A method and system for extracting and visualizing the structural relationship of historical and cultural resources based on a gravity transportation model

Abstract

The present invention relates to the field of information extraction and visualization, and specifically to a method and system for extracting and visualizing the structural relationship of historical and cultural resources based on a gravity traffic model. The present invention quantitatively extracts the impact index, pairwise correlation strength coefficient, and shortest path distance of each historical and cultural resource by performing text analysis and geographic spatial analysis on the historical and cultural resources within the target range. Then, based on the above data, a gravity model is applied to calculate the spatial correlation strength between historical and cultural resources, and they are allocated in a digital road network space to generate a visualization of the spatial structure of historical and cultural resources based on the actual road network space. Compared with existing analysis methods, the present invention effectively improves the scientificity, accuracy, and objectivity of the structural analysis of historical and cultural resources, and has broad application prospects and promotion potential.

Description

Technical Field

[0001] The invention relates to a method for extracting and visualizing the structural relationship of historical and cultural resources by using a gravity traffic model, and belongs to the technical field of information extraction and visualization. Background Art

[0002] At present, the protection and inheritance of historical and cultural heritage is increasingly valued, and new forms of heritage protection such as cultural routes are constantly proposed; however, in the face of a large number of historical and cultural resources with rich diversity, objectively and systematically extracting their spatial structural relationships is an especially important basic issue in this field. The existing methods for extracting the structure of historical and cultural resources mainly rely on the subjective feelings of planners and geographic information technology means such as kernel density analysis; however, both methods ignore the value differences and spatial associations of historical and cultural resources and the weak associations with the existing environmental road network structure, and therefore often require a lot of subjective deliberation, with problems such as low efficiency, strong subjectivity, and poor feasibility. At the same time, the existing visualization methods of structural relationships mainly rely on manual operations, and use drawing software to draw arrows, color blocks, etc. to express the structural relationships of axes and regions, which have strong subjectivity, low precision, and loose connection with the actual urban space. It is urgent to rely on data analysis to improve the accuracy of visualization and rely on road network space to achieve intuitive expression of visualization. Summary of the invention

[0003] Purpose of the invention: In view of the shortcomings of the prior art, the purpose of the present invention is to provide a method and system for extracting and visualizing the structural relationships of historical and cultural resources. Based on text analysis and spatial calculation, the method and system can quantitatively extract the strength of association between historical and cultural resources, and use the gravity traffic model to express the spatial association distribution between historical and cultural resources in the actual road network system. The method can effectively extract the structural relationships between historical and cultural resources and visualize them based on the actual urban road network space, thereby shifting from traditional subjective evaluation to objective analysis, and effectively improving the effectiveness, objectivity and scientificity of the analysis of the spatial structural relationships of historical and cultural resources.

[0004] Technical solution: To achieve the above-mentioned invention object, the present invention adopts the following technical solution:

[0005] A method for extracting and visualizing the structural relationship of historical and cultural resources based on a gravity transportation model includes the following steps:

[0006] Step 1: Obtain road network spatial data and spatial entities belonging to historical and cultural resources within the target range, and integrate them into a historical and cultural resource directory and a historical and cultural resource geospatial dataset integrating the digital road network space;

[0007] Step 2: Based on the geospatial dataset, calculate the shortest path length between historical and cultural resources in the digital road network space and integrate it into a road network distance matrix of historical and cultural resources;

[0008] Step 3: According to the historical and cultural resource list, relevant data texts are retrieved and natural language processing is performed on them to extract the impact index of each historical and cultural resource and the correlation strength coefficient between historical and cultural resources; the impact index is the academic impact index, social impact index or a combination of the two of the historical and cultural resources;

[0009] Step 4: Based on the road network distance between historical and cultural resources, the correlation strength coefficient and the influence index of each historical and cultural resource, the gravity model is used to calculate the spatial correlation strength between historical and cultural resources, and a historical and cultural resource-historical and cultural resource spatial correlation strength matrix is ​​constructed;

[0010] Step 5: Based on the historical and cultural resource geospatial dataset, allocate the historical and cultural resource-historical and cultural resource spatial correlation intensity matrix in the digital road network space and perform visualization output as the structural relationship extraction result of the historical and cultural resources within the target range.

[0011] Furthermore, the step 1 specifically includes:

[0012] Step 1.1, obtaining road network spatial data within the target range, and integrating and processing the road network information into digital road network spatial data;

[0013] Step 1.2: Obtain the spatial entity types belonging to historical and cultural resources from the texts related to the protection and inheritance of historical and cultural resources, and collect the historical and cultural resources and their geographic spatial coordinates within the target range, and integrate them into a list of historical and cultural resources;

[0014] Step 1.3: Input the digital road network spatial data and the historical and cultural resource list into the transportation geographic information data platform, and integrate them to obtain the historical and cultural resource geographic spatial data set of the integrated digital road network space.

