Digital operation management system and method for industrial park

Abstract

The invention discloses a digital operation management system and method for an industrial park, and relates to the field of operation management. According to the invention, operation management is carried out from two aspects of whether the enterprise distribution in the industrial park meets the operation positioning of the industrial park and whether the number of public technical equipment arranged in the industrial park meets the technical level of the internal enterprise; according to the method, a mapping tool is provided for subsequent classified mapping of feature data of each enterprise in the current industrial park by constructing a final main-marketing type mapping equation, so that whether the enterprises in the current industrial park meet operation positioning of the industrial park or not is judged; by constructing an intellectual property quantity mapping model, a judgment mapping model is provided for subsequent judgment of the optimization effect of technical equipment arranged in a current industrial park; by optimizing the setting of each technical device arranged in the current industrial park, the effect of improving the overall technical level of internal enterprises is optimal.

Description

Technical Field

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

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

[0003] Different industrial parks have their own development positioning, which makes the overall development positioning of the industrial park have a great correlation with the types of enterprises within it; if the types of enterprises distributed within the park are unreasonable, it will affect the development of the entire industrial park; in addition, the technical level of enterprises within the park has a great impact on the overall technical level of the park, and there may be small enterprises within the park with low technical levels. If the park does not provide sufficient and reasonable public technical equipment to meet their development needs, not only will the development of the enterprise be restricted, but it will also affect the overall technical level of the park. Summary of the invention

[0004] In response to the problems in the related technology, the present invention proposes a digital operation management system and method for an industrial park to overcome the above-mentioned technical problems existing in the existing related technology.

[0005] In order to solve the above technical problems, the present invention is achieved through the following technical solutions:

[0006] The present invention is a digital operation and management method for an industrial park, comprising the following steps:

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

[0008] S2. Use the historical enterprise characteristic data matrix set and the historical enterprise main business type data matrix to construct the final main business type mapping equation; use the historical technical equipment quantity matrix, the historical enterprise quantity set and the intellectual property quantity set to construct the intellectual property quantity mapping model;

[0009] S3. Use the final main business type mapping equation to map the current enterprise feature data matrix 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 settings of various technical equipment set in the current industrial park in conjunction 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.; industrial parks also have their own types, such as technology-based industrial parks; different types of enterprises have different characteristics; based on the above, this solution collects the characteristic data of each enterprise in the current industrial park to provide data support for the subsequent determination of the main business type of each enterprise in the current industrial park; by collecting the characteristic data of each enterprise in multiple historical industrial parks and the corresponding enterprise main business type data, it provides data support for the subsequent construction of the final main business type mapping equation; because the public technical equipment in the industrial park has a great relationship with the overall technical level of the industrial park, based on this, by collecting the characteristic data of multiple historical industrial parks, the corresponding enterprise main business type data The number of each type of public technical equipment, the number of internal enterprises and the number of intellectual property rights provide data support for the subsequent construction of an intellectual property quantity mapping model; by constructing the final main business type mapping equation, a mapping tool is provided for the subsequent classification and mapping of the characteristic data of each enterprise in the current industrial park, so as to determine whether the enterprises in the current industrial park meet the operational positioning of the industrial park; by constructing an intellectual property quantity mapping model, a judgment mapping model is provided for the subsequent judgment of the optimization effect of the technical equipment set up in the current industrial park; by optimizing the settings of various technical equipment set up in the current industrial park, the effect of improving the overall technical level of internal enterprises is optimized.

[0012] Preferably, the S1 comprises the following steps:

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

[0014] a={a1,a2,...,a i ,...,a a′}, a irepresents the main business type of the i-th industrial park, a′ represents the total number of main business types of the industrial park; and then multiple characteristic types that can reflect the type of the enterprise are set to obtain the enterprise characteristic type set represents the set i-th enterprise characteristic type, Indicates the total number of enterprise characteristic types set;

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

[0016]

[0017] in, represents the jth type of technical equipment required for technical development set for the i-th type of enterprise in the main business type set, Indicates the total number of technical equipment types required for technological development set for each main business type of the enterprise in the main business type set;

[0018] S12, collecting characteristic data of each enterprise in the current industrial park according to the enterprise characteristic type set, and obtaining the current enterprise characteristic data matrix b1; as follows,

[0019]

[0020] Among them, b 1ij represents the characteristic data of the jth type of the i-th enterprise in the current industrial park, and b1′ represents the total number of enterprises in the current industrial park;

