / (regional blueberry production + regional blueberry consumption);

[0023] Comparative advantage index = (regional blueberry sales / regional all agricultural product sales) / (national blueberry sales / national all agricultural product sales);

[0024] Competitive advantage index = comparative advantage index - (regional blueberry consumption amount / regional all agricultural product consumption amount) / (national blueberry consumption amount / national all agricultural product consumption amount).

[0025] Furthermore, the specific steps of step S2 are as follows:

[0026] S21. Use G1 method to determine the subjective evaluation weight;

[0027] S22. Determine the weight of each influencing factor according to the degree of difference of each evaluation index as an influencing factor by using the entropy method;

[0028] S23. The deviation maximization method is used to measure the weight by the proportion of the deviation of a single indicator to the total indicator deviation;

[0029] S24. Construct an objective function of the evaluation index weights by taking the maximum total deviation of all factors for all schemes, make a Lagrangian function of the objective function, calculate partial derivatives, and perform normalization processing;

[0030] S25. Determine the combination coefficient of the three weighting methods according to the Lagrange conditional extreme value principle, then calculate the combination weight according to the combination coefficient, and use the combination weight as the final weight of the evaluation index.

[0031] Furthermore, the specific steps of step S3 are as follows:

[0032] S31. Obtain the evaluation year, the blueberry industry region to be evaluated, and the evaluation indicators of the determined blueberry industry region;

[0033] S32. The values ​​of the rating indicators of the blueberry industry area to be evaluated are standardized as elements, and a standardized matrix is ​​constructed through the TOPSIS model;

[0034] S33. Determine the positive and negative ideal solutions of the standardized matrix according to the grey correlation model, calculate the distance between each solution and the positive and negative ideal solutions and the grey correlation matrix, and obtain the grey correlation between each solution and the positive and negative ideal solutions;

[0035] S34. The distances and grey correlations between each scheme and the positive and negative ideal solutions are normalized and then merged to obtain the approximate values ​​of the fit between each evaluation scheme and the positive and negative ideal solutions, and then the relative fit is calculated;

[0036] S35. The evaluation scheme with the greatest relative fit is taken as the closest evaluation scheme, and the comprehensive regional competitiveness index of the blueberry industry is calculated using the closest evaluation scheme.

[0037] Furthermore, the specific steps of step S4 are as follows:

[0038] S41. Determine the data acquisition method in advance and build a data collection platform;

[0039] S42. Upload the calculation method of the comprehensive index of regional competitiveness of the blueberry industry to the Linux server, set it to run in the Python environment, and complete the construction of the data calculation platform;

[0040] S43. Build the front and back ends of the index data publishing platform according to the product prototype and interface design requirements, and package the front and back ends of the built index data publishing platform and upload them to the server;

[0041] S44. The data collection platform collects the values ​​of the blueberry industry regional competitiveness evaluation indicators based on the determined data acquisition method and according to the set cycle, and cleans the collected data and stores it in the warehouse;

[0042] S45. The data computing platform verifies the original data in the database and calculates the comprehensive regional competitiveness index of the blueberry industry in accordance with steps S2 and S3;

[0043] S46. The data release platform will analyze and publish the calculated comprehensive index of regional competitiveness of the blueberry industry in the form of monthly index display, index ranking, index historical trend analysis and index report.

[0044] Furthermore, in step S43, the product prototype is designed using the Moknife design tool, the interface is designed using the Photoshop tool, and the index data publishing platform is developed using the JDK 1.8 programming language, SpringCloud and Vue2 development frameworks, and IDEA development tools.