[0015] Furthermore, the step 2 specifically includes:

[0016] Step 2.1, based on the geospatial dataset of historical and cultural resources integrated into the digital road network space, calculate the shortest road network distance between various historical and cultural resources in the transportation geographic information data platform;

[0017] Step 2.2: Export the shortest road network distance data between each historical and cultural resource to a spreadsheet data platform and integrate it into a road network distance matrix of historical and cultural resources.

[0018] Preferably, the weighted sum of the academic impact index and the social impact index is used as the impact index of historical and cultural resources; the academic impact index is extracted based on an academic text collection, and the social impact index is extracted based on a social text collection.

[0019] Furthermore, the step 3 specifically includes:

[0020] Step 3.1, obtain the relevant data and document texts of each historical and cultural resource in the historical and cultural resource catalog, and summarize them to form a relevant text set; perform natural language processing on the relevant text set, extract, quantify and normalize the academic impact index, social impact index and characteristic dimension vector of each historical and cultural resource;

[0021] Step 3.2: Based on the academic impact index and social impact index of each historical and cultural resource, measure the impact index of each historical and cultural resource according to the following formula:

[0022] M=P+Q

[0023] In the formula, M is the impact index of historical and cultural resources, P is the academic impact index of historical and cultural resources, and Q is the social impact index of historical and cultural resources. Both are normalized. All the impact indexes of historical and cultural resources are formed into the impact index data set of historical and cultural resources.

[0024] Step 3.3: Calculate the correlation strength coefficients between historical and cultural resources based on the characteristic dimension vectors of each historical and cultural resource, and form a correlation strength coefficient data set with all correlation strength coefficients.

[0025] Furthermore, the step 4 specifically includes:

[0026] Step 4.1: Based on the road network distance matrix, impact index dataset and association intensity coefficient dataset of historical and cultural resources, calculate the spatial association intensity between historical and cultural resources according to the following formula:

[0027]

[0028] In the formula, R ij is the spatial correlation intensity between historical and cultural resources i and j, K ij is the correlation strength coefficient between historical and cultural resources i and j, M i ,M j are the impact indexes of historical and cultural resources i and j, respectively, ij is the shortest path network distance of historical and cultural resources i,j;

[0029] Step 4.2: Construct a historical and cultural resource-historical and cultural resource spatial correlation intensity matrix based on the spatial correlation intensity between any two historical and cultural resources; the spatial correlation intensity between the same historical and cultural resources is not included in the matrix.

[0030] Furthermore, the step 5 specifically includes:

[0031] Step 5.1, import the historical and cultural resources-historical and cultural resources spatial correlation intensity matrix data into the transportation geographic information data platform, and cooperate with the historical and cultural resources geographic spatial data set of the integrated digital road network space to generate the spatial distribution of historical and cultural resources within the same target range and the spatial correlation intensity between them in the data platform;

[0032] Step 5.2: Use the traffic allocation tool in the traffic geographic information data platform to allocate the spatial correlation intensity between historical and cultural resources to the road network space, and generate the correlation intensity allocation result of the road network space;

[0033] Step 5.3: Use the line visualization tool in the transportation geographic information data platform to reflect the distribution of association intensity in the road network through line thickness, visualize the association intensity distribution results in the road network space, and complete the extraction and visualization output of the structural relationship of historical and cultural resources.

[0034] Based on the same inventive concept, the present invention provides a historical and cultural resource structure relationship extraction and visualization system based on a gravity traffic model, comprising:

[0035] The data integration module is used to obtain the road network spatial data and spatial entities belonging to historical and cultural resources within the target range, and integrate them into a historical and cultural resource directory and a historical and cultural resource geospatial data set integrating the digital road network space;

[0036] The road network distance calculation module is used to calculate the shortest path length between historical and cultural resources in the digital road network space based on the geospatial data set, and integrate it into the road network distance matrix of historical and cultural resources;

[0037] The impact index and correlation strength coefficient calculation module is used to retrieve relevant data texts and perform natural language processing on them according to the historical and cultural resource list, and extract the impact index of each historical and cultural resource and the correlation strength coefficient between historical and cultural resources; the impact index is the academic impact index, social impact index or a combination of the two of the historical and cultural resources;

[0038] The spatial correlation intensity matrix calculation module is used to calculate the spatial correlation intensity between historical and cultural resources based on the road network distance between historical and cultural resources, the correlation intensity coefficient and the influence index of each historical and cultural resource, and to construct the spatial correlation intensity matrix between historical and cultural resources;

[0039] The visualization output module is used to allocate the historical and cultural resources-historical and cultural resources spatial correlation intensity matrix in the digital road network space based on the historical and cultural resources geographic spatial dataset, and perform visualization output as the structural relationship extraction result of the historical and cultural resources within the target range.

