An engineering surveying and mapping information integration system and method

Abstract

The present invention relates to the field of engineering surveying and mapping technology, and discloses an engineering surveying and mapping information integration system and method. The system includes 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, object distribution, and three-dimensional point cloud data, wherein the terrain elevation data contains a specific waveform. The data fusion processing module calibrates the data and inputs it into the integrated analysis processing layer, which includes a data preprocessing unit and a topology analysis unit. The data preprocessing unit processes the original surveying and mapping data stream, and the topology analysis unit is obtained through joint modeling, and its various layers collaboratively generate digital surveying and mapping results. The method is applied to the above-mentioned system to realize multi-source data acquisition, processing, and analysis, generate high-precision digital surveying and mapping results, improve the efficiency and accuracy of engineering surveying and mapping, and provide strong data support for engineering construction.

Description

Technical Field

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

[0002] In modern engineering construction, engineering surveying and mapping is a key foundational link in project planning, design, construction, and operation, and its importance is self-evident. As projects continue to expand in scale and complexity, traditional surveying and mapping technologies and methods face numerous challenges and are unable to meet the growing demands of engineering.

[0003] Traditional surveying and mapping methods have obvious limitations in data collection. For example, relying on manual field measurements is not only inefficient, but also difficult for surveyors to reach areas with complex terrain and harsh environments, such as mountain gorges, swampy wetlands, and dense jungles, making data collection difficult or even impossible. Even with simple measuring instruments, the data obtained is often only discrete and local, and cannot fully reflect the topography and distribution of land features in the entire surveying area. When obtaining terrain elevation data, the traditional level measurement method requires setting measurement points at specific locations for each measurement, and the measurement range is limited. To complete large-scale terrain mapping, it requires a lot of manpower, material resources, and time.

[0004] The backwardness of data processing and analysis methods is also a major problem facing traditional surveying and mapping. When dealing with massive surveying and mapping data, traditional methods usually use manual calculations and simple data processing software, which makes it difficult to efficiently integrate and conduct in-depth analysis of multi-source, heterogeneous data. Different types of data, such as terrain elevation data, land feature distribution data, three-dimensional 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 dynamically changing land features, traditional methods cannot accurately extract their features, making it difficult to achieve accurate modeling and digital expression of the surveying and mapping area. In urban construction surveying and mapping, with the rapid development of cities, buildings are constantly updated. Traditional surveying and mapping methods find it difficult to obtain building change information in a timely and accurate manner and integrate it into the overall surveying and mapping results.

[0005] Furthermore, traditional surveying and mapping technologies perform poorly in terms of real-time and dynamic monitoring. During the construction process, the terrain and landforms change as construction progresses. Traditional surveying and mapping cannot track these changes in real time, making it difficult to provide accurate data support for engineering decisions. In large-scale water conservancy projects, operations such as river excavation and filling can cause significant changes in the terrain. Without real-time monitoring, construction plans cannot be adjusted in a timely manner, potentially affecting project quality and progress. Furthermore, traditional surveying and mapping results have a long update cycle and often cannot meet the requirements of modern engineering construction for the timeliness of surveying and mapping data.

[0006] Faced with these challenges, the engineering surveying and mapping field urgently needs innovative technologies that can efficiently collect multi-source data, accurately process and analyze it, and generate accurate digital surveying and mapping results in real time. This approach can adapt to the complex and ever-changing demands of engineering construction, improve its quality and efficiency, and reduce project risks. The engineering surveying and mapping information integration system and method proposed in this paper were developed precisely to address these issues. 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 background technology.

[0008] To achieve the above-mentioned object, the present invention provides the following technical solution: an engineering surveying and mapping information integration system, the system comprising:

[0009] A multi-source data acquisition module is configured to acquire spatial information data of a target surveying and mapping area, the spatial information data comprising a first surveying and mapping sequence corresponding to terrain elevation data, a second surveying and mapping sequence corresponding to feature distribution data, and a third surveying and mapping sequence corresponding to three-dimensional point cloud data. The terrain elevation data comprises 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.

[0010] A data fusion processing module is used to perform dynamic coordinate system calibration processing on the spatial information data and input the data into the integrated analysis processing layer for feature extraction, and generate digital mapping results of the target mapping area based on the output results of the integrated analysis processing layer;

[0011] The integrated analysis and processing layer includes a data preprocessing unit and a topology analysis unit, wherein the data preprocessing unit is used to spatially segment the original surveying and mapping data stream and eliminate outliers, and the topology analysis unit is obtained by joint modeling based on historical benchmark data and historical reflection data of multiple historical surveying and mapping cycles; the topology analysis unit includes a spatial alignment layer, a topology reconstruction layer and an outcome generation layer connected in sequence.

[0012] Preferably, the spatial registration layer is used to perform spatial domain alignment processing on multiple surveying and mapping sequences contained in the original surveying and mapping data stream to obtain coordinate association feature data; the topology reconstruction layer is used to model the dynamic association relationship between the coordinate association feature data corresponding to each surveying and mapping 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 association feature data to generate digital surveying and mapping results.

[0013] Preferably, the modeling of the dynamic association relationship between the coordinate association feature data corresponding to each surveying and mapping sequence to obtain the reconstructed topological feature data includes:

[0014] A dynamic benchmark calibration algorithm is used to identify key terrain nodes in the coordinate association feature data, and a spatial association sequence corresponding to each surveying and mapping sequence is determined based on the terrain type corresponding to each key terrain node;

[0015] The elevation similarity between nodes with the same terrain type in the spatial association sequences corresponding to any two surveying and mapping sequences is calculated, and the reconstructed topological feature data between the any two surveying and mapping sequences is determined based on the elevation similarity.