[0045] In a second aspect, the present invention provides a system for constructing a blueberry industry regional competitiveness evaluation model, including: an index evaluation system determination module, for determining a blueberry industry regional competitiveness evaluation index system according to comparative advantages and competitive advantages in industry competitiveness evaluation;

[0046] The evaluation index weight determination module is used to calculate the weight of each evaluation index using the weighting method, and determine the final weight of the evaluation index according to the Lagrange condition extreme value principle;

[0047] The comprehensive evaluation index calculation module is used to construct a standardized decision matrix through the TOPSIS model, obtain a standardized matrix after weighting the standardized decision matrix using the final weights of the indicators, and then calculate the closest evaluation scheme according to the grey correlation model, and use the closest evaluation scheme to calculate the comprehensive index of regional competitiveness of the blueberry industry;

[0048] The automated platform building module is used to pre-build the data collection platform, data calculation platform and data publishing platform to collect data corresponding to the blueberry industry regional competitiveness evaluation indicators, as well as to calculate, analyze and publish the blueberry industry regional competitiveness evaluation indicators.

[0049] In a third aspect, the present invention provides a device, comprising a processor and a memory;

[0050] The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the device executes the method described in the first aspect above.

[0051] In a fourth aspect, the present invention provides a storage medium,

[0052] The storage medium stores instructions, which, when executed on a computer, enable the computer to execute the method described in the first aspect.

[0053] The beneficial effects of the present invention are:

[0054] The method, system, device and medium for constructing a regional competitiveness evaluation model of the blueberry industry provided by the present invention realize the quantification of the regional competitiveness of the blueberry industry, so that the blueberry industry development conditions in different regions can be directly compared; automated data collection, index calculation, data storage and sharing are realized through a software platform, thereby improving work efficiency; multi-dimensional analysis and information release of blueberry index data are realized through an index release platform, thereby guiding blueberry production and operation enterprises to reasonably formulate production plans and operation strategies.

[0055] In addition, the invention has a reliable design principle, a simple structure and a very broad application prospect.

[0056] It can be seen that compared with the prior art, the present invention has outstanding substantive features and significant progress, and the beneficial effects of its implementation are also obvious. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0058] Figure 1 It is a flow chart of the method for constructing a regional competitiveness evaluation model for the blueberry industry of the present invention.

[0059] Figure 2 It is a schematic diagram of a blueberry industry regional competitiveness evaluation model construction system of the present invention. DETAILED DESCRIPTION

[0060] The method for constructing a regional competitiveness evaluation model of the blueberry industry provided by the present invention is used to scientifically evaluate the competitiveness of the blueberry industry in China's counties, guide the comprehensive development of the regional blueberry industry, and provide a scientific basis for government decision-making.

[0061] The method for constructing a regional competitiveness evaluation model for the blueberry industry can be applied to one or more devices, wherein the device is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (ASICs), programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0062] The device can be any electronic product that can interact with the user, such as a personal computer, tablet computer, smart phone, personal digital assistant (PDA), interactive network television (IPTV), etc. The network where the device is located includes but is not limited to the Internet, wide area network, metropolitan area network, local area network, virtual private network (VPN), etc.

[0063] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention. The key terms appearing in the present invention are explained below.

[0064] See also Figure 1 The figure is a flow chart of a method for constructing a regional competitiveness evaluation model for the blueberry industry in a specific embodiment, and the method comprises the following steps:

[0065] S1. Determine the evaluation index system of regional competitiveness of blueberry industry based on comparative advantages and competitive advantages in industrial competitiveness evaluation;

[0066] S2. Use the weighting method to calculate the weight of each evaluation index, and determine the final weight of the evaluation index according to the Lagrangian condition extreme value principle;

[0067] S3. Construct a standardized decision matrix through the TOPSIS model, weight the standardized decision matrix using the final weights of the indicators to obtain a standardized matrix, and then calculate the closest evaluation scheme according to the grey correlation model, and use the closest evaluation scheme to calculate the comprehensive index of regional competitiveness of the blueberry industry;

[0068] S4. Pre-build data collection platform, data calculation platform and data release platform to collect data corresponding to the evaluation indicators of regional competitiveness of blueberry industry, as well as calculate, analyze and release the evaluation indicators of regional competitiveness of blueberry industry.

