Three-dimensional terrain surface area measurement method and system based on elevation data

Abstract

The invention relates to the technical field of surveying and mapping control, in particular to a three-dimensional terrain surface area measurement method and system based on elevation data. Based on a plurality of irregular graph area measurement strategies associated with the target unmanned aerial vehicle metadata, performing grid shape drawing on the unmanned aerial vehicle alarm detection elevation information, and generating a plurality of pieces of grid shape drawing information of each irregular graph area measurement strategy in the plurality of irregular graph area measurement strategies; generating a grid shape determination result based on the multiple pieces of grid shape drawing information of each irregular graph area metering strategy; and performing unmanned aerial vehicle alarm adjustment on the area measurement central control end based on a grid shape determination result. According to the method, terrain data with elevation diversity can be effectively coped with, area measurement is carried out based on reasonable shape construction, and a relatively reliable scheme is provided for three-dimensional terrain measurement.

Description

Technical Field

[0001] The present invention relates to the field of surveying and mapping control technology, and in particular to a three-dimensional terrain surface area measurement method and system based on elevation data. Background Art

[0002] Traditional 2D WebGIS only measures the projected area of ​​terrain, and cannot effectively measure the surface area of ​​3D terrain. Moreover, due to its own limitations and high risk, it cannot measure areas that some people cannot reach. With the rapid development of geographic information technology and Internet technology, 3D real-life terrain models can now be presented in browsers.

[0003] In the prior art, for terrain data with various elevation changes, the measurement of effective area often requires the use of drones to perform identification and detection and then draw graphics to obtain area measurement results. However, when drones are faced with areas with complex elevations and irregular terrain shapes, their area measurements are also inaccurate.

[0004] Therefore, how to accurately identify the monitoring area of ​​the drone when faced with terrain data with diverse elevations is an urgent problem to be solved. Summary of the invention

[0005] To achieve the above object, the present invention provides the following technical solutions: According to a first aspect of the present invention, the present invention claims a method for measuring three-dimensional terrain surface area based on elevation data, the method comprising: Collecting drone warning detection elevation information of the area measurement central control end, the drone warning detection elevation information represents drone detection data associated with the sending time of the area grid division boundary of the area measurement central control end within the preset drone detection area and the elevation information associated with the drone warning data obtained by the area measurement central control end by drawing the completed drone detection data through the drone warning edge drawing model; Based on multiple irregular graphic area measurement strategies associated with the target drone metadata, grid shape drawing is performed on the drone warning detection elevation information to generate multiple grid shape drawing information of each irregular graphic area measurement strategy in the multiple irregular graphic area measurement strategies, wherein the grid shape drawing information represents the drawing information corresponding to the associated irregular graphic area measurement strategy in the drone warning detection elevation information; Based on the plurality of grid shape drawing information of each irregular graphic area measurement strategy, a grid shape determination result is generated, wherein the grid shape determination result indicates the association priority between the area measurement central control terminal and the target drone metadata; The drone alarm is adjusted on the area measurement central control terminal based on the grid shape determination result.

[0006] Furthermore, the plurality of irregular graphic area measurement strategies associated with the target drone metadata, performing grid shape drawing on the drone warning detection elevation information, and generating a plurality of grid shape drawing information of each irregular graphic area measurement strategy in the plurality of irregular graphic area measurement strategies, include at least two combinations of the following: Based on the first elevation model, the drone abnormal elevation information in the drone warning detection elevation information is grid-shaped drawn to generate drone abnormal drawing information of each irregular graphic area measurement strategy in the multiple irregular graphic area measurement strategies, wherein the drone abnormal drawing information represents the drawing information corresponding to the associated irregular graphic area measurement strategy in the drone abnormal elevation information; Based on the second elevation model, the UAV flight mutation elevation information in the UAV warning detection elevation information is grid-shaped drawn to generate UAV flight mutation drawing information of each irregular graphic area measurement strategy in the multiple irregular graphic area measurement strategies, wherein the UAV flight mutation drawing information represents the drawing information corresponding to the associated irregular graphic area measurement strategy in the UAV flight mutation elevation information; Based on the third elevation model, the drone flight interruption elevation information in the drone warning detection elevation information is grid-shaped drawn to generate drone flight interruption drawing information for each irregular graphic area measurement strategy in the multiple irregular graphic area measurement strategies, wherein the drone flight interruption drawing information represents the corresponding drawing information of the associated irregular graphic area measurement strategy in the drone flight interruption elevation information.

[0007] Further, the abnormal altitude information of the drone includes abnormal POIs within the preset drone detection area; The method of performing grid shape drawing on the abnormal elevation information of the drone in the drone warning detection elevation information based on the first elevation model to generate the abnormal drone drawing information of each irregular graphic area measurement strategy in the multiple irregular graphic area measurement strategies includes: Based on the first elevation model, a grid shape is drawn for the abnormal elevation information of the drone in the drone warning detection elevation information to generate an abnormal annotation log, where one abnormal annotation log represents an attribute log of an abnormal POI; For each irregular graphic area measurement strategy among the multiple irregular graphic area measurement strategies, splicing the target annotation log to generate an abnormal annotation splicing log, wherein the target annotation log is an abnormal annotation log associated with each irregular graphic area measurement strategy among the abnormal annotation logs; Based on the first elevation model, the anomaly annotated splicing log is grid-drawn to generate drone anomaly drawing information of each irregular graphic area measurement strategy.

[0008] Furthermore, the grid shape drawing of the abnormal elevation information of the drone in the drone warning detection elevation information based on the first elevation model to generate an abnormal annotation log includes: For each abnormal POI, the abnormal POI is converted and output into an abnormal association matrix based on a preset abnormal annotation database, wherein the preset abnormal annotation database represents the association information between abnormal backup coordinate segments and abnormal annotation parameters, the number of rows of the abnormal association matrix is ​​the number of abnormal backup coordinate segments in the abnormal POI, and the number of columns of the abnormal association matrix is ​​the number of annotation dimensions of the abnormal annotation parameters; A grid shape is drawn on the abnormal association matrix based on the first elevation model to generate an abnormal annotation log associated with the abnormal POI.

