CN112395101A - Big data fast rendering method based on bidirectional data processing mechanism - Google Patents
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Abstract
The invention provides a big data fast rendering method based on a bidirectional data processing mechanism, which has the core that mouse operation is monitored in real time during the parallel execution of rendering data generation and a rendering process, a rendering parameter set and a rendering data set are updated in real time, and cache data are screened by using an N-LRU mechanism according to a node layer according to a current rendering level, a query index and point cloud visibility of different levels while the interactive loading of internal and external memory data is carried out according to a current rendering parameter table; and storing the screened data into a bidirectional rendering data buffer queue, and continuously reading a current rendering parameter set and a current rendering data set to be rendered from the bidirectional rendering data buffer queue by a rendering thread. The bidirectional data mechanism is created, the efficiency of interaction between internal and external memories of big data is improved, cache data in the memories are refreshed and removed in real time, the pressure of the memories is reduced, and the overall situation and the details of point cloud data are displayed reasonably and efficiently in combination with the rendering parameter set.
Description
Technical Field
The invention belongs to the technical field of laser point cloud data processing, and particularly relates to a big data fast rendering method based on a bidirectional data processing mechanism.
Background
At present, the application of laser radars in various industries is gradually mature, and the amount of generated laser point cloud data is increased rapidly, so that the subsequent processing and analysis of the laser point cloud data put higher requirements on the loading and rendering of mass data of a platform. The storage of the spatial data is the basis for effective management, and compared with the two-dimensional data, the storage of the three-dimensional irregular data is more complex and needs to consider more factors. At present, mass laser point cloud data is less than 1 hundred million points and more than one billion points, traditional data loading based on a memory cannot meet requirements, an internal and external memory interaction mode and a memory mapping mechanism are adopted in the industry, but the internal and external memory interaction effect is greatly different according to different external memory files. The data load is usually increased, resulting in a somewhat reduced rendering smoothness.
In summary, in consideration of the wide application of the mass laser point cloud data post-processing in multiple industries and the rapid increase of mass data, in order to simultaneously satisfy the high efficiency of loading and rendering of large data, it is necessary to increase the speed of loading the large data, reduce the memory pressure, and increase the rendering speed. Separate data loading and rendering mechanisms need to be employed to address this problem. The invention provides a method and a system for fast rendering point cloud big data by a separated bidirectional data processing mechanism, which can relieve the contradiction between big data loading and fast rendering to a certain extent. The problem of large data loading and rendering is solved, the efficiency of point cloud post-processing is improved to a great extent, the application value of laser point cloud data in various industries is improved, additional effects are brought, and the output of derivative products is expanded.
Disclosure of Invention
In view of this, the present invention provides a method for fast rendering big data based on a bidirectional data processing mechanism, so as to solve the problem of pressure on memory capacity by big data loading, solve the problem of sacrificing rendering time response to obtain a larger data loading capacity, and improve rendering efficiency while fast loading big data.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
a big data fast rendering method based on a bidirectional data processing mechanism comprises the following steps:
s1, decomposing the file by using the self-defined big data and generating a rendering parameter set by combining with an actual pixel size factor;
s2, according to the current rendering parameter set, performing internal and external memory interaction according to the node layer in the user-defined big data decomposition file by using an N-LRU mechanism, and generating a rendering data set;
s3, storing the rendering parameter set and the rendering data set into a bidirectional rendering data buffer queue;
s4, calling a rendering parameter set and a rendering data set in the bidirectional rendering data cache queue in the rendering process, and rendering;
and S5, monitoring mouse operation in real time in the running process of producing the rendering parameter set, the rendering data set and the rendering process and executing the rendering, and updating the rendering parameter set and the rendering data set in real time.
Further, the rendering parameter set in step S1 includes the current LOD level, the level already displayed in the current rendering state, the level visible in the current rendering state, and the remaining points to be loaded of the current level.
Further, the rendering data set in step S1 is point cloud data that needs to be displayed on the current active frame.
