Engineering surveying and mapping informatization integration system and method

Abstract

The invention relates to the technical field of engineering surveying and mapping, and discloses an engineering surveying and mapping informatization integration system and method. The system comprises a multi-source data acquisition module, a data fusion processing module and an integrated analysis processing layer. The multi-source data acquisition module acquires spatial information data including terrain elevation, ground feature distribution and three-dimensional point cloud data, wherein the terrain elevation data includes a specific waveform. The data fusion processing module calibrates data and inputs the data into the integrated analysis processing layer, and the integrated analysis processing layer comprises a data preprocessing unit and a topology analysis unit. The data preprocessing unit processes original surveying and mapping data streams, the topology analysis unit obtains the data streams through joint modeling, and all layers cooperatively generate digital surveying and mapping results. The method is applied to the system, multi-source data acquisition, processing and analysis are realized, a high-precision digital surveying and mapping result is generated, the engineering surveying and mapping efficiency and accuracy are improved, and powerful data support is provided for engineering construction.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering surveying and mapping, and specifically to an information integration system and method for engineering surveying and mapping. Background Technique

[0002] In modern engineering construction, engineering surveying and mapping, as a key basic link in project planning, design, construction and operation, is of great importance. With the continuous expansion of the project scale and the continuous improvement of complexity, traditional surveying and mapping technologies and methods face many challenges and are difficult to meet the growing engineering needs.

[0003] Traditional surveying and mapping methods have obvious limitations in data collection. For example, relying on manual on-site measurement is not only inefficient, but also in some areas with complex terrain and harsh environment, such as alpine canyons, swamps, and dense jungles, it is difficult for surveyors to reach, resulting in difficult or even impossible data collection. Even when using some simple measuring instruments, the obtained data is often discrete and local, and cannot comprehensively reflect the terrain and landform and feature distribution of the entire surveyed area. When obtaining terrain elevation data, the traditional level measurement method requires setting measurement points at specific positions each time, with a limited measurement range. If a large area of terrain surveying and mapping is to be completed, it will consume a large amount of manpower, material resources and time.

[0004] The backwardness of data processing and analysis means is also a major problem faced by traditional surveying and mapping. When dealing with a large amount of surveying and mapping data in the traditional way, manual calculation and simple data processing software are usually used, which is difficult to efficiently integrate and deeply analyze multi-source and heterogeneous data. Different types of data, such as terrain elevation data, feature distribution data, 3D point cloud data, etc., are prone to data conflicts and information loss during the integration process due to different sources, formats and accuracies. Moreover, for complex terrain and landforms and dynamically changing features, traditional methods cannot accurately extract their features and are difficult to achieve accurate modeling and digital expression of the surveyed area. In urban construction surveying and mapping, with the rapid development of the city and the continuous update of buildings, it is very difficult for traditional surveying and mapping methods to timely and accurately obtain the change information of buildings and integrate it into the overall surveying and mapping results.

[0005] In addition, traditional surveying and mapping technologies perform poorly in real-time and dynamic monitoring. During the engineering construction process, the terrain and features will change with the progress of construction. Traditional surveying and mapping cannot track these changes in real-time and is difficult to provide accurate data support for engineering decisions in a timely manner. In the construction of large-scale water conservancy projects, operations such as river channel excavation and filling during the construction process will cause significant changes in the terrain and landform. If real-time monitoring cannot be carried out, the construction plan cannot be adjusted in time, which may affect the project quality and progress. Moreover, the update cycle of traditional surveying and mapping results is relatively long and often cannot meet the requirements of modern engineering construction for the timeliness of surveying and mapping data.

[0006] Facing these problems, the field of engineering surveying and mapping urgently needs an innovative technical means that can efficiently collect multi-source data, accurately process and analyze the data, and generate accurate digital surveying and mapping results in real time to adapt to the complex and changing engineering construction needs, improve the quality and efficiency of engineering construction, and reduce engineering risks. The engineering surveying and mapping information integration system and method proposed by the present invention are developed precisely to solve these problems. Summary of the Invention

[0007] The purpose of the present invention is to provide an engineering surveying and mapping information integration system and method to solve the problems raised in the above-mentioned background technology.

[0008] To achieve the above purpose, the present invention provides the following technical solution: An engineering surveying and mapping information integration system, the system includes: A multi-source data acquisition module, used to obtain spatial information data of the target surveying and mapping area, the spatial information data includes a first surveying sequence corresponding to terrain elevation data, a second surveying sequence corresponding to ground object distribution data, and a third surveying sequence corresponding to three-dimensional point cloud data, and the terrain elevation data includes a first reference waveform generated by a multi-band laser scanning device and a second reflected waveform collected by a distributed surveying sensor; A data fusion and processing module, used to perform dynamic coordinate system calibration processing on the spatial information data and input it into the integrated analysis and processing layer for feature extraction, and generate digital surveying and mapping results of the target surveying and mapping area according to the output result of the integrated analysis and processing layer; The integrated analysis and processing layer includes a data preprocessing unit and a topology analysis unit. Among them, the data preprocessing unit is used to perform spatial segmentation and outlier removal on the original surveying and mapping data stream, and the topology analysis unit is obtained by jointly modeling based on historical reference data and historical reflection data of multiple historical surveying cycles; the topology analysis unit includes a spatial registration layer, a topology reconstruction layer, and a result generation layer connected in sequence.

[0009] Preferably, the spatial registration layer is used to perform spatial domain alignment processing on multiple surveying sequences included in the original surveying and mapping data stream to obtain coordinate correlation feature data; the topology reconstruction layer is used to model the dynamic correlation relationship between the coordinate correlation feature data corresponding to each surveying sequence to obtain reconstructed topology feature data; the result generation layer is used to perform multi-dimensional superposition based on the reconstructed topology feature data and the coordinate correlation feature data to generate digital surveying and mapping results.