[0009] Furthermore, the UAV flight sudden change elevation information includes at least one set of obstacle feedback coordinate points within the preset UAV detection area; The method of performing grid shape drawing on the UAV flight mutation elevation information in the UAV warning detection elevation information based on the second elevation model to generate UAV flight mutation drawing information of each irregular graphic area measurement strategy in the multiple irregular graphic area measurement strategies includes: Based on the second elevation model, a grid shape is drawn for the UAV flight mutation elevation information in the UAV warning detection elevation information to generate a UAV flight mutation feature, where a UAV flight mutation feature represents an attribute log of a set of obstacle feedback coordinate points; For each irregular graphic area measurement strategy among the multiple irregular graphic area measurement strategies, the target flight mutation feature is processed based on the second elevation model to generate UAV flight mutation drawing information for each irregular graphic area measurement strategy, and the target flight mutation feature is the UAV flight mutation feature among the UAV flight mutation features associated with each irregular graphic area measurement strategy.

[0010] Further, the UAV flight interruption elevation information includes at least one alarm interruption feedback coordinate point within the preset UAV detection area; The method of performing grid shape drawing on the drone flight interruption elevation information in the drone warning detection elevation information based on the third elevation model to generate drone flight interruption drawing information of each irregular graphic area measurement strategy in the multiple irregular graphic area measurement strategies includes: Based on the third elevation model, a grid shape is drawn for the drone flight interruption elevation information in the drone alarm detection elevation information, and an alarm interruption feedback feature is generated, where an alarm interruption feedback feature represents an attribute log of an alarm interruption feedback coordinate point, and the alarm interruption feedback coordinate point is used to represent a feedback coordinate point where a flight interruption occurs when scheduling an associated drone control thread during the drone function implementation process; For each irregular graphic area measurement strategy among the multiple irregular graphic area measurement strategies, processing the target interruption feedback feature based on the second elevation model to generate drone flight interruption drawing information for each irregular graphic area measurement strategy, wherein the target interruption feedback feature is an alarm interruption feedback feature associated with each irregular graphic area measurement strategy in the abnormal annotation log; The method of drawing a grid shape of the UAV flight interruption elevation information in the UAV warning detection elevation information based on the third elevation model to generate an alarm interruption feedback feature includes: For each alarm interruption feedback coordinate point, a directed graph structure associated with the alarm interruption feedback coordinate point is constructed with the detection attribute coordinate in the alarm interruption feedback coordinate point as the coordinate point and the detection association information between the detection attribute coordinates as the edge; A grid shape is drawn on the directed graph structure associated with the alarm interruption feedback coordinate point based on the third elevation model to generate an alarm interruption feedback feature associated with the alarm interruption feedback coordinate point.

[0011] Further, the plurality of irregular graphic area measurement strategies associated with the target drone metadata perform grid shape drawing on the drone warning detection elevation information, generate a plurality of grid shape drawing information of each irregular graphic area measurement strategy in the plurality of irregular graphic area measurement strategies, and implement based on the first elevation model, the second elevation model and the third elevation model in the target grid directed graph, and the target grid directed graph is used to evaluate the association priority between the area measurement central control terminal and the target drone metadata; The steps of constructing the target grid directed graph include: Collecting the template drone warning detection elevation information of the calibration area measurement central control end, wherein the template drone warning detection elevation information represents the drone detection data associated with the sending time of the regional grid division boundary of the calibration area measurement central control end within the calibration drone detection area; The area measurement strategy of multiple calibrated irregular graphics associated with the metadata of collected calibrated drones; Constructing a directed graph and the plurality of calibrated irregular graphic area measurement strategies based on the grid shape associated with the current construction stage, performing grid shape drawing on the template drone warning detection elevation information, and generating a plurality of template grid shape drawing information of each calibrated irregular graphic area measurement strategy in the plurality of calibrated irregular graphic area measurement strategies, wherein the template grid shape drawing information represents drawing information of the associated calibrated irregular graphic area measurement strategy in the template drone warning detection elevation information; Constructing a directed graph based on the grid shape associated with the current construction stage to evaluate the multiple grid shape drawing information of each irregular graphic area measurement strategy, and generating grid shape evaluation data, wherein the grid shape evaluation data indicates the association priority of the matching relationship between the calibration area measurement central control terminal and the calibration drone metadata; Based on the mesh shape evaluation data, directed graph parameter information is adjusted for the mesh shape construction directed graph associated with the current construction phase to generate the target mesh directed graph.

[0012] Furthermore, the generating of the grid shape determination result based on the plurality of grid shape drawing information of each irregular graphic area measurement strategy includes: Aggregating the plurality of grid shape drawing information of each irregular graphic area measurement strategy respectively to generate a metadata map of each irregular graphic area measurement strategy; Based on the metadata atlas of the plurality of irregular graphic area measurement strategies, the grid shape determination result is obtained.

[0013] According to the second aspect of the present invention, the present invention requests protection for a three-dimensional terrain surface area measurement system based on elevation data, the three-dimensional terrain surface area measurement system based on elevation data comprising a processor and a machine-readable storage medium, the machine-readable storage medium storing machine-implementable instructions, the machine-implementable instructions being loaded and implemented by the processor to implement the three-dimensional terrain surface area measurement method based on elevation data.

[0014] The present invention relates to the field of surveying and mapping control technology, and in particular to a three-dimensional terrain surface area measurement method and system based on elevation data, which collects the drone warning detection elevation information of the area measurement central control end; based on multiple irregular graphic area measurement strategies associated with target drone metadata, the drone warning detection elevation information is grid-drawn, and multiple grid shape drawing information of each irregular graphic area measurement strategy in multiple irregular graphic area measurement strategies is generated; based on multiple grid shape drawing information of each irregular graphic area measurement strategy, a grid shape determination result is generated; based on the grid shape determination result, the drone warning is adjusted on the area measurement central control end. The present invention can effectively deal with terrain data with elevation diversity, perform area measurement based on reasonable shape construction, and provide a relatively reliable solution for three-dimensional terrain measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A workflow diagram of a three-dimensional terrain surface area measurement method based on elevation data as claimed in an embodiment of the present invention; Figure 2 A second working flow chart of a three-dimensional terrain surface area measurement method based on elevation data claimed in an embodiment of the present invention; Figure 3 A third workflow diagram of a three-dimensional terrain surface area measurement method based on elevation data claimed in an embodiment of the present invention; Figure 4 The fourth working flow chart of a three-dimensional terrain surface area measurement method based on elevation data claimed in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely attribute the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the protection area of ​​the present invention.

