Thermodynamic diagram drawing method and device based on point cloud, electronic equipment and medium

Abstract

The invention relates to a thermodynamic diagram drawing method and device based on point cloud, equipment and a medium. The method comprises the steps of firstly obtaining a to-be-processed data set; converting data points in the to-be-processed data set into point cloud data; then respectively calculating a weight value corresponding to each point cloud data; the weight value of each point cloud data is mapped to preset color gradient, and a color parameter corresponding to each point cloud data is obtained; and finally, drawing a thermodynamic diagram based on the color parameter of each point cloud data. Therefore, in the face of a large-scale data set, gridding is not needed, the data is directly converted into the point cloud data, the processing efficiency can be improved, and the details of the obtained thermodynamic diagram are richer.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device, equipment and medium for drawing a thermal map based on a point cloud. Background Art

[0002] Heat maps can show the strength and change trend of data and play an important role in many fields. For example, in high-precision maps, the density and distribution of data such as current rainfall can be displayed through the generated heat map to intuitively understand the data.

[0003] In the related art, a layer can be pasted on the map to draw thermal information, or the original data can be gridded and a thermal map can be drawn based on the gridded weights.

[0004] However, when faced with large-scale data sets, related technologies are unable to display rich detailed information and have poor processing efficiency. Summary of the invention

[0005] In order to solve or partially solve the problems existing in the related technology, the present application provides a point cloud-based heat map drawing method, device, equipment and medium, which can provide richer detailed information while drawing heat maps to ensure drawing efficiency when facing large-scale data sets, thereby more accurately reflecting the characteristics of data distribution.

[0006] The first aspect of the present application provides a method for drawing a heat map based on a point cloud, comprising:

[0007] Get the data set to be processed;

[0008] Converting data points in the data set to be processed into point cloud data;

[0009] Calculate the weight value corresponding to each point cloud data respectively;

[0010] The weight value of each point cloud data is mapped to the preset color gradient to obtain the color parameter corresponding to each point cloud data;

[0011] Draw a heat map based on the color parameters of each point cloud data.

[0012] A second aspect of the present application provides a point cloud-based thermal map drawing device, comprising:

[0013] An acquisition module is used to obtain the data set to be processed;

[0014] A conversion module, used for converting data points in the data set to be processed into point cloud data;

[0015] The weight calculation module is used to calculate the weight value corresponding to each point cloud data;

[0016] A mapping module is used to map the weight value of each point cloud data to a preset color gradient to obtain the color parameter corresponding to each point cloud data;

[0017] A generation module is used to draw a heat map based on the color parameters of each point cloud data.

[0018] A third aspect of the present application provides an electronic device, including:

[0019] Processor; and

[0020] The memory stores executable codes thereon, and when the executable codes are executed by the processor, the processor is caused to execute the method as described above.

[0021] A fourth aspect of the present application provides a computer-readable storage medium having executable code stored thereon. When the executable code is executed by a processor of an electronic device, the processor is caused to execute the method as described above.

[0022] The technical solution provided by this application may have the following beneficial effects:

[0023] In the embodiment of the present application, firstly, the data set to be processed is obtained; then the data points in the data set to be processed are converted into point cloud data; then the weight value corresponding to each point cloud data is calculated respectively; the weight value of each point cloud data is mapped to the preset color gradient respectively to obtain the color parameter corresponding to each point cloud data; finally, a heat map is drawn based on the color parameter of each point cloud data. Therefore, when facing a large-scale data set, there is no need to grid, but directly convert the data points into point cloud data, which can improve the processing efficiency and obtain a more detailed heat map.

[0024] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The above and other objects, features and advantages of the present application will become more apparent by describing in more detail exemplary embodiments of the present application in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments of the present application.