[0016] Preferably, the calculation of the elevation similarity between nodes with the same terrain type in the spatial association sequences corresponding to any two surveying and mapping sequences includes:

[0017] When there is a difference in the number of terrain nodes in the spatial association sequences corresponding to any two surveying and mapping sequences, virtual node compensation is performed based on the terrain type corresponding to the terminal terrain node in the sequence with the smaller number of terrain nodes, and the elevation similarity between nodes with the same terrain type is calculated based on the compensated data.

[0018] Preferably, the data preprocessing unit is specifically used to:

[0019] Performing grid division 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;

[0020] A dynamic weight allocation method is used to perform real-time smoothing on the standardized first surveying and mapping sequence and the standardized second surveying and mapping sequence, and a fixed weight filtering method is used to perform static noise reduction on the standardized third surveying and mapping sequence to generate a first fusion sequence, a second fusion sequence, and a third fusion sequence; wherein the first fusion sequence includes a processed first reference waveform and a processed second reflection waveform.

[0021] Preferably, the data preprocessing unit is further used for:

[0022] Calculating a spatial matching degree between the processed first reference waveform and the processed second reflected waveform within a historical surveying and mapping period;

[0023] Predicting a predicted elevation of the processed second reflected waveform in the real-time mapping period based on the spatial matching degree and the terrain characteristics of the processed first reference waveform in the real-time mapping period;

[0024] Target space registration data is generated according to the processed second reflection waveform and its predicted elevation, and a surveying and mapping sequence corresponding to the target space registration data is used as a first fusion sequence.

[0025] Preferably, the topology reconstruction layer specifically includes:

[0026] a spatial association unit, configured to perform terrain continuity analysis on each surveying and mapping sequence contained in the coordinate association feature data, so as to extract a corresponding terrain extension chain from each surveying and mapping sequence;

[0027] The topology matching unit is used to dynamically map the terrain extension chain extracted from each surveying and mapping sequence with the corresponding coordinate feature data to generate reconstructed topology feature data.

[0028] Preferably, the topology reconstruction layer further includes:

[0029] The error correction unit is used to perform redundant point cloud elimination processing on the reconstructed topological feature data.

[0030] Preferably, the achievement generation layer specifically includes:

[0031] A multi-dimensional overlay unit, comprising a plurality of data overlay nodes, each of which is connected to each mapping sequence in the reconstructed topological feature data and the coordinate association feature data through an association configuration;

[0032] A dynamic optimization and adjustment unit, configured to update the associated configuration using a dynamic optimization and adjustment algorithm to minimize the deviation between the digital surveying and mapping results and the actual terrain data;

[0033] A terrain fault identification unit is used to predict terrain fault zones based on the reconstructed topological feature data and coordinate association feature data, and generate digital surveying and mapping results.

[0034] Preferably, the present invention further includes an engineering surveying and mapping information integration method, which is applied to the engineering surveying and mapping information integration system as described in any one of the above, and the method includes the following steps:

[0035] Step 1: Acquire spatial information data of the target mapping area through a 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 feature distribution data, and a third mapping sequence corresponding to 3D 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 mapping sensor.

[0036] Step 2: Use the data fusion processing module to perform dynamic coordinate system calibration on the spatial information data, and input the calibrated spatial information data into the integrated analysis processing layer;

[0037] Step 3: The original surveying and mapping data stream is spatially segmented and outliers are eliminated by the data pre-processing 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 benchmark data and historical reflection data from multiple historical surveying and mapping cycles, and the topology analysis unit includes a spatial registration layer, a topology reconstruction layer, and an output generation layer connected in sequence;

[0038] Step 4: Based on the output results of the integrated analysis and processing layer, a digital mapping result of the target mapping area is generated through a data fusion processing module.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] During data collection, a rich and diverse range of spatial information can be acquired through the multi-source data acquisition module. The combination of the first reference waveform generated by the multi-band laser scanning device and the second reflected waveform collected by the distributed mapping sensor greatly improves the accuracy and completeness of terrain elevation data. Compared to traditional single-source measurement methods, this multi-source data acquisition method can detect terrain from different angles and scales, obtaining more comprehensive terrain information. When surveying in mountainous areas, multi-band laser scanning can penetrate certain vegetation cover to obtain authentic terrain and landform data. Distributed mapping sensors can compensate for the measurement blind spots of laser scanning in certain areas, ensuring the reliability of terrain elevation data.

[0041] The design of the data fusion processing module and the integrated analysis and processing layer has brought about a qualitative leap in data processing and analysis. The spatial segmentation and outlier removal operations of the data pre-processing unit have effectively improved the quality of the original surveying and mapping data. Grid segmentation is performed through preset spatial division rules, and complex terrain information is converted into a standardized surveying and mapping sequence to facilitate subsequent processing. The application of dynamic weight allocation method and fixed weight filtering method optimizes the processing of different types of data, removes noise interference, and retains key information. For the first surveying and mapping sequence containing the first reference waveform and the second reflected waveform, dynamic weight allocation can adjust the weight according to the real-time characteristics of the data to better reflect the trend of terrain changes and improve the accuracy of the data.