[0069] In this embodiment, the specific steps of step S1 are as follows:

[0070] S11. Obtaining evaluation dimensions for regional competitiveness evaluation of the blueberry industry; the evaluation dimensions include product quality, price level, market share and consumer satisfaction;

[0071] S12. According to the comparative advantages and competitive advantages in the industrial competitiveness evaluation, and according to the evaluation dimensions, determine the visible indicators and analytical indicators of the regional competitiveness evaluation of blueberry products; the visible indicators include the quality upgrade index, market share and price index; the analytical indicators include the quality competitiveness index, production price index, market competitiveness index, comparative advantage index and competitive advantage index;

[0072] S13. Determine the calculation formula for each evaluation index; the calculation formula for each evaluation index specifically includes:

[0073] Quality upgrade index = (blueberry sales in the reporting period / blueberry sales volume in the reporting period) / (blueberry sales in the base period / blueberry sales volume in the base period);

[0074] Quality competitiveness index = regional blueberry quality upgrade index / national blueberry quality upgrade index;

[0075] Price index = regional blueberry sales / regional blueberry sales volume;

[0076] Producer price index = blueberry producer price index in the reporting period / blueberry producer price index in the base period;

[0077] Market share = regional blueberry sales / national blueberry sales;

[0078] Market competitiveness index = (regional blueberry production - regional blueberry consumption) / (regional blueberry production + regional blueberry consumption);

[0079] Comparative advantage index = (regional blueberry sales / regional all agricultural product sales) / (national blueberry sales / national all agricultural product sales);

[0080] Competitive advantage index = comparative advantage index - (regional blueberry consumption amount / regional all agricultural product consumption amount) / (national blueberry consumption amount / national all agricultural product consumption amount);

[0081] The specific steps of step S2 are as follows:

[0082] S21. Use G1 method to determine the subjective evaluation weight;

[0083] Specifically, set any two evaluation indicators x j-1 With x j The importance ratio is r j , then the weight w of the kth evaluation index under the corresponding factor layer k for:

[0084]

[0085] By weight w k The weight calculation formula for the k-1, k-2, ..., 3, 2 ecological factors is: w j-1 =r j w j Where, j = k, k-1, k-2, …, 3, 2; w j-1 represents the G1 weight of the k-1th ecological element; r j Indicates the importance of pre-setting;

[0086] S22. Determine the weight of each influencing factor according to the degree of difference of each evaluation index as an influencing factor by using the entropy method;

[0087] Specifically, take m evaluation objects and n impact factors as an example, x ij It represents the jth factor of the i-th evaluation object. Since the dimensions and magnitudes of the influencing factors are different, they are standardized before comparison as follows:

[0088]

[0089] Among them, x' ij represents the standardized value of each factor, x jmax Represents x ij The maximum value of the jth factor in jmin Represents x ij The minimum value of the jth index in ;

[0090] Calculate the weight of the i-th object under the j-th factor:

[0091]

[0092] Calculate the information entropy of the factor:

[0093]

[0094] Calculate the coefficient of variation of the factors: d j =1-e j

[0095] Calculate factor weights:

[0096]

[0097] S23. The deviation maximization method is used to measure the weight by the proportion of the deviation of a single indicator to the total indicator deviation;

[0098] Specifically, let x ij (i=1,2,…,n;j=1,2,…,m) represents the standardized value of the jth ecological factor of the ith object; let w j is the weight of the jth factor. For factor j, use E ij (w) represents the total deviation of ecological factors between object i and all other objects, then:

[0099]

[0100] Assume E j (w) represents factor j, and the total deviation of all objects from other objects is:

[0101]

[0102] S24. Construct an objective function of the evaluation index weights by taking the maximum total deviation of all factors for all schemes, make a Lagrangian function of the objective function, calculate partial derivatives, and perform normalization processing;

