A real-time dynamic point cloud surveying data rapid processing method and real-time feedback system

Abstract

The application discloses a kind of real-time dynamic point cloud surveying and mapping data fast processing method and real-time feedback system, it is related to electric digital data processing technical field, the specific steps of this method are: signal conversion, feature construction, parallel processing, feedback interaction and parameter linkage;The application extracts the digital features of the spatial coordinates of point cloud and the collection time from electric digital signal, constructs the space-time correlation matrix based on the extracted digital features, relies on matrix traversal operation to complete noise filtering and redundant elimination operation in parallel synchronization in a traversal process, realizes the collaborative execution of the two operations in the preprocessing link, and configures preprocessing identification information for the generated effective point cloud electric digital data, the preprocessing operation type of point cloud data is labeled by identification combination, so that the effective point cloud electric digital data retains complete space-time feature information and processing trace information, realizes the collaborative development of point cloud data preprocessing and feature retention.

Description

Technical Field

[0001] This invention relates to the field of electronic digital data processing technology, specifically to a method for rapid processing of real-time dynamic point cloud mapping data and a real-time feedback system. Background Technology

[0002] Currently, spatial geographic information acquisition technology is developing rapidly. Real-time dynamic point cloud mapping technology, with its continuous data acquisition, has become an important technical means in the field of geographic information acquisition. It is widely used in mobile mapping, engineering site monitoring, 3D scene reconstruction and other fields. As various fields continue to increase their requirements for the efficiency and real-time performance of spatial geographic information acquisition, the processing of real-time dynamic point cloud mapping data has gradually become the key to the application of the technology. The industry's demand for the continuity and collaboration of mapping data processing is also constantly increasing.

[0003] However, existing technologies for noise filtering and redundancy removal of real-time dynamic point cloud mapping data are mostly performed in steps. They do not construct an association matrix based on the spatiotemporal digital characteristics of point clouds, nor do they achieve parallel and synchronous completion of the two types of preprocessing operations. Furthermore, they lack labeling and annotation of the preprocessed point cloud data, and the data processing and feedback interaction links are independent of each other. There is no unified signal transmission link to achieve communication between modules. The feedback link cannot standardize the processing of user interaction commands and convert them into control signals, nor can it adaptively adjust the processing parameters based on the commands. The operation of each link lacks coordination, which ultimately makes the processing flow of real-time dynamic point cloud mapping data an independent operation unit. It is impossible to achieve integrated connection between data preprocessing, feedback display, command interaction and parameter adjustment. The processing flow lacks complete closed-loop logic and is difficult to adapt to the continuity and coordination requirements of real-time dynamic point cloud mapping for data processing. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a method and system for rapid processing of real-time dynamic point cloud mapping data. This invention extracts the spatial coordinates and digital features of the acquisition time of point clouds from electrical digital signals, constructs a spatiotemporal correlation matrix based on the extracted digital features, and performs noise filtering and redundancy removal operations in parallel and synchronously during a single traversal based on matrix traversal operations. The preprocessing stage achieves the coordinated execution of the two types of operations, and at the same time, preprocessing identification information is configured for the generated valid point cloud electrical digital data. By marking the preprocessing operation type of the point cloud data through identification combination, the valid point cloud electrical digital data retains complete spatiotemporal feature information and processing traceability information, realizing the coordinated development of point cloud data preprocessing and feature retention.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: On one hand, a method for rapid processing of real-time dynamic point cloud mapping data, the specific steps of which are as follows: Signal conversion: The real-time dynamic point cloud analog signal is acquired by the data acquisition module, converted into an electrical digital signal, and then transmitted to the fast processing module through the electrical digital signal transmission link. Feature construction: The fast processing module receives the electrical digital signal, extracts the spatial coordinates and digital features of the acquisition time of the point cloud from the electrical digital signal, and constructs a spatiotemporal correlation matrix based on the extracted digital features; Parallel processing: The fast processing module performs matrix traversal operations based on the constructed spatiotemporal correlation matrix. During one matrix traversal, noise filtering and redundancy removal operations of real-time dynamic point cloud data are completed in parallel and synchronously to generate effective point cloud digital data. Feedback and Interaction: The effective point cloud electrical digital data is transmitted to the real-time feedback module through the electrical digital signal transmission link via the fast processing module. After receiving the effective point cloud electrical digital data, the real-time feedback module performs lightweight electrical digital rendering and displays relevant information, while also receiving semantic-level interactive commands input by the user. Parameter linkage: The real-time feedback module converts semantic-level interactive commands into electronic digital control signals and transmits them to the parameter linkage optimization module. The parameter linkage optimization module adaptively adjusts the processing parameters of the fast processing module according to the command type corresponding to the electronic digital control signal. The real-time dynamic point cloud simulation signal subsequently acquired by the data acquisition module completes the entire process according to the adjusted processing parameters.

