Method, device and system for visualizing equipment distribution information and storage medium

Abstract

The embodiment of the invention provides a device distribution information visualization method, device and system and a storage medium. Comprising the steps of determining a geographic coordinate interval corresponding to a to-be-rendered region; determining equipment data included in the to-be-rendered area based on the geographic coordinate interval, wherein each equipment data comprises longitude and latitude of a geographic position where equipment in the to-be-rendered area is located, an address of the equipment under the current scaling scale and an address code of the equipment under the current scaling scale; constructing data points corresponding to each piece of equipment data, and selecting any number of data points as target data points; generating a multi-dimensional feature vector corresponding to each target data point; inputting each multi-dimensional feature vector into a classification model of a corresponding scaling scale to output a thermodynamic value category corresponding to each target data point so as to determine an equipment heat value corresponding to each target data point; and rendering the to-be-rendered area into a thermodynamic diagram based on the equipment heat value corresponding to each target data point so as to realize equipment distribution information visualization.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method, device, system and storage medium for visualizing device distribution information. Background Art

[0002] With the popularity of the Internet of Things and smart devices, front-end applications need to process and display massive amounts of device data. Traditional data processing and display methods face problems such as large data transmission volume, low processing efficiency, and poor user experience. Data visualization, especially the display of heat maps, is crucial for presenting complex data sets and density information.

[0003] Existing device location data preprocessing solutions are generally based on data interpolation and gridding. Under different devices and resolutions, massive amounts of data can cause page freezes and poor user experience. Under the rendering requirements of dynamic data, existing solutions do not perform well when handling real-time data updates. In the prior art, the degree of support for massive data computing and processing varies, and it relies on the hardware capabilities of a single user device, which can easily lead to problems such as slow rendering speed and poor experience. Among them, when processing irregular areas, important spatial relationships are easily lost. When using gridding processing methods, improper selection of grid size may lead to information loss, and grids that are too large or too small will affect the results. At the same time, the prior art will perform global gridding on the entire map to be rendered, which requires a large amount of calculation and slow rendering speed. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a method, device, system and storage medium for visualizing equipment distribution information, so as to solve the technical defect of the prior art that the accuracy of visualization results is lost due to the loss of important spatial relationships in the process of visualizing equipment distribution information.

[0005] In order to achieve the above-mentioned object, the first aspect of the present application provides a method for visualizing device distribution information, the method comprising:

[0006] Determine the geographic coordinate interval corresponding to the area to be rendered, wherein the geographic coordinate interval includes the longitude interval and latitude interval covered by the area to be rendered;

[0007] Determine the device data included in the area to be rendered based on the geographic coordinate interval, wherein each device data includes the longitude and latitude of the geographic location of the device in the area to be rendered, the address of the device at the current zoom scale, and the address code at the current zoom scale;

[0008] Construct data points corresponding to each device data, and select any number of data points as target data points;

[0009] Generate a multi-dimensional feature vector corresponding to each target data point;

[0010] Inputting the multidimensional feature vector corresponding to each target data point into a classification model of a corresponding scaling scale, so as to output the thermal value category corresponding to each target data point through the classification model;

[0011] Determine the equipment thermal value corresponding to each target data point based on each thermal value category;

[0012] Based on the device heat value corresponding to each target data point, the area to be rendered is rendered into a heat map to visualize the device distribution information.

[0013] In an embodiment of the present application, determining the device data included in the area to be rendered based on the geographic coordinate interval includes: obtaining devices whose longitude and latitude corresponding to the geographic location are within the geographic coordinate interval; determining all zoom scales included in the area to be rendered; obtaining the detailed address of each device; determining the address of each device at each zoom scale and the address code at each zoom scale based on the detailed address and all zoom scales; obtaining the device data included in the area to be rendered according to the device type of each device, the longitude and latitude of the geographic location, the address at each zoom scale and the address code at each zoom scale.