[0026] Figure 1 It is a flow chart of a method for drawing a thermal map based on a point cloud shown in an embodiment of the present application;

[0027] Figure 2 It is a structural schematic diagram of a point cloud-based thermal map drawing device shown in an embodiment of the present application;

[0028] Figure 3It is a schematic diagram of the structure of an electronic device shown in an embodiment of the present application. DETAILED DESCRIPTION

[0029] The embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the 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 described herein. On the contrary, these embodiments are provided to make the present application more thorough and complete, and to fully convey the scope of the present application to those skilled in the art.

[0030] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0031] It should be understood that although the terms "first", "second", "third", etc. may be used in this application to describe various information, this information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of this application, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0032] The technical solution of the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0033] See also Figure 1 , Figure 1 It is a flow chart of a method for drawing a thermal map based on a point cloud shown in an embodiment of the present application.

[0034] The method in the embodiment of the present application may include:

[0035] S101, obtaining a data set to be processed;

[0036] S102, converting data points in the data set to be processed into point cloud data;

[0037] S103, respectively calculating the weight value corresponding to each point cloud data;

[0038] S104, mapping the weight value of each point cloud data to a preset color gradient to obtain a color parameter corresponding to each point cloud data;

[0039] S105. Draw a heat map based on the color parameters of each point cloud data.

[0040] In the related art, heat maps can explore data features and discover data patterns in large-scale data visualization scenarios. In the embodiments of the present application, it can be applied to various types of data, especially map data, and can also be biological data, community network data, etc.

[0041] The data set to be processed in the embodiment of the present application may be map data, and of course, may also be the above-mentioned biological data or community network data, etc., data corresponding to the scene for which a heat map needs to be drawn.

[0042] The data set to be processed may be obtained from a database, a cloud platform or other storage media.

[0043] After obtaining the data set to be processed, you can first perform data preprocessing on the data set, such as denoising, dimensionality reduction, normalization, etc.

[0044] The data set to be processed may include surface data, such as rainfall surface data, which can represent rainfall and the corresponding size coverage area. It can also be three-dimensional data, such as the distribution of pedestrian flow in a multi-storey shopping mall, the distribution of aircraft in a spatial area, etc.

[0045] After obtaining the data set to be processed, the embodiment of the present application converts it into point cloud data.

[0046] In one embodiment of the present application, taking the processing of three-dimensional data, such as the distribution of pedestrian flow in a multi-storey shopping mall as an example, a method for drawing a pedestrian flow point cloud distribution map of the shopping mall includes the following steps:

[0047] S1. Obtain a data set to be processed; the crowd flow data set can be obtained based on the monitoring device in the shopping mall, so as to obtain the crowd flow distribution information of each floor at a specific time;

[0048] S2. Convert the data points in the data set to be processed into point cloud data; abstract the obtained crowd flow distribution information into point cloud data, for example, abstract each person's head into a data point, and convert it into a crowd flow distribution point cloud;

[0049] S3, respectively calculating the weight value corresponding to each point cloud data; calculating the weight value of each point cloud data, for example, assigning a corresponding weight value to it according to the completeness of the human outline;

[0050] S4, mapping the weight value of each point cloud data to the preset color gradient, and obtaining the color parameter corresponding to each point cloud data;

[0051] S5. Draw a heat map based on the color parameters of each point cloud data, so as to obtain a three-dimensional point cloud distribution map of the shopping mall.

[0052] In the application, the surface data in the data set to be processed can be converted into point cloud data in a variety of ways. Among them, the converted point cloud data can be ordered or unordered, and the corresponding point cloud type can be selected according to the actual application scenario. Among them, ordered point cloud data can usually be arranged according to certain rules or in a specific order, such as from top to bottom, from left to right, which can be used in scenes that need to be arranged in pixel order in image processing. Unordered point cloud data can be a set of points that are not arranged in a specific order, and can be applied to scenes that only require feature extraction of point cloud registration.