[0006] Furthermore, in the signal conversion, the data acquisition module continuously acquires real-time dynamic point cloud information of the monitoring area through the acquisition unit to form a real-time dynamic point cloud analog signal. The acquisition unit includes a lidar acquisition device, a point cloud data acquisition card, and a signal preprocessing circuit. The conversion unit performs analog-to-digital conversion on the real-time dynamic point cloud analog signal to form a standardized electrical-digital signal. Then, the standardized electrical-digital signal is sent to the fast processing module via the electrical-digital signal transmission link through the transmission unit. The electrical-digital signal transmission link is a serial communication link or a wireless communication link. The electrical-digital signal is transmitted in the form of data packets in the electrical-digital signal transmission link. Each data packet contains the timing identifier and spatial location identifier of the point cloud acquisition.

[0007] Furthermore, in the feature construction, the fast processing module performs data parsing on the electrical digital signal, separating the spatial coordinate information and acquisition time information of the point cloud. It then extracts features from the separated spatial coordinate information and acquisition time information of the point cloud, introducing a spatiotemporal neighborhood correlation factor. Through the spatiotemporal correlation feature extraction formula, it quantifies and extracts the digital features of the spatial coordinates and acquisition time of the point cloud, forming a spatiotemporal fusion digital feature of the point cloud. Using the point cloud acquisition time sequence as the row dimension and the point cloud spatial coordinate sequence as the column dimension, the spatiotemporal fusion digital feature is filled into the corresponding positions in the matrix, completing the construction of the spatiotemporal correlation matrix. The elements in the spatiotemporal correlation matrix are the spatiotemporal fusion digital feature values ​​of the point cloud under the corresponding acquisition time and corresponding spatial coordinates. The spatiotemporal fusion digital feature values ​​of the point cloud are associated with the point cloud density value. The matrix dimension of the spatiotemporal correlation matrix is ​​dynamically adjusted according to the amount of real-time dynamic point cloud data acquired.

[0008] Furthermore, in the feature construction, the formula for extracting spatiotemporal correlation features is: ,in, For the spatiotemporal fusion digital feature values ​​of point clouds, For spatial feature weights, For time feature weights, All are determined based on the distribution characteristics analysis of point cloud spatiotemporal feature data corresponding to electrical digital signals. These are the spatial coordinate digital feature values ​​of the point cloud, obtained after standardizing the spatial coordinate information of the point cloud. The digital feature value representing the acquisition time of the point cloud is obtained by processing the acquisition time information of the point cloud through time series normalization encoding. The spatiotemporal neighborhood correlation factor of the point cloud is determined by the ratio of the distribution density of the point cloud in the spatiotemporal neighborhood of the feature point cloud to be extracted to the overall point cloud distribution density.

[0009] Furthermore, in the parallel processing, the fast processing module performs a row-by-row, column-by-column matrix traversal operation on the spatiotemporal correlation matrix. During the traversal, it extracts the spatiotemporal fusion digital feature value of the point cloud corresponding to each element in the matrix. Noise and redundancy are simultaneously determined on the extracted spatiotemporal fusion digital feature value of the point cloud. A redundancy correlation coefficient is introduced, and a comprehensive quantitative determination of noise and redundancy in the point cloud data is completed through a dual determination formula for point cloud data. This completes the noise filtering and redundancy removal operations for the corresponding point cloud data. The determined and retained point cloud data are integrated to generate valid point cloud electrical digital data. The valid point cloud electrical digital data includes digital feature information of the spatial coordinates and acquisition time of the point cloud, as well as preprocessing identification information. The preprocessing identification information is a combination of noise filtering operation identification and redundancy removal operation identification performed on the point cloud data, used to mark the specific operation type of noise filtering and redundancy removal performed on the point cloud data.

[0010] Furthermore, in the parallel processing, the dual-determination formula for point cloud data is: ,in, This is the comprehensive judgment value of point cloud data. The spatiotemporal fusion digital feature values ​​of the point cloud to be determined. The average value of the spatiotemporal fused digital features within the spatiotemporal neighborhood of the point cloud to be determined. The standard deviation of the spatiotemporal fusion digital features within the spatiotemporal neighborhood of the point cloud to be determined. All are determined by matrix elements in the spatiotemporal correlation matrix representing the neighborhood range of the point cloud to be determined. The redundancy correlation coefficient of the point cloud is determined by a comprehensive analysis of the spatial coordinate repetition and temporal series similarity between the point cloud to be judged and its neighboring point clouds. The number of feature overlaps between the point cloud to be determined and its neighboring point clouds. This represents the total number of point clouds within the spatiotemporal neighborhood of the point cloud to be determined. The comprehensive judgment value of the point cloud data is compared with the comprehensive judgment threshold of the point cloud data, and a judgment operation is performed: when When the point cloud data is classified as a noisy or redundant point cloud, corresponding filtering or removal operations are performed; When this happens, the point cloud data is determined to be a valid point cloud and is retained.