Further, the process of generating the rendering parameter set in step S1 is as follows:
calculating the base number of the currently rendered LOD level according to the actual pixel size factor, and calculating the total currently rendered LOD level by combining an adaptive threshold;
adding the parameters of the rendering layer of the current frame into the rendering parameter set of the current frame, and sequentially storing the parameters in the rendering parameter queue to form the current rendering parameter set.
Further, the N-LRU mechanism in step S2 is to use the Node as the KEY, and if the current Node is not used in the latest rendering, the current Node is considered to be the Node data that is not used in the current rendering, and after the new Node data is loaded later, the Node data will be eliminated when the cache queue is full of nodes, which reduces the pressure of the cache data on the memory to a certain extent, that is, updates the current rendering cache data, and improves the efficiency of data loading to a certain extent.
Further, the process of retrieving the rendering parameter set and the rendering data set in the bidirectional rendering data buffer queue by the rendering process in step S4 is as follows:
obtaining a rendering interface, judging whether to draw a 3D view, if not, updating and resetting the legend, if so, calling a rendering data set in a bidirectional rendering data cache queue, judging whether the flag bit of the current bounding box changes, if so, loading point cloud data from external data, analyzing an integral parameter table of the data, and updating the bounding box; judging the state of the current LOD, and judging the data reading mode by the current LOD, and directly rendering the previous frame data or reading a rendering parameter set and a rendering data set cached in a bidirectional mechanism; and synchronously updating the rendering cache data for rendering.
Compared with the prior art, the method for fast rendering the big data based on the bidirectional data processing mechanism has the following advantages:
the invention adopts a separated bidirectional data mechanism, improves the efficiency of interaction between internal and external memories of big data, refreshes and rejects the cache data in the memory in real time, greatly reduces the cache pressure of the memory, and reasonably and efficiently displays the overall situation and the details of point cloud data by combining with the rendering parameter set. The bidirectional data processing mechanism can utilize software resources to the maximum extent, so that the time difference between the loading of rendering data and the real-time rendering is minimum, and the rendering efficiency is improved. The N-LRU mechanism based on the node layer improves the screening efficiency of the rendering data to a certain extent, and can meet the rendering requirements of the global property and the detailed property of the point cloud by considering the consistency of the point cloud data in the node layer.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and, together with the description, serve to explain the invention without limitation. In the drawings:
FIG. 1 is a flowchart of generating a rendering parameter set and a rendering data set according to an embodiment of the present invention;
FIG. 2 is a flowchart of a frame data rendering mechanism according to an embodiment of the present invention;
fig. 3 is a rendering data reading sub-flowchart according to an embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
In the description of the present invention, it is to be understood that the terms "central," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like are used in the orientation or positional relationship indicated in the drawings, which are merely for convenience in describing the invention and to simplify the description, and are not intended to indicate or imply that the referenced device or element must have a particular orientation, be constructed and operated in a particular orientation, and are therefore not to be construed as limiting the invention. Furthermore, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first," "second," etc. may explicitly or implicitly include one or more of that feature. In the description of the invention, the meaning of "a plurality" is two or more unless otherwise specified.
In the description of the invention, it is to be noted that, unless otherwise explicitly specified or limited, the terms "mounted", "connected" and "connected" are to be construed broadly, e.g. as being fixed or detachable or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the creation of the present invention can be understood by those of ordinary skill in the art through specific situations.
The invention will be described in detail with reference to the following embodiments with reference to the attached drawings.
As shown in fig. 1 to 2, a method for fast rendering of big data based on a bidirectional data processing mechanism includes the following steps:
s1, decomposing the file by using the self-defined big data and generating a rendering parameter set by combining with an actual pixel size factor;
s2, according to the current rendering parameter set, performing internal and external memory interaction according to the node layer in the user-defined big data decomposition file by using an N-LRU mechanism, and generating a rendering data set;
s3, storing the rendering parameter set and the rendering data set into a bidirectional rendering data buffer queue;
s4, calling a rendering parameter set and a rendering data set in the bidirectional rendering data cache queue in the rendering process, and rendering;
and S5, monitoring mouse operation in real time in the running process of producing the rendering parameter set, the rendering data set and the rendering process and executing the rendering, and updating the rendering parameter set and the rendering data set in real time.