[0017] The terms "first", "second" and "third" in the present invention are only used for attribute purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined as "first", "second" and "third" can explicitly or implicitly include at least one of the features. In the attributes of the present invention, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. All directional indications (such as up, down, left, right, front, back...) in the embodiments of the present invention are only used to explain the relative position relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication also changes accordingly. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, geographical area or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, geographical areas or devices.

[0018] Reference to an "embodiment" herein means that a particular feature, structure, or characteristic in conjunction with the attributes of the embodiment may be included in at least one embodiment of the present invention. The presence of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0019] Figure 1 It is a flow chart of a three-dimensional terrain surface area measurement method based on elevation data provided by an embodiment of the present invention. The three-dimensional terrain surface area measurement method based on elevation data is introduced in detail below.

[0020] Step S101, collecting drone warning detection elevation information of the area measurement central control terminal, where the drone warning detection elevation information represents drone detection data associated with the sending time of the area grid division boundary within a preset drone detection area of ​​the area measurement central control terminal.

[0021] In another embodiment, the area measurement central control terminal may refer to a drone system for providing various services in area measurement. The drone warning detection elevation information represents the drone detection data associated with the sending time of the area measurement central control terminal at the regional grid division boundary in the preset drone detection area and the elevation information associated with the drone warning data obtained by the area measurement central control terminal by drawing the completed drone detection data through the drone warning edge drawing model.

[0022] Step S102, based on multiple irregular graphic area measurement strategies associated with the target drone metadata, grid shape drawing is performed on the drone warning detection elevation information to generate multiple grid shape drawing information for each irregular graphic area measurement strategy in the multiple irregular graphic area measurement strategies, and the grid shape drawing information represents the corresponding drawing information of the associated irregular graphic area measurement strategy in the drone warning detection elevation information.

[0023] In another embodiment, the target drone metadata is the drone metadata to be determined, and the target drone metadata may be associated with multiple irregular graphic area measurement strategies. In this embodiment, the above drone warning detection elevation information may be grid-shaped drawn from the dimension of the irregular graphic area measurement strategy, thereby obtaining multiple grid shape drawing information of each irregular graphic area measurement strategy, that is, the drawing information corresponding to each irregular graphic area measurement strategy in the above drone warning detection elevation information is obtained through grid shape drawing.

[0024] Step S103: generating a grid shape determination result based on the multiple grid shape drawing information of each irregular graphic area measurement strategy, wherein the grid shape determination result indicates the association priority between the area measurement central control terminal and the target drone metadata.

[0025] In another embodiment, after obtaining multiple grid shape drawing information of each irregular graphic area measurement strategy, since the multiple irregular graphic area measurement strategies are associated with the target drone metadata, the association priority between the area measurement central control terminal and the target drone metadata is determined based on the grid shape drawing information of the multiple irregular graphic area measurement strategies, and a grid shape determination result is generated.

[0026] Step S104: adjusting the drone alarm on the area measurement central control terminal based on the grid shape determination result.

[0027] Based on the above steps, by collecting the drone warning detection elevation information of the area measurement central control end, the drone detection data associated with the sending time of the regional grid division boundary in the preset drone detection area of ​​the area measurement central control end is obtained, and then the drone warning detection elevation information is grid-drawn based on the irregular graphic area measurement strategy associated with the target drone metadata to generate the drawing information of each irregular graphic area measurement strategy in the drone warning detection elevation information, and then the association priority between the area measurement central control end and the target drone metadata is determined based on the drawing information of each irregular graphic area measurement strategy. Compared with the regional grid division boundary in the prior art, more features with metadata calibration can be obtained by retrieving the drone warning detection elevation information, thereby improving the accuracy of drone metadata prediction.

[0028] Reference Figure 2 , the method steps of further embodiments are introduced below.

[0029] Step S201, collecting drone warning detection elevation information of the area measurement central control terminal, where the drone warning detection elevation information represents drone detection data associated with the sending time of the area grid division boundary within a preset drone detection area of ​​the area measurement central control terminal.

[0030] In another embodiment, the system ID of the area measurement control terminal can be collected based on the data loading operation, and then the drone warning detection elevation information of the area measurement control terminal can be collected based on the system ID, that is, based on the system ID, the drone detection data associated with the sending time of the regional grid division boundary in the preset drone detection area of ​​the area measurement control terminal is collected. The preset drone detection area can refer to a preset drone function detection area.

[0031] In another embodiment, the drone warning detection elevation information includes the drone abnormal elevation information, the drone flight sudden change elevation information, and the drone flight interruption elevation information. Based on the different data included in the drone warning detection elevation information, at least two of the following steps S202 to S204 are implemented.

[0032] Step S202, based on the first elevation model, grid shape drawing is performed on the abnormal elevation information of the drone in the drone alarm detection elevation information, and drone abnormal drawing information of each irregular graphic area measurement strategy in the multiple irregular graphic area measurement strategies is generated, and the drone abnormal drawing information represents the corresponding drawing information of the associated irregular graphic area measurement strategy in the abnormal elevation information of the drone.

[0033] In another embodiment, for the abnormal elevation information of the drone in the drone warning detection elevation information, the abnormal elevation information of the drone can be grid-shaped drawn based on the first elevation model. For each irregular graphic area measurement strategy in the above-mentioned multiple irregular graphic area measurement strategies, the abnormal drone drawing information associated with each irregular graphic area measurement strategy can be obtained through grid shape drawing, that is, the drawing information corresponding to each irregular graphic area measurement strategy in the abnormal elevation information of the drone can be obtained.