[0014] In an embodiment of the present application, the method further includes: for each data point, the data point is four-dimensional data including the longitude and latitude of the geographical location of the device, the address code of the device at the current zoom scale, and the number of devices included in the address code.

[0015] In an embodiment of the present application, generating a multidimensional feature vector corresponding to each target data point includes: for any target data point, determining the geographic coordinates corresponding to the longitude and latitude of the target data point as the center of the grid group; determining the side length of the grid group based on the zoom scale corresponding to the target data point and the preset map scale; constructing a first grid group for the target data point based on the grid group center and the grid group side length, wherein the first grid group includes a plurality of first sub-grids of the same specification; determining the distance between each remaining target data point and the target data point; determining the first sub-grid in which each remaining target data point is located based on the distance; for any first sub-grid, determining the number of devices corresponding to the first sub-grid based on the number of devices corresponding to the target data point in the first sub-grid; and sorting the number of devices corresponding to each first sub-grid based on a preset rule to generate a multidimensional feature vector corresponding to each target data point.

[0016] In an embodiment of the present application, determining the distance between each of the remaining target data points and the target data point includes determining according to formula (1):

[0017]

[0018] Among them, S is the distance, R is the radius of the earth, a=lat1-lat2 is the difference in radians of latitude between the target data point and any other target data point, b=lng1-lng2 is the difference in radians of longitude between the target data point and any other target data point, and (lng1, lat1) is the geographic coordinates corresponding to the longitude and latitude of the target data point.

[0019] In an embodiment of the present application, rendering the area to be rendered into a heat map based on the device heat value corresponding to each target data point to realize visualization of device distribution information includes: converting the area to be rendered into a second grid group to obtain a second sub-grid corresponding to each target data point, wherein the second grid group includes multiple second sub-grids of the same specification; for any second sub-grid, when there is a target data point in the second sub-grid, determining the grid assignment of the second sub-grid based on the device heat value corresponding to the target data point in the second sub-grid; for any second sub-grid, when there is no target data point in the second sub-grid, determining the grid assignment of the second sub-grid to be zero; generating a grid heat two-dimensional table for the area to be rendered based on the grid assignment of each second sub-grid; inputting the grid heat two-dimensional table into the Amap map framework, and rendering the heat map based on the Heat.js tool of the Amap map framework to realize visualization of device distribution information.

[0020] In an embodiment of the present application, for any second sub-grid, when there is a target data point in the second sub-grid, determining the grid assignment of the second sub-grid based on the device heat value corresponding to the target data point in the second sub-grid includes: for any second sub-grid, when there is any target data point in the second sub-grid, determining the device heat value corresponding to any target data point as the grid assignment of the second sub-grid; for any second sub-grid, when there are multiple target data points in the second sub-grid, determining the sum of the device heat values ​​corresponding to the multiple target data points as the grid assignment of the second sub-grid.

[0021] A second aspect of the present application provides a device for visualizing device distribution information, comprising:

[0022] a memory configured to store instructions;

[0023] The processor is configured to call the instructions from the memory and implement the above-mentioned method for visualizing device distribution information when executing the instructions.

[0024] A third aspect of the present application provides a visualization system, comprising the above-mentioned apparatus for visualizing equipment distribution information.

[0025] A fourth aspect of the present application provides a machine-readable storage medium having instructions stored thereon, which, when executed by a processor, configure the processor to execute the above-mentioned method for visualizing device distribution information.

[0026] The above technical solution determines the geographic coordinate interval corresponding to the area to be rendered; determines the device data included in the area to be rendered based on the geographic coordinate interval, each device data includes the longitude and latitude of the geographical location of the device in the area to be rendered, the address of the device at the current zoom scale, and the address code at the current zoom scale; constructs data points corresponding to each device data, and selects any number of data points as target data points; generates a multidimensional feature vector corresponding to each target data point; inputs each multidimensional feature vector into the classification model of the corresponding zoom scale to output the thermal value category corresponding to each target data point to determine the device heat value corresponding to each target data point; renders the area to be rendered into a heat map based on the device heat value corresponding to each target data point to realize the visualization of device distribution information. By capturing the correlation between adjacent grids, the target data points are made more representative, thereby effectively improving the accuracy of the visualization of device distribution information.

