A digital operation and management system and method for an industrial park

Abstract

The present invention discloses a digital operation management system and method for industrial parks, which relates to the field of operation management. The present invention conducts operation management from two aspects: whether the enterprise distribution within the industrial park meets the operation positioning of the industrial park and whether the number of public technical equipment set within the industrial park meets the technical levels of internal enterprises. Among them, by constructing a final main business type mapping equation, a mapping tool is provided for subsequent classification mapping of the characteristic data of each enterprise in the current industrial park, thereby determining whether the enterprises in the current industrial park meet the operation positioning of the industrial park; by constructing an intellectual property quantity mapping model, a judgment mapping model is provided for subsequent judgment of the optimization effect of the technical equipment set in the current industrial park; by optimizing the settings of each technical equipment set in the current industrial park, the improvement effect on the overall technical level of internal enterprises is optimized.

Description

Technical Field

[0001] The present invention belongs to the field of operation management. Specifically, it particularly relates to a digital operation management system and method for industrial parks. Background Art

[0002] Chinese patent application with publication number CN115018333A discloses a digital operation management method and system for industrial parks, including performing industrial analysis on the first industrial park to obtain a first industrial chain including a first enterprise cluster; traversing the first enterprise cluster for saturation analysis to obtain a first saturation; when the first saturation does not meet the first preset saturation, obtaining a first screening instruction; through screening, obtaining first screening information and sending it to a first decision-making channel to obtain a first decision result.

[0003] Different industrial parks have their own development orientations, which also makes the overall development orientation of the industrial park have a great correlation with the types of each enterprise inside; if the types of enterprises distributed inside the park are unreasonable, it will affect the development of the entire industrial park; in addition, the technical level of the enterprises inside the park has a great impact on the overall technical level of the park, and there may be small enterprises inside the park with a relatively low technical level of their own. If there is no sufficient and reasonable public technical equipment provided in the park to meet their development needs, not only the development of this enterprise is restricted, but also the overall technical level of the park is affected. Summary of the Invention

[0004] Aiming at the problems in the related art, the present invention proposes a digital operation management system and method for industrial parks to overcome the above-mentioned technical problems existing in the existing related technologies.

[0005] To solve the above technical problems, the present invention is realized through the following technical solutions:

[0006] The present invention is a digital operation management method for industrial parks, including the following steps:

[0007] S1. Collect the characteristic data of each enterprise inside the current industrial park to obtain the current enterprise characteristic data matrix; collect the characteristic data of each enterprise, the corresponding main business type data of the enterprise, the quantity of each public technical equipment, the number of internal enterprises, and the number of intellectual properties inside multiple historical industrial parks to obtain a historical enterprise characteristic data matrix set, a historical enterprise main business type data matrix, a historical technical equipment quantity matrix, a historical enterprise quantity set, and an intellectual property quantity set;

[0008] S2. Construct the final main business type mapping equation using the historical enterprise feature data matrix set and the historical enterprise main business type data matrix; construct the intellectual property quantity mapping model using the historical technical equipment quantity matrix, the historical enterprise quantity set, and the intellectual property quantity set;

[0009] S3. Map the current enterprise feature data matrix using the final main business type mapping equation to obtain the current enterprise main business type data set; optimize the enterprises in the current industrial park according to the current enterprise main business type data set to obtain the current final enterprise main business type data set;

[0010] S4. Optimize the setting of each technical equipment in the current industrial park in combination with the intellectual property quantity mapping model and the current final enterprise main business type data set to obtain the current final technical equipment quantity set;

[0011] Each enterprise in the industrial park has its own type, such as technology enterprises, entertainment enterprises, etc.; the industrial park also has its type, such as technology-based industrial park; different types of enterprises have different characteristics; based on the above, in this solution, by collecting the feature data of each enterprise inside the current industrial park, it provides data support for subsequent determination of the main business type of each enterprise in the current industrial park; by collecting the feature data of each enterprise and the corresponding enterprise main business type data inside multiple historical industrial parks, it provides data support for subsequent construction of the final main business type mapping equation; since the public technical equipment in the industrial park has a great relationship with the overall technical level in the industrial park, based on this, by collecting the quantity of each public technical equipment, the quantity of internal enterprises, and the intellectual property quantity inside multiple historical industrial parks, it provides data support for subsequent construction of the intellectual property quantity mapping model; by constructing the final main business type mapping equation, it provides a mapping tool for subsequent classification mapping of the feature data of each enterprise in the current industrial park, so as to determine whether the enterprises in the current industrial park meet the operation positioning of the industrial park; by constructing the intellectual property quantity mapping model, it provides a determination mapping model for subsequent determination of the optimization effect of the technical equipment set in the current industrial park; by optimizing the setting of each technical equipment set in the current industrial park, the improvement effect of the overall technical level of the internal enterprises reaches the optimal.

[0012] Preferably, S1 includes the following steps:

[0013] S11. Set multiple main business types of the enterprises in the industrial park to obtain the enterprise main business type set

[0014] a = {a1, a2,..., a i ,..., a a′}, a iDenote the main business type of the \(i\)-th industrial park set, and \(a'\) denote the total number of main business types of the industrial park set; further set multiple characteristic types that can reflect the type of an enterprise to obtain an enterprise characteristic type set Denote the \(i\)-th enterprise characteristic type set Denote the total number of enterprise characteristic types set

[0015] Set the type of technical equipment required for the technical development of each enterprise main business type in the enterprise main business type set to obtain a technical equipment type matrix As follows

[0016]

[0017] Among them Denote the \(j\)-th type of technical equipment required for the technical development set for the \(i\)-th enterprise main business type in the enterprise main business type set Denote the total number of types of technical equipment required for the technical development set for each enterprise main business type in the enterprise main business type set

[0018] S12. Collect the characteristic data of each enterprise within the current industrial park according to the enterprise characteristic type set to obtain the current enterprise characteristic data matrix \(b1\); as follows

[0019]

[0020] Among them, \(b\) 1ij Denote the characteristic data of the \(j\)-th type of the \(i\)-th enterprise within the current industrial park, and \(b1'\) denote the total number of enterprises within the current industrial park