[0010] Preferably, the modeling of the dynamic correlation relationship between the coordinate correlation feature data corresponding to each surveying sequence to obtain reconstructed topology feature data includes: The dynamic reference calibration algorithm is used to identify the key terrain nodes in the coordinate-associated feature data, and the spatial association sequences corresponding to each surveying and mapping sequence are determined based on the terrain types corresponding to each key terrain node; Calculate the elevation similarity between the nodes with the same terrain type in the spatial association sequences corresponding to any two surveying and mapping sequences, and determine the reconstructed topological feature data between the any two surveying and mapping sequences based on the elevation similarity.

[0011] Preferably, the calculating the elevation similarity between the nodes with the same terrain type in the spatial association sequences corresponding to any two surveying and mapping sequences includes: When there are differences in the number of terrain nodes in the spatial association sequences corresponding to the any two surveying and mapping sequences, virtual node compensation is performed based on the terrain type corresponding to the terminal terrain node in the one with fewer terrain nodes, and the elevation similarity between the nodes with the same terrain type is calculated based on the compensated data.

[0012] Preferably, the data preprocessing unit is specifically configured to: Perform grid segmentation on the terrain information included in the first surveying and mapping sequence, the second surveying and mapping sequence, and the third surveying and mapping sequence according to a preset spatial division rule to obtain a standardized first surveying and mapping sequence, a standardized second surveying and mapping sequence, and a standardized third surveying and mapping sequence; Perform real-time smoothing processing on the standardized first surveying and mapping sequence and the standardized second surveying and mapping sequence by using a dynamic weight allocation method, and perform static noise reduction on the standardized third surveying and mapping sequence by using a fixed weight filtering method to generate a first fusion sequence, a second fusion sequence, and a third fusion sequence; wherein, the first fusion sequence includes the processed first reference waveform and the processed second reflection waveform.

[0013] Preferably, the data preprocessing unit is further configured to: Calculate the spatial matching degree between the processed first reference waveform and the processed second reflection waveform in the historical surveying and mapping period; Predict the predicted elevation amount of the processed second reflection waveform in the real-time surveying and mapping period according to the spatial matching degree and the terrain features of the processed first reference waveform in the real-time surveying and mapping period; Generate target spatial registration data according to the processed second reflection waveform and its predicted elevation amount, and use the surveying and mapping sequence corresponding to the target spatial registration data as the first fusion sequence.

[0014] Preferably, the topological reconstruction layer specifically includes: A spatial association unit, configured to perform terrain continuity analysis on each surveying and mapping sequence included in the coordinate-associated feature data respectively, so as to extract the corresponding terrain extension chain from each surveying and mapping sequence; A topology matching unit for dynamically mapping the terrain extension chains extracted from each surveying sequence to the corresponding coordinate feature data to generate reconstructed topology feature data.

[0015] Preferably, the topology reconstruction layer further includes: An error correction unit for performing redundant point cloud elimination processing on the reconstructed topology feature data.

[0016] Preferably, the result generation layer specifically includes: A multi-dimensional superposition unit including a plurality of data superposition nodes, and each data superposition node is connected to each surveying sequence in the reconstructed topology feature data and the coordinate correlation feature data through associated configuration; A dynamic optimization and adjustment unit for updating the associated configuration through a dynamic optimization and adjustment algorithm to minimize the deviation between the digital surveying and mapping results and the actual terrain data; A terrain fault identification unit for predicting a terrain fracture zone based on the reconstructed topology feature data and the coordinate correlation feature data to generate digital surveying and mapping results.

[0017] Preferably, the present invention further includes an engineering surveying and mapping informatization integration method applied to the engineering surveying and mapping informatization integration system as described in any one of the above. The method includes the following steps: Step 1: Obtain spatial information data of a target surveying area through a multi-source data acquisition module. The spatial information data includes a first surveying sequence corresponding to terrain elevation data, a second surveying sequence corresponding to ground object distribution data, and a third surveying sequence corresponding to three-dimensional point cloud data. The terrain elevation data includes a first reference waveform generated by a multi-band laser scanning device and a second reflected waveform collected by a distributed surveying sensor; Step 2: Use a data fusion processing module to perform dynamic coordinate system calibration processing on the spatial information data, and input the calibrated spatial information data into the integrated analysis and processing layer; Step 3: Perform spatial segmentation and outlier removal on the original surveying data stream through a data preprocessing unit in the integrated analysis and processing layer; the topology analysis unit in the integrated analysis and processing layer performs joint modeling based on historical reference data and historical reflection data of multiple historical surveying periods. The topology analysis unit includes a spatial registration layer, a topology reconstruction layer, and a result generation layer connected in sequence; Step 4: Generate digital surveying and mapping results of the target surveying area through the data fusion processing module according to the output result of the integrated analysis and processing layer.

[0018] Compared with the prior art, the beneficial effects of the present invention are: In the data acquisition stage, through the multi-source data acquisition module, a rich variety of spatial information data can be obtained. The combination of the first reference waveform generated by the multi-band laser scanning device and the second reflection waveform collected by the distributed mapping sensor greatly improves the accuracy and integrity of the terrain elevation data. Compared with traditional single measurement methods, this multi-source data acquisition method can detect the terrain from different angles and scales, obtaining more comprehensive terrain information. When mapping mountainous areas, the multi-band laser scanning can penetrate a certain vegetation cover to obtain real terrain and landform data, and the distributed mapping sensor can make up for the measurement blind spots of laser scanning in some areas, ensuring the reliability of the terrain elevation data.

[0019] The design of the data fusion processing module and the integrated analysis processing layer has brought a qualitative leap to data processing and analysis. The spatial segmentation and outlier removal operations of the data preprocessing unit effectively improve the quality of the original mapping data. By performing grid segmentation according to the preset spatial division rules, the complex terrain information is transformed into a standardized mapping sequence, facilitating subsequent processing. The application of the dynamic weight allocation method and the fixed weight filtering method optimizes different types of data, removes noise interference, and retains key information. For the first mapping sequence containing the first reference waveform and the second reflection waveform, the dynamic weight allocation can adjust the weight according to the real-time characteristics of the data, better reflecting the terrain change trend and improving the data accuracy.

