Artificial Intelligence-Based Method and System for Optimizing Space Utilization Layout

Abstract

The present invention relates to the technical field of park management. The present invention discloses a method and system for optimizing the spatial utilization layout based on artificial intelligence. The method includes dividing a target industrial park into W park sub-regions based on the geographical location information of Q assets to be processed, calculating the residential kernel density value according to the residential space information, determining the industrial integration degree value according to the industrial distribution information, classifying the Q assets to be processed based on the residential kernel density value and the industrial integration degree value, finally determining P park planning layouts according to the asset classification results, calculating the imbalance index corresponding to each park planning layout, and optimizing the P park planning layouts according to the imbalance index and a preset natural inspiration algorithm. In this way, the present invention solves the problems in the prior art that the balance between asset classification and regional function coordination, and the integration of residence and industry cannot be achieved, effectively adjusts the imbalance of the resource distribution in each sub-region within the park, and realizes the balanced development of the residential function and the industrial function.

Description

Technical Field

[0001] The present invention relates to the technical field of park management. More specifically, the present invention relates to a method and system for optimizing the spatial utilization layout based on artificial intelligence. Background Art

[0002] Exploring methods for optimizing the spatial layout of industrial parks has become a key path to promoting the sustainable development of parks. Although existing technologies have covered relevant content on park layout optimization, there are still some deficiencies in practical applications. Especially in multi-dimensional aspects such as asset classification and regional function coordination, and the integration of residence and industry, the imbalance among regions within the industrial park has not been fully considered, which makes it impossible to effectively improve the efficiency and overall balance of park resource allocation. Although existing technologies have covered relevant content on park layout optimization, there are still some deficiencies in practical applications. Especially in multi-dimensional aspects such as asset classification and regional function coordination, and the integration of residence and industry, the imbalance among regions within the industrial park has not been fully considered, which makes it impossible to effectively improve the efficiency and overall balance of park resource allocation.

[0003] In view of this, the present invention proposes a method and system for optimizing the spatial utilization layout based on artificial intelligence to solve the above problems. Summary of the Invention

[0004] To overcome the above-mentioned defects of the prior art, the present invention provides a method and system for optimizing the spatial utilization layout based on artificial intelligence.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] In the first aspect, a method for optimizing the spatial utilization layout based on artificial intelligence is provided, including:

[0007] Obtain the geographical location information of Q assets to be processed in the target industrial park, and divide the target industrial park into W park sub-regions based on the geographical location information of the Q assets to be processed;

[0008] Obtain the residential space information and industrial distribution information corresponding to each park sub-region, calculate the residential kernel density value according to the residential space information, determine the industrial integration degree value according to the industrial distribution information, and classify the Q assets to be processed based on the residential kernel density value and the industrial integration degree value to obtain an asset classification result, where the asset classification result includes rental assets, intermediate assets, and commercial assets;

[0009] Determine P park planning layouts according to the asset classification result, calculate the imbalance index corresponding to each park planning layout, and optimize the P park planning layouts according to the imbalance index and a preset natural inspiration algorithm to obtain an optimal planning layout.

[0010] Further, the method for dividing the target industrial park into W park sub-regions based on the geographical location information of Q assets to be processed includes:

[0011] Cluster the Q assets to be processed according to the geographical location information to obtain W target asset sets, divide the target industrial park into W initial sub-regions according to the W target asset sets, and optimize the boundaries of the W initial sub-regions according to the Voronoi diagram algorithm, so as to obtain W park sub-regions, and the park sub-regions correspond to the target asset sets one by one.

[0012] Further, the residential space information includes the coordinates of each residential point and the target point coordinates in the park sub-region. The method for calculating the residential kernel density value according to the residential space information includes:

[0013] Perform kernel function cumulative calculation on the coordinates of each residential point and the target point coordinates in the park sub-region to obtain the residential kernel density value.

[0014] Further, the industrial distribution information includes the industrial category ratio, the industrial population ratio, and the industrial facility density. The method for determining the industrial integration degree value according to the industrial distribution information includes:

[0015] Perform weighted calculation according to the industrial category ratio, the industrial population ratio, and the industrial facility density to determine the industrial integration degree value.

[0016] Further, the method for classifying the Q assets to be processed based on the residential kernel density value and the industrial integration degree value to obtain the asset classification result includes:

[0017] Input each asset to be processed and the corresponding residential kernel density value and industrial integration degree value into a pre-constructed asset classification model to obtain the asset classification result.

