Model-based rapid retrieval method and system for road network and place name element information

Abstract

The present invention discloses a method and system for rapid retrieval of road network and place name element information based on a model. The method comprises: constructing a database of road network and place name element information, wherein the road network and place name element information comprises: road network characteristic values ​​and place name characteristic values; obtaining user query parameters, wherein the user query parameters comprise road network or place name element information; setting a comprehensive retrieval model, and calculating a comprehensive retrieval value of the user query parameter based on the user query parameter and the road network and place name element information in the database; and displaying the road network or place name element information corresponding to the comprehensive retrieval value that exceeds a preset retrieval threshold to the user.

Description

Technical Field

[0001] The present invention belongs to the technical field of road network and place name element information retrieval, and more specifically, relates to a model-based method and system for rapid retrieval of road network and place name element information. Background Art

[0002] The current state of information retrieval technology encompasses a wide range of methods and techniques, particularly in the retrieval of multimodal data such as text, images, audio, and video. The following are some key current and development trends in information retrieval:

[0003] 1. Keyword-based search (traditional method)

[0004] Early information retrieval methods relied primarily on keyword matching. Users entered query keywords, and the system returned documents or data containing those keywords through full-text search or inverted indexing. This approach is still widely used for structured data and document retrieval.

[0005] 2. Vector-based retrieval (semantic understanding)

[0006] Modern information retrieval systems are gradually moving towards semantic-based retrieval. Advances in natural language processing (NLP) technology, such as word embedding models (e.g., Word2Vec, GloVe) and large-scale pre-trained language models (e.g., BERT, GPT), enable systems to understand the semantics of queries and return relevant content, rather than just literal matches.

[0007] However, the existing technology is not accurate enough in retrieving information about road networks and place names, and the error rate is high. Summary of the Invention

[0008] To solve the above technical problems, the present invention proposes a model-based rapid retrieval method for road network and place name element information, comprising:

[0009] Constructing a database of road network and place name element information, wherein the road network and place name element information includes: road network characteristic values ​​and place name characteristic values;

[0010] Acquiring user query parameters, wherein the user query parameters include road network or place name element information;

[0011] Setting a comprehensive search model and calculating a comprehensive search value of the user query parameter based on the user query parameter and the road network and place name element information in the database;

[0012] The road network or place name element information corresponding to the comprehensive search value exceeding the preset search threshold is displayed to the user.

[0013] Furthermore, the comprehensive retrieval model includes:

[0014]

[0015] in, is the comprehensive search value, is a real number plane used to describe all points in a two-dimensional coordinate system, α1 is the first adjustment factor of the comprehensive retrieval value, R(x, y) is the road network characteristic value at the position (x, y), β′ is the third adjustment factor of the comprehensive retrieval value, α2 is the second adjustment factor of the comprehensive retrieval value, γ is the fourth adjustment factor of the comprehensive retrieval value, S R is the spatial index value of the road network, S P is the spatial index value of the place name, P(p i ) is the i-th place name p i The place name feature value, F is the matching degree between the user query parameter and the road network or place name element, and Q is the user query parameter.

[0016] Furthermore, the road network spatial index value S R include:

[0017]

[0018] Where n is the number of road network eigenvalues, ω k is the weight of the kth road network eigenvalue, R k (x, y) is the kth road network characteristic value at position (x, y), σ k is the adjustment factor of the kth road network eigenvalue.

[0019] Furthermore, the place name spatial index value S P include:

[0020]

[0021] Among them, m′ is the number of place name feature values, λ m is the adjustment factor of the mth place name characteristic value, P m (p i ) is the i-th place name p i The mth place name feature value, α is the first adjustment factor of the place name spatial index value, μ m is the expected value of the mth place name feature value, and β is the second adjustment factor of the place name spatial index value.

[0022] Furthermore, the matching degree F between the user query parameter and the road network or place name element includes:

[0023]

[0024] Among them, κ j is the weight of the j-th road network feature value or place name feature value, γj is the adjustment factor of the jth road network characteristic value, Q is the user query parameter, and the user query parameter is the road network or place name element, R j is the jth road network characteristic value, P j is the jth place name characteristic value, δ j is the adjustment factor of the j-th place name characteristic value, θ j is the adjustment factor of the j-th road network characteristic value or place name characteristic value.

[0025] The present invention also proposes a model-based rapid retrieval system for road network and place name element information, comprising:

[0026] A database construction module is used to construct a database of road network and place name element information, wherein the road network and place name element information include: road network characteristic values ​​and place name characteristic values;

[0027] A module for obtaining user query parameters, used to obtain user query parameters, wherein the user query parameters include road network or place name element information;

[0028] A calculation module, configured to set a comprehensive search model and calculate a comprehensive search value of the user query parameter based on the user query parameter and the road network and place name element information in the database;

[0029] The display module is used to display the road network or place name element information corresponding to the comprehensive search value exceeding the preset search threshold to the user.

