Intelligent optimization method of commercial space based on comprehensive knowledge graph and tourist data

Abstract

The present invention discloses an intelligent optimization method for commercial spaces based on a comprehensive knowledge graph and tourist data. High-precision drones and laser equipment are used to collect three-dimensional data on urban buildings and open spaces, and information on urban business distribution and transportation networks is integrated to construct a three-dimensional urban spatial data set. Based on the physical location of buildings, functional associations, and transportation network topology, the relationship between space, function, and transportation network is constructed and matched to form a knowledge graph of urban three-dimensional spatial business formats. A subgraph isomorphism algorithm is used to perform graph matching and optimization pedigree construction on the area to be optimized, and optimization solutions are screened based on homogeneous competition and passenger flow data. A graph network prediction algorithm is used to periodically predict and iteratively optimize the number of tourists in the optimized area. A path algorithm is combined to plan tourists' shopping and entertainment routes, and virtual reality and mixed reality devices are used to display the optimization results and adjustment solutions.

Description

Technical Field

[0001] The present invention relates to the field of urban planning, and specifically to a commercial space intelligent optimization method based on comprehensive knowledge graph and tourist data. Background Art

[0002] Traditional urban business renewal methods typically rely on manual measurement, data collection, manual updates, and modeling based on static data from a single data source. This leads to problems such as inefficiency, long and delayed data update cycles, a lack of real-time and accuracy in static urban models, small data volumes, limited coverage, and a lack of relevance. These methods struggle to adapt to rapidly changing urban environments and comprehensively reflect urban business forms and their connections. Furthermore, data silos are severe, hindering intelligent reasoning and decision support, and the provision of high-quality decision support and optimization recommendations. However, with the acceleration of urbanization, the spatial distribution and functional types of existing urban business forms have become highly dynamic and complex. Rapid renewal of urban business forms can improve urban management efficiency through real-time monitoring and management, optimize urban resource allocation, improve citizens' quality of life, and promote urban economic development. A knowledge graph-based intelligent urban business renewal method and device can help address these challenges. By integrating data from different sources through knowledge graph technology and 3D urban modeling, semantic technology can enhance the relevance and comprehensibility of data, and enable automated logical reasoning based on existing data. Geographic location data, building attribute data, and real-time sensor data can be integrated into the knowledge graph to construct a dynamic, semantically rich three-dimensional urban spatial model. This knowledge graph can then be used to predict business trends in a particular area or automatically update the city's three-dimensional spatial business model based on real-time data. Furthermore, edge computing and cloud computing technologies enable efficient processing and analysis of large-scale urban business data, supporting real-time updates and reasoning within the knowledge graph. Summary of the Invention

[0003] In order to address the deficiencies mentioned in the above background technology, the purpose of the present invention is to provide a commercial space intelligent optimization method based on comprehensive knowledge graph and tourist data. The present invention can achieve large-scale, high-precision and short-cycle intelligent optimization of urban three-dimensional space business formats.

[0004] The purpose of the present invention is achieved through the following technical solutions:

[0005] A commercial space intelligent optimization method based on comprehensive knowledge graph and tourist data includes the following steps:

[0006] Step S1: Data collection. Utilize a backpack drone with GPS+GLONASS dual-mode positioning and a maximum flight altitude greater than 500m, equipped with a laser module with a measurement range of 3-1200m and a high-definition camera with a resolution greater than 20MP, to collect three-dimensional building data (including building plans, building outlines, building heights, and three-dimensional floor data) and urban open space data (including open space plans and open space outlines). Use the API interface of an open map platform to collect business distribution data and popular facility data (including business types and locations). Use an open transportation information platform to collect the latest urban transportation network information and rail station data. The data collected in the above manner is converted and integrated into a unified format to form a complete urban three-dimensional spatial data set.

[0007] Step S2: Classification, construction, matching, and fusion of the 3D spatial knowledge graph. Based on the physical proximity of buildings, the relationship between the number of functional business formats, and the topological connectivity of the transportation network, spatial, functional, and transportation relationship graphs are constructed. The graph data is cleaned and the spatiotemporal network is aligned. Using the Blossom graph matching fusion algorithm, augmenting paths and contracting odd cycles are recursively searched to achieve maximum similarity matching of the relationship graphs, forming a 3D urban spatial business format knowledge graph.

[0008] Step S3: Sample graph matching and optimized pedigree construction for the area to be optimized. The graph of the area to be optimized is delineated. Using the Ullmann subgraph isomorphism algorithm, a candidate matrix is ​​initialized. Recursive backtracking and pruning rules are then used to find subgraph isomorphism relationships between the two graphs. Similar subgraph regions are identified in the urban 3D spatial business knowledge graph. K-means clustering is then performed to form an optimized pedigree for the business formats. Scores are calculated based on homogeneous competition distance and customer visit volume, and the optimized pedigree is selected. A terrestrial laser scanner is used to collect 3D data of the business format stores and popular amenities in the optimized pedigree.

[0009] Step S4: Periodic visitor volume prediction and iterative optimization. A GraphSAGE graph network prediction algorithm was constructed, and the case set model was trained 10,000 times. Visitor volume predictions were then made for the optimized areas. Quarterly, visitor volume for each business type was measured using an infrared counter with 95% accuracy within an 8-meter detection range and a video crowd analysis device equipped with the Faster R-CNN object detection algorithm. The actual visit volume was determined to be close to the predicted volume. If not, S3 was repeated.

[0010] Step S5: Tourist route planning. Input tourist needs and use the Dijkstra shortest path algorithm to plan shopping-oriented routes to minimize tour duration. Use the Floyd-Warshall multi-hop path algorithm to plan leisure and entertainment routes to maximize spatial coverage.

[0011] Step S6: Display virtual reality digital screens and mixed reality devices. Use virtual reality digital screens to display business layout and visitor flow planning, and use Apple Vision Pro to display the layout of popular facilities and store adjustments.

[0012] Furthermore, constructing the relationship map of space, function and transportation in S2 refers to importing the complete data set formed in S1 into the software ArcGis or ArcGisPro and generating models and data sets of urban space, business types and transportation respectively. The physical proximity relationship of buildings, urban open spaces and Internet celebrity facilities is determined by calculating the boundary distance between surface elements through the neighborhood analysis tool of ArcGis, including: proximity (boundary distance is 0-100m), close (boundary distance is 100m-500m) and far away (boundary distance is greater than 500m). Generate the plane and business distribution position of each floor of the building, associate the distribution coordinates of each business type and the floor number, view the feature count and summary statistical data through ArcGis to analyze the number and type of business distribution on each floor, and calculate the density distribution of business types through kernel density analysis.

