Laser mapping management method and system based on cloud platform

Abstract

The present application relates to the technical field of surveying and mapping management, in particular to a laser surveying and mapping management method and system based on a cloud platform, which obtains elevation reflection and three-dimensional profile data, processes marked occlusion point groups in layers, generates an occlusion index, determines a failure section list, extracts offset dense parameters to generate a correction instruction, sorts task priorities, judges upload capacity, and uploads high-priority sections to the cloud. The present application realizes spatial coincidence marking of the occlusion area by obtaining the elevation distribution and material reflection record in the partition, establishes a position index, and then combines the scanning track offset, echo failure frequency and path overlap rate to effectively identify the failure section and establish the task position mapping relationship. The direction vector and offset angle change rate are calculated in the track node, the path adjustment instruction is bound in combination with the boundary dense point density, the task response efficiency and data scheduling accuracy are improved, path omission and redundant rescan are avoided, and the utilization rate of surveying and mapping data is improved.

Description

Technical Field

[0001] This invention relates to the field of surveying and mapping management technology, and in particular to a laser surveying and mapping management method and system based on a cloud platform. Background Technology

[0002] Surveying and mapping management technology is an interdisciplinary application of geographic information science and engineering, mainly covering surveying, mapping, spatial data processing and organization management. Technical support in this field includes ground control surveying, remote sensing image processing, digital terrain modeling, spatial database construction, accuracy control and evaluation mechanisms, surveying and mapping production process management, and quality review of output data. Surveying and mapping management not only emphasizes the accuracy and efficiency of data acquisition but also stresses the standardization and automation of the planning, organization, resource allocation, task scheduling, and application of results in the surveying and mapping process, achieving full-process supervision and optimization of surveying and mapping work from data collection, processing, and storage to output.

[0003] Laser mapping management methods are used to organize and control each stage of the laser scanning mapping process. These methods mainly include laser equipment selection and configuration, scanning path planning, station deployment strategies, point cloud data acquisition parameter control, raw data management, processing workflow configuration, error assessment mechanisms, and the organization and allocation of mapping results. Their purpose is to improve the operational efficiency, data quality, and resource utilization of laser mapping tasks through efficient and systematic management.

[0004] Traditional management methods lack specific identification strategies for overlapping occlusion areas. During the execution of scan path planning and station deployment, they cannot dynamically identify missed sections, which can easily lead to duplication or omission between scan paths, affecting operational efficiency. In terms of task allocation, traditional methods are based solely on static settings of equipment configuration and process arrangement, lacking a dynamic task priority judgment mechanism that combines actual operation status and resource occupancy. This can easily lead to delays in uploading high-value data under bandwidth-limited or multi-task concurrent situations. For example, when repeatedly rescanning highly occluded sections, the importance of the area and the order of resource allocation cannot be accurately determined, resulting in decreased scheduling resource utilization and task delays. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a cloud-based laser mapping management method and system.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a cloud platform-based laser mapping management method, comprising the following steps:

[0007] S1: Obtain ground elevation distribution data and material surface reflection record data for each zone in the survey area, and obtain three-dimensional contour data of obstacles through laser scanning equipment. Mark the areas where the causes of occlusion overlap as feature point cluster areas and generate an index list of occlusion locations.

[0008] S2: Based on the occlusion location index list, calculate the failure score of the scan segment according to the located occlusion location, filter and identify the failed scan segments, and generate a list of missing path segments;

[0009] S3: Call the missing segment list of the path, calculate the rate of change of the offset angle of the adjacent segment and the number of dense points of the boundary of the current segment according to the position index of each missing segment, and determine whether the path change identification threshold is exceeded. Perform parameter binding on the path sector number, width scaling ratio and the corresponding scheduling node, and generate task correction parameter information.

[0010] S4: Based on the task correction parameter information, according to the listed scan segment position and sector number, collect the corresponding segment task's job deadline, area usage scenario category label, and historical rescan frequency, calculate the task ranking level value, and generate a scheduling task priority level list.

[0011] The present invention is improved in that the occlusion location index list includes occlusion location number, occlusion cause type, point group spatial coordinates, spatial occlusion label, and regional occlusion level; the path omission segment list specifically refers to task number index, omission segment path number, scan failure segment identification code, dead angle segment location label, and omission trigger source type; the task correction parameter information specifically includes sector identifier, path direction instruction, width compression parameter, scheduling binding code, and boundary point label; and the scheduling task priority level list includes task sorting value, sector task label, regional emergency flag, scheduling priority level, and task number mapping table.

[0012] The present invention is improved in that the step of obtaining the occlusion position index list is specifically as follows:

[0013] S111: Obtain ground elevation distribution data and material surface reflection record data for each zone in the survey area, and obtain obstacle three-dimensional contour data through laser scanning equipment. Perform coordinate mapping between zone number and ground elevation value, combine material surface reflectivity value and scan echo intensity value for position alignment, and use the projection range of the three-dimensional contour on the plane as the boundary to determine whether the area ratio occupied by the zone is greater than the projection ratio set threshold, and generate a set of terrain reflection overlap ratios.

[0014] S112: Based on the terrain reflection overlap ratio set, according to the partition identifier and the corresponding overlap ratio, filter the area numbers whose overlap ratio is greater than the overlap ratio threshold and whose reflectivity is less than the set reflection anomaly threshold by area number, extract the overlapping area grid index under the number, and obtain the point cloud density value and echo missing record number at the corresponding location, determine whether the number of missing records is greater than the missing trigger threshold, and obtain the occlusion candidate area index value group.

[0015] S113: Based on the occlusion candidate region index value group, according to the three-dimensional index number of each position in the scene coordinate system, call the task segment identifier in the task layer, bind it with the original ground scan trajectory number, and construct the task index relationship corresponding to each position point to establish an occlusion position index list.

[0016] The present invention is improved in that the steps for obtaining the list of missing segments of the path are specifically as follows:

[0017] S211: Based on the occlusion position index list, extract the Euclidean edge distance value between the occlusion position and the current preset path trajectory according to the located occlusion position, and calculate the average distance value of the scan segment to which each position belongs. Compare the average value with the scan critical distance threshold set for the path area. If it exceeds the threshold, mark it as a far segment and generate a path edge offset value group.

[0018] S212: Based on the path edge offset value group, according to the marked long-distance segments, call the echo reception record of the corresponding segment, extract the echo failure number of each segment and the average value of the echo failure records under the same sector, calculate the failure frequency deviation of each segment in its sector, extract the scan overlap ratio data in the sector, calculate and obtain the failure degree score of each scan segment, filter the segments with score values ​​greater than the set failure threshold, record the segment number and score result, and generate a scan segment failure score set;

[0019] S213: Based on the failure score set of the scan segment, extract the corresponding task number and establish a mapping structure between the segment number and the task number. Integrate the segments with a score value greater than the set failure threshold into an independent segment set, register the trajectory number, and generate a list of missing path segments.

[0020] The present invention is improved in that the step of obtaining the task correction parameter information is specifically as follows:

[0021] S311: Call the list of missing segments in the path, and perform cross-matching of the starting node position and ending node trajectory in the scanning task path file according to the position index of each missing segment to obtain the sector number of the missing segment in the task path graphic. Extract the scanning direction vector formed by the line connecting the current coordinate point and the edge point of each segment, and calculate the included angle value by combining it with the tangent direction formed by the three-dimensional boundary line of the coordinate point and extract it as the scanning offset angle value to generate a group of scanning direction offset angle values.