[0011] Furthermore, in the feedback interaction, the real-time feedback module unpacks the received valid point cloud digital data, extracting core spatial feature data composed of the spatial coordinates of the point cloud, digital features of the acquisition time, and spatiotemporal fusion digital feature values, as well as processing information composed of current processing parameters, preprocessing identification information, and data processing accuracy indicators. The current processing parameters consist of the currently configured point cloud data comprehensive judgment threshold and spatiotemporal correlation matrix construction parameters. The preprocessing identification information consists of noise filtering operation identifiers and redundancy removal operation identifiers. The data processing accuracy indicators consist of the proportion of valid point cloud data and the noise point cloud removal rate. The rendering unit then performs lightweight digital rendering of the unpacked data using a layered rendering method. The system generates visualized point cloud processing content; this visualized content is then displayed through an interactive unit, which simultaneously receives semantic-level interactive commands input by the user through the interactive unit and standardizes these commands. The interactive unit includes a touch input component, a voice recognition component, and a visualization display component. The semantic-level interactive commands include parameter adjustment commands and anomaly annotation commands. The parameter adjustment commands are generated based on the user's adjustment needs for the comprehensive judgment threshold and spatiotemporal correlation matrix construction parameters of the point cloud data. The anomaly annotation commands are generated based on the user's annotation needs for anomaly areas in the point cloud processing. The anomaly annotation commands contain spatiotemporal coordinate information composed of the spatial coordinates of the point cloud within the corresponding anomaly area and the digital features of the acquisition time.

[0012] Furthermore, in the parameter linkage, the processing parameters of the fast processing module include the point cloud data comprehensive judgment threshold and the spatiotemporal correlation matrix construction parameters; the parameter linkage optimization module has a built-in instruction parameter mapping table and parameter adjustment unit. The parameter linkage optimization module matches the corresponding adjustment relationship from the instruction parameter mapping table according to the electronic digital control signal, introduces the user instruction priority weight and the proportion of spatiotemporal features of abnormal areas, and completes the adjustment of the processing parameters through the adaptive adjustment formula of the processing parameters. The adjusted processing parameters are stored in the parameter configuration unit of the fast processing module.

[0013] Furthermore, in the parameter linkage, the adaptive adjustment formula for the processing parameters is as follows: ,in, These are the adjusted processing parameter values. These are the processing parameter values ​​before adjustment. User command priority weights For instruction parsing coefficients, All are determined through the parsing of standardized semantic-level interactive commands using digital encoding. The average comprehensive judgment value of point cloud data in the abnormal area is marked for the user. The proportion of spatiotemporal features of the point cloud within the anomalous region is determined by the ratio of the sum of the spatiotemporal fused digital feature values ​​of the point cloud within the anomalous region to the sum of the spatiotemporal fused digital feature values ​​of the entire point cloud. The average spatiotemporal neighborhood correlation factor of the point cloud within the abnormal region is obtained by averaging the spatiotemporal neighborhood correlation factors within the abnormal region.

[0014] On the other hand, a real-time dynamic point cloud mapping data rapid processing and real-time feedback system is provided, which includes: a data acquisition module, a rapid processing module, a real-time feedback module, and a parameter linkage optimization module. The data acquisition module acquires real-time dynamic point cloud simulation signals, converts them into digital signals, and transmits them to the fast processing module. The fast processing module receives the digital signals, extracts the spatial coordinates and digital features of the point cloud and the acquisition time from them, constructs a spatiotemporal correlation matrix, performs matrix traversal operations based on the matrix, and performs noise filtering and redundancy removal on the real-time dynamic point cloud data in parallel and synchronously to generate valid point cloud digital data. This valid point cloud digital data is then pushed to the real-time feedback module. The module can also receive adjusted processing parameters from the parameter linkage optimization module. The real-time feedback module receives the valid point cloud digital data, performs lightweight digital rendering and visualization on it, receives semantic-level interactive commands from the user, converts these commands into digital control signals, and transmits them to the parameter linkage optimization module. The parameter linkage optimization module receives the digital control signals, adaptively adjusts the processing parameters of the fast processing module according to the command type corresponding to the digital control signals, and transmits the adjusted processing parameters back to the fast processing module.