[0034] In another embodiment, the drone abnormal altitude information includes abnormal POIs in the preset drone detection area, wherein one abnormal POI is associated with one drone detection event in the preset drone detection area.

[0035] In another embodiment, the step of drawing a grid shape for the abnormal elevation information of the drone in the drone alarm detection elevation information based on the first elevation model, and generating the abnormal drawing information of the drone for each irregular graphic area measurement strategy in the multiple irregular graphic area measurement strategies includes: the terminal draws a grid shape for the abnormal elevation information of the drone in the drone alarm detection elevation information based on the first elevation model, and generates an abnormal annotation log, wherein an abnormal annotation log represents an attribute log of an abnormal POI. Then, for each irregular graphic area measurement strategy in the multiple irregular graphic area measurement strategies, the target annotation log is spliced ​​to generate an abnormal annotation splicing log, wherein the target annotation log is an abnormal annotation log associated with each irregular graphic area measurement strategy in the abnormal annotation log. Then, the abnormal annotation splicing log is drawn in a grid shape based on the first elevation model to generate the abnormal drawing information of the drone for each irregular graphic area measurement strategy. By performing grid shape drawing on the abnormal elevation information of the drone, the grid shape drawing information for marking each abnormal POI can be obtained, and then the grid shape drawing information is spliced ​​from the dimension of the irregular graphic area measurement strategy, and the grid shape drawing is performed to generate the drawing information of the irregular graphic area measurement strategy in the abnormal elevation information of the drone.

[0036] In another embodiment, the abnormal POI can be converted and output into a matrix first, and then the grid shape drawing is performed. Accordingly, for each abnormal POI, the abnormal POI can be converted and output into an abnormal association matrix based on a preset abnormal annotation database, and the preset abnormal annotation database represents the association information between the abnormal backup coordinate segments and the abnormal annotation parameters. The number of rows of the abnormal association matrix is ​​the number of abnormal backup coordinate segments in the abnormal POI, and the number of columns of the abnormal association matrix is ​​the number of annotation dimensions of the abnormal annotation parameters. Then the terminal performs a grid shape drawing on the abnormal association matrix based on the first elevation model, and generates an abnormal annotation log associated with the abnormal POI.

[0037] In another embodiment, for each irregular graphic area measurement strategy, if the irregular graphic area measurement strategy exists continuously in at least two abnormal POIs, the frequency of the irregular graphic area measurement strategy can be determined based on the time domain information associated with the irregular graphic area measurement strategy. Then, the features of the irregular graphic area measurement strategy in the associated time domain information are spliced ​​to construct an associated feature matrix, and then the grid shape is drawn based on the first elevation model. The time domain information represents the time sequence of the existence of the irregular graphic area measurement strategy, that is, the corresponding order of the irregular graphic area measurement strategy in the abnormal POI.

[0038] For the irregular graphic area measurement strategy that does not exist continuously in the abnormal POI, the abnormal annotation log associated with the irregular graphic area measurement strategy is spliced ​​according to the time domain information of the irregular graphic area measurement strategy in the abnormal POI, and then the grid shape of the abnormal association matrix is ​​drawn based on the first elevation model to generate the abnormal annotation log associated with the abnormal POI.

[0039] Step S203, based on the second elevation model, grid shape drawing is performed on the drone flight mutation elevation information in the drone warning detection elevation information, and drone flight mutation drawing information of each irregular graphic area measurement strategy in the multiple irregular graphic area measurement strategies is generated, and the drone flight mutation drawing information represents the corresponding drawing information of the associated irregular graphic area measurement strategy in the drone flight mutation elevation information.

[0040] In another embodiment, for the drone flight sudden change elevation information in the drone warning detection elevation information, the drone flight sudden change elevation information can be grid-shaped drawn based on the second elevation model. For each of the above-mentioned multiple irregular graphic area measurement strategies, the drone flight sudden change drawing information associated with each irregular graphic area measurement strategy can be obtained through grid shape drawing, that is, the drawing information corresponding to each irregular graphic area measurement strategy in the drone flight sudden change elevation information is obtained.

[0041] In another embodiment, the UAV flight sudden change elevation information includes at least one set of obstacle feedback coordinate points within the preset UAV detection area.

[0042] Correspondingly, the drone flight mutation elevation information in the drone warning detection elevation information can be grid-shaped drawn based on the second elevation model, and the step of generating the drone flight mutation drawing information of each irregular graphic area measurement strategy in the multiple irregular graphic area measurement strategies includes: the terminal performs grid-shaped drawing on the drone flight mutation elevation information in the drone warning detection elevation information based on the second elevation model to generate a drone flight mutation feature, and a drone flight mutation feature represents a set of attribute logs of obstacle feedback coordinate points. Then, for each irregular graphic area measurement strategy in the multiple irregular graphic area measurement strategies, the target flight mutation feature can be processed based on the second elevation model to generate the drone flight mutation drawing information of each irregular graphic area measurement strategy, and the target flight mutation feature is the drone flight mutation feature associated with each irregular graphic area measurement strategy in the drone flight mutation feature. By drawing the grid shape of the UAV flight mutation characteristics, the grid shape drawing information representing each group of obstacle feedback coordinate points can be obtained, and then the grid shape drawing information is processed from the dimension of the irregular graphic area measurement strategy based on the second elevation model to generate the drawing information of the irregular graphic area measurement strategy in the UAV flight mutation elevation information.

[0043] In another embodiment, for each irregular graphic area measurement strategy, a grid shape drawing can be performed on the UAV flight mutation characteristics of the irregular graphic area measurement strategy in the time domain information based on the second elevation model.

[0044] Step S204, based on the third elevation model, grid shape drawing is performed on the drone flight interruption elevation information in the drone warning detection elevation information, and drone flight interruption drawing information of each irregular graphic area measurement strategy in the multiple irregular graphic area measurement strategies is generated, and the drone flight interruption drawing information represents the corresponding drawing information of the associated irregular graphic area measurement strategy in the drone flight interruption elevation information.