[0022] S312: Based on the scanning direction offset angle value group, extract the angle change difference between two adjacent missing segments according to each offset angle value, extract the number of boundary grid points under the segment, calculate the angle change rate and boundary point density value of each segment, construct a path change trend index based on the two normalization results, calculate and obtain the path shape change trend value, determine whether it is greater than the trend identification threshold, if it is greater, mark it as a shape fluctuation segment, and generate a path change trend identification group.

[0023] S313: Based on the path change trend identification group, extract the corresponding sector number and scanning direction vector according to the segment number marked as the morphological fluctuation segment, combine the standard width configuration parameters set in the task, determine the width compression ratio of the angle offset value in the direction vector, execute the instruction to change direction and adjust width, and bind it with the scheduling node number to build a parameter index structure to generate task correction parameter information.

[0024] The present invention is improved in that the step of obtaining the priority list of scheduling tasks is specifically as follows:

[0025] S411: Based on the task correction parameter information, according to the listed scan segment positions and sector numbers, collect the task deadline time, area usage scenario category label and historical rescan frequency data associated with each segment, perform time difference calculation on the task deadline time, obtain the distance in days between the current time and the deadline time, and normalize it in units of days to generate a deadline urgency value group.

[0026] S412: Based on the deadline urgency value group, extract the regional usage scenario category label according to the value corresponding to each task number, obtain the scenario category score value corresponding to the label according to the preset scenario priority coefficient library, calculate and obtain the task ranking level value, and generate a task level score list based on the task number.

[0027] S413: Based on the task level rating list, bind and map each rating value to the task number according to the task number and the sorting level value, establish a number-corresponding level table, and sort the task level values ​​in descending order to generate a scheduling task priority level list.

[0028] The present invention has an improvement, wherein the method further includes the following steps:

[0029] S5: Based on the priority list of the scheduling tasks, compare the sorting level with the data transmission capacity required at the current rate. If the estimated amount of data packets is greater than the current transmission capacity threshold, extract the data segment number with a priority level value greater than the set reference level from the point cloud cache, execute the upload trigger command, transmit the mapping data to the cloud processing platform, and generate a priority upload task number set.

[0030] The priority upload task number set specifically includes the upload task sequence, data segment index code, channel sending identifier, cache extraction order, and throttling control label.

[0031] The present invention is improved in that the step of obtaining the priority upload task number set is specifically as follows:

[0032] S511: Based on the priority list of scheduling tasks, extract the sorting level value of the current task to be processed, and obtain the channel rate value returned by the real-time bandwidth detection unit of the current acquisition end. Compare the expected transmission volume of the data packet corresponding to the task with the transmission capacity threshold corresponding to the current available channel rate. When the expected data volume exceeds the threshold, record the task number and task level, and generate a set of bandwidth conflict task numbers.

[0033] S512: Based on the set of bandwidth conflict task numbers, according to the sorting level value of the task, filter the numbers that are greater than the set reference level, extract the corresponding task segment numbers in the point cloud cache, combine the task segment numbers with the transmission structure index table to complete the number mapping, and sort the numbers of each segment of data to generate a priority segment number sequence.

[0034] S513: Call the priority segment number sequence, execute the upload trigger command in sequence, write the point cloud data content in the data segment into the cloud task channel cache path, register the pointing relationship between the data segment number and the upload task number, and generate a priority upload task number set.

[0035] A cloud-based laser mapping management system, wherein the cloud-based laser mapping management system is used to implement the above-mentioned cloud-based laser mapping management method, the system comprising:

[0036] The occlusion location identification module acquires ground elevation distribution data and material surface reflection record data for each zone in the survey area, and obtains three-dimensional contour data of obstacles through laser scanning equipment. It marks areas where the causes of occlusion overlap as feature point cluster areas and generates an occlusion location index list.

[0037] The path omission identification module, based on the occlusion location index list, calculates the failure degree score of the scan segment according to the located occlusion location, filters and identifies failed scan segments, and generates a list of path omission segments;

[0038] The task correction analysis module calls the list of missing segments in the path, calculates the rate of change of the offset angle of adjacent segments and the number of dense points at the boundary of the current segment based on the location index of each missing segment, and determines whether the path change identification threshold is exceeded. It then binds the path sector number, width scaling ratio and the corresponding scheduling node execution parameters to generate task correction parameter information.

[0039] The task priority scheduling module, based on the task correction parameter information, collects the corresponding task's deadline, area usage scenario category label, and historical rescan frequency according to the listed scan segment position and sector number, calculates the task ranking level value, and generates a scheduling task priority level list.

[0040] The upload task adjustment module, based on the priority list of scheduled tasks, compares the sorting level with the data transmission capacity required at the current rate. If the estimated data packet volume is greater than the current transmission capacity threshold, it extracts the data segment number with a priority value greater than the set reference level from the point cloud cache, executes the upload trigger command, transmits the mapping data to the cloud processing platform, and generates a priority upload task number set.

[0041] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0042] In this invention, by acquiring the elevation distribution and material reflection records within a partition and performing layered processing in conjunction with three-dimensional contour data, spatial overlap marking of occluded areas is achieved. After establishing a position index, the scanning trajectory offset, echo failure frequency, and path overlap rate are combined for judgment to effectively identify failed segments and establish task position mapping relationships. The direction vector and offset angle change rate are calculated in the trajectory nodes, and the path adjustment command is bound by combining the density of boundary dense points. By constructing an accumulation and sorting mechanism of three parameters—job deadline, scene category, and rescanning frequency—the priority level of scheduling tasks is clarified. The estimated data volume is dynamically compared with the available bandwidth value, and high-priority segments in the cache are extracted and uploaded. This achieves rapid identification of failed segments, dynamic trajectory correction, clear sorting of task levels, and priority transmission of high-priority data, improving task response efficiency and data scheduling accuracy, avoiding path omissions and rescanning redundancy, and improving the utilization rate of surveying data. Attached Figure Description

[0043] Figure 1 This is a flowchart of the method of the present invention;

[0044] Figure 2 This is a flowchart illustrating the process of obtaining the occlusion location index list according to the present invention;

[0045] Figure 3 This is a flowchart illustrating the process of obtaining a list of missing path segments according to the present invention.

[0046] Figure 4 This is a flowchart illustrating the process of obtaining task correction parameter information according to the present invention;

[0047] Figure 5 This is a flowchart illustrating how the present invention obtains a priority list of scheduled tasks;

[0048] Figure 6 This is a flowchart for obtaining the priority upload task number set according to the present invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0050] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0051] Please see Figure 1 This invention provides a technical solution: a laser mapping management method based on a cloud platform, comprising the following steps:

[0052] S1: Obtain ground elevation distribution data and material surface reflection record data for each partition in the survey area, and obtain obstacle 3D contour data through laser scanning equipment. Perform data layering processing according to the area number in each partition, and mark the areas where the occlusion causes overlap as feature point group areas. Call the position index number of the marked points in the scene coordinate system to generate an occlusion position index list.