[0018] Further, the method for determining P park planning layouts according to the asset classification result includes:

[0019] Determine the rental assets and commercial assets in the target industrial park according to the asset classification result, obtain the asset quantity corresponding to the intermediate assets based on the asset classification result, and construct P park planning layouts through the enumeration combination method and the asset quantity.

[0020] Further, the method for calculating the imbalance index corresponding to each park planning layout includes:

[0021] Obtain the total number of sub - regions in each park planning layout, the number of rental assets in the park sub - regions, the number of commercial assets in the park sub - regions, and the number of assets to be processed in the park sub - regions. Calculate based on the total number of park sub - regions, the number of rental assets in the park sub - regions, the number of commercial assets in the park sub - regions, and the number of assets to be processed in the park sub - regions to obtain the corresponding imbalance index.

[0022] Furthermore, the nature - inspired algorithm is the whale optimization algorithm. The method for optimizing P park planning layouts according to the imbalance index and the preset nature - inspired algorithm to obtain the optimal planning layout includes:

[0023] S301: Define the initial number of whales as P and the maximum number of iterations , and each initial whale individual represents a park planning layout. The random value rand is a random number sampled from a uniform distribution on [0, 1];

[0024] S302: Obtain the imbalance index corresponding to the park planning layout and use the imbalance index as the objective fitness value of the initial whale individual;

[0025] S303: When the random value rand < 0.5, perform the shrinking encircling strategy. When the random value rand ≥ 0.5, perform the spiral hunting strategy;

[0026] S304: Repeat the above S303 to continuously update the park planning layout until the maximum number of iterations is reached, then output the current optimal park planning layout and use the optimal park planning layout as the optimal planning layout.

[0027] Furthermore, the method for performing the shrinking encircling strategy includes:

[0028] ;

[0029] where, is the position of the next - generation whale, is the position of the current whale, is the position of the pursuit target, is the shrinking coefficient of the shrinking encircling strategy, is the control factor of the shrinking encircling strategy, is the current number of iterations.

[0030] On the second aspect, a spatial utilization layout optimization system based on artificial intelligence is provided, which is used to implement the above - mentioned spatial utilization layout optimization method based on artificial intelligence, including:

[0031] Region Division Module: It is used to obtain the geographical location information of Q assets to be processed in the target industrial park, and divide the target industrial park into W sub-regions based on the geographical location information of the Q assets to be processed;

[0032] Data Processing Module: It is used to obtain the residential space information and industrial distribution information corresponding to each sub-region of the park, calculate the residential kernel density value according to the residential space information, determine the industrial integration degree value according to the industrial distribution information, classify the Q assets to be processed based on the residential kernel density value and the industrial integration degree value, and obtain the asset classification result. The asset classification result includes rental assets, intermediate assets, and commercial assets;

[0033] Layout Optimization Module: It is used to determine P park planning layouts according to the asset classification result, calculate the imbalance index corresponding to each park planning layout, and optimize the P park planning layouts according to the imbalance index and the preset natural inspiration algorithm to obtain the optimal planning layout.

[0034] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0035] The present invention first divides the target industrial park into W sub-regions of the park based on the geographical location information of Q assets to be processed, then obtains the residential space information and industrial distribution information corresponding to each sub-region of the park, calculates the residential kernel density value according to the residential space information, determines the industrial integration degree value according to the industrial distribution information, classifies the Q assets to be processed based on the residential kernel density value and the industrial integration degree value, and obtains the asset classification result. Finally, it determines P park planning layouts according to the asset classification result, calculates the imbalance index corresponding to each park planning layout, and optimizes the P park planning layouts according to the imbalance index and the preset natural inspiration algorithm to obtain the optimal planning layout. In this way, by dividing and analyzing the geographical location information of the assets to be processed, comprehensively considering the residential kernel density value and the industrial integration degree value, classifying the assets to be processed and constructing multiple park planning layouts, the present invention solves the problems in the prior art that the asset classification and regional function coordination, and the integration of residence and industry cannot be balanced; by calculating the imbalance index and using the natural inspiration algorithm to optimize the planning layout, it effectively adjusts the imbalance of the resource distribution in each sub-region of the park and realizes the balanced development of the residential function and the industrial function. Description of the Drawings

[0036] Figure 1 It is a schematic flow chart of the spatial utilization layout optimization method based on artificial intelligence in the present invention;

[0037] Figure 2 It is a schematic structural diagram of the spatial utilization layout optimization system based on artificial intelligence in the present invention;