[0021] S13. Set the proportion of the main business type; collect the characteristic data, the corresponding enterprise main business type data, and the main business type data of the corresponding industrial park of each enterprise within multiple historical industrial parks according to the enterprise characteristic type set and the enterprise main business type set, and the ratio of the number of internal enterprises with the same main business type as the industrial park to the total number of internal enterprises within the historical industrial park is greater than or equal to the proportion of the main business type; obtain the historical enterprise characteristic data matrix set \(c1 = \{c\) 11 , c 12 ,..., c 1i ,..., c 1c′ \}, the historical enterprise main business type data matrix \(c2\), and the historical park main business type data set \(c3 = \{c\) 31 , c 32 ,..., c 3i ,..., c 3c′ \}; \(c\) 1iDenote the matrix of enterprise feature data of the \(i\)-th historical industrial park collected, \(c'\) denote the total number of historical industrial parks collected, \(c\) 3i Denote the main business type data of the \(i\)-th historical industrial park collected; \(c\) 1i \(c_1\), \(c_2\) are respectively as follows

[0022]

[0023]

[0024] Among them, \(c\) 1ijk Denote the feature data of the \(k\)-th type of the \(j\)-th enterprise in the \(i\)-th historical industrial park collected, \(b_2'\) i Denote the total number of enterprises in the \(i\)-th historical industrial park collected; \(c\) 2ij Denote the main business type data of the \(j\)-th enterprise in the \(i\)-th historical industrial park collected;

[0025] S14. According to the technical equipment type matrix, for each main business type of enterprises in the concentration of enterprise main business types, collect the quantity of each public technical equipment, the quantity of internal enterprises, and the quantity of intellectual property rights of multiple historical industrial parks respectively, to obtain the historical technical equipment quantity matrix Historical enterprise quantity set And the intellectual property rights quantity set Respectively denote the total number of enterprises and the total number of intellectual property rights in the \(i\)-th historical industrial park collected, \(f\) denote the total number of industrial parks collected for each main business type of enterprises in the concentration of enterprise main business types according to the technical equipment type matrix; As follows

[0026]

[0027] Among them, Denote the quantity of the \(j\)-th type of technical equipment set in the \(i\)-th historical industrial park;

[0028] The concentration of enterprise main business types includes industrial type, construction type, geological exploration and water conservancy type, transportation and warehousing type, post and telecommunications type, wholesale and retail type, catering type, financial type, real estate type, information technology service type, and social service type, etc.; the concentration of enterprise feature types includes industrial nature feature, enterprise scale feature, ownership form feature, market positioning feature, organizational feature, rules and regulations feature, technology and organizational feature, etc.; the technical equipment type matrix includes Internet of Things devices, cloud computing devices, big data devices, artificial intelligence devices, network devices, storage devices, monitoring devices, system software, cloud computing software, and big data software, etc.

[0029] Preferably, S2 includes the following steps:

[0030] S21. Construct an initial main business type mapping equation as follows:

[0031]

[0032] In the formula, is the dependent variable of the initial main business type mapping equation, representing the enterprise main business type data; is the i-th independent variable of the initial main business type mapping equation, representing the enterprise characteristic data of the i-th type, is the corresponding independent variable coefficient; d represents the bias of the initial main business type mapping equation, and ceil represents the rounding function;

[0033] S22. Optimize the initial main business type mapping equation by using the historical enterprise characteristic data matrix set and the historical enterprise main business type data matrix to obtain the final main business type mapping equation;

[0034] S23. Construct an intellectual property quantity mapping model by using the historical technology equipment quantity matrix, the historical enterprise quantity set, and the intellectual property quantity set;

[0035] In the initial main business type mapping equation, by performing a rounding operation on the right side of the equation, the mapping result can better match the enterprise main business type data. In this solution, a series of integers are used to represent the enterprise main business type data.

[0036] Preferably, S22 includes the following steps:

[0037] S221. Substitute each row of data in each historical enterprise characteristic data matrix in the historical enterprise characteristic data matrix set into the initial main business type mapping equation for mapping to obtain a historical main business type mapping data matrix c4 as follows:

[0038]

[0039] where c 4ij represents the main business type mapping data obtained by substituting the characteristic data set of the j-th enterprise in the i-th historical industrial park in the historical enterprise characteristic data matrix set into the initial main business type mapping equation for mapping;

[0040] S222. Set the error ratio threshold. When the ratio of the main business type mapping data that is different from the corresponding main business type data in the historical enterprise main business type data matrix c2 in the historical main business type mapping data matrix c4 is greater than or equal to the error ratio threshold, optimize the initial main business type mapping equation until the ratio of the main business type mapping data that is different from the corresponding main business type data in the historical enterprise main business type data matrix c2 in the historical main business type mapping data matrix c4 is less than the error ratio threshold, and then obtain the final main business type mapping equation; otherwise, there is no need to optimize the initial main business type mapping equation, and the initial main business type mapping equation is used as the final main business type mapping equation.

[0041] By substituting each row of data in each historical enterprise feature data matrix in the historical enterprise feature data matrix set into the mapping equation before optimization for mapping, if the mapped data can match the actual data, there is no need to optimize, thus saving operation steps.

[0042] Preferably, the S3 includes the following steps:

[0043] S31. Set the main business ratio data. Collect the main business type data of the current industrial park according to the enterprise main business type set, denoted as the current park main business type data. Substitute each row of data in the current enterprise feature data matrix b1 into the final main business type mapping equation for mapping to obtain the current enterprise main business type data set.

[0044] S32. When the ratio of the number of the same enterprise main business type data in the current enterprise main business type data set and the current park main business type data to the total number of data in the current enterprise main business type data set is greater than or equal to the main business ratio data, the enterprises in the current industrial park meet the positioning of the current industrial park, and there is no need to adjust the internal enterprises. The current enterprise main business type data set is used as the current final enterprise main business type data set; otherwise, adjust the enterprises in the current industrial park in combination with the final main business type mapping equation until the ratio of the number of the same enterprise main business type data in the current enterprise main business type data set and the current park main business type data to the total number of data in the current enterprise main business type data set is greater than or equal to the main business ratio data, and then obtain the current final enterprise main business type data set.