[0013] Furthermore, the atlas data in S2 is cleaned and the spatiotemporal network is aligned, which means that the datasets from three different sources, namely urban space, business and transportation, are mapped to the WGS_1984_UTM_Zone_50N projection coordinate system using the ArcGis projection tool, and the converted data are spatially aligned to ensure that the geographical locations of all data sources on the map are consistent. The ArcGis map visualization tool is used to ensure that all geographical locations are correct, and any errors are corrected. Time data from different data sources are collected, including timestamps and time formats. The ArcGis Convert Time Field tool is used to convert the time formats of the three data sources, namely urban space, business and transportation, into a supported unified format, and the ArcGis Time Zone (Environment Settings) tool is used to convert the timestamps of different data sources to a unified time scale of UTC+8 to ensure time consistency. For data collected at different times, the linear difference method is used to synchronize the exact time points in the dataset. Finally, the time alignment results are checked to ensure that the timestamps are consistent, and any errors are corrected.

[0014] Furthermore, the Blossom graph matching fusion algorithm is used in S2: 1. Graph structure construction: The data collected in S1 and S2 are modeled as a graph structure, where nodes represent different spatial entities (surface elements), including buildings, urban open spaces, popular facilities, urban road networks and rail stations, and edges represent the physical proximity between nodes (surface elements). Each node and edge has additional area and functional type attributes. 2. Constructing initial graph matching: Using the Blossom graph matching fusion algorithm, data from different sources are matched and fused, the graph data is constructed as a subgraph and initial matching is performed based on node attributes. 3. Augmenting paths and shrinking odd rings: In the initial matching, an augmenting path from an unmatched node to another unmatched node is searched. If an odd ring is encountered when searching for an augmenting path, it is reduced to a single point and the search for augmenting paths continues on the way after reduction. 4. Optimizing matching: After finding the augmenting path in the contracted graph, the matching expansion is performed in the original graph through the reverse operation, and the augmenting path and flower contraction steps are repeated until no augmenting path is found, and the maximum match is obtained.

[0015] Furthermore, in S3, the Ullmann subgraph isomorphism algorithm is used to initialize a candidate matrix and, using recursive backtracking and pruning rules, to search for subgraph isomorphisms between the two graphs. This involves constructing a matching matrix M of size m×n, where m is the number of vertices in the pattern graph and n is the number of vertices in the target graph. For a pattern graph vertex i and a target graph vertex j, if M[i][j] = 1, it indicates that pattern graph vertex i can be matched with target graph vertex j; if M[i][j] = 0, it indicates that pattern graph vertex i cannot be matched with target graph vertex j. During initialization, all M[i][j] are set to 1. Based on vertex and edge attributes, impossible vertex pairings are filtered out, and the matching matrix M is updated, with impossible pairings set to 0. A recursive method is used to gradually construct an isomorphic mapping between the pattern graph and the target graph. Specifically, for each unmatched pattern graph vertex i, the candidate matrix is ​​searched for a possible matching target graph vertex j, i.e., a pairing that satisfies M[i][j] = 1. Whenever a vertex pairing is determined, it is added to the current partial isomorphism mapping and the recursive process continues with the next pattern graph vertex. If at any step it is found that the current pairing does not satisfy the subgraph isomorphism condition (for example, the matching relationship of some edges does not hold in the target graph), the process backtracks to the previous step, cancels the current pairing, and tries other possible pairing paths. Pruning rules are applied to prune impossible matching paths. By checking in advance whether the current partial isomorphism mapping violates the necessary conditions for subgraph isomorphism, the decision to continue recursion is made.

[0016] Furthermore, the homogeneous competition distance and passenger flow visit number in S3 are calculated and scored as follows: 1) the functional relationship map in S2 that has been cleaned and aligned with the spatiotemporal network is converted into point feature data using the feature conversion tool in the ArcGIS data management tool, and the passenger flow visit number n is assigned to each point feature using the field calculation tool; 2) the business nodes and passenger flow visit number n of the same functional type are selected using the ArcGIS attribute selection tool and the distance between the feature points is calculated using the ArcGIS neighborhood analysis tool; 3) the homogeneous competition distance d and the passenger flow visit number n are normalized by the formula to obtain the normalized homogeneous competition distance d norm and normalized passenger flow n norm , the calculation formula is as follows:

[0017] (1)

[0018] Where: d i : The original value of the homogeneous competition distance of the i-th in the dataset; d min : the minimum value of all homogeneous competition distances in the data set; d max : the maximum value of all homogeneous competition distances in the dataset; d norm,i : The normalized value of the i-th homogeneous competition distance; n i : the original value of the number of visits of the i-th passenger flow in the data set; n min : the minimum value of all passenger traffic visits in the data set; n max : the maximum value of all passenger traffic visits in the data set; n norm,i : The normalized value of the i-th passenger flow visit volume;

[0019] Use the normalized d norm and n norm Calculate the score a of each store using the following formula:

[0020] (2)a i =d norm,i ×n norm,i

[0021] Among them: a i : The rating of the i-th store;

[0022] And sum up the scores of all stores to get the total score A total and maximum score A max , the calculation formula is as follows:

[0023] (3)

[0024] (4)A max =max(a1,a2,…,a m);

[0025] Among them: A total : The total score of all stores; A total : The maximum value of the rating among all stores; m: The total number of stores.

[0026] Furthermore, the GraphSAGE graph network prediction algorithm in S4 refers to constructing a multi-layer sampling aggregation (SAGEConv) neural network layer, where each layer is defined by an input feature dimension, a hidden layer feature dimension, and an output feature dimension. By sampling and aggregating neighbor nodes in the graph, the output features of each layer are used as the input of the next layer. In the model initialization stage, the feature dimensions of each layer are defined, the input feature dimension is X, the hidden layer feature dimension is H, and the output feature dimension is Y, and each layer uses a mean aggregation operation. The graph dataset is divided into a training set, a validation set, and a test set. The training set is used for model training, the validation set is used for model parameter adjustment, and the test set is used for model performance evaluation. The method for training the model includes defining a loss function and an optimizer, calculating the predicted value and loss function value of each node through forward propagation, and updating the model parameters through backpropagation until the preset number of training rounds is reached or the early termination condition is met. The optimized pedigree and its feature data screened in S3 are added to the graph structure, and the adjacency matrix and feature matrix of the graph are updated. The trained GraphSAGE model is used to propagate the new node forward, calculate the representation vector of the new node, and input it into the regression layer to output the predicted visit volume of the new node. The regression layer calculates the dot product of the new node representation vector and the preset weight matrix to obtain the predicted visit volume value d of the new node. 预测i , sum up the visits of all new nodes to get the total amount d 预测总 .

[0027] Furthermore, the judgment in S4 on whether the actual volume is close to the predicted volume refers to the total flow rate d at the entrance and exit of the area measured every quarter after the business optimization by an infrared counter with 95% accuracy within a detection range of 8m. 实测总 , using the video crowd flow analysis device equipped with the Faster R-CNN target detection algorithm to identify the daily passenger flow of business nodes 实测i ; Compare the measured value and the predicted value. If the following conditions cannot be met at the same time, repeat step S3;

[0028] (5)

[0029] (6)0.6*d 预测总 -d 实测总 >0;

[0030] Where: d 实测i : the actual passenger flow of the i-th business node; d 预计i: predicted passenger flow of the i-th business node; m: the total number of business nodes; d 预测总 : The total predicted passenger flow of all business nodes; d 实测总 : The sum of actual passenger flow of all business nodes.