[0038] Figure 3Flow diagram of the method for dividing a target industrial park into W park sub - regions based on the geographical location information of Q assets to be processed in the present invention;

[0039] Figure 4 Flow diagram of the method for determining P park planning layouts according to the asset classification result in the present invention. Detailed implementation manners

[0040] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0041] Embodiment 1

[0042] Please refer to Figure 1 As shown, the present embodiment discloses and provides a method for optimizing spatial utilization layout based on artificial intelligence, including:

[0043] S10: Obtain the geographical location information of Q assets to be processed in the target industrial park, and divide the target industrial park into W park sub - regions based on the geographical location information of the Q assets to be processed;

[0044] In this embodiment, the assets to be processed can be office buildings or office towers, etc. There are mainly two uses for the assets to be processed. One use is to rent the assets to be processed (such as office buildings or office towers) as commercial office spaces for enterprises, companies, etc. to use, which can ensure that the enterprises in the park have office places and promote commercial activities and economic development in the park. The other use is to rent the assets to be processed to individuals, freelancers or other types of tenants, which can meet the needs of different - scale tenants and improve the utilization rate of the assets.

[0045] As Figure 3 shown, the method for dividing the target industrial park into W park sub - regions based on the geographical location information of Q assets to be processed includes:

[0046] Cluster the Q assets to be processed according to the geographical location information to obtain W target asset sets, divide the target industrial park into W initial sub - regions according to the W target asset sets, and optimize the boundaries of the W initial sub - regions according to the Voronoi diagram algorithm to obtain W park sub - regions, and the park sub - regions correspond to the target asset sets one by one.

[0047] It should be noted that the target asset set refers to a group of assets divided from the assets to be processed. These assets have spatial similarity. In this embodiment, the method of clustering Q assets to be processed can be K-means clustering or DBSCAN clustering. The assets to be processed with close spatial distances are divided into a target asset set. After obtaining W target asset sets, the target industrial park can be divided into W initial sub-regions in an equal-area manner, and there is only one target asset set in each initial sub-region.

[0048] In this embodiment, the Voronoi diagram algorithm refers to a method based on geometric space division. By a given set of points (usually called seed points or generating points), the space is divided into multiple regions, so that each point in the space belongs to the region of the seed point closest to it. Then, in this embodiment, the central coordinates of the target asset set can be used as the seed points in the Voronoi diagram algorithm. Then, by smoothing or adjusting the boundary lines of each initial sub-region, overly complex or irregular shapes are avoided, so as to obtain W park sub-regions. Optimizing the boundary by the Voronoi diagram algorithm is a prior art, and this embodiment will not elaborate on it too much. Above, the central coordinates of the target asset set refer to the geometric center coordinates of the geographical locations of all the assets to be processed in the target asset set.

[0049] S20: Obtain the residential space information and industrial distribution information corresponding to each park sub-region, calculate the residential kernel density value according to the residential space information, determine the industrial integration degree value according to the industrial distribution information, and classify the Q assets to be processed based on the residential kernel density value and the industrial integration degree value to obtain an asset classification result. The asset classification result includes rental assets, intermediate assets, and commercial assets;

[0050] In some embodiments, the residential space information at least includes the coordinates of each residential point and the target point coordinates in the park sub-region. The residential point refers to the geographical location of the space or facility for residence in the park sub-region, including residential buildings, apartments and other buildings for people to live in. The target point coordinates can be the geometric center coordinates of the geographical locations of all the assets to be processed in the target asset set. Then, each park sub-region corresponds to a target point coordinate.

[0051] The method for calculating the residential kernel density value according to the residential space information includes:

[0052] Perform kernel function cumulative calculation on the coordinates of each residential point and the target point coordinates in the park sub-region to obtain the residential kernel density value.

[0053] The specific method for calculating the residential kernel density value includes:

[0054] RKD = ;

[0055] Wherein, RKD is the residential kernel density value, is the total number of residential points in the sub-region of the park, is the Gaussian kernel function, is the coordinate of the target point, is the coordinate of the th residential point,

[0056] In this embodiment, the search radius is a preset fixed value, which is used to limit which residential points around the target point will affect the residential kernel density value. The residential kernel density value represents the distribution density of residential points around the target point, that is, the residential space density of the sub-region of the park where the target point is located. Through the kernel density analysis method, the residential kernel density value can quantify the distribution characteristics of residential points around the target point. The larger the residential kernel density value, the denser the distribution of residential points in the sub-region of the park where the target point is located, and the higher the utilization degree of the residential space. The larger the residential kernel density value, the more the assets to be processed should be given priority to be rented as commercial office space. This is to achieve the balanced development of residence and industry, avoid the over-simplification of functions in the region, and using the assets to be processed as commercial office space can introduce more employment opportunities and economic activities, enhance the industrial function of the region, and balance the needs of residence and industry.

