Urban data visualization method and system based on Beidou grid code

Abstract

The invention relates to the technical field of data processing, and particularly discloses a city data visualization method and system based on Beidou grid codes. The method comprises the following steps: acquiring city data from a plurality of data sources, wherein the city data comprises a data type, position information of a data acquisition point and data content; determining the grid hierarchy of the city data at least according to the data type of the city data; determining a grid unit corresponding to the city data in combination with the grid hierarchy and the position information of the data acquisition points; mapping the city data into the fixed fields of the determined grid units, and generating a grid model of the city data; processing the data of the same grid unit or multiple grid units, and determining data to be displayed; based on the grid model and the to-be-displayed data, drawing an initial display graph and each detail display graph; and outputting the initial display graph and each detail display graph. Based on the method and the device, the city data visualization precision and efficiency can be improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method and system for visualizing urban data based on Beidou grid codes. Background Art

[0002] In many fields such as urban planning, traffic monitoring, and public safety, the visualization of urban data is of great significance for decision makers to understand the operation status of the city and formulate corresponding policies. With the acceleration of urbanization, the demand for refined management of urban data is growing.

[0003] At present, urban data visualization mainly relies on geographic information system (GIS) technology, which combines urban data with geographic location through map services to achieve spatial display of data. However, GIS technology has some limitations when processing large-scale, highly dynamic urban data. Specifically, the main shortcomings of existing technologies include: (1) data granularity limitations. GIS technology can usually only provide a relatively coarse spatial division, making it difficult to achieve more refined grid management; (2) insufficient real-time performance. When dynamically updating urban data, the existing system often has insufficient response speed and cannot meet the needs of real-time monitoring; (3) weak interactivity. The user interface of the existing visualization system is complex and not intuitive enough, resulting in poor user understanding and interaction experience; (4) limited scalability. As the amount of urban data continues to grow, the existing system has bottlenecks in scalability and is difficult to adapt to future development.

[0004] Therefore, a new urban data visualization solution is needed. Summary of the invention

[0005] The present application provides a method and system for visualizing urban data based on Beidou grid code, in an effort to solve or at least alleviate at least one of the above problems.

[0006] According to one aspect of the present application, a method for visualizing urban data based on Beidou grid codes is provided, which is suitable for execution in a computing device, and the method comprises: acquiring urban data from multiple data sources, the urban data comprising data types, location information of data collection points, and data content, the data types comprising: infrastructure data, energy and resource data, environmental monitoring data, social and economic data, transportation data, safety and emergency data; determining a grid level of the urban data at least according to the data type of the urban data; determining a grid unit corresponding to the urban data in combination with the grid level and the location information of the data collection points, wherein the grid unit is based on The urban space is gridded according to the Beidou grid code, and multi-level grid units are obtained, and grid units of different levels correspond to different grid sizes, and each grid unit has a unique grid code; the urban data is mapped to the fixed fields of the determined grid units to generate a grid model of the urban data; the data of the same grid unit or multiple grid units are processed to determine the data to be displayed; based on the grid model and the data to be displayed, an initial display diagram is drawn using a map framework, and a detailed display diagram corresponding to each data module is generated using a chart tool, wherein the data module is related to the data type; the initial display diagram and each detailed display diagram are output.

[0007] According to another aspect of the present application, a city data visualization system based on Beidou grid code is provided, including: a data acquisition module, suitable for acquiring city data from multiple data sources, the city data including data types, location information of data acquisition points, and data content, the data types including: infrastructure data, energy and resource data, environmental monitoring data, social and economic data, transportation data, safety and emergency data; a data preprocessing module, suitable for preprocessing city data to obtain standardized city data; a Beidou grid encoding module, suitable for using Beidou grid code to grid the urban space, dividing the urban space into multi-level grid units, and different levels of grid units correspond to different grid sizes; and determining the grid code of each grid unit; a data A mapping module is adapted to determine the grid level of the urban data at least according to the data type of the urban data; and determine the corresponding grid unit in combination with the grid level and the location information of the data collection point, and map the urban data to the determined grid unit to generate a grid model of the urban data; a data processing module is adapted to process the data of the same grid unit or multiple grid units to determine the corresponding data to be displayed; a visualization display module is adapted to draw an initial display diagram based on the grid model and the data to be displayed using a map framework, and generate a detailed display diagram corresponding to each data module using a chart tool, wherein the data module is related to the data type; the visualization display module is also adapted to output the initial display diagram and each of the detailed display diagrams.

[0008] According to another aspect of the present application, a computing device is provided, comprising: one or more processor memories; one or more programs, wherein the one or more programs are stored in the memories and configured to be executed by one or more processors, and the one or more programs include instructions for executing any of the above methods.

[0009] According to another aspect of the present application, a computer-readable storage medium storing one or more programs is provided. The one or more programs include instructions. When the instructions are executed by a computing device, the computing device executes any of the methods described above.

[0010] According to another aspect of the present application, a computer program product is provided, comprising a computer program / instruction, wherein the computer program / instruction implements the steps of the above-mentioned method when executed by a processor.

[0011] In summary, according to the solution of the present application, the Beidou grid code is used to perform fine and dynamic spatial division of urban data, and to realize real-time dynamic visualization of data. Specifically, the urban space is divided based on the Beidou grid code, and the data type of the urban data and the location information of the data collection point are combined to map each city data to grid units of different sizes, and corresponding fixed fields are set for different types of data in the grid units to store urban data, so as to ensure the accuracy and efficiency of the visualization of urban data. This solution can not only improve the accuracy and efficiency of urban data visualization, but also provide users with a more friendly interactive experience, and provide strong technical support for the construction of smart cities.

[0012] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to achieve the above and related purposes, certain illustrative aspects are described herein in conjunction with the following description and accompanying drawings, which indicate various ways in which the principles applied herein can be practiced, and all aspects and their equivalents are intended to fall within the scope of the claimed subject matter. By reading the following detailed description in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. Throughout the application, the same reference numerals generally refer to the same parts or elements.

[0014] Figure 1 A schematic diagram of a city data visualization system 100 based on Beidou grid code according to some embodiments of the present application is shown;

[0015] Figure 2 A schematic diagram of a computing device 200 according to some embodiments of the present application is shown;

[0016] Figure 3 A visualization diagram of city data according to some embodiments of the present application is shown;

[0017] Figure 4 A schematic flow chart of a method 400 for visualizing urban data based on Beidou grid codes according to some embodiments of the present application is shown. DETAILED DESCRIPTION

[0018] The exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0019] In view of the limitations of existing technologies, the solution of this application aims to achieve the following goals: provide more sophisticated grid management, enhance the granularity and accuracy of data visualization; realize real-time dynamic update of data, improve the response speed and real-time performance of the system; optimize the user interaction experience, provide a simple and intuitive user interface; enhance the scalability of the system to adapt to the growing amount of urban data. Specifically, based on the Beidou grid code to process various types of urban data, this solution can not only improve the accuracy and efficiency of urban data visualization, but also provide users with a more friendly interactive experience, and provide strong technical support for the construction of smart cities.