[0045] By mapping the feature data of the enterprises in the current industrial park, the main business type data of each enterprise is obtained. By setting the main business ratio data, a quantitative determination standard is provided for determining whether the enterprises inside the current industrial park meet the operation positioning of the current industrial park.

[0046] Preferably, the S4 includes the following steps:

[0047] S41. Collect the quantity of each public technical equipment, the total number of internal enterprises, and the number of intellectual properties in the current industrial park in combination with the current final main business type dataset of enterprises and the technical equipment type matrix, and obtain the current technical equipment quantity set The current final number of enterprises and the current number of intellectual properties f2′; f1′ i Represents the quantity of the i-th type of technical equipment in the current industrial park;

[0048] S42. Set the intellectual property quantity threshold; when the current intellectual property quantity is greater than or equal to the intellectual property quantity threshold, there is no need to adjust the quantity of each public technical equipment in the current industrial park, and use the current technical equipment quantity set as the current final technical equipment quantity set; otherwise, adjust the current technical equipment quantity set until the current intellectual property quantity is greater than or equal to the intellectual property quantity threshold, and obtain the current final technical equipment quantity set;

[0049] By setting the intellectual property quantity threshold, a quantitative judgment criterion is provided for subsequent judgment on whether the overall intellectual property quantity in the current industrial park meets the requirements; furthermore, it is determined whether it is necessary to optimize the quantity of public technical equipment set in the current industrial park.

[0050] Preferably, the adjustment of the current technical equipment quantity set in S42 includes the following steps:

[0051] S421. Construct an improved tern population for optimizing the quantity of technical equipment d2′ i Represents the i-th tern in the improved tern population for optimizing the quantity of technical equipment, Represents the scale of the improved tern population for optimizing the quantity of technical equipment; set the maximum number of iterations of the improved tern population for optimizing the quantity of technical equipment as And the current number of iterations as Respectively as the second maximum number of iterations and the second current number of iterations; the search space dimension of the improved tern population for optimizing the quantity of technical equipment is

[0052] S422. Set the value range of the quantity of various technical equipment set in the current industrial park in combination with the technical equipment type matrix, and obtain the technical equipment quantity value range set e3; as follows,

[0053]

[0054] Among them, Respectively represent the lower limit and upper limit of the value range of the quantity of the i-th technical equipment set in the current industrial park;

[0055] Set the initial position of each Sooty Tern in the population of Sooty Terns optimized for the number of technical devices according to the value range set e3 of the number of technical devices, and obtain the second initial position matrix e'2; as follows,

[0056]

[0057] where e' 2ji represents the component of the initial position of the j-th Sooty Tern in the population of Sooty Terns optimized for the number of technical devices in the dimension of the number of installations of the i-th technical device in the current industrial park; e' 2ji The calculation formula of is as follows,

[0058]

[0059] In the formula, rand 2ji represents a random number between 0 and 1 generated for e' 2ji ;

[0060] S423. Construct the fitness function of the population of Sooty Terns optimized for the number of technical devices according to the current number of intellectual properties f2' as follows,

[0061]

[0062] S424. Start iteration. Before iteration, set the second current iteration number to 1; in the first round of iteration, use the fitness function of the population of Sooty Terns optimized for the number of technical devices and cooperate with the intellectual property number mapping model to calculate the fitness value of the initial position of each Sooty Tern in the second initial position matrix e'2, and obtain the third fitness value set; take the largest fitness value in the third fitness value set and the corresponding initial position of the Sooty Tern as the third global best fitness and the third global best position respectively; update the initial position of each Sooty Tern in the second initial position matrix e'2 according to the third global best fitness and the third global best position; after the update is completed, add 1 to the second current iteration number and enter the next round of iteration;

[0063] In each subsequent round of iteration, use the fitness function of the population of Sooty Terns optimized for the number of technical devices And in combination with the intellectual property quantity mapping model, calculate the fitness value of the position of each sooty tern updated in the previous iteration process to obtain the fourth fitness value set; use the maximum fitness value in the fourth fitness value set and the corresponding position of the sooty tern as the fourth global best fitness and the fourth global best position respectively; according to the fourth global best fitness and the fourth global best position, update the position of each sooty tern updated in the previous iteration process again; after the update is completed, add 1 to the second current iteration number and enter the next iteration;

[0064] S425. When holds, stop the iteration to obtain the second final global best position; otherwise, continue the iteration until holds; use the second final global best position as the current optimized technical equipment quantity set;

[0065] Map the current optimized technical equipment quantity set and the current final enterprise quantity into the intellectual property quantity mapping model to obtain the current optimized intellectual property quantity; when the current optimized intellectual property quantity is greater than or equal to the intellectual property quantity threshold, use the current optimized technical equipment quantity set as the current final technical equipment quantity set; otherwise, return to S424 to continue the iteration until the current optimized intellectual property quantity is greater than or equal to the intellectual property quantity threshold;

[0066] By using the sooty tern optimization algorithm to simultaneously iteratively optimize the quantities of various types of technical equipment in the current industrial park, and in combination with the intellectual property quantity mapping model, map the quantities of various types of technical equipment in the current industrial park obtained in each iteration process into the corresponding intellectual property quantities, so that the superiority and inferiority of the solution obtained in the current iteration can be calculated through the fitness function; with continuous iteration, the overall intellectual property quantity corresponding to the current industrial park continuously increases and finally meets the requirements.

[0067] A digital operation and management method for an industrial park, including a data collection type setting module, a current enterprise feature data collection module, a first historical industrial park data collection module, a second historical industrial park data collection module, a mapping equation construction module, a mapping model construction module, a mapping module, a current industrial park enterprise optimization module, and a technical equipment optimization module.

[0068] The present invention has the following beneficial effects:

[0069] 1. In the present invention, operation management is carried out from two aspects: whether the enterprise distribution within the industrial park meets the operation positioning of the industrial park and whether the number of public technical equipment set within the industrial park meets the technical levels of the internal enterprises. Among them, by constructing the final main business type mapping equation, a mapping tool is provided for subsequent classification mapping of the characteristic data of each enterprise in the current industrial park, thereby determining whether the enterprises in the current industrial park meet the operation positioning of the industrial park; by constructing the intellectual property quantity mapping model, a judgment mapping model is provided for subsequent judgment of the optimization effect of the technical equipment set in the current industrial park; by optimizing the setting of each technical equipment set in the current industrial park, the improvement effect on the overall technical level of the internal enterprises is optimized to the best.