[0021] S13, set the proportion of the main business type; collect the characteristic data of each enterprise in multiple historical industrial parks, the corresponding enterprise main type data and the main type data of the corresponding industrial park according to the enterprise characteristic type set and the enterprise main type set, the ratio of the internal enterprises in the historical industrial park with the same main type as the industrial park to the total number of internal enterprises is greater than or equal to the proportion of the main type; obtain the historical enterprise characteristic data matrix set c1 = {c 11 ,c 12 ,...,c 1i ,...,c 1c′}, historical enterprise main business type data matrix c2 and historical park main business type data set c3 = {c 31 ,c 32 ,...,c 3i ,...,c 3c′};c 1irepresents the enterprise characteristic data matrix of the i-th historical industrial park collected, c′ represents the total number of historical industrial parks collected, c 3i represents the main business type data of the i-th historical industrial park collected; c 1i , c2 are as follows,

[0022]

[0023]

[0024] Among them, c 1ijk b2′ represents the kth type of characteristic data collected from the jth enterprise in the i-th historical industrial park. i represents the total number of enterprises in the i-th historical industrial park collected; c 2ij It represents the main business type data of the j-th enterprise in the i-th historical industrial park;

[0025] S14. According to the technology equipment type matrix, for each main type of enterprise, the number of each public technology equipment, the number of internal enterprises and the number of intellectual property rights in multiple historical industrial parks are collected for each main type of enterprise, and the historical technology equipment quantity matrix is ​​obtained. Historical Enterprise Quantity Set and the number of intellectual property sets Respectively represent the total number of enterprises and the total number of intellectual property rights collected in the i-th historical industrial park, and f represents the total number of industrial parks collected for each main type of enterprise according to the technology equipment type matrix; as follows,

[0026]

[0027] in, represents the number of technical equipment of the jth type installed in the i-th historical industrial park collected;

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

[0029] Preferably, S2 comprises the following steps:

[0030] S21, construct the 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 main business type data of the enterprise; is the ith independent variable of the initial main business type mapping equation, representing the characteristic data of the ith type of enterprise, for 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, optimizing the initial main business type mapping equation using the historical enterprise feature data matrix set and the historical enterprise main business type data matrix to obtain a final main business type mapping equation;

[0034] S23, constructing an intellectual property quantity mapping model using the historical technical equipment quantity matrix, the historical enterprise quantity set, and the intellectual property quantity set;

[0035] In the initial main business type mapping equation, the right side of the equal sign is rounded to make the mapping result better match the main business type data of the enterprise. In this solution, the main business type data of the enterprise is expressed by using a series of integers.

[0036] Preferably, the S22 comprises the following steps:

[0037] S221, substitute each row of data of each historical enterprise characteristic data matrix of the historical enterprise characteristic data matrix set into the initial main business type mapping equation for mapping, and obtain the historical main business type mapping data matrix c4; as follows,

[0038]

[0039] Among them, c 4ij represents the main business type mapping data obtained by substituting the feature data set of the jth 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;

[0040] S222, setting an error ratio threshold; when the proportion of the main business type mapping data in the historical main business type mapping data matrix c4 that is different from the corresponding enterprise main business type data in the historical enterprise main business type data matrix c2 is greater than or equal to the error ratio threshold, optimizing the initial main business type mapping equation until the proportion of the main business type mapping data in the historical main business type mapping data matrix c4 that is different from the corresponding enterprise main type data in the historical enterprise main business type data matrix c2 is less than the error ratio threshold, and obtaining 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 of each historical enterprise feature data matrix in the historical enterprise feature data matrix set into the mapping equation before optimization, if the mapped data can match the actual data, no optimization is required, thus saving operation steps.

[0042] Preferably, S3 comprises the following steps:

[0043] S31, setting the main business proportion data; collecting the main business type data of the current industrial park according to the main business type set of the enterprise, and recording it as the main business type data of the current park; substituting each row of data in the current enterprise feature data matrix b1 into the final main business type mapping equation for mapping, and obtaining the main business type data set of the current enterprise;

[0044] S32. When the ratio of the number of enterprise main type data in the current enterprise main type data set that is the same as the enterprise main type data in the current park main type data set to the total number of data in the current enterprise main 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 there is no need to adjust the internal enterprises, and the current enterprise main type data set is used as the current final enterprise main type data set; otherwise, the enterprises in the current industrial park are adjusted in accordance with the final main type mapping equation until the ratio of the number of enterprise main type data in the current enterprise main type data set that is the same as the enterprise main type data in the current park main type data set to the total number of data in the current enterprise main type data set is greater than or equal to the main business proportion data, and the current final enterprise main type data set is obtained;

[0045] By mapping the characteristic data of enterprises in the current industrial park, the main business type data of each enterprise is obtained; by setting the main business proportion data, a quantitative judgment standard is provided to determine whether the enterprises within the current industrial park meet the operational positioning of the current industrial park.