[0027] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present application, but do not constitute a limitation on the embodiments of the present application. In the accompanying drawings:

[0029] Figure 1 A schematic diagram of a process flow of a method for visualizing device distribution information according to an embodiment of the present application is schematically shown;

[0030] Figure 2 The internal structure diagram of a computer device according to an embodiment of the present application is schematically shown. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific implementation methods described herein are only used to illustrate and explain the embodiments of the present application, and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0032] In addition, if the descriptions such as "first" and "second" are involved in the embodiments of the present application, the descriptions of "first", "second", etc. are only for descriptive purposes and should not be construed as indicating or implying their relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions between various embodiments may be combined with each other, but it must be based on the fact that those of ordinary skill in the art can implement them. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present application.

[0033] Figure 1 Schematically shown is a flowchart of a method for visualizing device distribution information according to an embodiment of the present application. As Figure 1 shown, the embodiment of the present application provides a method for visualizing device distribution information, and the method may include the following steps.

[0034] Step 101, determine the geographical coordinate range corresponding to the area to be rendered, where the geographical coordinate range includes the longitude range and the latitude range covered by the area to be rendered.

[0035] In the embodiment of the present application, it should be noted that the area to be rendered may refer to the map range required to be displayed according to the actual needs of the user, and the geographical coordinate range may be the longitude range and the latitude range covered by the map range required to be displayed. After obtaining the location of the area to be rendered, the longitude range and the latitude range covered by the area to be rendered can be quickly extracted through tools such as the satellite positioning system, so as to obtain the geographical coordinate range corresponding to the area to be rendered.

[0036] Step 102, determine the device data included in the area to be rendered based on the geographical coordinate range, where each device data includes the longitude and latitude of the location of the device in the area to be rendered, the address of the device at the current zoom scale, and the address code at the current zoom scale.

[0037] In the embodiment of the present application, it should be noted that the zoom scale may refer to the zoom scale of the map, indicating the range and detail level of the map display. The larger the zoom value, the smaller the area displayed on the map, but the more detailed and accurate it is. On the contrary, the smaller the zoom value, the larger the area displayed on the map, but the more blurred it is. The address code may refer to the code, and the code is the code at the current zoom scale. For example, according to the current zoom value, it can be determined which specific scale of device data in the multi-scale device data is required. If 3 < zoom < 8, the city / county code is used. If 8 <= zoom < 13, the administrative region code is used. If zoom >= 13, the street code is used.

[0038] After obtaining the geographic coordinate interval corresponding to the area to be rendered, the device data at all zoom scales within the latitude and longitude range covered by the geographic coordinate interval can be further determined, and at the same time, the number of device data at all zoom scales can be obtained, for example, a total of N. It should be noted that each device data should include the latitude and longitude of the geographical location of the device in the area to be rendered, the address of the device at the current zoom, and the code at the current zoom.

[0039] In an embodiment of the present application, determining the device data included in the area to be rendered based on the geographic coordinate interval includes: obtaining devices whose longitude and latitude corresponding to the geographic location are within the geographic coordinate interval; determining all zoom scales included in the area to be rendered; obtaining the detailed address of each device; determining the address of each device at each zoom scale and the address code at each zoom scale based on the detailed address and all zoom scales; obtaining the device data included in the area to be rendered according to the device type of each device, the longitude and latitude of the geographic location, the address at each zoom scale and the address code at each zoom scale.