[0057] In some embodiments, the industrial distribution information at least includes the industrial category ratio, the industrial population ratio, and the industrial facility density. The industrial category ratio is the ratio of the number of people engaged in the secondary industry to the number of people engaged in the tertiary industry. The industrial population ratio is the ratio of the number of employees engaged in the secondary industry to the number of employees engaged in the tertiary industry. The industrial facility density is the number of tertiary industry facilities per unit area in the sub-region of the park.

[0058] The method for determining the industrial integration degree value according to the industrial distribution information includes:

[0059] Performing weighted calculation according to the industrial category ratio, the industrial population ratio, and the industrial facility density to determine the industrial integration degree value.

[0060] The specific method for determining the industrial integration degree value includes:

[0061] IND = ;

[0062] Wherein, IND is the industrial integration degree value, is the hyperbolic cosine function, is the logarithmic function with base e, is the arccotangent function, is the industrial facility density, is the industrial category ratio, is the industrial population ratio, , are all weighting factors, and e is the natural constant.

[0063] In this embodiment, the industrial facility density and the industrial category ratio are taken as examples. When the industrial facility density is larger, it indicates that the tertiary industry activities in the sub-region of the park are relatively concentrated. When the industrial category ratio is larger, it indicates that the number of facilities in the secondary industry is significantly more than that in the tertiary industry, indicating that the sub-region of the park is mainly engaged in productive activities such as manufacturing and industry. Therefore, from the above content, it can be seen that when the industrial integration degree value is smaller, it indicates that the industrial structure in the sub-region of the park is relatively single, the secondary industry occupies a dominant position, and the degree of coordination and integration between industries is relatively low. Therefore, it is necessary to use the assets to be processed as commercial office space to increase the proportion of the regional service industry and balance the industrial structure.

[0064] The method for classifying Q assets to be processed based on the residential kernel density value and the industrial integration degree value to obtain the asset classification result includes:

[0065] Input each asset to be processed and the corresponding residential kernel density value and industrial integration degree value into a pre-constructed asset classification model to obtain the asset classification result, and the asset classification result includes rental assets, intermediate assets, and commercial assets.

[0066] The method for constructing the asset classification model includes:

[0067] Obtain Z sets of training data, where Z is a positive integer greater than 1. The training data includes historical assets to be processed, historical residential kernel density values, historical industrial integration degree values, and historical asset classification results. Take the historical assets to be processed, historical residential kernel density values, historical industrial integration degree values, and historical asset classification results as the sample set, divide the sample set into a training set and a test set, construct a classifier, take the historical assets to be processed, historical residential kernel density values, and historical industrial integration degree values in the training set as input data, take the historical asset classification results in the training set as output data, train the classifier to obtain an initial classifier, and use the test set to test the initial classifier, and output a classifier that meets the preset accuracy as the asset classification model. The classifier is preferably one of the naive Bayes model or the support vector machine model.

[0068] In this embodiment, the asset classification results include rental assets, intermediate assets, and commercial assets. It can be understood that when the asset classification result is a rental asset, it means that the asset to be processed needs to be rented to individuals, freelancers, or other types of tenants. When the asset classification result is a commercial asset, it means that the asset to be processed needs to be used for renting commercial office spaces for enterprises, companies, etc. When the asset classification result is an intermediate asset, it means that the asset to be processed can be used for renting to individuals or for renting commercial office spaces. From the above, it can be seen that the greater the residential nuclear density value or the smaller the industrial integration value in the sub-region of the park, the greater the probability that the asset classification result is a commercial asset. Correspondingly, when the residential nuclear density value in the sub-region of the park is smaller or the industrial integration value is greater, the greater the probability that the asset classification result is a rental asset. Taking the residential nuclear density value as an example, the residential nuclear density value reflects the density of residential points in the target area. When the residential nuclear density value is large, it indicates that the residential points in the area are highly concentrated, and the residential demand has been fully met or is excessive. In an area with a large residential nuclear density value, classifying the asset to be processed as a commercial asset helps to optimize the regional function and promote the coordinated development of the secondary and tertiary industries.

