Urban entity data updating method and device and storage medium

Abstract

The invention discloses a city entity data updating method and device and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: if it is detected that a difference exists between CIM platform data corresponding to a historical time node and CIM platform data corresponding to a current time node, determining the matching degree between the current time node and the CIM platform data corresponding to each data source; if the matching degree is greater than or equal to a preset threshold value, determining target update data from the current time node and the CIM platform data corresponding to each data source; and according to the target update data, updating a preset city entity database and a knowledge graph database. The accuracy of the city database updating result can be improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device and storage medium for updating urban entity data. Background Art

[0002] With the acceleration of urbanization, urban entity data is also changing frequently. In order to maintain the timeliness and accuracy of data, it is necessary to accurately obtain the updated data that has changed.

[0003] At present, when obtaining updated data, the new version of urban entity data and the old version of urban entity data are usually compared by graphics or coding to extract the inconsistent parts, and then the entity data with inconsistent graphics or coding is manually compared with the remote sensing image data, and the data with the highest similarity is selected as the updated data. In this process, the new version of urban entity data has a single source. When the urban entity data from this single source is not updated in time and fails to reflect the latest changes in the city in a timely manner, the updated data obtained from this single source is less accurate.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of this application is to provide a city entity data updating method, device and storage medium, aiming to solve the technical problem of how to improve the accuracy of city database update results in the process of obtaining updated data of city entities.

[0006] To achieve the above objectives, the present application proposes a method for updating urban entity data, the method comprising:

[0007] If it is detected that there is a difference between the CIM platform data corresponding to the historical time node and the current time node, then determining the matching degree between the CIM platform data corresponding to each data source at the current time node;

[0008] If the matching degree is greater than or equal to a preset threshold, determining target update data from the CIM platform data corresponding to each data source at the current time node;

[0009] Update data according to the target, and update the preset city entity database and knowledge graph library.

[0010] In one embodiment, the step of determining the matching degree between the CIM platform data corresponding to each data source at the current time node includes:

[0011] According to the data source, the CIM platform data corresponding to each data source at the current time node is divided into reference data corresponding to the reference data source and comparison data corresponding to the comparison data source;

[0012] Superimposing each of the reference data according to a preset reference weight to obtain target reference data;

[0013] Determining a degree of match between the target reference data and each of the comparison data;

[0014] If the matching degree between the target reference data and the comparison data is greater than or equal to a preset threshold, the target reference data is determined to be the target update data.

[0015] In one embodiment, the step of determining the matching degree between the target reference data and each of the comparison data comprises:

[0016] Loading the target reference data and the comparison data on a map layer of the CIM platform;

[0017] The matching degree between the target reference data and each of the comparison data is determined according to the loading result.

[0018] In one embodiment, the step of loading the target reference data and the comparison data on a map layer of the CIM platform includes:

[0019] Dividing the target reference data and the comparison data into tile data;

[0020] Organizing the tile data according to levels and row and column numbers to form a data directory;

[0021] The target reference data and the comparison data are loaded onto the map layer respectively according to the data directory.

[0022] In one embodiment, if it is detected that there is a difference between the CIM platform data corresponding to the historical time node and the current time node, before the step of determining the matching degree between the CIM platform data corresponding to each data source at the current time node, the step further includes:

[0023] Acquire the CIM platform data corresponding to the historical time node;

[0024] Building a city entity data model and the city entity database corresponding to the city entity data model based on the CIM platform data corresponding to the historical time node;

[0025] The knowledge graph library is acquired and established based on the city entity data in the city entity database.

[0026] In one embodiment, the step of acquiring and establishing a knowledge graph library based on the city entity data in the city entity database includes:

[0027] Acquire the city entity data, the city entity data including: structured data, semi-structured data and unstructured data;

[0028] Performing knowledge extraction on the semi-structured data and the unstructured data, acquiring entity information, attribute information and relationship information in the semi-structured data and the unstructured data, and generating knowledge units;

[0029] The knowledge units and the structured data are fused to merge the same entity information and generate the knowledge graph library.

[0030] In one embodiment, before the step of determining the target update data from the CIM platform data corresponding to each data source at the current time node if the matching degree is greater than or equal to a preset threshold, the step further includes:

[0031] Inputting the comparison data into the knowledge graph library for entity recognition;

[0032] The entities in the comparison data and the association relationships between different entities are obtained according to the recognition results.

[0033] In one embodiment, the method further comprises:

[0034] Performing time series modeling on the city entities in the CIM platform according to the CIM platform data corresponding to the historical time nodes and the target update data;

[0035] The results of the temporal modeling are presented based on the BIM model in the CIM platform.

[0036] In addition, to achieve the above-mentioned purpose, the present application also proposes a city entity data updating device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the city entity data updating method described above.