[0042] The innovative design of the topological analysis unit is a highlight of the present invention. The spatial registration layer performs spatial domain alignment processing on multiple surveying and mapping sequences to establish accurate coordinate-related feature data, so that data from different sources can be analyzed under a unified spatial framework. The topological reconstruction layer models the dynamic correlation relationship between coordinate-related feature data, uses a dynamic benchmark calibration algorithm to identify key terrain nodes, determine spatial correlation sequences, and calculate elevation similarity, thereby more accurately reflecting the true structure of the terrain. In areas with complex terrain, this method can accurately capture subtle changes in the terrain and construct a high-precision terrain model. The error correction unit eliminates redundant point clouds on the reconstructed topological feature data to further improve data quality, reduce data redundancy, and improve processing efficiency.

[0043] The results generation layer generates high-quality digital surveying and mapping results through the collaborative work of a multi-dimensional overlay unit, a dynamic optimization and adjustment unit, and a terrain fault identification unit. The multi-dimensional overlay unit's multiple data overlay nodes enable comprehensive integration of different surveying and mapping sequences. The dynamic optimization and adjustment unit continuously optimizes correlation configurations through a dynamic optimization and adjustment algorithm, minimizing the deviation between digital surveying and mapping results and actual terrain data. The terrain fault identification unit predicts terrain fault zones based on reconstructed topological feature data and coordinate correlation feature data, providing important geological information for engineering construction, helping engineers plan ahead, avoid construction in dangerous areas, and ensure project safety.

[0044] Overall, the system and method of the present invention realize the information integration of engineering surveying and mapping, can quickly and accurately acquire and process multi-source surveying and mapping data, generate high-precision digital surveying and mapping results, and provide reliable data support for engineering planning, design, construction and operation. It effectively improves the efficiency and quality of engineering construction, reduces engineering costs and risks, and has broad application prospects and significant economic and social benefits in the field of engineering surveying and mapping. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a working principle diagram of an engineering surveying and mapping information integration system according to the present invention;

[0046] Figure 2 Flowchart for topological reconstruction of engineering surveying and mapping data;

[0047] Figure 3 This is a design diagram of the dynamic benchmark calibration algorithm;

[0048] Figure 4 Workflow diagram for terrain node compensation in surveying and mapping sequence;

[0049] Figure 5 This is a flowchart of multi-surveying and mapping sequence data fusion processing. DETAILED DESCRIPTION

[0050] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0051] See also Figure 1-Figure 5 The present invention provides a technical solution: The present invention provides an engineering surveying and mapping information integration system. The specific implementation steps are as follows:

[0052] The system mainly includes a multi-source data acquisition module and a data fusion processing module. The multi-source data acquisition module is used to obtain spatial information data of the target mapping area. These spatial information data include 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. Among them, 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 mapping sensor. In actual operation, the multi-band laser scanning device will emit laser beams of different frequency bands to the target mapping area. These laser beams will be reflected back after encountering the terrain surface, thereby generating a first reference waveform, which can accurately reflect the basic characteristics of the terrain. The distributed mapping sensors are distributed at different locations in the mapping area, collecting signals reflected from the terrain surface in real time to form a second reflected waveform. The two complement each other to obtain terrain elevation data more comprehensively.

[0053] The data fusion processing module performs dynamic coordinate system calibration on spatial information data. This processed data is then fed into the integrated analysis processing layer for feature extraction. The output of the integrated analysis processing layer generates digital mapping results for the target mapping area. The integrated analysis processing layer comprises a data preprocessing unit and a topology analysis unit. The data preprocessing unit is responsible for spatially segmenting the raw mapping data stream and removing outliers. Spatial segmentation divides the mapping area into distinct spatial segments based on pre-set rules, facilitating more detailed data processing. Outlier removal utilizes specific algorithms and thresholds to remove data points that significantly deviate from the normal range, ensuring data accuracy. The topology analysis unit is jointly modeled based on historical baseline data and historical reflection data from multiple historical mapping cycles. It comprises a sequentially connected spatial registration layer, a topology reconstruction layer, and a result generation layer. These layers work together to perform in-depth data analysis and processing, supporting the generation of digital mapping results.

[0054] The present invention will be further described below in conjunction with Examples 1 to 5:

[0055] Example 1: During system operation, the spatial registration layer performs spatial domain alignment on multiple mapping sequences contained in the original mapping data stream. Specifically, through specific algorithms and technical means, the data in different mapping sequences are matched and aligned in spatial coordinates, enabling them to be compared and analyzed in the same spatial reference system, thereby obtaining coordinate-related feature data. These coordinate-related feature data contain spatial association information between each mapping sequence, providing a basis for subsequent processing. The topological reconstruction layer models the dynamic association relationship between the coordinate-related feature data corresponding to each mapping sequence. In this process, a dynamic benchmark calibration algorithm is first used to identify key terrain nodes in the coordinate-related feature data. This algorithm identifies nodes that have a significant impact on the terrain structure and changes based on the characteristics and change patterns of the terrain. Then, based on the terrain type corresponding to each key terrain node, the spatial association sequence corresponding to each mapping sequence is determined. In this way, the terrain information in different mapping sequences can be associated and integrated according to the terrain type. Next, the elevation similarity between nodes with the same terrain type in the spatial association sequence corresponding to any two mapping sequences is calculated. During the calculation process, if there is a difference in the number of terrain nodes in the spatial association 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 the smaller number of terrain nodes, so that the number of nodes in the two sequences is the same. Then, the elevation similarity between nodes with the same terrain type is calculated based on the compensated data. Through these steps, reconstructed topological feature data is finally obtained. These data reflect the topological relationship and similarity of terrain features between different surveying and mapping sequences. The result generation layer performs multi-dimensional superposition based on the reconstructed topological feature data and coordinate association feature data to generate digital surveying and mapping results. During the multi-dimensional superposition process, the reconstructed topological feature data and coordinate association feature data will be fused according to certain rules and algorithms to display terrain information from multiple dimensions and generate more accurate and detailed digital surveying and mapping results.