[0040] Based on the same inventive concept, the present invention provides a computer system, including a memory, a processor, and a computer program / instructions stored in the memory and executable on the processor. When the computer program / instructions are executed by the processor, the steps of the method for extracting and visualizing the structural relationships of historical and cultural resources based on a gravity transportation model are implemented.

[0041] Based on the same inventive concept, the present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of a method for extracting and visualizing the structural relationships of historical and cultural resources based on a gravity transportation model.

[0042] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0043] 1. The present invention is based on intelligent data processing, and discovers the spatial correlation of historical and cultural resources based on a large amount of text data and spatial calculations, thereby transforming the traditional subjective evaluation method of structural relationship extraction into an objective analysis method, thereby improving the accuracy and objectivity of the extraction and analysis of the structural relationship of historical and cultural resources.

[0044] 2. The present invention innovatively introduces gravity traffic model analysis, thereby successfully quantifying and expressing the diversity and correlation between historical and cultural resources, overcoming the defects of previous work that neglected the existing urban-rural spatial relationship and the ontological correlation of historical and cultural resources, and making up for the one-sided and inefficient problems of previous working methods.

[0045] 3. The present invention outputs the structural relationship of historical and cultural resources as an instant visualization result, providing users with an intuitive analysis tool; it eliminates the step of drawing to express the structural relationship in the traditional method, greatly improving work efficiency.

[0046] 4. The present invention uses objective data analysis and relies on road network space to achieve high-level visual and intuitive expression, which can effectively improve the effectiveness, objectivity and scientificity of the analysis of the spatial structural relationship of historical and cultural resources, and has broad application prospects and promotion potential. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art description are briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0048] Figure 1 is a flow chart of an embodiment of the present invention;

[0049] Figure 2 It is a visualized result diagram of the embodiment of the present invention. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] The gravity traffic model is an analytical model that measures the strength of association between objects and performs traffic assignment calculations based on the gravity model, and can accurately assign the strength of association between objects to a given road network space. The embodiment of the present invention is based on the gravity traffic model, performs automatic extraction of the structural relationship of historical and cultural resources through data analysis, and realizes intuitive visual expression of the structural relationship based on the road network structure diagram.

[0052] The following is a detailed description of the technical solution of the present invention by taking the extraction and visualization of the structural relationship of historical and cultural resources within the target range as a specific embodiment. Figure 1 As shown, the present invention provides a method for extracting and visualizing the structural relationship of historical and cultural resources based on a gravity transportation model, comprising the following steps:

[0053] Step 1: Obtain the road network spatial data within the target range and the spatial entities belonging to historical and cultural resources, and integrate them into a historical and cultural resource directory and a historical and cultural resource geospatial dataset that integrates the digitized road network space.

[0054] In specific implementation, the steps include:

[0055] Step 1.1, obtain and collect road network spatial data within the target range, and integrate and process the road network information into digital road network spatial data;

[0056] The specific method is to obtain the osm file of road network data within the target range through the OSM map data platform, input it into the Arcmap10.8 geographic information data platform, and use the LoadOSM File tool in the platform open source plug-in ArcGIS Editor for OSM to convert the osm file into a shp file for further data processing; import the road network data in the shp file format, use the "Buffer" tool under the "Analysis Tools-Neighborhood Analysis" category, set the buffer width to 30m, process and export it into a 1-bit monochrome threshold tiff format file for further data processing; import the road network data in the tiff format file, set the import symbol system to "binary classification", use the "Raster to Turnline" tool under the "Conversion Tools-From Raster" category to integrate the data into digital road network spatial data that supports spatial structure extraction, and export it to the processed shp file.