[0031] Furthermore, the Dijkstra shortest path algorithm in S5 plans the shopping destination-oriented streamline, assuming that there are n access nodes in the business knowledge graph, denoted as N = {n1, n2, ..., n n}, the connection relationship between nodes forms a directed graph G = (N, E)G = (N, E)G = (N, E), where E is the edge set, including the weight w of each edge ij , represents node n i To node n j Distance weights are calculated. First, the distances of all nodes are initialized, with the distance to the starting node set to 0 and the distances to all other nodes set to infinity. The predecessor node array is also set. Next, the node with the shortest current distance is selected from the set of unvisited nodes, removed from the set, and the distances and predecessor node information of all its neighboring nodes are updated. This process is repeated until all nodes have been visited or the shortest distance to the current node is infinite.

[0032] Furthermore, the Floyd-Warshall multi-hop path algorithm in S5 plans the leisure and entertainment roaming route, which means initializing a distance matrix D, where D[i][j] represents node n i To node n j The path weight of node n i and n j Directly connected, then D[i][j] is equal to the edge weight w ij Otherwise, D[i][j] = ∞, and for all nodes i, D[i][i] = 0. Then, by introducing the intermediate node k, the shortest paths between all pairs of nodes are gradually updated. The specific update formula is D[i][j] = min(D[i][j], D[i][k] + D[k][j]). By performing the above update operation on all nodes, the shortest path matrix is ​​output, ensuring that tourists cover the maximum space and business types during their leisure and entertainment roaming.

[0033] Furthermore, the mixed reality device in S6 displays the layout of Internet celebrity facilities and store adjustments, which refers to the display of three-dimensional data collected by the ground laser scanner in S3, and the visual integration of spatial position and surface texture with real scenes through Apple Vision Pro to assist owners in choosing business style, layout and surrounding facilities.

[0034] Beneficial effects:

[0035] 1. This invention reduces the time and labor costs of urban renewal projects. Traditional urban renewal projects covering 15-20 square kilometers require a team of 6-10 people to collaborate and spend 4-6 weeks to complete the design. This invention uses intelligent algorithms to rapidly generate and adjust multiple plans. With just 2-3 people, plans can be generated and adjusted within 7 days, significantly reducing the need for human resources, improving the efficiency of urban renewal project design, and reducing labor costs.

[0036] 2. This invention improves the accuracy and predictability of urban business updates. The accuracy of data collected by intelligent data collection equipment is 60%-70% higher than that of traditional manual data collection. Open platform data can obtain the latest business data and road network information, ensuring the comprehensiveness and accuracy of the data. Compared with traditional methods, the accuracy of business data is improved by 40%-50%, and the accuracy of road network data is improved by 50%-60%. The Blossom graph matching fusion algorithm ensures the accuracy of the knowledge graph and the reliability of data analysis, which can be improved by 35%-45% compared with traditional methods. The GraphSAGE graph network algorithm is used to predict the number of visitors to the optimized area and ensure the reliability of the prediction model, which can be improved by 30% compared with traditional methods.

[0037] 3. This invention expands the design scope and depth of urban business renewal solutions. By utilizing backpack drones and multi-source data integration, it can efficiently collect urban 3D spatial data, business data, and road network data for areas of 15-20 square kilometers or even larger. High-precision data and the ability to quickly generate and adjust solutions enable planners to iterate and optimize design solutions more frequently within a limited timeframe, effectively completing large-scale urban business renewal projects while increasing design depth. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a flow chart of the method of the present invention;

[0039] Figure 2 It is a schematic diagram of the three-dimensional spatial knowledge graph;

[0040] Figure 3 It is a schematic diagram of sample map matching and optimization spectrum of the area to be optimized. DETAILED DESCRIPTION

[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0042] A commercial space intelligent optimization method based on comprehensive knowledge graph and tourist data, such as Figure 1-3 As shown, the following steps are included:

[0043] Step 1: Data Collection. Using a backpack drone with GPS+GLONASS dual-mode positioning and a maximum flight altitude greater than 500m, equipped with a laser module with a measurement range of 3-1200m and a high-definition camera with a resolution greater than 20MP, collect 3D building data (including building plans, building outlines, building heights, and 3D floor data) and urban open space data (including open space plans and open space outlines). Business distribution data and popular facility data (including business types and locations) are collected through the API interface of an open map platform. The latest urban transportation network information and rail station data are collected through an open transportation information platform. The data collected in this way is converted and integrated into a unified format to form a complete urban 3D spatial data set.

[0044] Step 2: Classify, construct, match, and fuse the 3D spatial knowledge graph. Based on the physical proximity of buildings, the relationship between functional business formats and the topological connectivity of the transportation network, we construct spatial, functional, and transportation relationship graphs. The graph data is cleaned and aligned with the spatiotemporal network. Using the Blossom graph matching fusion algorithm, we recursively search for augmenting paths and contracting odd cycles to achieve maximum similarity matching of the relationship graphs, thus forming a 3D urban spatial business format knowledge graph.

[0045] The construction of the relationship map of space, function and transportation refers to importing the complete data set formed in step one into the software ArcGis or ArcGisPro and generating models and data sets of urban space, business types and transportation respectively. The physical proximity relationship of buildings, urban open spaces and Internet celebrity facilities is determined by calculating the boundary distance between surface elements through the neighborhood analysis tool of ArcGis, including: proximity (boundary distance is 0-100m), close (boundary distance is 100m-500m) and far away (boundary distance is greater than 500m). Generate the plane and business distribution position of each floor of the building, associate the distribution coordinates of each business type and the floor number, view the feature count and summary statistical data through ArcGis to analyze the number and type of business distribution on each floor, and calculate the density distribution of business types through kernel density analysis.

[0046] The atlas data is cleaned and the spatiotemporal network is aligned, which means that the datasets from three different sources, namely urban space, business and transportation, are mapped to the WGS_1984_UTM_Zone_50N projection coordinate system using the ArcGis projection tool, and the converted data are spatially aligned to ensure that the geographical locations of all data sources on the map are consistent. The ArcGis map visualization tool is used to ensure that all geographical locations are correct, and any errors are corrected. Time data from different data sources are collected, including timestamps and time formats. The ArcGis time field conversion tool is used to convert the time formats of the three data sources, namely urban space, business and transportation, into a supported unified format, and the ArcGis time zone (environment setting) tool is used to convert the timestamps of different data sources to a unified time scale of UTC+8 to ensure time consistency. For data collected at different times, the linear difference method is used to synchronize the actual time points in the dataset. Finally, the time alignment results are checked to ensure that the timestamps are consistent, and any errors are corrected.