[0037] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the urban entity data updating method described above are implemented.

[0038] One or more technical solutions proposed in this application have at least the following technical effects:

[0039] If it is detected that there is a difference between the CIM platform data corresponding to the historical time node and the current time node, the matching degree between the CIM platform data corresponding to each data source at the current time node is determined; the same city entity in the CIM platform has multiple data sources. By detecting the CIM platform data corresponding to the historical time node and the current time node, the latest data from multiple different data sources at the current time node after the city entity has changed can be obtained; the problem of low data accuracy caused by the failure to update the city entity data from a single source in a timely manner is avoided. If the matching degree is greater than or equal to the preset threshold, the target update data is determined from the CIM platform data corresponding to each data source at the current time node; by comparing the matching degree between the CIM platform data corresponding to each data source at the current time node, the latest data at the current time is verified, thereby improving the accuracy of the latest data. According to the target update data, the preset city entity database and knowledge graph library are updated; the database is updated by the latest data at the current time node obtained after verification, and the latest city entity database and knowledge graph library are obtained, thereby improving the efficiency and accuracy of data update. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0041] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0042] Figure 1 A flowchart of the first embodiment of the method for updating city entity data of the present application is provided;

[0043] Figure 2 The first detailed flow chart provided for the first embodiment of the method for updating city entity data of the present application;

[0044] Figure 3 A second detailed flow chart provided for the first embodiment of the method for updating city entity data of this application;

[0045] Figure 4 A third detailed flow chart provided for the first embodiment of the method for updating city entity data of this application;

[0046] Figure 5 A flow chart of the second embodiment of the method for updating city entity data of the present application;

[0047] Figure 6 An entity-relationship diagram of a city entity provided in an embodiment of the city entity data updating method of the present application;

[0048] Figure 7 A detailed flow chart of the second embodiment of the method for updating city entity data of the present application;

[0049] Figure 8 A flowchart of the third embodiment of the method for updating city entity data of the present application is provided;

[0050] Fig. 9 This is a schematic diagram of the device structure of the hardware operating environment involved in the urban entity data updating method in the embodiment of the present application.

[0051] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0052] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0053] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0054] The main solution of the embodiment of the present application is: if it is detected that there is a difference between the CIM platform data corresponding to the historical time node and the current time node, then determine the matching degree between the CIM platform data corresponding to each data source at the current time node; if the matching degree is greater than or equal to a preset threshold, then determine the target update data from the CIM platform data corresponding to each data source at the current time node; and update the preset city entity database and knowledge graph library according to the target update data.

[0055] At present, when obtaining updated data, the new version of urban entity data and the old version of urban entity data are usually compared by graphics or coding to extract the inconsistent parts, and then the entity data with inconsistent graphics or coding is manually compared with the remote sensing image data, and the data with the highest similarity is selected as the updated data. In this process, the source of the new version of urban entity data is single, and if the urban entity data from this single source is not updated in time, the accuracy of the updated data obtained is low. In addition, the data comparison by data processing personnel has a high labor cost and is inefficient when processing large amounts of data.

[0056] The present application provides a solution. If a difference is detected between the CIM platform data corresponding to the historical time node and the current time node, the matching degree between the CIM platform data corresponding to each data source at the current time node is determined; the same city entity in the CIM platform has multiple data sources. By detecting the CIM platform data corresponding to the historical time node and the current time node, the latest data from multiple different data sources at the current time node after the city entity has changed can be obtained; the problem of low data accuracy caused by the failure to update the city entity data from a single source in a timely manner is avoided. If the matching degree is greater than or equal to the preset threshold, the target update data is determined from the CIM platform data corresponding to each data source at the current time node; by comparing the matching degree between the CIM platform data corresponding to each data source at the current time node, the latest data at the current time is verified, thereby improving the accuracy of the latest data. According to the target update data, the preset city entity database and knowledge graph library are updated; the database is updated by the latest data at the current time node obtained after verification, thereby obtaining the latest city entity database and knowledge graph library. Through the above steps, the efficiency and accuracy of data update are improved.

[0057] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a program process abnormality monitoring device, etc. The following takes the city entity data update device as an example to illustrate this embodiment and the following embodiments.

[0058] Based on this, the embodiment of the present application provides a method for updating city entity data, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the city entity data updating method of the present application.