[0069] In this embodiment, first, by clustering and dividing the geographical location information of the assets to be processed in the target industrial park, the target industrial park is divided into multiple sub-regions to ensure the spatial similarity of the assets to be processed in each sub-region, laying a foundation for subsequent data analysis. Based on the divided sub-regions of the park, the residential space information and industrial distribution information are further obtained, and the residential nuclear density value and industrial integration value are calculated to accurately classify the assets to be processed, so as to achieve the balanced development of residential and industrial functions in different regions and avoid the over-simplification of regional functions.

[0070] S30: Determine P park planning layouts according to the asset classification results, calculate the imbalance index corresponding to each park planning layout, and optimize the P park planning layouts according to the imbalance index and the preset natural inspiration algorithm to obtain the optimal planning layout;

[0071] As Figure 4 shown, the method for determining P park planning layouts according to the asset classification results includes:

[0072] Determine the rental assets and commercial assets in the target industrial park according to the asset classification results, obtain the asset quantity corresponding to the intermediate assets based on the asset classification results, and construct P park planning layouts through the enumeration combination method and the asset quantity.

[0073] It should be noted that the park planning layout represents the distribution result after classifying the assets to be processed in the target industrial park, including the specific distribution of rental assets, commercial assets, and intermediate assets in the park. Since intermediate assets can be used as rental assets and then rented to individuals, freelancers, or other types of tenants, or as commercial assets for renting commercial office spaces, there are multiple park planning layouts. Therefore, in this embodiment, the rental assets and commercial assets are first determined, and then different allocation methods of intermediate assets are enumerated through the enumeration combination method to construct multiple park planning layouts. In this way, each park planning layout completely represents the spatial distribution of rental assets, commercial assets, and intermediate assets in the target industrial park, providing a comprehensive initial solution set for subsequent optimization of the optimal planning layout.

[0074] The method for calculating the imbalance index corresponding to each park planning layout includes:

[0075] Obtain the total number of park sub-regions, the number of rental assets in the park sub-region, the number of commercial assets in the park sub-region, and the number of assets to be processed in the park sub-region corresponding to each park planning layout, and calculate according to the total number of park sub-regions, the number of rental assets in the park sub-region, the number of commercial assets in the park sub-region, and the number of assets to be processed in the park sub-region to obtain the corresponding imbalance index.

[0076] The specific method for calculating the imbalance index includes:

[0077] IMX = ;

[0078] In the formula, IMX is the imbalance index, is the total number of park sub-regions, is the number of rental assets in the th park sub-region, the th number of commercial assets in the park sub-region, is the th number of assets to be processed in the park sub-region, is the arccotangent function.

[0079] It can be understood that from the above, since the intermediate assets can be used as rental assets and commercial assets, in this embodiment, different allocation methods of the intermediate assets are enumerated by the enumeration combination method, so as to construct multiple park planning layouts. Therefore, there are only rental assets and commercial assets in each park planning layout. Then, in order to avoid an excessive number of rental assets or commercial assets in the same park sub-region in this embodiment, the imbalance index is calculated. The imbalance index represents the degree of balance between the distribution of rental assets and commercial assets in each park sub-region. The imbalance index reflects the distribution balance of rental assets and commercial assets in each sub-region of the park planning layout. The smaller its value, the more balanced the distribution; the larger its value, the more unbalanced the distribution. It is an important indicator to measure the rationality of the planning layout.

[0080] The nature-inspired algorithm is the whale optimization algorithm. The method for optimizing the P park planning layouts according to the imbalance index and the preset nature-inspired algorithm to obtain the optimal planning layout includes:

[0081] S301: Define the initial number of whales as P and the maximum number of iterations , and each initial whale individual represents a park planning layout. The random value rand is a random number sampled from a uniform distribution on [0, 1];

[0082] S302: Obtain the imbalance index corresponding to the park planning layout, and use the imbalance index as the objective fitness value of the initial whale individual;

[0083] It can be understood that the imbalance index corresponds to the "fitness value" in the whale optimization algorithm, representing the "survival ability" of an individual in the entire population. In the whale optimization algorithm, each individual (whale) has a fitness value, which is used to measure the quality of the individual. The smaller the fitness value, the closer the individual is to the optimal solution.

[0084] S303: When the random value rand < 0.5, the shrinking encircling strategy is performed; when the random value rand ≥ 0.5, the spiral hunting strategy is performed;

[0085] Among them, when the shrinking encircling strategy is executed, the whale will shrink and encircle around the optimal solution; when the spiral hunting strategy is executed, it simulates the whale moving around the target along a spiral path.