[0040] In this embodiment, it should be noted that after obtaining the longitude and latitude range covered by the area to be rendered, the device whose longitude and latitude corresponding to the geographical location is within the longitude and latitude range covered by the area to be rendered can be obtained first, and the detailed address of each device is obtained, and then all the required zooms are determined. In this way, the address of each device under each zoom and the code under each zoom can be further determined based on the detailed address of each device and all zooms. In this way, all device data included in the area to be rendered are obtained.

[0041] Step 103, constructing data points corresponding to each device data, and selecting any number of data points as target data points.

[0042] In the embodiment of the present application, it should be noted that after obtaining the device data of the area to be rendered at all scales, a data point corresponding to each device data can be constructed. Then, among the multiple data points obtained, any number of data points can be randomly selected as our target data points, wherein the random number selected should be less than the total number of multiple data points.

[0043] In the embodiment of the present application, for each data point, the data point is four-dimensional data including the longitude and latitude of the geographical location of the device, the address code of the device at the current zoom scale, and the number of devices included in the address code.

[0044] In this embodiment, it should be noted that each data point is four-dimensional data including the longitude and latitude of the device's geographic location, the address code of the device under the current zoom, and the number of devices included in the address code. Specifically, each data point is four-dimensional data including (longitude, latitude, code, num), where longitude is the longitude of the device's geographic location, latitude is the latitude of the device's geographic location, and num is the number of devices included in the code.

[0045] Step 104: Generate a multi-dimensional feature vector corresponding to each target data point.

[0046] In the embodiment of the present application, it should be noted that the multidimensional feature vector may include but is not limited to being set to a 25-dimensional feature vector. After constructing the data point corresponding to each target device data, a 25-dimensional feature vector corresponding to each target data point may be further generated.

[0047] In an embodiment of the present application, generating a multidimensional feature vector corresponding to each target data point includes: for any target data point, determining the geographic coordinates corresponding to the longitude and latitude of the target data point as the center of the grid group; determining the side length of the grid group based on the zoom scale corresponding to the target data point and the preset map scale; constructing a first grid group for the target data point based on the grid group center and the grid group side length, wherein the first grid group includes a plurality of first sub-grids of the same specification; determining the distance between each remaining target data point and the target data point; determining the first sub-grid in which each remaining target data point is located based on the distance; for any first sub-grid, determining the number of devices corresponding to the first sub-grid based on the number of devices corresponding to the target data point in the first sub-grid; and sorting the number of devices corresponding to each first sub-grid based on a preset rule to generate a multidimensional feature vector corresponding to each target data point.

[0048] In this embodiment, it should be noted that the multi-dimensional feature vector can include but is not limited to being set as a 25-dimensional feature vector. After constructing the data points corresponding to each target device data, first, for the longitude and latitude of each target data point, a 5*5 grid group composed of 25 square grids is constructed. Among them, the target data point falls at the center of the grid group, that is, the center point of the central 1*1 grid. The side length Smin of this grid group can be jointly determined according to the current zoom and the current map scale SE. Among them, the map scale SE will change with the current zoom. Smin represents the expected calculation distance at the current scale SE and zoom. When 3 < zoom < 8, Smin = SE; when 8 <= zoom < 13, Smin = SE / 2; when zoom >= 13, Smin = SE / 4. Further, after the grid group is constructed, the distance between each remaining target data point and this target data point can be calculated. From this, it can be obtained which specific grid in this 5*5 grid each remaining target data point falls into. Those outside the grid group are ignored, and the data points falling within the same 1*1 grid are grouped together. In this way, the number of devices corresponding to each 1*1 grid in the 5*5 grid group can be obtained, and these device numbers are sorted according to a fixed rule to obtain a 25-dimensional feature vector. In this way, the 25-dimensional feature vector corresponding to each target data point is obtained.

[0049] In the embodiment of the present application, determining the distance between each remaining target data point and the target data point includes determining according to formula (1):

[0050]

[0051] Where S is the distance, R is the radius of the earth, a = lat1 - lat2 is the radian difference in latitude between the target data point and any remaining target data point, b = lng1 - lng2 is the radian difference in longitude between the target data point and any remaining target data point, and (lng1, lat1) is the geographical coordinate corresponding to the longitude and latitude of the target data point.