[0015] Compared with existing technologies, this method for rapid processing of real-time dynamic point cloud mapping data and its real-time feedback system have the following advantages: I. This invention extracts the spatial coordinates and digital features of the acquisition time of point clouds from electrical digital signals, constructs a spatiotemporal correlation matrix based on the extracted digital features, and performs noise filtering and redundancy removal operations in parallel and synchronously during a single traversal by relying on matrix traversal operations. The preprocessing stage achieves the coordinated execution of the two types of operations, and at the same time, configures preprocessing identification information for the generated valid point cloud electrical digital data. By marking the preprocessing operation type of the point cloud data through identification combination, the valid point cloud electrical digital data retains complete spatiotemporal feature information and processing traceability information, realizing the coordinated development of point cloud data preprocessing and feature retention.

[0016] II. This invention constructs a system consisting of data acquisition, rapid processing, real-time feedback, and parameter linkage optimization modules. The modules establish communication connections through an electro-digital signal transmission link, forming a complete signal transmission and data interaction link. The rapid processing module pushes effective point cloud electro-digital data to the real-time feedback module. The feedback module unpacks the data and performs lightweight electro-digital rendering for display. Simultaneously, it receives and standardizes the user's semantic-level interaction commands, converts the commands into electro-digital control signals, and transmits them to the parameter linkage optimization module. The module then adaptively adjusts the processing parameters based on the command matching and adjustment relationship. Subsequent point cloud data is processed in the adjusted manner to complete the entire process, realizing a closed-loop linkage of data processing, feedback display, command interaction, and parameter adjustment.

[0017] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0019] Figure 1 A flowchart of a method for rapid processing of real-time dynamic point cloud mapping data; Figure 2 This is a framework diagram of a real-time dynamic point cloud mapping data rapid processing and real-time feedback system. Figure 3 This is a framework diagram of parallel processing in a method for rapid processing of real-time dynamic point cloud mapping data. Detailed Implementation

[0020] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0021] Example: In the preliminary stage of road reconstruction and expansion projects in the core urban area, a customized vehicle-mounted real-time dynamic point cloud mapping data rapid processing and real-time feedback system is used to carry out operations. The mapping scope covers the main roads, secondary roads and surrounding side streets and alleys in the core urban area. The mapping vehicle travels at a constant speed along the planned mapping route and needs to accurately collect full-dimensional point cloud data of road surface, traffic facilities and surrounding buildings and structures. This provides high-precision geographic information support for the 3D modeling and scheme design of the road reconstruction and expansion project. The system realizes real-time acquisition, rapid processing, visual feedback of effects and dynamic adaptive adjustment of processing parameters throughout the process, adapting to the dynamic and complex needs of urban road mapping.

[0022] Signal Conversion: The surveying vehicle travels at a constant speed along the planned surveying route. The data acquisition module inside the rooftop surveying compartment continuously collects point cloud information from targets such as the road surface, streetlights, traffic signs, guardrails, and bus stops. The acquisition unit consists of a lidar acquisition device, a point cloud data acquisition card, and a signal preprocessing circuit. During the acquisition process, it effectively avoids temporary obstructions from pedestrians and other vehicles. After acquisition, a continuous, real-time dynamic point cloud simulation signal is generated, such as... Figure 1 As shown; subsequently, the data acquisition module performs analog-to-digital conversion on the real-time dynamic point cloud analog signal through the conversion unit, forming a standardized electrical digital signal, allowing point cloud data from different acquisition periods and locations to form a unified signal standard; then, the standardized electrical digital signal is transmitted to the in-vehicle operating terminal's rapid processing module via a wireless communication link through the transmission unit, such as... Figure 2 As shown, the electronic digital signal is transmitted stably in the link in the form of data packets. Each data packet contains the timing identifier and spatial location identifier of the point cloud acquisition, which can realize lossless and high-fidelity transmission of the acquired signal, so that subsequent data analysis can accurately correspond to the specific surveyed road section and acquisition time.