[0059] In this embodiment, the city entity data updating method includes steps S10 to S30:

[0060] Step S10, if it is detected that there is a difference between the CIM platform data corresponding to the historical time node and the current time node, then determining the matching degree between the CIM platform data corresponding to each data source at the current time node;

[0061] It should be noted that the CIM platform (platform of city information modeling, the city-wide spatiotemporal information platform) is based on technologies such as BIM (Building Information Modeling), GIS (Geographic Information System) and IoT (Internet of Things). The CIM platform is a three-dimensional digital model that manages and expresses urban three-dimensional space, buildings and infrastructure, and supports the basic operation platform for urban planning, construction, management and operation. It directly provides diversified spatiotemporal information services to various administrative departments, enterprises, institutions and the public. BIM (Building Information Modeling) is a general term for the process and results of digitally expressing the physical and functional characteristics of construction projects and facilities throughout their life cycle, and designing, constructing and operating them accordingly. GIS (Geographic Information System) is a technical system that collects, stores, manages, calculates, analyzes, displays and describes relevant geographical distribution data in the entire or part of the earth's surface (including the atmosphere) space with the support of computer hardware and software systems. IoT (Internet of Things) collects real-time data on any object or process that needs to be monitored, connected, or interacted with through various devices and technologies such as information sensors, radio frequency identification technology, global positioning system, infrared sensors, laser scanners, etc., and collects various required information such as sound, light, heat, electricity, mechanics, chemistry, biology, and location. Through various possible network accesses, it realizes ubiquitous connection between objects and objects, and between objects and people, and realizes intelligent perception, identification, and management of objects and processes.

[0062] In addition, it should be noted that urban entities refer to the general term for natural or artificial structures in the city that have spatial locations and common attributes in the real world, also known as geographical entities. These entities can be buildings, roads, parks, rivers, lakes, bridges, public facilities, etc.

[0063] In this embodiment, the various data sources of the CIM platform include: data from the Internet, business data from a certain unit, and basic spatiotemporal data. The data in the above data sources can specifically include: basic geographic information, including information on geographical elements such as topography, road network, water system, and administrative divisions. Building BIM information, including geometric information, material information, construction progress, etc. of the building. Internet of Things (IoT) data, data collected in real time by various sensors, cameras and other equipment in the city, such as temperature, humidity, pedestrian flow, vehicle flow, etc. Oblique photography data, using oblique photography technology to shoot buildings and terrain from different angles to generate high-precision three-dimensional models, providing the appearance information of the building, as well as the spatial structure and layout of the building. Laser point cloud data, CAD data, including design drawings, construction drawings, etc. Remote sensing image data, image data taken by remote sensing satellites and drones, provides macro information such as urban surface coverage, vegetation distribution, and water changes. Industry-specific data, thematic data generated by different industry departments, such as urban planning data, environmental monitoring data, traffic flow data, etc.

[0064] In this embodiment, the CIM platform data of the historical time node is compared with the CIM platform data of the current time node to see if there is a difference. If there is a difference, it means that the data in the CIM platform has been updated, and it is necessary to verify the data with these differences to ensure the accuracy of the data update. Since the CIM platform includes data from multiple different data sources, data from one or more data sources with higher confidence are selected as reference data, and data from other data sources are used as comparison data. The matching degree between all reference data with differences and the comparison data is compared. If the matching degree is greater than or equal to the preset threshold, the reference data is considered to be credible and has passed the verification. According to the reference data that has passed the verification (i.e., the target update data), the preset city entity database and knowledge graph library are updated to ensure that the data in the city entity database and the knowledge graph library can reflect the latest status of the city entity object.

[0065] In this embodiment, first, a historical time node is selected, and the CIM platform data corresponding to the historical node is obtained by searching from the database of the CIM platform. Secondly, the CIM platform data corresponding to the current time node is collected, and the CIM platform data corresponding to the current time node needs to cover the city entity information contained in the CIM platform data corresponding to the historical node. Then, a data matching algorithm is used to compare multiple dimensions such as spatial position, name, and attributes, and the city entities in the CIM platform data corresponding to the historical time node and the current time node are matched. After the matching is completed, the CIM platform data corresponding to the historical time node and the current time node are compared to see if there are differences. These differences may include newly added city entities, demolished entities, movement of entity locations, changes in entity attributes, etc. When there are differences, all current time nodes with differences are obtained, and the CIM data corresponding to each data source are compared. The CIM data includes object information and attribute information.

[0066] In a feasible implementation, the CIM data corresponding to each data source at the current time node with differences can be obtained based on the image recognition method. Specifically, first, image data containing urban entities before and after the preset time point is collected, such as city panoramas taken by drones, street surveillance video screenshots, satellite images, etc. The collected image data is preprocessed to remove noise and interference factors in the image, and the entity features in the image are made more obvious through image enhancement techniques such as contrast adjustment and sharpening, and the images of the same place at different times are registered to ensure the spatial consistency between images. Secondly, the key features in the image are extracted using a deep learning algorithm (such as a convolutional neural network CNN), and specific urban entities such as buildings, bridges, vehicles, etc. are identified in the image, and their locations and attributes are marked. Finally, the image recognition results at different time points are compared and analyzed to identify changes in urban entities, such as new buildings, demolished buildings, road widening, etc. Result visualization: The change detection results are displayed in a visual form, such as a heat map, highlighting of the changed area, etc.