[0047] The Blossom graph matching fusion algorithm refers to 1. Graph structure construction: modeling the data collected in steps one and two into a graph structure, wherein nodes represent different spatial entities (surface elements), including buildings, urban open spaces, Internet celebrity facilities, urban road networks and rail stations, and edges represent the physical proximity between nodes (surface elements). Each node and edge has additional area and functional type attributes. 2. Constructing initial graph matching: using the Blossom graph matching fusion algorithm to match and fuse data from different sources, construct the graph data into a subgraph and perform initial matching based on node attributes. 3. Augmenting paths and shrinking odd rings: searching for an augmenting path from an unmatched node to another unmatched node in the initial matching. If an odd ring is encountered when searching for an augmenting path, it is reduced to a single point, and the search for an augmenting path continues on the way after the reduction. 4. Optimizing matching: After finding the augmenting path in the contracted graph, the matching expansion is performed in the original graph through the reverse operation, and the augmenting path and the shrinking steps of the flower are repeated until no augmenting path is found, and the maximum match is obtained.

[0048] Step 3: Sample graph matching and optimization pedigree construction for the area to be optimized. The graph of the area to be optimized is delineated. Using the Ullmann subgraph isomorphism algorithm, a candidate matrix is ​​initialized. Recursive backtracking and pruning rules are then used to find subgraph isomorphism relationships between the two graphs. Similar subgraph regions are identified in the urban 3D spatial business knowledge graph. K-means clustering is then performed to form an optimized pedigree for the business formats. Scores are calculated based on homogeneous competition distance and customer visit volume, and the optimized pedigree is selected. A terrestrial laser scanner is used to collect 3D data of the business format stores and popular amenities in the optimized pedigree.

[0049] The Ullmann subgraph isomorphism algorithm, described above, initializes a candidate matrix and uses recursive backtracking and pruning rules to search for subgraph isomorphisms between two graphs. This involves constructing a matching matrix M of size m×n, where m is the number of vertices in the pattern graph and n is the number of vertices in the target graph. For a pattern graph vertex i and a target graph vertex j, if M[i][j] = 1, it indicates that pattern graph vertex i can be matched with target graph vertex j; if M[i][j] = 0, it indicates that pattern graph vertex i cannot be matched with target graph vertex j. Initially, all M[i][j] are set to 1. Based on vertex and edge attributes, impossible vertex pairings are filtered out, and the matching matrix M is updated, with impossible pairings set to 0. A recursive method is used to gradually construct an isomorphic mapping between the pattern graph and the target graph. Specifically, for each unmatched pattern graph vertex i, the candidate matrix is ​​searched for a possible matching target graph vertex j, i.e., a pairing that satisfies M[i][j] = 1. Whenever a vertex pairing is determined, it is added to the current partial isomorphism mapping and the recursive process continues with the next pattern graph vertex. If at any step it is found that the current pairing does not satisfy the subgraph isomorphism condition (for example, the matching relationship of some edges does not hold in the target graph), the process backtracks to the previous step, cancels the current pairing, and tries other possible pairing paths. Pruning rules are applied to prune impossible matching paths. By checking in advance whether the current partial isomorphism mapping violates the necessary conditions for subgraph isomorphism, the decision to continue recursion is made.

[0050] The calculation score of the homogeneous competition distance and passenger flow visit quantity refers to 1. Using the feature conversion tool in the ArcGIS data management tool to convert the functional relationship map of the business format stores and Internet celebrity facilities into point feature data after data cleaning and spatiotemporal network alignment in step 2, and assigning the passenger flow visit quantity n to each point feature through the field calculation tool. 2. Using the ArcGIS attribute screening tool to filter business format nodes and Internet celebrity facilities of the same functional type and using the ArcGIS neighborhood analysis tool to calculate the distance between feature points, that is, the homogeneous competition distance d. 3. Normalizing the homogeneous competition distance d and the passenger flow visit quantity n by formula to obtain d norm and n norm , the calculation formula is as follows:

[0051]

[0052] Where: d i : The original value of the homogeneous competition distance of the i-th in the dataset; d min : the minimum value of all homogeneous competition distances in the data set; d max : the maximum value of all homogeneous competition distances in the dataset; d norm,i : The normalized value of the i-th homogeneous competition distance; n i : the original value of the number of visits of the i-th passenger flow in the data set; nmin : the minimum value of all passenger traffic visits in the data set; n max : the maximum value of all passenger traffic visits in the data set; n norm,i : The normalized value of the i-th passenger flow visit volume.

[0053] Use the normalized d norm and n norm Calculate the score a of each store using the following formula:

[0054] (2)a i =d norm,i ×n norm,i

[0055] Among them: a i : The rating of the i-th store.

[0056] And sum up the scores of all stores to get the total score A total and maximum score A max , the calculation formula is as follows:

[0057] (3)

[0058] (4)A max =max(a1,a2,…,a m ).

[0059] Among them: A total : The total score of all stores; A total : The maximum value of the rating among all stores; m: The total number of stores.

[0060] Step 4: Periodic visitor volume prediction and iterative optimization. A GraphSAGE graph network prediction algorithm was constructed, and the case set model was trained 10,000 times. Visitor volume forecasts were then generated for the optimized areas. Quarterly, visitor volume was measured using an infrared counter with 95% accuracy within an 8-meter detection range and a video crowd analysis device equipped with the Faster R-CNN object detection algorithm to determine if actual visit volume was close to the predicted value. If not, step 3 was repeated.

[0061] The GraphSAGE graph network prediction algorithm refers to constructing a multi-layer sampling aggregation (SAGEConv) neural network layer, each layer is defined by the input feature dimension, the hidden layer feature dimension and the output feature dimension, and the output features of each layer are used as the input of the next layer by sampling and aggregating the neighbor nodes in the graph. In the model initialization stage, the feature dimension of each layer is defined, the input feature dimension is X, the hidden layer feature dimension is H, the output feature dimension is Y, and each layer uses the mean aggregation operation. The graph data set is divided into a training set, a validation set and a test set. The training set is used for model training, the validation set is used for model parameter adjustment, and the test set is used for model performance evaluation. The method for training the model includes defining a loss function and an optimizer, calculating the predicted value and loss function value of each node by forward propagation, and updating the model parameters by backpropagation until the preset number of training rounds is reached or the early termination condition is met. The optimized pedigree and its feature data screened in step three are added to the graph structure, and the adjacency matrix and feature matrix of the graph are updated. The trained GraphSAGE model is used to propagate the new node forward, calculate the representation vector of the new node, and input it into the regression layer to output the predicted visit volume of the new node. The regression layer calculates the dot product of the new node representation vector and the preset weight matrix to obtain the predicted visit volume value d of the new node. 预测i , sum up the visits of all new nodes to get the total amount d 预测总 .

[0062] The judgment of whether the actual volume is close to the predicted volume refers to the measurement of the total flow rate d at the entrance and exit of the area every quarter after the business optimization by using an infrared counter with 95% accuracy within a detection range of 8m. 实测总 , using the video crowd flow analysis device equipped with the Faster R-CNN target detection algorithm to identify the daily passenger flow of business nodes 实测i Compare the measured value with the predicted value. If the following conditions cannot be met at the same time, repeat the third step.

[0063] (5)

[0064] (6)0.6*d 预测总 -d 实测总 >0.