[0053] In the application, the embodiment of the present application can be implemented in the browser of the autonomous driving vehicle, for example, a heat map of pedestrian flow or vehicle flow can be drawn. When drawing a heat map of vehicle flow, a collected data set to be processed can be obtained. The data set to be processed can be a massive data set obtained through a cloud server. The massive data set can be data points collected by multiple autonomous driving vehicles and uploaded to the server. The data points in the data set to be processed can be data points of surrounding vehicles collected by each autonomous driving vehicle. Each data point can correspond to a vehicle, and each data point corresponds to the coordinate information of the vertex, edge, face, and normal in the image.

[0054] After obtaining the massive data set, the data points in the massive data set can be converted into point cloud data, where the data points are surface data or three-dimensional data, and the point cloud data corresponding to each data point can be constructed, so as to realize the drawing of the heat map without meshing the original data, and to provide more detailed information, such as three-dimensional coordinates, color, timestamp, etc.

[0055] After the point cloud data is constructed, the weight value corresponding to each point cloud data can be calculated based on the data structure of the point cloud data. The weight value is used as the basic data for subsequent mapping. Then the weight value of each point cloud data can be mapped to the preset color gradient, and the color parameters corresponding to each point cloud data are obtained. Then the heat map is drawn based on the color parameters of each point cloud data. The thermal distribution of the vehicle is displayed in the browser.

[0056] Since this application does not add additional layers or grid the original data, the point cloud data can reflect more detailed information. Compared with related technologies, the conversion into point cloud data is more efficient, which can solve the contradiction between the amount of details and the speed of processing efficiency, and can provide more detailed information while ensuring processing efficiency.

[0057] The step of converting data points in the data set to be processed into point cloud data may include:

[0058] Acquire data point attribute information of each data point in the data set to be processed; create a point cloud data structure corresponding to each data point based on the attribute information; and establish point cloud data corresponding to each data point based on the data point data information and the point cloud data structure corresponding to each data point.

[0059] In an embodiment of the present application, the data set to be processed may include data point attribute information of each data point, and the data point attribute information may include at least one of position information and feature information. Among them, the feature information may be texture information, and each data point may be represented by a preset point cloud data structure. The data point data information of each data point may be written into the point cloud data structure. Among them, each parameter in the point cloud data structure may be set with a default value, and the information not included in the data point attribute information may be replaced with the default value. It is understandable that the conversion function or tool may be used to convert to point cloud data, wherein the point cloud data may only have two-dimensional data, and the data of other dimensions are represented by default values.

[0060] Of course, you can also use the interpolation reconstruction method, take the points in the surface data as sampling points, interpolate between the sampling points, generate dense point cloud data, and obtain accurate point cloud data after optimizing and denoising.

[0061] Another way, since the surface data may include multiple triangular faces, the surface data may be split into multiple minimum basic units such as triangles or polygons, and then each split triangle or polygon is sampled, such as sampling vertices, a certain number of boundary points, etc., and finally point cloud data is generated based on these sampling points. The point cloud data may include location information and possible attribute information, so as to obtain point cloud data that matches the data to be processed.

[0062] In the embodiment of the present application, as long as the surface data in the data set to be processed can be converted into point cloud data, for example, a data interface of a preset software can be called, and the software can convert the input data into point cloud data. The specific implementation process of the present application is not limited.

[0063] After conversion to point cloud data, in the embodiment of the present application, the weight value corresponding to each point cloud data can be calculated respectively. In the application, a preset function can be called to calculate the weight value of each point cloud data from the center to the edge of the surface data.

[0064] In the embodiment of the present application, the center to the edge of the surface data can correspond to a weight transition, such as a simple identification of the effect of the center to the boundary. Based on this rule, the weight value of each point cloud can be calculated, for example, the weight is calculated using a Gaussian kernel function or a polynomial kernel function. Among them, the weight of each point in the surface data can change according to its distance from the center. Generally, the closer the distance to the center point, the greater the weight, and the farther the distance from the center point, the smaller the weight. Based on this, a gradual display effect can be achieved.

[0065] After obtaining the weight value of each point cloud, color mapping can be performed. For example, the weight of each point cloud can be mapped to a color gradient, and a linear gradient or a nonlinear gradient can be used to present a transition effect when the weight changes.