[0020] The innovative design of the topology analysis unit is a major highlight of this invention. The spatial registration layer performs spatial domain alignment processing on multiple mapping sequences, establishing accurate coordinate correlation feature data, enabling data from different sources to be analyzed within a unified spatial framework. The topology reconstruction layer models the dynamic association relationships between the coordinate correlation feature data, uses the dynamic reference calibration algorithm to identify key terrain nodes, determines the spatial association sequence, and calculates the elevation similarity, thus more accurately reflecting the real structure of the terrain. In areas with complex terrain, this method can accurately capture the subtle changes in the terrain and construct a high-precision terrain model. The error correction unit performs redundant point cloud elimination processing on the reconstructed topology feature data, further improving the data quality, reducing data redundancy, and enhancing the processing efficiency.

[0021] The result generation layer generates high-quality digital mapping results through the collaborative work of the multi-dimensional superposition unit, the dynamic optimization and adjustment unit, and the terrain fault identification unit. The multiple data superposition nodes of the multi-dimensional superposition unit achieve the comprehensive integration of different mapping sequences; the dynamic optimization and adjustment unit continuously optimizes the associated configuration through the dynamic optimization and adjustment algorithm to minimize the deviation between the digital mapping results and the actual terrain data; the terrain fault identification unit predicts the terrain fracture zone based on the reconstructed topological feature data and the coordinate association feature data, providing important geological information for engineering construction, helping engineers plan in advance, avoid construction in dangerous areas, and ensure project safety.

[0022] Overall, the system and method of the present invention realize the informatization integration of engineering surveying and mapping, can quickly and accurately acquire and process multi-source surveying and mapping data, generate high-precision digital mapping results, provide reliable data support for engineering planning, design, construction and operation, effectively improve the efficiency and quality of engineering construction, reduce engineering costs and risks, and have broad application prospects and significant economic and social benefits in the field of engineering surveying and mapping. Brief Description of the Drawings

[0023] Figure 1 It is the working principle diagram of an engineering surveying and mapping informatization integration system described in the present invention; Figure 2 It is the flow chart of the topological reconstruction processing of engineering surveying and mapping data; Figure 3 It is the design diagram of the dynamic reference calibration algorithm; Figure 4 It is the working flow chart of the terrain node compensation of the mapping sequence; Figure 5 It is the flow chart of the data fusion processing of multiple mapping sequences. Detailed Embodiment

[0024] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0025] Please refer to Figures 1-5 , the present invention provides a technical solution: The present invention provides an engineering surveying and mapping informatization integration system. The specific implementation steps are as follows: The system mainly includes a multi-source data acquisition module and a data fusion and processing module. The multi-source data acquisition module is used to obtain the spatial information data of the target surveying and mapping area. These spatial information data include the first surveying and mapping sequence corresponding to the terrain elevation data, the second surveying and mapping sequence corresponding to the ground object distribution data, and the third surveying and mapping sequence corresponding to the 3D point cloud data. Among them, the terrain elevation data includes the first reference waveform generated by a multi-band laser scanning device and the second reflected waveform collected by distributed surveying and mapping sensors. In actual operation, the multi-band laser scanning device emits laser beams of different frequencies to the target surveying and mapping area. These laser beams are reflected back after encountering the terrain surface, thereby generating the first reference waveform, which can accurately reflect the basic characteristics of the terrain. The distributed surveying and mapping sensors are distributed at different positions in the surveying and mapping area to collect the signals reflected from the terrain surface in real time, forming the second reflected waveform. The two complement each other to obtain the terrain elevation data more comprehensively.

[0026] The data fusion and processing module is used to perform dynamic coordinate system calibration processing on the spatial information data, and then input the processed data into the integrated analysis and processing layer for feature extraction, and generate the digital surveying and mapping results of the target surveying and mapping area according to the output results of the integrated analysis and processing layer. The integrated analysis and processing layer includes a data preprocessing unit and a topology analysis unit. The data preprocessing unit is used to perform spatial segmentation and outlier removal on the original surveying and mapping data stream. When performing spatial segmentation, the surveying and mapping area will be divided into different spatial segments according to preset rules for more detailed data processing in the follow-up. For outlier removal, specific algorithms and thresholds will be used to judge and remove those data points that significantly deviate from the normal range to ensure the accuracy of the data. The topology analysis unit is obtained by jointly modeling based on the historical reference data and historical reflection data of multiple historical surveying and mapping cycles. The topology analysis unit includes a spatial registration layer, a topology reconstruction layer, and a result generation layer connected in sequence. Each layer cooperates with each other to perform in-depth analysis and processing on the data, providing support for generating digital surveying and mapping results.

[0027] The present invention will be further described below in conjunction with Embodiments 1 to 5: Embodiment 1: During the operation of the system, the spatial registration layer performs spatial domain alignment processing on multiple surveying and mapping sequences contained in the original surveying and mapping data stream. Specifically, through specific algorithms and technical means, the data in different surveying and mapping sequences are matched and aligned in spatial coordinates, enabling them to be compared and analyzed under the same spatial reference system, thereby obtaining coordinate correlation feature data. These coordinate correlation feature data contain the spatial correlation information of each surveying and mapping sequence, providing a basis for subsequent processing. The topology reconstruction layer models the dynamic correlation relationship between the coordinate correlation feature data corresponding to each surveying and mapping sequence. In this process, first, the dynamic reference calibration algorithm is used to identify the key terrain nodes in the coordinate correlation feature data. This algorithm will find out those nodes that have an important impact on the terrain structure and changes according to the characteristics and change rules of the terrain. Then, based on the terrain type corresponding to each key terrain node, the spatial correlation sequences corresponding to each surveying and mapping sequence are determined. In this way, the terrain information in different surveying and mapping sequences can be correlated and integrated according to the terrain type. Next, the elevation similarity between the nodes with the same terrain type in the spatial correlation sequences corresponding to any two surveying and mapping sequences is calculated. During the calculation process, if there is a difference in the number of terrain nodes in the spatial correlation sequences corresponding to any two surveying and mapping sequences, virtual node compensation will be performed based on the terrain type corresponding to the terminal terrain node in the sequence with fewer terrain nodes to make the number of nodes in the two sequences the same, and then the elevation similarity between the nodes with the same terrain type is calculated based on the compensated data. Through these steps, the reconstructed topology feature data are finally obtained, which reflect the topological relationship and the similarity of terrain features between different surveying and mapping sequences. The result generation layer performs multi-dimensional superposition based on the reconstructed topology feature data and the coordinate correlation feature data to generate digital surveying and mapping results. During the multi-dimensional superposition process, the reconstructed topology feature data and the coordinate correlation feature data are fused according to certain rules and algorithms to display the terrain information from multiple dimensions and generate more accurate and detailed digital surveying and mapping results.