[0046] Preferably, S4 comprises the following steps:

[0047] S41. Collect the number of each type of public technical equipment, the total number of internal enterprises and the number of intellectual property rights in the current industrial park in conjunction with the current final enterprise main business type data set and the technical equipment type matrix to obtain the current technical equipment quantity data set. Current final number of enterprises and current number of intellectual property rights f2′; f1′ i represents the number of technical equipment of the i-th type in the current industrial park;

[0048] S42, setting a threshold value for the number of intellectual property rights; when the current number of intellectual property rights is greater than or equal to the threshold value for the number of intellectual property rights, there is no need to adjust the number of each type of public technical equipment in the current industrial park, and the current technical equipment quantity set is used as the current final technical equipment quantity set; otherwise, the current technical equipment quantity set is adjusted until the current number of intellectual property rights is greater than or equal to the threshold value for the number of intellectual property rights, and the current final technical equipment quantity set is obtained;

[0049] By setting a threshold for the number of intellectual property rights, a quantitative judgment standard is provided for the subsequent determination of whether the overall number of intellectual property rights in the current industrial park meets the requirements; and then it is determined whether it is necessary to optimize the number of public technical equipment installed in the current industrial park.

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

[0051] S421. Build technical equipment to optimize the population of sooty terns d2′ i It means that the number of technical equipments is optimized for the i-th sooty tern in the sooty tern population, Indicates that the number of technical equipment optimizes the size of the black tern population; Sets the maximum number of iterations of the number of technical equipment to optimize the black tern population And the current iteration number is As the second maximum number of iterations and the second current number of iterations respectively; the search space dimension of the black tern population optimized by the number of technical equipment is

[0052] S422, setting the value intervals of the number of various technical equipments set in the current industrial park in conjunction with the technical equipment type matrix, and obtaining a technical equipment quantity value interval set e3; as follows,

[0053]

[0054] in, They respectively represent the lower limit and upper limit of the number of the i-th technical equipment in the current industrial park;

[0055] According to the technical equipment quantity value interval set e3, the technical equipment quantity is set to optimize the initial position of each sooty tern in the sooty tern population, and the second initial position matrix e′2 is obtained; as follows,

[0056]

[0057] Among them, e′ 2ji represents the component of the initial position of the jth sooty tern in the sooty tern population in the dimension of the number of settings of the i-th technical equipment in the current industrial park; e′ 2ji The calculation formula is as follows,

[0058]

[0059] In the formula, rand 2ji Indicates for e′ 2ji Generate a random number between 0 and 1;

[0060] S423, constructing a fitness function for optimizing the population of sooty terns based on the current number of intellectual property rights f2′ as follows,

[0061]

[0062] S424, start iteration, before iteration, set the second current iteration number Set to 1; in the first round of iterations, the number of technical equipment is used to optimize the fitness function of the black tern population And cooperate 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 e′2 to obtain a third fitness value set; the maximum fitness value in the third fitness value set and the corresponding initial position of the sooty tern are respectively used as the third global optimal fitness and the third global optimal position; according to the third global optimal fitness and the third global optimal position, the initial position of each sooty tern in the second initial position matrix e′2 is updated; after the update is completed, the second current iteration number Add 1 and enter the next iteration;

[0063] In each other round of iteration, the number of technical equipment is used to optimize the fitness function of the black tern population And cooperate 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 iterations to obtain a fourth fitness value set; the maximum fitness value in the fourth fitness value set and the corresponding position of the sooty tern are respectively used as the fourth global optimal fitness and the fourth global optimal position; according to the fourth global optimal fitness and the fourth global optimal position, the position of each sooty tern updated in the previous round of iterations is updated again; after the update is completed, the second current iteration number Add 1 and enter the next iteration;

[0064] S425, when When , stop the iteration and get the second final global optimal position; otherwise, continue to iterate until until the second final global optimal position is used as the current optimized technical equipment quantity set;

[0065] Input the current optimized technical equipment quantity set and the current final enterprise quantity into the intellectual property quantity mapping model for mapping 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 and continue to iterate until the current optimized intellectual property quantity is greater than or equal to the intellectual property quantity threshold;

[0066] By adopting the Sooty Tern optimization algorithm to iteratively optimize the number of various types of technical equipment in the current industrial park at the same time, and cooperating with the intellectual property quantity mapping model, the number of various types of technical equipment in the current industrial park obtained in each round of iteration is mapped to the corresponding number of intellectual property rights, so that the fitness function can be used to calculate the quality of the solution obtained in the current iteration; with continuous iterations, the overall number of intellectual property rights corresponding to the current industrial park continues to increase and eventually meets the requirements.