[0070] 2. In the present invention, by setting the intellectual property quantity threshold, a quantitative judgment criterion is provided for subsequent judgment of whether the overall intellectual property quantity of the current industrial park meets the requirements.

[0071] 3. In the present invention, the number of various types of technical equipment in the current industrial park is simultaneously iteratively optimized by adopting the Sooty Tern optimization algorithm, and the number of various types of technical equipment in the current industrial park obtained in each round of iteration process is mapped to the corresponding intellectual property quantity, so that the quality of the solution obtained in the current iteration can be calculated through the fitness function; with continuous iteration, the overall intellectual property quantity corresponding to the current industrial park continuously increases and finally meets the requirements.

[0072] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the technical solutions of the embodiments of the invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the invention, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0074] Figure 1 It is a schematic flow chart of a digital operation management method for an industrial park of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0075] The technical solutions in the embodiments of the invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the invention. Obviously, the described embodiments are only some embodiments of the invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the invention without creative efforts belong to the scope of protection of the invention.

[0076] In the description of the present invention, it should be understood that the terms "opening", "upper", "lower", "top", "middle", "inner", etc. indicating the orientation or positional relationship are only for the convenience of describing the invention and simplifying the description, rather than indicating or implying that the referred components or elements must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to the invention.

[0077] Embodiment 1

[0078] Please refer to Figure 1 , this embodiment is a digital operation and management method for an industrial park, including the following steps:

[0079] S1. Collect the characteristic data of each enterprise within the current industrial park to obtain the current enterprise characteristic data matrix; collect the characteristic data of each enterprise, the corresponding main business type data of the enterprise, the quantity of each public technical equipment, the quantity of internal enterprises, and the quantity of intellectual property rights within multiple historical industrial parks to obtain a historical enterprise characteristic data matrix set, a historical enterprise main business type data matrix, a historical technical equipment quantity matrix, a historical enterprise quantity set, and an intellectual property right quantity set;

[0080] The said S1 includes the following steps:

[0081] S11. Set multiple main business types of the enterprises in the industrial park to obtain an enterprise main business type set

[0082] a = {a1, a2,..., a i ,..., a a′}, where a i represents the i-th main business type set for the industrial park, and a' represents the total number of main business types set for the industrial park; then set multiple characteristic types that can reflect the types of enterprises to obtain an enterprise characteristic type set represents the i-th enterprise characteristic type set, represents the total number of enterprise characteristic types set;

[0083] Set the types of technical equipment required for the technical development of each main business type in the enterprise main business type set to obtain a technical equipment type matrix as follows,

[0084]

[0085] Among them, represents the j-th technical equipment type required for the technical development set for the i-th main business type in the enterprise main business type set, Represents the total quantity of types of technical equipment required for technological development set for each main business type of the enterprise, concentrated on the main business types of the enterprise;

[0086] S12. Collect the characteristic data of each enterprise within the current industrial park according to the set of enterprise characteristic types to obtain the current enterprise characteristic data matrix b1; as follows,

[0087]

[0088] where b 1ij represents the characteristic data of the j-th type of the i-th enterprise within the current industrial park, and b1' represents the total number of enterprises within the current industrial park;

[0089] S13. Set the proportion of the main business type; collect the characteristic data of each enterprise, the corresponding main business type data of the enterprise, and the main business type data of the corresponding industrial park within multiple historical industrial parks according to the set of enterprise characteristic types and the set of enterprise main business types. The ratio of the number of internal enterprises with the same main business type as the industrial park to the total number of internal enterprises within the historical industrial park is greater than or equal to the proportion of the main business type; obtain the historical enterprise characteristic data matrix set c1 = {c 11 , c 12 ,..., c 1i ,..., c 1c′}, the historical enterprise main business type data matrix c2, and the historical park main business type data set c3 = {c 31 , c 32 ,..., c 3i ,..., c 3c′}; c 1i represents the enterprise characteristic data matrix of the i-th historical industrial park collected, c' represents the total number of historical industrial parks collected, and c 3i represents the main business type data of the i-th historical industrial park collected; c 1i , c2 are as follows respectively,

[0090]

[0091]

[0092] where c 1ijk represents the characteristic data of the k-th type of the j-th enterprise within the i-th historical industrial park collected, and b2' i represents the total number of enterprises within the i-th historical industrial park collected; c 2ij represents the main business type data of the j-th enterprise within the i-th historical industrial park collected;

[0093] S14. According to the technical equipment type matrix, for each main business type of the enterprise, collect the quantity of each public technical equipment, the quantity of internal enterprises, and the quantity of intellectual property rights in multiple historical industrial parks respectively, and obtain the historical technical equipment quantity matrix. Historical enterprise quantity set And the intellectual property quantity set respectively represent the total quantity of enterprises and the total quantity of intellectual property rights in the i-th historical industrial park collected, and f represents the total quantity of industrial parks collected for each main business type of the enterprise according to the technical equipment type matrix. As follows,

[0094]

[0095] Among them, represents the quantity of the j-th type of technical equipment set in the i-th historical industrial park collected.

[0096] S2. Construct the final main business type mapping equation by using the historical enterprise feature data matrix set and the historical enterprise main business type data matrix; construct the intellectual property quantity mapping model by using the historical technical equipment quantity matrix, the historical enterprise quantity set, and the intellectual property quantity set.

[0097] S2 includes the following steps:

[0098] S21. Construct the initial main business type mapping equation; as follows,

[0099]

[0100] In the formula, is the dependent variable of the initial main business type mapping equation, representing the enterprise main business type data; is the i-th independent variable of the initial main business type mapping equation, representing the i-th type of enterprise feature data, is the corresponding independent variable coefficient; d represents the bias of the initial main business type mapping equation, and ceil represents the rounding function.

[0101] S22. Optimize the initial main business type mapping equation by using the historical enterprise feature data matrix set and the historical enterprise main business type data matrix to obtain the final main business type mapping equation.