[0028] Suppose engineering surveying and mapping is to be carried out on a mountainous area. This mountainous area has a complex terrain with various landforms such as high mountains, canyons, and rivers.

[0029] During surveying and mapping, the multi-source data acquisition module starts to work. The multi-band laser scanning device emits laser beams of different bands towards the mountainous area. These laser beams are reflected back after encountering the terrain surface, generating the first reference waveform. For example, when scanning a mountain peak, the laser beams return from different angles, carrying terrain information such as the height and slope of the mountain peak. At the same time, distributed surveying and mapping sensors are distributed at various positions in the mountainous area, such as at key locations like valleys and riverbanks, to collect in real-time the signals reflected from the terrain surface, forming the second reflected waveform. These waveform data constitute the first surveying and mapping sequence corresponding to the terrain elevation data. The second surveying and mapping sequence corresponding to the ground object distribution data is obtained through specialized ground object detection equipment, such as identifying the distribution positions and shape information of trees, buildings, etc. in the mountainous area. The third surveying and mapping sequence corresponding to the three-dimensional point cloud data is generated by a three-dimensional laser scanner, which can accurately measure the three-dimensional coordinates of each point in the mountainous area, forming a dense point cloud data.

[0030] When these spatial information data are obtained, the data enters the spatial registration layer of the integrated analysis and processing layer. Taking the terrain data in the first surveying and mapping sequence and the ground object distribution data in the second surveying and mapping sequence as an example, the spatial registration layer matches and aligns the two in terms of spatial coordinates through a specific algorithm. For example, the terrain height data at a certain position in the first surveying and mapping sequence is spatially corresponded to the ground object distribution data near the same position in the second surveying and mapping sequence, thus obtaining coordinate correlation feature data, which indicates the spatial connection between different surveying and mapping sequences.

[0031] Then, the topological reconstruction layer starts to work. The dynamic reference calibration algorithm is used to identify the key terrain nodes in the coordinate correlation feature data. In this mountainous area, the vertices of mountain peaks, the lowest points of valleys, and the confluence points of rivers, etc. may all be identified as key terrain nodes. Based on the terrain type corresponding to each key terrain node, the spatial correlation sequences corresponding to each surveying and mapping sequence are determined. For example, for the key terrain node of the mountain peak vertex, a series of terrain data related to it in the first surveying and mapping sequence and the ground object data distributed around the mountain peak in the second surveying and mapping sequence form their respective corresponding spatial correlation sequences.

[0032] Then, calculate the elevation similarity between the nodes with the same terrain type in the spatial association sequences corresponding to any two surveying and mapping sequences. Suppose both the first surveying and mapping sequence and the second surveying and mapping sequence have terrain data on hillslopes in a certain area. When there is a difference in the number of terrain nodes on the hillslope in the two sequences, virtual node compensation is performed based on the terrain type corresponding to the terminal terrain node in the sequence with fewer terrain nodes. If the first surveying and mapping sequence has 10 terrain nodes on this hillslope and the second surveying and mapping sequence has only 8, and the terminal terrain node of the second surveying and mapping sequence is a gentle slope terrain, then 2 virtual gentle slope terrain nodes are added behind it to make the number of nodes in the two sequences the same. After that, based on the compensated data, calculate the elevation similarity between the nodes with the same terrain type. By comparing the elevation information of these nodes, the reconstructed topological feature data between the two sequences are obtained, and these data reflect the similarity degree of the terrain features of the two surveying and mapping sequences in the hillslope area.

[0033] Finally, the result generation layer performs multi-dimensional superposition based on the reconstructed topological feature data and the coordinate association feature data. Integrate the terrain data of the first surveying and mapping sequence, the ground object data of the second surveying and mapping sequence, and the three-dimensional point cloud data of the third surveying and mapping sequence according to certain rules, and display the information of the mountainous area from multiple dimensions such as terrain height, ground object distribution, and three-dimensional spatial position, generating detailed and accurate digital surveying and mapping results. For example, generate an electronic map containing the terrain and landform, ground object distribution, and precise three-dimensional model of the mountainous area, providing a reliable basis for subsequent engineering planning and construction.

[0034] Embodiment 2: When the data preprocessing unit is working, first, it performs grid segmentation on the terrain information included in the first surveying sequence, the second surveying sequence, and the third surveying sequence according to the preset space division rules. This grid segmentation can divide the complex terrain information into regular grids, facilitating subsequent processing and analysis, thereby obtaining a standardized first surveying sequence, a standardized second surveying sequence, and a standardized third surveying sequence. Then, a dynamic weight allocation method is used to perform real-time smoothing processing on the standardized first surveying sequence and the standardized second surveying sequence. The dynamic weight allocation method will automatically adjust the weights according to the real-time changes and characteristics of the data, perform smoothing processing on the data, remove the noise and fluctuations in the data, and make the data more stable and accurate. At the same time, a fixed weight filtering method is used to perform static noise reduction on the standardized third surveying sequence, filter the data through fixed weights, remove the noise interference therein, and generate a first fusion sequence, a second fusion sequence, and a third fusion sequence. Among them, the first fusion sequence includes the processed first reference waveform and the processed second reflection waveform. In addition, the data preprocessing unit will also calculate the spatial matching degree between the processed first reference waveform and the processed second reflection waveform in the historical surveying period. By analyzing the matching situation of their spatial positions and characteristics in the historical period, the correlation between them can be understood. Then, according to the spatial matching degree and the terrain characteristics of the processed first reference waveform in the real-time surveying period, the predicted elevation of the processed second reflection waveform in the real-time surveying period is predicted. Using this information, target space registration data is generated based on the processed second reflection waveform and its predicted elevation, and the surveying sequence corresponding to the target space registration data is used as the first fusion sequence to further improve the accuracy and reliability of the data.