[0067] A digital operation and management method for an industrial park, comprising a data collection type setting module, a current enterprise characteristic 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. The present invention conducts operation management from two aspects: whether the distribution of enterprises within the industrial park meets the operational positioning of the industrial park and whether the number of public technical equipment installed in the industrial park meets the technical level of the internal enterprises; wherein, by constructing the final main business type mapping equation, a mapping tool is provided for the subsequent classification and mapping of the characteristic data of each enterprise in the current industrial park, so as to determine whether the enterprises in the current industrial park meet the operational positioning of the industrial park; by constructing an intellectual property quantity mapping model, a determination mapping model is provided for the subsequent determination of the optimization effect of the technical equipment installed in the current industrial park; by optimizing the settings of various technical equipment installed in the current industrial park, the effect of improving the overall technical level of internal enterprises is optimized.

[0070] 2. The present invention sets a threshold for the number of intellectual property rights, thereby providing a quantitative judgment standard for subsequent determination of whether the overall number of intellectual property rights in 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 iteratively optimized simultaneously by using 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 is mapped to the corresponding number of intellectual property rights, so that the quality of the solution obtained in the current iteration can be calculated through the fitness function; with continuous iterations, the overall number of intellectual property rights corresponding to the current industrial park continues to increase, and finally meets the requirements.

[0072] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. 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 described below are only some embodiments of the invention. For ordinary technicians in this field, they can also obtain drawings based on these drawings without paying creative work.

[0074] Figure 1 It is a flow chart of a digital operation and management method for an industrial park according to the present invention. DETAILED DESCRIPTION

[0075] The following will be combined with the drawings in the embodiments of the invention to clearly and completely describe the technical solutions in the embodiments of the invention. Obviously, the described embodiments are only part of the embodiments of the invention, not all of the embodiments. Based on the embodiments in the invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the invention.

[0076] In the description of the present invention, it is necessary to understand that the terms "opening", "upper", "lower", "top", "middle", "inside" and the like indicating orientation or positional relationship are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the components or elements referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the invention.

[0077] Embodiment 1

[0078] See also Figure 1 This embodiment is a digital operation and management method for an industrial park, comprising the following steps:

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

[0080] The S1 comprises the following steps:

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

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

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

[0084]

[0085] in, represents the jth type of technical equipment required for technical development set for the i-th type of enterprise in the main business type set, Indicates the total number of technical equipment types required for technological development set for each main business type of the enterprise in the main business type set;

[0086] S12, collecting characteristic data of each enterprise in the current industrial park according to the enterprise characteristic type set, and obtaining the current enterprise characteristic data matrix b1; as follows,

[0087]

[0088] Among them, b 1ij represents the characteristic data of the jth type of the i-th enterprise in the current industrial park, and b1′ represents the total number of enterprises in the current industrial park;

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

[0090]

[0091]

[0092] Among them, c 1ijk b2′ represents the kth type of characteristic data collected from the jth enterprise in the i-th historical industrial park. i represents the total number of enterprises in the i-th historical industrial park collected; c 2ij It represents the main business type data of the j-th enterprise in the i-th historical industrial park;

[0093] S14. According to the technology equipment type matrix, for each main type of enterprise, the number of each public technology equipment, the number of internal enterprises and the number of intellectual property rights in multiple historical industrial parks are collected for each main type of enterprise, and the historical technology equipment quantity matrix is ​​obtained. Historical Enterprise Quantity Set and the number of intellectual property sets Respectively represent the total number of enterprises and the total number of intellectual property rights collected in the i-th historical industrial park, and f represents the total number of industrial parks collected for each main type of enterprise according to the technology equipment type matrix; as follows,

[0094]

[0095] in, represents the number of technical equipment of the jth type installed in the i-th historical industrial park collected;

[0096] S2. Use the historical enterprise characteristic data matrix set and the historical enterprise main business type data matrix to construct the final main business type mapping equation; use the historical technical equipment quantity matrix, the historical enterprise quantity set and the intellectual property quantity set to construct the intellectual property quantity mapping model;