[0102] S22 includes the following steps:

[0103] S221. Substitute each row of data in each historical enterprise feature data matrix in the historical enterprise feature data matrix set into the initial main business type mapping equation for mapping to obtain the historical main business type mapping data matrix c4 as follows:

[0104]

[0105] where c 4ij represents the main business type mapping data obtained by substituting the feature data set of the j-th enterprise in the i-th historical industrial park in the historical enterprise feature data matrix set into the initial main business type mapping equation for mapping;

[0106] S222. Set the error ratio threshold. When the ratio of the main business type mapping data that is different from the corresponding enterprise main business type data in the historical enterprise main business type data matrix c2 in the historical main business type mapping data matrix c4 is greater than or equal to the error ratio threshold, optimize the initial main business type mapping equation until the ratio of the main business type mapping data that is different from the corresponding enterprise main business type data in the historical enterprise main business type data matrix c2 in the historical main business type mapping data matrix c4 is less than the error ratio threshold, and then obtain the final main business type mapping equation; otherwise, there is no need to optimize the initial main business type mapping equation, and the initial main business type mapping equation is used as the final main business type mapping equation.

[0107] The optimization of the initial main business type mapping equation in S222 includes the following steps:

[0108] S2221. Construct a mapping equation to optimize the Sooty Tern population d1′ i represents the i-th Sooty Tern in the mapping equation to optimize the Sooty Tern population, represents the size of the mapping equation to optimize the Sooty Tern population; set the maximum number of iterations of the mapping equation to optimize the Sooty Tern population as and the current number of iterations as as the first maximum number of iterations and the first current number of iterations respectively; the search space dimension of the mapping equation to optimize the Sooty Tern population is

[0109] S2222. Set the value range of each independent variable coefficient and the bias in the initial main business type mapping equation to obtain the independent variable coefficient value range set e1 and the bias value range [e 21 , e 22 , e 21 , e 22 represent the lower limit and upper limit of the bias value in the initial main business type mapping equation respectively; e1 is as follows

[0110]

[0111] Among them, respectively represent the lower limit and the upper limit of the value of the coefficient of the i-th independent variable in the initial main business type mapping equation;

[0112] According to the set e1 of the value ranges of the independent variable coefficients and the value range [e 21 , e 22 of the bias, set the mapping equation to optimize the initial positions of each sooty tern in the sooty tern population to obtain the first initial position matrix e1'; as follows,

[0113]

[0114] Among them, e1' ji represents the component of the initial position of the j-th sooty tern in the sooty tern population optimized by the mapping equation in the dimension of the coefficient of the i-th independent variable in the initial main business type mapping equation, represents the component of the initial position of the j-th sooty tern in the sooty tern population optimized by the mapping equation in the dimension of the bias of the initial main business type mapping equation; the calculation formulas of e1' ji , are as follows respectively,

[0115]

[0116]

[0117] In the formula, rand 1ji , respectively represent random numbers between 0 and 1 generated for e1' ji , ;

[0118] S2223. Construct the fitness function of the sooty tern population optimized by the mapping equation according to the historical main business type mapping data matrix c4 and the historical enterprise main business type data matrix c2 As follows,

[0119]

[0120] In the formula, β is a positive number, representing the correction parameter;

[0121] S2224. Start the iteration. Before the iteration, set the first current iteration number to 1; in the first round of iteration, use the fitness function of the sooty tern population optimized by the mapping equation Calculate the fitness value of the initial position of each sooty tern in the first initial position matrix e1′ to obtain the first fitness value set; take the maximum fitness value in the first fitness value set and the corresponding initial position of the sooty tern as the first global best fitness and the first global best position respectively; update the initial position of each sooty tern in the first initial position matrix e1′ according to the first global best fitness and the first global best position; after the update is completed, increment the first current iteration count by 1 and enter the next round of iteration;

[0122] In each other round of iteration, optimize the fitness function of the sooty tern population using the mapping equation Calculate the fitness value of the position of each sooty tern updated in the previous round of iteration to obtain the second fitness value set; take the maximum fitness value in the second fitness value set and the corresponding position of the sooty tern as the second global best fitness and the second global best position respectively; update the position of each sooty tern updated in the previous round of iteration again according to the second global best fitness and the second global best position; after the update is completed, increment the first current iteration count by 1 and enter the next round of iteration;

[0123] S2225. When stop the iteration to obtain the first final global best position; otherwise, continue the iteration until Substitute each position component in the first final global best position into the initial main business type mapping equation to obtain the optimized main business type mapping equation; substitute each row of data in each historical enterprise feature data matrix in the historical enterprise feature data matrix set into the optimized main business type mapping equation for mapping to obtain the historical optimized main business type mapping data matrix;

[0124] When the proportion of the main business type mapping data that is different from the corresponding enterprise main business type data in the historical enterprise main business type data matrix c2 in the historical optimized main business type mapping data matrix is less than the error proportion threshold, take the optimized main business type mapping equation as the final main business type mapping equation; otherwise, return to S2224 to continue the iteration until the proportion of the main business type mapping data that is different from the corresponding enterprise main business type data in the historical enterprise main business type data matrix c2 in the historical optimized main business type mapping data matrix is less than the error proportion threshold;

[0125] The Sooty Tern Optimization Algorithm has strong global search ability and can quickly find potential optimal solutions in the search space. It adopts an "exploration and exploitation" strategy, that is, seeking a balance between global search and local search, which helps to improve the search efficiency and convergence speed of the algorithm. It has fewer parameters, which makes the parameter tuning process of the algorithm simpler. It is less sensitive to initial conditions and parameter settings and has strong robustness. Based on the above advantages, in this solution, the Sooty Tern Optimization Algorithm is used to iteratively optimize the coefficients of each independent variable and the bias of the initial main business type mapping equation for multiple times, and the error between the mapped data and the actual data is used as the fitness function. Therefore, as the iteration progresses, the coefficients of the independent variables and the bias obtained by iteration are substituted into the mapping equation, making the mapping accuracy of the mapping equation higher and higher, and finally meeting the requirements.

[0126] S23. Construct an intellectual property quantity mapping model by using the historical technology equipment quantity matrix, the historical enterprise quantity set, and the intellectual property quantity set.