[0067] In a feasible implementation, the CIM data corresponding to each data source at the current time node with differences can be obtained based on the semantic analysis method. Specifically, first, collect text data related to urban entities, such as news reports and social media posts. The obtained text is preprocessed, and the preprocessing process may include: word segmentation processing, dividing the text into independent words or phrases. Stop words are removed to remove common but meaningless words in the text. Stem extraction, restore the words to their basic form for unified processing. Secondly, use a topic model (such as LDA (Linear Discriminant Analysis)) to extract the topic information in the text to understand the hot topics related to urban entities. Analyze the emotional tendency in the text to understand the public's views and attitudes on the changes in urban entities. Use named entity recognition technology to extract the names and attributes of urban entities in the text. Finally, analyze the changing trends of topics and emotions over time, identify the key time points related to the changes in urban entities, associate the entity recognition results with time changes, and extract the semantic information of changes in urban entity data.

[0068] In a feasible implementation, the CIM data corresponding to each data source at the current time node with differences can be obtained based on a data mining method. First, the data is obtained and preprocessed to convert the data into a format and type suitable for data mining. Secondly, a classification algorithm is used to identify different types of urban entity changes (such as addition, reduction, transformation, etc.). Optionally, similar urban entity changes can be classified into the same category through a clustering algorithm to obtain the correlation between different urban entity changes.

[0069] Please refer to Figure 2 In a feasible implementation manner, step S10 may include steps S11 to S14:

[0070] Step S11, dividing the CIM platform data corresponding to each data source at the current time node into reference data corresponding to the reference data source and comparison data corresponding to the comparison data source according to the data source;

[0071] Step S12, superimposing each of the reference data according to a preset reference weight to obtain target reference data;

[0072] Step S13, determining the matching degree between the target reference data and each of the comparison data;

[0073] Step S14: if the matching degree between the target reference data and the comparison data is greater than or equal to a preset threshold, determining that the target reference data is the target update data.

[0074] In this implementation, first, according to the different data sources, at the current time node, the CIM platform data corresponding to each data source is divided into reference data and comparison data. The reference data is from a data source with a higher confidence level, and the data from other data sources is used as comparison data. Secondly, a weight is assigned to each reference data source from different data sources according to its importance or credibility. The weight can be a numerical value (such as a decimal between 0 and 1) and is used to adjust the influence of different data sources during the comparison process. The target reference data and comparison data obtained by superimposing each reference data according to the weights are compared. If the degree of match between the target reference data and the comparison data is greater than or equal to the preset threshold, the target reference data is used as the target update data.

[0075] Optionally, business data or spatiotemporal basic data from a certain unit is selected as reference data. Reference data is selected by predefined indicators for evaluating the credibility of data sources. Exemplarily, the data source credibility indicators include: authority and reputation of the data source, for example, whether it is published by a certain unit or well-known institution; historical accuracy, the accuracy of data provided by the data source in the past; update frequency, the frequency of data source updates; data integrity, whether the data provided by the data source is comprehensive; user feedback, the user's evaluation of the data source. The target data source is determined by scoring each data source and selecting the data source with the highest score, or by weighted fusion of data from multiple data sources based on the scores of the data sources.

[0076] Exemplarily, the reference data are remote sensing images, BIM models and emergency thematic data from an authoritative organization, which are assigned weights of 0.4, 0.4 and 0.3 respectively, and each reference data is superimposed according to the weight and compared with the comparison data.

[0077] Please refer to Figure 3 In a feasible implementation manner, step S13 may include steps A10 to A20:

[0078] Step A10, loading the target reference data and the comparison data onto a map layer of the CIM platform;

[0079] Step A20: determining the matching degree between the target reference data and each of the comparison data according to the loading result.

[0080] In this embodiment, the comparison data and the target reference data are converted into a format and protocol acceptable to the CIM platform, and the corresponding layer is updated on the map layer of the CIM platform according to the comparison data and the target reference data. Optionally, new city entity markers are added to the map, entities that no longer exist are removed, and the location or attributes of the entities are updated. The comparison data is displayed in an intuitive manner through the visualization tools of the CIM platform. For example, highlights, animations, or different colors can be used to distinguish different types of changes. By loading the target reference data and the comparison data on the map layer for automatic data comparison, the time required for manual comparison can be reduced, the comparison speed can be accelerated, and the comparison efficiency and accuracy can be improved.