The rendering parameter set in step S1 includes the current LOD level, the level already displayed in the current rendering state, the level visible in the current rendering state, and the number of points remaining to be loaded in the current level.
The rendering data set in step S1 is point cloud data that needs to be displayed on the current active window.
The process of generating the rendering parameter set in step S1 is as follows:
calculating the base number of the currently rendered LOD level according to the actual pixel size factor, and calculating the total currently rendered LOD level by combining an adaptive threshold;
adding the parameters of the rendering layer of the current frame into the rendering parameter set of the current frame, and sequentially storing the parameters in the rendering parameter queue to form the current rendering parameter set.
In the step S2, the N-LRU mechanism uses the Node as the KEY, and if the current Node is not used in the latest rendering, the Node is considered to be the Node data that is not used in the current rendering, and after the new Node data is loaded later, the Node is eliminated when the cache queue is full of nodes, which reduces the pressure of the cache data on the memory to a certain extent, that is, updates the current rendering cache data, and improves the efficiency of data loading to a certain extent.
The process of calling the rendering parameter set and the rendering data set in the bidirectional rendering data buffer queue in the step S4 is as follows:
obtaining a rendering interface, judging whether to draw a 3D view, if not, updating and resetting the legend, if so, calling a rendering data set in a bidirectional rendering data cache queue, judging whether the flag bit of the current bounding box changes, if so, loading point cloud data from external data, analyzing an integral parameter table of the data, and updating the bounding box; judging the state of the current LOD, and judging the data reading mode by the current LOD, and directly rendering the previous frame data or reading a rendering parameter set and a rendering data set cached in a bidirectional mechanism; and synchronously updating the rendering cache data for rendering.
The specific process is as follows:
the invention provides a method and a system for fast rendering point cloud big data based on a separated bidirectional data processing mechanism. And (the step is an integral operation process), carrying out parameter self-adaptive fine adjustment on the overall situation and the details according to a currently rendered parameter table, and screening the cached data of the data set by using an N-LRU mechanism according to the current rendering level, the query index and the point cloud visibility of different levels while carrying out interactive loading on the internal and external memory data. The screened data is stored in a bidirectional rendering data buffer queue, and a rendering thread continuously reads a current rendering parameter set and a current rendering data set to be rendered from the bidirectional rendering data buffer queue, specifically as follows:
1) and performing self-defined internal and external memory interaction on the decomposed data cache file, keeping original point cloud data and establishing an index for the point cloud data by combining an octree and an LOD mechanism. Screening the cache files by using the rendering parameters, improving the internal and external storage efficiency of big data loading, and reducing the cache pressure of the memory;
2) based on the N-LRU mechanism of the Node layer, the Node is taken as the KEY, if the current Node is not used in the latest rendering, the Node is considered as the Node data which is not commonly used in the current rendering, and after new Node data is loaded later, the Node data is eliminated when the cache queue is full of nodes, so that the pressure of the cache data on a memory is reduced to a certain extent, namely the current rendering cache data is updated. The method mainly comprises the steps of carrying out LRU processing in a cache on a node layer in a data structure constructed by the octree, and removing outdated data taking the node layer as a molecular layer in the cache. The point cloud data in the cache is continuously supplemented in the rendering thread in the bidirectional rendering data cache queue before rendering, so that the data loading and rendering efficiency is greatly improved.
As shown in fig. 1, the process of generating the rendering parameter set and the rendering data set mainly includes loading a self-defined big data decomposition file, reading point cloud data from the big data decomposition file, determining a length threshold of a cache queue, determining whether a 3D view has a mouse operation, and determining whether the number of the point cloud layers under the control of a current camera view cone has been read, to determine whether to generate a next frame of rendering parameter set and rendering data set, and storing the next frame of rendering parameter set and rendering data set into a bidirectional rendering data cache queue;
zwlas, on the basis of keeping original las data, adding public area data and data area data which are decomposed by octree. The public area is used for placing the data record length and the layer number of the Node nodes, and the data area is used for placing the data of each Node in sequence.