[0030] Furthermore, the comprehensive retrieval model includes:

[0031]

[0032] in, is the comprehensive search value, is a real number plane used to describe all points in a two-dimensional coordinate system, α1 is the first adjustment factor of the comprehensive retrieval value, R(x, y) is the road network characteristic value at the position (x, y), β′ is the third adjustment factor of the comprehensive retrieval value, α2 is the second adjustment factor of the comprehensive retrieval value, γ is the fourth adjustment factor of the comprehensive retrieval value, S R is the spatial index value of the road network, S P is the spatial index value of the place name, P(p i ) is the i-th place name p i The place name feature value, F is the matching degree between the user query parameter and the road network or place name element, and Q is the user query parameter.

[0033] Furthermore, the road network spatial index value S R include:

[0034]

[0035] Where n is the number of road network eigenvalues, ω k is the weight of the kth road network eigenvalue, R k (x, y) is the kth road network characteristic value at position (x, y), σ k is the adjustment factor of the kth road network eigenvalue.

[0036] Furthermore, the place name spatial index value S P include:

[0037]

[0038] Among them, m′ is the number of place name feature values, λ m is the adjustment factor of the mth place name characteristic value, P m (p i ) is the i-th place name p i The mth place name feature value, α is the first adjustment factor of the place name spatial index value, μ m is the expected value of the mth place name feature value, and β is the second adjustment factor of the place name spatial index value.

[0039] Furthermore, the matching degree F between the user query parameter and the road network or place name element includes:

[0040]

[0041] Among them, κ j is the weight of the j-th road network feature value or place name feature value, γ j is the adjustment factor of the jth road network characteristic value, Q is the user query parameter, and the user query parameter is the road network or place name element, R j is the jth road network characteristic value, P j is the jth place name characteristic value, δ j is the adjustment factor of the j-th place name characteristic value, θ j is the adjustment factor of the j-th road network characteristic value or place name characteristic value.

[0042] In general, the above technical solutions conceived by the present invention have the following beneficial effects compared with the prior art:

[0043] The present invention can accurately search the query requirements raised by users based on the constructed road network and place name element information database. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a flow chart of the method of embodiment 1 of the present invention;

[0045] Figure 2 It is a structural diagram of the system of embodiment 2 of the present invention. DETAILED DESCRIPTION

[0046] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0047] The method provided by the present invention can be implemented in the following terminal environment, wherein the terminal may include one or more of the following components: a processor, a storage medium, and a display screen. The storage medium stores at least one instruction, which is loaded and executed by the processor to implement the method described in the following embodiments.

[0048] A processor can include one or more processing cores. It connects various components within the terminal using various interfaces and circuits. It executes instructions, programs, code sets, or instruction sets stored in storage media, and accesses data stored in storage media to perform various terminal functions and process data.

[0049] The storage medium may include a random access memory (RAM) or a read-only memory (ROM). The storage medium may be used to store instructions, programs, codes, code sets, or instructions.

[0050] The display is used to show the user interface of each application.

[0051] In addition, those skilled in the art will appreciate that the structure of the terminal described above does not limit the terminal. The terminal may include more or fewer components, or a combination of certain components, or a different arrangement of components. For example, the terminal may also include a radio frequency circuit, an input unit, a sensor, an audio circuit, a power supply, and other components, which will not be described in detail here.

[0052] Example 1

[0053] like Figure 1 As shown, an embodiment of the present invention provides a method for quickly retrieving road network and place name element information based on a model, comprising:

[0054] Step 101: constructing a database of road network and place name element information, wherein the road network and place name element information includes: road network characteristic values ​​and place name characteristic values;

[0055] The following is an example of building a database:

[0056] The spatial analysis capabilities of vector data make it widely used in various industries, including construction planning, resource management, and environmental analysis. Multi-source data fusion can effectively increase data dimensionality, which in turn increases object attribute information and is more beneficial for expressing certain spatial relationships. The theory and method of multi-source vector spatial data fusion is a powerful tool for addressing these issues. It can ensure consistency in the spatial datum, mathematical foundation, scale, content, and spatial relationships of multi-source vector spatial data, ensuring data consistency.