[0056] Suppose you want to conduct engineering surveying in a mountainous area. The terrain of this mountainous area is complex, with various landforms such as mountains, canyons, and rivers.

[0057] During surveying and mapping, the multi-source data acquisition module begins operating. A multi-band laser scanning device emits laser beams of different frequencies into the mountainous area. These laser beams reflect back upon encountering the terrain surface, generating a first baseline waveform. For example, when scanning a mountain peak, the laser beams return from different angles, carrying terrain information such as the peak's height and slope. Simultaneously, distributed mapping sensors are deployed throughout the mountainous area, such as at key locations like valleys and riverbanks, collecting signals reflected from the terrain surface in real time to generate a second reflected waveform. These waveforms form the first mapping sequence corresponding to terrain elevation data. The second mapping sequence, corresponding to feature distribution data, is acquired using specialized feature detection equipment. For example, this sequence identifies the location and shape of trees, buildings, and other structures within the mountainous area. The third mapping sequence, corresponding to 3D point cloud data, is generated by a 3D laser scanner, which accurately measures the 3D coordinates of each point in the mountainous area, generating a dense point cloud.

[0058] After acquiring this spatial information data, it enters the spatial registration layer of the integrated analysis and processing layer. Taking the terrain data from the first mapping sequence and the feature distribution data from the second mapping sequence as an example, the spatial registration layer uses a specific algorithm to match and align the two in spatial coordinates. For example, the terrain height data at a certain location in the first mapping sequence is spatially aligned with the feature distribution data near the same location in the second mapping sequence, thereby generating coordinate association feature data that demonstrates the spatial connection between the different mapping sequences.

[0059] Next, the topology reconstruction layer begins its work. A dynamic benchmark calibration algorithm is used to identify key terrain nodes in the coordinate-association feature data. In this mountainous area, key terrain nodes might include the apex of a mountain peak, the lowest point in a valley, and the confluence of a river. The spatial association sequence corresponding to each mapping sequence is determined based on the terrain type corresponding to each key terrain node. For example, for a key terrain node, the terrain data associated with the peak in the first mapping sequence and the feature data surrounding the peak in the second mapping sequence form their respective spatial association sequences.

[0060] Then, the elevation similarity between nodes with the same terrain type in the spatially associated sequences corresponding to any two mapping sequences is calculated. Assuming that both the first and second mapping sequences have terrain data related to hillsides in a certain area, if there is a difference in the number of terrain nodes related to hillsides between the two sequences, virtual node compensation is performed based on the terrain type corresponding to the terminal node in the sequence with the fewer terrain nodes. For example, if the first mapping sequence has 10 terrain nodes related to the hillside, while the second sequence has only 8, and the terminal terrain node of the second mapping sequence is a gentle slope, two virtual gentle slope nodes are added after it to make the number of nodes in the two sequences the same. Next, the elevation similarity between nodes with the same terrain type is calculated based on the compensated data. By comparing the elevation information of these nodes, reconstructed topological feature data between the two sequences is obtained. This data reflects the similarity of the terrain characteristics of the hillside area between the two mapping sequences.

[0061] Finally, the results generation layer performs multi-dimensional overlay based on the reconstructed topological feature data and coordinate-related feature data. The terrain data from the first surveying sequence, the feature data from the second surveying sequence, and the 3D point cloud data from the third surveying sequence are integrated according to specific rules. This layer displays information about the mountainous area from multiple dimensions, including terrain height, feature distribution, and 3D spatial position. This generates detailed and accurate digital surveying results, such as an electronic map that includes the mountain's topography, feature distribution, and precise 3D models, providing a reliable basis for subsequent project planning and construction.

[0062] Example 2: During operation, the data preprocessing unit first grids the terrain information contained in the first, second, and third mapping sequences according to preset spatial division rules. This gridding divides the complex terrain information into regular grids, facilitating subsequent processing and analysis, thereby generating standardized first, second, and third mapping sequences. Next, a dynamic weight allocation method is used to perform real-time smoothing on the standardized first and second mapping sequences. This dynamic weight allocation method automatically adjusts weights based on real-time data changes and characteristics, smoothing the data and removing noise and fluctuations, making the data more stable and accurate. Simultaneously, a fixed-weight filtering method is used to perform static noise reduction on the standardized third mapping sequence. The data is filtered using fixed weights to remove noise interference, generating the first, second, and third fused sequences. The first fused sequence contains the processed first reference waveform and the processed second reflected waveform. Furthermore, the data preprocessing unit calculates the spatial matching between the processed first reference waveform and the processed second reflected waveform over the historical mapping period. By analyzing the matching of their spatial positions and features over historical periods, the correlation between them is understood. The predicted elevation of the processed second reflected waveform during the real-time mapping period is then predicted based on the spatial matching and the terrain characteristics of the processed first reference waveform during the real-time mapping period. Using this information, target spatial registration data is generated based on the processed second reflected waveform and its predicted elevation. The mapping sequence corresponding to the target spatial registration data is then used as the first fusion sequence, further improving the accuracy and reliability of the data.