[0081] Please refer to Figure 4 In a feasible implementation manner, step A10 may include steps A11 to A13:

[0082] A11, dividing the target reference data and the comparison data into tile data;

[0083] A12, organizing the tile data according to levels and row and column numbers to form a data directory;

[0084] A13, loading the target reference data and the comparison data onto the map layer respectively according to the data directory.

[0085] It should be noted that tile data refers to a number of model data or units formed by dividing the information model of a certain spatial range into blocks according to certain grid division rules. This data organization method facilitates the storage, management and rapid retrieval of data at different levels.

[0086] In this embodiment, first, according to the requirements of the CIM platform and the zoom level of the map, the resolution and size of the slice are determined, and the processed data is sliced ​​using a slicing tool or algorithm to generate multi-resolution tile data. Secondly, the generated tile data is organized into directories according to the tile level (Zoom Level) and tile coordinates (Tile X, Tile Y), and stored in a suitable storage medium, such as a file system, database, or cloud storage. And an index is built for the tile data to quickly locate and load tiles in a specific area. The index can be based on tile coordinates, geographic location, or other identifiers. Finally, a dynamic loading mechanism is configured on the map layer of the CIM platform to dynamically request and load the corresponding tile data based on user interaction (such as zooming, panning).

[0087] In this embodiment, the comparison data and target reference data are loaded on the map layer of the CIM platform, which can efficiently and intuitively compare the data changes of urban entities. By dividing the map into tiles of different resolutions, the map tiles of the corresponding resolution can be dynamically loaded and displayed when the user browses the map, thereby improving the user experience and efficiency of map browsing.

[0088] In this embodiment, the same city entity in the CIM platform has multiple data sources. By detecting the CIM platform data corresponding to the historical time nodes and the current time nodes, the latest data from multiple different data sources at the current time node after the city entity has changed can be obtained, thereby avoiding the problem of low data accuracy caused by the failure to update the city entity data from a single source in a timely manner.

[0089] Step S20: if the matching degree is greater than or equal to a preset threshold, determining target update data from the CIM platform data corresponding to each data source at the current time node;

[0090] In this embodiment, during the data verification process, if the matching degree between the CIM platform data corresponding to each data source at the current time node is greater than or equal to a preset threshold, the target update data is determined from the CIM platform data corresponding to each data source at the current time node; if the matching degree is less than the threshold, it means that there is a data conflict, and manual judgment is required to comprehensively confirm the CIM platform data corresponding to each data source to select the target update data.

[0091] Exemplarily, the preset threshold may be 0.9. When the matching degree is greater than 0.9, the target update data is determined from the CIM platform data corresponding to each data source at the current time node; when the matching degree is less than or equal to 0.9, it is considered that there is a data conflict and further verification is required.

[0092] Step S30, updating the preset city entity database and knowledge graph library according to the target update data.

[0093] In this embodiment, data is updated and stored in the database. According to the city entity data model standard, the target updated data is integrated and imported into the entity database, the city entity data is updated, and the knowledge graph database is updated synchronously.

[0094] In this embodiment, the city entity database and the knowledge graph library are updated through the target update data, and the target update data obtained after verification is updated to the entity database, which ensures the freshness of the data and improves the accuracy of the database update.

[0095] In this embodiment, if it is detected that there is a difference between the CIM platform data corresponding to the historical time node and the current time node, the matching degree between the CIM platform data corresponding to each data source at the current time node is determined; the same city entity in the CIM platform has multiple data sources. By detecting the CIM platform data corresponding to the historical time node and the current time node, the latest data from multiple different data sources at the current time node after the city entity has undergone data changes can be obtained; the problem of low data accuracy caused by the failure to update the city entity data from a single source in a timely manner is avoided. If the matching degree is greater than or equal to the preset threshold, the target data source is selected; by comparing the matching degree between the CIM platform data corresponding to each data source at the current time node, the latest data at the current time is verified, thereby improving the accuracy of the latest data. According to the target update data, the preset city entity database and knowledge graph library are updated; the database is updated with the latest data at the current time node obtained after verification, and the latest city entity database and knowledge graph library are obtained.

[0096] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 5 Before step S10, the city entity data updating method further includes steps S01 to S03:

[0097] Step S01, obtaining the CIM platform data corresponding to the historical time node;

[0098] Step S02, constructing a city entity data model and the city entity database corresponding to the city entity data model based on the CIM platform data corresponding to the historical time node;

[0099] In this embodiment, the city entity data in the CIM platform is obtained at the historical time node, and the city entity data model is constructed to initialize the city entity database. The CIM platform data corresponding to the historical time node is the city entity standard data of the historical time node.