[0127] S23 includes the following steps:

[0128] S231. Construct an initial SVM model and set the training data ratio; divide the historical technology equipment quantity matrix, the historical enterprise quantity set, and the intellectual property quantity set according to the training data ratio to obtain a historical technology equipment quantity training matrix, a historical enterprise quantity training set, an intellectual property quantity training set, a historical technology equipment quantity test matrix, a historical enterprise quantity test set, and an intellectual property quantity test set.

[0129] S232. Set the training error threshold; input the historical technology equipment quantity training matrix and the historical enterprise quantity training set as training data and the intellectual property quantity training set as training labels into the initial SVM model for training; during the training process, when the training error is less than the training error threshold, stop training to obtain a trained SVM model; otherwise, continue training until the training error is less than the training error threshold.

[0130] S233. Set the test accuracy threshold; input the historical technology equipment quantity test matrix and the historical enterprise quantity test set as test data and the intellectual property quantity test set as test labels into the trained SVM model for testing; after the testing is completed, obtain the test accuracy; when the test accuracy is greater than or equal to the test accuracy threshold, use the trained SVM model as the intellectual property quantity mapping model; otherwise, return to S232 to continue training the trained SVM model until the test accuracy is greater than or equal to the test accuracy threshold.

[0131] By using the historical technology equipment quantity training matrix, historical enterprise quantity training set, intellectual property quantity training set, historical technology equipment quantity test matrix, historical enterprise quantity test set, and intellectual property quantity test set to train and test the initial SVM model respectively, the obtained intellectual property quantity mapping model has a good mapping ability for the relationship between technology equipment quantity, enterprise quantity, and intellectual property quantity;

[0132] S3. Map the current enterprise feature data matrix using the final main business type mapping equation to obtain the current enterprise main business type data set; optimize the enterprises in the current industrial park according to the current enterprise main business type data set to obtain the current final enterprise main business type data set;

[0133] The S3 includes the following steps:

[0134] S31. Set the main business proportion data; collect the main business type data of the current industrial park according to the enterprise main business type set, denoted as the current park main business type data; substitute each row of data in the current enterprise feature data matrix b1 into the final main business type mapping equation for mapping to obtain the current enterprise main business type data set;

[0135] S32. When the ratio of the number of enterprise main business type data that are the same in the current enterprise main business type data set and the current park main business type data to the total number of data in the current enterprise main business type data set is greater than or equal to the main business proportion data, the enterprises in the current industrial park meet the positioning of the current industrial park and do not need to adjust the internal enterprises, and use the current enterprise main business type data set as the current final enterprise main business type data set; otherwise, adjust the enterprises in the current industrial park in cooperation with the final main business type mapping equation until the ratio of the number of enterprise main business type data that are the same in the current enterprise main business type data set and the current park main business type data to the total number of data in the current enterprise main business type data set is greater than or equal to the main business proportion data, and obtain the current final enterprise main business type data set;

[0136] S4. Optimize the settings of each technical equipment set in the current industrial park in cooperation with the intellectual property quantity mapping model and the current final enterprise main business type data set to obtain the current final technical equipment quantity set;

[0137] The S4 includes the following steps:

[0138] S41. Collect the quantity of each public technical equipment, the total number of internal enterprises, and the intellectual property quantity in the current industrial park in cooperation with the current final enterprise main business type data set and the technical equipment type matrix to obtain the current technical equipment quantity set Current final enterprise quantity and current intellectual property quantity f2′; f1′i represents the quantity of the i-th type of technical equipment in the current industrial park;

[0139] S42. Set the intellectual property quantity threshold; when the current intellectual property quantity is greater than or equal to the intellectual property quantity threshold, there is no need to adjust the quantity of each public technical equipment in the current industrial park, and use the current technical equipment quantity set as the current final technical equipment quantity set; otherwise, adjust the current technical equipment quantity set until the current intellectual property quantity is greater than or equal to the intellectual property quantity threshold, and obtain the current final technical equipment quantity set;

[0140] The adjustment of the current technical equipment quantity set in S42 includes the following steps:

[0141] S421. Construct an optimized tern population for technical equipment quantity d2′ i represents the i-th tern in the optimized tern population for technical equipment quantity, represents the scale of the optimized tern population for technical equipment quantity; set the maximum number of iterations of the optimized tern population for technical equipment quantity as and the current number of iterations as as the second maximum number of iterations and the second current number of iterations respectively; the search space dimension of the optimized tern population for technical equipment quantity is

[0142] S422. Cooperate with the technical equipment type matrix to set the value range of the quantity of various technical equipment settings in the current industrial park, and obtain the technical equipment quantity value range set e3; as follows,

[0143]

[0144] Among them, respectively represent the lower limit and upper limit of the value range of the quantity of the i-th technical equipment setting in the current industrial park;

[0145] According to the technical equipment quantity value range set e3, set the initial position of each tern in the optimized tern population for technical equipment quantity, and obtain the second initial position matrix e′2; as follows,

[0146]

[0147] Among them, e′ 2ji represents the component of the initial position of the j-th tern in the optimized tern population for technical equipment quantity in the dimension of the quantity of the i-th technical equipment setting in the current industrial park; e′ 2ji The calculation formula of is as follows,

[0148]

[0149] where rand 2ji represents a random number between 0 and 1 generated for e′ 2ji ;

[0150] S423. Construct a fitness function for optimizing the population of sooty terns with the number of technical devices according to the current number of intellectual properties f2′ as follows,

[0151]

[0152] S424. Start iteration. Before iteration, set the second current iteration number to 1; in the first round of iteration, use the fitness function of the population of sooty terns optimized by the number of technical devices and cooperate with the intellectual property number mapping model to calculate the fitness values of the initial positions of each sooty tern in the second initial position matrix e′2, obtaining a third set of fitness values; take the maximum fitness value in the third set of fitness values and the corresponding initial position of the sooty tern as the third global best fitness and the third global best position respectively; update the initial positions of each sooty tern in the second initial position matrix e′2 according to the third global best fitness and the third global best position; after the update is completed, add 1 to the second current iteration number and enter the next round of iteration;