[0023] Feature Construction: The in-vehicle operating terminal's rapid processing module receives the transmitted electrical digital signals in real time. Considering the dynamic variation in point cloud data collection volume across different urban road sections with varying facility density, the electrical digital signals are first refined to accurately separate the spatial coordinates and acquisition time information of the point cloud. Then, feature extraction is performed on these two types of basic information. During the extraction process, a spatiotemporal neighborhood correlation factor is introduced. The spatiotemporal correlation feature extraction formula is used to quantify and extract the digital features of the point cloud's spatial coordinates and acquisition time, forming a spatiotemporal fusion digital feature of the point cloud that accurately represents the attributes of the surveyed target. The spatiotemporal correlation feature extraction formula is as follows: ,in, For the spatiotemporal fusion digital feature values ​​of point clouds, For spatial feature weights, For time feature weights, All are determined based on the distribution characteristics analysis of point cloud spatiotemporal feature data corresponding to electrical digital signals. These are the spatial coordinate digital feature values ​​of the point cloud, obtained after standardizing the spatial coordinate information of the point cloud. The digital feature value representing the acquisition time of the point cloud is obtained by processing the acquisition time information of the point cloud through time series normalization encoding. The spatiotemporal neighborhood correlation factor of the point cloud is determined by the ratio of the distribution density of the point cloud within the spatiotemporal neighborhood of the feature point cloud to the overall point cloud distribution density. Then, using the point cloud acquisition time series as the row dimension and the point cloud spatial coordinate sequence as the column dimension, the spatiotemporal fusion digital features are systematically filled into the corresponding positions of the matrix to complete the construction of the spatiotemporal correlation matrix. The elements in this spatiotemporal correlation matrix are the spatiotemporal fusion digital feature values ​​of the point cloud at the corresponding acquisition time and corresponding spatial coordinates. The spatiotemporal fusion digital feature values ​​of the point cloud are associated with the point cloud reflection intensity value. The matrix dimension of the spatiotemporal correlation matrix can be flexibly and dynamically adjusted according to the amount of real-time dynamic point cloud data collected during vehicle movement, so that the matrix construction always adapts to the real-time requirements of mobile mapping, effectively improving the accuracy of point cloud feature extraction and the scene adaptability of matrix construction.

[0024] Parallel processing: The fast processing module performs row-by-row and column-by-column matrix traversal operations based on the constructed spatiotemporal correlation matrix. During a single traversal, it simultaneously extracts the spatiotemporal fusion digital feature values ​​of the point cloud corresponding to each element in the matrix. Addressing the industry pain points of noisy point clouds caused by pedestrians and vehicles in urban road mapping, and redundant point clouds generated by continuous acquisition, the module simultaneously performs noise and redundancy assessments on the extracted feature values. A redundancy correlation coefficient is introduced during the assessment process, and a dual-assessment formula for point cloud data is used to comprehensively quantify and assess noise and redundancy in the point cloud data. This accurately distinguishes between valid point clouds and various types of interfering point clouds. Based on the assessment results, noise filtering and redundancy removal operations are then performed on the corresponding point cloud data. The dual-assessment formula for point cloud data is as follows: ,in, This is the comprehensive judgment value of point cloud data. The spatiotemporal fusion digital feature values ​​of the point cloud to be determined. The average value of the spatiotemporal fused digital features within the spatiotemporal neighborhood of the point cloud to be determined. The standard deviation of the spatiotemporal fusion digital features within the spatiotemporal neighborhood of the point cloud to be determined. All are determined by matrix elements in the spatiotemporal correlation matrix representing the neighborhood range of the point cloud to be determined. The redundancy correlation coefficient of the point cloud is determined by a comprehensive analysis of the spatial coordinate repetition and temporal series similarity between the point cloud to be judged and its neighboring point clouds. The number of feature overlaps between the point cloud to be determined and its neighboring point clouds. The total number of point clouds within the spatiotemporal neighborhood of the point cloud to be determined; the comprehensive determination value of the point cloud data is compared with the comprehensive determination threshold of the point cloud data, and a determination operation is performed: when When the point cloud data is classified as a noisy or redundant point cloud, corresponding filtering or removal operations are performed; At that time, the point cloud data is determined to be a valid point cloud and is retained; then, the determined and retained valid point cloud data are integrated to generate valid point cloud digital data, such as... Figure 3 As shown, the effective point cloud digital data completely includes the spatial coordinates and digital feature information of the point cloud and the acquisition time, as well as preprocessing identification information. The preprocessing identification information is a combination of noise filtering operation identification and redundancy removal operation identification performed on the point cloud data. It can clearly mark the specific operation types of noise filtering and redundancy removal performed on the point cloud data, and realize the traceability management of point cloud data preprocessing.

[0025] Feedback and Interaction: The rapid processing module transmits the generated valid point cloud electrical digital data to the real-time feedback module of the in-vehicle operating terminal in real time via an electrical digital signal transmission link. The real-time feedback module unpacks the received valid point cloud electrical digital data, comprehensively extracting core spatial feature data composed of the spatial coordinates of the point cloud, digital features of the acquisition time, and spatiotemporal fusion digital feature values, as well as processing information composed of point cloud data comprehensive judgment thresholds, spatiotemporal correlation matrix construction parameters, preprocessing identification information, valid point cloud data ratio, and noise point cloud removal rate. Subsequently, the rendering unit uses a layered rendering method to perform lightweight electrical digital rendering on the unpacked data, quickly generating a visual point cloud processing display, allowing surveyors to intuitively see the points. The spatial distribution and processing effect of the cloud are visualized and clearly displayed on the in-vehicle operating terminal screen through the interactive unit. The interactive unit consists of a touch input component, a voice recognition component, and a visualization display component. Surveyors can view the point cloud processing effect, current processing parameters, and data accuracy indicators of each road segment in real time on the operating terminal. At the same time, for the point cloud processing effect of complex road segments such as road intersections and bus stops, semantic-level interactive commands can be input through the touch input component or the voice recognition component. The real-time feedback module will standardize the semantic-level interactive commands, so that the interactive commands can accurately convey the surveyors' parameter adjustment and anomaly annotation needs, realizing the real-time visualization of the point cloud processing effect and the accurate and efficient input of interactive commands.