[0063] Suppose that engineering surveying is carried out on an area to be developed in a city. The area contains different types of terrain, such as flat land, small hills, and some existing buildings and roads.

[0064] In this embodiment, the data preprocessing unit initiates the workflow. First, the terrain information contained in the first mapping sequence (terrain elevation data), the second mapping sequence (feature distribution data), and the third mapping sequence (3D point cloud data) from the multi-source data acquisition module is gridded according to preset spatial division rules. For example, the entire development area is divided into square grids with a side length of 5 meters, and the corresponding data is collected and organized for each grid. After this gridding process, the originally complex and irregular terrain information is organized into standardized first mapping sequence, standardized second mapping sequence, and standardized third mapping sequence, facilitating subsequent processing.

[0065] After gridding, the data preprocessing unit performs targeted processing on the different survey sequences. A dynamic weighting method is used for real-time smoothing of the standardized first and second survey sequences. For example, the terrain elevation data from the first survey sequence fluctuates within this undeveloped area, potentially due to measurement errors or subtle undulations in the terrain itself. The dynamic weighting method automatically adjusts the weight of each data point based on real-time data changes. In relatively flat areas, closely spaced data points with minimal fluctuations are given higher weights, giving them a greater impact on the final smoothing result and highlighting the stability of the terrain. Data points with unusual fluctuations are weighted lower to minimize their impact on the overall data. For example, in a flat area, the terrain elevation data for several consecutive grids are 100 meters, 100.1 meters, 100 meters, and 100.2 meters. The dynamic weighting method assigns higher weights to the relatively stable data points at 100 meters and 100.1 meters, ensuring that after smoothing, the terrain elevation data for this area more closely resembles the actual flat terrain.

[0066] For the standardized third surveying sequence (3D point cloud data), a fixed-weight filtering method is used for static noise reduction. Due to the large volume of 3D point cloud data, it is easily affected by noise during the acquisition process. The fixed-weight filtering method filters each point cloud data according to pre-set weights. For example, 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. This fixed-weight combination is used to process the data, removing noise points that significantly deviate from the normal range, allowing the 3D point cloud data to more accurately reflect the actual terrain and ground features of the 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 baseline waveform and the processed second reflection waveform.

[0067] The data preprocessing unit further analyzes the data. It calculates the spatial match between the processed first reference waveform and the processed second reflected waveform over the historical surveying cycle. Assuming the area has been surveyed multiple times before, resulting in historical surveying data, the unit compares the currently processed waveform data with the historical data to analyze their matching in terms of spatial position and topographical features. For example, the first reference waveform and the second reflected waveform can be used to measure topographical changes over different surveying cycles. If the two waveforms in a particular area maintain a high degree of consistency in spatial position and features over the historical period, this indicates that the terrain in that area is relatively stable and the spatial match is high. Conversely, if the differences are significant, the spatial match is low.

[0068] The predicted elevation of the processed second reflected waveform during the real-time mapping cycle is predicted based on the spatial match and the terrain characteristics of the processed first baseline waveform during the real-time mapping cycle. For example, if the first baseline waveform shows a slight upward trend in the terrain of a certain area during the real-time mapping cycle, combined with the high spatial match between the two waveforms in the same area during the previous historical mapping cycle, the predicted elevation of the processed second reflected waveform in that area will also increase accordingly.

[0069] Finally, the processed second reflection waveform and its predicted elevation are used to generate target spatial registration data, and the mapping sequence corresponding to the target spatial registration data is used as the first fused sequence. This process results in a first fused sequence that more accurately reflects the terrain elevation information of the area, providing a more reliable data foundation for subsequent data processing and the generation of digital mapping results.

[0070] Example 3: The spatial association unit performs terrain continuity analysis on each surveying and mapping sequence contained in the coordinate association feature data. By analyzing the continuity of the terrain data, the trend and law of terrain changes are found, and the corresponding terrain extension chain is extracted from each surveying and mapping sequence. These terrain extension chains reflect the extension and change of the terrain in space. The topology matching unit dynamically maps the terrain extension chain extracted from each surveying and mapping sequence with the corresponding coordinate feature data. During the mapping process, a connection between the characteristics of the terrain extension chain and the coordinate feature data is established based on the relationship between the two to generate reconstructed topological feature data. The error correction unit performs redundant point cloud elimination processing on the reconstructed topological feature data. During the data acquisition and processing process, 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 topological feature data.

[0071] Suppose an engineering survey is conducted on a mining area with various complex terrains, such as mining pits, piles of ore, and surrounding mountains and valleys.