[0153] In each subsequent round of iteration, use the fitness function of the population of sooty terns optimized by the number of technical devices and cooperate with the intellectual property number mapping model to calculate the fitness values of the positions of each sooty tern updated in the previous round of iteration, obtaining a fourth set of fitness values; take the maximum fitness value in the fourth set of fitness values and the corresponding position of the sooty tern as the fourth global best fitness and the fourth global best position respectively; update the positions of each sooty tern updated in the previous round of iteration again according to the fourth global best fitness and the fourth global best position; after the update is completed, add 1 to the second current iteration number and enter the next round of iteration;

[0154] S425. When stop iteration and obtain the second final global best position; otherwise, continue iteration until ; take the second final global best position as the current optimized set of the number of technical devices;

[0155] Input the set of the currently optimized technical equipment quantities and the current final number of enterprises into the intellectual property quantity mapping model for mapping to obtain the currently optimized intellectual property quantity; when the currently optimized intellectual property quantity is greater than or equal to the intellectual property quantity threshold, take the set of the currently optimized technical equipment quantities as the current final set of technical equipment quantities; otherwise, return to S424 to continue the iteration until the currently optimized intellectual property quantity is greater than or equal to the intellectual property quantity threshold.

[0156] Embodiment 2

[0157] This embodiment discloses a digital operation and management system for an industrial park. The system can implement the method of the above embodiment, including a data collection type setting module, a current enterprise feature data collection module, a first historical industrial park data collection module, a second historical industrial park data collection module, a mapping equation construction module, a mapping model construction module, a mapping module, a current industrial park enterprise optimization module, and a technical equipment optimization module;

[0158] The data collection type setting module is used to set multiple main business types, enterprise feature types of the enterprises in the industrial park, and the types of technical equipment required for the technical development of each enterprise main business type, to obtain a set of enterprise main business types, a set of enterprise feature types, and a technical equipment type matrix;

[0159] The current enterprise feature data collection module is used to collect the feature data of each enterprise inside the current industrial park according to the set of enterprise feature types, to obtain a current enterprise feature data matrix;

[0160] The first historical industrial park data collection module is used to collect the feature data of each enterprise inside multiple historical industrial parks, the corresponding enterprise main business type data, and the main business type data of the corresponding industrial parks according to the set of enterprise feature types and the set of enterprise main business types, to obtain a set of historical enterprise feature data matrices, a historical enterprise main business type data matrix, and a historical park main business type data set;

[0161] The second historical industrial park data collection module is used to collect the quantity of each public technical equipment, the number of internal enterprises, and the number of intellectual properties of multiple historical industrial parks according to the technical equipment type matrix, to obtain a historical technical equipment quantity matrix, a historical enterprise quantity set, and an intellectual property quantity set;

[0162] The mapping equation construction module is used to construct a final main business type mapping equation by using the set of historical enterprise feature data matrices and the historical enterprise main business type data matrix;

[0163] The mapping model construction module is used to construct an intellectual property quantity mapping model by using the historical technical equipment quantity matrix, the historical enterprise quantity set, and the intellectual property quantity set;

[0164] The mapping module is configured to map the current enterprise feature data matrix by using the final main business type mapping equation to obtain the current enterprise main business type data set;

[0165] The current industrial park enterprise optimization module is configured to optimize the enterprises in the current industrial park according to the current enterprise main business type data set to obtain the current final enterprise main business type data set;

[0166] The technical equipment optimization module is configured to cooperate with the intellectual property quantity mapping model and the current final enterprise main business type data set to optimize the settings of each technical equipment set in the current industrial park to obtain the current final technical equipment quantity set.

[0167] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0168] The preferred embodiments of the invention disclosed above are only used to help illustrate the invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the invention, so that those skilled in the art can understand and utilize the invention well.

Claims

1. A digital operation and management method for industrial parks, characterized in that It includes the following steps: S1. Collect the characteristic data of each enterprise within the current industrial park to obtain the current enterprise characteristic data matrix; Collect the characteristic data of each enterprise, the corresponding main business type data of the enterprise, the quantity of each public technical equipment, the number of internal enterprises, and the number of intellectual properties within multiple historical industrial parks to obtain a historical enterprise characteristic data matrix set, a historical enterprise main business type data matrix, a historical technical equipment quantity matrix, a historical enterprise number set, and an intellectual property quantity set; S2. Construct a final main business type mapping equation by using the historical enterprise characteristic data matrix set and the historical enterprise main business type data matrix; construct an intellectual property quantity mapping model by using the historical technical equipment quantity matrix, the historical enterprise number set, and the intellectual property quantity set; S3. Map the current enterprise characteristic data matrix by using the final main business type mapping equation to obtain the current enterprise main business type data set; Optimize the enterprises within the current industrial park according to the current enterprise main business type data set to obtain the current final enterprise main business type data set; S4. Optimize the setting of each technical equipment set within the current industrial park in combination with the intellectual property quantity mapping model and the current final enterprise main business type data set to obtain the current final technical equipment quantity set; The S4 includes the following steps: S41. Collect the quantity of each public technical equipment, the total number of internal enterprises, and the number of intellectual properties within the current industrial park in combination with the current final enterprise main business type data set and the technical equipment type matrix to obtain the current technical equipment quantity set, the current final enterprise number, and the current intellectual property quantity; S42. Set an intellectual property quantity threshold; when the current intellectual property quantity is greater than or equal to the intellectual property quantity threshold, there is no need to adjust the quantity of each public technical equipment within the current industrial park, and use the current technical equipment quantity set as the current final technical equipment quantity set; otherwise, adjust the current technical equipment quantity set until the current intellectual property quantity is greater than or equal to the intellectual property quantity threshold, and obtain the current final technical equipment quantity set.