[0052] In this embodiment, the distance between each remaining target data point and the target data point is calculated according to the above formula (1).

[0053] Step 105, input the multi-dimensional feature vector corresponding to each target data point into the classification model corresponding to the corresponding zoom scale, so as to output the heat value category corresponding to each target data point through the classification model.

[0054] In the embodiment of the present application, it should be noted that the present technical solution designs a neural network model, which includes a one-dimensional convolution layer, a maximum pooling layer, a fully connected layer, and an output layer. Specifically, the width of the input sample can be set to 5, the height can be set to 1, and the number of input channels can be 5 (i.e., the input data of the deep learning mode is 25-dimensional data). Convolution kernels are used for convolution calculations to extract high-dimensional features and capture the correlation between adjacent grids. Then, all channels are subjected to maximum pooling, and the output values ​​are connected as vectors to reduce feature latitudes, reduce the amount of calculation, retain the most important features, and prevent overfitting. The obtained vector is then passed through a fully connected layer to map the extracted features to the output space (the output is a 4-dimensional data, each dimension is the confidence corresponding to one of the four classes of 'blue', 'green', 'yellow', and 'red' corresponding to the data point. Softmax is used as the activation function, the final output is standardized to between 0 and 1, and the category corresponding to the maximum probability value is taken as the classification result for the final classification task, and categorical cross-entropy is used as the loss function of this model.

[0055] It should be noted that, since the technical solution involves multiple zoom scales, it is necessary to train multiple deep learning models to be suitable for heat value estimation at different zoom scales. For example, if the zoom scale is city / county, administrative district, street, etc., three models need to be trained. Specifically, a heat map of the number of devices generated by any map at a certain zoom scale can be generated by traditional methods, and then a corresponding training sample can be constructed for any coordinate point in the heat map, and its corresponding feature vector is converted into a corresponding 5*5 feature vector. The true value label is the classification result corresponding to the thermal value of the coordinate point obtained by traditional methods. Among them, the technical solution uses four types in the thermal distribution, namely 'blue', 'green', 'yellow', 'red', that is, blue, green, yellow and red. The thermal value thresholds corresponding to these four categories can be set to (0.3, 0.5, 0.7, 0.9) respectively. Therefore, during training, it is necessary to input the training samples into the neural network model to obtain the output value (a 4-dimensional data), calculate the classification cross entropy loss function between the output and the true value, and backpropagate to update the parameters until the training end condition is reached.

[0056] It should be noted that after obtaining the multidimensional feature vector corresponding to each target data point, each multidimensional feature vector is input into the classification model at the corresponding scale to output the thermal value category corresponding to each target data point through the classification model. Specifically, each 25-dimensional feature vector is input into the deep learning model of the corresponding scale to obtain multiple corresponding 4-dimensional output vectors. At this time, for each 4-dimensional output vector, the category corresponding to the highest dimension value in the 4-dimensional output vector, such as the 'green' category, is taken as the thermal value category corresponding to the corresponding target data point.

[0057] Step 106, determining the equipment thermal value corresponding to each target data point based on each thermal value category.

[0058] In the embodiment of the present application, it should be noted that for each thermal value category, there is a corresponding thermal value threshold. For example, the thermal value threshold corresponding to 'green' is 0.5. Therefore, after obtaining the thermal value category corresponding to each target data point, the thermal value threshold corresponding to each thermal value category can be used as the device thermal value of the corresponding target data point. Thus, the device thermal value corresponding to each target data point is obtained.

[0059] Step 107 , based on the device heat value corresponding to each target data point, the area to be rendered is rendered into a heat map to visualize the device distribution information.