[0053] Ground elevation distribution data originates from digital surface models and digital elevation models, and is frequently used for surveying and 3D modeling; obstacle 3D contour data can be obtained through point cloud boundary extraction algorithms generated by laser scanning equipment; material surface reflection recording data consists of echo intensity values ​​recorded by LiDAR equipment, which are related to parameters such as material type, surface angle, and distance, and are commonly available parameters in LiDAR acquisition;

[0054] S2: Based on the occlusion location index list, extract the shortest edge distance value between the location and the preset path trajectory, the number of echo reception failures in the scanning history, and the overlap rate data of the task sector in the segment according to the located occlusion location. Perform segment combination judgment on the three data items. Calculate the failure degree score of the scanning segment based on the low edge overlap ratio, high reflection failure frequency, and far distance from the scanning trajectory. Screen and identify failed scanning segments, establish the relationship between index location and task number, and generate a list of missing path segments.

[0055] The shortest edge distance value refers to the Euclidean shortest distance between the scan path segment and the edge of the point cloud coverage, which is a common measure of spatial geometry; the number of echo reception failures comes from the echo detection failure records of the scanning device, which is often caused by too small a reflection angle or occlusion; the overlap rate data refers to the point cloud repetition rate in the overlapping area of ​​two scans, which is usually measured as a percentage and is obtained by registration overlap algorithm or mesh statistics;

[0056] S3: Call the list of missing segments in the path. Based on the position index of each missing segment, perform cross-matching with the starting node position and ending node trajectory in the existing scanning task path file to obtain the sector number, scanning direction vector and current position edge point offset angle value of the missing segment in the path graph. Calculate the rate of change of offset angle between adjacent segments and the number of boundary dense points of the current segment, and determine whether it exceeds the path change recognition threshold. If it does, generate a command to change the scanning path direction and a scanning width compression ratio instruction. Bind the path sector number, width scaling ratio and corresponding position scheduling node execution parameters to generate task correction parameter information.

[0057] The scanning direction vector refers to the unit direction of the scanning device in three-dimensional space, used to represent the scanning path direction; the edge point offset angle value is the angle between the vector where the edge point is located and the main scanning path; the rate of change of the offset angle between adjacent segments refers to the difference ratio of the angle changes of continuous path segments, which is one of the commonly used curvature estimation methods; the number of dense points at the boundary is the number of point clouds per unit area in the edge region, used to determine the boundary complexity; the path change recognition threshold is a preset upper limit of angle change, used to trigger path change actions;

[0058] S4: Based on the task correction parameter information, according to the listed scan segment position and sector number, collect the corresponding segment task's job deadline, area usage scenario category label, and historical rescan frequency. Perform cumulative sorting on the three data items according to the set weight, calculate the task sorting level value, bind the sorting level value to the task number, and generate a scheduling task priority level list.

[0059] The deadline for each surveying project is the delivery node defined in the task management system; the area usage scenario category tags, such as "transportation facilities", "urban buildings", "woodland", etc., are derived from the task classification standards; the historical rescanning frequency is the number of times the same area has been scanned and repaired in historical operations, which is recorded by the operation record system.

[0060] S5: Based on the priority list of scheduling tasks, compare the data transmission capacity required by the current rate with the data transmission capacity required by the current rate, according to the current sorting level of the task to be processed and the available channel rate value returned by the real-time bandwidth detection unit of the acquisition end. If the estimated amount of data packets is greater than the current transmission capacity threshold, extract the data segment number with the priority level value greater than the set reference level from the point cloud cache, execute the upload trigger command, transmit the mapping data to the cloud processing platform, and generate a set of priority upload task numbers.

[0061] The real-time bandwidth detection unit is the channel rate fed back by the link monitoring unit commonly used in the mapping cloud platform; the available channel rate value is measured in Mbps or Gbps, representing the current remaining bandwidth; the data transmission capacity is calculated based on the number of point cloud data points, the bit width of each data point, and the encoding format to determine the required transmission capacity for each batch of data.

[0062] The occlusion location index list includes occlusion location number, occlusion cause type, point group spatial coordinates, spatial occlusion label, and area occlusion level. The path omission segment list specifically refers to the task number index, omission segment path number, scan failure segment identification code, dead angle segment location label, and omission trigger source type. The task correction parameter information specifically includes sector identifier, path direction instruction, width compression parameter, scheduling binding code, and boundary point label. The scheduling task priority level list includes task sorting value, sector task label, area emergency flag, scheduling priority level, and task number mapping table. The priority upload task number set specifically includes upload task sequence, data segment index code, channel sending identifier, cache extraction order, and throttling control label.

[0063] Please see Figure 2 The specific steps for obtaining the occlusion location index list are as follows:

[0064] S111: Obtain ground elevation distribution data and material surface reflection record data for each zone in the survey area, and obtain obstacle three-dimensional contour data through laser scanning equipment. Perform coordinate mapping between zone number and ground elevation value, combine material surface reflectivity value and scan echo intensity value for position alignment, and use the projection range of the three-dimensional contour on the plane as the boundary to determine whether the area ratio occupied by the zone is greater than the projection ratio set threshold, and generate a set of terrain reflection overlap ratios.

[0065] To obtain ground elevation distribution data and material surface reflection record data for each zone in the surveying area, it is necessary to first retrieve the surveying planning information containing multiple zones from the task layer and set the zone number values ​​according to the block division. For example, the task layer with dimensions of [missing information]... The area was divided into 400 grid sub-regions numbered Z001 to Z400. Then, the elevation values ​​corresponding to the center point, corner points, and edge center points of each grid cell were retrieved from the DSM and DEM layers of the surveyed area to form an initial set of ground elevation values ​​in matrix format. The units for the ground elevation values ​​are... Data precision set to Subsequently, the reflection intensity values ​​recorded by the LiDAR device at the same location were matched. The reflection value sequence was obtained by indexing the same location in the echo intensity record table, and interfering echo items were removed. The most recent value at the sampling time was taken as the current valid reflection value, and the reflectivity value of the material surface was normalized to make it fall within the range of the LiDAR device. Within a certain range, for example, the echo intensity value corresponding to a certain measuring point Z123 is... The reflectance obtained after normalization is Simultaneously, after acquiring 3D point cloud data of obstacles using laser scanning equipment, the contour extraction module is called to form the point cloud boundary envelope volume. This volume is then orthographically projected onto a 2D surface mesh layer to obtain the number of meshes covered by each projected volume in each zone. The area ratio of each projected volume in each zone is then calculated. For example, in zone Z123, the projected area of ​​a certain obstacle is... The total area of ​​the district is The projection ratio is then The partition number is linked with elevation data, material reflectivity, echo intensity value and projection boundary to form an index binding. A five-data combination containing coordinate index, ground elevation value, material reflectivity, echo intensity value and obstacle projection ratio is constructed. A mapping verification operation is performed on the combination. When the coordinates are consistent, the data is jointly indexed to obtain the final set of terrain reflection overlap ratios.

[0066] S112: Based on the terrain reflection overlap ratio set, according to the partition identifier and the corresponding overlap ratio, filter the area number by area number, the overlap ratio is greater than the overlap ratio threshold and the reflectivity is less than the set reflection anomaly threshold, extract the grid index of the overlapping area under the number, and obtain the point cloud density value and the number of echo missing records at the corresponding location. Determine whether the number of missing records is greater than the missing trigger threshold, and obtain the occlusion candidate area index value group.