[0057] Traffic resource data includes two types of road network data: dedicated road network data, which contains vector data with characteristics such as road grades; and civil road network data, which contains road network topology and enables route planning and vehicle navigation. The difference between these two data sources is that traffic network data is only spatial vector data without road topology attributes, while civil road network data, such as navigation topology data, can be used for navigation and route planning. This project requires data fusion using civil road network navigation topology data combined with traffic network data to achieve vehicle route planning and navigation functions.

[0058] This example is based on the analysis of spatial geometric relationships in geographic information. The tool software used is the PGsql database. PGsql, short for postgresql, is a mature, open-source, and powerful relational database. Generally speaking, the database supports complex SQL syntax, enabling left-join queries, right-join queries, functions, triggers, constraints, and other functions. By utilizing the GIS functions for processing spatial data provided by PGsql, efficient and optimized data fusion is achieved.

[0059] Geographic information spatial geometric relationship analysis primarily includes proximity analysis, overlay analysis, and network analysis. Buffer analysis is a type of proximity analysis. Buffer zones are established around a geographic entity or spatial object to identify its influence on surrounding features. Overlay analysis of buffer zones, as independent data layers, can be applied to spatial analysis of roads and bridges, providing a scientific basis for specific applications.

[0060] Data fusion is based on line feature matching, and adopts a buffer overlap-based approach, that is, a buffer is established based on matching line segments, and the length of the line segment to be matched entering the buffer range is used to determine whether the two are entities with the same name. The buffer growth method is used to match the road network. In terms of selecting the basic matching unit, the "node-arc segment" method is used for matching. At the same time, based on the matching idea of ​​global consistency, according to the road network structure, the road data is divided into separate matches based on road grade. At the same time, drawing on the mathematical optimization problem of global optimization, the distance and topological structure are used to measure the similarity between targets, optimize the road network matching problem, and integrate the road grade, road base width, bridge deck width, design load level, technical condition assessment level, all bridges and tunnels in the arc segment and other information related to the special road network into the civil road network data, which enhances the attribute information characteristics of the software's topological network data.

[0061] Buffer radius confirmation:

[0062] 1) The civil navigation data was superimposed with a dedicated dataset (national, provincial, and county roads), and 100 line segments were randomly sampled. Buffer analysis was performed using PostGIS. Based on the civil navigation data, buffers were established for the line segments, starting points, center points, and end points. The intersection of the three buffers was calculated, and the matching rate was calculated at 20, 30, 40, 50, 60, and 70 meters. The analysis found that the optimal buffer distances were 50 meters for national and provincial roads and 20 meters for county roads.

[0063] 2) Civilian navigation data was overlaid with different specialized datasets (national and provincial highway bridges, and rural bridges) to establish buffer zones for line segments. Matching rates were calculated at 20, 30, 40, 50, 60, and 70 meter intervals. Analysis revealed that the optimal buffer zones for national and provincial highway bridges were 50 meters and 20 meters for rural bridges.

[0064] Fusion processing flow:

[0065] Algorithms for matching entities with the same name based on the geometric characteristics of geographic features are the most basic and commonly used of all matching algorithms. The principle behind using geometric features for entity matching is to measure the similarity of one or more geometric features of geographic features and then use a preset threshold to determine whether they are identical geographic features. The differences between different methods primarily lie in the choice of geometric features and matching units, as well as the method for determining similarity.

[0066] Road data fusion process:

[0067] The road network data of the same road is divided into several arc segments. After data analysis, it is found that the arc segments of dedicated road network data are longer than those of civilian navigation data. To improve the accuracy of matching, four methods are used to match the arc segments, and the intersection of the matching results is calculated.

[0068] (1) For different road levels, establish different buffer zones, traverse them, match them with dedicated road network data, and obtain the value A;

[0069] (2) Using the starting point of the civil navigation data segment as a buffer zone, traverse and match the dedicated road network data and obtain the value B;

[0070] (3) Using the center point of the civil navigation data segment as a buffer zone, traverse and match the dedicated road network data to obtain the value C;

[0071] (4) Matching the dedicated road network data with the end point of the civil navigation data segment as a buffer zone and obtaining the value D;

[0072] The values ​​obtained by the four types of analysis methods are intersected to obtain the final matching data, and the corresponding data details of the dedicated road network are extracted and updated to the navigation dataset.

[0073] Bridge data fusion process:

[0074] For bridges, based on civil navigation data, the correspondence between line segments and bridge points is queried. Through function processing, multiple bridge points corresponding to civil navigation line segments are stored in the format of "a, b, c..." to complete the fusion of civil navigation data bridge information.

[0075] Tunnel data fusion process:

[0076] For tunnels, we use civil navigation data as a basis to query the correspondence between line segments and tunnel points. Through function processing, we store the multiple bridge points corresponding to the civil navigation line segments in the "a, b, c..." format, completing the integration of civil navigation data tunnel information. The specific process is the same as for bridges and will not be described in detail here.