As shown in fig. 2, reading a frame of data in rendering mainly includes the following steps:
1) calculating a rendering parameter set of the current frame: calculating the base number of the currently rendered LOD levels according to the actual pixel size factor, calculating the total LOD levels of the current rendering, the loading levels of the current rendering state, the displayed levels in the current rendering state, the visible levels in the current rendering state and the number of points to be loaded in the current level, adding the parameters of the rendering layer of the current frame into the rendering parameter set of the current frame, and sequentially storing the parameters in a rendering parameter queue to form the current rendering parameter set.
2) Calculating a rendering data set of the current frame: a node-based LRU mechanism (N-LRU) is used to read the point cloud data to be rendered. The Node is used as the KEY, if the current Node is not used in the latest rendering, the Node is considered to be the Node data which is not commonly used in the current rendering, after new Node data is loaded later, the Node data is eliminated when the cache queue is full of nodes, the pressure of the cache data on a memory is reduced to a certain extent, namely the current rendering cache data is updated, and the data loading efficiency is improved to a certain extent. And acquiring the current point cloud according to the hierarchy, the display nodes and the node layer and storing the current point cloud into a cache subset. And sequentially performing an internal and external memory interactive data mechanism, and storing rendering data in the rendering layer of the current frame into a common buffer queue of the bidirectional data mechanism. Rendering data in the rendering layer of the current frame is defined as atomic-level data which are 34 unsigned bytes and describe the position and relevant attribute information of the point cloud data, and molecular-level data which are data subsets after a plurality of atomic-level data are stored.
3) Calculating a current rendering data cache end mark: whether the current layer has no residual points or not needs to be judged, the rendering level is smaller than the total level, the converted rendering level is smaller than the number of visible rendering layers, and the number of rendering layers is sequentially judged to be increased or stopped to read point data sequentially from the current layer.
The internal and external memory interaction data caching mechanism is a key process of caching big data into a data queue to be rendered in the separated bidirectional data processing mechanism, improves the internal and external memory data interaction efficiency, lowers the internal memory pressure, and meets the requirement of big data loading. In addition, another important process in the bidirectional data processing mechanism is how to quickly load the cache data set to be rendered in the rendering process, so that the large data loading and the data cache are completely separated. The key in a frame data rendering mechanism in the separated bidirectional data processing mechanism is to interact with a rendering parameter set and a rendering data set in a data loading and caching mechanism.
As shown in fig. 2, in the one-frame data rendering mechanism, rendering parameters and rendering data of a current frame are obtained mainly through a separate bidirectional data processing mechanism. The main process comprises the following steps:
1) acquiring a rendering interface: the rendering process is based on OPENGL rendering, and the validity of the current rendering interface needs to be judged;
2) acquiring a rendering mode: whether a 3D view or a 2D view is rendered. The 3D view mainly renders the point cloud data according to a selected rendering mode; the 2D view is mainly to update and reset the legend;
3) acquiring a latest bounding box: judging whether the flag bit of the current bounding box changes, if so, loading point cloud data from the external memory data, analyzing an integral parameter table of the data, and updating the bounding box;
4) acquiring a frame of rendering parameters and rendering data to be rendered of a common buffer queue of a bidirectional data mechanism: and judging the state of the current LOD so as to judge the data reading mode, and directly rendering the previous frame data or reading the rendering parameter set and the rendering data set cached in the bidirectional mechanism. And synchronously updating the rendering cache data for rendering.