[0026] Parameter Linkage: The real-time feedback module converts standardized semantic-level interactive commands into electronic digital control signals and transmits these signals to the parameter linkage optimization module in real time. The processing parameters of the rapid processing module include the point cloud data comprehensive judgment threshold and the spatiotemporal correlation matrix construction parameters. The parameter linkage optimization module has a built-in command parameter mapping table and parameter adjustment unit. After receiving the electronic digital control signal, it can quickly match the corresponding parameter adjustment relationship from the command parameter mapping table. During the matching process, the user command priority weight and the proportion of spatiotemporal features of abnormal areas are introduced. The processing parameters are accurately adjusted through the adaptive adjustment formula, which is as follows: ,in, These are the adjusted processing parameter values. These are the processing parameter values ​​before adjustment. User command priority weights For instruction parsing coefficients, All are determined through the parsing of standardized semantic-level interactive commands using digital encoding. The average comprehensive judgment value of point cloud data in the abnormal area is marked for the user. The proportion of spatiotemporal features of the point cloud within the anomalous region is determined by the ratio of the sum of the spatiotemporal fused digital feature values ​​of the point cloud within the anomalous region to the sum of the spatiotemporal fused digital feature values ​​of the entire point cloud. The average spatiotemporal neighborhood correlation factor of the point cloud within the abnormal area is obtained by averaging the spatiotemporal neighborhood correlation factors within the abnormal area. After adjustment, the new processing parameters are immediately stored in the parameter configuration unit of the fast processing module. When subsequent surveying vehicles pass through similar complex surveying sections such as road intersections and bus stops, the point cloud data will be processed directly according to the optimized processing parameters. This allows the point cloud data processing to dynamically adapt to the surveying needs of different road sections in the city, realize real-time adaptive optimization of processing parameters, continuously ensure the accuracy of point cloud data processing in complex surveying scenarios of urban roads, and enable the collected point cloud data to better meet the high-precision requirements of 3D modeling for road reconstruction and expansion projects.

[0027] In summary, in the mobile mapping scenario of urban road reconstruction and expansion projects, a full-process operation of real-time dynamic point cloud mapping data processing is carried out. First, point cloud information of roads and surrounding facilities is continuously collected and signal converted. Then, the spatial coordinates and digital features of the acquisition time of the point cloud are extracted from the signal to construct a spatiotemporal correlation matrix. Based on matrix traversal operations, noise filtering and redundancy removal are completed in parallel to generate effective point cloud electrical digital data with pre-processed identification information. After lightweight electrical digital rendering and display, interactive instructions are received and processed. Then, the processing method is adjusted according to the instructions. Subsequently, the mapping data is processed in the optimized way. The overall process adapts to the dynamic and complex needs of urban road mapping, provides high-precision geographic information support for engineering 3D modeling and scheme design, and ensures the real-time and accuracy of mapping data processing.

[0028] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for rapid processing of real-time dynamic point cloud mapping data, characterized in that, The specific steps of this method are as follows: Signal conversion: The real-time dynamic point cloud analog signal is acquired by the data acquisition module, converted into an electrical digital signal, and then transmitted to the fast processing module through the electrical digital signal transmission link. Feature construction: The fast processing module receives the electrical digital signal, extracts the spatial coordinates and digital features of the acquisition time of the point cloud from the electrical digital signal, and constructs a spatiotemporal correlation matrix based on the extracted digital features; Parallel processing: The fast processing module performs matrix traversal operations based on the constructed spatiotemporal correlation matrix. During one matrix traversal, noise filtering and redundancy removal operations of real-time dynamic point cloud data are completed in parallel and synchronously to generate effective point cloud digital data. Feedback and Interaction: The effective point cloud electrical digital data is transmitted to the real-time feedback module through the electrical digital signal transmission link via the fast processing module. After receiving the effective point cloud electrical digital data, the real-time feedback module performs lightweight electrical digital rendering and displays relevant information, while also receiving semantic-level interactive commands input by the user. Parameter linkage: The real-time feedback module converts semantic-level interactive commands into electronic digital control signals and transmits them to the parameter linkage optimization module. The parameter linkage optimization module adaptively adjusts the processing parameters of the fast processing module according to the command type corresponding to the electronic digital control signal. The real-time dynamic point cloud simulation signal subsequently acquired by the data acquisition module completes the entire process according to the adjusted processing parameters.