[0045] In another embodiment, for the drone flight interruption elevation information in the drone warning detection elevation information, the drone flight interruption elevation information can be grid-shaped drawn based on the third elevation model. For each of the above-mentioned multiple irregular graphic area measurement strategies, the drone flight interruption drawing information associated with each irregular graphic area measurement strategy can be obtained through grid shape drawing, that is, the drawing information corresponding to each irregular graphic area measurement strategy in the drone flight interruption elevation information can be obtained.

[0046] In another embodiment, the UAV flight interruption elevation information includes at least one alarm interruption feedback coordinate point in the preset UAV detection area, wherein one alarm interruption feedback coordinate point is associated with a UAV function detection event in the preset UAV detection area.

[0047] Correspondingly, the drone flight interruption elevation information in the drone warning detection elevation information can be grid-shaped based on the third elevation model to generate the drone flight interruption drawing information for each irregular graphic area measurement strategy in the multiple irregular graphic area measurement strategies, including: the terminal grid-shapes the drone flight interruption elevation information in the drone warning detection elevation information based on the third elevation model to generate an alarm interruption feedback feature, where an alarm interruption feedback feature represents an attribute log of an alarm interruption feedback coordinate point. Then, for each irregular graphic area measurement strategy in the multiple irregular graphic area measurement strategies, the target interruption feedback feature can be processed based on the second elevation model to generate the drone flight interruption drawing information for each irregular graphic area measurement strategy, where the target interruption feedback feature is the alarm interruption feedback feature associated with each irregular graphic area measurement strategy in the abnormal annotation log. By drawing the grid shape of the UAV flight interruption elevation information, the grid shape drawing information representing each alarm interruption feedback coordinate point can be obtained, and then the grid shape drawing information is processed based on the second elevation model from the dimension of the irregular graphic area measurement strategy to generate the drawing information of the irregular graphic area measurement strategy in the UAV flight interruption elevation information.

[0048] In another embodiment, the coordinate entity of the detection attribute coordinates can be extracted first, and then a directed graph structure can be constructed with the detection attribute coordinates as coordinate points, and then a grid shape drawing can be performed. Correspondingly, for each alarm interruption feedback coordinate point, the detection attribute coordinates in the alarm interruption feedback coordinate point can be used as coordinate points, and the detection association information between the detection attribute coordinates can be used as edges to construct a directed graph structure associated with the alarm interruption feedback coordinate point. Then, based on the third elevation model, a grid shape drawing is performed on the directed graph structure associated with the alarm interruption feedback coordinate point to generate an alarm interruption feedback feature associated with the alarm interruption feedback coordinate point.

[0049] Step S205: generating a grid shape determination result based on the multiple grid shape drawing information of each irregular graphic area measurement strategy, wherein the grid shape determination result indicates the association priority between the area measurement central control terminal and the target drone metadata.

[0050] In another embodiment, after the grid shape drawing from step S202 to step S204, for each irregular graphic area measurement strategy, three grid shape drawing information associated with each irregular graphic area measurement strategy can be obtained, and the three grid shape drawing information can indicate the drawing information of each irregular graphic area measurement strategy. The three grid shape drawing information associated with each irregular graphic area measurement strategy can be spliced ​​to generate a metadata map of each irregular graphic area measurement strategy, and then the metadata maps of multiple irregular graphic area measurement strategies are processed through an average pooling layer to generate a representation vector of the area measurement central control terminal, and finally the grid shape determination result is determined through a fully connected layer.

[0051] In another embodiment, the multiple grid shape drawing information of each irregular graphic area measurement strategy can be feature spliced ​​separately to generate a metadata map of each irregular graphic area measurement strategy, and then based on the metadata map of the multiple irregular graphic area measurement strategies, the grid shape determination result is determined and output.

[0052] In another embodiment, the above steps S202 to S204 can be implemented based on the first elevation model, the second elevation model and the third elevation model in the target grid directed graph, and the target grid directed graph is used to evaluate the association priority between the area measurement central control terminal and the target drone metadata.

[0053] Reference Figure 3 , the steps of constructing the target grid directed graph are introduced below. The steps of constructing the target grid directed graph include steps S301 to S305; S301. Collect template drone warning detection elevation information of the area measurement control terminal, which represents drone detection data associated with the sending time of the regional grid division boundary of the calibration area measurement control terminal within the calibration drone detection area.

[0054] In another embodiment, the template drone warning detection elevation information includes at least two of drone abnormal elevation information, drone flight sudden change elevation information, and drone flight interruption elevation information. The template drone warning detection elevation information of the calibration area measurement control terminal is the elevation information data corresponding to a certain drone metadata for multiple consecutive times by the calibration area measurement control terminal. The certain drone metadata is the drone metadata associated with the target grid directed graph.

[0055] S302. Obtaining a plurality of calibrated irregular graphic area measurement strategies associated with the drone metadata.

[0056] In another embodiment, for the calibrated drone metadata d to be evaluated, z calibrated irregular graphic area measurement strategies related to the calibrated drone metadata are obtained, where z is a positive integer. In order to obtain the time domain characteristics of each calibrated irregular graphic area measurement strategy, for each calibrated irregular graphic area measurement strategy, the time domain information associated with each calibrated irregular graphic area measurement strategy can be determined respectively.

[0057] S303. In the construction phase, the grid shapes associated with the directed graph are constructed and the multiple calibrated irregular graphic area measurement strategies, and a grid shape is drawn for the template drone warning detection elevation information to generate multiple template grid shape drawing information for each of the multiple calibrated irregular graphic area measurement strategies, wherein the template grid shape drawing information represents the drawing information of the associated calibrated irregular graphic area measurement strategy in the template drone warning detection elevation information.

[0058] S304. A directed graph is constructed based on the grid shape associated with the current construction stage to evaluate multiple grid shape drawing information of each irregular graphic area measurement strategy, and generate grid shape evaluation data, which represents the priority of the control terminal associated with the calibration drone metadata for the calibration area measurement.