[0060] In the embodiments of the present application, it should be noted that a heat map may refer to a visualization tool that uses color to express data density. A heat map presents data in a two-dimensional space in the form of hot spots of different colors to show the distribution and concentration of data, thereby helping to observe trends and patterns in the data. It represents the size of data values ​​by the depth or intensity of the color, making complex data more intuitive and easy to understand. Heat maps are usually displayed in a two-dimensional coordinate system, where the color of each cell represents the value of the point. Color gradients can range from cold colors (low values) to warm colors (high values), and commonly used color scales include red, green, blue, and the like. Specifically, after obtaining the device heat value corresponding to each target data point, the area to be rendered can be further rendered into a heat map based on the device heat value corresponding to each target data point, thereby visualizing the device distribution information.

[0061] In an embodiment of the present application, rendering the area to be rendered into a heat map based on the device heat value corresponding to each target data point to realize visualization of device distribution information includes: converting the area to be rendered into a second grid group to obtain a second sub-grid corresponding to each target data point, wherein the second grid group includes multiple second sub-grids with the same specifications; for any second sub-grid, when there is a target data point in the second sub-grid, determining the grid assignment of the second sub-grid based on the device heat value corresponding to the target data point in the second sub-grid; for any second sub-grid, when there is no target data point in the second sub-grid, determining the grid assignment of the second sub-grid to be zero; generating a grid heat two-dimensional table for the area to be rendered based on the grid assignment of each second sub-grid; inputting the grid heat two-dimensional table into the Amap map framework, and rendering the heat map based on the Heat.js tool of the Amap map framework to realize visualization of device distribution information.

[0062] In this embodiment, it should be noted that the Amap map framework may refer to a map development framework provided by Amap, which is mainly used to integrate map functions in Web and mobile applications. The Amap map framework provides a wealth of map components and functions, including map display, layer management, vector graphics drawing, point marking, map controls, etc. Heat.js is a lightweight JavaScript library specifically used to generate customizable heat maps, charts and statistics to visualize date-related activities and trends. It has the characteristics of zero dependency, small size and fast loading speed, and is suitable for various data visualization requirements. In this technical solution, after obtaining the device heat value corresponding to each target data point, the area to be rendered can be gridded, thereby dividing the subgrid where each target data point is located, wherein the grid size is customized. For any subgrid, if there is a target data point in the subgrid, the grid assignment of the subgrid can be determined based on the device heat value corresponding to the target data point in the subgrid. If there is no target data point in the subgrid, the grid assignment of the subgrid can be considered to be zero. By analogy, after calculating the grid value of each sub-grid, a two-dimensional grid heat table for the area to be rendered is generated based on the grid value of each sub-grid, and the two-dimensional grid heat table is input into the Amap map framework, and the heat map is rendered using the Heat.js tool based on the Amap map framework to visualize the equipment distribution information.

[0063] In an embodiment of the present application, for any second sub-grid, when there is a target data point in the second sub-grid, determining the grid assignment of the second sub-grid based on the device heat value corresponding to the target data point in the second sub-grid includes: for any second sub-grid, when there is any target data point in the second sub-grid, determining the device heat value corresponding to any target data point as the grid assignment of the second sub-grid; for any second sub-grid, when there are multiple target data points in the second sub-grid, determining the sum of the device heat values ​​corresponding to the multiple target data points as the grid assignment of the second sub-grid.

[0064] In this embodiment, it should be noted that for any sub-grid, if there is a target data point in the sub-grid, it is necessary to further calculate the number of target data points in the sub-grid. If there is one and only one target data point in the sub-grid, the device heat value of the target data point can be used as the grid assignment of the sub-grid. If there are multiple target data points in the sub-grid, the sum of the device heat values ​​of each target data point is used as the grid assignment of the sub-grid.