[0086] S304: Repeat the above S303 to continuously update the park planning layout until the maximum number of iterations is reached, and the current optimal park planning layout is output, and the optimal park planning layout is used as the optimal planning layout.

[0087] In this embodiment, in each iteration, the whale population is continuously updated until the maximum number of iterations is reached After each iteration, the algorithm updates the weight factor combination of the whale individuals and calculates the new fitness value. Through repeated iterations, the weight factor combination of the whale individuals is gradually improved step by step each time, making the whale population gradually approach the optimal solution.

[0088] The methods for implementing the shrinking encircling strategy include:

[0089] ;

[0090] Among them, is the position of the next-generation whale, is the position of the current whale, is the position of the pursuit target, is the shrinking coefficient of the shrinking encircling strategy, is the control factor of the shrinking encircling strategy, is the current iteration number.

[0091] In this embodiment, the whale individuals gradually shrink when approaching the optimal solution, precisely adjust the weight factors, and perform local optimization. Through this strategy, the whale individuals gradually approach the current optimal solution, ensuring a fine search in the solution space.

[0092] The methods for implementing the spiral hunting strategy include:

[0093] = ;

[0094] Among them, is the spiral shrinking coefficient of the spiral hunting strategy, is a random value within the range of [-1, 1].

[0095] In this embodiment, first, based on the geographical location information of Q assets to be processed, the target industrial park is divided into W sub-regions. Then, the residential space information and industrial distribution information corresponding to each sub-region are obtained. The residential kernel density value is calculated according to the residential space information, and the industrial integration degree value is determined according to the industrial distribution information. The Q assets to be processed are classified based on the residential kernel density value and the industrial integration degree value to obtain the asset classification result. Finally, P park planning layouts are determined according to the asset classification result, the imbalance index corresponding to each park planning layout is calculated, and the P park planning layouts are optimized according to the imbalance index and a preset natural inspiration algorithm to obtain the optimal planning layout. In this way, in this embodiment, by dividing and analyzing the geographical location information of the assets to be processed, comprehensively considering the residential kernel density value and the industrial integration degree value, classifying the assets to be processed and constructing multiple park planning layouts, the problems in the prior art that the asset classification and regional function coordination, and the integration of residence and industry cannot be balanced are solved. By calculating the imbalance index and using the natural inspiration algorithm to optimize the planning layout, the imbalance of the resource distribution in each sub-region within the park is effectively adjusted, and the balanced development of the residential function and the industrial function is realized.

[0096] Embodiment 2

[0097] Please refer to Figure 2 As shown, based on the same inventive concept, this embodiment discloses and provides a spatial utilization layout optimization system based on artificial intelligence. For the details not described in this embodiment, please refer to the relevant parts in Embodiment 1. The system includes:

[0098] Region division module: used to obtain the geographical location information of Q assets to be processed in the target industrial park, and divide the target industrial park into W sub-regions based on the geographical location information of the Q assets to be processed;

[0099] In this embodiment, the assets to be processed can be office buildings or office blocks, etc. There are mainly two uses for the assets to be processed. One use is to rent the assets to be processed (such as office buildings or office blocks) as commercial office spaces for enterprises, companies, etc. to use, which can ensure that the enterprises in the park have office places and promote the commercial activities and economic development in the park. The other use is to rent the assets to be processed to individuals, freelancers or other types of tenants, which can meet the needs of different-scale tenants and improve the utilization rate of the assets.

[0100] The method for dividing the target industrial park into W sub-regions based on the geographical location information of Q assets to be processed includes:

[0101] Cluster Q assets to be processed according to geographical location information to obtain W target asset sets. Divide the target industrial park into W initial sub-regions according to the W target asset sets, and optimize the boundaries of the W initial sub-regions according to the Voronoi diagram algorithm to obtain W park sub-regions, where the park sub-regions correspond to the target asset sets one by one.

[0102] Data processing module: used to obtain the residential space information and industrial distribution information corresponding to each park sub-region, calculate the residential kernel density value according to the residential space information, determine the industrial integration degree value according to the industrial distribution information, and classify the Q assets to be processed based on the residential kernel density value and the industrial integration degree value to obtain an asset classification result, where the asset classification result includes rental assets, intermediate assets, and commercial assets;

[0103] In some embodiments, the residential space information at least includes the coordinates of each residential point in the park sub-region and the target point coordinates. The residential point refers to the geographical location of the space or facility for living in the park sub-region, including residential buildings, apartments, and other buildings for people to live in. The target point coordinates can be the geometric center coordinates of the geographical locations of all assets to be processed in the target asset set. Then, each park sub-region corresponds to a target point coordinate.