[0097] The S2 comprises 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 main business type data of the enterprise; is the ith independent variable of the initial main business type mapping equation, representing the characteristic data of the ith type of enterprise, for 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, optimizing the initial main business type mapping equation using the historical enterprise feature data matrix set and the historical enterprise main business type data matrix to obtain a final main business type mapping equation;

[0102] The S22 comprises the following steps:

[0103] S221, substitute each row of data of each historical enterprise characteristic data matrix of the historical enterprise characteristic data matrix set into the initial main business type mapping equation for mapping, and obtain the historical main business type mapping data matrix c4; as follows,

[0104]

[0105] Among them, c 4ij represents the main business type mapping data obtained by substituting the feature data set of the jth 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, setting an error ratio threshold; when the proportion of the main business type mapping data in the historical main business type mapping data matrix c4 that is different from the corresponding enterprise main business type data in the historical enterprise main business type data matrix c2 is greater than or equal to the error ratio threshold, optimizing the initial main business type mapping equation until the proportion of the main business type mapping data in the historical main business type mapping data matrix c4 that is different from the corresponding enterprise main type data in the historical enterprise main business type data matrix c2 is less than the error ratio threshold, and obtaining 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] Optimizing the initial main business type mapping equation in S222 includes the following steps:

[0108] S2221. Constructing mapping equations to optimize the population of sooty terns d1′ i It means that the mapping equation optimizes the i-th sooty tern in the sooty tern population, Indicates that the mapping equation optimizes the scale of the black tern population; Sets the maximum number of iterations of the mapping equation to optimize the black tern population And the current iteration number is 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, setting the value interval of each independent variable coefficient and the offset in the initial main business type mapping equation, and obtaining the independent variable coefficient value interval set e1 and the offset value interval [e 21 ,e 22 ],e 21 、e 22 They respectively represent the lower limit and upper limit of the bias value in the initial main business type mapping equation; e1 is as follows,

[0110]

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

[0112] According to the independent variable coefficient value interval set e1 and the bias value interval [e 21 ,e 22 ]Set the mapping equation to optimize the initial position of each sooty tern in the sooty tern population, and obtain the first initial position matrix e1′; as follows,

[0113]

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

[0115]

[0116]

[0117] In the formula, rand 1ji , Respectively for e1′ ji , Generate a random number between 0 and 1;

[0118] S2223, constructing a mapping equation to optimize the fitness function of the sooty tern population based on 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, indicating the correction parameter;

[0121] S2224, start iteration, before iteration, set the first current iteration number Set to 1; in the first round of iteration, the mapping equation is used to optimize the fitness function of the black tern population Calculate the fitness value of the initial position of each sooty tern in the first initial position matrix e1′ to obtain a first fitness value set; use the maximum fitness value in the first fitness value set and the corresponding initial position of the sooty tern as the first global optimal fitness and the first global optimal position respectively; update the initial position of each sooty tern in the first initial position matrix e1′ according to the first global optimal fitness and the first global optimal position; after the update is completed, the first current iteration number Add 1 and enter the next iteration;

[0122] In each other round of iteration, the mapping equation is used to optimize the fitness function of the sooty tern population. Calculate the fitness value of the position of each sooty tern updated in the previous round of iterations to obtain a second fitness value set; use the maximum fitness value in the second fitness value set and the corresponding position of the sooty tern as the second global optimal fitness and the second global optimal position respectively; update the position of each sooty tern updated in the previous round of iterations according to the second global optimal fitness and the second global optimal position; after the update is completed, update the first current iteration number Add 1 and enter the next iteration;

[0123] S2225, when When , stop the iteration and get the first final global optimal position; otherwise, continue to iterate until ; Substitute each position component in the first final global optimal position into the initial main business type mapping equation to obtain the optimized main business type mapping equation; Substitute each row of data of each historical enterprise feature data matrix of 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 main business type mapping data in the historical optimized main business type mapping data matrix that is different from the corresponding enterprise main type data in the historical enterprise main business type data matrix c2 is less than the error proportion threshold, the optimized main business type mapping equation is used as the final main business type mapping equation; otherwise, return to S2224 to continue iterating until the proportion of main business type mapping data in the historical optimized main business type mapping data matrix that is different from the corresponding enterprise main type data in the historical enterprise main type data matrix c2 is less than the error proportion threshold;