[0065] Where: d 实测i : the actual passenger flow of the i-th business node; d 预计i : predicted passenger flow of the i-th business node; m: the total number of business nodes; d 预测总 : The total predicted passenger flow of all business nodes; d 实测总 : The sum of actual passenger flow of all business nodes.

[0066] Step 5: Tourist route planning. Input tourist needs and use the Dijkstra shortest path algorithm to plan shopping-oriented routes to minimize tour duration. Use the Floyd-Warshall multi-hop path algorithm to plan leisure and entertainment routes to maximize spatial coverage.

[0067] The Dijkstra shortest path algorithm plans the shopping destination-oriented streamline, assuming that there are n access nodes in the business knowledge graph, denoted as N = {n1, n2, ..., n n}, the connection relationship between nodes forms a directed graph G = (N, E)G = (N, E)G = (N, E), where E is the edge set, including the weight w of each edge ij , represents node n i To node n j Distance weights are calculated. First, the distances of all nodes are initialized, with the distance to the starting node set to 0 and the distances to all other nodes set to infinity. The predecessor node array is also set. Next, the node with the shortest current distance is selected from the set of unvisited nodes, removed from the set, and the distances and predecessor node information of all its neighboring nodes are updated. This process is repeated until all nodes have been visited or the shortest distance to the current node is infinite.

[0068] The Floyd-Warshall multi-hop path algorithm for planning leisure and entertainment roaming routes is to initialize a distance matrix D, where D[i][j] represents node n i To node n j The path weight of node n i and n j Directly connected, then D[i][j] is equal to the edge weight w ij Otherwise, D[i][j] = ∞, and for all nodes i, D[i][i] = 0. Then, by introducing the intermediate node k, the shortest paths between all pairs of nodes are gradually updated. The specific update formula is D[i][j] = min(D[i][j], D[i][k] + D[k][j]). By performing the above update operation on all nodes, the shortest path matrix is ​​output, ensuring that tourists cover the maximum space and business types during their leisure and entertainment roaming.

[0069] Step 6: Display virtual reality digital screens and mixed reality devices. Use virtual reality digital screens to display business layout and visitor flow planning, and use Apple Vision Pro to display the layout of popular facilities and store adjustments.

[0070] The mixed reality device displays the layout of Internet celebrity facilities and store adjustments, which refers to the display of three-dimensional data collected by the ground laser scanner in the s3, and the visual integration of spatial position and surface texture with the real scene through Apple Vision Pro to assist owners in choosing business style, layout and surrounding facilities.

[0071] The technical solution of the present invention will be described in detail below by taking the urban design of a certain area in a certain city as an example.

[0072] The specific implementation is as follows:

[0073] (1) Data collection. Taking a certain area of ​​a city as the target city, a backpack drone with GPS+GLONASS dual-mode positioning and a maximum flight altitude greater than 500m is used, equipped with a laser module with a measurement range of 3-1200m and a high-definition camera with a resolution greater than 20MP. 3D building data (including building plan, building outline, building height and 3D floor data) and urban open space data (including open space plan and open space outline) are collected. Business distribution data and popular facility data (including business type and location) are collected through the API interface of the open map platform. The latest urban traffic network information and rail station data are collected through the open traffic information platform. The data collected by the above method are converted and integrated into a unified format to form a complete urban 3D spatial data set. The three types of online consumption data and urban population data are obtained to form the actual online consumption data set of the target city, and the said data set is used to build a digital space sandbox model.

[0074] (2) Classification construction and matching fusion of three-dimensional spatial knowledge graphs. Based on the physical proximity of buildings, the relationship between the number of functional business types, and the topological connection relationship of the traffic network, the relationship graphs of space, function, and traffic are constructed respectively. The graph data is cleaned and the spatiotemporal network is aligned. Through the Blossom graph matching fusion algorithm, the augmenting path and the shrinking odd ring are recursively searched to achieve the maximum similarity matching of the relationship graph and form the urban three-dimensional spatial business type knowledge graph. Specifically, it includes:

[0075] (2.1) Import the complete dataset generated in step 1 into the software ArcGis or ArcGisPro and generate models and datasets for urban space, business types, and transportation respectively. Use ArcGis’s neighborhood analysis tool to calculate the boundary distance between surface features to determine the physical proximity of buildings, urban open spaces, and popular facilities, including: proximity (boundary distance is 0-100m), close (boundary distance is 100m-500m), and far away (boundary distance is greater than 500m). Generate the plan and business type distribution position of each floor of the building, associate the distribution coordinates of each business type with the floor number, view the feature count and summary statistical data through ArcGis to analyze the number and type of business type distribution on each floor, and calculate the density distribution of business types through kernel density analysis.

[0076] (2.2) Use ArcGis's projection tool to map the datasets from three different sources, namely urban space, business, and transportation, to the WGS_1984_UTM_Zone_50N projection coordinate system. Spatially align the converted data to ensure that the geographical locations of all data sources on the map are consistent. Use ArcGis map visualization tools to ensure that all geographical locations are correct. If errors are found, correct them. Collect time data from different data sources, including timestamps and time formats. Use ArcGis's Convert Time Field tool to convert the time formats of the three data sources, namely urban space, business, and transportation, to a supported unified format, and use ArcGis's Time Zone (Environment Settings) tool to convert the timestamps of different data sources to a unified time scale of UTC+8 to ensure time consistency. For data collected at different times, use the linear interpolation method to synchronize the exact time points in the dataset. Finally, check the time alignment results to ensure that the timestamps are consistent and correct any errors.

[0077] (2.3) Through 1. Graph Structure Construction: The data collected in steps 1 and 2 are modeled as a graph structure, where nodes represent different spatial entities (surface elements), including buildings, urban open spaces, popular facilities, urban road networks, and rail stations. Edges represent the physical proximity between nodes (surface elements). Each node and edge is assigned area and functional type attributes. 2. Initial Graph Matching Construction: The Blossom graph matching fusion algorithm is used to match and fuse data from different sources. The graph data is constructed into subgraphs and initial matching is performed based on node attributes. 3. Augmenting Paths and Contracting Odd Cycles: During the initial matching, augmenting paths are searched from unmatched nodes to other unmatched nodes. If an odd cycle is encountered while searching for an augmenting path, it is reduced to a single point and the search for augmenting paths continues along the way. 4. Optimizing Matching: After finding an augmenting path in the contracting graph, the augmenting path is expanded in the original graph through the reverse operation. The augmenting path and contraction steps are repeated until no augmenting path is found, resulting in a maximum match. Ultimately, a knowledge graph of urban three-dimensional spatial business formats is formed.