[0077] Road network data fusion pilot implementation process:

[0078] Data preparation:

[0079] The dedicated national road network data consists of the line datasets of national highways "ROAD_GD", provincial highways "ROAD_SD", county roads "ROAD_X", township roads "ROAD_Y_par1, ROAD_Y_par2", town roads "ROAD_Z", rural roads "ROAD_Y_par1, ROAD_Y_par2, ROAD_Y_par3, ROAD_Y_par4, ROAD_Y_par5, ROAD_Y_par6" and the point datasets of national and provincial highway bridges "QLP_GSD", rural bridges "QLP_NCGL", national and provincial highway tunnels "SDP_GSD", and rural tunnels "SDP_NCGL".

[0080] The original civil data file is a MIF file. The project import function converts the MIF file into a udbx file, performs topology preprocessing, and then constructs a 2D network topology to generate a navigation dataset. The navigation data table is named NetWork and contains 67 data attributes, including the map sheet number.

[0081] Confirmation of road network data coordinate system:

[0082] It is necessary to confirm that the coordinate systems of the traffic network data and the navigation dataset are consistent. If they are inconsistent, the coordinate system conversion function in the project software needs to be converted to the same coordinate system to ensure data accuracy. The coordinate system of the dedicated data and the civil navigation data in this project is both WGS84, so the coordinate system of the final fused map data is WGS84.

[0083] Road network data attribute reconstruction:

[0084] In order to meet the needs of data fusion, it is necessary to reconstruct the table structure of the civil road network data set and the traffic road network data set.

[0085] Dedicated road network data attribute reconstruction:

[0086] Road data in the road network includes national highways, provincial highways, county highways, township roads, and town roads. The methods for reconstructing road data attributes are basically the same. This article describes the attribute reconstruction of national highways as an example.

[0087] Reconstructing traffic network data primarily involves adding attribute fields and assigning values ​​to them using appropriate algorithms. The attribute fields added to dedicated road network data are the arc start node (number 39), center point (number 40), and arc end node (number 41). These fields are set to wide character strings with a length of 255. Taking the dedicated national highway attribute table ROAD_GD as an example, the reconstructed attribute structure is shown in the following table:

[0088] National Highway Table Reconstruction Structure Table

[0089]

[0090]

[0091]

[0092] Civilian navigation road network data attribute reconstruction:

[0093] The reconstruction of the road network data table for civil navigation is divided into three parts: adding road attribute content, adding bridge attribute content, and adding tunnel attribute content.

[0094] The additional fields related to roads are the arc start node start_center, center point center and arc end node end_center; dedicated road name road_name, dedicated road grade dldj, dedicated road surface net width lmjk, dedicated route number lxbh and dedicated arc id.

[0095] Bridge data in the road network includes two categories: national and provincial highway bridges and rural bridges. Bridge data attribute reconstruction involves adding new attribute fields to the civil navigation dataset: national and provincial highway bridge name (qlmc), national and provincial highway bridge code (qlbm), national and provincial highway bridge deck clear width (qmjk), design load level code (sjhzdjdm), technical condition assessment level (jszkpddj), bridge ID (jtb_smid), and dataset (qlsssjj) (1: national and provincial highway bridge dataset; 2: rural bridge dataset; 3: national and provincial highway tunnel dataset; 4: rural tunnel dataset).

[0096] The newly added attribute fields for the civil navigation dataset are tunnel name sdmc, tunnel code sdbm, tunnel clear width sdjk, tunnel clear height sdjg, overall technical condition assessment ztjszkpd, tunnel ID: jtb_sdid, and dataset qlsssjj (1: National and Provincial Highway Bridge Dataset; 2: Rural Bridge Dataset; 3: National and Provincial Highway Tunnel Dataset; 4: Rural Tunnel Dataset). The reconstructed attribute structure is shown in the following table:

[0097] Network reconstruction structure table

[0098]

[0099]

[0100]

[0101]

[0102] Data fusion processing:

[0103] Road network data assignment:

[0104] Assigning values ​​to the extended fields of the traffic network and civil navigation data. Since the location information of the road network data is spatial vector data, this project uses GIS software to calculate and assign values. This is achieved by using a program to read the geographic information of each arc segment in the road network, calling a function to obtain the arc's latitude and longitude information, and then calculating and assigning these values ​​to three newly created attribute fields (start_center, center point, and end_center).