[0125] The black tern optimization algorithm has a strong global search capability and can quickly find potential optimal solutions in the search space. It adopts an "exploration and development" strategy, that is, seeking a balance between global search and local search, which helps to improve the algorithm's search efficiency and convergence speed. It has fewer parameters, which makes the algorithm's parameter adjustment process simpler, has low sensitivity to initial conditions and parameter settings, and has strong robustness. Based on the above advantages, the black tern optimization algorithm is used in this scheme to perform multiple iterations on the coefficients and biases of the initial main business type mapping equation, and the error between the mapped data and the actual data is used as the fitness function. Therefore, as the iteration proceeds, the independent variable coefficients and bias obtained by the iteration are substituted into the mapping equation, so that the mapping accuracy of the mapping equation becomes higher and higher, and finally meets the requirements.

[0126] S23, constructing an intellectual property quantity mapping model using the historical technical equipment quantity matrix, the historical enterprise quantity set, and the intellectual property quantity set;

[0127] The S23 comprises the following steps:

[0128] S231, constructing an initial SVM model and setting a training data ratio; dividing the historical technical equipment quantity matrix, the historical enterprise quantity set, and the intellectual property quantity set according to the training data ratio to obtain a historical technical equipment quantity training matrix, a historical enterprise quantity training set, an intellectual property quantity training set, a historical technical equipment quantity test matrix, a historical enterprise quantity test set, and an intellectual property quantity test set;

[0129] S232, setting a training error threshold; inputting the historical technical equipment quantity training matrix, 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, stopping the training to obtain a trained SVM model; otherwise, continuing the training until the training error is less than the training error threshold;

[0130] S233, set a test accuracy threshold; input the historical technical equipment quantity test matrix, 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 test 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 technical equipment quantity training matrix, historical enterprise quantity training set, intellectual property quantity training set and historical technical equipment quantity test matrix, historical enterprise quantity test set, and intellectual property quantity test set to train and test the initial SVM model, the obtained intellectual property quantity mapping model has a good mapping ability between the technical equipment quantity, enterprise quantity and intellectual property quantity;

[0132] S3. Use the final main business type mapping equation to map the current enterprise feature data matrix 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 comprises the following steps:

[0134] S31, setting the main business proportion data; collecting the main business type data of the current industrial park according to the main business type set of the enterprise, and recording it as the main business type data of the current park; substituting each row of data in the current enterprise feature data matrix b1 into the final main business type mapping equation for mapping, and obtaining the main business type data set of the current enterprise;

[0135] S32. When the ratio of the number of enterprise main type data in the current enterprise main type data set that is the same as the enterprise main type data in the current park main type data set to the total number of data in the current enterprise main 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 there is no need to adjust the internal enterprises, and the current enterprise main type data set is used as the current final enterprise main type data set; otherwise, the enterprises in the current industrial park are adjusted in accordance with the final main type mapping equation until the ratio of the number of enterprise main type data in the current enterprise main type data set that is the same as the enterprise main type data in the current park main type data set to the total number of data in the current enterprise main type data set is greater than or equal to the main business proportion data, and the current final enterprise main type data set is obtained;

[0136] S4. Optimize the settings of various technical equipment set in the current industrial park in conjunction 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 comprises the following steps:

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

[0139] S42, setting a threshold value for the number of intellectual property rights; when the current number of intellectual property rights is greater than or equal to the threshold value for the number of intellectual property rights, there is no need to adjust the number of each type of public technical equipment in the current industrial park, and the current technical equipment quantity set is used as the current final technical equipment quantity set; otherwise, the current technical equipment quantity set is adjusted until the current number of intellectual property rights is greater than or equal to the threshold value for the number of intellectual property rights, and the current final technical equipment quantity set is obtained;

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

[0141] S421. Build technical equipment to optimize the population of sooty terns d2′ i It means that the number of technical equipments is optimized for the i-th sooty tern in the sooty tern population, Indicates that the number of technical equipment optimizes the size of the black tern population; Sets the maximum number of iterations of the number of technical equipment to optimize the black tern population And the current iteration number is As the second maximum number of iterations and the second current number of iterations respectively; the search space dimension of the black tern population optimized by the number of technical equipment is

[0142] S422, setting the value intervals of the number of various technical equipments set in the current industrial park in conjunction with the technical equipment type matrix, and obtaining a technical equipment quantity value interval set e3; as follows,

[0143]

[0144] in, They respectively represent the lower limit and upper limit of the number of the i-th technical equipment in the current industrial park;