[0104] The method for calculating the residential kernel density value according to the residential space information includes:

[0105] Perform kernel function accumulation calculation on the coordinates of each residential point and the target point coordinates in the park sub-region to obtain the residential kernel density value.

[0106] The specific method for calculating the residential kernel density value includes:

[0107] RKD = ;

[0108] In the formula, RKD is the residential kernel density value, is the total number of residential points in the park sub-region, is the Gaussian kernel function, is the target point coordinates, is the coordinates of the th residential point,

[0109] is the preset search radius.

[0110] The method for determining the industrial integration degree value according to the industrial distribution information includes:

[0111] The specific method for determining the industrial integration degree value includes:

[0112] IND = ;

[0113] Where IND is the industrial integration degree value, is the hyperbolic cosine function, is the logarithmic function with base e, is the arccotangent function, is the industrial facility density, is the industrial category ratio, is the industrial population ratio, , are both weight factors, and e is the natural constant.

[0114] A method for classifying Q assets to be processed based on the residential kernel density value and the industrial integration degree value to obtain an asset classification result includes:

[0115] Input each asset to be processed, the corresponding residential kernel density value, and the industrial integration degree value into a pre-constructed asset classification model to obtain an asset classification result, where the asset classification result includes rental assets, intermediate assets, and commercial assets.

[0116] The construction method of the asset classification model includes:

[0117] Obtain Z sets of training data, where Z is a positive integer greater than 1. The training data includes historical assets to be processed, historical residential kernel density values, historical industrial integration degree values, and historical asset classification results. Use the historical assets to be processed, historical residential kernel density values, historical industrial integration degree values, and historical asset classification results as a sample set. Divide the sample set into a training set and a test set, construct a classifier, use the historical assets to be processed, historical residential kernel density values, and historical industrial integration degree values in the training set as input data, use the historical asset classification results in the training set as output data, train the classifier to obtain an initial classifier, use the test set to test the initial classifier, and output a classifier that meets the preset accuracy as the asset classification model. The classifier is preferably one of the naive Bayes model or the support vector machine model.

[0118] Layout optimization module: used to determine P park planning layouts according to the asset classification result, calculate the imbalance index corresponding to each park planning layout, and optimize the P park planning layouts according to the imbalance index and a preset natural inspiration algorithm to obtain the optimal planning layout;

[0119] The method for determining P park planning layouts according to the asset classification result includes:

[0120] Determine the rental assets and commercial assets in the target industrial park according to the asset classification result, obtain the number of assets corresponding to the intermediate assets based on the asset classification result, and construct P park planning layouts through the enumeration combination method and the number of assets.

[0121] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data and performing software simulations to get a formula closest to the actual situation. The preset parameters, weights, and threshold selections in the formulas are set by those skilled in the art according to the actual situation.

[0122] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that contains a set of one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0123] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in the present invention can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0124] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be repeated here.

[0125] In several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only one way, and in actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling, direct coupling, or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the devices or units can be in electrical, mechanical, or other forms.

[0126] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0127] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0128] As mentioned above, the above are only specific implementation manners of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

[0129] Finally: The above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should all be included in the protection scope of the present invention.

Claims

1. A space utilization layout optimization method based on artificial intelligence, characterized in that: include: Obtaining geographic location information of Q assets to be processed in the target industrial park, and dividing the target industrial park into W park sub-areas based on the geographic location information of the Q assets to be processed; Obtain the residential space information and industrial distribution information corresponding to each park sub-area, calculate the residential kernel density value based on the residential space information, determine the industrial integration value based on the industrial distribution information, and classify the Q assets to be processed based on the residential kernel density value and the industrial integration value to obtain the asset classification results, which include rental assets, intermediate assets and commercial assets; Determine the planning layout of P parks according to the asset classification results, calculate the imbalance index corresponding to each park planning layout, optimize the planning layout of P parks according to the imbalance index and the preset natural inspiration algorithm, and obtain the optimal planning layout; The method for determining the planning layout of P parks according to the asset classification results includes: Determine the rental assets and commercial assets in the target industrial park based on the asset classification results, obtain the asset quantity corresponding to the intermediate assets based on the asset classification results, and construct the planning layout of P parks through the enumeration combination method and the asset quantity; The method for calculating the imbalance index corresponding to each park planning layout includes: The total number of park sub-areas, the number of rental assets in the park sub-areas, the number of commercial assets in the park sub-areas, and the number of assets to be processed in the park sub-areas corresponding to each park planning layout are obtained, and the corresponding imbalance index is obtained based on the total number of park sub-areas, the number of rental assets in the park sub-areas, the number of commercial assets in the park sub-areas, and the number of assets to be processed in the park sub-areas.