[0065] The above technical solution determines the geographic coordinate interval corresponding to the area to be rendered; determines the device data included in the area to be rendered based on the geographic coordinate interval, each device data includes the longitude and latitude of the geographical location of the device in the area to be rendered, the address of the device at the current zoom scale, and the address code at the current zoom scale; constructs data points corresponding to each device data, and selects any number of data points as target data points; generates a multidimensional feature vector corresponding to each target data point; inputs each multidimensional feature vector into the classification model of the corresponding zoom scale to output the thermal value category corresponding to each target data point to determine the device heat value corresponding to each target data point; renders the area to be rendered into a heat map based on the device heat value corresponding to each target data point to realize the visualization of device distribution information. By capturing the correlation between adjacent grids, the target data points are made more representative, thereby effectively improving the accuracy of the visualization of device distribution information.

[0066] The device distribution visualization method of this solution combines random sampling, multi-scale feature extraction and deep learning to achieve efficient data processing and feature extraction. Random sampling reduces the amount of input data for the interpolation method, significantly improving computational efficiency. The deep learning model is used to capture the correlation between adjacent grids, making the target data points more representative and reducing precision loss. At the same time, the device data of different scales corresponding to the regional encoding is dynamically selected according to the zoom level, and different feature receptive fields and specific deep learning models are used to enhance the computational adaptability to different scales and irregular areas. This solution improves the rendering speed of the device quantity heat map and improves the user experience.

[0067] The present application provides a device for visualizing device distribution information, including:

[0068] a memory configured to store instructions;

[0069] The processor is configured to call the instructions from the memory and implement the above-mentioned method for visualizing device distribution information when executing the instructions.

[0070] An embodiment of the present application provides a visualization system, including the above-mentioned apparatus for visualizing equipment distribution information.

[0071] The embodiment of the present application provides a machine-readable storage medium, on which instructions are stored, and when the instructions are executed by a processor, the processor is configured to execute the above-mentioned method for visualizing device distribution information.

[0072] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 2 As shown. The computer device includes a processor A01, a network interface A02, a memory (not shown in the figure) and a database (not shown in the figure) connected via a system bus. Among them, the processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an internal memory A03 and a non-volatile storage medium A04. The non-volatile storage medium A04 stores an operating system B01, a computer program B02 and a database (not shown in the figure). The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A04. The database of the computer device is used to store method data for visualization of device distribution information. The network interface A02 of the computer device is used to communicate with an external terminal through a network connection. When the computer program B02 is executed by the processor A01, a method for visualization of device distribution information is implemented.

[0073] Those skilled in the art will understand that Figure 2 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0074] An embodiment of the present application provides a device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, method steps for visualizing device distribution information are implemented.

[0075] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program for initializing the method steps for visualizing device distribution information.

[0076] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0077] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0078] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0079] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0080] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0081] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0082] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0083] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0084] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for visualizing equipment distribution information, characterized in that: The method comprises: Determine a geographic coordinate interval corresponding to the area to be rendered, wherein the geographic coordinate interval includes a longitude interval and a latitude interval covered by the area to be rendered; Determining device data included in the area to be rendered based on the geographic coordinate interval, wherein each device data includes the longitude and latitude of the geographical location of the device in the area to be rendered, the address of the device at the current zoom scale, and the address code at the current zoom scale; Construct data points corresponding to each device data, and select any number of data points as target data points; Generate a multi-dimensional feature vector corresponding to each target data point; Inputting the multidimensional feature vector corresponding to each target data point into a classification model of a corresponding scaling scale, so as to output the thermal value category corresponding to each target data point through the classification model; Determine the equipment thermal value corresponding to each target data point based on each thermal value category; The area to be rendered is rendered into a heat map based on the device heat value corresponding to each target data point to visualize the device distribution information.