[0020] Figure 1 FIG. 1 is a schematic diagram of a city data visualization system 100 based on Beidou grid code according to some embodiments of the present application. Figure 1 As shown, the system 100 includes: a data acquisition module 110, a data preprocessing module 120, a Beidou grid encoding module 130, a data mapping module 140, a data processing module 150 and a visualization display module 160.

[0021] The data acquisition module 110 can be connected to multiple data sources (such as traffic monitoring, weather stations, demographics, etc.) and is responsible for collecting urban data from multiple data sources. According to the implementation mode of the present application, the urban data includes data type, location information of data collection points, and data content. The data type can be determined by a preset identifier, or it can be determined by the data acquisition module 110 according to the data source of the acquired data, and the embodiment of the present application does not limit this. In the present embodiment, the data acquisition module 110 assigns a unique type identifier to the urban data according to the data type. The location information of the data collection point is usually represented by longitude and latitude or longitude and latitude and altitude.

[0022] The data types include at least: infrastructure data, energy and resource data, environmental monitoring data, social and economic data, transportation data, safety and emergency data. According to the implementation mode of the present application, the above data types are large categories, and under each large category, more detailed classification can be performed to obtain subcategories under each large category. For example, the data is classified based on its application scenario. It should be noted that the present application does not limit the data type, and any data related to the city can be used as city data and visualized through the embodiments of the present application.

[0023] The city data in this embodiment is further described below. Under each major category (the following (1)-(6) correspond to the major category), one or more subcategories (each subcategory is identified by English letters a, b...) and the data content corresponding to each subcategory are shown by way of example.

[0024] (1) Infrastructure data

[0025] a. Road and bridge data

[0026] · Number of lanes: The number of lanes per highway or street.

[0027] Length: The actual length of the road.

[0028] · Condition and maintenance records: The maintenance status, maintenance records and damage of roads and bridges.

[0029] b. Building data

[0030] Building use: such as residential, commercial, office, industrial, etc.

[0031] Height: Building height.

[0032] Area: The ground area covered by the building.

[0033] Construction Year: The construction and renovation time of the building.

[0034] c. Parks and green spaces

[0035] Area: The total area of ​​the green space or park.

[0036] Vegetation cover: vegetation type and coverage.

[0037] Facilities: The condition of public facilities in the park, such as benches, toilets, sports facilities, etc.

[0038] (2) Energy and resource data

[0039] a. Power usage data

[0040] Electricity consumption: Electricity consumption in residential, commercial and industrial areas.

[0041] Power supply status: power outage records, grid load information.

[0042] b. Water resource usage data

[0043] Water consumption: water supply and water consumption in each area.

[0044] Water quality monitoring data: water quality parameters such as pH, turbidity, and chemical content.

[0045] c. Gas usage data

[0046] Gas consumption: Residential, commercial and industrial gas consumption.

[0047] Pipeline network status: The health status and maintenance records of the gas pipeline network.

[0048] (3) Environmental monitoring data

[0049] a. Air quality

[0050] Pollutant concentration: such as PM2.5, PM10, SO2, NO2, CO, O3, etc.

[0051] Air Quality Index (AQI): Overall air quality level.

[0052] b. Noise pollution

[0053] Noise Level: Real-time noise monitoring data in different regions.

[0054] Noise sources: types and distribution of noise pollution sources.

[0055] c. Waste management

[0056] Waste collection: The amount and rate of waste collection in each area.

[0057] Waste treatment facilities: the distribution and operation of treatment facilities.

[0058] d. Weather data

[0059] Temperature: The temperature of each area at each time period.

[0060] Humidity: humidity in each area and at each time period.

[0061] Precipitation: Precipitation in each region and time period.

[0062] Wind speed and direction: wind direction and speed in each area and at each time period.

[0063] (4) Social and economic data

[0064] a. Population data

[0065] Population density: The density of residents in different areas.

[0066] Population structure: distribution of age, gender, occupation and education level.

[0067] b. Economic data

[0068] Economic activities and enterprise distribution: Enterprise distribution and industry classification in different regions.

[0069] Income level: Residents’ income and consumption level.

[0070] c. Public health

[0071] Distribution of medical facilities: location and distribution of hospitals, clinics, and pharmacies.

[0072] Epidemic monitoring: monitoring data of infectious diseases and public health events.

[0073] (5) Traffic and transportation data

[0074] a. Traffic flow data

[0075] Traffic density: Traffic flow data for each major road.

[0076] Average speed: vehicle speed information for each road section.

[0077] b. Public transportation

[0078] ·Bus routes: bus, subway and other public transportation routes and schedules.

[0079] Real-time vehicle location: Real-time location and estimated arrival time of public transport vehicles.

[0080] c. Parking data

[0081] Parking lot location: Distribution data of parking lots and parking spaces.

[0082] Available parking spaces: Display the parking space usage of each parking lot in real time.

[0083] (6) Safety and emergency data

[0084] a. Public safety data

[0085] Crime hotspots: the frequency and type of crime in different areas.

[0086] · Police force distribution: relevant police departments and police force deployment.

[0087] b. Disasters and emergency response:

[0088] Disaster monitoring: Monitoring data of natural disasters such as earthquakes, floods, storms, etc.

[0089] Emergency facilities: location and status of emergency shelters and emergency material storage points.

[0090] The data preprocessing module 120 preprocesses the acquired various city data to obtain standardized city data.

[0091] According to the implementation of the present application, preprocessing includes data cleaning, formatting, standardization, etc. In practical applications, one of the goals of data preprocessing is to convert all city data into GeoJSON format for visualization, and to manually calibrate and add data that lacks longitude and latitude.

[0092] The Beidou grid coding module 130 uses the Beidou grid code to grid the urban space, dividing the urban space into multiple levels of grid units, and different levels of grid units correspond to different grid sizes. In addition, the Beidou grid coding module 130 also determines the grid code (Grid Code) of each grid unit.

[0093] Beidou grid code is a geospatial coding method based on the Beidou Navigation Satellite System (BDS). It divides the earth's surface into grid units of different levels according to longitude and latitude.

[0094] Specifically, the size of the first-level grid is 6° x 4°, representing a larger area;

[0095] The size of the second-level grid is 30' x 30', corresponding to a 55.66km x 55.66km grid at the equator;

[0096] The dimensions of the third-level grid are 15' x 10' (27.83km x 18.55km);

[0097] The size of the fourth-level grid is 1.85km x 1.85km;

[0098] The dimensions of the fifth-level grid are 123.69m x 123.69m;

[0099] The dimensions of the sixth level grid are 61.84m x 61.84m;

[0100] The dimensions of the seventh-level grid are 7.73m x 7.73m;

[0101] The dimensions of the eighth-level grid are 0.97m x 0.97m;

[0102] …

[0103] The dimensions of the tenth level grid are 0.12m x 0.12m.

[0104] Based on the above, the size of each grid unit is fixed. However, in the embodiments of the present application, multiple scale factors s are adjusted according to data requirements. Specifically, for urban data that requires finer granularity processing or display, a finer grid unit is matched for it, that is, the smaller the size of the grid unit; conversely, the larger the size of the matched grid unit.