[0057] Step 1.2: Collect relevant texts related to the protection and inheritance of historical and cultural resources, sort out the spatial entity types subordinate to historical and cultural resources, and collect historical and cultural resources and their geographic spatial coordinates within the target range, and integrate them into a list of historical and cultural resources;

[0058] Among them, the relevant texts related to the protection and inheritance of historical and cultural resources can be the effective policy and legal texts approved by the central and local governments, such as the legal and regulatory texts related to the protection and inheritance of historical and cultural resources published on the official websites of cultural relics and housing and construction departments at all levels in the country in the past 10 years. By constructing a special dictionary, machine learning word segmentation, and named entity recognition text mining methods for natural language processing, the spatial entity types belonging to historical and cultural resources are identified, as shown in Table 1.

[0059] Table 1: Types of spatial entities belonging to historical and cultural resources

[0060] Types of historical and cultural resources Historical and cultural resource space entity types Regulations: Cultural relics protection units National key / provincial / municipal and county-level cultural relics protection units Area: Historical and cultural town Historical and cultural district Type: Non-cultural heritage site spatial element Historic buildings / immovable cultural relics / industrial heritage

[0061] The names and geographic coordinate information of spatial entities belonging to the historical and cultural resource types within the target scope can be obtained by crawling electronic maps or collecting them through field surveys, and all the acquired information can be integrated into a historical and cultural resource directory, which includes three types of information: number, name, and geographic spatial coordinates.

[0062] Step 1.3: Input the digital road network spatial data and the historical and cultural resource list into the transportation geographic information data platform, and integrate them to obtain the historical and cultural resource geographic spatial data set of the integrated digital road network space.

[0063] The specific method is to input the processed shp file obtained in step 1.1 into the Transcad6.0 traffic geographic information data platform to complete the placement of the digital road network space data in the platform; at the same time, according to the name and geographic spatial coordinate information in the historical and cultural resource list, complete the placement of the historical and cultural resource data in the platform. Use the Networks / Paths-Create Network tool to process the digital road network space and historical and cultural resources that have completed the data placement into a historical and cultural resource geographic spatial data set of an integrated digital road network space in the "network" format.

[0064] Step 2: Based on the geospatial dataset, calculate the shortest path length between historical and cultural resources in the digital road network space and integrate it into the road network distance matrix of historical and cultural resources.

[0065] In specific implementation, the steps include:

[0066] Step 2.1: Based on the geospatial dataset of historical and cultural resources integrated into the digital road network space, calculate the shortest road network distance between each historical and cultural resource in the transportation geographic information data platform.

[0067] The specific method is to use the Networks / Multiple ShortestPath tool in the Transcad6.0 transportation geographic information data platform to calculate and process the historical and cultural resource geospatial dataset of the integrated digitized road network space obtained in step 1.3 into the mtx file format, and calculate the road network shortest distance information between each historical and cultural resource point.

[0068] Step 2.2: Export the shortest road network distance data between each historical and cultural resource to a spreadsheet data platform and integrate it into a road network distance matrix of historical and cultural resources.

[0069] The specific method is to use the Matrix Export command in the Transcad6.0 transportation geographic information data platform to export the road network shortest distance information between each historical and cultural resource point obtained in step 2.1 and save it as a historical and cultural resource road network distance matrix in csv file format, a part of which is shown in Table 2. Among them, H1, H2, H3, etc. are the historical and cultural resource numbers obtained in step 1.2.

[0070] Table 2: Road network distance matrix of historical and cultural resources (partial diagram)

[0071] H1 H2 H3 ... H1 / 414.3 373.2 ... H2 414.3 / 1514.8 ... H3 373.2 1514.8 / ... ... ... ... ... ...

[0072] Step 3: According to the historical and cultural resource list, retrieve the relevant data text and perform natural language processing on it, extract the impact index of each historical and cultural resource and the correlation strength coefficient between historical and cultural resources. The impact index here is used to calculate the endogenous variable of each historical and cultural resource in the gravity model. It can be measured from a single or multiple dimensions according to user needs. For example, the academic impact index is obtained by measuring the intensity of academic research, and the social impact index is obtained by measuring the heat of social attention. This embodiment takes the weighted composition of the academic impact index and the social impact index as an example.

[0073] In specific implementation, the steps include:

[0074] Step 3.1, obtain the relevant data and document texts of each historical and cultural resource in the historical and cultural resource catalog, and summarize them to form a relevant text set; perform natural language processing on the relevant text set, extract, quantify and normalize the academic impact index, social impact index and characteristic dimension vector of each historical and cultural resource.

[0075] Specifically, the relevant data and document texts of historical and cultural resources can be obtained in the following ways: input the name of each historical and cultural resource into common Internet search engines and authoritative academic platforms for retrieval, and use the top 100 search results in the comprehensive ranking as relevant data and document texts to form academic text collections, social text collections, and comprehensive text collections. For academic text collections and social text collections, the academic impact index and social impact index of each historical and cultural resource can be extracted and quantified through relevant technical solutions of text mining such as word frequency statistics. Among them, the quantitative coefficient should be normalized to a real number between (0,1] through the standard score (Z-score) method, corresponding to the low academic impact index / social impact index-high academic impact index / social impact index interval, as shown in Table 3.