[0072] The topology reconstruction layer begins its work. The spatial association unit begins by performing a terrain continuity analysis for each mapping sequence contained in the coordinate-association feature data. For example, when analyzing the pit area of ​​a mining area, the spatial association unit analyzes the data for continuity along the pit's boundaries and interior. By comprehensively evaluating information such as changes in elevation and slope between adjacent data points, it identifies trends and patterns in terrain change. If the elevation in a particular data segment consistently decreases while the slope remains relatively stable, it can be determined that this segment exhibits a certain degree of terrain continuity. The corresponding terrain extension chain can then be extracted from this mapping sequence. For example, a terrain extension chain reflecting the pit's topographic changes is formed around the pit's boundary, from the pit's edge to the pit's bottom. Similarly, when analyzing the distribution of ore piles in the second mapping sequence, the continuity of feature data along the pit's boundaries and interior is analyzed to extract terrain extension chains associated with the ore piles.

[0073] The topology matching unit dynamically maps the terrain extension chain extracted from each mapping sequence with the corresponding coordinate feature data to generate reconstructed topology feature data. In this mining area, the mine pit terrain extension chain extracted from the first mapping sequence is associated with its coordinate feature data, such as its actual position and range in the coordinate system. For example, each point in the mine 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. The terrain extension chain of the ore pile in the second mapping sequence is also mapped with its coordinate feature data. In this way, by dynamically mapping the terrain extension chain with the coordinate feature data, reconstructed topology feature data reflecting the topological relationship between the mining area's terrain and features is generated. This data details the spatial distribution and interrelationships of the mine pits, ore piles, and so on.

[0074] The error correction unit performs redundant point cloud elimination processing on the reconstructed topological feature data. During the data collection process, some redundant point cloud data may be generated due to accuracy issues of the measuring equipment or interference from environmental factors. For example, when scanning the mountains in the mining area, the shaking of vegetation or interference from reflected signals may result in the collection of redundant point cloud data at certain locations. This data does not truly reflect the actual terrain of the mountains. The error correction unit uses specific algorithms and technical means to identify these redundant point cloud data. For example, by comparing the position, elevation and other information of adjacent point cloud data, if it is found that some point cloud data differs too much from the surrounding data and does not conform to the overall terrain change trend, these points are judged to be redundant points. The error correction unit then removes these redundant point cloud data from the reconstructed topological feature data, thereby improving the accuracy and quality of the reconstructed topological feature data, so that the final digital surveying and mapping results can more accurately reflect the actual terrain and ground features of the mining area.

[0075] Example 4: The multi-dimensional overlay unit includes multiple data overlay nodes, and each data overlay node is connected to each mapping sequence in the reconstructed topological feature data and coordinate associated feature data through an associated configuration. These data overlay nodes can fuse and overlay different data to display terrain information from multiple dimensions. The dynamic optimization adjustment unit is used to update the associated configuration through a dynamic optimization adjustment algorithm. During the operation of the system, the generated digital mapping results will be continuously compared with the actual terrain data. According to the deviation between the two, the associated configuration will be adjusted using a dynamic optimization adjustment algorithm to minimize the deviation between the digital mapping results and the actual terrain data, thereby improving the accuracy of the mapping results. The terrain fault identification unit predicts terrain fault zones based on the reconstructed topological feature data and coordinate associated feature data. By analyzing the terrain features and topological relationships in the data, possible terrain fault zones are identified, and this information is integrated into the digital mapping results to make the mapping results more complete and accurate.

[0076] Suppose that engineering surveying is carried out on a large park with various landscapes such as rolling hills, artificial lakes, pavilions, and towers.

[0077] The results generation layer starts working. The results generation layer mainly includes multi-dimensional superposition unit, dynamic optimization adjustment unit and terrain fault identification unit.

[0078] The multidimensional overlay unit contains multiple data overlay nodes, each of which is connected to various mapping sequences within the reconstructed topological feature data and coordinate association feature data through association configuration. For example, for a hill within a park, the first mapping sequence corresponding to terrain elevation data, the second mapping sequence corresponding to feature distribution data (such as trees and pavilions on the hill), and the third mapping sequence corresponding to 3D point cloud data are all connected to specific data overlay nodes within the multidimensional overlay unit. These data overlay nodes fuse data from different mapping sequences. For example, for a specific area of ​​the hill, terrain elevation information (from the first mapping sequence), feature distribution (from the second mapping sequence, such as the location of trees and a pavilion) and precise topographic details presented by the 3D point cloud data (from the third mapping sequence) are integrated to present detailed information about the area from multiple dimensions. This multidimensional overlay enables the digital mapping of the park to present richer and more comprehensive information about the park landscape, allowing users to not only visualize the hill's topography but also clearly understand the distribution of features within it.

[0079] The dynamic optimization and adjustment unit is used to update the associated configuration through the dynamic optimization and adjustment algorithm to minimize the deviation between the digital mapping results and the actual terrain data. In the process of surveying and mapping the park, as the data is collected and processed, the generated digital mapping results will be continuously compared with the actual terrain data. For example, when surveying the artificial lake, it was found that there was a certain deviation between the boundary of the artificial lake in the digital mapping results and the actual boundary. Based on this deviation, 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. It may adjust the weights of different mapping sequence data when superimposed, 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, thereby improving the accuracy of the entire digital mapping results.

[0080] The terrain fault identification unit predicts terrain fault zones based on the reconstructed topological feature data and coordinate-related feature data, and generates digital mapping results. In the park, although there are no obvious terrain fault zones like in geologically complex areas such as mountainous areas, there may be some areas of potential terrain changes due to engineering construction or natural factors. For example, a 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 location of possible terrain fault zones by analyzing the reconstructed topological feature data and coordinate-related feature data, such as analyzing the elevation change trend of the terrain under the road, the continuity of the terrain in adjacent areas, and other information. In the digital mapping results, the information of these predicted potential terrain fault zones will be marked, providing an important reference for the subsequent construction and maintenance of the park, and avoiding inappropriate engineering activities in these potentially dangerous areas.