[0145] According to the technical equipment quantity value interval set e3, the technical equipment quantity is set to optimize the initial position of each sooty tern in the sooty tern population, and the second initial position matrix e′2 is obtained; as follows,

[0146]

[0147] Among them, e′ 2ji represents the component of the initial position of the jth sooty tern in the sooty tern population in the dimension of the number of settings of the i-th technical equipment in the current industrial park; e′ 2ji The calculation formula is as follows,

[0148]

[0149] In the formula, rand 2ji Indicates for e′ 2ji Generate a random number between 0 and 1;

[0150] S423, constructing a fitness function for optimizing the population of sooty terns based on the current number of intellectual property rights f2′ as follows,

[0151]

[0152] S424, start iteration, before iteration, set the second current iteration number Set to 1; in the first round of iterations, the number of technical equipment is used to optimize the fitness function of the black tern population And cooperate 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 e′2 to obtain a third fitness value set; the maximum fitness value in the third fitness value set and the corresponding initial position of the sooty tern are respectively used as the third global optimal fitness and the third global optimal position; according to the third global optimal fitness and the third global optimal position, the initial position of each sooty tern in the second initial position matrix e′2 is updated; after the update is completed, the second current iteration number Add 1 and enter the next iteration;

[0153] In each other round of iteration, the number of technical equipment is used to optimize the fitness function of the black tern population And cooperate 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 iterations to obtain a fourth fitness value set; the maximum fitness value in the fourth fitness value set and the corresponding position of the sooty tern are respectively used as the fourth global optimal fitness and the fourth global optimal position; according to the fourth global optimal fitness and the fourth global optimal position, the position of each sooty tern updated in the previous round of iterations is updated again; after the update is completed, the second current iteration number Add 1 and enter the next iteration;

[0154] S425, when When , stop the iteration and get the second final global optimal position; otherwise, continue to iterate until until the second final global optimal position is used as the current optimized technical equipment quantity set;

[0155] The current optimized technical equipment quantity set and the current final enterprise quantity are input into the intellectual property quantity mapping model for mapping 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, the current optimized technical equipment quantity set is used as the current final technical equipment quantity set; otherwise, return to S424 to continue iterating until the current 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 management system for an industrial park, which 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 characteristic types and technical equipment types required for technical development of each main business type of enterprises in the industrial park, and obtain a set of main business types, a set of enterprise characteristic types and a matrix of technical equipment types;

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

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

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

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

[0163] The mapping model building module is used to build an intellectual property quantity mapping model using a historical technical equipment quantity matrix, a historical enterprise quantity set, and an intellectual property quantity set;

[0164] The mapping module is used to 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;

[0165] The current industrial park enterprise optimization module is used 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 used to optimize the settings of various technical equipment set in the current industrial park in conjunction 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.

[0167] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0168] The preferred embodiments of the invention disclosed above are only used to help explain the invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the invention, so that those skilled in the art can understand and use the invention well.

Claims

1. A digital operation and management method for an industrial park, characterized in that: The following steps are involved: S1. Collect the characteristic data of each enterprise in the current industrial park to obtain the current enterprise characteristic data matrix; Collect characteristic data of each enterprise within multiple historical industrial parks, corresponding enterprise main business type data, the number of each public technical equipment, the number of internal enterprises, and the number of intellectual property rights, and 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; S2. Use the historical enterprise characteristic data matrix set and the historical enterprise main business type data matrix to construct the final main business type mapping equation; use the historical technical equipment quantity matrix, the historical enterprise quantity set and the intellectual property quantity set to construct the intellectual property quantity mapping model; S3, using the final main business type mapping equation to map the current enterprise feature data matrix 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; S4. Optimize the settings of various technical equipment in the current industrial park in conjunction 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.

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

3. The digital operation and management method of an industrial park according to claim 2, characterized in that: The S2 comprises the following steps: S21, constructing an initial main business type mapping equation; S22, optimizing the initial main business type mapping equation using the historical enterprise feature data matrix set and the historical enterprise main business type data matrix to obtain a final main business type mapping equation; S23. Construct an intellectual property quantity mapping model 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 is characterized in that: The S22 comprises the following steps: S221, substituting each row of data of each historical enterprise characteristic data matrix of 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; S222. Set an error percentage threshold; when the proportion of main business type mapping data in the historical main business type mapping data matrix that is different from the corresponding enterprise main type data in the historical enterprise main business type data matrix is ​​greater than or equal to the error percentage threshold, optimize the initial main business type mapping equation until the proportion of main business type mapping data in the historical main business type mapping data matrix that is different from the corresponding enterprise main type data in the historical enterprise main type data matrix is ​​less than the error percentage threshold, and 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.