[0076] Table 3: Academic impact index & social impact index of historical and cultural resources (partial diagram)

[0077] Historical and cultural resource number Academic Impact Index of Historical and Cultural Resources Social Impact Index of Historical and Cultural Resources H1 0.76 0.63 H2 0.15 0.03 H3 0.03 0.01 … … …

[0078] For comprehensive text collections, we can extract the characteristic dimension vectors of various historical and cultural resources through deep learning-related technical solutions such as corpus vectorization and bag-of-words model construction.

[0079] Step 3.2: Based on the academic impact index and social impact index of each historical and cultural resource, measure the impact index of each historical and cultural resource according to the following formula:

[0080] M=P+Q

[0081] In the formula, M is the impact index of historical and cultural resources, P is the academic impact index of historical and cultural resources, and Q is the social impact index of historical and cultural resources. Both are normalized; all the impact indices of historical and cultural resources are formed into an impact index data set of historical and cultural resources.

[0082] The specific method is to add the academic impact index and social impact index of the same historical and cultural resource, summarize the data, input and save it as an impact index dataset of historical and cultural resources in csv file format, as shown in Table 4.

[0083] Table 4: Impact index dataset of historical and cultural resources (partial illustration)

[0084] Historical and cultural resource number Impact Index of Historical and Cultural Resources H1 1.39 H2 0.18 H3 0.04 … …

[0085] Step 3.3: Calculate the correlation strength coefficients between historical and cultural resources based on the characteristic dimension vectors of each historical and cultural resource, and form a correlation strength coefficient data set with all correlation strength coefficients.

[0086] The specific method is to calculate the cosine similarity between the characteristic dimension vectors of each historical and cultural resource obtained in step 3.1, and normalize the calculation results to real numbers between (0,1] using the standard score (Z-score) method, corresponding to the weak correlation strength-strong correlation strength interval. All correlation strength coefficients are input and saved as a historical and cultural resource correlation strength coefficient dataset in csv file format, as shown in Table 5.

[0087] Table 5: Dataset of correlation intensity coefficients between historical and cultural resources (partial illustration)

[0088] H1 H2 H3 ... H1 1 0.82 0.91 ... H2 0.82 1 0.60 ... H3 0.91 0.60 1 ... ... ... ... ... ...

[0089] Step 4: Based on the road network distance between historical and cultural resources, the correlation strength coefficient and the influence index of each historical and cultural resource, the gravity model is used to calculate the spatial correlation strength between historical and cultural resources, and a historical and cultural resource-historical and cultural resource spatial correlation strength matrix is ​​constructed.

[0090] In specific implementation, the steps include:

[0091] Step 4.1: Based on the road network distance matrix, impact index dataset and association intensity coefficient dataset of historical and cultural resources, calculate the spatial association intensity between historical and cultural resources according to the following formula.

[0092]

[0093] In the formula, R ij is the spatial correlation intensity between historical and cultural resources i and j, Kij is the correlation strength coefficient between historical and cultural resources i and j, M i ,M j are the impact indexes of historical and cultural resources i and j, respectively, ij is the shortest path network distance between historical and cultural resources i, j.

[0094] The specific method is to import the impact index dataset, association intensity coefficient dataset, and road network distance matrix of historical and cultural resources in csv file format obtained in step 3.2, step 3.3, and step 2.2 into Excel spreadsheet software, construct the above formula in the spreadsheet software, substitute each parameter into the above formula, and finally calculate the spatial association intensity R between historical and cultural resources.

[0095] Step 4.2: Construct a historical and cultural resource-historical and cultural resource spatial correlation intensity matrix based on the spatial correlation intensity between any two historical and cultural resources; the spatial correlation intensity between the same historical and cultural resources is not included in the matrix.

[0096] Among them, in this embodiment, the historical and cultural resources-historical and cultural resources spatial correlation intensity matrix within the target range is shown in Table 6.

[0097] Table 6: Historical and cultural resources-historical and cultural resources spatial correlation intensity matrix (partial diagram)

[0098] H1 H2 H3 ... H1 / 1.458 0.418 ... H2 1.458 / 0.003 ... H3 0.418 0.003 / ... ... ... ... ... ...