[0100] Please refer to Figure 6, for example, taking the house entity as an example, the house entity includes business attributes, spatial attributes, and the relationship with other urban entities; among them, the business attributes include basic attributes, management attributes and extended attributes. Basic attributes include measurement information (such as: floor area, building scale, height, geometric shape, spatial location, etc.) and description information (such as: district, county, street, community name, etc.). Management attributes include land management attributes (plot nature, land use type, land use status, etc.) and house registration attributes (such as: use nature, right type, age, house code, number of households, etc.). Extended attributes include population quantity information (such as: number of resident population, number of registered population, number of employed population, number of managed population), energy consumption information (such as: water, electricity, gas, etc.) and economic information (such as: industrial and commercial information and output value information, etc.). The above basic attributes, management attributes and extended attributes are all obtained based on public data.

[0101] Step S03, obtaining and establishing the knowledge graph library based on the city entity data in the city entity database.

[0102] It should be noted that the knowledge graph is a structured semantic knowledge base that aggregates information, data, and link relationships into knowledge, making resources easier to calculate, understand, and evaluate, and enabling rapid response and reasoning. Knowledge graphs usually use the triple form of "entity-relationship-entity" to express knowledge, where entities represent things in the real world, such as people, places, etc., and relationships express the connection between different entities.

[0103] Please refer to Figure 7 In a feasible implementation manner, step S03 may include steps S031 to S033:

[0104] Step S031, obtaining the city entity data, the city entity data including: structured data, semi-structured data and unstructured data;

[0105] Step S032, performing knowledge extraction on the semi-structured data and the unstructured data, acquiring entity information, attribute information and relationship information in the semi-structured data and the unstructured data, and generating a knowledge unit;

[0106] Step S033, performing knowledge fusion on the knowledge unit and the structured data, merging the same entity information, and generating the knowledge graph library.

[0107] It should be noted that structured data refers to data stored in a clear format and rules, which can be stored and managed through tables, databases or other programmable data models. This type of data has clear fields and attributes, and each field has a specific data type and value range. Semi-structured data refers to data with a basic fixed structure pattern in a non-relational model, but the structure is implicit in the data, or the patterns are numerous and often change. This type of data includes log files, XML documents, JSON documents, E-mail, etc. Unstructured data refers to data with irregular or incomplete structure and no predefined data model. This type of data includes text, images, audio, video, etc.

[0108] In this embodiment, the city entity data can be derived from the CIM platform, an open data website of a certain unit, social media, a specific website, etc. The obtained city entity data can be divided into structured data, semi-structured data and unstructured data. For semi-structured data and unstructured data, first, knowledge extraction is performed to extract entities, attributes and relationships from the city entity data to form knowledge units. Secondly, the extracted knowledge is sorted and integrated, including steps such as entity alignment and entity disambiguation, to form a high-quality knowledge base. Among them, in the entity alignment step, the records from different data sources but representing the same city entity are matched and merged by calculating the similarity between the entities. Optionally, the Levenstein Distance algorithm is used to calculate the similarity between entities to ensure that the same entity in different data sources is regarded as the same entity in the knowledge graph. In the entity elimination step, the specific meaning of the entity in a specific scenario is determined by analyzing the context information, attribute information and relationship information of the entity with other entities, and the ambiguity problem that may exist in the entity in different contexts is solved. Finally, the fused knowledge is represented in a computer-understandable form, such as using a graph structure to represent the association between entities, relationships and attributes.

[0109] Based on the first embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those in the first embodiment can be referred to the above description, and will not be described in detail later. The step S20 also includes the following steps:

[0110] Inputting the comparison data into the knowledge graph library for entity recognition;

[0111] The entities in the comparison data and the association relationships between different entities are obtained according to the recognition results.

[0112] It should be noted that the entities in the comparison data are divided into change entities and change association entities. Change association entities refer to city entities that have an association relationship with change entities.

[0113] In this embodiment, when the comparison data is input into the knowledge graph library, the knowledge graph will first perform entity recognition and map the elements in the comparison data to the corresponding entities in the knowledge graph. Subsequently, through association analysis, the knowledge graph will identify the association relationship between entities and identify other change associations that have an association mapping relationship with the change ontology.

[0114] In this embodiment, entities and the relationships between entities are obtained based on the knowledge graph library. By displaying the relationships between city entities, the association verification can be performed during the data update process. In addition, it helps to find possible omissions in data updates. When a city entity changes, by viewing its associated entities, those data items that need to be changed due to indirect associations can be discovered and updated in a timely manner, avoiding errors or inconsistencies caused by data omissions, and improving the accuracy and completeness of data updates.

[0115] Based on the first embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 8 The city entity data updating method further includes steps S40 to S50:

[0116] Step S40, performing time series modeling on the city entities in the CIM platform according to the CIM platform data corresponding to the historical time nodes and the target update data;

[0117] Step S50: display the result of the temporal modeling based on the BIM model in the CIM platform.