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 using the Sooty Tern optimization algorithm.

6. The digital operation and management method of an industrial park according to claim 5, characterized in that: The intellectual property quantity mapping model described in S23 adopts the SVM model.

7. The digital operation and management method of an industrial park according to claim 6, characterized in that: The S3 comprises the following steps: S31, setting the main business proportion data; collecting the main business type data of the current industrial park according to the main business type set of the enterprise, and recording it as the main business type data of the current park; substituting each row of data in the current enterprise feature data matrix into the final main business type mapping equation for mapping, and obtaining the main business type data set of the current enterprise; S32. When the ratio of the number of enterprise main type data in the current enterprise main type data set that is identical to the enterprise main type data in the current park main type data set to the total number of data in the current enterprise main 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 there is no need to adjust the internal enterprises. The current enterprise main type data set is used as the current final enterprise main type data set; otherwise, the enterprises in the current industrial park are adjusted in accordance with the final main business type mapping equation until the ratio of the number of enterprise main type data in the current enterprise main type data set that is identical to the enterprise main type data in the current park main type data set to the total number of data in the current enterprise main type data set is greater than or equal to the main business proportion data, and the current final enterprise main type data set is obtained.

8. The digital operation and management method of an industrial park according to claim 7, characterized in that: The S4 comprises the following steps: S41. Collect the number of each type of public technical equipment, the total number of internal enterprises and the number of intellectual property rights in the current industrial park in conjunction 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 quantity and the current intellectual property quantity; S42. Set a threshold for the number of intellectual property rights; when the current number of intellectual property rights is greater than or equal to the threshold for the number of intellectual property rights, there is no need to adjust the number of each type of public technical equipment in the current industrial park, and the current set of technical equipment quantities is used as the current final set of technical equipment quantities; otherwise, adjust the current set of technical equipment quantities until the current number of intellectual property rights is greater than or equal to the threshold for the number of intellectual property rights, and obtain the current final set of technical equipment quantities.

9. The digital operation and management method of an industrial park according to claim 8, characterized in that: The step of adjusting the current technical equipment quantity set in S42 includes the following steps: S421, constructing the number of technical equipment to optimize the sooty tern population; setting the maximum number of iterations of the number of technical equipment to optimize the sooty tern population is And the current number of iterations is as the second maximum number of iterations and the second current number of iterations respectively; S422, setting the value interval of the number of various technical equipment settings in the current industrial park in conjunction with the technical equipment type matrix to obtain a technical equipment quantity value interval set; setting the technical equipment quantity to optimize the initial position of each sooty tern in the sooty tern population according to the technical equipment quantity value interval set to obtain a second initial position matrix; S423, constructing a fitness function for optimizing the sooty tern population based on the current number of intellectual property rights; S424, start iteration, and set the second current iteration number to 1 before iteration; in the first round of iteration, use the number of technical equipment to optimize the fitness function of the sooty tern population 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 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 optimal fitness and the third global optimal position respectively; update the initial position of each sooty tern in the second initial position matrix according to the third global optimal fitness and the third global optimal position; after the update is completed, add 1 to the second current iteration number and enter the next round of iteration; In each other round of iteration, the fitness function of the sooty tern population is optimized by using the number of technical equipment and the fitness value of the position of each sooty tern updated in the previous round of iteration is calculated in conjunction with the intellectual property quantity mapping model to obtain a fourth fitness value set; the maximum fitness value in the fourth fitness value set and the corresponding position of the sooty tern are respectively used as the fourth global optimal fitness and the fourth global optimal position; the position of each sooty tern updated in the previous round of iteration is updated again according to the fourth global optimal fitness and the fourth global optimal position; after the update is completed, the second current iteration number is increased by 1 and the next round of iteration is entered; S425, when When , stop the iteration and get the second final global optimal position; otherwise, continue to iterate until until the time; take the second final global optimal position as the current optimized technical equipment quantity set; input the current optimized technical equipment quantity set and the current final enterprise quantity into the intellectual property quantity mapping model for mapping, and 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, take the current optimized technical equipment quantity set as the current final technical equipment quantity set; otherwise, return to S424 and continue to iterate until the current optimized intellectual property quantity is greater than or equal to the intellectual property quantity threshold.

10. A system for implementing a digital operation and management method for an industrial park as described in any one of claims 1 to 9.

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