[0081] Example 5: A method for engineering surveying and mapping information integration is applied to the aforementioned engineering surveying and mapping information integration system. First, a multi-source data acquisition module acquires spatial information data for the target surveying area. This spatial information data includes a first surveying sequence corresponding to terrain elevation data, a second surveying sequence corresponding to feature 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 reflection waveform collected by distributed surveying sensors. Next, a data fusion processing module performs dynamic coordinate system calibration on the spatial information data and inputs the calibrated spatial information data into an integrated analysis processing layer. The data preprocessing unit in the integrated analysis processing layer then spatially segments the raw surveying and mapping data stream and removes outliers. A topology analysis unit in the integrated analysis processing layer performs joint modeling based on historical reference data and historical reflection data from multiple historical surveying cycles. The topology analysis unit comprises a sequentially connected spatial registration layer, a topology reconstruction layer, and a result generation layer. Finally, based on the output of the integrated analysis processing layer, the data fusion processing module generates digital surveying and mapping results for the target surveying area. Throughout the entire process, various modules and units collaborate with each other to continuously collect, process and analyze data, ultimately generating high-quality digital surveying and mapping results to meet the actual needs of engineering surveying and mapping.

[0082] Suppose you want to conduct engineering surveying for a newly planned residential community to provide accurate data support for subsequent community construction and facility layout.

[0083] The multi-source data acquisition module begins operation. A multi-band laser scanner emits laser beams of different frequencies into the residential area. These laser beams reflect off objects such as the ground and buildings, generating a first baseline waveform. For example, when scanning the planned building foundations within the residential area, laser beams return from various angles, carrying information such as the area's terrain height and slope. This information forms part of the first baseline waveform. Distributed mapping sensors are located at various locations within the residential area, such as along planned roads and in green areas, collecting signals reflected from the terrain surface to form a second reflected waveform. This terrain elevation data corresponds to the first mapping sequence. Simultaneously, specialized object detection equipment is used to obtain object distribution data, such as the location and shape of planned residential gates and walls, forming the second mapping sequence. A 3D laser scanner scans the entire area, measuring the 3D coordinates of each point and generating 3D point cloud data, representing the third mapping sequence.

[0084] The data fusion processing module starts. It performs dynamic coordinate system calibration on the acquired spatial information data. Because the data collected by different devices may be based on different coordinate systems during the data acquisition process, unified calibration is required. Assume that there is a coordinate conversion formula: , (in, are the coordinates of the original data, are the calibrated coordinates, 、 、 、 、 、 Through this coordinate transformation, all data are unified into a standard coordinate system, and then the calibrated spatial information data is input into the integrated analysis and processing layer.

[0085] The data preprocessing unit in the integrated analysis and processing layer begins spatial segmentation and outlier removal of the raw mapping data stream. Taking the first mapping sequence as an example, it is divided into different spatial segments based on the planned area of ​​the residential complex, such as by different building clusters. Within each segment, outliers are identified by setting a certain data range threshold. For example, if the terrain elevation data of a measured point deviates from the data of surrounding points by more than a set threshold (e.g., ±5 meters from the mean elevation), it is identified as an outlier and removed. The topological analysis unit performs joint modeling based on historical benchmark data and historical reflection data from multiple historical mapping cycles (if relevant mapping data has been previously obtained for the area). The spatial registration layer spatially aligns the mapping sequences to make the data from different sequences spatially comparable. The topological reconstruction layer analyzes the dynamic relationships between the mapping sequences. The output generation layer performs multidimensional overlay based on these processing results.

[0086] Based on the output of the integrated analysis and processing layer, the data fusion processing module generates a digital mapping result for the target survey area. This digital mapping result is presented as an electronic map, containing information such as the community's topography, feature distribution, and a precise 3D model. For example, the electronic map clearly shows the terrain elevation changes in different areas, the location and shape of planned buildings, and the layout of roads and green areas within the community, providing accurate and reliable data for community planning, design, and construction.

[0087] It should be noted that, in this document, relational terms such as first and second, etc., are used only 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 terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0088] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An engineering surveying and mapping information integration system, characterized in that: include: A multi-source data acquisition module is configured to acquire spatial information data of a target surveying and mapping area, the spatial information data comprising a first surveying and mapping sequence corresponding to terrain elevation data, a second surveying and mapping sequence corresponding to feature distribution data, and a third surveying and mapping sequence corresponding to three-dimensional point cloud data. The terrain elevation data comprises 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 processing module is used to perform dynamic coordinate system calibration processing on the spatial information data and input the data into the integrated analysis processing layer for feature extraction, and generate digital mapping results of the target mapping area based on the output results of the integrated analysis processing layer; The integrated analysis and processing layer includes a data preprocessing unit and a topology analysis unit, wherein the data preprocessing unit is used to spatially segment the original surveying and mapping data stream and eliminate outliers, and the topology analysis unit is obtained by joint modeling based on historical benchmark 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 an output generation layer connected in sequence; The topology reconstruction layer is used to model the dynamic association relationship between the coordinate association feature data corresponding to each surveying and mapping sequence to obtain reconstructed topology feature data; The dynamic correlation relationship between the coordinate correlation feature data corresponding to each surveying and mapping sequence is modeled to obtain reconstructed topological feature data, including: A dynamic benchmark calibration algorithm is used to identify key terrain nodes in the coordinate association feature data, and a spatial association sequence corresponding to each surveying and mapping sequence is determined based on the terrain type corresponding to each key terrain node; The elevation similarity between nodes with the same terrain type in the spatial association sequences corresponding to any two surveying and mapping sequences is calculated, and the reconstructed topological feature data between the any two surveying and mapping sequences is determined based on the elevation similarity.