[0103] Specifically, the objective function of w is constructed to maximize the total deviation of all factors for all solutions:

[0104]

[0105]

[0106] Make a Lagrange function for the model and find the partial derivatives, then normalize it to get:

[0107]

[0108] S25. Determine the combination coefficient of the three weighting methods according to the Lagrange conditional extreme value principle, calculate the combination weight according to the combination coefficient, and use the combination weight as the final weight of the evaluation index;

[0109] Specifically, let the weight of a single weighting method be w v (v=1,2,3), the combination coefficient is cv , the combined weight is:

[0110]

[0111]

[0112] Minimize the generalized distance between the weighted result of each object and the ideal point:

[0113]

[0114] The function is constructed using Jaynes maximum entropy principle to minimize the difference in single weighted results:

[0115]

[0116] Establish the objective function:

[0117]

[0118]

[0119] Among them, α is the balance coefficient between the two objectives, generally α = 0.5;

[0120] According to the above two objective function factors, the Lagrange function is constructed to obtain the combination coefficient:

[0121]

[0122] The specific steps of step S3 are as follows:

[0123] S31. Obtain the evaluation year, the blueberry industry region to be evaluated, and the evaluation indicators of the determined blueberry industry region;

[0124] Specifically, for a single evaluation year, there are m evaluation objects and n evaluation indicators;

[0125] S32. The values ​​of the rating indicators of the blueberry industry area to be evaluated are standardized as elements, and a standardized matrix is ​​constructed through the TOPSIS model;

[0126] Specifically, v ij (i=1,2,…,n;j=1,2,…,m) represents the standardized value of the i-th and j-th ecological factors, forming a standardized decision matrix, denoted as Z, that is:

[0127]

[0128] Weight the matrix Z according to x ij =w j vij Weighted, to obtain the weighted normalization matrix H;

[0129] S33. Determine the positive and negative ideal solutions of the standardized matrix according to the grey correlation model, calculate the distance between each solution and the positive and negative ideal solutions and the grey correlation matrix, and obtain the grey correlation between each solution and the positive and negative ideal solutions;

[0130] Specifically, the positive and negative ideal solutions are determined according to the weighted matrix, expressed as x + and x - :

[0131]

[0132]

[0133] Determine the distance between each solution and the positive and negative ideal solutions and

[0134]

[0135] Determine the grey correlation matrix between each scheme and the positive and negative ideal solutions and

[0136]

[0137]

[0138] in, is the separation coefficient, Generally take Determine the grey correlation between each solution and the positive and negative ideal solutions and

[0139]

[0140] Standardize the distance and correlation between each solution and the positive and negative ideal solutions:

[0141]

[0142]

[0143] Combine the standardized distance and grey correlation degree, and calculate the relative closeness value of each evaluation scheme to the positive and negative ideal schemes:

[0144]

[0145] S34. The distances and grey correlations between each scheme and the positive and negative ideal solutions are normalized and then merged to obtain the approximate values ​​of the fit between each evaluation scheme and the positive and negative ideal solutions, and then the relative fit is calculated;

[0146] Specifically, the relative fit C is calculated by the following formula: i :

[0147]

[0148] S35. Take the evaluation scheme with the greatest relative fit as the closest evaluation scheme, and use the closest evaluation scheme to calculate the comprehensive index of regional competitiveness of the blueberry industry;

[0149] Specifically,;

[0150] The specific steps of step S4 are as follows:

[0151] S41. Determine the data acquisition method in advance and build a data collection platform; the data acquisition method can be purchased from a data company, authorized by the government, or crawled from the web; create data collection tasks based on the data integration, data governance, and data storage functions of the big data platform to achieve periodic collection, cleaning, and storage of the above data; for example, in some embodiments, Wind Information Technology Co., Ltd. purchases sales and consumption data services for blueberries and all agricultural products in Yunnan and across the country, creates an API data integration task based on the Inspur Chuangzhi data base platform, collects the above data on a monthly basis, and stores it in a MySQL database in a certain format;