[0066] After mapping to a color gradient, a heat map can be drawn.

[0067] In the application, for example, an OpenGL (Open Graphics Library) drawing tool or a layered point cloud rendering algorithm can be called to draw a heat map based on the color parameters of each point cloud data. For example, the primary color gradient of each point cloud point is drawn on the screen using an OpenGL graphics drawing tool. Of course, a layered point cloud rendering method can also be used to implement the drawing process.

[0068] It can be understood that points of the same color can be drawn in batches. Except for the different positions, the texture color is the same. Therefore, the drawing efficiency can be improved. Moreover, after conversion into point cloud data, clearer and more accurate boundaries can be provided. Therefore, compared with the gridding method of drawing surface data, it can be more accurate and clear.

[0069] The heat map may be a three-dimensional map, and the three-dimensional map may display more detailed information and may dynamically display the dynamic changes of the target displayed in the heat map based on the timestamp.

[0070] The embodiment of the present application can process the original data, that is, the data points in the data set to be processed, as point cloud data instead of performing grid processing. Compared with the grid processing method, it can retain more detailed information, can more accurately reflect the distribution characteristics of the data, and improve the efficiency and accuracy of drawing the heat map.

[0071] Corresponding to the aforementioned application function implementation method embodiment, the present application also provides a point cloud-based thermal map drawing device, electronic device and corresponding embodiments.

[0072] Figure 2 It is a structural schematic diagram of a point cloud-based thermal map drawing device shown in an embodiment of the present application.

[0073] A point cloud-based thermal map drawing device, comprising:

[0074] Acquisition module 1, used to acquire the data set to be processed;

[0075] Conversion module 2, used for converting data points in the data set to be processed into point cloud data;

[0076] The weight calculation module 3 is used to calculate the weight value corresponding to each point cloud data;

[0077] Mapping module 4, used to map the weight value of each point cloud data to a preset color gradient, and obtain the color parameter corresponding to each point cloud data;

[0078] The generating module 5 is used to draw a heat map based on the color parameters of each point cloud data.

[0079] Optionally, the conversion module 2 may include:

[0080] A data point attribute information acquisition submodule is used to acquire the data point attribute information of each data point in the data set to be processed;

[0081] A point cloud data structure creation submodule is used to create a point cloud data structure corresponding to each data point based on attribute information;

[0082] The point cloud data construction submodule is used to establish the point cloud data corresponding to each data point based on the data point data information corresponding to each data point and the point cloud data structure.

[0083] Optionally, the dataset to be processed is surface data.

[0084] Optionally, the weight calculation module 3 may include:

[0085] The weight calculation submodule is used to call the preset function to calculate the weight value of each point cloud data from the center to the edge of the surface data.

[0086] Optionally, before converting the data points in the data set to be processed into point cloud data, the device may further include:

[0087] The preprocessing module is used to preprocess the data set to be processed; wherein the preprocessing includes denoising, dimensionality reduction and / or normalization.

[0088] Optionally, the generating module 5 may include:

[0089] The drawing submodule is used to call the OpenGL drawing tool or the layered point cloud rendering algorithm to draw the heat map based on the color parameters of each point cloud data.

[0090] Optionally, the data set to be processed is map data, biological data, geographic data or social network data.

[0091] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated again here.

[0092] Figure 3 It is a schematic diagram of the structure of an electronic device shown in an embodiment of the present application.

[0093] See also Figure 3 , the electronic device 300 includes a memory 310 and a processor 320.