[0035] Suppose engineering surveying is carried out on a to-be-developed area in a certain city. This area contains different types of terrains, such as flat land, small mounds, and there are also some existing buildings and roads, etc.

[0036] In this embodiment, the data preprocessing unit starts the work process. First, it performs grid segmentation on the terrain information included in the first surveying sequence (terrain elevation data), the second surveying sequence (ground object distribution data), and the third surveying sequence (3D point cloud data) from the multi-source data acquisition module according to the preset space division rules. For example, the entire to-be-developed area is divided into square grids with a side length of 5 meters, and the corresponding data is collected and sorted for each grid. After such grid processing, the originally complex and irregular terrain information is sorted into a standardized first surveying sequence, a standardized second surveying sequence, and a standardized third surveying sequence, facilitating subsequent processing.

[0037] After the grid segmentation is completed, the data preprocessing unit will perform targeted processing on different surveying and mapping sequences. For the standardized first surveying and mapping sequence and the standardized second surveying and mapping sequence, a dynamic weight allocation method is used for real-time smoothing. Taking the terrain elevation data in the first surveying and mapping sequence as an example, in this undeveloped area, due to measurement errors or slight undulations of the terrain itself, there may be certain fluctuations in the data. The dynamic weight allocation method will automatically adjust the weights of each data point according to the real-time changes of the data. In relatively flat areas, if several adjacent data points do not change much, higher weights will be given to make these data points have a greater impact on the final result during the smoothing process, so as to highlight the stability of the terrain in this area; for those data points with abnormal changes, their weights will be reduced to reduce their interference with the overall data. For example, in a flat area, the terrain elevation data of several consecutive grids are 100 meters, 100.1 meters, 100 meters, and 100.2 meters respectively. The dynamic weight allocation method will give higher weights to the relatively stable data points such as 100 meters and 100.1 meters, so that after smoothing, the terrain elevation data in this area is closer to the real flat terrain.

[0038] For the standardized third surveying and mapping sequence (3D point cloud data), a fixed weight filtering method is used for static noise reduction. Since the amount of 3D point cloud data is huge, it is easily interfered by noise during the acquisition process. The fixed weight filtering method will perform filtering operations on each point cloud data according to the pre-set weights. For example, if the weight of the central data point is set to 0.6 and the weight of its adjacent data points is set to 0.2, the data is processed through this fixed weight combination to remove those noise points that are significantly deviated from the normal range, so that the 3D point cloud data can more accurately reflect the actual terrain and ground features of this area. After processing, the first fusion sequence, the second fusion sequence, and the third fusion sequence are generated, where the first fusion sequence contains the processed first reference waveform and the processed second reflection waveform.

[0039] The data preprocessing unit will further analyze the data. It will calculate the spatial matching degree between the processed first reference waveform and the processed second reflection waveform during the historical surveying and mapping cycle. Assume that this area has been surveyed multiple times before, forming historical surveying and mapping data. By comparing the current processed waveform data with the historical data, analyze their matching degree in terms of spatial position and terrain features. For example, observe the terrain changes reflected by the first reference waveform and the second reflection waveform in a certain area during different surveying and mapping cycles. If the two waveforms in a certain area have always maintained a high degree of consistency in terms of spatial position and features during the historical cycle, it indicates that the terrain in this area is relatively stable and the spatial matching degree is high; on the contrary, if the difference is large, the spatial matching degree is low.

[0040] Predict the predicted elevation of the processed second reflection waveform during the real-time mapping cycle based on the spatial matching degree and the terrain characteristics of the processed first reference waveform during the real-time mapping cycle. For example, during the real-time mapping cycle, the first reference waveform shows a slight upward trend in the terrain of a certain area. Considering that the spatial matching degree between the two waveforms in this area was relatively high during the previous historical mapping cycles, it is predicted that the elevation of the processed second reflection waveform in this area will also increase accordingly.

[0041] Finally, generate target space registration data based on the processed second reflection waveform and its predicted elevation, and use the mapping sequence corresponding to the target space registration data as the first fusion sequence. In this way, after a series of processes, the obtained first fusion sequence can more accurately reflect the terrain elevation information of the area, providing a more reliable data basis for subsequent data processing and the generation of digital mapping results.

[0042] Example 3: The spatial association unit performs terrain continuity analysis on each mapping sequence included in the coordinate association feature data. By analyzing the continuity of the terrain data, identify the trends and patterns of terrain changes, and thus extract the corresponding terrain extension chains from each mapping sequence. These terrain extension chains reflect the extension and changes of the terrain in space. The topology matching unit dynamically maps the terrain extension chains extracted from each mapping sequence to the corresponding coordinate feature data. During the mapping process, based on the characteristics of the terrain extension chains and the relationship with the coordinate feature data, establish the connection between the two to generate reconstructed topology feature data. The error correction unit performs redundant point cloud elimination processing on the reconstructed topology feature data. During the data acquisition and processing, some redundant point cloud data may be generated, which will affect the accuracy and processing efficiency of the data. The error correction unit will use specific algorithms and technical means to identify and remove these redundant point cloud data to improve the quality of the reconstructed topology feature data.

[0043] Suppose an engineering survey is carried out on a mining area. There are various complex terrains in this mining area, such as mined-out pits formed by mining, ore piles, and surrounding mountains and valleys.