[0105] Civilian navigation road network data assignment:

[0106] Navigation arc data assignment:

[0107] The values ​​assigned to the extended fields of civil navigation data are consistent with those of the road network. Because the location information in the road network data is spatial vector data, the project uses GIS software to calculate and assign values. This is achieved by using a program to read the geographic information of each arc in the road network, calling a function to obtain the arc's latitude and longitude information, and then calculating and assigning these values ​​to three newly created attribute fields (start_center, center point, and end_center).

[0108] Road data assignment:

[0109] Data fusion is performed based on the spatial function characteristics of POSTGIS. Based on civil navigation data and the road level corresponding to FUNCCLASS, special data is queried and buffers are established for line segments, starting points, center points, and end points. The intersection of the three buffers is calculated to obtain the corresponding special data information. The special data primary key (jtb-smid), road name (roadname), road level (dldj), road width (dlkd), and route number (lxbh) attributes are fused.

[0110] Data analysis is performed on the fused line data set. For the unfused data, a buffer is only established for the line segments to obtain dedicated corresponding data information, further improving the data fusion rate.

[0111] Bridge data assignment:

[0112] For bridges, based on civil navigation data, the correspondence between line segments and bridge points is queried. Through function processing, multiple bridge points corresponding to civil navigation line segments are stored in the format of "a, b, c...", and the special data bridge name (qlmc), bridge code (qlbm), bridge deck clear width (qmjk), special bridge primary key (jtb_qlid), bridge data set (qlsssjj), technical condition assessment level (jszkpddjdm), and design load level (sjhzdjdm) are completed.

[0113] Tunnel data assignment:

[0114] For tunnels, based on civil navigation data, the correspondence between line segments and tunnel points is queried. Through function processing, multiple bridge points corresponding to civil navigation line segments are stored in the format of "a, b, c...", and the dedicated data tunnel name (sdmc), tunnel code (sdbm), tunnel clear width (sdjk), tunnel clear height (sdjg), dedicated bridge primary key (jtb_sdid), tunnel data set (qlsssjj), technical condition assessment level (jszkpddjdm), and design load level (sjhzdjdm) are completed.

[0115] Step 102: obtaining user query parameters, wherein the user query parameters include road network or place name element information;

[0116] Step 103: setting a comprehensive search model and calculating a comprehensive search value of the user query parameter based on the user query parameter and the road network and place name element information in the database;

[0117] Specifically, the comprehensive retrieval model includes:

[0118]

[0119] in, is the comprehensive search value, is a real number plane used to describe all points in a two-dimensional coordinate system, α1 is the first adjustment factor of the comprehensive retrieval value, R(x, y) is the road network characteristic value at the position (x, y), β′ is the third adjustment factor of the comprehensive retrieval value, α2 is the second adjustment factor of the comprehensive retrieval value, γ is the fourth adjustment factor of the comprehensive retrieval value, S R is the spatial index value of the road network, S P is the spatial index value of the place name, P(p i ) is the i-th place name p i The place name feature value, F is the matching degree between the user query parameter and the road network or place name element, and Q is the user query parameter.

[0120] Specifically, the road network spatial index value S R include:

[0121]

[0122] Where n is the number of road network eigenvalues, ω k is the weight of the kth road network eigenvalue, R k (x, y) is the kth road network characteristic value at position (x, y), σ kis the adjustment factor of the kth road network eigenvalue.

[0123] Specifically, the place name spatial index value S P include:

[0124]

[0125] Among them, m′ is the number of place name feature values, λ m is the adjustment factor of the mth place name characteristic value, P m (p i ) is the i-th place name p i The mth place name feature value, α is the first adjustment factor of the place name spatial index value, μ m is the expected value of the mth place name feature value, and β is the second adjustment factor of the place name spatial index value.

[0126] Specifically, the matching degree F between the user query parameter and the road network or place name element includes:

[0127]

[0128] Among them, κ j is the weight of the j-th road network feature value or place name feature value, γ j is the adjustment factor of the jth road network characteristic value, Q is the user query parameter, and the user query parameter is the road network or place name element, R j is the jth road network characteristic value, P j is the jth place name characteristic value, δ j is the adjustment factor of the j-th place name characteristic value, θ j is the adjustment factor of the j-th road network characteristic value or place name characteristic value.

[0129] Step 104 : Displaying the road network or place name element information corresponding to the comprehensive search value exceeding the preset search threshold to the user.