[0067] Based on the zoning numbers and overlap ratio data recorded in the terrain reflection overlap ratio set, a dual judgment operation needs to be performed simultaneously on the projection ratio and reflectivity according to a preset judgment threshold. The overlap ratio threshold is set as follows: This value is based on the The statistical distribution of the proportion of obstacle projection area in each sample region was used as a reference standard, with the first sample region being the first sample region. The projected percentage value corresponding to the percentile, which is [value missing] in the experimental data. Therefore, rounded down to The abnormal reflection threshold is set to The basis is that the ratio between material reflectivity and echo intensity is lower than that of conventional ground features. At that time, the reflection failure rate began to be higher than This turning point serves as the basis for the settings, and the specific settings statistics are shown below.

[0068] Table 1: Statistical Table of Overlap and Reflectivity

[0069]

[0070] Referring to Table 1, the projection ratio will be satisfied during the filtering operation. and reflectivity The region IDs are filtered out. For each region ID, all grid cell IDs are extracted, and the point cloud density is retrieved within each grid cell. Density is defined as the number of points per unit area, for example, points per square meter. The unit area is set to... The base grid window, if a certain grid contains If there are 1 point, then the density is 1. Simultaneously, the number of missing echo records for each grid is counted. The criteria for determining missing echoes is the absence of valid echo responses in multiple scans. The missing echo trigger threshold is set to... This value was obtained through statistical analysis. The locations in the obscured areas are The distribution of effective reception counts under each scan revealed that when the number of missing data reaches or exceeds a certain threshold... At this time, it was in the subsequent scan. The probability of this needs to be repeatedly collected, therefore this threshold is used as the occlusion judgment benchmark to form a clear occlusion recognition boundary. During the collection process, if the echo response of a certain grid point is... The number of records reached or exceeded If the occlusion candidate grid is marked, an index value group of occlusion candidate regions is finally formed. This index value group records the spatial location coordinates of all regions that satisfy the conditions of high projection overlap, low reflection, and significant echo loss.

[0071] S113: Based on the occlusion candidate region index value group, according to the 3D index number of each location in the scene coordinate system, call the task segment identifier in the task layer, perform task binding operation with the original ground scan trajectory number, and construct the task index relationship corresponding to each location point to establish an occlusion location index list.

[0072] For each grid position coordinate value recorded in the occlusion candidate region index value group, its 3D index number in the scene coordinate system needs to be called. The 3D index uses... The composite structure records the absolute position of each point in the world coordinate system. After calling the corresponding index, it extracts the task segment identifier marked in the task layer file. The task segment identifier is the sub-task segment number corresponding to each scan path in the path graphic, such as T089, T090, etc. Then, according to the task trajectory record table, the grid position of the occlusion point is bound to the original ground scan path number. If an occlusion point is numbered P345 and is located within scan path segment T090, then a... The binding relationship is established, and then a mapping index is created for each occlusion point and its corresponding task segment number. All mapping indices are integrated into an occlusion location index list, for example, the list records as follows: This index list, with its structure, serves as a key input for subsequent path omission analysis and task repair operations.

[0073] Please see Figure 3 The specific steps for obtaining the list of missing segments in the path are as follows:

[0074] S211: Based on the occlusion location index list, extract the Euclidean edge distance value between the located occlusion location and the current preset path trajectory, calculate the average distance value of the scan segment to which each location belongs, compare the average value with the scan critical distance threshold set for the path area, and if it exceeds the threshold, mark it as a distant segment and generate a path edge offset value group.

[0075] After obtaining the point cluster position index data from the occlusion position index list, the three-dimensional coordinates corresponding to each point cluster are first parsed in the scene coordinate system. Then, the set of centerline vectors of the preset path trajectory in the task layer is loaded. The trajectory is stored in a line segment structure, and its constituent points are continuous scan control nodes. For each point group index point and the trajectory line segment set, the shortest distance is calculated using the Euclidean distance formula. The minimum edge distance value from the trajectory is obtained. All point groups are clustered according to their respective scan segment numbers, and the arithmetic mean of the edge distance values ​​of all point groups in each segment is calculated to obtain the average edge offset value of the scan segment. Let this average value be... ,in The scan segment is numbered; for example, scan segment S104 contains 13 point groups, and their edge distances are respectively... Then its average edge offset is The preset critical scanning distance threshold is This threshold is set based on the measurement results of the average offset distribution of the outer contours of buildings, facilities, etc. in the surveyed scene. Its reference standard is the statistical analysis of the common maximum values ​​between the road center and the building edge. quantile value, across the sample region The average value obtained from edge measurements of each scan segment is: Finally set to Used to divide distant segments, averaging the offset value across all scanned segments. and Perform a comparison item by item, if If the paragraph is marked as a distant paragraph, a path edge offset value group is generated, which is used to reflect the spatial deviation of each paragraph from the preset trajectory.

[0076] S212: Based on the path edge offset value group, according to the marked long-distance segments, call the echo reception records of the corresponding segments, extract the echo failure count of each segment and the average value of echo failure records in the same sector, calculate the failure frequency deviation of each segment in its sector, and extract the scan overlap ratio data in the sector using the formula:

[0077] ;

[0078] The failure score of each scan segment is obtained by calculation. Segments with scores greater than the set failure threshold are filtered out, and the segment number and score result are recorded to generate a scan segment failure score set.

[0079] in, The score indicates the degree of failure of the scanned segment. This represents the normalized value indicating the number of failed echo receptions. The normalized value representing the offset distance at the path edge. This represents the normalized value of the scan overlap ratio. This represents the task frequency deviation value of the scan segment in its respective sector, which is used to adjust the evaluation sensitivity of frequently operated segments. The value is the difference between the segment task frequency and the regional task mean, and after normalization, it is used to calculate the nonlinear amplification adjustment factor.

[0080] For the distant segments marked in the path edge offset value group, retrieve their echo reception records recorded in the task layer in sequence, and count the number of echo failures for each segment. Simultaneously, load the set of echo failure counts for all scan segments within its sector and calculate the average value of this set. Then, the deviation of the failure frequency for each segment is calculated and defined as follows: ,Will Perform normalization operation to map to At the same time, the offset distance value of the corresponding paragraph will be... Scan overlap ratio Unified normalization process to obtain normalized parameters , , ,in , , The maximum value of each parameter is set based on the statistical extreme values ​​of all segments within the sector. For example, if the maximum number of echo failures in a certain sector is 21 and the maximum offset distance is... Minimum overlap ratio is The maximum values ​​of each parameter are respectively , , (Since the overlap ratio is normalized as a percentage), after normalization, the formula is entered.

[0081] ;

[0082] The calculation is performed to obtain the failure score for each distant segment. The natural logarithm term is used to amplify the weight of frequently failing segments. This structure reflects the nonlinear amplification risk of frequently interfering segments in actual operations. The higher the score, the more serious the structural occlusion or path deviation problem may be in that segment. The failure threshold is set to... This threshold was determined experimentally and selected. Ground-based verification was conducted on high-scoring segments, with the score set corresponding to a significant increase in the effective scan failure rate being [value missing]. Therefore, it is set to For all rating values and The system compares the scores, records the segment numbers whose scores exceed the threshold and their calculated scores, and generates a scan segment failure score set.

[0083] As shown in Table 2, some of the calculation results are as follows:

[0084] Table 2 Sample Table of Scan Segment Failure Scoring

[0085]

[0086] Table 2 lists the calculated score values ​​for the selected paragraphs. The value is derived from the difference between the actual number of failures and the average number of failures per sector. All indicators are normalized and then entered into the scoring formula.