[0044] The topological reconstruction layer starts to work. First, the spatial association unit plays a role. It performs terrain continuity analysis on each surveying and mapping sequence contained in the coordinate association feature data. Taking the first surveying and mapping sequence corresponding to the terrain elevation data as an example, when analyzing the pit area in the mining area, the spatial association unit will conduct data continuity analysis along the boundary and inside of the pit. By comprehensively judging information such as the elevation change and slope change of adjacent data points, the trend and law of terrain change are found. If in a certain section of data, the elevation continuously decreases and the slope remains relatively stable, it can be judged that this section of terrain has a certain continuity, and then the corresponding terrain extension chain is extracted from this surveying and mapping sequence. For example, around the boundary of a pit, from the pit edge to the bottom, a terrain extension chain reflecting the terrain change of the pit is formed. Similarly, for the second surveying and mapping sequence corresponding to the ground object distribution data, when analyzing the distribution of ore piles, the terrain extension chain related to the ore piles is extracted by analyzing the continuity of the boundary and internal ground object data of the ore piles.

[0045] The topological matching unit dynamically maps the terrain extension chains extracted from each surveying and mapping sequence to the corresponding coordinate feature data to generate reconstructed topological feature data. In this mining area, the pit terrain extension chain extracted from the first surveying and mapping sequence is associated with the coordinate feature data such as its actual position and range in the coordinate system. For example, each point in the pit terrain extension chain corresponds to a specific coordinate position. Through this correspondence, a dynamic connection is established between the terrain extension chain and the coordinate feature data. For the terrain extension chain of the ore piles in the second surveying and mapping sequence, it is also mapped to its coordinate feature data in the same way. In this way, through the dynamic mapping of the terrain extension chains and the coordinate feature data, the reconstructed topological feature data reflecting the topological relationship between the terrain and ground objects in the mining area is generated. These data detail the distribution and mutual relationship of pits, ore piles, etc. in space.

[0046] The error correction unit performs redundant point cloud elimination processing on the reconstructed topological feature data. During the data acquisition process, due to the accuracy problem of the measurement equipment or the interference of environmental factors, some redundant point cloud data may be generated. For example, when scanning the mountainous area in the mining area, due to the shaking of vegetation or the interference of reflection signals, redundant point cloud data may be collected at some positions, and these data do not truly reflect the actual terrain of the mountain. The error correction unit will use specific algorithms and technical means to identify these redundant point cloud data. For example, by comparing the position, elevation, etc. of adjacent point cloud data, if it is found that some point cloud data are too different from the surrounding data and do not conform to the overall terrain change trend, these points are judged as redundant points. Then, the error correction unit will remove these redundant point cloud data from the reconstructed topological feature data, thereby improving the accuracy and quality of the reconstructed topological feature data, and enabling the final generated digital surveying and mapping results to more accurately reflect the actual terrain and ground objects in the mining area.

[0047] Example 4: The multi-dimensional superposition unit includes multiple data superposition nodes, and each data superposition node is connected to each surveying and mapping sequence in the reconstructed topological feature data and the coordinate-associated feature data through an association configuration. These data superposition nodes can fuse and superpose different data, and display terrain information from multiple dimensions. The dynamic optimization and adjustment unit is used to update the association configuration through a dynamic optimization and adjustment algorithm. During the operation of the system, the generated digital surveying and mapping results will be continuously compared with the actual terrain data. According to the deviation between the two, the association configuration is adjusted using the dynamic optimization and adjustment algorithm to minimize the deviation between the digital surveying and mapping results and the actual terrain data, and improve the accuracy of the surveying and mapping results. The terrain fault identification unit predicts terrain fracture zones based on the reconstructed topological feature data and the coordinate-associated feature data. By analyzing the terrain features and topological relationships in the data, potential terrain fracture zones are identified and this information is incorporated into the digital surveying and mapping results to make the surveying and mapping results more complete and accurate.

[0048] Suppose an engineering survey is carried out on a large park, which has various landscapes such as rolling hills, artificial lakes, pavilions and towers.

[0049] The result generation layer starts to work. The result generation layer mainly includes a multi-dimensional superposition unit, a dynamic optimization and adjustment unit, and a terrain fault identification unit.

[0050] The multi-dimensional superposition unit includes multiple data superposition nodes, and each data superposition node is connected to each surveying and mapping sequence in the reconstructed topological feature data and the coordinate-associated feature data through an association configuration. For example, for a hill in the park, the first surveying and mapping sequence corresponding to the terrain elevation data, the second surveying and mapping sequence corresponding to the ground object distribution data (such as trees and pavilions on the hill), and the third surveying and mapping sequence corresponding to the three-dimensional point cloud data are all connected to a specific data superposition node in the multi-dimensional superposition unit. These data superposition nodes will fuse the data of different surveying and mapping sequences. Taking a certain area of the hill as an example, the terrain height information of this area (from the first surveying and mapping sequence), the ground object distribution (from the second surveying and mapping sequence, such as the location information of several trees and a pavilion), and the precise terrain details presented by the three-dimensional point cloud data (from the third surveying and mapping sequence) are integrated to display the detailed information of this area from multiple dimensions. Through this multi-dimensional superposition, richer and more comprehensive park landscape information can be presented in the digital surveying and mapping results. Users can not only see the terrain undulations of the hill, but also clearly know the distribution of ground objects on it.

[0051] The dynamic optimization and adjustment unit is used to update the associated configuration through a dynamic optimization and adjustment algorithm to minimize the deviation between the digital mapping results and the actual terrain data. During the mapping of the park, as data is collected and processed, the generated digital mapping results will be continuously compared with the actual terrain data. For example, when mapping an artificial lake, it is found that there is a certain deviation between the boundary of the artificial lake in the digital mapping results and the actual boundary. The dynamic optimization and adjustment unit will use the dynamic optimization and adjustment algorithm to adjust the associated configuration of the data overlay nodes related to the artificial lake in the multi-dimensional overlay unit according to this deviation. It may adjust the weights of different mapping sequence data during overlay, or change the order of data fusion, etc. After multiple adjustments, the boundary of the artificial lake in the digital mapping results is closer to the actual situation, thus improving the accuracy of the entire digital mapping results.

