Method and system for integrating base mapping data

Abstract

The application discloses a basic surveying and mapping data integration method and system, relates to the technical field of surveying and mapping data integration, and comprises the following steps: collecting image data of a region to be surveyed and mapped, performing image data slicing processing, and obtaining surveying and mapping image slice data; acquiring region node information in the surveying and mapping image slice data, acquiring node change data, performing region type division, and obtaining region type information; predicting according to the node change data, obtaining node prediction data, dynamically additionally collecting the surveying and mapping image slice data, and obtaining additional image slice data; and dynamically updating and integrating the surveying and mapping image slice data and the additional image slice data; the application is used to solve the problem that the existing surveying and mapping data integration technology cannot select and save image data collected each time while ensuring the timeliness and availability of the image data when integrating the image data of basic surveying and mapping, thereby avoiding the problem of affecting data redundancy.

Description

Technical Field

[0001] This invention relates to the field of surveying and mapping data integration technology, specifically to a method and system for integrating basic surveying and mapping data. Background Technology

[0002] Surveying and mapping data integration technology refers to the technology of integrating, processing, fusing, and uniformly managing surveying and mapping data from multiple sources, in different formats, and with different levels of precision, in order to form a dataset with consistency, integrity, and usability.

[0003] Existing surveying and mapping data integration technologies often preserve the complete data from each acquisition when integrating basic surveying and mapping imagery. Over time and with increased acquisition frequency, the data volume grows exponentially, placing immense pressure on storage systems. Furthermore, monitoring surveying and mapping data within a specific location range often employs a periodic acquisition and integration method. This periodic acquisition, conducted at fixed intervals, fails to capture timely information on sudden, short-term changes. By the time the next periodic acquisition occurs, the critical period of change may have been missed, resulting in data that does not accurately reflect the actual situation and impacting data timeliness. The timeliness and availability of image data cannot provide accurate information for real-time decision-making. For example, patent application CN116821777A discloses a novel method and system for integrating basic surveying and mapping data. Although this method filters the collected surveying and mapping data, it only removes identical data from the same collection. It still saves all image data collected each time, putting a huge burden on the storage system. Therefore, existing surveying and mapping data integration technologies cannot ensure the timeliness and availability of image data while selectively saving each collected image data to avoid data redundancy. Summary of the Invention

[0004] This invention aims to at least partially solve one of the technical problems in the prior art. It obtains surveying image slice data by collecting image data of the area to be surveyed and processing the image data into slices; acquiring regional node information and node change data, classifying the region into types, and obtaining regional type information; making predictions based on the node change data to obtain node prediction data; and dynamically acquiring additional surveying image slice data to obtain additional image slice data; then dynamically updating and integrating the data. This addresses the problem that existing surveying data integration technologies, when integrating basic surveying image data, cannot simultaneously ensure the timeliness and availability of the image data while selectively saving each acquired image data to avoid data redundancy.

[0005] To achieve the above objectives, firstly, this application provides a method for integrating basic surveying and mapping data, comprising the following steps:

[0006] Collect image data of the area to be surveyed and perform image data slicing to obtain survey image slice data;

[0007] Obtain regional node information from the surveying and mapping image tile data, acquire node change data, classify regional types, and obtain regional type information;

[0008] Based on the node change data, predictions are made to obtain node prediction data. Then, based on the node prediction data, additional image tile data is dynamically acquired to obtain additional image tile data.

[0009] Dynamically update and integrate survey image tile data and additional image tile data.

[0010] Further, image data of the area to be mapped is collected, and image data slicing is performed to obtain mapping image slice data, including the following sub-steps:

[0011] Set the basic acquisition cycle to t1, and acquire orthophotos of the area to be surveyed at time intervals of t1. Ensure that the acquisition position, acquisition height and acquisition device settings remain consistent each time orthophotos of the area to be surveyed are acquired, and record the acquisition time as the regular acquisition time. Sort the acquired orthophotos according to the acquisition time and mark them as original survey images.

[0012] Furthermore, the process of tiling the image data to obtain the mapping image tile data also includes the following sub-steps:

[0013] Set the slicing level to n. For each acquired original surveying image, perform image data slicing processing, including: designating each acquired original surveying image as the image to be sliced; uniformly dividing the image to be sliced ​​into k0 rectangular grid regions, resulting in the first-level sliced ​​image; uniformly dividing any rectangular grid region in the first-level sliced ​​image into k0 rectangular grid regions, resulting in the second-level sliced ​​image; uniformly dividing any rectangular grid region in the second-level sliced ​​image into k0 rectangular grid regions, resulting in the third-level sliced ​​image; repeating the operation until the nth-level sliced ​​image is obtained; and marking the sliced ​​images of the first to nth levels as the corresponding original surveying images.

[0014] Repeatedly acquire all original mapping images and record them as mapping image slice data.

[0015] Further, the process involves obtaining regional node information from the surveying and mapping image tile data, acquiring node change data, classifying regional types, and obtaining regional type information, including the following sub-steps:

[0016] For any given surveying segmentation image, denoted as the first surveying segmentation image, the portion of the edge of the area to be surveyed corresponding to the first surveying segmentation image is denoted as the area edge line. The endpoints, inflection points, and curvature change points with curvature changes greater than K of the area edge line are marked as area nodes. For any two area nodes connected by the area edge line, the distance along the area edge line is obtained and denoted as L1. If L1 is greater than L0, L2 is calculated. Mark the L2 division points of the region edge line between the two corresponding region nodes as region nodes; where K is the set curvature change threshold and L0 is the set distance threshold;

[0017] Obtain all region nodes in the first surveying and mapping segmented image, and obtain the coordinates of all region nodes in the first surveying and mapping segmented image, which are recorded as region node coordinate information; for any rectangular grid region in the nth level segmented image of the first surveying and mapping segmented image, it is recorded as the nth level grid region; obtain the region nodes in the nth level grid region, obtain the size of the corresponding ordinate, and number them in ascending order. Repeat the numbering of all region nodes in the first surveying and mapping segmented image to ensure that the numbers of any two region nodes are different;

[0018] The coordinates and numbers of all regional nodes in the first surveyed and segmented image are recorded as the regional node information of the first surveyed and segmented image; the regional node information corresponding to all surveyed and segmented images is repeatedly obtained.