[0059] S305. Adjust the directed graph parameter information of the mesh shape construction directed graph associated with the current construction stage based on the mesh shape evaluation data to generate the target mesh directed graph.

[0060] In another embodiment, if the directed graph constructed by the mesh shape meets the directed graph convergence requirements, the directed graph constructed by the mesh shape associated with the current construction stage is determined as the target mesh directed graph; if the directed graph constructed by the mesh shape meets the directed graph convergence requirements, the directed graph weight information of the directed graph constructed by the mesh shape associated with the current construction stage is updated based on the loss function value between the mesh shape evaluation data and the calibrated drone metadata and the loop is performed.

[0061] Based on the above steps, by collecting the drone warning detection elevation information of the area measurement central control end, the drone detection data associated with the sending time of the regional grid division boundary in the preset drone detection area of ​​the area measurement central control end is obtained, and then the drone warning detection elevation information is grid-drawn based on the irregular graphic area measurement strategy associated with the target drone metadata to generate the drawing information of each irregular graphic area measurement strategy in the drone warning detection elevation information, and then based on the drawing information of each irregular graphic area measurement strategy, the association priority between the area measurement central control end and the target drone metadata can be determined.

[0062] In another embodiment, referring to Figure 4, step S101, can be implemented through the following exemplary steps.

[0063] Step S1011, collecting first candidate drone detection data, candidate drone warning data associated with the first candidate drone detection data, first historical drone warning data, and second historical drone warning data, wherein the first historical drone warning data is obtained by drawing warning detection data for the first candidate drone detection data based on a first drone warning edge drawing model, and the second historical drone warning data is obtained by drawing warning detection data for the first candidate drone detection data based on a second drone warning edge drawing model; Step S1012, determining a first calibration memory deviation value based on the candidate drone warning data associated with the first candidate drone detection data, the first historical drone warning data, and the second historical drone warning data; Step S1013, collecting and updating the memory historical drone detection data, the third historical drone warning data and the fourth historical drone warning data, wherein the third historical drone warning data is obtained by drawing the warning detection data of the updated memory historical drone detection data based on the first drone warning edge drawing model, and the fourth historical drone warning data is obtained by drawing the warning detection data of the updated memory historical drone detection data based on the second drone warning edge drawing model, the candidate drone detection data is used to construct a model of an existing construction decision, and the updated memory historical drone detection data is used to update the memory of the decision to be memorized in combination with the existing construction decision of the candidate drone detection data; Step S1014, determining a first updated memory deviation value based on the third historical drone warning data and the fourth historical drone warning data; Step S1015, based on the first calibrated memory deviation value and the first updated memory deviation value, the first UAV warning edge drawing model is adjusted in a model loop, and based on the adjusted UAV warning edge drawing model, the completed UAV detection data of the area measurement central control end is drawn to obtain the associated UAV warning data, and the elevation information associated with the UAV warning data is collected.

[0064] In another embodiment, a first calibrated memory deviation value is determined based on the candidate drone warning data associated with the first candidate drone detection data, the first historical drone warning data and the second historical drone warning data, including: when the first candidate drone detection data is determined as negative historical drone detection data based on the second drone warning edge drawing model, a third calibrated memory deviation value is determined based on the candidate drone warning data and the first historical drone warning data; a fourth calibrated memory deviation value is determined based on the second historical drone warning data and the first historical drone warning data; the number of adjustment implementations associated with this model adjustment task is collected; if the number of adjustment implementations is not less than the first threshold and not greater than the second threshold, the first calibrated memory deviation value is determined based on the third calibrated memory deviation value, the fourth calibrated memory deviation value, the number of adjustment implementations, the first threshold and the second threshold.

[0065] In another embodiment, the present embodiment may further collect second candidate drone detection data, candidate drone warning data associated with the second candidate drone detection data, and fifth historical drone warning data, wherein the fifth historical drone warning data is obtained by drawing the warning detection data for the second candidate drone detection data based on the first drone warning edge drawing model. When the second candidate drone detection data is output as non-negative historical drone detection data based on the second drone warning edge drawing model, a second calibration memory deviation value is determined based on the candidate drone warning data associated with the second candidate drone detection data and the fifth historical drone warning data. Based on the first calibration memory deviation value, the second calibration memory deviation value, and the first updated memory deviation value, the first drone warning edge drawing model is subjected to a model loop adjustment.

[0066] In another embodiment, the third historical drone warning data includes third recorded calibrated warning navigation data associated with each drone detection POI in the updated memory historical drone detection data, the fourth historical drone warning data includes fourth recorded calibrated warning navigation data associated with each drone detection POI in the updated memory historical drone detection data, and the determining the first updated memory deviation value based on the third historical drone warning data and the fourth historical drone warning data includes: Determining a second similarity based on the third recorded calibrated warning navigation data and the fourth recorded calibrated warning navigation data associated with each drone detection POI, wherein the second similarity represents a degree of overlap between the third historical drone warning data and the fourth historical drone warning data; Determine an updated memory sub-deviation value associated with any drone detection POI based on the second similarity, the third record calibration warning navigation data associated with any drone detection POI, and the fourth record calibration warning navigation data; Determining the first update memory deviation value based on the determined multiple update memory sub-deviation values; The method further comprises: Performing warning recognition on the first historical drone warning data based on the recognition directed graph to generate first warning recognition data; Performing warning identification on the third historical drone warning data based on the identification directed graph to generate second warning identification data; Determining an alarm identification deviation value based on the first alarm identification data, the second alarm identification data, and the third historical drone alarm data; Based on the warning identification deviation value, the first UAV warning edge drawing model is subjected to model loop adjustment and the identification directed graph.