[0087] The operations in the formula have clear interactions: the molecular part This is an enhancement term for the degree of failure in the scan segment, indicating that there are many echo failures ( Large), path offset far ( Large), significant deviation in task frequency ( When the value is large, the overall value of this item increases significantly; among which As a non-linear adjustment term, it is able to Increasing the denominator causes a logarithmic amplification of the aforementioned products, thus preventing single extreme values ​​from dominating the score and making outliers easier to identify; while the denominator Then it is used as an attenuation factor in the scoring, if the scan overlap ratio If the score is high (i.e., the paragraph area has been scanned multiple times), the square root of this item will be large, suppressing the overall score and effectively offsetting the risk of excessively high scores for repeatedly scanned paragraphs. This combined logic reflects the identification strategy of "few overlaps, many failures, and large deviations" as high-risk segments; the final calculated score value It is a dimensionless discrimination index, whose physical meaning can be regarded as a "comprehensive failure index". It is used to characterize the superimposed performance of a certain segment under the three abnormal factors of structural occlusion, path deviance and signal loss. The higher the score, the more severe the comprehensive abnormality of the segment. The score serves as the basis for subsequent decision-making for path omission segment extraction and task priority ranking, and its threshold setting is... This serves as the boundary for identifying failure states. All segments with scores exceeding this threshold will be identified as failure segments and included in the subsequent rescanning scheduling process.

[0088] S213: Based on the failure score set of the scan segment, extract the corresponding task number and establish a mapping structure between the segment number and the task number. Integrate the segments with a score value greater than the set failure threshold into an independent segment set, and perform trajectory number registration operation to generate a list of missing path segments.

[0089] Based on the segment numbers in the scan segment failure score set, the mapping table between segments and task numbers is called one by one to extract the task number associated with each segment and generate a binding structure between segment numbers and task numbers, such as... , Integrate all scores that are greater than the failure threshold The paragraphs are a set of independent paragraphs, and this set is marked as the set of missing paragraphs in the path. For each segment in the set, a trajectory number registration operation is performed based on the main path number where the trajectory is located. The path name, segment number, and task number are then combined and registered to form a unified index list structure as a list of missing segments. For example, one item in the list records... It is used for subsequent task correction and path rescanning operations.

[0090] Please see Figure 4 The specific steps for obtaining task correction parameter information are as follows:

[0091] S311: Call the list of missing segments in the path, and perform cross-matching between the position index of each missing segment and the starting node position and ending node trajectory in the scanning task path file to obtain the sector number of the missing segment in the task path graphic. Extract the scanning direction vector formed by the line connecting the current coordinate point and the edge point of each segment, and calculate the included angle value by combining it with the tangent direction formed by the three-dimensional boundary line of the coordinate point and extract it as the scanning offset angle value to generate a group of scanning direction offset angle values.

[0092] After retrieving the list of missing path segments, the spatial location index information of each missing segment is read one by one. For each missing segment, the path segment structure associated with it in the task path graph is loaded, and the starting node of that path segment is obtained. With the termination node By cross-matching the node numbers in the path file using the location index, the corresponding sector number for that segment can be retrieved. Then, the 3D coordinates of all current scan point groups for the missing segment are extracted from the path map, and the coordinates of each scan point group are... Its nearest edge point in the current paragraph boundary point set Construct the direction vector:

[0093] ;

[0094] This represents the scan direction vector, and simultaneously analyzes the 3D boundary segments surrounding this coordinate point to extract its local tangent direction vector. Calculate the angle between the two vectors mentioned above:

[0095] ;

[0096] The scanning offset angle value of the current point is calculated in radians. After all points in each missing segment are calculated, the average offset angle of each segment is extracted as the main offset angle value of that segment. The offset angle values ​​of all segments are combined to form a scanning direction offset angle value group for further path trend analysis and change identification.

[0097] S312: Based on the scan direction offset angle value group, extract the angle change difference between two adjacent missing segments according to each offset angle value, and extract the number of boundary grid points under the segment. Calculate the angle change rate and boundary point density value for each segment, and construct a path change trend index based on the two normalized results, using the formula:

[0098] ;

[0099] The path shape change trend value is obtained by calculation. It is determined whether it is greater than the trend recognition threshold. If it is greater, it is marked as a shape fluctuation segment and a path change trend identification group is generated.

[0100] in, This value represents the trend of path shape changes and is used to determine the degree of change in the shape of path segments. The normalized value representing the difference in scanning direction angle between two adjacent scan segments is calculated by extracting the angle between the main scanning direction vector of each segment and the direction vector of the adjacent segment, and then performing minimum-maximum normalization on the angle value. This represents the normalized value indicating the number of boundary grid points in the current paragraph. The original value is obtained by counting the number of point clouds within the boundary grid of this paragraph, and then normalized using the maximum value. This represents the normalized value indicating the amplitude of the scan frequency perturbation. It is obtained by dividing the absolute difference in the task trigger time interval between adjacent segments by the maximum time difference between segments. This represents the perturbation amplification parameter, used to enhance the influence of frequency perturbations on trend values. In practical applications, it is determined based on the sensitivity to changes in the scanning period. The normalized value representing path density is obtained by normalizing the number of task triggers associated with a path segment of unit length, using the min-max normalization method.

[0101] Based on the obtained scanning direction offset angle value set, a segment pair structure is constructed for every two adjacent missing segments according to the segment order, and the difference in offset angle is extracted to form a set of angle change difference values. Each item represents the degree of directional abrupt change between adjacent paragraphs. Then, the corresponding boundary grid point set for each paragraph is loaded, and the paragraph boundaries are then... The number of point clouds in a cell grid is used as the number of boundary points. And extract the difference in task trigger time intervals between segments. The maximum time difference between all segments As a normalization benchmark, let the scanning frequency perturbation amplitude be... All the above parameters are normalized, including the difference in the included angle. Use the minimum value With the maximum value Perform normalized calculations:

[0102] ;

[0103] Number of boundary points By maximum points Perform normalization:

[0104] ;

[0105] Path density The number of task triggers per unit length paragraph, normalized to the minimum and maximum values:

[0106] ;

[0107] Scan perturbation amplification parameters Set a value for experience, set it to Based on the sensitivity test of operation frequency from the path segment, where when At that time, the false positive rate of trend identification was the lowest, and the median was finally selected. Substitute the above normalized values ​​into the path trend formula:

[0108] ;

[0109] The specific calculation logic for each parameter is as follows: The numerator consists of three terms, the first term... The second item indicates the magnitude of the abrupt change in direction between paragraphs. The third term is the boundary point complexity index. The frequency disturbance influence factor, where the logarithmic function constitutes a nonlinear amplification, causing the scanning frequency to change more rapidly; the denominator This is a path density adjustment term; the denser the paths, the lower the score, effectively reducing the sensitivity of trend indicators in dense path segments. The final calculation result... Characterizing the trend of path morphology changes, reflecting the stability of segments in terms of geometric structure and task scheduling, a trend recognition threshold is set. This value comes from the... Statistics on the distribution of trend scores in the path segment, among which The abnormal trend segment scores are concentrated in The above serves as the boundary for identifying trend fluctuation segments. If the score value is greater than the threshold, the segment is marked as a morphological fluctuation segment.