[0052] The terrain fault identification unit predicts terrain fault zones based on reconstructed topological feature data and coordinate association feature data to generate digital mapping results. In the park, although there are no obvious terrain fault zones like in mountainous areas with complex geology, there may still be some potential terrain change areas caused by engineering construction or natural factors. For example, a certain road in the park may have a potential risk of collapse due to underground cavities, forming a situation similar to a terrain fault. The terrain fault identification unit predicts the possible locations of terrain fault zones by analyzing the reconstructed topological feature data and coordinate association feature data, such as analyzing the elevation change trend of the terrain under the road and the continuity of the terrain in adjacent areas. In the digital mapping results, the information on these predicted potential terrain fault zones will be marked, providing important references for the subsequent construction and maintenance of the park and avoiding improper engineering activities in these potentially dangerous areas.

[0053] Embodiment 5: An integrated method for engineering surveying and mapping informatization, which is applied to the above-mentioned integrated system for engineering surveying and mapping informatization. First, the multi-source data acquisition module obtains the spatial information data of the target surveying and mapping area. These spatial information data include the first surveying sequence corresponding to the terrain elevation data, the second surveying sequence corresponding to the ground object distribution data, and the third surveying sequence corresponding to the three-dimensional point cloud data. The terrain elevation data includes the first reference waveform generated by the multi-band laser scanning device and the second reflected waveform collected by the distributed surveying sensors. Then, the data fusion processing module performs dynamic coordinate system calibration processing on the spatial information data and inputs the calibrated spatial information data into the integrated analysis and processing layer. Next, the data preprocessing unit in the integrated analysis and processing layer performs spatial segmentation and outlier removal on the original surveying and mapping data stream. The topology analysis unit in the integrated analysis and processing layer performs joint modeling based on the historical reference data and historical reflection data of multiple historical surveying cycles. The topology analysis unit includes a spatial registration layer, a topology reconstruction layer, and a result generation layer connected in sequence. Finally, according to the output result of the integrated analysis and processing layer, the data fusion processing module generates the digital surveying and mapping results of the target surveying and mapping area. Throughout the process, each module and unit cooperate with each other, continuously collect, process, and analyze the data, and finally generate high-quality digital surveying and mapping results to meet the actual needs of engineering surveying and mapping.

[0054] Suppose an engineering surveying and mapping is to be carried out on a newly planned residential community to provide accurate data support for subsequent community construction and facility layout.

[0055] The multi-source data acquisition module starts to work. The multi-band laser scanning device emits laser beams of different frequencies to the area where the community is located. After the laser beams are reflected by objects such as the ground and buildings, the first reference waveform is generated. For example, when scanning the foundation position of the planned building in the community, the laser beams return from various angles, carrying information such as the terrain height and slope of the area, which constitute part of the first reference waveform. The distributed surveying sensors are distributed at different positions in the community, such as beside the planned roads and in the greening areas, to collect the signals reflected from the terrain surface and form the second reflected waveform. The terrain elevation data corresponding to these is the first surveying sequence. At the same time, special ground object detection equipment is used to obtain the ground object distribution data, such as recording the position and shape information of the planned community gate, fence, etc., to form the second surveying sequence. The three-dimensional laser scanner scans the entire area to measure the three-dimensional coordinates of each point and generates the three-dimensional point cloud data, which is the third surveying sequence.

[0056] The data fusion processing module is started. It performs dynamic coordinate system calibration processing on the obtained spatial information data. Because during the data acquisition process, the data collected by different devices may be based on different coordinate systems, unified calibration is required. Suppose there is a coordinate transformation formula: , (where are the coordinates of the original data, are the calibrated coordinates, and , , , , , are parameters determined according to the conversion relationship between different coordinate systems). Through this coordinate transformation, all data are unified into a standard coordinate system, and then the calibrated spatial information data are input into the integrated analysis and processing layer.

[0057] The data preprocessing unit in the integrated analysis and processing layer starts to perform spatial segmentation and outlier removal on the original surveying and mapping data stream. Taking the first surveying and mapping sequence as an example, according to the planned area of the community, it is divided into different spatial segments, such as by different building clusters. Within each spatial segment, outliers are judged by setting a certain data range threshold. For example, if the terrain elevation data of a certain measurement point deviates from the data of surrounding points by more than the set threshold (such as more than ±5 meters from the average elevation value), it is judged as an outlier and removed. The topology analysis unit performs joint modeling based on the historical reference data and historical reflection data of multiple historical surveying and mapping cycles (if there has been relevant surveying and mapping data in this area before). Among them, the spatial registration layer performs spatial domain alignment processing on each surveying and mapping sequence to make the data of different sequences comparable in space; the topology reconstruction layer analyzes the dynamic correlation relationship between each surveying and mapping sequence; the result generation layer performs multi-dimensional superposition based on these processing results.

[0058] According to the output result of the integrated analysis and processing layer, the data fusion processing module generates the digital surveying and mapping results of the target surveying area. This digital surveying and mapping result is presented in the form of an electronic map, which contains information such as the terrain and landforms, distribution of ground objects, and accurate 3D models of the community. For example, on the electronic map, the terrain height changes in different areas, the positions and shapes of planned buildings, and the layout of roads and greening areas in the community can be clearly seen, providing accurate and reliable data basis for the planning and design, construction of the community.

[0059] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0060] ​​​​​​​​​​​​​​​​Although embodiments of the present invention have been shown and described, those of ordinary skill in the art will appreciate that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An information integration system for engineering surveying and mapping, characterized in that, Including: A multi-source data acquisition module for obtaining spatial information data of a target surveying and mapping area. The spatial information data includes a first surveying and mapping sequence corresponding to terrain elevation data, a second surveying and mapping sequence corresponding to ground feature distribution data, and a third surveying and mapping sequence corresponding to three-dimensional point cloud data. The terrain elevation data includes a first reference waveform generated by a multi-band laser scanning device and a second reflected waveform collected by a distributed surveying and mapping sensor; A data fusion and processing module for performing dynamic coordinate system calibration processing on the spatial information data and inputting it into an integrated analysis and processing layer for feature extraction, and generating a digital surveying and mapping result of the target surveying and mapping area according to the output result of the integrated analysis and processing layer; The integrated analysis and processing layer includes a data preprocessing unit and a topology analysis unit. Among them, the data preprocessing unit is used for spatial segmentation and outlier removal of the original surveying and mapping data stream, and the topology analysis unit is obtained by jointly modeling based on historical reference data and historical reflection data of multiple historical surveying and mapping cycles; the topology analysis unit includes a spatial registration layer, a topology reconstruction layer, and a result generation layer connected in sequence.