[0019] Further, the process involves obtaining regional node information from the surveying and mapping image tile data, acquiring node change data, classifying regional types, and obtaining regional type information, including the following sub-steps:

[0020] For any region node, we denote it as the first node. We obtain the coordinates of the first node in all the surveyed and segmented images and sort them from far to near according to the corresponding acquisition order, which is recorded as the node coordinate sequence. If a certain surveyed and segmented image does not have a first node, we mark the coordinates of the first node in the surveyed and segmented image as (-1, -1).

[0021] For any coordinate in the node coordinate sequence, let it be denoted as M. i The coordinate change value is calculated for any two adjacent coordinates in the node coordinate sequence using the coordinate change formula, and the corresponding values ​​are ordered and denoted as the coordinate change value sequence. The coordinate change formula is as follows: Q i =|M i (x)-M i-1 (x)|+|M i (y)-M i-1 (y)|, where Q i M(x) represents the change value of the i-th coordinate, M(y) represents the x-coordinate, and M(y) represents the y-coordinate.

[0022] Repeatedly acquire the node coordinate sequence and coordinate change value sequence of all regional nodes, which are recorded as node change data;

[0023] For a first node, acquire k1 coordinate change values with the largest sequence numbers in the coordinate change value sequence of the first node, which are recorded as recent coordinate change values; if all the recent coordinate change values are less than V0, mark the first node as a stable node, otherwise mark the first node as an active node, where V0 is a set change threshold; repeat the marking for all nodes;

[0024] Each acquired surveying and mapping split image is recorded as the latest split image. For any rectangular grid region in any level of split image in the latest split image, it is recorded as any grid region. If there is an active node in any grid region, mark any grid region as an active grid region, otherwise mark it as a stable grid region; repeat the marking for all grid regions in the latest split image, and obtain the latest marked split image after completion.

[0025] Further, prediction is performed according to the node change data to obtain node prediction data, which includes the following sub-steps:

[0026] Any active node is recorded as a second node. Linear fitting is performed according to the coordinate change value sequence of the second node and the corresponding acquisition time to obtain the change relationship of the coordinate change value of the second node over time, and the coordinate change value between the moment when the original surveying and mapping image was last acquired and the moment when the original surveying and mapping image will be acquired next time is acquired at the second time interval, sorted from near to far in chronological order, which is recorded as the node prediction sequence of the corresponding active node, wherein the second time interval is t2, and t2 < t1; the node prediction sequences of all active nodes are repeatedly acquired and recorded as node prediction data.

[0027] Further, dynamic additional acquisition is performed on the surveying and mapping image slice data according to the node prediction data to obtain additional image slice data, which includes the following sub-steps:

[0028] Any predicted coordinate change value in the node prediction sequence of the second node is recorded as Bj; the moment corresponding to Bj is recorded as Bj prediction moment; the total coordinate change value from the start moment of the node prediction sequence to Bj prediction moment is calculated according to a cumulative change formula, which is recorded as a cumulative change value, and the cumulative change formula is as follows: wherein LBj represents the cumulative change value at the moment corresponding to Bj, E represents the total number of predicted coordinate change values in the node prediction sequence; repeatedly acquire the cumulative change values corresponding to all prediction moments in the node prediction sequence, sort them according to the order of the corresponding prediction moments, and record them as the cumulative change sequence of the corresponding active node.

[0029] Furthermore, based on the node prediction data, dynamic additional acquisition of the mapping image tile data is performed to obtain additional image tile data, including the following sub-steps:

[0030] Repeatedly obtain the cumulative change sequence of all active nodes; and based on the cumulative change sequence of all active nodes, sum the cumulative change values ​​belonging to different active nodes at the same time to obtain the total cumulative change sequence of all active nodes;

[0031] Set the total cumulative change threshold to ZB0. Starting from the first data in the total cumulative change sequence, compare the data with ZB0 sequentially. If any data is greater than or equal to ZB0, stop the comparison and obtain the time corresponding to the data that is greater than or equal to ZB0, which is recorded as the additional acquisition time. At the additional acquisition time, an additional original mapping image of the area to be mapped is acquired, which is recorded as the additional mapping image. If there is no data greater than or equal to ZB0, no additional acquisition is performed.

[0032] The additional mapping images are then processed using dynamic image data slicing based on the latest labeled and segmented images. This includes: uniformly dividing the additional mapping images into k0 rectangular grid regions to obtain the first-level additional segmented images; uniformly dividing any active grid region in the first-level segmented images into k0 rectangular grid regions to obtain the second-level segmented images; uniformly dividing any active grid region in the second-level additional segmented images into k0 rectangular grid regions to obtain the third-level additional segmented images; repeating this process until the nth-level additional segmented images are obtained, and marking the first to nth-level additional segmented images as additional image slice data.

[0033] Furthermore, the dynamic updating and integration of surveying and mapping image tile data and additional image tile data includes the following sub-steps:

[0034] All surveying and mapping image tile data and additional image tile data are collectively referred to as regional image tile data.

[0035] The first obtained regional image tile data is denoted as the first tile data. All rectangular grid regions contained in the nth level segmentation image of the first tile data are stored separately, and the storage location of each rectangular grid region is recorded; this is recorded as the first mapping block storage information.

[0036] Each time a regional image slice data is obtained, it is recorded as the a-th slice data. Any rectangular grid region contained in the n-th level segmentation image of the a-th slice data is recorded as the first grid region. If the first grid region is an active grid region, it is re-stored and the storage location is recorded. If the first grid region is a stable grid region, the storage location of the corresponding rectangular grid region in the (a-1)-th survey block storage information is obtained and marked as the storage location of the corresponding rectangular grid region. The processing of all rectangular grid regions is repeated to obtain the a-th survey block storage information.