[0099] Step 5: Based on the historical and cultural resource geospatial dataset, allocate the historical and cultural resource-historical and cultural resource spatial correlation intensity matrix in the digital road network space and perform visualization output as the structural relationship extraction result of the historical and cultural resources within the target range.

[0100] In specific implementation, the steps include:

[0101] Step 5.1: Import the historical and cultural resources-historical and cultural resources spatial correlation intensity matrix data into the transportation geographic information data platform, and use the historical and cultural resources geographic spatial data set integrated into the digital road network space to generate the spatial distribution of historical and cultural resources within the same target range and the spatial correlation intensity between them in the data platform.

[0102] The specific method is to import the historical and cultural resource geospatial data set of the integrated digital road network space in the "network" format obtained in step 1.3 into the Transcad6.0 traffic geographic information data platform, and create a new matrix mtx file; the number of rows and columns of the newly created matrix should be equal to the total number of historical and cultural resource points, and sorted by the number of historical and cultural resource points. According to the numbering order, input the historical and cultural resource-historical and cultural resource spatial correlation intensity matrix data to generate the historical and cultural resource-historical and cultural resource spatial correlation intensity matrix that can be used for traffic distribution calculations within the platform.

[0103] Step 5.2: Use the traffic allocation tool in the traffic geographic information data platform to allocate the spatial correlation intensity between historical and cultural resources to the road network space and generate the correlation intensity allocation result of the road network space.

[0104] Among them, the specific method is, in the Transcad6.0 traffic geographic information data platform, according to the historical and cultural resource geographic spatial data set of the integrated digital road network space in the "network network" format obtained in step 1.3, and the historical and cultural resource-historical and cultural resource spatial correlation intensity matrix obtained in step 5.1, use the Planning-Static TrafficAssignment-Traffic Assignment tool to perform traffic assignment calculations. It should be pointed out that the TrafficAssignment tool covers different traffic assignment calculation methods, and the All or Nothing method is most in line with the actual considerations of the present invention for the calculation of correlation intensity, so the All or Nothing method is selected for calculation. Therefore, the All or Nothing method should be selected in the Method column to calculate and obtain the traffic assignment result dataview file. The Tot-flow column in the dataview file is the correlation intensity result assigned to each section in the road network space.

[0105] Step 5.3: Use the line visualization tool in the transportation geographic information data platform to reflect the distribution of association intensity in the road network through line thickness, visualize the association intensity distribution results in the road network space, and complete the extraction and visualization output of the structural relationship of historical and cultural resources.

[0106] The specific method is to use the Scaled-SymbolTheme tool in the Transcad6.0 traffic geographic information data platform, select the Tot-flow column in the dataview file obtained in step 5.2 in the Choose a field box, that is, the association strength results assigned to each road segment in the road network space, as the visualization data source; in the Symbol Sizes box, select Map Wizard, that is, adaptively determine the absolute width of each association strength level line segment as the scale control method of the line segment width, and finally complete the visualization and output of the association strength distribution results in the road network space, such as Figure 2 As shown in the figure, the greater the association strength of each road segment, the greater the width of its line segment, so it can intuitively reflect the distribution of association strength in the road network space, thereby realizing the visualization analysis of the historical and cultural resource association network structure based on data analysis.

[0107] Based on the same inventive concept, an embodiment of the present invention discloses a system for extracting and visualizing the structural relationship of historical and cultural resources based on a gravity traffic model, including:

[0108] The data integration module is used to obtain the road network spatial data and spatial entities belonging to historical and cultural resources within the target range, and integrate them into a historical and cultural resource directory and a historical and cultural resource geospatial data set integrating the digital road network space;

[0109] The road network distance calculation module is used to calculate the shortest path length between historical and cultural resources in the digital road network space based on the geospatial data set, and integrate it into the road network distance matrix of historical and cultural resources;

[0110] The impact index and correlation strength coefficient calculation module is used to retrieve relevant data texts and perform natural language processing on them according to the historical and cultural resource list, and extract the impact index of each historical and cultural resource and the correlation strength coefficient between historical and cultural resources; the impact index is the academic impact index, social impact index or a combination of the two of the historical and cultural resources;

[0111] The spatial correlation intensity matrix calculation module is used to calculate the spatial correlation intensity between historical and cultural resources based on the road network distance between historical and cultural resources, the correlation intensity coefficient and the influence index of each historical and cultural resource, and to construct the spatial correlation intensity matrix between historical and cultural resources;

[0112] The visualization output module is used to allocate the historical and cultural resources-historical and cultural resources spatial correlation intensity matrix in the digital road network space based on the historical and cultural resources geographic spatial dataset, and perform visualization output as the structural relationship extraction result of the historical and cultural resources within the target range.