[0078] (3) Sample graph matching and optimization spectrum construction of the area to be optimized. Delineate the graph of the area to be optimized, initialize the candidate matrix through the Ullmann subgraph isomorphism algorithm, and use recursive backtracking and pruning rules to find the subgraph isomorphism relationship between the two graphs, identify similar subgraph areas in the urban three-dimensional spatial business knowledge graph, and perform k-means clustering to form the business optimization spectrum. Calculate the score and screen the optimization spectrum based on the homogeneous competition distance and the number of customer visits. Use a ground laser scanner to collect three-dimensional data of the business stores and Internet celebrity facilities in the optimization spectrum. Specifically including:

[0079] (3.1) Initialize the candidate matrix and construct a matching matrix M of size m×n, where m is the number of vertices in the pattern graph and n is the number of vertices in the target graph. For a pattern graph vertex i and a target graph vertex j, if M[i][j] = 1, it means that pattern graph vertex i can be matched with target graph vertex j. If M[i][j] = 0, it means that pattern graph vertex i cannot be matched with target graph vertex j. During initialization, all M[i][j] are set to 1. Based on vertex and edge attributes, impossible vertex pairings are filtered out, and the matching matrix M is updated, with impossible pairings set to 0. A recursive method is used to gradually construct an isomorphic mapping between the pattern graph and the target graph. Specifically, for each unmatched pattern graph vertex i, the candidate matrix is ​​searched for a possible matching target graph vertex j that satisfies M[i][j] = 1. Whenever a vertex pairing is determined, it is added to the current partial isomorphic mapping, and the recursive process continues for the next pattern graph vertex. If at a certain step it is found that the current pairing fails to satisfy the subgraph isomorphism condition (for example, the matching relationship of some edges does not hold in the target graph), it backtracks to the previous step, cancels the current pairing, and tries other possible pairing paths. It applies pruning rules to prune impossible matching paths, and decides whether to continue recursion by checking in advance whether the current partial isomorphism mapping violates the necessary conditions for subgraph isomorphism.

[0080] (3.2) Use the Feature to Point tool in the ArcGIS data management tool to convert the functional relationship map of the business stores and Internet celebrity facilities in step 2, which has been cleaned and aligned with the spatiotemporal network, into point feature data, and assign the number of passenger visits n to each point feature using the field calculation tool. 2. Use the ArcGIS attribute selection tool to select business nodes and Internet celebrity facilities of the same functional type and use the ArcGIS neighborhood analysis tool to calculate the distance between feature points, that is, the homogeneous competition distance d. 3. Use the formula to normalize the homogeneous competition distance d and the passenger flow visit n to obtain d norm and n norm , the calculation formula is as follows:

[0081] (1)

[0082] Where: d i : The original value of the homogeneous competition distance of the i-th in the dataset; d min : the minimum value of all homogeneous competition distances in the data set; d max : the maximum value of all homogeneous competition distances in the dataset; d norm,i : The normalized value of the i-th homogeneous competition distance; n i : the original value of the number of visits of the i-th passenger flow in the data set; n min : the minimum value of all passenger traffic visits in the data set; n max : the maximum value of all passenger traffic visits in the data set; n norm,i : The normalized value of the i-th passenger flow visit volume.

[0083] Use the normalized d norm and n norm Calculate the score a of each store using the following formula:

[0084] (2)a i =d norm,i ×n norm,i

[0085] Among them: a i : The rating of the i-th store.

[0086] And sum up the scores of all stores to get the total score A total and maximum score A max , the calculation formula is as follows:

[0087] (3)

[0088] (4)A max =max(a1,a2,…,a m ).

[0089] Among them: A total : The total score of all stores; A total : The maximum value of the rating among all stores; m: The total number of stores.

[0090] (4) Periodic prediction and iterative optimization of the number of tourists. Build a GraphSAGE graph network prediction algorithm, train the case set model for 10,000 iterations, and predict the number of visitors to the optimized area. Every quarter, measure the number of visitors to the business format using an infrared counter with a 95% accuracy within a detection range of 8m and a video crowd analysis device equipped with the FasterR-CNN target detection algorithm to determine whether the actual number is close to the predicted number. If not, repeat the third step. Specifically, it includes:

[0091] (4.1) Construct a multi-layer sampling and aggregation (SAGEConv) neural network layer. Each layer is defined by the input feature dimension, the hidden layer feature dimension, and the output feature dimension. By sampling and aggregating neighboring nodes in the graph, the output features of each layer serve as the input of the next layer. During the model initialization phase, the feature dimensions of each layer are defined as X for the input feature dimension, H for the hidden layer feature dimension, and Y for the output feature dimension. Each layer uses the mean aggregation operation. The graph dataset is divided into a training set, a validation set, and a test set. The training set is used for model training, the validation set is used for model parameter adjustment, and the test set is used for model performance evaluation. The model training method includes defining a loss function and an optimizer. The predicted value and loss function value of each node are calculated through forward propagation. The model parameters are updated through backpropagation until the preset number of training rounds is reached or the early termination condition is met. The optimized lineage and its feature data selected in S3 are added to the graph structure, and the adjacency matrix and feature matrix of the graph are updated. The trained GraphSAGE model is used to propagate the new node forward, calculate the representation vector of the new node, and input it into the regression layer to output the predicted visit volume of the new node. The regression layer calculates the dot product of the new node representation vector and the preset weight matrix to obtain the predicted visit volume value d of the new node. 预测i , sum up the visits of all new nodes to get the total amount d 预测总 .

[0092] (4.2) After the business model is optimized, the total flow rate d at the entrance and exit of the area is measured every quarter using an infrared counter with a 95% accuracy within a detection range of 8m. 实测总 , using the video crowd flow analysis device equipped with the Faster R-CNN target detection algorithm to identify the daily passenger flow of business nodes 实测i Compare the measured value with the predicted value. If the following conditions cannot be met at the same time, repeat the third step.

[0093] (5)

[0094] (6)0.6*d 预测总 -d 实测总 >0.

[0095] Where: d 实测i : the actual passenger flow of the i-th business node; d 预计i : predicted passenger flow of the i-th business node; m: the total number of business nodes; d 预测总 : The total predicted passenger flow of all business nodes; d 实测总 : The sum of actual passenger flow of all business nodes.

[0096] (5) Tourist route organization and planning. Input tourist needs and use the Dijkstra shortest path algorithm to plan shopping destination-oriented streamlines to minimize tour duration; use the Floyd-Warshall multi-hop path algorithm to plan leisure and entertainment roaming routes to maximize spatial tour coverage. Specifically include:

[0097] (5.1) Assume that there are n access nodes in the business knowledge graph, denoted as N = {n1, n2, ..., n n}, the connection relationship between nodes forms a directed graph G = (N, E)G = (N, E)G = (N, E), where E is the edge set, including the weight w of each edge ij , represents node n i To node n j Distance weights are calculated. First, the distances of all nodes are initialized, with the distance to the starting node set to 0 and the distances to all other nodes set to infinity. The predecessor node array is also set. Next, the node with the shortest current distance is selected from the set of unvisited nodes, removed from the set, and the distances and predecessor node information of all its neighboring nodes are updated. This process is repeated until all nodes have been visited or the shortest distance to the current node is infinite.