2. The digital operation and management method of an industrial park according to claim 1, characterized in that The S1 includes the following steps: S11. Set multiple main business types of the enterprises in the industrial park to obtain an enterprise main business type set; then set multiple characteristic types that can reflect the types of enterprises to obtain an enterprise characteristic type set; set the technical equipment types required for the technical development of each enterprise main business type in the enterprise main business type set to obtain a technical equipment type matrix; S12. Collect the characteristic data of each enterprise within the current industrial park according to the enterprise characteristic type set to obtain the current enterprise characteristic data matrix; S13. Set the proportion of the main business types; collect the characteristic data of each enterprise, the corresponding enterprise main business type data, and the main business type data of the corresponding industrial park within multiple historical industrial parks according to the enterprise characteristic type set and the enterprise main business type set, where the ratio of the number of internal enterprises with the same main business type as the industrial park to the total number of internal enterprises within the historical industrial park is greater than or equal to the proportion of the main business type; obtain the historical enterprise characteristic data matrix set, the historical enterprise main business type data matrix, and the historical park main business type data set; S14. According to the technical equipment type matrix, collect the quantity of each public technical equipment, the number of internal enterprises, and the number of intellectual properties in each historical industrial park for each enterprise main business type in the enterprise main business type set, to obtain the historical technical equipment quantity matrix, the historical enterprise quantity set, and the intellectual property quantity set.

3. The digital operation and management method of an industrial park according to claim 2, characterized in that The said S2 includes the following steps: S21. Construct the initial main business type mapping equation; S22. Optimize the initial main business type mapping equation by using the historical enterprise characteristic data matrix set and the historical enterprise main business type data matrix to obtain the final main business type mapping equation; S23. Construct an intellectual property quantity mapping model by using the historical technical equipment quantity matrix, the historical enterprise quantity set, and the intellectual property quantity set.

4. The digital operation and management method of an industrial park according to claim 3, characterized in that, The said S22 includes the following steps: S221. Substitute each row of data in each historical enterprise characteristic data matrix in the historical enterprise characteristic data matrix set into the initial main business type mapping equation for mapping to obtain the historical main business type mapping data matrix; S222. Set the error proportion threshold; when the proportion of the main business type mapping data that is different from the corresponding enterprise main business type data in the historical enterprise main business type data matrix in the historical main business type mapping data matrix is greater than or equal to the error proportion threshold, optimize the initial main business type mapping equation until the proportion of the main business type mapping data that is different from the corresponding enterprise main business type data in the historical enterprise main business type data matrix in the historical main business type mapping data matrix is less than the error proportion threshold, and then obtain the final main business type mapping equation; otherwise, there is no need to optimize the initial main business type mapping equation, and take the initial main business type mapping equation as the final main business type mapping equation.

5. The digital operation and management method of an industrial park according to claim 4, characterized in that: In S222, the initial main business type mapping equation is optimized by using the sooty tern optimization algorithm.

6. The digital operation management method of an industrial park according to claim 5, wherein: In S23, the intellectual property quantity mapping model uses the SVM model.

7. A digital operation and management method for an industrial park according to claim 6, characterized in that, The said S3 includes the following steps: S31. Set the main business proportion data; collect the main business type data of the current industrial park according to the enterprise main business type set, denoted as the current park main business type data; substitute each row of data in the current enterprise characteristic data matrix into the final main business type mapping equation for mapping to obtain the current enterprise main business type data set; S32. When the ratio of the number of the enterprise main business type data that is the same in the current enterprise main business type data set and the current industrial park main business type data to the total number of data in the current enterprise main business type data set is greater than or equal to the main business ratio data, the enterprises in the current industrial park meet the positioning of the current industrial park, and there is no need to adjust the internal enterprises. Then, use the current enterprise main business type data set as the current final enterprise main business type data set; otherwise, adjust the enterprises in the current industrial park in combination with the final main business type mapping equation until the ratio of the number of the enterprise main business type data that is the same in the current enterprise main business type data set and the current industrial park main business type data to the total number of data in the current enterprise main business type data set is greater than or equal to the main business ratio data, and obtain the current final enterprise main business type data set.

8. A digital operation and management method for an industrial park according to claim 1, characterized in that The adjustment of the current technical equipment quantity set in S42 includes the following steps: S421. Construct the number of technical devices to optimize the population of black noddy terns; set the maximum number of iterations for optimizing the population of black noddy terns by the number of technical devices as and the current number of iterations as , which are respectively used as the second maximum number of iterations and the second current number of iterations; S422. Set the value range of the quantity of various technical equipment set in the current industrial park in combination with the technical equipment type matrix to obtain a technical equipment quantity value range set; set the initial position of each sooty tern in the sooty tern population optimized for technical equipment quantity according to the technical equipment quantity value range set to obtain a second initial position matrix; S423. Construct a fitness function of the sooty tern population optimized for technical equipment quantity according to the current intellectual property quantity; S424. Start iteration. Set the second current iteration number to 1 before iteration; in the first round of iteration, use the fitness function of the sooty tern population optimized for technical equipment quantity and combine it with the intellectual property quantity mapping model to calculate the fitness value of the initial position of each sooty tern in the second initial position matrix to obtain a third fitness value set; use the maximum fitness value in the third fitness value set and the corresponding initial position of the sooty tern as the third global best fitness and the third global best position respectively; update the initial position of each sooty tern in the second initial position matrix according to the third global best fitness and the third global best position; after the update is completed, add 1 to the second current iteration number and enter the next round of iteration; In each subsequent round of iteration, use the fitness function of the sooty tern population optimized for technical equipment quantity and combine it with the intellectual property quantity mapping model to calculate the fitness value of the position of each sooty tern updated in the previous round of iteration to obtain a fourth fitness value set; use the maximum fitness value in the fourth fitness value set and the corresponding position of the sooty tern as the fourth global best fitness and the fourth global best position respectively; update the position of each sooty tern updated in the previous round of iteration again according to the fourth global best fitness and the fourth global best position; after the update is completed, add 1 to the second current iteration number and enter the next round of iteration; S425. When is satisfied, stop the iteration to obtain the second final global best position; otherwise, continue the iteration until is satisfied; use the second final global best position as the current optimized set of the number of technical devices; input the current optimized set of the number of technical devices and the current final number of enterprises into the intellectual property quantity mapping model for mapping to obtain the current optimized number of intellectual properties; when the current optimized number of intellectual properties is greater than or equal to the intellectual property quantity threshold, use the current optimized set of the number of technical devices as the current final set of the number of technical devices; otherwise, return to S424 to continue the iteration until the current optimized number of intellectual properties is greater than or equal to the intellectual property quantity threshold.

9. A system for implementing the digital operation and management method of an industrial park as described in any one of claims 1-8.

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