As shown in fig. 3, the rendering data reading sub-process mainly includes the following steps:
1) acquiring the current total number of rendered layers and the LOD structure state: judging the state to be rendered in the rendering parameters and a rendering mark of a higher level;
2) acquiring the current layer number: initializing the state of a current rendering frame before rendering, and acquiring the current layer number;
3) updating the rendering environment and intervening in the current data caching thread: obtaining the current viewport and OPENGL matrices, calculating the visibility of the computer view cone and the reset LOD cell. Initializing a rendering starting point and adaptively calculating a total point cloud threshold value. Obtaining a rendering parameter set and a rendering data set of a current rendering frame; and analyzing and serializing the point cloud data set of the current rendering frame into a rendering data format.
4) Updating the rendering state: the main purpose is to perform state setting for drawing a point at the next level.
A difficulty in the split bi-directional data processing mechanism is the fast decoupling of the rendering parameter set and the rendering data set. When cache data are interacted with internal and external memory data, mouse operation needs to be monitored in real time to update a rendering parameter set and a rendering data set, and the rendering parameter set needs to be corrected in real time in combination with the current LOD state in the data rendering process. The rendering parameter set ensures the fluency and integrity of the current point cloud rendering.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the invention, so that any modifications, equivalents, improvements and the like, which are within the spirit and principle of the present invention, should be included in the scope of the present invention.
Claims (6)
1. A big data fast rendering method based on a bidirectional data processing mechanism is characterized by comprising the following steps:
s1, decomposing the file by using the self-defined big data and generating a rendering parameter set by combining with an actual pixel size factor;
s2, according to the current rendering parameter set, performing internal and external memory interaction according to the node layer in the user-defined big data decomposition file by using an N-LRU mechanism, and generating a rendering data set;
s3, storing the rendering parameter set and the rendering data set into a bidirectional rendering data buffer queue;
s4, calling a rendering parameter set and a rendering data set in the bidirectional rendering data cache queue in the rendering process, and rendering;
and S5, monitoring mouse operation in real time in the running process of producing the rendering parameter set, the rendering data set and the rendering process and executing the rendering, and updating the rendering parameter set and the rendering data set in real time.
2. The method for fast big data rendering based on the bidirectional data processing mechanism as claimed in claim 1, wherein: the rendering parameter set in step S1 includes the current LOD level, the level already displayed in the current rendering state, the level visible in the current rendering state, and the number of points remaining to be loaded in the current level.
3. The method for fast big data rendering based on the bidirectional data processing mechanism as claimed in claim 1, wherein: the rendering data set in step S1 is point cloud data that needs to be displayed on the current active window.
4. The method for fast big data rendering based on the bidirectional data processing mechanism as claimed in claim 1, wherein: the process of generating the rendering parameter set in step S1 is as follows:
calculating the base number of the currently rendered LOD level according to the actual pixel size factor, and calculating the total currently rendered LOD level by combining an adaptive threshold;
adding the parameters of the rendering layer of the current frame into the rendering parameter set of the current frame, and sequentially storing the parameters in the rendering parameter queue to form the current rendering parameter set.
5. The method for fast big data rendering based on the bidirectional data processing mechanism as claimed in claim 1, wherein: in the step S2, the LRU mechanism uses the Node as KEY, and if the current Node is not used in the latest rendering, the current Node is considered to be Node data that is not used in the current rendering, and after new Node data is loaded later, the current Node data is eliminated when the cache queue is full of nodes, which reduces the pressure of the cache data on the memory to a certain extent, that is, updates the current rendering cache data, and improves the efficiency of data loading to a certain extent.
6. The method for fast big data rendering based on the bidirectional data processing mechanism as claimed in claim 1, wherein: the process of calling the rendering parameter set and the rendering data set in the bidirectional rendering data buffer queue in the step S4 is as follows:
obtaining a rendering interface, judging whether to draw a 3D view, if not, updating and resetting the legend, if so, calling a rendering data set in a bidirectional rendering data cache queue, judging whether the flag bit of the current bounding box changes, if so, loading point cloud data from external data, analyzing an integral parameter table of the data, and updating the bounding box; judging the state of the current LOD, and judging the data reading mode by the current LOD, and directly rendering the previous frame data or reading a rendering parameter set and a rendering data set cached in a bidirectional mechanism; and synchronously updating the rendering cache data for rendering.
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