[0067] In another embodiment, after determining the first calibrated memory deviation value based on the candidate drone warning data associated with the first candidate drone detection data, the first historical drone warning data, and the second historical drone warning data, the method further includes: If the first calibrated memory deviation value is less than the target deviation value, the first candidate drone detection data is output as non-negative historical drone detection data; or, if the first calibrated memory deviation value is not less than the target deviation value, the first candidate drone detection data is output as negative historical drone detection data, and a sixth calibrated memory deviation value is determined based on the candidate drone warning data associated with the first candidate drone detection data, the first historical drone warning data and the sixth historical drone warning data, where the sixth historical drone warning data is obtained by drawing the warning detection data of the first candidate drone detection data based on the second drone warning edge drawing model; a second updated memory deviation value is determined based on the third historical drone warning data and the seventh historical drone warning data, where the seventh historical drone warning data is obtained by drawing the warning detection data of the updated memory historical drone detection data based on the second drone warning edge drawing model; and a model weight parameter of the second drone warning edge drawing model is adjusted based on the sixth calibrated memory deviation value and the second updated memory deviation value.

[0068] Based on the above steps, when the model weight parameters of the first UAV warning edge drawing model are adjusted using the candidate UAV detection data and the updated memory historical UAV detection data, not only the warning detection data navigation results of the first UAV warning edge drawing model are combined, but also the warning detection data navigation results of the second UAV warning edge drawing model are integrated into the calibration memory deviation value and the updated memory deviation value, so that the first UAV warning edge drawing model makes a decision memory on the warning detection data navigation results of the second UAV warning edge drawing model. In this way, the model decision memory process of the first UAV warning edge drawing model can be supervised by the warning detection data navigation results of the second UAV warning edge drawing model, thereby obtaining a higher accuracy of UAV warning data navigation.

[0069] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the above-mentioned device embodiments are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not implemented. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0070] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of a drone functional unit. The above is only an implementation method of the present invention, and does not limit the patent area of ​​the present invention. Any equivalent structure or equivalent process transformation made using the contents of the specification and drawings of the present invention, or directly or indirectly used in other related technical fields, are also included in the patent protection area of ​​the present invention.

[0071] The above detailed description of the specific implementation of the invention is only used as an example, and the present invention is not limited to the specific implementation of the above attributes. For those skilled in the art, any equivalent modification or substitution of the invention is also within the scope of the present invention. Therefore, the equalization, modification, improvement, etc. made without departing from the spirit and principle of the present invention should be included in the scope of the present invention.

Claims

1. A three-dimensional terrain surface area measurement method based on elevation data, characterized in that: The method comprises: Collecting drone warning detection elevation information of the area measurement central control end, the drone warning detection elevation information represents drone detection data associated with the sending time of the area grid division boundary of the area measurement central control end within the preset drone detection area and the elevation information associated with the drone warning data obtained by the area measurement central control end by drawing the completed drone detection data through the drone warning edge drawing model; Based on multiple irregular graphic area measurement strategies associated with the target drone metadata, grid shape drawing is performed on the drone warning detection elevation information to generate multiple grid shape drawing information of each irregular graphic area measurement strategy in the multiple irregular graphic area measurement strategies, wherein the grid shape drawing information represents the drawing information corresponding to the associated irregular graphic area measurement strategy in the drone warning detection elevation information; Based on the plurality of grid shape drawing information of each irregular graphic area measurement strategy, a grid shape determination result is generated, wherein the grid shape determination result indicates the association priority between the area measurement central control terminal and the target drone metadata; The drone alarm is adjusted on the area measurement central control terminal based on the grid shape determination result.

2. The three-dimensional terrain surface area measurement method based on elevation data according to claim 1, characterized in that: The plurality of irregular graphic area measurement strategies associated with the target drone metadata, performing grid shape drawing on the drone warning detection elevation information, and generating a plurality of grid shape drawing information of each irregular graphic area measurement strategy in the plurality of irregular graphic area measurement strategies, include at least two of the following combinations: Based on the first elevation model, the drone abnormal elevation information in the drone warning detection elevation information is grid-shaped drawn to generate drone abnormal drawing information of each irregular graphic area measurement strategy in the multiple irregular graphic area measurement strategies, wherein the drone abnormal drawing information represents the drawing information corresponding to the associated irregular graphic area measurement strategy in the drone abnormal elevation information; Based on the second elevation model, the UAV flight mutation elevation information in the UAV warning detection elevation information is grid-shaped drawn to generate UAV flight mutation drawing information of each irregular graphic area measurement strategy in the multiple irregular graphic area measurement strategies, wherein the UAV flight mutation drawing information represents the drawing information corresponding to the associated irregular graphic area measurement strategy in the UAV flight mutation elevation information; Based on the third elevation model, the drone flight interruption elevation information in the drone warning detection elevation information is grid-shaped drawn to generate drone flight interruption drawing information for each irregular graphic area measurement strategy in the multiple irregular graphic area measurement strategies, wherein the drone flight interruption drawing information represents the corresponding drawing information of the associated irregular graphic area measurement strategy in the drone flight interruption elevation information.

3. The three-dimensional terrain surface area measurement method based on elevation data as claimed in claim 2, characterized in that: The abnormal altitude information of the drone includes abnormal POIs within the preset drone detection area; The method of performing grid shape drawing on the abnormal elevation information of the drone in the drone warning detection elevation information based on the first elevation model to generate the abnormal drone drawing information of each irregular graphic area measurement strategy in the multiple irregular graphic area measurement strategies includes: Based on the first elevation model, a grid shape is drawn for the abnormal elevation information of the drone in the drone warning detection elevation information to generate an abnormal annotation log, where one abnormal annotation log represents an attribute log of an abnormal POI; For each irregular graphic area measurement strategy among the multiple irregular graphic area measurement strategies, splicing the target annotation log to generate an abnormal annotation splicing log, wherein the target annotation log is an abnormal annotation log associated with each irregular graphic area measurement strategy among the abnormal annotation logs; Based on the first elevation model, the anomaly annotated splicing log is grid-drawn to generate drone anomaly drawing information of each irregular graphic area measurement strategy.