2. The method for visualizing equipment distribution information according to claim 1, characterized in that: The device data included in the area to be rendered determined based on the geographic coordinate interval includes: Acquire a device whose latitude and longitude corresponding to the geographical location is within the geographical coordinate interval; Determine all scaling scales included in the area to be rendered; Get the detailed address of each device; Determine the address of each device at each zoom scale and the address code at each zoom scale based on the detailed address and the total zoom scale; The device data included in the to-be-rendered area is obtained according to the device type of each device, the longitude and latitude of the geographical location, the address at each zoom scale, and the address code at each zoom scale.

3. The method for visualizing equipment distribution information according to claim 1, characterized in that: The method further comprises: For each data point, the data point is four-dimensional data including the longitude and latitude of the geographical location of the device, the address code of the device at the current zoom scale, and the number of devices included in the address code.

4. The method for visualizing equipment distribution information according to claim 1, characterized in that: Generating a multidimensional feature vector corresponding to each target data point comprises: For any target data point, the geographical coordinates corresponding to the latitude and longitude of the target data point are determined as the center of the grid group; Determine the side length of the grid group based on the zoom scale corresponding to the target data point and a preset map scale; Constructing a first grid group for the target data point based on the grid group center and the grid group side length, wherein the first grid group includes a plurality of first sub-grids with the same specifications; determining the distance between each remaining target data point and the target data point; Determine the first subgrid where each remaining target data point is located based on the distance; For any first sub-grid, determine the number of devices corresponding to the first sub-grid based on the number of devices corresponding to the target data points in the first sub-grid; The number of devices corresponding to each first sub-grid is sorted based on a preset rule to generate a multi-dimensional feature vector corresponding to each target data point.

5. The method for visualizing equipment distribution information according to claim 4, characterized in that: Determining the distance between each of the remaining target data points and the target data point includes determining according to formula (1): Among them, S is the distance, R is the radius of the earth, a=lat1-lat2 is the difference in radians of latitude between the target data point and any other target data point, b=lng1-lng2 is the difference in radians of longitude between the target data point and any other target data point, and (lng1, lat1) are the geographic coordinates corresponding to the longitude and latitude of the target data point.

6. The method for visualizing equipment distribution information according to claim 1, characterized in that: The rendering of the area to be rendered into a heat map based on the device heat value corresponding to each target data point to realize the visualization of device distribution information includes: Converting the area to be rendered into a second grid group to obtain a second sub-grid corresponding to each target data point, wherein the second grid group includes a plurality of second sub-grids with the same specifications; For any second sub-grid, when there is a target data point in the second sub-grid, determining a grid value of the second sub-grid based on a device heat value corresponding to the target data point in the second sub-grid; For any second sub-grid, when there is no target data point in the second sub-grid, determining that the grid value of the second sub-grid is zero; Generate a two-dimensional grid heat table for the area to be rendered based on the grid value assignment of each second sub-grid; The grid heat two-dimensional table is input into the Amap map framework, and the heat map is rendered based on the Heat.js tool of the Amap map framework to realize the visualization of the equipment distribution information.

7. The method for visualizing equipment distribution information according to claim 6, characterized in that: For any second sub-grid, when there is a target data point in the second sub-grid, determining the grid value of the second sub-grid based on the device heat value corresponding to the target data point in the second sub-grid includes: For any second sub-grid, when any target data point exists in the second sub-grid, the device heat value corresponding to the any target data point is determined as the grid assignment of the second sub-grid; For any second sub-grid, when there are multiple target data points in the second sub-grid, the sum of the device heat values ​​corresponding to the multiple target data points is determined as the grid assignment of the second sub-grid.

8. A device for visualizing equipment distribution information, characterized in that: include: a memory configured to store instructions; A processor is configured to call the instructions from the memory and implement the method for visualizing equipment distribution information according to any one of claims 1 to 7 when executing the instructions.

9. A visualization system, characterized in that: It comprises the device for visualizing equipment distribution information according to claim 8.

10. A machine-readable storage medium having instructions stored thereon, characterized in that: When the instruction is executed by a processor, the processor is configured to perform the method for visualizing equipment distribution information according to any one of claims 1 to 7.

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