[0110] Table 3: Example Table of Path Trend Recognition

[0111]

[0112] As shown in Table 3, the trend scores of paragraphs S205 and S207 are both higher than the recognition threshold. This will be marked as a path morphology fluctuation segment.

[0113] S313: Based on the path change trend identification group, extract the corresponding sector number and scanning direction vector according to the segment number marked as the morphological fluctuation segment, combine the standard width configuration parameters set in the task, determine the width compression ratio of the angle offset value in the direction vector, execute the command to change direction and adjust width to generate, and bind it with the scheduling node number to build a parameter index structure to generate task correction parameter information.

[0114] Based on the segment numbers marked as morphological fluctuation segments in the path change trend identifier group, extract their corresponding sector numbers. and the main scan direction vector Obtain the standard width configuration value from the task configuration file. The unit is meters. If the current scanning direction is offset by an angle Exceed Then, the scanning direction switching determination mechanism is activated, and the direction switching command is set to... Meanwhile, if the path direction changes abruptly and the boundary point density are both high (corresponding to...) Then, based on the offset angle The width compression ratio is determined, and the compression ratio is defined as follows:

[0115] ;

[0116] For example , ,but This generates a compression command. Finally, the swap command, compression command, and the scheduling node number of that segment will be used. Bind the parameters to form a parameter structure: This information is recorded in the task correction parameter information table for subsequent rescanning path reconstruction and dynamic adjustment of task configuration.

[0117] Please see Figure 5 The specific steps for obtaining the priority list of scheduled tasks are as follows:

[0118] S411: Based on the task correction parameter information, according to the listed scan segment positions and sector numbers, collect the job deadline time, area usage scenario category label and historical rescan frequency data of the task associated with each segment, perform time difference calculation on the job deadline time, obtain the distance in days between the current time and the deadline time, and normalize it in units of days to generate a group of deadline urgency values.

[0119] Based on the scanned segment positions and corresponding sector numbers listed in the task correction parameter information, the task registration table in the task scheduling system is loaded sequentially to obtain the task number associated with each segment. Further extract the task's set deadline. The time record is accurate to "year, month, and day," and the current system time is calculated after a unified conversion using timestamps. (Set as July 29, 2024) Time difference with each deadline This time difference represents the remaining time of the task in "days" and is recorded as an integer. After forming a set of remaining days for all tasks, a normalization operation needs to be performed, setting the maximum remaining time of this set to [value missing]. The minimum value is The normalization method employs "reverse stretch normalization," meaning that the shorter the remaining time, the closer the normalized value is to 1, thus enhancing the priority of time-critical tasks. The normalization formula is:

[0120] ;

[0121] For example, suppose the deadlines for the three tasks are 2024-08-02 ( ), 2024-08-15 ), 2024-08-20 ), then the maximum value Minimum value The normalized result for a task with a deadline of 2024-08-15 is:

[0122] ;

[0123] And so on, to obtain the time urgency value for each task number. And generate a group of deadline urgency values. This value describes the time urgency of a task relative to the overall task queue. The closer the value is to 1, the shorter the remaining time for the task, and the higher its priority in subsequent scheduling.

[0124] S412: Based on the deadline urgency value group, extract the regional usage scenario category label according to the value corresponding to each task number, and obtain the scenario category score value corresponding to the label according to the preset scenario priority coefficient library, using the formula:

[0125] ;

[0126] The task ranking value is calculated and summarized according to the task number to generate a task ranking score list;

[0127] in, This indicates the task ranking level value, and the overall ranking level. is the base of the natural logarithm. This represents the regional usage scenario rating, a fixed score mapped from task scenario tags, derived from a predefined task tag level library. This represents the normalized value of the deadline urgency, derived from the normalized result of the difference in days between the task deadline and the current system time. This indicates the priority start time threshold, and the reference critical value indicating the urgency of the time. This represents the normalized value of historical rescanning frequency, derived from the minimum-maximum normalized result of historical rescanning frequency within the same area in the task record. This represents scheduling impact parameters, used to amplify the effect of priority task rating on the sorting mechanism;

[0128] In terms of the time urgency of acquiring each task Next, extract the area usage scenario category tags corresponding to each task. The tag originates from the classification setting of the work area type during task initialization. Tag types include, but are not limited to, "transportation facilities", "urban buildings", "woodland", and "industrial plant area". For each tag, it is mapped to a preset fixed score value in the priority level scoring library. The score reflects the priority of the scenario's impact on task scheduling resources; for example, the score for traffic facility mapping is... Urban building clusters are Woodland is Industrial plant area is Subsequently, the re-sweeping records in the historical job record system were extracted, and the re-sweeping frequency of the area to which each task belongs was calculated. and according to the maximum value and minimum value Normalization is performed, and the normalized form is:

[0129] ;

[0130] When the number of rescans in the task history is high, the normalized value A value approaching 1 indicates that the task has experienced multiple scan failures or supplementary actions in its history, and the priority value should be increased accordingly; the above three indicators... , , After merging, substitute the results into the sorting level calculation formula:

[0131] ;

[0132] The logical structure of each term in the formula is as follows: First term Scoring the task scenario is a statically determined inherent priority of the task; the second item This is the urgency deviation response item, used to measure the current task relative to the set initiation threshold. distance, Set as This value is based on the average urgency level corresponding to high urgency states in past task scheduling. The greater the deviation, the greater the potential for abrupt changes in the task's initiation state. Therefore, this deviation is amplified by an exponential function; the third term... Used to describe the scheduling pressure caused by the increased frequency of task rescanning;

[0133] Overall rating This is a quantitative expression of the overall task ranking level, representing the total scheduling priority value of a task across three dimensions: time urgency, regional importance, and historical task complexity. A higher value indicates a higher priority for the task. This ranking level will serve as a key ranking criterion in the subsequent generation of the task scheduling ranking list.

[0134] Table 4: Task Level Rating Calculation Table

[0135]

[0136] As shown in Table 4, task number T101 has a high scene score, high time urgency and high rescan frequency. Under the influence of these three factors, its ranking level value is significantly higher than that of other tasks. This value will directly determine its upload and processing priority in the subsequent scheduling process.

[0137] S413: Based on the task level rating list, bind and map each rating value to the task number according to the task number and the sorting level value, establish a number-to-level table, sort the task level values ​​in descending order, and generate a priority level list for scheduling tasks.

[0138] Based on the task level rating results in Table 4, a binding structure between task number and level value is established for each item, forming a binary mapping group. Then press Sort the values ​​in descending order and build a priority list of scheduled tasks. This sorting structure will serve as the priority for queues during data upload, bandwidth scheduling, and node processing. For example, when bandwidth is a bottleneck, the system will prioritize the execution of point cloud data transmission and computing resource allocation for tasks with higher rankings. In addition, this list will also drive the execution order of tasks in processes such as task batch scheduling and rescanning scheduling, thereby ensuring that tasks with tight deadlines, high workloads, and high-value areas can be completed first, achieving complete mapping control from task level to execution strategy across the entire chain.

[0139] Please see Figure 6 The specific steps for obtaining the task ID set to be uploaded first are as follows:

[0140] S511: Based on the priority list of scheduling tasks, extract the sorting level value of the current task to be processed, and obtain the channel rate value returned by the real-time bandwidth detection unit of the current acquisition end. Compare the expected transmission volume of the data packet corresponding to the task with the transmission capacity threshold corresponding to the current available channel rate. When the expected data volume exceeds the threshold, record the task number and task level, and generate a set of bandwidth conflict task numbers.