[0118] It should be noted that time series modeling, also known as time series modeling, is the process of building mathematical models based on time series data.

[0119] In this embodiment, the city entity objects and their historical traceability relationships are modeled in time series, and a time series relationship map is constructed to store the city entity change information. Based on the time series modeling results, the city entity object change results are published as services, called on the CIM platform, and based on the BIM model, a two-dimensional and three-dimensional visualization of the city entity change trend is achieved, which can intuitively perceive the dynamic development process of the city entity and monitor the city entity objects.

[0120] In this implementation, first, various entity objects (such as buildings, roads, parks, public facilities, etc.) in the city are classified and identified. The historical connections and evolution processes between these entity objects are analyzed, including how they change over time and their mutual influence. Based on the above analysis, the hierarchical structure of the map is designed. For example, the city can be used as the highest-level node, and then the sub-nodes can be divided according to different functional areas (such as commercial areas, residential areas, industrial areas, etc.), and then further subdivided into specific entity objects under the sub-nodes. Secondly, entity nodes and relationship edges are established, a unique node is created for each city entity object, and a globally unique identifier (such as UUID) is assigned to it, and basic information of the entity object, such as name, type, location, construction time, etc., is stored in the node. According to the historical traceability relationship between entity objects, relationship edges are established between nodes, and the relationship edges contain relationship types (such as "built on", "renovated on", "adjacent to", etc.) and timestamps (indicating the time when the relationship occurs). If the relationship is directional (such as "from... to..."), the direction is clearly marked in the relationship edge. Finally, select a suitable storage tool, such as a graph database (such as Neo4j, OrientDB), a relational database (such as MySQL, PostgreSQL), etc., and define the table structure, field type, index, etc. of nodes and edges. Import the data of nodes and edges into the database and perform data verification to ensure the integrity and consistency of the data. Verification can include checking whether the data is missing, whether it is repeated, whether it meets specific constraints, etc.

[0121] In this implementation, a Web service interface (such as a RESTful API) is constructed, and the city entity change information stored in the time series relationship graph is published as a service. The published service is connected to the CIM platform to ensure that the CIM platform can call the service to obtain the change results of the city entity object. The change results in the service are bound to the BIM model, and the change trends and prediction results are presented on the BIM model in the form of visual elements such as color, height, and transparency. The visualization function of the CIM platform is used to achieve a two-dimensional and three-dimensional visualization of the change trend of urban entities. Users can observe the changes of urban entities at different time points through operations such as zooming, rotating, and panning. Allow users to query the change information and attribute data of specific entity objects through operations such as clicking and selecting. At the same time, through the timeline control, users can view the city status at different time points.

[0122] In this implementation, by performing time series modeling, it is possible to capture the dynamic changes of urban entity objects (such as buildings, roads, infrastructure, etc.) and their historical traceability relationships, including construction, renovation, expansion and other processes, to form a complete record of changes. In the process of performing time series modeling, detailed information on changes in urban entities, including time, location, type of change, etc., can be stored and analyzed to provide data support for urban management and planning, helping decision makers to quickly understand the historical changes and current status of urban entities, so as to more accurately judge future development trends and formulate scientific and reasonable urban planning and management strategies. The two- and three-dimensional visualization display based on the BIM model can intuitively present the changing trends and dynamic development process of urban entity objects, so that decision makers can more intuitively understand the changes in the city. At the same time, it can monitor the status and changes of urban entity objects in real time, discover potential problems in a timely manner and take corresponding measures to improve the efficiency and response speed of urban management.

[0123] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the urban entity data updating method of the present application. More simple transformations based on this technical concept are all within the protection scope of the present application.

[0124] The urban entity data updating device provided by the present application adopts the urban entity data updating method in the above-mentioned embodiment, and can solve the technical problem of how to improve the accuracy of the urban database update result in the process of obtaining the updated data of the urban entity. Compared with the prior art, the beneficial effects of the urban entity data updating device provided by the present application are the same as the beneficial effects of the urban entity data updating method provided by the above-mentioned embodiment, and the other technical features in the urban entity data updating device are the same as the features disclosed in the above-mentioned embodiment method, which will not be repeated here.

[0125] The present application provides a city entity data updating device, which includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the city entity data updating method in the above-mentioned embodiment 1.

[0126] Reference below Fig. 9, which shows a schematic diagram of the structure of a city entity data update device suitable for implementing the embodiment of the present application. The city entity data update device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Fig. 9 The urban entity data updating device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0127] like Fig. 9 As shown, the urban entity data updating device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the urban entity data updating device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the urban entity data update device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows an urban entity data update device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or provided instead.