[0105] Taking traffic flow monitoring data as an example, first determine the basic level of the grid unit as the seventh level according to the data type, that is, the grid size is 7.73mx 7.73m, which is usually used for traffic flow monitoring. When the application scenario is low-density demand, it is necessary to expand the traffic flow data of the data collection point to a larger scale grid. At this time, a larger scale factor can be set, such as scale factor s=8, then the final grid size is 7.73m×8=61.84m, that is, the grid level is the sixth level grid (61.84mx 61.84m).

[0106] Taking air quality monitoring data as an example, first determine the basic level of the grid unit as the seventh level according to the data type, that is, the grid size is 7.73mx 7.73m, which is suitable for general monitoring needs. When the application scenario requires high-precision geographic location, you can set a smaller scale factor, such as scale factor s=0.125, then the final grid size is 7.73m×0.125=0.97m, that is, the grid level is the eighth level grid (0.97mx 0.97m).

[0107] The data mapping module 140 is responsible for mapping the city data (ie, the city data processed by the data preprocessing module 120) to each grid unit. First, the grid level of the city data is determined at least according to the data type of the city data.

[0108] According to some embodiments, different basic levels are first determined for different data types, and then the scale factor is set to adjust the grid level according to the needs or application scenarios or subcategories. Usually, the size of the grid unit = the grid size of the basic level × the scale factor s. For example, for environmental monitoring data, the sixth-level grid is usually used to represent it. When the pollutant monitoring data is to be processed more accurately, a higher-precision grid unit is required. Then, the grid level can be adjusted by setting a scale factor s less than 1 so that the size of the grid unit storing the pollutant monitoring data is smaller than the size of the sixth-level grid.

[0109] Afterwards, the grid unit corresponding to the city data is determined by combining the grid level and the location information of the data collection point, and the city data is mapped to the determined grid unit to generate a grid model of the city data. In other words, the grid unit to which the location information of the data collection point belongs is determined, and the level of the grid unit is consistent with the determined grid level.

[0110] Furthermore, when mapping the city data to the grid unit, the data mapping module 140 also sets a fixed field in the grid unit according to the data type to store the data content and location information. At the same time, a corresponding identifier is set according to the data type of the city data, and the identifier is associated with the fixed field to form a complete city data. The following are common type identifiers and fixed fields. It should be noted that in each data type, a corresponding field is set to store timestamp information and location information. According to this embodiment, the location information can be represented by a grid code.

[0111] 1. Traffic data

[0112] Identifier: TrafficID.

[0113] Fixed fields: VehicleCount, Speed, Incidents, Timestamp, LocationGridCode.

[0114] 2. Weather data

[0115] Identifier: WeatherID.

[0116] Fixed fields: Temperature, Humidity, Precipitation, WindSpeed, WindDirection, Timestamp, LocationGridCode.

[0117] 3. Air quality data

[0118] Identifier: AirQualityID.

[0119] Fixed fields: PM2.5, PM10, SO2, NO2, CO, O3, air quality index AQI, timestamp, location information LocationGridCode.

[0120] 4. Energy usage data

[0121] Identifier: EnergyID.

[0122] Fixed fields: ElectricityUsage, WaterUsage, GasUsage, Timestamp, LocationGridCode.

[0123] 5. Safety and emergency data

[0124] Identifier: SecurityID.

[0125] Fixed fields: incident type IncidentType, severity Severity, timestamp Timestamp, location information LocationGridCode.

[0126] 6. Environmental monitoring data

[0127] Identifier: EnvironmentalID.

[0128] Fixed fields: Noise power NoiseLevel, water quality WaterQuality, timestamp Timestamp, location information LocationGridCode.

[0129] When all city data are stored in corresponding grid units according to their location information, and the data content is stored according to the set fixed fields, a grid model of city data is formed.

[0130] According to some further embodiments of the present application, the data mapping module 140 further includes a real-time update submodule 142. The real-time update submodule 142 is adapted to, when receiving new city data, map the new city data to a fixed field of the determined grid unit by determining the corresponding grid unit, so as to update the grid model of the city data.

[0131] Afterwards, a series of data analysis and processing tasks can be performed on the grid model of the city data to ensure the accuracy and effectiveness of the data visualization display.

[0132] The data processing module 150 processes the data of the same grid unit or multiple grid units to determine the corresponding data to be displayed.

[0133] In some embodiments, multiple pieces of data in the same grid unit are aggregated (such as summing, averaging, counting, clustering, etc.) to determine the data to be displayed. Data of the same data type in multiple grid units are processed to calculate corresponding indicator data, such as a traffic congestion index. Using a time series model, time series data in the same grid unit or multiple grid units are processed to analyze and predict trends, such as the weather change trend this summer.

[0134] According to some other embodiments, the data processing module 150 also detects abnormal values ​​in the grid unit and corrects them. Taking meteorological data as an example, in a grid unit, multiple meteorological data can be stored in the order of data update (i.e., the order of timestamps). For example, the standard deviation method can be used to detect abnormal values ​​of temperature data (Temperature) within a period of time (such as too high or too low temperature data can be considered as abnormal values), and then replace the abnormal values ​​with the mean or median.

[0135] The visualization module 160 visualizes the data status of the grid model using charts, color coding, 3D models, etc. In some embodiments, based on the grid model and the data to be displayed, an initial display diagram is drawn using a map framework, and a detailed display diagram corresponding to each data module is generated using a chart tool, where the data module is related to the data type.

[0136] According to an embodiment of the present application, the initial display diagram is a basic view generated when the system 100 is started or when the data is initially loaded, and is used to display all key city data overviews, such as various real-time city data mapped to grid units. In some embodiments, the initial display diagram can also display the data stored in each grid unit through the grid unit. For example, the view is divided into a number of grid units according to the proportional size of the grid unit, and the data overview associated with each grid unit is displayed on each grid unit.

[0137] The detail display diagram is used to display detailed data for a certain data type, which is defined as various data modules, such as traffic module, air quality module, etc.

[0138] Finally, the initial display image and each detail display image are output by the visualization display module 160. In some embodiments, the initialization display image is used as the base view, and the detail display images of each data module are displayed on the base view by overlaying layers or the like, or directly or indirectly, and each detail display image can be viewed by switching views.

[0139] Figure 3 FIG. 4 shows a visualization diagram of city data according to some embodiments of the present application. Figure 3 As shown, the initial display diagram shows the data overview in the urban area. Different contents can be displayed by different colors, icons, etc. This application does not impose too many restrictions on this. The blue square area superimposed on it is the detailed display diagram. Taking the detailed display diagram on the left as an example, it corresponds to the air quality data module, which specifically displays information such as air quality monitoring, pollution warning, and ranking.

[0140] In some other embodiments, the visualization display module 160 further includes a user interaction submodule 162 for receiving a query condition input by a user, wherein the query condition includes one or more of time information, location information, and data type. Optionally, the location information can be represented by a text description (such as ** street), or by latitude and longitude or a grid code.