2. The method for rapid processing of real-time dynamic point cloud mapping data according to claim 1, characterized in that, In the signal conversion, the data acquisition module continuously acquires real-time dynamic point cloud information of the monitoring area through the acquisition unit to form a real-time dynamic point cloud analog signal. The acquisition unit includes a lidar acquisition device, a point cloud data acquisition card, and a signal preprocessing circuit. The conversion unit performs analog-to-digital conversion on the real-time dynamic point cloud analog signal to form a standardized electrical-digital signal. Then, the standardized electrical-digital signal is sent to the fast processing module through the electrical-digital signal transmission link via the transmission unit. The electrical-digital signal transmission link is a serial communication link or a wireless communication link. The electrical-digital signal is transmitted in the form of data packets in the electrical-digital signal transmission link. Each data packet contains the timing identifier and spatial location identifier of the point cloud acquisition.

3. The method for rapid processing of real-time dynamic point cloud mapping data according to claim 1, characterized in that, In the feature construction process, the fast processing module performs data parsing on the electrical digital signal, separates the spatial coordinate information and acquisition time information of the point cloud, extracts features from the separated spatial coordinate information and acquisition time information of the point cloud, introduces the spatiotemporal neighborhood correlation factor, and completes the quantitative extraction of the digital features of the spatial coordinates and acquisition time of the point cloud through the spatiotemporal correlation feature extraction formula, forming the spatiotemporal fusion digital features of the point cloud. With the point cloud acquisition time sequence as the row dimension and the point cloud spatial coordinate sequence as the column dimension, the spatiotemporal fusion digital features are filled into the corresponding positions of the matrix to complete the construction of the spatiotemporal correlation matrix.

4. The method for rapid processing of real-time dynamic point cloud mapping data according to claim 3, characterized in that, In the feature construction, the formula for extracting spatiotemporal correlation features is: ,in, For the spatiotemporal fusion digital feature values ​​of point clouds, For spatial feature weights, For time feature weights, All are determined based on the distribution characteristics analysis of point cloud spatiotemporal feature data corresponding to electrical digital signals. These are the spatial coordinate digital feature values ​​of the point cloud, obtained after standardizing the spatial coordinate information of the point cloud. The digital feature value representing the acquisition time of the point cloud is obtained by processing the acquisition time information of the point cloud through time series normalization encoding. The spatiotemporal neighborhood correlation factor of the point cloud is determined by the ratio of the distribution density of the point cloud in the spatiotemporal neighborhood of the feature point cloud to be extracted to the overall point cloud distribution density.

5. The method for rapid processing of real-time dynamic point cloud mapping data according to claim 1, characterized in that, In the parallel processing, the fast processing module performs a row-by-row, column-by-column matrix traversal operation on the spatiotemporal correlation matrix. During the traversal, it extracts the spatiotemporal fusion digital feature value of the point cloud corresponding to each element in the matrix. Noise and redundancy are simultaneously determined on the extracted spatiotemporal fusion digital feature value of the point cloud. A redundancy correlation coefficient is introduced, and a comprehensive quantitative determination of noise and redundancy in the point cloud data is completed through a dual determination formula for point cloud data. The noise filtering and redundancy removal operations of the corresponding point cloud data are completed. The point cloud data that has been determined and retained are integrated to generate effective point cloud electrical digital data. The effective point cloud electrical digital data includes the spatial coordinates and digital feature information of the point cloud and the acquisition time, as well as preprocessing identification information. The preprocessing identification information is a combination of noise filtering operation identification and redundancy removal operation identification performed on the point cloud data.

6. The method for rapid processing of real-time dynamic point cloud mapping data according to claim 5, characterized in that, In the parallel processing, the dual-determination formula for point cloud data is: ,in, This is the comprehensive judgment value of point cloud data. The spatiotemporal fusion digital feature values ​​of the point cloud to be determined. The average value of the spatiotemporal fused digital features within the spatiotemporal neighborhood of the point cloud to be determined. The standard deviation of the spatiotemporal fusion digital features within the spatiotemporal neighborhood of the point cloud to be determined. All are determined by matrix elements in the spatiotemporal correlation matrix representing the neighborhood range of the point cloud to be determined. The redundancy correlation coefficient of the point cloud is determined by a comprehensive analysis of the spatial coordinate repetition and temporal series similarity between the point cloud to be judged and its neighboring point clouds. The number of feature overlaps between the point cloud to be determined and its neighboring point clouds. This represents the total number of point clouds within the spatiotemporal neighborhood of the point cloud to be determined. The comprehensive judgment value of the point cloud data is compared with the comprehensive judgment threshold of the point cloud data, and a judgment operation is performed: when When the point cloud data is classified as a noisy or redundant point cloud, corresponding filtering or removal operations are performed; When this happens, the point cloud data is determined to be a valid point cloud and is retained.