[0037] Secondly, this application provides a basic surveying and mapping data integration system, including an image acquisition module, a node division module, a predictive acquisition module, and a dynamic integration module;

[0038] The image acquisition module includes an acquisition unit and a slicing unit. The acquisition unit is used to acquire image data of the area to be surveyed, and the slicing unit is used to perform image data slicing processing to obtain survey image slice data.

[0039] The node division module is used to obtain regional node information in the mapping image slice data, obtain node change data, perform regional type division, and obtain regional type information.

[0040] The prediction acquisition module includes a prediction unit and an additional acquisition unit. The prediction unit makes predictions based on node change data to obtain node prediction data. The additional acquisition unit dynamically acquires additional image slice data based on the node prediction data to obtain additional image slice data.

[0041] The dynamic integration module is used to dynamically update and integrate the surveying and mapping image slice data and additional image slice data.

[0042] The beneficial effects of this invention are as follows: This invention acquires image data of the area to be surveyed and performs image data slicing to obtain survey image slice data; it obtains regional node information from the survey image slice data and acquires node change data, classifies regional types, and obtains regional type information; it makes predictions based on node change data to obtain node prediction data, and dynamically acquires additional survey image slice data based on the node prediction data to obtain additional image slice data; it dynamically updates and integrates the survey image slice data and the additional image slice data; when integrating basic survey image data, it can select and save each acquired image data while ensuring the timeliness and availability of the image data, avoiding data redundancy and reducing storage costs;

[0043] This invention uses historical change data of nodes for prediction and additionally collects image data of the surveyed area based on the prediction results. This allows for timely and accurate recording of sudden changes in the monitored area, ensuring the timeliness and accuracy of the data. By slicing the collected images and analyzing the areas of change, only the changed image areas are saved, and unchanged sub-slices are not stored repeatedly. This significantly reduces data redundancy and storage costs while ensuring the availability of integrated data. For additionally collected images, only the active grid area is sliced, avoiding repeated operations on a large amount of unchanged data, reducing the workload and time cost of data processing, and improving the efficiency of processing and integration. Attached Figure Description

[0044] Figure 1 This is a schematic diagram of the system of the present invention;

[0045] Figure 2 This is a flowchart illustrating the steps of the method of the present invention;

[0046] Figure 3 This is a schematic diagram of the regional nodes of the present invention;

[0047] Figure 4 This is a schematic diagram of the electronic device of the present invention. Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] Example 1, please refer to Figure 1 As shown, this application provides a basic surveying and mapping data integration system, including an image acquisition module, a node division module, a predictive acquisition module, and a dynamic integration module;

[0050] The image acquisition module includes an acquisition unit and a slicing unit. The acquisition unit is used to acquire image data of the area to be surveyed, and the slicing unit is used to perform image data slicing processing to obtain survey image slice data.

[0051] The acquisition unit is configured with an acquisition strategy, which includes: setting the basic acquisition cycle to t1, i.e., the update frequency of the surveying data for the area to be surveyed; acquiring orthophotos of the area to be surveyed at time intervals of t1, ensuring that the acquisition position, acquisition height, and acquisition equipment settings remain consistent each time the orthophotos are acquired, so that the corresponding position of the area to be surveyed is consistent in each acquired image; recording the acquisition time as the regular acquisition time, and sorting the acquired orthophotos according to the acquisition time, marking them as original surveying images; an orthophoto is a corrected remote sensing image or aerial photographic image that eliminates image distortion caused by factors such as terrain undulation and sensor attitude, so that each pixel on the image has accurate geographic coordinates and scale, just like a picture taken vertically from directly above. Figure 1 In this way, it can accurately reflect the actual position and shape of objects on the ground;

[0052] The slicing unit is configured with a slicing strategy, which includes: setting the slicing level to n, and performing image data slicing processing on each acquired original surveying image, including: recording each acquired original surveying image as the image to be sliced, uniformly dividing the image to be sliced ​​into k0 rectangular grid regions, resulting in the first-level slicing image; uniformly dividing any rectangular grid region in the first-level slicing image into k0 rectangular grid regions, resulting in the second-level slicing image; uniformly dividing any rectangular grid region in the second-level slicing image into k0 rectangular grid regions, resulting in the third-level slicing image; repeating the operation until the nth-level slicing image is obtained, and marking the nth-level slicing image as the slicing image of the corresponding original surveying image; in this embodiment, k0 = 4, and n can be set according to the resolution of the acquired image, generally ensuring that the resolution of the rectangular grid region of the nth-level slicing image is less than or equal to 256*256.

[0053] Repeatedly acquire all original mapping images and record them as mapping image slice data;

[0054] In the specific implementation process, obtaining orthophotos of the area to be mapped is because it focuses only on the morphological changes of the area to be mapped, which is suitable for various application scenarios that require long-term monitoring of the boundary changes of a specific area.

[0055] The node segmentation module is used to obtain regional node information in the surveying and mapping image tile data, obtain node change data, perform regional type segmentation, and obtain regional type information.

[0056] The node partitioning module is configured with a node partitioning strategy, which includes: any given mapping segmentation image is designated as the first mapping segmentation image. Please refer to [link to relevant documentation]. Figure 3As shown, the portion of the edge of the area to be mapped corresponding to the first mapped segmented image is denoted as the area edge line. The endpoints, inflection points, and curvature change points with curvature changes greater than K of the area edge line are marked as area nodes. For any two area nodes connected by the area edge line, the distance along the area edge line is obtained and denoted as L1. If L1 is greater than L0, L2 is calculated. Mark the L2 equidistant points of the region edge line between the two corresponding region nodes as region nodes; where K is the set curvature change threshold and L0 is the set distance threshold; in this embodiment, K = 2 degrees and L0 = 5 meters, and K and L0 can be set according to the actual application scenario; for example, if L1 = 18 meters of the region edge line between the two region nodes, then... If L1 = 3, then the three-part division point of the region edge line between the two region nodes is marked as a region node;

[0057] When L1 is greater than L0, it indicates that the edge line between the two nodes is relatively long and may contain more morphological information. By calculating L2 and dividing the region edge line into L2 equal parts, and marking the division points as new region nodes, we can increase the detailed description of the edge line segment, more accurately capture the shape changes of the region, and help with the subsequent accurate analysis and processing of the region morphology.