[0141] At this point, the data processing module 150 can filter the data in the grid model and perform necessary data processing according to the query conditions, and finally select the data to be displayed. For example, the query condition is: weather changes in area A over the past week. The data processing module 150 first determines the longitude and latitude range of area A and preliminarily determines the grid code; then, based on the data type to which the weather changes belong, determines the corresponding identifier "WeatherID". Based on the grid code and identifier, combined with the time information "past week", the initial city data can be filtered out. Next, the filtered data is further processed to obtain the data to be displayed.

[0142] According to the implementation of the present application, the data acquisition module 110, the data preprocessing module 120, the Beidou grid encoding module 130, the data mapping module 140, the data processing module 150 and other entities can be arranged as one or more servers (such as application servers, database servers, Web servers, etc.), and the visualization display module 160 can be arranged as a client. In this way, the urban data visualization system 100 includes a server and a client connected in communication. The server performs steps such as data acquisition, preprocessing, Beidou grid encoding, data mapping, data processing, and real-time updating. The client performs visualization display and user interaction steps.

[0143] In addition, the system 100 is designed to support multi-platform operation, including the Web and mobile devices, which improves the system's availability and flexibility. The system 100 is designed with data security and privacy protection in mind, and encryption and access control technology are used in communications between the server and the client to ensure data security.

[0144] In addition, the system 100 adopts a modular design, which is easy to expand new functions and maintain and upgrade.

[0145] In addition, the architectural design of system 100 fully considers high concurrency and high load conditions, enhancing the stability and robustness of the system.

[0146] According to the city data visualization system 100 of the present application, the Beidou grid code is used to perform fine and dynamic spatial division of the city data, and realize real-time dynamic visualization of the data. Specifically, the city space is divided based on the Beidou grid code, and the data type of the city data and the location information of the data collection point are combined to map each city data to grid units of different sizes, and corresponding fixed fields are set for different types of data in the grid units to store the city data, so as to ensure the accuracy and efficiency of the visualization of the city data. This system 100 can not only improve the accuracy and efficiency of the visualization of the city data, but also provide users with a more friendly interactive experience, and provide strong technical support for the construction of smart cities.

[0147] The city data visualization system 100 according to the present application may be implemented by one or more computing devices. Figure 2 FIG. 2 shows a schematic diagram of a computing device 200 according to some embodiments of the present application. It should be noted that: Figure 2 The computing device 200 shown is only an example. In practice, the computing device used to implement the method of the present application can be any type of device, and its hardware configuration can be different from the above. Figure 2 The computing device 200 shown is the same as Figure 2 The computing device 200 shown is different. In practice, the computing device used to implement the embodiment of the present application can be Figure 2 The hardware components of the computing device 200 shown are increased or deleted, and the present application does not limit the specific hardware configuration of the computing device.

[0148] like Figure 2 In a basic configuration, computing device 200 includes at least one processing unit 202 and system memory 204. According to one aspect, depending on the configuration and type of computing device, processing unit 202 can be implemented as a processor. System memory 204 includes, but is not limited to, volatile storage (e.g., random access memory), non-volatile storage (e.g., read-only memory), flash memory, or any combination of such memories. According to one aspect, system memory 204 includes operating system 205 and program modules 206.

[0149] According to one aspect, operating system 205 is suitable for controlling the operation of computing device 200, for example. Furthermore, examples are practiced in conjunction with graphics libraries, other operating systems, or any other application programs, and are not limited to any particular application or system. Figure 2This basic configuration is illustrated in FIG. 2 by those components within dashed line 208. According to one aspect, computing device 200 has additional features or functionality. For example, according to one aspect, computing device 200 includes additional data storage devices (removable and / or non-removable), such as magnetic disks, optical disks, or tapes. Such additional storage Figure 2 2 is illustrated by removable storage 209 and non-removable storage 210.

[0150] As stated above, according to one aspect, program modules are stored in the system memory 204. According to one aspect, the program modules may include one or more application programs, and the present application does not limit the types of application programs, for example, the application programs may include: email and contact applications, word processing applications, spreadsheet applications, database applications, slide show applications, drawing or computer-aided applications, web browser applications, etc.

[0151] According to one aspect, the examples may be practiced on a circuit comprising discrete electronic components, a packaged or integrated electronic chip containing logic gates, a circuit utilizing a microprocessor, or a single chip containing electronic components or a microprocessor. Figure 2 Each or many components shown in can be integrated in a system on chip (SOC) on a single integrated circuit to practice examples. According to one aspect, such a SOC device may include one or more processing units, a graphics unit, a communication unit, a system virtualization unit, and various application functions, all of which are integrated (or "burned") into a chip substrate as a single integrated circuit. When operated via SOC, the functions described in the present application can be operated via a dedicated logic integrated with other components of a computing device 200 on a single integrated circuit (chip). Other technologies that can perform logical operations (such as AND, OR, and NOT) can also be used to practice embodiments of the present application, including but not limited to mechanical, optical, fluid, and quantum technologies. In addition, embodiments of the present application can be practiced in a general-purpose computer or in any other circuit or system.

[0152] According to one aspect, the computing device 200 may also have one or more input devices 212, such as a keyboard, a mouse, a pen, a voice input device, a touch input device, a VR motion capture input device, etc. Output devices 214 may also be included, such as a display, a speaker, a printer, etc. The aforementioned devices are examples and other devices may also be used. The computing device 200 may include one or more communication connections 216 that allow communication with other computing devices 218. Examples of suitable communication connections 216 include, but are not limited to: RF transmitters, receivers and / or transceiver circuits; Universal Serial Bus (USB), parallel and / or serial ports.

[0153] The term computer-readable media as used in this application includes computer storage media. Computer storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (e.g., computer-readable instructions, data structures, or program modules). System memory 204, removable storage 209, and non-removable storage 210 are all examples of computer storage media (i.e., memory storage). Computer storage media may include random access memory (RAM), read-only memory (ROM), electrically erasable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical storage, cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other products that can be used to store information and can be accessed by computing device 200. According to one aspect, any such computer storage media may be part of computing device 200. Computer storage media do not include carrier waves or other propagated data signals.

[0154] According to one aspect, communication media is implemented by computer readable instructions, data structures, program modules, or other data in a modulated data signal (e.g., a carrier wave or other transport mechanism), and includes any information delivery media. According to one aspect, the term "modulated data signal" describes a signal that has one or more characteristics set or changed in a manner that encodes information in the signal. By way of example and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media.

[0155] In an embodiment according to the present application, the computing device 200 is configured to execute the Beidou grid code-based city data visualization method 400 according to the present application. The program module 206 includes a plurality of program instructions for executing the Beidou grid code-based city data visualization method 400, and these program instructions can instruct the processing unit 202 to execute the method 400 of the present application.

[0156] Figure 4 A flow chart of a Beidou grid code-based city data visualization method 400 according to some embodiments of the present application is shown. It should be noted that the method 400 can be executed by the aforementioned city data visualization system 100, so the contents of the method 400 and the aforementioned system 100 complement each other, and the relevant parts are not repeated here.