2. The engineering surveying and mapping informatization integration system according to claim 1, characterized in that, The spatial registration layer is used for performing spatial domain alignment processing on multiple surveying and mapping sequences included in the original surveying and mapping data stream to obtain coordinate correlation feature data; the topology reconstruction layer is used for modeling the dynamic correlation relationship between the coordinate correlation feature data corresponding to each surveying and mapping sequence to obtain reconstructed topology feature data; the result generation layer is used for performing multi-dimensional superposition based on the reconstructed topology feature data and the coordinate correlation feature data to generate a digital surveying and mapping result.

3. The engineering surveying and mapping informatization integration system according to claim 2, characterized in that, The modeling of the dynamic correlation relationship between the coordinate correlation feature data corresponding to each surveying and mapping sequence to obtain reconstructed topology feature data includes: Using a dynamic reference calibration algorithm to identify key terrain nodes in the coordinate correlation feature data, and determining spatial correlation sequences corresponding to each surveying and mapping sequence based on the terrain type corresponding to each key terrain node; Calculating the elevation similarity between nodes with the same terrain type in the spatial correlation sequences corresponding to any two surveying and mapping sequences, and determining the reconstructed topology feature data between the any two surveying and mapping sequences based on the elevation similarity.

4. The engineering surveying and mapping informatization integration system according to claim 3, wherein, The calculation of the elevation similarity between nodes with the same terrain type in the spatial correlation sequences corresponding to any two surveying and mapping sequences includes: When there is a difference in the number of terrain nodes in the spatial correlation sequences corresponding to the any two surveying and mapping sequences, performing virtual node compensation based on the terrain type corresponding to the end terrain nodes in the one with fewer terrain nodes, and calculating the elevation similarity between nodes with the same terrain type based on the compensated data.

5. The engineering surveying and mapping informatization integration system according to claim 1, wherein The data preprocessing unit is specifically used for: Performing grid segmentation on the terrain information included in the first surveying and mapping sequence, the second surveying and mapping sequence, and the third surveying and mapping sequence according to a preset spatial division rule to obtain a standardized first surveying and mapping sequence, a standardized second surveying and mapping sequence, and a standardized third surveying and mapping sequence; The dynamic weight allocation method is adopted to perform real-time smoothing processing on the standardized first mapping sequence and the standardized second mapping sequence, and the fixed weight filtering method is adopted to perform static noise reduction on the standardized third mapping sequence, generating a first fusion sequence, a second fusion sequence and a third fusion sequence; wherein, the first fusion sequence includes the processed first reference waveform and the processed second reflection waveform.

6. The engineering surveying and mapping informatization integration system according to claim 5, characterized in that, The data preprocessing unit is further configured to: Calculate the spatial matching degree between the processed first reference waveform and the processed second reflection waveform in the historical mapping period; Predict the predicted elevation amount of the processed second reflection waveform in the real-time mapping period according to the spatial matching degree and the terrain feature of the processed first reference waveform in the real-time mapping period; Generate target space registration data according to the processed second reflection waveform and its predicted elevation amount, and use the mapping sequence corresponding to the target space registration data as the first fusion sequence.

7. The engineering surveying and mapping informatization integration system according to claim 2, characterized in that, The topology reconstruction layer specifically includes: A space association unit, configured to perform terrain continuity analysis on each mapping sequence included in the coordinate association feature data respectively, so as to extract corresponding terrain extension chains from each mapping sequence; A topology matching unit, configured to perform dynamic mapping on the terrain extension chains extracted from each mapping sequence and the corresponding coordinate feature data to generate reconstructed topology feature data.

8. The engineering surveying and mapping informatization integration system according to claim 7, characterized in that, The topology reconstruction layer further includes: An error correction unit, configured to perform redundant point cloud elimination processing on the reconstructed topology feature data.

9. The engineering surveying and mapping informatization integration system according to claim 2, characterized in that, The result generation layer specifically includes: A multi-dimensional superposition unit, including a plurality of data superposition nodes, and each data superposition node is connected to each mapping sequence in the reconstructed topology feature data and the coordinate association feature data through associated configuration; A dynamic optimization adjustment unit, configured to update the associated configuration through a dynamic optimization adjustment algorithm to minimize the deviation between the digital mapping result and the actual terrain data; A terrain fault identification unit, configured to perform terrain fracture zone prediction based on the reconstructed topology feature data and the coordinate association feature data to generate a digital mapping result.

10. An engineering surveying and mapping informatization integration method, applied to the engineering surveying and mapping informatization integration system according to any one of claims 1 to 9, characterized in that, Including the following steps: Step 1: Obtain the spatial information data of the target mapping area through the multi-source data acquisition module. The spatial information data includes a first mapping sequence corresponding to terrain elevation data, a second mapping sequence corresponding to ground object distribution data, and a third mapping sequence corresponding to three-dimensional point cloud data. The terrain elevation data includes a first reference waveform generated by a multi-band laser scanning device and a second reflection waveform collected by a distributed mapping sensor; Step 2: Use the data fusion processing module to perform dynamic coordinate system calibration processing on the spatial information data, and input the calibrated spatial information data into the integrated analysis processing layer; Step 3: Perform spatial segmentation and outlier rejection on the original mapping data stream through the data preprocessing unit in the integrated analysis processing layer; the topology analysis unit in the integrated analysis processing layer performs joint modeling based on the historical reference data and historical reflection data of multiple historical mapping periods. The topology analysis unit includes a space registration layer, a topology reconstruction layer and a result generation layer connected in sequence; Step 4: Generate a digital mapping result of the target mapping area through a data fusion processing module according to the output result of the integrated analysis and processing layer.

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