7. The method for rapid processing of real-time dynamic point cloud mapping data according to claim 1, characterized in that, In the feedback interaction, the real-time feedback module unpacks the received valid point cloud digital data, extracts the core spatial feature data consisting of the spatial coordinates of the point cloud, the digital features of the acquisition time, and the spatiotemporal fusion digital feature values, as well as the processing information consisting of the current processing parameters, preprocessing identification information, and data processing accuracy indicators; and uses a layered rendering method to perform lightweight digital rendering on the unpacked data through the rendering unit to generate a visual point cloud processing display. The system then displays visual content through interactive units, while simultaneously receiving semantic-level interactive commands input by users through these units. These semantic-level interactive commands are then standardized. The interactive units include touch input components, voice recognition components, and visual display components. The semantic-level interactive commands include parameter adjustment commands and anomaly annotation commands. The parameter adjustment commands are generated based on the user's adjustment requirements for the comprehensive judgment threshold and spatiotemporal correlation matrix construction parameters of the point cloud data. The anomaly annotation commands are generated based on the user's annotation requirements for anomaly areas in the point cloud processing. The anomaly annotation commands contain spatiotemporal coordinate information composed of the spatial coordinates of the point cloud within the corresponding anomaly area and the digital features of the acquisition time.

8. The method for rapid processing of real-time dynamic point cloud mapping data according to claim 1, characterized in that, In the parameter linkage, the processing parameters of the fast processing module include the point cloud data comprehensive judgment threshold and the spatiotemporal correlation matrix construction parameters; the parameter linkage optimization module has a built-in instruction parameter mapping table and parameter adjustment unit. The parameter linkage optimization module matches the corresponding adjustment relationship from the instruction parameter mapping table according to the electronic digital control signal, introduces the user instruction priority weight and the proportion of spatiotemporal features of abnormal areas, and completes the adjustment of the processing parameters through the adaptive adjustment formula of the processing parameters. The adjusted processing parameters are stored in the parameter configuration unit of the fast processing module.

9. The method for rapid processing of real-time dynamic point cloud mapping data according to claim 8, characterized in that, In the parameter linkage, the adaptive adjustment formula for the processing parameters is: ,in, These are the adjusted processing parameter values. These are the processing parameter values ​​before adjustment. User command priority weights For instruction parsing coefficients, All are determined through the parsing of standardized semantic-level interactive commands using digital encoding. The average comprehensive judgment value of point cloud data in the abnormal area is marked for the user. The proportion of spatiotemporal features of the point cloud within the anomalous region is determined by the ratio of the sum of the spatiotemporal fused digital feature values ​​of the point cloud within the anomalous region to the sum of the spatiotemporal fused digital feature values ​​of the entire point cloud. The average spatiotemporal neighborhood correlation factor of the point cloud within the abnormal region is obtained by averaging the spatiotemporal neighborhood correlation factors within the abnormal region.

10. A real-time dynamic point cloud mapping data rapid processing and real-time feedback system, the system being applicable to the real-time dynamic point cloud mapping data rapid processing method according to any one of claims 1-9, characterized in that, The system includes: a data acquisition module, a rapid processing module, a real-time feedback module, and a parameter linkage optimization module; The data acquisition module acquires real-time dynamic point cloud simulation signals, converts them into digital signals, and transmits them to the fast processing module. The fast processing module receives the digital signals, extracts the spatial coordinates and digital features of the point cloud and the acquisition time from them, constructs a spatiotemporal correlation matrix, performs matrix traversal operations based on the matrix, and performs noise filtering and redundancy removal on the real-time dynamic point cloud data in parallel and synchronously to generate valid point cloud digital data. This valid point cloud digital data is then pushed to the real-time feedback module. The module can also receive adjusted processing parameters from the parameter linkage optimization module. The real-time feedback module receives the valid point cloud digital data, performs lightweight digital rendering and visualization on it, receives semantic-level interactive commands from the user, converts these commands into digital control signals, and transmits them to the parameter linkage optimization module. The parameter linkage optimization module receives the digital control signals, adaptively adjusts the processing parameters of the fast processing module according to the command type corresponding to the digital control signals, and transmits the adjusted processing parameters back to the fast processing module.

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