[0058] Obtain all region nodes in the first surveying and mapping segmented image, and obtain the coordinates of all region nodes in the first surveying and mapping segmented image, denoted as region node coordinate information; for any rectangular grid region in the nth level segmented image of the first surveying and mapping segmented image, denoted as the nth level grid region; obtain the region nodes in the nth level grid region, obtain the corresponding ordinate size, and number them in ascending order, repeating the numbering of all region nodes in the first surveying and mapping segmented image to ensure that any two region nodes have different numbers; that is, any two region nodes in the entire first surveying and mapping segmented image have different numbers;

[0059] The coordinates and numbers of all regional nodes in the first surveyed and segmented image are recorded as the regional node information of the first surveyed and segmented image; the regional node information corresponding to all surveyed and segmented images is repeatedly obtained;

[0060] For any given region node, we denote it as the first node. We obtain the coordinates of the first node in all the surveyed and segmented images and sort them from far to near according to the corresponding acquisition order, which is recorded as the node coordinate sequence. If a certain surveyed and segmented image does not have a first node, we mark the coordinates of the first node in that surveyed and segmented image as (-1, -1). Because changes in the shape of the area to be surveyed may cause the number of region nodes to increase or decrease, it is possible that there is a first node in one surveyed and segmented image but not in another.

[0061] For any coordinate in the node coordinate sequence, let it be denoted as M. i The coordinate change value is calculated for any two adjacent coordinates in the node coordinate sequence using the coordinate change formula, and the corresponding values ​​are ordered and denoted as the coordinate change value sequence. The coordinate change formula is as follows: Q i =|M i (x)-M i-1 (x)|+|M i (y)-M i-1 (y)|, where Q i M(x) represents the change value of the i-th coordinate, M(y) represents the x-coordinate, and M(y) represents the y-coordinate.

[0062] Repeatedly obtain the node coordinate sequence and coordinate change value sequence of all region nodes, and record them as node change data;

[0063] For the first node, obtain the k1 coordinate change values ​​with the largest sequence number in the coordinate change value sequence of the first node, and record them as the most recent coordinate change values; if all the most recent coordinate change values ​​are less than V0, then mark the first node as a stable node, otherwise mark the first node as an active node, where V0 is the set change threshold; repeat the marking process for all nodes; in this embodiment, k1 = 5, that is, if no change is detected in the last 5 acquired images, then mark it as a stable node, otherwise mark the first node as an active node;

[0064] Each acquired mapping segmented image is recorded as the latest segmented image. Any rectangular grid region in any level segmented image of the latest segmented image is recorded as an arbitrary grid region. If there is an active node in the arbitrary grid region, the arbitrary grid region is marked as an active grid region; otherwise, it is marked as a stable grid region. The marking of all grid regions in the latest segmented image is repeated, and the latest marked segmented image is obtained after completion.

[0065] In practice, by focusing on node data to monitor morphological changes in a location area, subtle changes in ground features can be captured more accurately. Node data represents key feature points of a location area, and monitoring changes in these nodes can accurately reflect morphological changes in the area, avoiding the problem that traditional methods may overlook subtle local changes due to overall data comparison.

[0066] The predictive acquisition module includes a prediction unit and an additional acquisition unit. The prediction unit makes predictions based on the node change data to obtain node prediction data. The additional acquisition unit dynamically acquires additional image tile data based on the node prediction data to obtain additional image tile data.

[0067] The prediction unit is configured with a prediction strategy, and the prediction strategy comprises: for any active node recorded as a second node, performing linear fitting according to the coordinate change value sequence of the second node and the corresponding acquisition time to obtain the change relationship of the coordinate change value of the second node over time, acquiring the coordinate change value between the moment when the original surveying and mapping image was acquired last time at the second time interval and the moment when the original surveying and mapping image will be acquired next time, sorting the coordinate change values from recent to remote in chronological order, and recording the sorted result as a node prediction sequence corresponding to the active node, wherein the second time interval is t2, and t2<t1; repeatedly acquiring node prediction sequences of all active nodes and recording the obtained result as node prediction data; that is, predicting the coordinate change value between the current normal acquisition and the next normal acquisition; the prediction is performed after each acquisition at the regular acquisition time;

[0068] The additional acquisition unit is configured with an additional acquisition strategy, and the additional acquisition strategy comprises: recording any predicted coordinate change value in the node prediction sequence of the second node as Bj; recording the moment corresponding to Bj as a Bj prediction moment; calculating a sum of coordinate change values from the starting moment of the node prediction sequence to the Bj prediction moment according to a cumulative change formula, and recording the sum as a cumulative change value, wherein the cumulative change formula is as follows: wherein LBj represents the cumulative change value at the moment corresponding to Bj, and E represents the total number of predicted coordinate change values in the node prediction sequence; repeatedly acquiring cumulative change values corresponding to all prediction moments in the node prediction sequence, sorting the cumulative change values according to the order of corresponding prediction moments, and recording the sorted result as a cumulative change sequence corresponding to the active node; for example, if a certain node prediction sequence is {2, 3, 4, 3, 2, 3}, the corresponding cumulative change sequence is {2, 5, 9, 12, 14, 17};

[0069] repeatedly acquiring cumulative change sequences of all active nodes; and according to the cumulative change sequences of all active nodes, summing cumulative change values belonging to different active nodes at the same moment to obtain a total cumulative change sequence of all active nodes;

[0070] setting a total cumulative change threshold as ZB0, sequentially comparing each data with ZB0 starting from the first data in the total cumulative change sequence, if a certain data is greater than or equal to ZB0, stopping the comparison, acquiring the moment corresponding to the data greater than or equal to ZB0, and recording the moment as an additional acquisition moment; additionally acquiring an original surveying and mapping image of a to-be-surveyed area at the additional acquisition moment, and recording the additionally acquired image as an additional surveying and mapping image; if no data is greater than or equal to ZB0, no additional acquisition is performed; ZB0 can be set according to actual application scenarios, for example, when ZB0=10 and the cumulative change sequence is {2, 5, 9, 12, 14, 17}, 12 is the first value greater than or equal to 10, and thus the moment corresponding to 12 is recorded as the additional acquisition moment;