[0128] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0129] The urban entity data updating device provided by the present application adopts the urban entity data updating method in the above embodiment, which can solve the technical problem of how to improve the accuracy of the urban database update results in the process of obtaining the updated data of the urban entity. Compared with the prior art, the beneficial effects of the urban entity data updating device provided by the present application are the same as the beneficial effects of the urban entity data updating method provided by the above embodiment, and the other technical features in the urban entity data updating device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0130] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0131] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0132] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the urban entity data updating method in the above-mentioned embodiment.

[0133] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0134] The computer-readable storage medium may be included in the urban entity data updating device; or may exist independently without being assembled into the urban entity data updating device.

[0135] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the urban entity data updating device, the urban entity data updating device: if it is detected that there is a difference between the CIM platform data corresponding to the historical time node and the current time node, then determine the matching degree between the CIM platform data corresponding to each data source at the current time node; if the matching degree is greater than or equal to a preset threshold, then determine the target update data from the CIM platform data corresponding to each data source at the current time node; and update the preset urban entity database and knowledge graph library according to the target update data.

[0136] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0137] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0138] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0139] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned city entity data update method, and can solve the technical problem of how to improve the accuracy of the city database update results in the process of obtaining the updated data of the city entity. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the city entity data update method provided by the above-mentioned embodiment, and will not be repeated here.

[0140] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned urban entity data updating method when executed by a processor.

[0141] The computer program product provided by the present application can solve the technical problem of how to improve the accuracy of the city database update results in the process of obtaining the updated data of the city entity. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the city entity data update method provided by the above embodiment, which will not be repeated here.

[0142] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for updating city entity data, characterized in that: The method includes: If it is detected that there is a difference between the CIM platform data corresponding to the historical time node and the current time node, then determining the matching degree between the CIM platform data corresponding to each data source at the current time node; If the matching degree is greater than or equal to a preset threshold, determining target update data from the CIM platform data corresponding to each data source at the current time node; Update data according to the target, and update the preset city entity database and knowledge graph library.

2. The method according to claim 1, characterized in that The step of determining the matching degree between the CIM platform data corresponding to each data source at the current time node includes: According to the data source, the CIM platform data corresponding to each data source at the current time node is divided into reference data corresponding to the reference data source and comparison data corresponding to the comparison data source; Superimposing each of the reference data according to a preset reference weight to obtain target reference data; Determining a degree of match between the target reference data and each of the comparison data; If the matching degree between the target reference data and the comparison data is greater than or equal to the preset threshold, the target reference data is determined to be the target update data.

3. The method according to claim 2, characterized in that The step of determining the matching degree between the target reference data and each of the comparison data comprises: Loading the target reference data and the comparison data on a map layer of the CIM platform; The matching degree between the target reference data and each of the comparison data is determined according to the loading result.

4. The method according to claim 3, characterized in that The step of loading the target reference data and the comparison data on the map layer of the CIM platform includes: Dividing the target reference data and the comparison data into tile data; Organizing the tile data according to levels and row and column numbers to form a data directory; The target reference data and the comparison data are loaded onto the map layer respectively according to the data directory.

5. The method according to claim 1, characterized in that If it is detected that there is a difference between the CIM platform data corresponding to the historical time node and the current time node, before the step of determining the matching degree between the CIM platform data corresponding to each data source at the current time node, the step further includes: Acquire the CIM platform data corresponding to the historical time node; Building a city entity data model and the city entity database corresponding to the city entity data model based on the CIM platform data corresponding to the historical time node; The knowledge graph library is acquired and established based on the city entity data in the city entity database.

6. The method according to claim 5, characterized in that The step of acquiring and establishing the knowledge graph library based on the city entity data in the city entity database comprises: Acquire the city entity data, the city entity data including: structured data, semi-structured data and unstructured data; Performing knowledge extraction on the semi-structured data and the unstructured data, acquiring entity information, attribute information and relationship information in the semi-structured data and the unstructured data, and generating knowledge units; The knowledge units and the structured data are fused to merge the same entity information and generate the knowledge graph library.

7. The method according to claim 2, characterized in that Before the step of determining target update data from the CIM platform data corresponding to each data source at the current time node if the matching degree is greater than or equal to a preset threshold, the method further includes: Inputting the comparison data into the knowledge graph library for entity recognition; The entities in the comparison data and the association relationships between different entities are obtained according to the recognition results.

8. The method according to any one of claims 1 to 7, characterized in that The method further comprises: Performing time series modeling on the city entities in the CIM platform according to the CIM platform data corresponding to the historical time nodes and the target update data; The results of the temporal modeling are presented based on the BIM model in the CIM platform.

9. A city entity data updating device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the urban entity data updating method according to any one of claims 1 to 8.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the urban entity data updating method according to any one of claims 1 to 8 are implemented.

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