[0130] Example 2

[0131] like Figure 2 As shown, an embodiment of the present invention further provides a model-based rapid retrieval system for road network and place name element information, comprising:

[0132] A database construction module is used to construct a database of road network and place name element information, wherein the road network and place name element information include: road network characteristic values ​​and place name characteristic values;

[0133] A module for obtaining user query parameters, used to obtain user query parameters, wherein the user query parameters include road network or place name element information;

[0134] A calculation module, configured to set a comprehensive search model and calculate a comprehensive search value of the user query parameter based on the user query parameter and the road network and place name element information in the database;

[0135] Specifically, the comprehensive retrieval model includes:

[0136]

[0137] in, is the comprehensive search value, is a real number plane used to describe all points in a two-dimensional coordinate system, α1 is the first adjustment factor of the comprehensive retrieval value, R(x, y) is the road network characteristic value at the position (x, y), β′ is the third adjustment factor of the comprehensive retrieval value, α2 is the second adjustment factor of the comprehensive retrieval value, γ is the fourth adjustment factor of the comprehensive retrieval value, S R is the spatial index value of the road network, S P is the spatial index value of the place name, P(p i ) is the i-th place name p i The place name feature value, F is the matching degree between the user query parameter and the road network or place name element, and Q is the user query parameter.

[0138] Specifically, the road network spatial index value S R include:

[0139]

[0140] Where n is the number of road network eigenvalues, ω k is the weight of the kth road network eigenvalue, R k (x, y) is the kth road network characteristic value at position (x, y), σ k is the adjustment factor of the kth road network eigenvalue.

[0141] Specifically, the place name spatial index value S P include:

[0142]

[0143] Among them, m′ is the number of place name feature values, λ m is the adjustment factor of the mth place name characteristic value, P m (p i ) is the i-th place name p i The mth place name feature value, α is the first adjustment factor of the place name spatial index value, μ m is the expected value of the mth place name feature value, and β is the second adjustment factor of the place name spatial index value.

[0144] Specifically, the matching degree F between the user query parameter and the road network or place name element includes:

[0145]

[0146] Among them, к j is the weight of the j-th road network feature value or place name feature value, γ j is the adjustment factor of the jth road network characteristic value, Q is the user query parameter, and the user query parameter is the road network or place name element, R j is the jth road network characteristic value, P j is the jth place name characteristic value, δ j is the adjustment factor of the j-th place name characteristic value, θ j is the adjustment factor of the j-th road network characteristic value or place name characteristic value.

[0147] The display module is used to display the road network or place name element information corresponding to the comprehensive search value exceeding the preset search threshold to the user.

[0148] Example 3

[0149] An embodiment of the present invention further provides a storage medium storing a plurality of instructions, wherein the instructions are used to implement the method for quickly retrieving the model-based road network and place name element information.

[0150] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0151] Optionally, in this embodiment, the storage medium is configured to store program codes for executing the following steps: Step 101, constructing a database of road network and place name element information, wherein the road network and place name element information includes: road network feature values ​​and place name feature values;

[0152] Step 102: obtaining user query parameters, wherein the user query parameters include road network or place name element information;

[0153] Step 103: setting a comprehensive search model and calculating a comprehensive search value of the user query parameter based on the user query parameter and the road network and place name element information in the database;

[0154] Specifically, the comprehensive retrieval model includes:

[0155]

[0156] in, is the comprehensive search value, is a real number plane used to describe all points in a two-dimensional coordinate system, α1 is the first adjustment factor of the comprehensive retrieval value, R(x, y) is the road network characteristic value at the position (x, y), β′ is the third adjustment factor of the comprehensive retrieval value, α2 is the second adjustment factor of the comprehensive retrieval value, γ is the fourth adjustment factor of the comprehensive retrieval value, S R is the spatial index value of the road network, S P is the spatial index value of the place name, P(p i ) is the i-th place name p i The place name feature value, F is the matching degree between the user query parameter and the road network or place name element, and Q is the user query parameter.

[0157] Specifically, the road network spatial index value S R include:

[0158]

[0159] Where n is the number of road network eigenvalues, ω k is the weight of the kth road network eigenvalue, R k (x, y) is the kth road network characteristic value at position (x, y), σ k is the adjustment factor of the kth road network eigenvalue.

[0160] Specifically, the place name spatial index value S P include:

[0161]

[0162] Among them, m′ is the number of place name feature values, λ m is the adjustment factor of the mth place name characteristic value, P m (p i ) is the i-th place name p i The mth place name feature value, α is the first adjustment factor of the place name spatial index value, μ m is the expected value of the mth place name feature value, and β is the second adjustment factor of the place name spatial index value.