[0094] The processor 320 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0095] The memory 310 may include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage devices. Among them, ROM can store static data or instructions required by the processor 320 or other modules of the computer. The permanent storage device may be a readable and writable storage device. The permanent storage device may be a non-volatile storage device that does not lose the stored instructions and data even after the computer is powered off. In some embodiments, the permanent storage device uses a large-capacity storage device (such as a magnetic or optical disk, flash memory) as a permanent storage device. In some other embodiments, the permanent storage device may be a removable storage device (such as a floppy disk, optical drive). The system memory may be a readable and writable storage device or a volatile readable and writable storage device, such as a dynamic random access memory. The system memory may store some or all instructions and data required by the processor at run time. In addition, the memory 310 may include any combination of computer-readable storage media, including various types of semiconductor memory chips (such as DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and disks and / or optical disks may also be used. In some embodiments, the memory 310 may include a readable and / or writable removable storage device, such as a laser disc (CD), a read-only digital versatile disc (such as a DVD-ROM, a double-layer DVD-ROM), a read-only Blu-ray disc, an ultra-density optical disc, a flash memory card (such as an SD card, a mini SD card, a Micro-SD card, etc.), a magnetic floppy disk, etc. Computer-readable storage media do not include carrier waves and transient electronic signals transmitted wirelessly or wired.

[0096] The memory 310 stores executable codes, and when the executable codes are processed by the processor 320 , the processor 320 can execute part or all of the methods described above.

[0097] In addition, the method according to the present application may also be implemented as a computer program or a computer program product, which includes computer program code instructions for executing some or all of the steps in the above method of the present application.

[0098] Alternatively, the present application can also be implemented as a computer-readable storage medium (or non-transitory machine-readable storage medium or machine-readable storage medium) on which executable code (or computer program or computer instruction code) is stored. When the executable code (or computer program or computer instruction code) is executed by a processor of an electronic device (or server, etc.), the processor executes part or all of the steps of the above-mentioned method according to the present application.

[0099] The embodiments of the present application have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for drawing a thermal map based on point cloud, characterized in that: include: Get the data set to be processed; Converting data points in the data set to be processed into point cloud data; Calculate the weight value corresponding to each point cloud data respectively; The weight value of each point cloud data is mapped to the preset color gradient to obtain the color parameter corresponding to each point cloud data; Draw a heat map based on the color parameters of each point cloud data.

2. The method according to claim 1, characterized in that The step of converting the data points in the to-be-processed data set into point cloud data comprises: Obtaining data point attribute information of each data point in the data set to be processed; Creating a point cloud data structure corresponding to each data point based on the attribute information; Point cloud data corresponding to each data point is established based on the data point data information corresponding to each data point and the point cloud data structure.

3. The method according to claim 2, characterized in that The data set to be processed is surface data or three-dimensional data.

4. The method according to claim 3, characterized in that: The step of calculating the weight value corresponding to each point cloud data includes: Call the preset function to calculate the weight value of each point cloud data from the center to the edge of the surface data; Alternatively, a preset function is called to respectively calculate the weight value of each point cloud data from the center to the edge of the three-dimensional data.

5. The method according to claim 2, characterized in that: Before converting the data points in the to-be-processed data set into point cloud data, the method further includes: Preprocessing the data set to be processed; wherein the preprocessing includes one or more of denoising, dimensionality reduction and normalization.

6. The method according to claim 1, characterized in that Drawing a heat map based on the color parameters of each point cloud data includes: Call OpenGL drawing tools or layered point cloud rendering algorithms to draw heat maps based on the color parameters of each point cloud data.

7. The method according to any one of claims 1 to 6, characterized in that: The data set to be processed is map data, biological data, geographic data or social network data.

8. A point cloud-based thermal map drawing device, characterized in that: include: An acquisition module is used to obtain the data set to be processed; A conversion module, used for converting data points in the data set to be processed into point cloud data; The weight calculation module is used to calculate the weight value corresponding to each point cloud data; A mapping module is used to map the weight value of each point cloud data to a preset color gradient to obtain the color parameter corresponding to each point cloud data; A generation module is used to draw a heat map based on the color parameters of each point cloud data.

9. An electronic device, characterized in that: include: processor; as well as A memory having executable codes stored thereon, which, when executed by the processor, causes the processor to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having executable codes stored thereon, which, when executed by a processor of an electronic device, causes the processor to execute the method according to any one of claims 1 to 7.

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