[0098] (5.2) Initialize a distance matrix D, where D[i][j] represents node n i To node n j The path weight of node n i and n j Directly connected, then D[i][j] is equal to the edge weight w ij Otherwise, D[i][j] = ∞, and for all nodes i, D[i][i] = 0. Then, by introducing the intermediate node k, the shortest paths between all pairs of nodes are gradually updated. The specific update formula is D[i][j] = min(D[i][j], D[i][k] + D[k][j]). By performing the above update operation on all nodes, the shortest path matrix is ​​output, ensuring that tourists cover the maximum space and business types during their leisure and entertainment roaming.

[0099] (6) Virtual reality digital screens and mixed reality equipment displays. Use virtual reality digital screens to display business layout and visitor flow planning, and use Apple Vision Pro to display the layout of popular facilities and store adjustments.

[0100] (6.1) Display the 3D data collected by the terrestrial laser scanner in step 3, and use Apple VisionPro to visually integrate the spatial position and surface texture with the real scene to assist the owner in selecting the business style, layout and surrounding facilities.

Claims

1. A commercial space intelligent optimization method based on comprehensive knowledge graph and tourist data, characterized by: Step S1: Data Collection: Utilize a backpack drone equipped with GPS+GLONASS dual-mode positioning and a maximum flight altitude greater than 500m, equipped with a laser module with a measurement range of 3-1200m and a high-definition camera with a resolution greater than 20MP, to collect 3D building data and urban open space data. Business distribution data and popular facility data are collected through the API interface of an open map platform, and the latest urban transportation network information and rail station data are collected through an open transportation information platform. The data collected by these methods are converted and integrated into a unified format to form a complete 3D urban spatial dataset. Step S2: Classification, construction, matching and fusion of 3D spatial knowledge graphs. Based on the physical proximity of buildings, the relationship between the number of functional business types, and the topological connection of the transportation network, spatial, functional, and transportation relationship graphs are constructed respectively. The graph data is cleaned and the spatiotemporal network is aligned. Using the Blossom graph matching fusion algorithm, the augmenting paths and contracting odd cycles are recursively searched to achieve maximum similarity matching of the relationship graphs, thus forming a 3D urban spatial business type knowledge graph. Step S3: Sample graph matching and optimization spectrum construction for the area to be optimized; delineate the graph of the area to be optimized, initialize the candidate matrix using the Ullmann subgraph isomorphism algorithm, and use recursive backtracking and pruning rules to find the subgraph isomorphism relationship between the two graphs. Identify similar subgraph areas in the urban three-dimensional spatial business knowledge graph, and perform k-means clustering to form the business optimization spectrum; calculate scores based on homogeneous competition distance and customer flow visit volume and select the optimization spectrum; use a terrestrial laser scanner to collect three-dimensional data of the business stores and popular facilities in the optimization spectrum; Step S4: Periodic prediction and iterative optimization of visitor numbers. A GraphSAGE graph network prediction algorithm was constructed, and the case set model was trained 10,000 times. Visitor volume predictions were then made for the optimized areas. Every quarter, visitor volume was measured using an infrared counter with a 95% accuracy within an 8-meter detection range and a video crowd analysis device equipped with the Faster R-CNN object detection algorithm to determine whether the actual volume was close to the predicted volume. If not, step 3 was repeated. Step S5: Tourist route organization and planning: Input tourist needs and use the Dijkstra shortest path algorithm to plan shopping-oriented routes to minimize tour duration; use the Floyd-Warshall multi-hop path algorithm to plan leisure and entertainment routes to maximize spatial tour coverage; Step S6: Display of virtual reality digital screens and mixed reality devices; display of business layout and visitor flow planning through virtual reality digital screens, and display of internet celebrity facility layout and store adjustments using Apple Vision Pro.

2. The commercial space intelligent optimization method based on comprehensive knowledge graph and tourist data according to claim 1 is characterized in that: The construction of the relationship map of space, function and transportation in S2 refers to importing the complete data set formed in S1 into the software ArcGis or ArcGisPro and generating models and data sets of urban space, business type and transportation respectively; calculating the boundary distance between surface elements through the ArcGis neighborhood analysis tool to determine the physical proximity relationship of buildings, urban open spaces and popular facilities, including: proximity, closeness and distance; generating the plane of each floor of the building and the distribution position of the business type, associating the distribution coordinates of each business type with the number of floors, analyzing the number and type of business type distribution on each floor through ArcGis viewing feature counts and summary statistical data, and calculating the density distribution of business types through kernel density analysis; The atlas data in S2 are cleaned and the spatiotemporal network is aligned, which means that the datasets from three different sources, namely urban space, business and transportation, are mapped to the WGS_1984_UTM_Zone_50N projection coordinate system using the ArcGis projection tool, and the converted data are spatially aligned to ensure that the geographical locations of all data sources on the map are consistent. The ArcGis map visualization tool is used to ensure that all geographical locations are correct, and any errors are corrected; time data from different data sources are collected, including timestamps and time formats; the ArcGis time field conversion tool is used to convert the time formats of the three data sources, namely urban space, business and transportation, into a supported unified format, and the ArcGis time zone tool is used to convert the timestamps of different data sources to the unified time scale UTC+8 to ensure time consistency; for data collected at different times, the linear difference method is used to synchronize the actual time points in the dataset; finally, the time alignment results are checked to ensure that the timestamps are consistent, and any errors are corrected.

3. The commercial space intelligent optimization method based on comprehensive knowledge graph and tourist data according to claim 2 is characterized in that: In S2, the Blossom graph matching fusion algorithm is used: 1) Graph structure construction: the data collected in S1 and S2 are modeled into a graph structure, where nodes represent different spatial entities, including buildings, urban open spaces, popular facilities, urban road networks and rail stations, and edges represent the physical proximity between nodes. Each node and edge is attached with area and function type attributes; 2) Initial graph matching is constructed: Blossom is used to m The graph matching fusion algorithm matches and fuses data from different sources, constructs the graph data into subgraphs, and performs initial matching based on node attributes; 3) Augmenting paths and shrinking odd rings: In the initial matching, an augmenting path from an unmatched node to another unmatched node is searched. If an odd ring is encountered while searching for an augmenting path, it is reduced to a single point and the search for an augmenting path continues along the way after the reduction; 4) Optimizing matching: After finding an augmenting path in the shrinking graph, the matching is expanded in the original graph through the reverse operation, and the augmenting path and shrinking steps are repeated until no augmenting path is found, resulting in a maximum match.