[0157] like Figure 4As shown, the method 400 starts at step S410 . In S410 , the data acquisition module 110 acquires city data from multiple data sources.

[0158] As mentioned above, city data includes data types, location information of data collection points, and data content. The data types include: infrastructure data, energy and resource data, environmental monitoring data, social and economic data, transportation data, and safety and emergency data. For the relevant description of city data, please refer to the relevant content of the data collection model 110 above, which will not be repeated here.

[0159] According to the implementation of the present application, after obtaining the city data, the data and processing module 120 will also pre-process the city data to obtain standardized city data. One of the goals of data pre-processing is to convert all data into a standard format for visualization, and to manually calibrate and add data that lacks longitude and latitude. Among them, the standard format is GeoJSON format.

[0160] Among them, the preprocessing of urban data includes the following three situations.

[0161] First, if the city data contains image data, then:

[0162] (1) First, the image is denoised and feature extracted to extract geographic feature data in the image and calculate the edges of the geographic features. The denoising process, for example, uses a filter to remove random noise in the image. The geographic feature data can be features such as buildings and roads, and their contour boundaries (i.e., feature edges) are calculated.

[0163] (2) Map the geographic feature data in the image to the geographic coordinate system to generate coordinate values ​​in a standard format, that is, generate coordinate points in GeoJSON;

[0164] (3) Convert the extracted geographic feature data and coordinate values ​​into standard format data as standardized city data. The following is a code example for converting the format of geographic feature data and coordinate values:

[0165]

[0166] Second, if the city data contains single-value data, then:

[0167] (1) Perform data cleaning on each numerical data and calibrate its corresponding location information to obtain the preprocessed numerical data and its calibrated geographic coordinates. Data cleaning mainly removes outliers, such as data whose values ​​are not within the threshold range, and calibration of location information mainly involves manually filling in the missing longitude and latitude;

[0168] (2) Convert the preprocessed numerical data and its calibrated geographic coordinates into standard format data as standardized city data. The following is a code example of format conversion:

[0169]

[0170]

[0171] The third method is to clean and smooth the time series data to ensure that it contains valid geographic locations and convert it into GeoJSON format. Specifically:

[0172] (1) Perform data cleaning on each value in the time series data and calibrate its corresponding location information to obtain the preliminarily processed time series data and its calibrated geographic coordinates. Data cleaning includes removing outliers and filling missing values, and calibration of location information includes manually filling in longitude and latitude information.

[0173] (2) processing the time series data after preliminary processing to obtain processed time series data, wherein the processing includes at least one of the following: smoothing and difference;

[0174] (3) Convert the processed time series data and its calibrated geographic coordinates into standard format data, i.e., GeoJSON with time series information, as standardized city data. The following is a code example of format conversion:

[0175]

[0176]

[0177] In S420, a grid level of the city data is determined at least according to the data type of the city data.

[0178] Regarding the division of Beidou grid units, please refer to the previous description related to the Beidou grid encoding module 130, which will not be repeated here.

[0179] First, the basic level of the grid unit is determined according to the data type. Specifically, if the data type is transportation data, the basic level is the seventh-level grid or the eighth-level grid; if the data type is environmental monitoring data, the basic level is the sixth-level grid or the seventh-level grid; if the data type is social and economic data, energy and resource data, the basic level is the fifth-level grid or the sixth-level grid; if the data type is infrastructure data, safety and emergency data, the basic level is the seventh-level grid or the eighth-level grid.

[0180] Afterwards, the scale factor is determined according to the application scenario. According to the implementation mode of the present application, the scale factor can be any value and can be dynamically adjusted according to the accuracy requirements of the application scenario. Generally speaking, when higher accuracy (or, a finer-grained geographical range) is required, the scale factor is usually less than 1; but when lower accuracy (or, a larger geographical range) is required, the scale factor is usually larger.

[0181] Afterwards, the final grid level is calculated based on the base level and the scale factor. In some embodiments, the size of the grid level = the size of the base level × the scale factor s. According to the present application, by dynamically adjusting the grid division accuracy, data mapping and visualization are made more refined and meet actual needs.

[0182] The following further illustrates how to determine the grid level in some data types and application scenarios. It should be understood that the following is only an example, and in a specific application scenario, the scale factor can be dynamically adjusted according to demand.

[0183] 1. For transportation data

[0184] In the following application scenarios: traffic flow monitoring, traffic accident analysis, road optimization; the accuracy requirements are: high precision, which needs to be refined to secondary streets and traffic intersections.

[0185] At this time, the base level is the seventh-level grid (7.73m x 7.73m) or the eighth-level grid (0.97m x 0.97m).

[0186] According to the requirements of the application scenario, a smaller scale factor s is used to reduce the grid size. For example, by setting a scale factor less than 1, the final grid level is set to be higher than the seventh-level grid, that is, the grid size is less than 7.73m x 7.73m, or even less than 0.97m x 0.97m.

[0187] 2. Environmental monitoring data

[0188] In the following application scenarios: air quality monitoring, pollution source detection, water quality monitoring; the accuracy requirements are: moderate accuracy, covering pollution sources and affected areas.

[0189] At this time, the base level is the sixth-level grid (61.84m x 61.84m) or the seventh-level grid (7.73m x 7.73m).

[0190] According to the requirements of the application scenario, a medium-precision scale factor s (for example, a scale factor in units of 1, 2, 3, etc.) is selected to adjust the grid size. The final grid level can be maintained at the sixth or seventh level grid, for example, the scale factor s is set to 1.

[0191] 3. For social and economic data

[0192] In the following application scenarios: population density analysis, resource allocation, and urban planning, the accuracy requirements are: lower accuracy and need to cover a larger area.

[0193] At this time, the base level is the fifth-level grid (123.69m x 123.69m) or the sixth-level grid (61.84m x 61.84m).

[0194] According to the requirements of the application scenario, it is usually to process macro data such as population density and economic activities in a larger area, and the accuracy requirement is low, so a larger scale factor s is selected. For example, the scale factor s can be in units of hundreds, such as 100, 150, 200, ... and so on.

[0195] 4. For infrastructure data, safety and emergency data

[0196] In the following application scenarios: public facility distribution, emergency management, and public security monitoring, the accuracy requirements are: refined management, with an accuracy of 10m.

[0197] At this time, the basic level is: seventh-level grid (7.73m x 7.73m) or eighth-level grid (0.97m x 0.97m).

[0198] According to the requirements of the application scenario, a smaller scale factor s is used to reduce the grid size so that the grid size corresponding to the grid level can accurately map the facility location.

[0199] According to some other embodiments of the present application, the scale factor s is also related to the display method. Specifically, the scale factor s affects the final display method, and different display methods have different requirements for grid accuracy. The display method is, for example, a heat map, a three-dimensional model, a time series graph, a dot graph, or a bar graph. The following describes the size factors corresponding to these four types of display graphs.

[0200] 1. Heatmap

[0201] Application: Display continuous data such as air quality and temperature distribution.