[0152] S42. Upload the calculation method of the comprehensive index of regional competitiveness of the blueberry industry to the Linux server, set it to run in the Python environment, and complete the construction of the data calculation platform;

[0153] S43. Build the front and back ends of the index data publishing platform according to the product prototype and interface design requirements, and package the front and back ends of the index data publishing platform and upload them to the server; in step S43, the product prototype is designed using the ink knife design tool, the interface is designed using the Photoshop tool, and the index data publishing platform is developed using the JDK 1.8 programming language, SpringCloud and Vue2 development framework, and IDEA development tool;

[0154] S44. The data collection platform collects the values ​​of the blueberry industry regional competitiveness evaluation indicators based on the determined data acquisition method and according to the set cycle, and cleans the collected data and stores it in the warehouse;

[0155] S45. The data calculation platform verifies the original data in the database and calculates the comprehensive index of regional competitiveness of the blueberry industry in accordance with steps S2 and S3; uses Python 3.6 programming language and PyCharm 2021.1.2 development tools to develop an index compilation program in accordance with steps S2 and S3, and runs the program every week to ensure timely data updates;

[0156] S46. The data publishing platform will analyze and publish the calculated comprehensive index of regional competitiveness of the blueberry industry in the form of monthly index display, index ranking, index historical trend analysis and index report; for example, the backend program of the data publishing platform can be packaged using Maven, and the front-end program can be packaged using Node.js, and uploaded to the server for operation;

[0157] It should be noted that through the deployment of data calculation platform and data release platform, the automatic calculation, analysis and release of the blueberry industry regional competitiveness evaluation index can be realized.

[0158] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0159] The following is an embodiment of a system for constructing a blueberry industry regional competitiveness evaluation model provided by an embodiment of the present disclosure. This system and the method for constructing a blueberry industry regional competitiveness evaluation model of the above-mentioned embodiments belong to the same inventive concept. For details not fully described in the embodiment of the system for constructing a blueberry industry regional competitiveness evaluation model, reference can be made to the embodiment of the method for constructing a blueberry industry regional competitiveness evaluation model described above.

[0160] like Figure 2 As shown, the system includes:

[0161] The indicator evaluation system determination module is used to determine the blueberry industry regional competitiveness evaluation indicator system based on the comparative advantages and competitive advantages in the industry competitiveness evaluation;

[0162] The evaluation index weight determination module is used to calculate the weight of each evaluation index using the weighting method, and determine the final weight of the evaluation index according to the Lagrange condition extreme value principle;

[0163] The comprehensive evaluation index calculation module is used to construct a standardized decision matrix through the TOPSIS model, obtain a standardized matrix after weighting the standardized decision matrix using the final weights of the indicators, and then calculate the closest evaluation scheme according to the grey correlation model, and use the closest evaluation scheme to calculate the comprehensive index of regional competitiveness of the blueberry industry;

[0164] The automated platform building module is used to pre-build the data collection platform, data calculation platform and data publishing platform to collect data corresponding to the blueberry industry regional competitiveness evaluation indicators, as well as to calculate, analyze and publish the blueberry industry regional competitiveness evaluation indicators.