[0071] The additional mapping image is then dynamically sliced ​​based on the latest labeled and segmented image. This process includes: uniformly dividing the additional mapping image into k0 rectangular grid regions to obtain the first-level additional segmented image; uniformly dividing any active grid region in the first-level segmented image into k0 rectangular grid regions to obtain the second-level segmented image; uniformly dividing any active grid region in the second-level additional segmented image into k0 rectangular grid regions to obtain the third-level additional segmented image; repeating this process until the nth-level additional segmented image is obtained, and then marking the nth-level additional segmented image as additional image slice data. In other words, only active grid regions are sliced. For example, if the first-level segmented image of the latest labeled and segmented image has four grid regions, with the top two being active and the others being stable, then only the top two rectangular grid regions corresponding to the first-level additional segmented image of the additional mapping image are sliced, while the others are not sliced.

[0072] In the actual implementation process, sudden changes may occur in the area to be mapped; for example, a large-scale infrastructure construction project may be suddenly launched in a city, or a natural disaster may occur in the natural environment, causing changes in the topography. Regular data collection may not be able to capture these sudden changes in time, while additional data collection based on prediction can determine the areas and times that may change in advance based on the prediction results, and collect data when or shortly after the change occurs, so as to record these changes in a timely and accurate manner, ensuring the timeliness and accuracy of the data. For additional mapping images, only the corresponding active grid areas are processed, avoiding repeated operations on a large amount of unchanged data, thereby reducing the workload and time cost of data processing.

[0073] The dynamic integration module is used to dynamically update and integrate surveying and mapping image tile data and additional image tile data.

[0074] The dynamic integration module is configured with a tiling strategy, which includes: uniformly recording survey image tiling data and additional image tiling data as regional image tiling data.

[0075] The first obtained regional image tile data is denoted as the first tile data. All rectangular grid regions contained in the nth level segmentation image of the first tile data are stored separately, and the storage location of each rectangular grid region is recorded; this is recorded as the first mapping block storage information.

[0076] Each acquired regional image slice is then denoted as the a-th slice data. Any rectangular grid region contained in the n-th level segmentation image of the a-th slice data is denoted as the first grid region. If the first grid region is an active grid region, it is re-stored and its storage location is recorded. If the first grid region is a stable grid region, the storage location of the corresponding rectangular grid region in the (a-1)-th survey block storage information is obtained and marked as the storage location of the corresponding rectangular grid region. This process is repeated for all rectangular grid regions to obtain the a-th survey block storage information. That is, the changed rectangular grid regions are re-stored, and the unchanged rectangular grid regions only need to obtain the previously stored location and combine it with the re-stored location to form new survey block storage information. When it is needed to view, the storage location of each rectangular grid region is read and merged to form the corresponding regional image slice data.

[0077] In practice, traditional surveying and mapping data integration techniques typically save all image data collected each time, which occupies a large amount of storage space. This method, however, only saves the changed parts and does not repeatedly store the unchanged parts, greatly reducing data redundancy and significantly lowering storage costs. For example, when conducting timed surveys of a long-term stable water body, most parts will not change during multiple collections. Using this method, there is no need to repeatedly save the data of these unchanging sub-slices, thus saving a lot of storage resources.

[0078] Example 2, please refer to Figure 2 As shown, this application provides a method for integrating basic surveying and mapping data, including the following steps:

[0079] Step S1 involves acquiring image data of the area to be surveyed and performing image data tiling to obtain survey image tile data. Step S1 includes the following sub-steps:

[0080] Step S101: Set the basic acquisition period to t1, and acquire orthophotos of the area to be surveyed at time intervals of t1, ensuring that the acquisition position, acquisition height and acquisition device settings remain consistent each time the orthophotos of the area to be surveyed are acquired.

[0081] Step S102, and record the acquisition time as the regular acquisition time, and sort the acquired orthophotos according to the acquisition time, and mark them as the original mapping images;

[0082] Step S103: Set the slicing level to n. Perform image data slicing processing on the original surveying images acquired each time, including: recording the original surveying images acquired each time as the surveying images to be sliced; uniformly dividing the surveying images to be sliced ​​into k0 rectangular grid regions, thus obtaining the first-level sliced ​​image; uniformly dividing any rectangular grid region in the first-level sliced ​​image into k0 rectangular grid regions, thus obtaining the second-level sliced ​​image; uniformly dividing any rectangular grid region in the second-level sliced ​​image into k0 rectangular grid regions, thus obtaining the third-level sliced ​​image; repeating the operation until the nth-level sliced ​​image is obtained, and marking the sliced ​​images of the corresponding original surveying images as the surveying sliced ​​images;

[0083] Step S104: Repeatedly acquire all original mapping images and record them as mapping image slice data.

[0084] Step S2 involves acquiring regional node information from the surveying image tile data, obtaining node change data, classifying regional types, and obtaining regional type information. Step S2 includes the following sub-steps:

[0085] Step S201: For any surveying segmentation image, it is denoted as the first surveying segmentation image. The part of the edge of the area to be surveyed that corresponds to the first surveying segmentation image is denoted as the area edge line. The endpoints, inflection points, and curvature change points with curvature change greater than K of the area edge line are marked as area nodes.

[0086] Step S202: For any two region nodes connected by the region edge line, obtain the distance along the region edge line, denoted as L1. If L1 is greater than L0, calculate L2. Mark the L2 division points of the region edge line between the two corresponding region nodes as region nodes; where K is the set curvature change threshold and L0 is the set distance threshold;

[0087] Step S203: Obtain all region nodes in the first surveying and mapping segmented image, and obtain the coordinates of all region nodes in the first surveying and mapping segmented image, and record them as region node coordinate information; for any rectangular grid region in the nth level segmented image of the first surveying and mapping segmented image, record it as the nth level grid region;

[0088] Step S204: Obtain the region nodes in the n-level grid region, obtain the corresponding ordinate size, number them in ascending order, and repeat the numbering of all region nodes in the first surveying and mapping segmentation image to ensure that the numbers of any two region nodes are different.