4. The commercial space intelligent optimization method based on comprehensive knowledge graph and tourist data according to claim 3 is characterized in that: In the S3, the Ullmann subgraph isomorphism algorithm is used to initialize the candidate matrix and use recursive backtracking and pruning rules to find the subgraph isomorphism relationship between the two graphs; it refers to constructing a matching matrix M of size m×n, where m is the number of vertices of the pattern graph and n is the number of vertices of the target graph; the pattern graph vertex is i and the target graph vertex is j, if M[i][j]=1, it means that the pattern graph vertex i is matched with the target graph vertex j, if M[i][j]=0, it means that the pattern graph vertex i cannot be matched with the target graph vertex j; during initialization, all M[i][j] are set to 1, and according to the vertex attributes and edge attributes, impossible vertex pairings are screened out, the matching matrix M is updated, and the impossible pairings are set to 0; the isomorphism mapping between the pattern graph and the target graph is gradually constructed by a recursive method; for each unmatched pattern graph vertex i, its possible matching target graph vertex j is searched in the candidate matrix, that is, the pairing that satisfies M[i][j]=1; whenever a vertex pairing is determined, it is added to the current partial isomorphism mapping, and the next pattern graph vertex is recursively processed; If it is found at a certain step that the current pairing cannot meet the subgraph isomorphism condition, then backtrack to the previous step, cancel the current pairing, and try other possible pairing paths; apply pruning rules to prune impossible matching paths, and decide whether to continue recursion by checking in advance whether the current partial isomorphism mapping violates the necessary conditions for subgraph isomorphism.

5. The commercial space intelligent optimization method based on comprehensive knowledge graph and tourist data according to claim 4 is characterized in that: The scoring of homogeneous competition distance and passenger flow visit number in S3 is calculated as follows: 1) the functional relationship map in S2, which has been cleaned and aligned with the spatiotemporal network, is converted into point feature data using the Feature to Point tool in the ArcGIS data management tool, and the number of passenger flow visits n is assigned to each point feature using the Field Calculation Tool; 2) the business nodes and passenger flow visit number of the same functional type are selected using the ArcGIS Attribute Selection Tool, and the distance between the feature points, i.e., the homogeneous competition distance d, is calculated using the ArcGIS Neighborhood Analysis Tool; 3) The homogeneous competition distance d and the passenger flow n are normalized by the formula to obtain the normalized homogeneous competition distance d norm and normalized passenger flow n norm , the calculation formula is as follows: (1) Where: d i : The original value of the homogeneous competition distance of the i-th in the dataset; d min : the minimum value of all homogeneous competition distances in the data set; d max : the maximum value of all homogeneous competition distances in the dataset; d norm,i : The normalized value of the i-th homogeneous competition distance; n i : the original value of the number of visits of the i-th passenger flow in the data set; n min : the minimum value of all passenger traffic visits in the data set; n max : the maximum value of all passenger traffic visits in the data set; n norm，i : The normalized value of the i-th passenger flow visit volume; Use the normalized d norm and n norm Calculate the score a of each store using the following formula: (2)a i =d norm,i ×n norm,i Among them: a i : The rating of the i-th store; And sum up the scores of all stores to get the total score A total and maximum score A max , the calculation formula is as follows: (3) (4)A max =max(a1,a2,...,a m ); Among them: A total : The total score of all stores; A total : The maximum value of the rating among all stores; m: The total number of stores.

6. The commercial space intelligent optimization method based on comprehensive knowledge graph and tourist data according to claim 5 is characterized in that: The GraphSAGE graph network prediction algorithm in S4 refers to constructing a multi-layer sampling aggregation neural network layer, where each layer is defined by input feature dimension, hidden layer feature dimension and output feature dimension. By sampling and aggregating neighbor nodes in the graph, the output features of each layer are used as the input of the next layer; in the model initialization stage, the feature dimension of each layer is defined, the input feature dimension is X, the hidden layer feature dimension is H, and the output feature dimension is Y, and the mean aggregation operation is used for each layer; the graph dataset is divided into a training set, a validation set and a test set, the training set is used for model training, the validation set is used for model parameter adjustment, and the test set is used for model performance evaluation; training The method of training the model includes defining the loss function and optimizer, calculating the predicted value and loss function value of each node through forward propagation, and updating the model parameters through backpropagation until the preset number of training rounds is reached or the early termination condition is met; adding the optimized lineage and its feature data screened in S3 to the graph structure, updating the adjacency matrix and feature matrix of the graph; forward propagating the new node through the trained GraphSAGE model, calculating the representation vector of the new node, and inputting it into the regression layer, outputting the predicted visit volume of the new node, and the regression layer calculates the dot product of the new node representation vector and the preset weight matrix to obtain the predicted visit volume value d of the new node. 预测i , sum up the visits of all new nodes to get the total amount d 预测总 .

7. The commercial space intelligent optimization method based on comprehensive knowledge graph and tourist data according to claim 6 is characterized in that: The judgment in S4 on whether the actual volume is close to the predicted volume refers to the total flow rate d at the entrance and exit of the area measured every quarter after the business optimization by an infrared counter with 95% accuracy within a detection range of 8m. 实测总 , using the video crowd flow analysis device equipped with the Faster R-CNN target detection algorithm to identify the daily passenger flow of business nodes 实测i ; Compare the measured value and the predicted value. If the following conditions cannot be met at the same time, repeat step S3; (5) (6)0.6*d 预测总 -d 实测总 >0; Where: d 实测i : the actual passenger flow of the i-th business node; d 预计i : predicted passenger flow of the i-th business node; m: the total number of business nodes; d 预测总 : The total predicted passenger flow of all business nodes; d 实测总 : The sum of actual passenger flow of all business nodes.

8. The commercial space intelligent optimization method based on comprehensive knowledge graph and tourist data according to claim 7 is characterized in that: The Dijkstra shortest path algorithm in S5 plans the shopping destination-oriented streamline, assuming that there are n access nodes in the business knowledge graph, denoted as N={n1, n2, ..., n n }, the connection relationship between nodes forms a directed graph G = (N, E)G = (N, E)G = (N, E), where E is the edge set, including the weight w of each edge ij , represents node n i To node n j Distance weight; First, initialize the distances of all nodes, set the distance of the starting node to 0, and the distances of other nodes to infinity, and set the predecessor node array; Then, select a node with the shortest current distance from the set of unvisited nodes, remove it from the set, and update the distances and predecessor node information of all its neighbor nodes; Repeat this process until all nodes are visited or the shortest distance of the current node is infinite.

9. The commercial space intelligent optimization method based on comprehensive knowledge graph and tourist data according to claim 8 is characterized in that: The Floyd-Warshall multi-hop path algorithm in S5 plans the leisure and entertainment roaming route, which is to initialize a distance matrix D, where D[i][j] represents the node n i To node n j The path weight of node n i and n j Directly connected, then D[i][j] is equal to the edge weight w ij , otherwise D[i][j]=∞, and for all nodes i, D[i][i]=0; by introducing the intermediate node k, the shortest paths between all node pairs are gradually updated, and the specific update formula is D[i][j]=min(D[i][j], D[i][k]+D[k][j]); the above update operation is performed on all nodes to obtain the shortest path matrix, ensuring that tourists cover the maximum space and business formats during their leisure and entertainment roaming.

10. The commercial space intelligent optimization method based on comprehensive knowledge graph and tourist data according to claim 9 is characterized in that: The mixed reality device in S6 displays the layout of Internet celebrity facilities and store adjustments, which refers to the display of three-dimensional data collected by the ground laser scanner in S3, and the visual integration of spatial position and surface texture with real scenes through Apple Vision Pro to assist owners in choosing business style, layout and surrounding facilities.

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