[0163] Specifically, the matching degree F between the user query parameter and the road network or place name element includes:

[0164]

[0165] Among them, κ j is the weight of the j-th road network feature value or place name feature value, γ j is the adjustment factor of the jth road network characteristic value, Q is the user query parameter, and the user query parameter is the road network or place name element, R j is the jth road network characteristic value, P j is the jth place name characteristic value, δj is the adjustment factor of the j-th place name characteristic value, θ j is the adjustment factor of the j-th road network characteristic value or place name characteristic value.

[0166] Step 104 : Displaying the road network or place name element information corresponding to the comprehensive search value exceeding the preset search threshold to the user.

[0167] Example 4

[0168] An embodiment of the present invention also proposes an electronic device, including a processor and a storage medium connected to the processor, wherein the storage medium stores multiple instructions, which can be loaded and executed by the processor to enable the processor to execute the model-based method for rapid retrieval of road network and place name element information.

[0169] Specifically, the electronic device of this embodiment may be a computer terminal, which may include: one or more processors and a storage medium.

[0170] Among them, the storage medium can be used to store software programs and modules, such as a method for rapid retrieval of road network and place name element information based on a model in an embodiment of the present invention, and corresponding program instructions / modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the storage medium, that is, realizing the above-mentioned method for rapid retrieval of road network and place name element information based on a model. The storage medium may include high-speed random access storage media, and may also include non-volatile storage media, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage media. In some instances, the storage medium may further include storage media remotely located relative to the processor, and these remote storage media may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, corporate intranet, local area network, mobile communication network, and combinations thereof.

[0171] The processor can call the information and application stored in the storage medium through the transmission system to perform the following steps: Step 101, constructing a database of road network and place name element information, wherein the road network and place name element information includes: road network feature values ​​and place name feature values;

[0172] Step 102: obtaining user query parameters, wherein the user query parameters include road network or place name element information;

[0173] Step 103: setting a comprehensive search model and calculating a comprehensive search value of the user query parameter based on the user query parameter and the road network and place name element information in the database;

[0174] Specifically, the comprehensive retrieval model includes:

[0175]

[0176] in, is the comprehensive search value, is a real number plane used to describe all points in a two-dimensional coordinate system, α1 is the first adjustment factor of the comprehensive retrieval value, R(x, y) is the road network characteristic value at the position (x, y), β′ is the third adjustment factor of the comprehensive retrieval value, α2 is the second adjustment factor of the comprehensive retrieval value, γ is the fourth adjustment factor of the comprehensive retrieval value, S R is the spatial index value of the road network, S P is the spatial index value of the place name, P(p i ) is the i-th place name p i The place name feature value, F is the matching degree between the user query parameter and the road network or place name element, and Q is the user query parameter.

[0177] Specifically, the road network spatial index value S R include:

[0178]

[0179] Where n is the number of road network eigenvalues, ω k is the weight of the kth road network eigenvalue, R k (x, y) is the kth road network characteristic value at position (x, y), σ k is the adjustment factor of the kth road network eigenvalue.

[0180] Specifically, the place name spatial index value S P include:

[0181]

[0182] Among them, m′ is the number of place name feature values, λ m is the adjustment factor of the mth place name characteristic value, P m (p i ) is the i-th place name p i The mth place name feature value, α is the first adjustment factor of the place name spatial index value, μ m is the expected value of the mth place name feature value, and β is the second adjustment factor of the place name spatial index value.

[0183] Specifically, the matching degree F between the user query parameter and the road network or place name element includes:

[0184]

[0185] Among them, κ j is the weight of the j-th road network feature value or place name feature value, γ jis the adjustment factor of the jth road network characteristic value, Q is the user query parameter, and the user query parameter is the road network or place name element, R j is the jth road network characteristic value, P j is the jth place name characteristic value, δ j is the adjustment factor of the j-th place name characteristic value, θ j is the adjustment factor of the j-th road network characteristic value or place name characteristic value.

[0186] Step 104 : Displaying the road network or place name element information corresponding to the comprehensive search value exceeding the preset search threshold to the user.

[0187] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0188] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0189] In the several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the system embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, and can be electrical or other forms.

[0190] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0191] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0192] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only storage medium (ROM, Read-Only Memory), random access storage medium (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, and other media that can store program code.