[0202] Relationship with scale factor: Heat maps require moderate grid accuracy and need to smoothly display data changes, so medium scale factors are usually used.

[0203] The grid unit size (such as four-level or five-level grid) of the heat map display is expressed as:

[0204] heatmap_grid_size=base_grid_size*1, where heatmap_grid_size is the grid unit size displayed in the heat map, and base_grid_size is the grid unit size of the base level.

[0205] 2.3D Model

[0206] Application: Display three-dimensional spatial data such as urban buildings and terrain.

[0207] Relationship with scale factor: 3D models require high mesh accuracy and need to be refined to buildings or terrain slopes, so smaller scale factors are usually used.

[0208] The grid unit size (such as seven-level or eight-level grid) displayed in the 3D model is expressed as:

[0209] building_grid_size=base_grid_size*0.125, where building_grid_size is the grid unit size displayed in the 3D model, and base_grid_size is the basic level grid unit size.

[0210] 3. Time Series Plot

[0211] Application: Display data about a location changing over time, such as temperature changes, traffic flow changes, etc.

[0212] Relationship with scale factor: Time series graphs are not directly affected by grid accuracy, and usually pay more attention to the accuracy of the time axis, but the grid size of the collected data still needs to be determined to integrate it with the time series observation.

[0213] The grid cell size for time series display is expressed as:

[0214] timeseries_grid_size=base_grid_size*1, where timeseries_grid_size is the grid unit size for time series display, and base_grid_size is the base level grid unit size.

[0215] 4. Dot chart or bar chart

[0216] Application: Displaying the distribution of discrete data points or data at different locations, such as police force distribution and pollutant monitoring points.

[0217] Relationship with scale factor: Point charts or bar charts generally have a low demand for grid accuracy, and are usually selected based on the required refinement range.

[0218] The grid unit size (such as a five-level or six-level grid) displayed in a dot chart or bar chart is expressed as:

[0219] point_chart_grid_size=base_grid_size*1, where point_chart_grid_size is the grid unit size displayed in the point chart or bar chart, and base_grid_size is the grid unit size of the basic level.

[0220] In S430, the grid unit corresponding to the urban data is determined by combining the grid level and the location information of the data collection point. The grid unit is a multi-level grid unit obtained by gridding the urban space based on the Beidou grid code, and grid units of different levels correspond to different grid sizes, and each grid unit has a unique grid code.

[0221] According to some embodiments of the present application, each grid unit has a corresponding longitude range and latitude range, and of course, for a three-dimensional grid, a corresponding altitude range. According to the location information of the data collection point, the grid unit to which it belongs can be determined, and the level of the grid unit must be consistent with the grid level determined by S330.

[0222] In S440, the city data is mapped to the fixed fields of the determined grid units to generate a grid model of the city data.

[0223] In some embodiments, according to the data type of the city data, the location information and data content of the data collection point are written into the fixed field of the corresponding grid unit. Specifically, for different data types, the data mapping module 140 pre-sets different fixed fields to facilitate subsequent identification, retrieval and other processing. At the same time, since each data type has a corresponding type identifier, the type identifier is associated with the corresponding fixed field to obtain a complete piece of city data. For the fixed fields of data under each type, please refer to the relevant content of the data mapping module 140 in the previous text.

[0224] In this way, the grid model is composed of grid units of different sizes, and the spatial range of all grid units just corresponds to the spatial range of the city. In each grid unit, a piece of city data is stored in a predetermined format, and each piece of city data at least includes: a grid code, a timestamp, a type identifier, and specific data content. The timestamp can be the time information of data collection, or it can be the timestamp when the data collection module 110 obtains the data. This embodiment does not impose too many restrictions on this.

[0225] According to other embodiments, the data acquisition module 110 may acquire new city data from the data source in real time, or may acquire city data from the data source at predetermined time intervals. Therefore, after generating the grid model of the city data, the data mapping module 140 further includes: when receiving new city data, by determining the corresponding grid unit, mapping the new city data to the fixed field of the determined grid unit to update the grid model of the city data.

[0226] It should be noted that steps S420 - S440 are all implemented by the data mapping module 140 , so the relevant contents may refer to the relevant description in the above system 100 , and the two complement each other.

[0227] Different from the general data mapping scheme based on Beidou grid code, this scheme is unique in that:

[0228] (1) Dynamically adjust grid precision: flexibly adjust grid division precision according to different application requirements and data types to make data mapping more refined and applicable;

[0229] (2) Multi-scale processing: In the same urban area, data can be refined to different levels according to the nature and needs of specific data collection points, and hierarchical processing can be performed in combination with different scale factors s;

[0230] (3) Combination of automatic and manual work: Missing geographic information data is manually calibrated and filled in, making GIS data more complete and accurate, and distinguishing data scope and precision.

[0231] After mapping the city data to the grid, necessary analysis and calculation of the city data is a key step to ensure the accuracy and effectiveness of the visualization of the city data. Therefore, in S450, the data of the same grid unit or multiple grid units are processed to determine the data to be displayed. The following shows a method for processing city data according to some embodiments of the present application.

[0232] (1) Aggregate multiple data in the same grid unit. Specifically, multiple data in the same grid unit are processed by aggregation methods such as summation, averaging, counting, and clustering. The following is a code example for calculating each data to be displayed in the grid through data aggregation, where the calculated data to be displayed is explained in the form of comments after each calculation instruction.

[0233] grid_data=df.groupby('LocationGridCode').agg({

[0234] 'Temperature':'mean', / / Calculate the mean temperature

[0235] 'Humidity':'mean', / / Calculate the mean humidity

[0236] 'Precipitation':'sum', / / Calculate the sum of precipitation

[0237] 'WindSpeed':'mean', / / Calculate the mean wind speed

[0238] 'WindDirection': lambda x: x.value_counts().idxmax(), / / Calculate the most common wind direction

[0239] }).reset_index()

[0240] (2) Processing data of the same data type in multiple grid cells to calculate corresponding index data. In some embodiments, index data refers to new indicators to be calculated as needed in addition to the data content contained in the city data for further analysis and visualization. For example, the index data may be an air quality index (AQI), a traffic congestion index, and the like. By calculating the PM2.5, PM10, SO2, NO2, CO, and O3 values ​​in each grid cell contained in a certain area, the air quality index (AQI) of the area can be obtained. The code instructions for calculating the AQI are exemplarily shown below.

[0241] grid_data['AQI']=grid_data.apply(lambda row:calculate_aqi(row['PM2.5'],row['PM10'],row['SO2'],row['NO2'],row['CO'],row['O3']),axis=1)

[0242] (3) Using a time series model (such as ARIMA, LSTM, etc.), the time series data in the same grid cell or multiple grid cells are processed to analyze and predict trends. The following is an example of a code instruction for calculating the temperature values ​​predicted for the next 10 moments using the ARIMA model.

[0243] from statsmodels.tsa.arima.model import ARIMA

[0244] model=ARIMA(grid_data['Temperature'],order=(5,1,0))

[0245] model_fit = model.fit()

[0246] forecast=model_fit.forecast(steps=10)

[0247] In some other embodiments, the time series data may be smoothed to remove short-term fluctuations in the time series data and extract long-term trends. For example, the trend of the time series data may be predicted by a moving average method.