[0089] Step S205: Record the coordinates and numbers of all regional nodes in the first surveying and mapping segmentation image as the regional node information of the first surveying and mapping segmentation image; repeatedly obtain the regional node information corresponding to all surveying and mapping segmentation images;

[0090] Step S206: For any region node, denoted as the first node, obtain the coordinates of the first node in all the surveying and mapping segmented images, and sort them from far to near according to the corresponding acquisition order, and denoted as the node coordinate sequence; if a certain surveying and mapping segmented image does not have a first node, then mark the coordinates of the first node in the surveying and mapping segmented image as (-1, -1).

[0091] Step S207: For any coordinate in the node coordinate sequence, denoted as M i The coordinate change value is calculated for any two adjacent coordinates in the node coordinate sequence using the coordinate change formula, and the corresponding values ​​are ordered and denoted as the coordinate change value sequence. The coordinate change formula is as follows: Q i =|M i (x)-M i-1 (x)|+|M i (y)-M i-1 (y)|, where Q i M(x) represents the change value of the i-th coordinate, M(y) represents the x-coordinate, and M(y) represents the y-coordinate.

[0092] Step S208: Repeatedly obtain the node coordinate sequence and coordinate change value sequence of all regional nodes, and record them as node change data;

[0093] Step S209: For the first node, obtain the k1 coordinate change values ​​with the largest index in the coordinate change value sequence of the first node, and record them as the most recent coordinate change values; if all the most recent coordinate change values ​​are less than V0, then mark the first node as a stable node, otherwise mark the first node as an active node, where V0 is the set change threshold; repeat the marking process for all nodes.

[0094] Step S210: For each acquired mapping segmented image, record it as the latest segmented image. For any rectangular grid region in any first-level segmented image in the latest segmented image, record it as an arbitrary grid region. If there is an active node in the arbitrary grid region, mark the arbitrary grid region as an active grid region; otherwise, mark it as a stable grid region. Repeat the marking of all grid regions in the latest segmented image to obtain the latest marked segmented image.

[0095] Step S3 involves making predictions based on node change data to obtain node prediction data, and then dynamically acquiring additional image tile data based on the node prediction data to obtain additional image tile data. Step S3 includes the following sub-steps:

[0096] In step S301, for any active node denoted as a second node, linear fitting is performed according to the coordinate change value sequence of the second node and the corresponding acquisition time, to obtain the change relationship of the coordinate change value of the second node over time;

[0097] In step S302, the coordinate change values between the moment when the original surveying and mapping image was acquired last time and the moment when the original surveying and mapping image will be acquired next time are obtained at a second time interval, sorted in chronological order from near to far, and recorded as a node prediction sequence of the corresponding active node, wherein the second time interval is t2, and t2<t1; repeating the process for all active nodes to obtain the node prediction sequences thereof, which are recorded as node prediction data;

[0098] In step S303, for any predicted coordinate change value in the node prediction sequence of the second node, it is denoted as Bj; the moment corresponding to Bj is denoted as Bj prediction moment; the total coordinate change value from the start moment of the node prediction sequence to the Bj prediction moment is calculated according to a cumulative change formula, and denoted as a cumulative change value, the cumulative change formula is as follows: Wherein, LBj represents the cumulative change value at the moment corresponding to Bj, and E represents the total number of predicted coordinate change values in the node prediction sequence; repeatedly obtaining the cumulative change values corresponding to all prediction moments in the node prediction sequence, sorting them according to the order of the corresponding prediction moments, and recording them as the cumulative change sequence of the corresponding active node;

[0099] In step S304, repeatedly obtaining the cumulative change sequences of all active nodes; and according to the cumulative change sequences of all active nodes, summing the cumulative change values belonging to different active nodes at the same moment to obtain a total cumulative change sequence of all active nodes;

[0100] In step S305, setting a total cumulative change threshold as ZB0, sequentially comparing the size with ZB0 starting from the first data in the total cumulative change sequence, if a certain data is greater than or equal to ZB0, stopping the comparison, and obtaining the moment corresponding to the data that is greater than or equal to ZB0, which is recorded as an additional acquisition moment; an additional acquisition of the original surveying and mapping image of the area to be surveyed is performed at the additional acquisition moment, which is recorded as an additional surveying and mapping image; if there is no data greater than or equal to ZB0, no additional acquisition is performed;

[0101] Step S306 involves performing dynamic image data slicing processing on the additional mapping image based on the latest marked segmented image, including: uniformly dividing the additional mapping image into k0 rectangular grid regions to obtain the first-level additional segmented image; uniformly dividing any active grid region in the first-level segmented image into k0 rectangular grid regions based on the latest marked segmented image to obtain the second-level segmented image; uniformly dividing any active grid region in the second-level additional segmented image into k0 rectangular grid regions to obtain the third-level additional segmented image; repeating the operation until the nth-level additional segmented image is obtained, and marking the nth-level additional segmented image as additional image slice data.

[0102] Step S4 involves dynamically updating and integrating the surveying image tile data and additional image tile data; Step S4 includes the following sub-steps:

[0103] Step S401: Mapping image tile data and additional image tile data are uniformly referred to as regional image tile data;

[0104] Step S402: The first obtained regional image tile data is recorded as the first tile data. All rectangular grid regions contained in the nth level segmentation image of the first tile data are stored separately, and the storage location of each rectangular grid region is recorded; this is recorded as the first mapping block storage information.

[0105] Step S403: Afterwards, each obtained area image slice data is recorded as the a-th slice data. Any rectangular grid region contained in the n-th level segmented image of the a-th slice data is recorded as the first grid region. If the first grid region is an active grid region, the first grid region is re-stored and the storage location is recorded.