[0165] The method for constructing a blueberry industry regional competitiveness evaluation model provided by the present invention is a module and algorithm step of each example described in combination with the embodiments disclosed herein, and can be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0166] Those skilled in the art will appreciate that the blueberry industry regional competitiveness evaluation model construction method provided by the present invention can be implemented as a system, method or program product. Therefore, various aspects of the present disclosure can be specifically implemented as the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or hardware and software combined implementation, which can be collectively referred to as "circuit", "module" or "system" here. In the non-transient computer-readable storage medium of the blueberry industry regional competitiveness evaluation model construction method, it can be but not limited to electrical, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or devices, or any combination of the above. More specific examples (non-exhaustive lists) of readable storage media include: electrical connection with one or more wires, portable disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0167] Although the present invention has been described in detail by referring to the accompanying drawings and in combination with the preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, a person of ordinary skill in the art may make various equivalent modifications or substitutions to the embodiments of the present invention, and these modifications or substitutions shall be within the scope of the present invention. Any person of ordinary skill in the art may easily think of changes or substitutions within the technical scope disclosed by the present invention, and these shall be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A method for constructing a blueberry industry regional competitiveness evaluation model, characterized in that: The steps include: S1. Determine the evaluation index system of regional competitiveness of blueberry industry based on comparative advantages and competitive advantages in industrial competitiveness evaluation; S2. Use the weighting method to calculate the weight of each evaluation index, and determine the final weight of the evaluation index according to the Lagrangian condition extreme value principle; S3. Construct a standardized decision matrix through the TOPSIS model, weight the standardized decision matrix using the final weights of the indicators to obtain a standardized matrix, and then calculate the closest evaluation scheme according to the grey correlation model, and use the closest evaluation scheme to calculate the comprehensive index of regional competitiveness of the blueberry industry; S4. Pre-build data collection platform, data calculation platform and data release platform to collect data corresponding to the evaluation indicators of regional competitiveness of blueberry industry, as well as calculate, analyze and release the evaluation indicators of regional competitiveness of blueberry industry.

2. The method for constructing a blueberry industry regional competitiveness evaluation model according to claim 1, characterized in that: The specific steps of step S1 are as follows: S11. Obtaining evaluation dimensions for regional competitiveness evaluation of the blueberry industry; the evaluation dimensions include product quality, price level, market share and consumer satisfaction; S12. According to the comparative advantages and competitive advantages in the industrial competitiveness evaluation, and according to the evaluation dimensions, determine the visible indicators and analytical indicators of the regional competitiveness evaluation of blueberry products; the visible indicators include the quality upgrade index, market share and price index; the analytical indicators include the quality competitiveness index, production price index, market competitiveness index, comparative advantage index and competitive advantage index; S13. Determine the calculation formula for each evaluation index.

3. The method for constructing a blueberry industry regional competitiveness evaluation model according to claim 2, characterized in that: In step S13, the calculation formulas of various evaluation indicators specifically include: Quality upgrade index = (blueberry sales in the reporting period / blueberry sales volume in the reporting period) / (blueberry sales in the base period / blueberry sales volume in the base period); Quality competitiveness index = regional blueberry quality upgrade index / national blueberry quality upgrade index; Price index = regional blueberry sales / regional blueberry sales volume; Producer price index = blueberry producer price index in the reporting period / blueberry producer price index in the base period; Market share = regional blueberry sales / national blueberry sales; Market competitiveness index = (regional blueberry production - regional blueberry consumption) / (regional blueberry production + regional blueberry consumption); Comparative advantage index = (regional blueberry sales / regional all agricultural product sales) / (national blueberry sales / national all agricultural product sales); Competitive advantage index = comparative advantage index - (regional blueberry consumption amount / regional all agricultural product consumption amount) / (national blueberry consumption amount / national all agricultural product consumption amount).

4. The method for constructing a blueberry industry regional competitiveness evaluation model according to claim 2, characterized in that: The specific steps of step S2 are as follows: S21. Use G1 method to determine the subjective evaluation weight; S22. Determine the weight of each influencing factor according to the degree of difference of each evaluation index as an influencing factor by using the entropy method; S23. The deviation maximization method is used to measure the weight by the proportion of the deviation of a single indicator to the total indicator deviation; S24. Construct an objective function of the evaluation index weights by taking the maximum total deviation of all factors for all schemes, make a Lagrangian function of the objective function, calculate partial derivatives, and perform normalization processing; S25. Determine the combination coefficient of the three weighting methods according to the Lagrange conditional extreme value principle, then calculate the combination weight according to the combination coefficient, and use the combination weight as the final weight of the evaluation index.