[0248] According to some further embodiments, the data processing module 150 processes the city data further including: detecting and correcting abnormal values ​​of the data stored in the grid cells. Specifically, the abnormal values ​​may be detected using the standard deviation method and then filled with the mean or median.

[0249] As shown in the following code instruction, when the temperature value is detected, when the absolute value of the difference between the temperature value (data) and the temperature mean is greater than 3 times the temperature standard deviation, the temperature value is an abnormal value. At this time, the temperature value is replaced by the mean.

[0250]

[0251] In S460, based on the grid model and the data to be displayed, an initial display diagram is drawn using a map framework, and a detailed display diagram corresponding to each data module is generated using a chart tool, wherein the data module is related to the data type.

[0252] According to an implementation mode of the present application, when the system is started or the data is initially loaded, the above steps are performed on the city data to generate a grid model of the city data and the data to be displayed, and an initial display map is drawn using a map framework (such as Cesium) as a basic view to display a data overview of all key city data.

[0253] At the same time, for some specific data modules (such as traffic, air quality, population, etc.), the corresponding data to be displayed is obtained by performing more detailed data processing and analysis on the corresponding data. Afterwards, a detailed display diagram corresponding to each data module is generated using a chart tool (echart) to display the detailed data of these specific data modules. Specifically, specific data modules can be configured by the system according to the data type, such as determining each data module through a type identifier; or they can be determined according to user selection, and this application does not impose too many restrictions on this.

[0254] The present application does not limit the display method of each view, such as the aforementioned heat map, three-dimensional model, time series, point map, bar chart, etc. You can choose the appropriate interface layout, interactive elements, visual elements, etc. according to the application scenario and user needs.

[0255] In S470, the initial display image and each detail display image are output.

[0256] Specifically, the initial display image is used as the basic view, and various detail display images or interfaces of detail display images can be superimposed on it. In addition, the initial display image should also include an input interface to allow users to perform operations such as data query, view switching, and detail viewing.

[0257] Method 400 also includes: the visualization module 160 receives query conditions input by the user, the query conditions include one or more of time information, location information, and data types; the data processing module 150 filters out the data to be displayed according to the query conditions; the visualization module 160 draws a new detail display diagram based on the data to be displayed using a chart tool.

[0258] In addition, every time the data mapping module 140 updates the grid model, the visualization display module 160 will draw a new initial display diagram and a new detail display diagram based on the updated grid model.

[0259] According to the solution of this application, the urban space is divided based on the Beidou grid code, and the data type of the urban data and the location information of the data collection point are combined to map the urban data to grid units of different sizes, and corresponding fixed fields are set for different types of data in the grid units to store urban data, so as to ensure the accuracy and efficiency of the visualization of urban data. The solution of this application has the following advantages:

[0260] (1) Refined spatial analysis: The Beidou grid code is used to divide the urban space in detail, and each grid unit has a unique grid code, which enables refined spatial analysis and management of urban data;

[0261] (2) Improve data visualization accuracy: The accuracy of urban data visualization is improved through the precise mapping of Beidou grid codes. At the same time, dynamic mapping of urban data is achieved based on data types and application scenarios, making data display more accurate and intuitive.

[0262] (3) Support for multi-dimensional data display: Considering the multi-dimensional characteristics of urban data, different types of urban data are collected. At the same time, each piece of urban data contains timestamp, location information, and data type, so that the system can display multiple dimensions of urban data (such as time, space, category, etc.), fully display the complexity and diversity of urban data, and provide comprehensive information for user decision-making;

[0263] (4) Enhanced real-time performance: Once new urban data is obtained from the data source, the grid model can be updated, which can quickly respond to the real-time update of urban data and provide instant information for urban management and decision-making;

[0264] (5) Optimize user experience: The user interface is intuitive and easy to operate, so that non-professional users can easily query and analyze data;

[0265] In short, the solution of this application can provide important data support for the construction and management of smart cities, and help promote the modernization of urban management.

[0266] This application also discloses:

[0267] Optionally, the method of the present application also includes: receiving query conditions input by a user, the query conditions including one or more of time information, location information, and data type; filtering out data to be displayed according to the query conditions; and drawing a new detail display diagram based on the data to be displayed using a chart tool.

[0268] Optionally, after generating the grid model of the city data, the method further includes: when new city data is received, by determining the corresponding grid unit, mapping the new city data to the fixed field of the determined grid unit to update the grid model of the city data.

[0269] Optionally, the method of the present application further includes: drawing a new initial display diagram and a new detail display diagram based on the updated grid model.

[0270] Optionally, in the system of the present application, the data mapping module also includes: a real-time update submodule, which is suitable for, when new city data is received, mapping the new city data to the fixed field of the determined grid unit by determining the corresponding grid unit to update the grid model of the city data.

[0271] Optionally, in the system of the present application, the data mapping module is further adapted to set fixed fields for storing data content and location information according to data types.

[0272] Optionally, in the system of the present application, the visualization display module also includes a user interaction submodule, which is suitable for receiving query conditions input by a user, and the query conditions include one or more of time information, location information, and data type; the data processing module is also suitable for filtering the data to be displayed according to the query conditions.

[0273] Optionally, in the system of the present application, the data processing module is also suitable for detecting abnormal values ​​in the grid units and making corrections.

[0274] Optionally, a computing device comprises: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing the method described above.

[0275] Optionally, a computer-readable storage medium stores one or more programs, wherein the one or more programs include instructions, and when the instructions are executed by a computing device, the computing device performs the method as described above.

[0276] Optionally, a computer program product comprises a computer program / instruction, wherein the computer program / instruction implements the method described above when executed by a processor.

[0277] The various techniques described herein may be implemented in combination with hardware or software, or a combination thereof. Thus, the method and apparatus of the present application, or certain aspects or portions of the method and apparatus of the present application may be in the form of program codes (i.e., instructions) embedded in a tangible medium, such as a removable hard disk, a USB flash drive, a floppy disk, a CD-ROM, or any other machine-readable storage medium, wherein when the program is loaded into a machine such as a computer and executed by the machine, the machine becomes a device for practicing the present application.

[0278] In the case where the program code is executed on a programmable computer, the computing device generally includes a processor, a storage medium readable by the processor (including volatile and non-volatile memory and / or storage elements), at least one input device, and at least one output device. The memory is configured to store the program code; the processor is configured to execute the method of the present application according to the instructions in the program code stored in the memory.

[0279] By way of example and not limitation, readable media include readable storage media and communication media. Readable storage media stores information such as computer readable instructions, data structures, program modules or other data. Communication media generally embody computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transmission mechanism, and include any information transfer medium. Any combination of the above is also included within the scope of readable media.

[0280] In the description provided herein, algorithms and displays are not inherently related to any particular computer, virtual system or other device. Various general purpose systems can also be used together with the examples of the present application. According to the above description, it is obvious to construct the structure required for such systems. In addition, the present application is not directed to any specific programming language either. It should be understood that the content of the present application described herein can be realized by various programming languages, and the description of the specific language above is to disclose the preferred implementation of the present application.