2. The method for optimizing space utilization layout based on artificial intelligence according to claim 1, characterized in that: The method for dividing the target industrial park into W park sub-areas based on the geographic location information of Q assets to be processed includes: The Q assets to be processed are clustered according to the geographic location information to obtain W target asset sets. The target industrial park is divided into W initial sub-areas according to the W target asset sets. The boundaries of the W initial sub-areas are optimized according to the Voronoi diagram algorithm to obtain W park sub-areas, and the park sub-areas correspond to the target asset sets one by one.

3. The method for optimizing space utilization layout based on artificial intelligence according to claim 1, characterized in that: The residential space information includes the coordinates of each residential point in the park sub-area and the coordinates of the target point. The method for calculating the residential kernel density value according to the residential space information includes: Perform kernel function accumulation calculation on the coordinates of each residential point in the sub-area of ​​the park and the coordinates of the target point to obtain the residential kernel density value; The target point coordinates are the geometric center coordinates of the geographical locations of all assets to be processed in the target asset set, and each park sub-area corresponds to a target point coordinate.

4. The method for optimizing space utilization layout based on artificial intelligence according to claim 3 is characterized in that: The industrial distribution information includes industrial category ratio, industrial staff ratio and industrial facility density. The method for determining the industrial integration value according to the industrial distribution information includes: The industrial integration value is determined by weighted calculation based on the proportion of industrial categories, the proportion of industrial personnel and the density of industrial facilities.

5. The method for optimizing space utilization layout based on artificial intelligence according to claim 4 is characterized in that: The method of classifying Q assets to be processed based on the residential kernel density value and the industrial integration value to obtain the asset classification result includes: Input each asset to be processed and the corresponding residential core density value and industrial integration value into the pre-built asset classification model to obtain the asset classification result.

6. The method for optimizing space utilization layout based on artificial intelligence according to claim 1, characterized in that: The natural heuristic algorithm is a whale optimization algorithm. The method for optimizing the planning layout of P parks according to the imbalance index and the preset natural heuristic algorithm to obtain the optimal planning layout includes: S301: Define the initial number of whales as P and the maximum number of iterations , each initial whale individual is represented as a park planning layout, and the random value rand is a random number sampled from a uniform distribution of [0,1]; S302: Obtain an imbalance index corresponding to the park planning layout, and use the imbalance index as the target fitness value of the initial whale individual; S303: When the random value rand is less than 0.5, a shrinking and encircling strategy is performed; when the random value rand is greater than or equal to 0.5, a spiral hunting strategy is performed; S304: Repeat S303 to continuously update the park planning layout until the maximum number of iterations is reached When the optimal park planning layout is output, the optimal park planning layout is used as the optimal planning layout.

7. The method for optimizing space utilization layout based on artificial intelligence according to claim 6, characterized in that: The method for performing the shrinking and surrounding strategy comprises: ； in, For the next generation of whale positions, is the current whale position, To pursue the target location, is the shrinkage coefficient of the shrinkage and encirclement strategy, is the control factor of the shrinking and encircling strategy, is the current iteration number.

8. A space utilization layout optimization system based on artificial intelligence, which is used to implement the space utilization layout optimization method based on artificial intelligence according to any one of claims 1 to 7, characterized in that: include: Area division module: used to obtain the geographical location information of Q assets to be processed in the target industrial park, and divide the target industrial park into W park sub-areas based on the geographical location information of the Q assets to be processed; Data processing module: used to obtain the residential space information and industrial distribution information corresponding to each park sub-area, calculate the residential kernel density value according to the residential space information, determine the industrial integration value according to the industrial distribution information, classify the Q assets to be processed based on the residential kernel density value and the industrial integration value, and obtain the asset classification results, which include rental assets, intermediate assets and commercial assets; Layout optimization module: used to determine the planning layout of P parks according to the asset classification results, calculate the imbalance index corresponding to the planning layout of each park, optimize the planning layout of P parks according to the imbalance index and the preset natural inspiration algorithm to obtain the optimal planning layout.

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