4. The three-dimensional terrain surface area measurement method based on elevation data as claimed in claim 3, characterized in that: The step of drawing a grid shape for the abnormal elevation information of the drone in the drone warning detection elevation information based on the first elevation model to generate an abnormal annotation log includes: For each abnormal POI, the abnormal POI is converted and output into an abnormal association matrix based on a preset abnormal annotation database, wherein the preset abnormal annotation database represents the association information between abnormal backup coordinate segments and abnormal annotation parameters, the number of rows of the abnormal association matrix is ​​the number of abnormal backup coordinate segments in the abnormal POI, and the number of columns of the abnormal association matrix is ​​the number of annotation dimensions of the abnormal annotation parameters; A grid shape is drawn on the abnormal association matrix based on the first elevation model to generate an abnormal annotation log associated with the abnormal POI.

5. The three-dimensional terrain surface area measurement method based on elevation data as claimed in claim 3, characterized in that: The UAV flight sudden elevation information includes at least one set of obstacle feedback coordinate points within the preset UAV detection area; The method of performing grid shape drawing on the UAV flight mutation elevation information in the UAV warning detection elevation information based on the second elevation model to generate UAV flight mutation drawing information of each irregular graphic area measurement strategy in the multiple irregular graphic area measurement strategies includes: Based on the second elevation model, a grid shape is drawn for the UAV flight mutation elevation information in the UAV warning detection elevation information to generate a UAV flight mutation feature, where a UAV flight mutation feature represents an attribute log of a set of obstacle feedback coordinate points; For each irregular graphic area measurement strategy among the multiple irregular graphic area measurement strategies, the target flight mutation feature is processed based on the second elevation model to generate UAV flight mutation drawing information for each irregular graphic area measurement strategy, and the target flight mutation feature is the UAV flight mutation feature among the UAV flight mutation features associated with each irregular graphic area measurement strategy.

6. The three-dimensional terrain surface area measurement method based on elevation data according to claim 3, characterized in that: The UAV flight interruption elevation information includes at least one alarm interruption feedback coordinate point within the preset UAV detection area; The method of performing grid shape drawing on the drone flight interruption elevation information in the drone warning detection elevation information based on the third elevation model to generate drone flight interruption drawing information of each irregular graphic area measurement strategy in the multiple irregular graphic area measurement strategies includes: Based on the third elevation model, a grid shape is drawn for the drone flight interruption elevation information in the drone alarm detection elevation information, and an alarm interruption feedback feature is generated, where an alarm interruption feedback feature represents an attribute log of an alarm interruption feedback coordinate point, and the alarm interruption feedback coordinate point is used to represent a feedback coordinate point where a flight interruption occurs when scheduling an associated drone control thread during the drone function implementation process; For each irregular graphic area measurement strategy among the multiple irregular graphic area measurement strategies, processing the target interruption feedback feature based on the second elevation model to generate drone flight interruption drawing information for each irregular graphic area measurement strategy, wherein the target interruption feedback feature is an alarm interruption feedback feature associated with each irregular graphic area measurement strategy in the abnormal annotation log; The method of drawing a grid shape of the UAV flight interruption elevation information in the UAV warning detection elevation information based on the third elevation model to generate an alarm interruption feedback feature includes: For each alarm interruption feedback coordinate point, a directed graph structure associated with the alarm interruption feedback coordinate point is constructed with the detection attribute coordinate in the alarm interruption feedback coordinate point as the coordinate point and the detection association information between the detection attribute coordinates as the edge; A grid shape is drawn on the directed graph structure associated with the alarm interruption feedback coordinate point based on the third elevation model to generate an alarm interruption feedback feature associated with the alarm interruption feedback coordinate point.

7. The three-dimensional terrain surface area measurement method based on elevation data according to any one of claims 2 to 6, characterized in that: The plurality of irregular graphic area measurement strategies associated with the target drone metadata perform grid shape drawing on the drone warning detection elevation information, generate a plurality of grid shape drawing information of each irregular graphic area measurement strategy in the plurality of irregular graphic area measurement strategies, and implement based on the first elevation model, the second elevation model and the third elevation model in the target grid directed graph, the target grid directed graph is used to evaluate the association priority between the area measurement central control terminal and the target drone metadata; The steps of constructing the target grid directed graph include: Collecting the template drone warning detection elevation information of the calibration area measurement central control end, wherein the template drone warning detection elevation information represents the drone detection data associated with the sending time of the regional grid division boundary of the calibration area measurement central control end within the calibration drone detection area; The area measurement strategy of multiple calibrated irregular graphics associated with the metadata of collected calibrated drones; Constructing a directed graph and the plurality of calibrated irregular graphic area measurement strategies based on the grid shape associated with the current construction stage, performing grid shape drawing on the template drone warning detection elevation information, and generating a plurality of template grid shape drawing information of each calibrated irregular graphic area measurement strategy in the plurality of calibrated irregular graphic area measurement strategies, wherein the template grid shape drawing information represents drawing information of the associated calibrated irregular graphic area measurement strategy in the template drone warning detection elevation information; Constructing a directed graph based on the grid shape associated with the current construction stage to evaluate the multiple grid shape drawing information of each irregular graphic area measurement strategy, and generating grid shape evaluation data, wherein the grid shape evaluation data indicates the association priority of the matching relationship between the calibration area measurement central control terminal and the calibration drone metadata; Based on the mesh shape evaluation data, directed graph parameter information is adjusted for the mesh shape construction directed graph associated with the current construction phase to generate the target mesh directed graph.

8. The three-dimensional terrain surface area measurement method based on elevation data according to claim 1, characterized in that: The generating of the grid shape determination result based on the plurality of grid shape drawing information of each irregular graphic area measurement strategy comprises: Aggregating the plurality of grid shape drawing information of each irregular graphic area measurement strategy respectively to generate a metadata map of each irregular graphic area measurement strategy; Based on the metadata atlas of the plurality of irregular graphic area measurement strategies, the grid shape determination result is obtained.

9. A three-dimensional terrain surface area measurement system based on elevation data, characterized in that: The three-dimensional terrain surface area measurement system based on elevation data includes a processor and a machine-readable storage medium, wherein the machine-readable storage medium stores machine-implementable instructions, and the machine-implementable instructions are loaded and implemented by the processor to implement the three-dimensional terrain surface area measurement method based on elevation data as described in any one of claims 1-8.

Images