[0113] An embodiment of the present invention also discloses a computer system, including a memory, a processor, and a computer program / instruction stored in the memory and executable on the processor. When the computer program / instruction is executed by the processor, the steps of the method for extracting and visualizing the structural relationships of historical and cultural resources based on a gravity transportation model are implemented.

[0114] An embodiment of the present invention also discloses a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of a method for extracting and visualizing structural relationships of historical and cultural resources based on a gravity transportation model.

[0115] The program / instruction code for implementing the method of the present invention can be written in any combination of one or more programming languages. These programs / instruction codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program / instruction code, when executed by the processor or controller, enables the steps of the method of the present invention to be implemented. The program / instruction code can be executed completely on the machine, partially on the machine, partially on the machine as an independent software package and partially on a remote machine or completely on a remote machine or server. The parts not described in detail in the present invention are all known technologies of those skilled in the art.

Claims

1. A method for extracting and visualizing the structural relationship of historical and cultural resources based on a gravity transportation model, characterized in that: The steps include: Step 1: Obtain road network spatial data and spatial entities belonging to historical and cultural resources within the target range, and integrate them into a historical and cultural resource directory and a historical and cultural resource geospatial dataset integrating the digital road network space; Step 2: Based on the geospatial dataset, calculate the shortest path length between historical and cultural resources in the digital road network space and integrate it into a road network distance matrix of historical and cultural resources; Step 3: According to the historical and cultural resource list, relevant data texts are retrieved and natural language processing is performed on them to extract the impact index of each historical and cultural resource and the correlation strength coefficient between historical and cultural resources; the impact index is the academic impact index, social impact index or a combination of the two of the historical and cultural resources; Step 4: Based on the road network distance between historical and cultural resources, the correlation strength coefficient and the impact index of each historical and cultural resource, the gravity model is used to calculate the spatial correlation strength between historical and cultural resources, and a historical and cultural resource-historical and cultural resource spatial correlation strength matrix is ​​constructed; including: calculating the spatial correlation strength between historical and cultural resources according to the road network distance matrix, the impact index data set and the correlation strength coefficient data set of the historical and cultural resources according to the following formula: In the formula, R ij is the spatial correlation intensity between historical and cultural resources i and j, K ij is the correlation strength coefficient between historical and cultural resources i and j, M i ,M j are the impact indexes of historical and cultural resources i and j, respectively, ij is the shortest path network distance of historical and cultural resources i,j; According to the spatial correlation intensity of any two historical and cultural resources, a historical and cultural resource-historical and cultural resource spatial correlation intensity matrix is ​​constructed; the spatial correlation intensity between the same historical and cultural resources is not included in the matrix; Step 5: Based on the historical and cultural resource geospatial dataset, allocate the historical and cultural resource-historical and cultural resource spatial correlation intensity matrix in the digital road network space and perform visualization output as the structural relationship extraction result of the historical and cultural resources within the target range.

2. The method for extracting and visualizing the structural relationship of historical and cultural resources based on the gravity transportation model according to claim 1 is characterized in that: The step 1 specifically includes: Step 1.1, obtaining road network spatial data within the target range, and integrating and processing the road network information into digital road network spatial data; Step 1.2: Obtain the spatial entity types belonging to historical and cultural resources from the texts related to the protection and inheritance of historical and cultural resources, and collect the historical and cultural resources and their geographic spatial coordinates within the target range, and integrate them into a list of historical and cultural resources; Step 1.3: Input the digital road network spatial data and the historical and cultural resource list into the transportation geographic information data platform, and integrate them to obtain the historical and cultural resource geographic spatial data set of the integrated digital road network space.

3. The method for extracting and visualizing the structural relationship of historical and cultural resources based on the gravity transportation model according to claim 1 is characterized in that: The step 2 specifically includes: Step 2.1, based on the geospatial dataset of historical and cultural resources integrated into the digital road network space, calculate the shortest road network distance between various historical and cultural resources in the transportation geographic information data platform; Step 2.2: Export the shortest road network distance data between each historical and cultural resource to a spreadsheet data platform and integrate it into a road network distance matrix of historical and cultural resources.

4. The method for extracting and visualizing the structural relationship of historical and cultural resources based on the gravity transportation model according to claim 1 is characterized in that: The weighted sum of the academic impact index and the social impact index is used as the impact index of historical and cultural resources; the academic impact index is extracted based on an academic text collection, and the social impact index is extracted based on a social text collection.