[0141] Based on the generated priority list of scheduled tasks, the system retrieves the priority value of the currently pending tasks in order of task number within the scheduling cycle. This value represents the overall scheduling priority determined by the combined influence of task urgency, scene weight, and rescan frequency; simultaneously, the real-time bandwidth detection unit returns the currently available channel rate at the acquisition end. The unit is This value originates from the link resource monitoring module, and is determined by periodically sampling port throughput and using feedback from the backhaul window to obtain the remaining bandwidth value; for each task Read the estimated transmission volume of point cloud data packets in its cache. Calculate the theoretical transmission capacity threshold of this data volume at the current channel rate. ,in The current task trigger interval window (unit: seconds), for example, the current bandwidth value is The task upload window is Then the transmission capacity is If the task data volume Therefore, The task will be marked as a bandwidth conflict task; the system will check all tasks in sequence, record whether they are limited by the current link rate due to excessive data volume, and record their task number and level value, forming a set of bandwidth conflict task numbers. This provides a basis for prioritizing and filtering subsequent uploads.

[0142] S512: Based on the set of bandwidth conflict task numbers, according to the sorting level value of the task, filter the numbers that are greater than the set reference level, extract the corresponding task segment numbers in the point cloud cache, combine the task segment numbers with the transmission structure index table to complete the number mapping, and perform number sorting operation on each segment of data to generate a priority segment number sequence.

[0143] Based on the set of bandwidth conflict task numbers Each task number in The system retrieves its corresponding sorting level value. and set reference level threshold The threshold is set for comparison. Its source is the lowest priority task score that ensures scheduling effectiveness during link congestion in historical scheduling. This value is at the 65th percentile among 1200 task samples, effectively identifying high-importance tasks and prioritizing their upload resource allocation. If a task is identified as a priority task, then the task segment number is extracted from the point cloud cache. It also loads the transport structure index table and completes the task segment numbering and data block numbering. A one-to-one mapping, for example, the segment number of task T101 is... The mapping data number is ; Form a data segment set from all mapped number sets. And sort them in ascending order according to their original numbering order or logical position in the task structure to generate a priority segment number sequence. This ensures that the upload process is scheduled to complete in the order of paragraph execution.

[0144] Table 5: Examples of Bandwidth Conflicts and Priority Segment Numbering

[0145]

[0146] As shown in Table 5, tasks T101 and T103 exceed the current bandwidth's supported threshold, forming a set of bandwidth-conflicting tasks, and their level values ​​are both higher than [a certain threshold]. Therefore, it is included in the priority segment numbering sequence.

[0147] S513: Call the priority segment number sequence, execute the upload trigger command in sequence, write the point cloud data content in the data segment to the cloud task channel cache path, and register the pointing relationship between the data segment number and the upload task number to generate a priority upload task number set;

[0148] Call priority segment number sequence All data segment numbers in The upload scheduling action is initiated one by one according to the sorting order. After the upload command is triggered, the point cloud data encapsulated in the corresponding numbered data segment is written to the task channel cache path specified by the cloud platform. The path structure follows a two-level index specification of task number / timestamp, ensuring that all uploaded data is searchable and task traceable. Simultaneously, the mapping structure between upload numbers and their respective tasks is recorded in the task scheduling log. This forms a structure set corresponding to the upload tasks; the system merges all completed upload task numbers to form a priority upload task number set. This set is used by the link scheduling module to identify currently uploading tasks and their resource usage structure, while also providing task execution integrity verification data for the subsequent statistics module. This process ensures that high-priority tasks can still successfully complete critical segment data transmission under bandwidth-constrained conditions, avoiding task terminal failure or data loss due to resource scheduling errors.

[0149] A cloud-based laser mapping management system is used to implement the aforementioned cloud-based laser mapping management method. The system includes:

[0150] The occlusion location identification module acquires ground elevation distribution data and material surface reflection record data for each zone in the survey area, and obtains three-dimensional contour data of obstacles through laser scanning equipment. It marks areas where the causes of occlusion overlap as feature point cluster areas and generates an occlusion location index list.

[0151] The path omission identification module, based on the occlusion location index list, calculates the failure degree score of the scan segment according to the located occlusion location, filters and identifies failed scan segments, and generates a list of path omission segments;

[0152] The task correction analysis module calls the list of missing segments in the path, calculates the rate of change of the offset angle of adjacent segments and the number of dense points at the boundary of the current segment based on the location index of each missing segment, and determines whether it exceeds the path change identification threshold. It then binds the path sector number, width scaling ratio and the corresponding scheduling node to the execution parameters to generate task correction parameter information.

[0153] The task priority scheduling module, based on task correction parameter information, collects the job deadline, area usage scenario category label, and historical rescan frequency of the corresponding segment task according to the listed scan segment position and sector number, calculates the task ranking level value, and generates a priority level list of scheduling tasks.

[0154] The upload task adjustment module, based on the priority list of scheduled tasks, compares the sorting level with the data transmission capacity required at the current rate. If the estimated data packet volume is greater than the current transmission capacity threshold, it extracts the data segment number with a priority value greater than the set reference level from the point cloud cache, executes the upload trigger command, transmits the mapping data to the cloud processing platform, and generates a set of priority upload task numbers.