2. The engineering surveying and mapping information integration system according to claim 1, characterized in that: The spatial registration layer is used to perform spatial domain alignment processing on multiple surveying and mapping sequences contained in the original surveying and mapping data stream to obtain coordinate-related feature data; the result generation layer is used to perform multi-dimensional superposition based on the reconstructed topological feature data and coordinate-related feature data to generate digital surveying and mapping results.

3. The engineering surveying and mapping information integration system according to claim 1, characterized in that: The calculation of the elevation similarity between nodes with the same terrain type in the spatial association sequences corresponding to any two surveying and mapping sequences includes: When there is a difference in the number of terrain nodes in the spatial association sequences corresponding to any two surveying and mapping sequences, virtual node compensation is performed based on the terrain type corresponding to the terminal terrain node in the sequence with the smaller number of terrain nodes, and the elevation similarity between nodes with the same terrain type is calculated based on the compensated data.

4. The engineering surveying and mapping information integration system according to claim 1, characterized in that: The data preprocessing unit is specifically used for: Performing grid division 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; A dynamic weight allocation method is used to perform real-time smoothing on the standardized first surveying and mapping sequence and the standardized second surveying and mapping sequence, and a fixed weight filtering method is used to perform static noise reduction on the standardized third surveying and mapping sequence to generate a first fusion sequence, a second fusion sequence, and a third fusion sequence; wherein the first fusion sequence includes a processed first reference waveform and a processed second reflection waveform.

5. The engineering surveying and mapping information integration system according to claim 4, characterized in that: The data pre-processing unit is further used for: Calculating a spatial matching degree between the processed first reference waveform and the processed second reflected waveform within a historical surveying and mapping period; Predicting a predicted elevation of the processed second reflected waveform in the real-time mapping period based on the spatial matching degree and the terrain characteristics of the processed first reference waveform in the real-time mapping period; Target space registration data is generated according to the processed second reflection waveform and its predicted elevation, and a surveying and mapping sequence corresponding to the target space registration data is used as a first fusion sequence.

6. The engineering surveying and mapping information integration system according to claim 1, characterized in that: The topology reconstruction layer specifically includes: a spatial association unit, configured to perform terrain continuity analysis on each surveying and mapping sequence contained in the coordinate association feature data, so as to extract a corresponding terrain extension chain from each surveying and mapping sequence; The topology matching unit is used to dynamically map the terrain extension chain extracted from each surveying and mapping sequence with the corresponding coordinate feature data to generate reconstructed topology feature data.

7. The engineering surveying and mapping information integration system according to claim 6, characterized in that: The topology reconstruction layer further includes: The error correction unit is used to perform redundant point cloud elimination processing on the reconstructed topological feature data.

8. The engineering surveying and mapping information integration system according to claim 2, characterized in that: The achievement generation layer specifically includes: A multi-dimensional overlay unit, comprising a plurality of data overlay nodes, each of which is connected to each mapping sequence in the reconstructed topological feature data and the coordinate association feature data through an association configuration; A dynamic optimization and adjustment unit, configured to update the associated configuration using 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 is used to predict terrain fault zones based on the reconstructed topological feature data and coordinate association feature data, and generate digital surveying and mapping results.

9. An engineering surveying and mapping information integration method, applied to the engineering surveying and mapping information integration system according to any one of claims 1 to 8, characterized in that: The following steps are involved: Step 1: Acquire spatial information data of the target mapping area through a 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 feature distribution data, and a third mapping sequence corresponding to 3D 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 mapping sensor. Step 2: Use the data fusion processing module to perform dynamic coordinate system calibration on the spatial information data, and input the calibrated spatial information data into the integrated analysis processing layer; Step 3: The original surveying and mapping data stream is spatially segmented and outliers are eliminated by the data pre-processing 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 benchmark data and historical reflection data from multiple historical surveying and mapping cycles, and the topology analysis unit includes a spatial registration layer, a topology reconstruction layer, and an output generation layer connected in sequence; Step 4: Based on the output results of the integrated analysis and processing layer, a digital mapping result of the target mapping area is generated through a data fusion processing module; The topology reconstruction layer is used to model the dynamic association relationship between the coordinate association feature data corresponding to each surveying and mapping sequence to obtain reconstructed topology feature data; The method of modeling the dynamic association relationship between the coordinate association feature data corresponding to each surveying and mapping sequence to obtain reconstructed topological feature data includes: A dynamic benchmark calibration algorithm is used to identify key terrain nodes in the coordinate association feature data, and a spatial association sequence corresponding to each surveying and mapping sequence is determined based on the terrain type corresponding to each key terrain node; The elevation similarity between nodes with the same terrain type in the spatial association sequences corresponding to any two surveying and mapping sequences is calculated, and the reconstructed topological feature data between the any two surveying and mapping sequences is determined based on the elevation similarity.

Images