5. The method for constructing a blueberry industry regional competitiveness evaluation model according to claim 4, characterized in that: The specific steps of step S3 are as follows: S31. Obtain the evaluation year, the blueberry industry region to be evaluated, and the evaluation indicators of the determined blueberry industry region; S32. The values ​​of the rating indicators of the blueberry industry area to be evaluated are standardized as elements, and a standardized matrix is ​​constructed through the TOPSIS model; S33. Determine the positive and negative ideal solutions of the standardized matrix according to the grey correlation model, calculate the distance between each solution and the positive and negative ideal solutions and the grey correlation matrix, and obtain the grey correlation between each solution and the positive and negative ideal solutions; S34. The distances and grey correlations between each scheme and the positive and negative ideal solutions are normalized and then merged to obtain the approximate values ​​of the fit between each evaluation scheme and the positive and negative ideal solutions, and then the relative fit is calculated; S35. The evaluation scheme with the greatest relative fit is taken as the closest evaluation scheme, and the comprehensive regional competitiveness index of the blueberry industry is calculated using the closest evaluation scheme.

6. The method for constructing a blueberry industry regional competitiveness evaluation model according to claim 5, characterized in that: The specific steps of step S4 are as follows: S41. Determine the data acquisition method in advance and build a data collection platform; S42. Upload the calculation method of the comprehensive index of regional competitiveness of the blueberry industry to the Linux server, set it to run in the Python environment, and complete the construction of the data calculation platform; S43. Build the front and back ends of the index data publishing platform according to the product prototype and interface design requirements, and package the front and back ends of the built index data publishing platform and upload them to the server; S44. The data collection platform collects the values ​​of the blueberry industry regional competitiveness evaluation indicators based on the determined data acquisition method and according to the set cycle, and cleans the collected data and stores it in the warehouse; S45. The data computing platform verifies the original data in the database and calculates the comprehensive regional competitiveness index of the blueberry industry in accordance with steps S2 and S3; S46. The data release platform will analyze and publish the calculated comprehensive index of regional competitiveness of the blueberry industry in the form of monthly index display, index ranking, index historical trend analysis and index report.

7. The method for constructing a blueberry industry regional competitiveness evaluation model according to claim 6, characterized in that: In step S43, the product prototype is designed using the Moknife design tool, the interface is designed using the Photoshop tool, and the index data publishing platform is developed using the JDK 1.8 programming language, SpringCloud and Vue2 development frameworks, and IDEA development tools.

8. A blueberry industry regional competitiveness evaluation model construction system, characterized in that: include: The indicator evaluation system determination module is used to determine the blueberry industry regional competitiveness evaluation indicator system based on the comparative advantages and competitive advantages in the industry competitiveness evaluation; The evaluation index weight determination module is used to calculate the weight of each evaluation index using the weighting method, and determine the final weight of the evaluation index according to the Lagrange condition extreme value principle; The comprehensive evaluation index calculation module is used to construct a standardized decision matrix through the TOPSIS model, obtain a standardized matrix after weighting the standardized decision matrix using the final weights of the indicators, and then calculate the closest evaluation scheme according to the grey correlation model, and use the closest evaluation scheme to calculate the comprehensive index of regional competitiveness of the blueberry industry; The automated platform building module is used to pre-build the data collection platform, data calculation platform and data publishing platform to collect data corresponding to the blueberry industry regional competitiveness evaluation indicators, as well as to calculate, analyze and publish the blueberry industry regional competitiveness evaluation indicators.

9. A device, characterized in that: It comprises a processor and a memory; wherein the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the device executes the method described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores instructions, which, when executed on a computer, enable the computer to execute the method described in any one of claims 1 to 7.

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