[0155] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A cloud platform-based laser mapping management method, characterized in that, Comprise the following steps: S1: Obtain the ground elevation distribution data and material surface reflection record data of each partition in the surveying and mapping area, and obtain the three-dimensional profile data of the obstacle by laser scanning equipment, mark the section with overlapping and crossing occlusion causes as a feature point group area, and generate an occlusion position index list; S2: Based on the occlusion position index list, calculate the scanning segment failure degree score according to the located occlusion position, screen and identify the failed scanning segment, and generate a path omission segment list; S3: Call the path omission segment list, calculate the adjacent segment offset angle change rate and the boundary dense point number of the current segment according to the position index of each omission segment, and judge whether it exceeds the path change recognition threshold, perform parameter binding on the path sector number, width scaling ratio and scheduling node corresponding to the position, and generate task correction parameter information; S4: Based on the task correction parameter information, according to the listed scanning segment position and sector number, collect the task deadline, regional use scene category label and historical supplementary scanning frequency of the corresponding segment, calculate the task sorting level value, and generate a scheduling task priority level list; The acquisition step of the scheduling task priority level list is specifically: S411: Based on the task correction parameter information, according to the listed scanning segment position and sector number, collect the task deadline, regional use scene category label and historical supplementary scanning frequency data of each segment, perform time difference calculation on the task deadline, obtain the distance between the current time and the deadline in days, and normalize the data in days to generate a deadline urgency value group; S412: Based on the deadline urgency value group, according to the value corresponding to each task number, extract the regional use scene category label, obtain the scene category score value corresponding to the label according to the preset scene priority coefficient library mapping, calculate the task sorting level value, and according to the task number, generate a task level score list; S413: Based on the task level score list, according to the task number and sorting level value, bind and map each score value according to the task number, establish a number corresponding level table, and sort the task level value in descending order, and generate a scheduling task priority level list. 2.The cloud platform-based laser mapping management method according to claim 1, characterized in that, The occlusion position index list includes occlusion position number, occlusion cause type, point group space coordinates, space occlusion label and regional occlusion level, the path omission segment list specifically refers to task number index, omission segment path number, scanning failure segment identification code, dead angle segment position label and omission trigger source type, the task correction parameter information specifically refers to sector identification number, path direction instruction, width compression parameter, scheduling binding code and boundary point label, and the scheduling task priority level list includes task sorting value, sector task label, regional emergency symbol, scheduling priority level and task number mapping table. 3.The cloud platform-based laser mapping management method of claim 2, wherein, The acquisition step of the occlusion position index list is specifically: S111: Obtain the ground elevation distribution data and material surface reflection record data of each subzone in the surveying area, obtain the three-dimensional profile data of the obstacles through the laser scanning device, perform coordinate mapping on the subzone number and the ground elevation value, align the position in combination with the material surface reflectivity value and the scanning echo intensity value, and determine whether the area ratio of the subzone is greater than the projection ratio threshold value in the projection range of the three-dimensional profile on the plane as a boundary, to generate a terrain reflection overlap ratio set; S112: Based on the terrain reflection overlap ratio set, according to the subzone identifier and the corresponding overlap ratio, the area number is screened according to the area number, the overlap ratio is greater than the overlap ratio threshold value, and the reflectivity is less than the set reflection abnormal threshold value, the overlap area grid index under the number is extracted, and the point cloud quantity density value and the echo missing record number of the corresponding position are obtained, it is judged whether the missing record number is greater than the missing trigger threshold value, and the occlusion candidate area index value group is obtained; S113: Based on the occlusion candidate area index value group, according to the three-dimensional index number of each position in the scene coordinate system, the task fragment identifier in the task layer is called, the original ground scanning track number is bound, and the work index relationship corresponding to each position point is constructed, and the occlusion position index list is established. 4.The cloud platform-based laser mapping management method of claim 3, wherein, The acquisition step of the path omission segment list is specifically: S211: Based on the occlusion position index list, according to the located occlusion position, the Euclidean edge distance value between the position and the current preset path track is extracted, and the average distance value of each position belonging to the scanning segment is calculated, and the average value is compared with the scanning critical distance threshold value set by the path area, if it exceeds the threshold value, it is marked as a far distance paragraph, and a path edge offset value group is generated; S212: Based on the path edge offset value group, according to the marked far distance paragraph, the echo receiving record of the corresponding segment is called, the echo failure number of each segment and the average value of the echo failure record in the same sector are extracted, the failure frequency deviation of each segment in the belonging sector is calculated, the scanning overlap ratio data in the sector is extracted, the failure degree score value of each scanning segment is obtained by operation, the paragraph number and the score result are recorded, and the scanning segment failure score set is generated; S213: According to the scanning segment failure score set, the corresponding task number is extracted and the mapping structure of the paragraph number and the task number is established, the paragraphs with score values greater than the set failure critical value are integrated into an independent paragraph set, and the track number is registered, to generate a path omission segment list. 5.The cloud platform-based laser mapping management method of claim 4, wherein, The acquisition step of the task correction parameter information is specifically: S311: Call the path omission segment list, cross-match the starting node position and the terminal node track in the scanning task path file according to the position index of each omission segment, obtain the sector number of the omission segment in the task path graph, extract the scanning direction vector composed of the current coordinate point and the edge point of each segment, combine the tangent direction composed of the three-dimensional boundary line of the coordinate point, calculate the included angle value and extract it as the scanning offset angle value, and generate a scanning direction offset angle value group; S312: Based on the scan direction offset angle value group, according to each offset angle value, the angle change difference value of the adjacent two missing sections is extracted, and the boundary grid point number under the paragraph is extracted, the angle change rate of each section is calculated and the boundary point density value is calculated, and the path change trend index is constructed according to the two normalization results, the path form change trend value is obtained by operation, and it is judged whether it is greater than the trend identification threshold. If it is greater than the form fluctuation section is marked, and the path change trend identification group is generated; S313: According to the path change trend identification group, according to the paragraph number marked as the form fluctuation section, the corresponding sector number and scan direction vector are extracted, the angle offset value in the direction vector is judged according to the standard width configuration parameter set in the task, the instruction generation of switching direction and adjusting width is executed, and the parameter index structure is bound with the scheduling node number to construct the parameter index structure, and the task correction parameter information is generated. 6.The cloud platform-based laser mapping management method of claim 5, wherein, The method further comprises the following steps: S5: Based on the scheduling task priority level list, compare the sorting level and the data transmission capacity required by the current rate, if the data packet estimation is greater than the current transmission capacity threshold, extract the data section number with priority level value greater than the set reference level from the point cloud cache, execute the upload trigger instruction, and transmit the surveying and mapping data to the cloud processing platform to generate the priority upload task number set; The priority upload task number set specifically includes upload task sequence, data section index code, channel sending identification, cache extraction sequence and throttling control label. 7.The cloud platform-based laser mapping management method of claim 6, wherein, The acquisition step of the priority upload task number set is specifically: S511: Based on the scheduling task priority level list, extract the sorting level value of the current to-be-processed task, and obtain the channel rate value returned by the current acquisition end real-time bandwidth detection unit. Compare the estimated transmission amount of the task data packet with the transmission capacity threshold corresponding to the current available channel rate. When the estimated data amount exceeds the threshold, record the task number and task level, and generate a bandwidth conflict task number set; S512: Based on the bandwidth conflict task number set, according to the sorting level value of the task, filter the numbers greater than the set reference level, extract the task section number in the point cloud cache, complete the number mapping combined with the task section number and the transmission structure index table, and sort the numbers of each data section to generate a priority section number sequence; S513: Call the priority section number sequence, execute the upload trigger instruction in sequence, write the point cloud data content in the data section into the cloud task channel cache path, and record the pointing relationship between the data section number and the upload task number, and generate the priority upload task number set.

8. A cloud platform-based laser mapping management system, characterized in that, The system is used to realize the cloud platform-based laser surveying and mapping management method in any one of claims 1-7, and the system comprises: An occlusion position identification module obtains the ground elevation distribution data and material surface reflection record data of each subarea in the surveying and mapping area, and obtains the obstacle three-dimensional profile data through a laser scanning device. The section with overlapping occlusion causes is marked as a feature point group area to generate an occlusion position index list. A path omission identification module calculates a scanning segment failure degree score based on the occlusion position index list according to the located occlusion positions, screens and identifies a failure scanning segment, and generates a path omission segment list; A task correction analysis module calls the path omission segment list, calculates an adjacent segment offset angle change rate and a boundary dense point number of a current segment according to each omission segment position index, judges whether the path change recognition threshold is exceeded, performs parameter binding on path sector numbering, width scaling ratio, and scheduling nodes corresponding to positions, and generates task correction parameter information; A task priority scheduling module calculates a task sorting level value based on the listed scanning segment position and sector number according to the task correction parameter information, collects a job deadline, a region use scene category label, and a historical supplementary scanning frequency of a corresponding segment task, and generates a scheduling task priority level list; An upload task adjustment module compares a sorting level and a data transmission capacity required by a current rate, extracts data segment numbers with a priority level greater than a set reference level from a point cloud cache if a data packet estimation is greater than a current transmission capacity threshold, executes an upload trigger instruction, transmits surveying and mapping data to a cloud processing platform, and generates a priority upload task number set.

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