[0193] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will readily appreciate that other variations or modifications based on the above descriptions are possible. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A method for rapid retrieval of road network and place name element information based on a model, characterized in that: include: Constructing a database of road network and place name element information, wherein the road network and place name element information includes: road network characteristic values ​​and place name characteristic values; Acquiring user query parameters, wherein the user query parameters include road network or place name element information; Setting a comprehensive search model and calculating a comprehensive search value of the user query parameter based on the user query parameter and the road network and place name element information in the database; The comprehensive retrieval model includes: in, is the comprehensive search value, is a real number plane used to describe all points in a two-dimensional coordinate system, α1 is the first adjustment factor of the comprehensive retrieval value, R(x, y) is the road network characteristic value at the position (x, y), β′ is the third adjustment factor of the comprehensive retrieval value, α2 is the second adjustment factor of the comprehensive retrieval value, γ is the fourth adjustment factor of the comprehensive retrieval value, S R is the spatial index value of the road network, S P is the spatial index value of the place name, P(p i ) is the i-th place name p i The place name feature value, F is the matching degree between the user query parameter and the road network or place name element, and Q is the user query parameter; The road network or place name element information corresponding to the comprehensive search value exceeding the preset search threshold is displayed to the user.

2. A method for rapid retrieval of road network and place name element information based on a model as claimed in claim 1, characterized in that: The road network spatial index value S R include: Where n is the number of road network eigenvalues, ω k is the weight of the kth road network eigenvalue, R k (x, y) is the kth road network characteristic value at position (x, y), σ k is the adjustment factor of the kth road network eigenvalue.

3. A method for rapid retrieval of road network and place name element information based on a model as claimed in claim 1, characterized in that: Place name spatial index value S P include: Among them, m′ is the number of place name feature values, λ m is the adjustment factor of the mth place name characteristic value, P m (p i ) is the i-th place name p i The mth place name feature value, α is the first adjustment factor of the place name spatial index value, μ m is the expected value of the mth place name feature value, and β is the second adjustment factor of the place name spatial index value.

4. A method for rapid retrieval of road network and place name element information based on a model as claimed in claim 1, characterized in that: The matching degree F between the user query parameters and the road network or place name elements includes: Among them, κ j is the weight of the j-th road network feature value or place name feature value, γ j is the adjustment factor of the jth road network characteristic value, Q is the user query parameter, and the user query parameter is the road network or place name element, R j is the jth road network characteristic value, P j is the jth place name characteristic value, δ j is the adjustment factor of the j-th place name characteristic value, θ j is the adjustment factor of the j-th road network characteristic value or place name characteristic value.

5. A rapid retrieval system for road network and place name element information based on a model, characterized in that: include: A database construction module is used to construct a database of road network and place name element information, wherein the road network and place name element information include: road network characteristic values ​​and place name characteristic values; A module for obtaining user query parameters, used to obtain user query parameters, wherein the user query parameters include road network or place name element information; A calculation module, configured to set a comprehensive search model and calculate a comprehensive search value of the user query parameter based on the user query parameter and the road network and place name element information in the database; The comprehensive retrieval model includes: Among them, is the comprehensive search value, is a real number plane used to describe all points in a two-dimensional coordinate system, α1 is the first adjustment factor of the comprehensive retrieval value, R(x, y) is the road network characteristic value at the position (x, y), β′ is the third adjustment factor of the comprehensive retrieval value, α2 is the second adjustment factor of the comprehensive retrieval value, γ is the fourth adjustment factor of the comprehensive retrieval value, S R is the spatial index value of the road network, S P is the spatial index value of the place name, P(p i ) is the i-th place name p i The place name feature value, F is the matching degree between the user query parameter and the road network or place name element, and Q is the user query parameter; The display module is used to display the road network or place name element information corresponding to the comprehensive search value exceeding the preset search threshold to the user.

6. A rapid retrieval system for road network and place name element information based on a model as claimed in claim 5, characterized in that: The road network spatial index value S R include: Where n is the number of road network eigenvalues, ω k is the weight of the kth road network eigenvalue, R k (x, y) is the kth road network characteristic value at position (x, y), σ k is the adjustment factor of the kth road network eigenvalue.

7. A rapid retrieval system for road network and place name element information based on a model as claimed in claim 5, characterized in that: Place name spatial index value S P include: Among them, m′ is the number of place name feature values, λ m is the adjustment factor of the mth place name characteristic value, P m (p i ) is the i-th place name p i The mth place name feature value, α is the first adjustment factor of the place name spatial index value, μ m is the expected value of the mth place name feature value, and β is the second adjustment factor of the place name spatial index value.

8. A method for rapid retrieval of road network and place name element information based on a model as claimed in claim 5, characterized in that: The matching degree F between the user query parameters and the road network or place name elements includes: Among them, κ j is the weight of the j-th road network feature value or place name feature value, γ j is the adjustment factor of the jth road network characteristic value, Q is the user query parameter, and the user query parameter is the road network or place name element, R j is the jth road network characteristic value, P j is the jth place name characteristic value, δ j is the adjustment factor of the j-th place name characteristic value, θ j is the adjustment factor of the j-th road network characteristic value or place name characteristic value.

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