[0106] Step S404: If the first grid region is a stable grid region, obtain the storage location of the corresponding rectangular grid region in the (a-1)th survey block storage information, mark it as the storage location of the corresponding rectangular grid region, repeat the processing of all rectangular grid regions, and obtain the ath survey block storage information.

[0107] Example 3, please refer to Figure 4 As shown, Figure 4A schematic diagram of an electronic device is provided, which may include a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other via the communication bus. The memory stores computer-readable instructions, which the processor can call. When the processor executes these computer-readable instructions, it performs steps similar to those in the basic surveying and mapping data integration method to achieve the following functions: acquiring image data of the area to be surveyed and performing image data slicing to obtain surveying and mapping image slice data; acquiring regional node information from the surveying and mapping image slice data, acquiring node change data, and classifying regional types to obtain regional type information; making predictions based on node change data to obtain node prediction data, and dynamically acquiring additional surveying and mapping image slice data based on the node prediction data to obtain additional image slice data; and dynamically updating and integrating the surveying and mapping image slice data and the additional image slice data.

[0108] Furthermore, when the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0109] Example 4: This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it performs the steps of the above-mentioned basic surveying and mapping data integration method to achieve the following functions: collecting image data of the area to be surveyed and performing image data slicing processing to obtain surveying and mapping image slice data; obtaining regional node information in the surveying and mapping image slice data and obtaining node change data, performing regional type division to obtain regional type information; making predictions based on node change data to obtain node prediction data, and dynamically acquiring additional surveying and mapping image slice data based on node prediction data to obtain additional image slice data; and dynamically updating and integrating the surveying and mapping image slice data and the additional image slice data.

[0110] Based on the above description of the embodiments, the embodiments of the present invention can be provided as methods, systems, or computer program products. Based on this understanding, the technical solutions described above, or the parts that contribute to the prior art, can be embodied in the form of software products. These computer software products can be stored in computer-readable storage media, such as ROM / RAM, magnetic disks, optical disks, etc., and include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or certain parts of the embodiments.

[0111] In the embodiments provided in this application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces. The indirect coupling or communication connection between systems, modules, and units may be electrical, mechanical, or other forms.

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method of integrating base mapping data, characterized by, Comprising the following steps: Collecting image data of a region to be surveyed, performing slicing processing on the image data to obtain surveyed image slice data; Acquiring regional node information in the surveyed image slice data, acquiring node change data, performing regional type division to obtain regional type information; Performing prediction according to the node change data to obtain node prediction data, performing additional dynamic collection on the surveyed image slice data according to the node prediction data to obtain additional image slice data; Dynamically updating and integrating the surveyed image slice data and the additional image slice data; Performing prediction according to the node change data to obtain node prediction data, comprising the following sub-steps: Recording any active node as a second node, performing linear fitting according to the coordinate change value sequence of the second node and the corresponding collection time to obtain the change relationship of the coordinate change value of the second node over time, acquiring the coordinate change value between the moment of the last collection of the original surveyed image and the moment of the next collection of the original surveyed image at a second time interval, sorting the coordinate change values from the nearest to the farthest in chronological order, recording the sorted result as a node prediction sequence corresponding to the active node, wherein the second time interval is t2, and t2 < t1; repeatedly acquiring the node prediction sequences of all active nodes, which is recorded as node prediction data; Performing additional dynamic collection on the surveyed image slice data according to the node prediction data to obtain additional image slice data, comprising the following sub-steps: For any predicted coordinate change value in the node prediction sequence of the second node, denoted as Bj; the time corresponding to Bj is denoted as the prediction time of Bj; the sum of coordinate changes from the start time of the node prediction sequence to the prediction time of Bj is calculated according to the cumulative change formula, and denoted as the cumulative change value. The cumulative change formula is as follows: , where LBj represents the cumulative change value at time Bj, and E represents the total number of predicted coordinate change values ​​in the node prediction sequence; repeatedly obtain the cumulative change values ​​corresponding to all prediction times in the node prediction sequence, and sort them according to the order of the corresponding prediction times, and record them as the cumulative change sequence of the corresponding active node. Repeatedly acquiring the cumulative change sequences of all active nodes; And summing the cumulative change values belonging to different active nodes at the same moment according to the cumulative change sequences of all active nodes to obtain a total cumulative change sequence of all active nodes; Setting the total cumulative change threshold as ZB0, sequentially comparing each data with ZB0 starting from the first data in the total cumulative change sequence, if a certain data is greater than or equal to ZB0, stopping the comparison, and acquiring the moment corresponding to the data greater than or equal to ZB0, which is recorded as an additional collection moment; additionally collecting the original surveyed image of the region to be surveyed once at the additional collection moment, which is recorded as an additional surveyed image; if no data is greater than or equal to ZB0, no additional collection is performed; And performing dynamic image data slicing processing on the additional surveyed image according to the latest marker segmentation image, comprising: uniformly dividing the additional surveyed image into k0 rectangular grid regions, so as to obtain a 1st-level additional segmented image after completion; And uniformly dividing any active grid region in the 1st-level segmented image into k0 rectangular grid regions according to the latest marker segmentation image, so as to obtain a 2nd-level additional segmented image after completion; uniformly dividing any active grid region in the 2nd-level additional segmented image into k0 rectangular grid regions, so as to obtain a 3rd-level additional segmented image after completion; repeating the operation until an nth-level additional segmented image is obtained, and marking the 1st to nth-level additional segmented images as additional image slice data; Dynamically updating and integrating the surveyed image slice data and the additional image slice data comprises the following sub-steps: Uniformly recording the surveyed image slice data and the additional image slice data as regional image slice data; The first obtained regional image tile data is denoted as the first tile data. All rectangular grid regions contained in the nth level segmentation image of the first tile data are stored separately, and the storage location of each rectangular grid region is recorded; this is recorded as the first mapping block storage information. Each time a regional image slice data is obtained, it is recorded as the a-th slice data. Any rectangular grid region contained in the n-th level segmentation image of the a-th slice data is recorded as the first grid region. If the first grid region is an active grid region, it is re-stored and the storage location is recorded. If the first grid region is a stable grid region, the storage location of the corresponding rectangular grid region in the (a-1)-th survey block storage information is obtained and marked as the storage location of the corresponding rectangular grid region. The processing of all rectangular grid regions is repeated to obtain the a-th survey block storage information.