5. The method for extracting and visualizing the structural relationship of historical and cultural resources based on the gravity transportation model according to claim 1 is characterized in that: The step 3 specifically includes: Step 3.1, obtain the relevant data and document texts of each historical and cultural resource in the historical and cultural resource catalog, and summarize them to form a relevant text set; perform natural language processing on the relevant text set, extract, quantify and normalize the academic impact index, social impact index and characteristic dimension vector of each historical and cultural resource; Step 3.2: Based on the academic impact index and social impact index of each historical and cultural resource, measure the impact index of each historical and cultural resource according to the following formula: M=P+Q In the formula, M is the impact index of historical and cultural resources, P is the academic impact index of historical and cultural resources, and Q is the social impact index of historical and cultural resources. Both are normalized. All the impact indexes of historical and cultural resources are formed into the impact index data set of historical and cultural resources. Step 3.3: Calculate the correlation strength coefficients between historical and cultural resources based on the characteristic dimension vectors of each historical and cultural resource, and form a correlation strength coefficient data set with all correlation strength coefficients.

6. The method for extracting and visualizing the structural relationship of historical and cultural resources based on the gravity transportation model according to claim 1 is characterized in that: The step 5 specifically includes: Step 5.1, import the historical and cultural resources-historical and cultural resources spatial correlation intensity matrix data into the transportation geographic information data platform, and cooperate with the historical and cultural resources geographic spatial data set of the integrated digital road network space to generate the spatial distribution of historical and cultural resources within the same target range and the spatial correlation intensity between them in the data platform; Step 5.2: Use the traffic allocation tool in the traffic geographic information data platform to allocate the spatial correlation intensity between historical and cultural resources to the road network space, and generate the correlation intensity allocation result of the road network space; Step 5.3: Use the line visualization tool in the transportation geographic information data platform to reflect the distribution of association intensity in the road network through line thickness, visualize the association intensity distribution results in the road network space, and complete the extraction and visualization output of the structural relationship of historical and cultural resources.

7. A historical and cultural resource structural relationship extraction and visualization system based on a gravity traffic model, characterized in that: include: The data integration module is used to obtain the road network spatial data and spatial entities belonging to historical and cultural resources within the target range, and integrate them into a historical and cultural resource directory and a historical and cultural resource geospatial data set integrating the digital road network space; The road network distance calculation module is used to calculate the shortest path length between historical and cultural resources in the digital road network space based on the geospatial data set, and integrate it into the road network distance matrix of historical and cultural resources; The impact index and correlation strength coefficient calculation module is used to retrieve relevant data texts and perform natural language processing on them according to the historical and cultural resource list, and extract the impact index of each historical and cultural resource and the correlation strength coefficient between historical and cultural resources; the impact index is the academic impact index, social impact index or a combination of the two of the historical and cultural resources; The spatial correlation intensity matrix calculation module is used to calculate the spatial correlation intensity between historical and cultural resources based on the road network distance between historical and cultural resources, the correlation intensity coefficient and the influence index of each historical and cultural resource, and to construct a historical and cultural resource-historical and cultural resource spatial correlation intensity matrix; including: according to the road network distance matrix of historical and cultural resources, the influence index data set and the correlation intensity coefficient data set, the spatial correlation intensity between historical and cultural resources is calculated according to the following formula: In the formula, R ij is the spatial correlation intensity between historical and cultural resources i and j, K ij is the correlation strength coefficient between historical and cultural resources i and j, M i ,M j are the impact indexes of historical and cultural resources i and j, respectively, ij is the shortest path network distance of historical and cultural resources i,j; According to the spatial correlation intensity of any two historical and cultural resources, a historical and cultural resource-historical and cultural resource spatial correlation intensity matrix is ​​constructed; the spatial correlation intensity between the same historical and cultural resources is not included in the matrix; The visualization output module is used to allocate the historical and cultural resources-historical and cultural resources spatial correlation intensity matrix in the digital road network space based on the historical and cultural resources geographic spatial dataset, and perform visualization output as the structural relationship extraction result of the historical and cultural resources within the target range.

8. A computer system comprising a memory, a processor, and a computer program / instruction stored in the memory and executable on the processor, characterized in that: When the computer program / instructions are executed by the processor, the steps of a method for extracting and visualizing structural relationships of historical and cultural resources based on a gravity transportation model according to any one of claims 1 to 6 are implemented.

9. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by the processor, the steps of a method for extracting and visualizing structural relationships of historical and cultural resources based on a gravity transportation model according to any one of claims 1 to 6 are implemented.

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