[0281] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0282] Those skilled in the art will appreciate that the modules or units or components of the devices in the examples disclosed herein may be arranged in the devices described in the embodiment, or alternatively may be located in one or more devices different from the devices in the examples. The modules in the foregoing examples may be combined into one module or may be divided into multiple submodules.

[0283] Those skilled in the art will appreciate that the modules in the devices in the embodiments can be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments can be combined into one module or unit or component, and in addition they can be divided into multiple submodules or subunits or subassemblies. Except that at least some of such features and / or processes or units are mutually exclusive, all features disclosed in this specification and all processes or units of any method or device disclosed in this manner can be combined in any combination. Unless otherwise clearly stated, each feature disclosed in this specification can be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0284] Furthermore, those skilled in the art will appreciate that although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features from different embodiments is meant to be within the scope of the present application and to form different embodiments.

[0285] In addition, some of the embodiments are described herein as methods or combinations of method elements that can be implemented by a processor of a computer system or by other devices that perform the functions. Therefore, a processor with necessary instructions for implementing the method or method elements forms a device for implementing the method or method elements. In addition, the elements described herein of the device embodiments are examples of devices that are used to implement the functions performed by the elements for the purpose of implementing the disclosure.

[0286] As used herein, unless otherwise specified, the use of ordinal numbers "first," "second," "third," etc. to describe common objects merely refers to different instances of similar objects and is not intended to imply that the objects so described must have a given order in time, space, order, or in any other manner. In addition, the quantifier "plurality" means "two" and / or "more than two."

[0287] Although the present application is described according to a limited number of embodiments, it will be appreciated by those skilled in the art, with the benefit of the above description, that other embodiments may be envisioned within the scope of the present application thus described. In addition, it should be noted that the language used in this specification is selected primarily for the purpose of readability and teaching, rather than for the purpose of explaining or limiting the subject matter of the present application. Therefore, many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the appended claims. For the scope of the present application, the disclosure made to the present application is illustrative, not restrictive, and the scope of the present application is defined by the appended claims.

Claims

1. A method for visualizing urban data based on Beidou grid code, suitable for execution in a computing device, the method comprising: Acquire urban data from multiple data sources, the urban data including data types, location information of data collection points, and data content, the data types including: infrastructure data, energy and resource data, environmental monitoring data, social and economic data, transportation data, and safety and emergency data; Determining a grid level of the city data at least according to a data type of the city data; Determine the grid unit corresponding to the urban data in combination with the grid level and the location information of the data collection point, wherein the grid unit is a multi-level grid unit obtained by gridding the urban space based on the Beidou grid code, and grid units of different levels correspond to different grid sizes, and each grid unit has a unique grid code; Mapping the city data to the fixed fields of the determined grid cells to generate a grid model of the city data; Processing data of the same grid unit or multiple grid units to determine data to be displayed; Based on the grid model and the data to be displayed, an initial display diagram is drawn using a map framework, and a detailed display diagram corresponding to each data module is generated using a chart tool, wherein the data module is related to the data type; The initial display image and each of the detailed display images are output.

2. The method of claim 1, further comprising: The city data is preprocessed to obtain standardized city data.

3. The method of claim 2, wherein: The preprocessing of the city data comprises: If the data content of the city data includes image data, then De-noising and feature extraction are performed on the image to extract geographic feature data in the image and calculate the edges of the geographic features; Map the geographic feature data in the image to the geographic coordinate system to generate coordinate values ​​in a standard format; Convert the extracted geographic feature data and coordinate values ​​into standard format data as standardized city data; If the data content of the city data contains single-value data, then Perform data cleaning on each numerical data and calibrate its corresponding location information to obtain preprocessed numerical data and its calibrated geographic coordinates; Convert the preprocessed numerical data and their calibrated geographic coordinates into standard format data as standardized city data; If the data content of the city data contains time series data, then Perform data cleaning on each value in the time series data and calibrate its corresponding location information to obtain the time series data after preliminary processing and its calibrated geographic coordinates; Processing the time series data after preliminary processing to obtain processed time series data, wherein the processing includes at least one of the following: smoothing and difference; The processed time series data and their calibrated geographic coordinates are converted into standard format data as standardized city data.

4. The method of claim 3, wherein: The standard format is GeoJSON format.

5. The method according to any one of claims 1 to 4, wherein: The step of determining the grid level of the city data at least according to the data type of the city data comprises: Determining a base level of a grid cell according to the data type; Determine the scale factor based on the application scenario; Based on the base level and the scale factor, a final grid level is calculated.

6. The method of claim 5, wherein: The determining the basic level of the grid unit according to the data type includes: If the data type is transportation data, the basic level is the seventh-level grid or the eighth-level grid; If the data type is environmental monitoring data, the basic level is the sixth-level grid or the seventh-level grid; If the data type is social and economic data, energy and resource data, the basic level is the fifth-level grid or the sixth-level grid; If the data type is infrastructure data, safety and emergency data, the base level is the seventh-level grid or the eighth-level grid.

7. The method according to any one of claims 1 to 6, wherein: The determining the grid unit corresponding to the city data by combining the grid level and the location information of the data collection point includes: The grid unit to which the location information of the data collection point belongs is determined, and the level of the grid unit is consistent with the grid level.

8. The method according to any one of claims 1 to 7, wherein: The mapping of the city data to the fixed field of the determined grid unit includes: According to the data type of the city data, the location information and data content of the data collection point are written into the fixed field.

9. The method according to any one of claims 1 to 8, wherein: The processing of data of the same grid unit or multiple grid units includes: Aggregate multiple data in the same grid unit; Process the data of the same data type in multiple grid cells to calculate the corresponding indicator data; The time series model is used to process the time series data within the same grid unit or multiple grid units to analyze and predict trends.

10. Urban data visualization system based on Beidou grid code, including: A data collection module, adapted to obtain city data from multiple data sources, wherein the city data includes data types, location information of data collection points, and data content, wherein the data types include: infrastructure data, energy and resource data, environmental monitoring data, social and economic data, transportation data, and safety and emergency data; A data preprocessing module, suitable for preprocessing urban data to obtain standardized urban data; The Beidou grid coding module is suitable for using the Beidou grid code to grid the urban space, dividing the urban space into multi-level grid units, and different levels of grid units correspond to different grid sizes; and determining the grid code of each grid unit; A data mapping module, adapted to determine a grid level of the city data at least according to a data type of the city data; and determine a corresponding grid unit in combination with the grid level and location information of a data collection point, and map the city data to the determined grid unit to generate a grid model of the city data; A data processing module, adapted to process data of the same grid unit or multiple grid units to determine corresponding data to be displayed; The visualization display module is adapted to draw an initial display diagram based on the grid model and the data to be displayed using a map framework, and to generate a detailed display diagram corresponding to each data module using a chart tool, wherein The data module is related to the data type; the visual display module is also suitable for outputting the initial display diagram and each of the detailed display diagrams.

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