2. The basic surveying and mapping data integration method according to claim 1, characterized in that, The process of collecting image data of the area to be mapped and performing image data tiling to obtain mapping image tile data includes the following sub-steps: Set the basic acquisition cycle to t1, and acquire orthophotos of the area to be surveyed at time intervals of t1. Ensure that the acquisition position, acquisition height and acquisition device settings remain consistent each time orthophotos of the area to be surveyed are acquired, and record the acquisition time as the regular acquisition time. Sort the acquired orthophotos according to the acquisition time and mark them as original survey images.

3. The basic surveying and mapping data integration method according to claim 2, characterized in that, The process of collecting image data of the area to be mapped and performing image data tiling to obtain mapping image tile data also includes the following sub-steps: Set the slicing level to n. For each acquired original surveying image, perform image data slicing processing, including: designating each acquired original surveying image as the image to be sliced; uniformly dividing the image to be sliced ​​into k0 rectangular grid regions, resulting in the first-level sliced ​​image; uniformly dividing any rectangular grid region in the first-level sliced ​​image into k0 rectangular grid regions, resulting in the second-level sliced ​​image; uniformly dividing any rectangular grid region in the second-level sliced ​​image into k0 rectangular grid regions, resulting in the third-level sliced ​​image; repeating the operation until the nth-level sliced ​​image is obtained; and marking the sliced ​​images of the first to nth levels as the corresponding original surveying images. Repeatedly acquire all original mapping images and record them as mapping image slice data.

4. The basic surveying and mapping data integration method according to claim 3, characterized in that, Obtaining regional node information from surveying and mapping image tile data, acquiring node change data, and classifying regional types to obtain regional type information includes the following sub-steps: For any given mapping segmentation image, denoted as the first mapping segmentation image, the portion of the edge of the area to be mapped corresponding to the first mapping segmentation image is denoted as the area edge line. The endpoints, inflection points, and curvature change points with curvature changes greater than K of the area edge line are marked as area nodes. For any two area nodes connected by the area edge line, the distance along the area edge line is obtained and denoted as L1. If L1 is greater than L0, L2 is calculated, where L2 = ... L1 / L0 -1 marks the L2 division points of the region edge line between the two corresponding region nodes as region nodes; where K is the set curvature change threshold and L0 is the set distance threshold; Obtain all region nodes in the first surveying and mapping segmented image, and obtain the coordinates of all region nodes in the first surveying and mapping segmented image, which are recorded as region node coordinate information; for any rectangular grid region in the nth level segmented image of the first surveying and mapping segmented image, it is recorded as the nth level grid region; obtain the region nodes in the nth level grid region, obtain the size of the corresponding ordinate, and number them in ascending order. Repeat the numbering of all region nodes in the first surveying and mapping segmented image to ensure that the numbers of any two region nodes are different; The coordinates and numbers of all regional nodes in the first surveyed and segmented image are recorded as the regional node information of the first surveyed and segmented image; the regional node information corresponding to all surveyed and segmented images is repeatedly obtained.

5. The basic surveying and mapping data integration method according to claim 4, characterized in that, Obtaining regional node information from surveying and mapping image tile data, acquiring node change data, and classifying regional types to obtain regional type information includes the following sub-steps: For any given region node, we denote it as the first node. We obtain the coordinates of the first node in all the surveyed and segmented images and sort them from far to near according to the corresponding acquisition order, which is recorded as the node coordinate sequence. If a certain surveyed and segmented image does not have a first node, we mark the coordinates of the first node in the surveyed and segmented image as (-1, -1). For any coordinate in the node coordinate sequence, let it be denoted as M. i The coordinate change value is calculated for any two adjacent coordinates in the node coordinate sequence using the coordinate change formula, and the corresponding values ​​are ordered and denoted as the coordinate change value sequence. The coordinate change formula is as follows: Q i M(x) represents the change value of the i-th coordinate, M(y) represents the horizontal coordinate, and M(y) represents the vertical coordinate. Repeatedly obtain the node coordinate sequence and coordinate change value sequence of all region nodes, and record them as node change data; For the first node, obtain the k1 largest coordinate change values ​​in the sequence of coordinate change values ​​of the first node, and record them as the most recent coordinate change values; if all the most recent coordinate change values ​​are less than V0, then mark the first node as a stable node, otherwise mark the first node as an active node, where V0 is the set change threshold; repeat the marking process for all nodes. Each acquired mapping segmented image is recorded as the latest segmented image. Any rectangular grid region in any level segmented image of the latest segmented image is recorded as an arbitrary grid region. If there are active nodes in the arbitrary grid region, the arbitrary grid region is marked as an active grid region; otherwise, it is marked as a stable grid region. Repeat the marking process for all grid regions in the latest segmented image to obtain the latest marked segmented image.

6. A basic surveying and mapping data integration system, used to implement the basic surveying and mapping data integration method according to any one of claims 1-5, characterized in that, It includes an image acquisition module, a node segmentation module, a predictive acquisition module, and a dynamic integration module; The image acquisition module includes an acquisition unit and a slicing unit. The acquisition unit is used to acquire image data of the area to be surveyed, and the slicing unit is used to perform image data slicing processing to obtain survey image slice data. The node division module is used to obtain regional node information in the mapping image slice data, obtain node change data, perform regional type division, and obtain regional type information. The prediction acquisition module includes a prediction unit and an additional acquisition unit. The prediction unit makes predictions based on node change data to obtain node prediction data. The additional acquisition unit dynamically acquires additional image slice data based on the node prediction data to obtain additional image slice data. The dynamic integration module is used to dynamically update and integrate the surveying and mapping image slice data and additional image slice data.

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