Three-dimensional data acquisition management system and method

Abstract

The invention relates to the technical field of data acquisition, and discloses a three-dimensional data acquisition management system and method, and the method comprises the following steps: S1, obtaining a three-dimensional space model of a to-be-acquired region, and discretizing the space into a plurality of sampling units to form a voxel grid; s2, identifying a shielding area in the three-dimensional space model, and constructing an effective and reachable three-dimensional acquisition space; s3, generating an initial layout point location according to the equipment view field parameters; s4, acquiring parameters and sampling unit positions based on the equipment; s5, constructing a joint optimization objective function; and S6, under the constraint condition, solving an optimal equipment layout point location and path control function through an optimization method. According to the method, the optimal equipment layout scheme and path control strategy are obtained by constructing the three-dimensional space acquisition model and adopting a plurality of factors such as joint optimization of the objective function, dynamic optimization of equipment layout points and paths, energy consumption and shielding area punishment, and the acquisition efficiency and the reasonability of path planning are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data acquisition, and in particular to a management system and method for three-dimensional data acquisition. Background Art

[0002] With the rapid development of three-dimensional data acquisition technology, efficient and accurate acquisition and processing of three-dimensional spatial data has become a research hotspot in many application areas such as environmental monitoring, urban modeling, and robotic navigation. However, traditional three-dimensional data acquisition methods typically rely on fixed deployment points and simple path planning strategies, lacking dynamic optimization of the acquisition area and equipment configuration. This approach has several practical problems. In particular, as the complexity of the acquisition area increases, issues such as equipment energy consumption, the impact of obstructions, and acquisition coverage become more prominent. This leads to low efficiency, high energy consumption, and inadequate utilization of available resources during the data acquisition process.

[0003] In existing technologies, common 3D data acquisition and management systems typically employ static path planning, lacking dynamic selection of equipment placement points and real-time path optimization. Even some methods consider optimizing acquisition paths, but these typically focus on a single factor, such as minimizing energy consumption or maximizing acquisition coverage, while ignoring the need for multi-objective optimization. Furthermore, due to obstacles in the environment, traditional systems fail to effectively address occlusions, resulting in frequent data omissions or incomplete acquisition in real-world applications. Summary of the Invention

[0004] In order to remedy the above deficiencies, the present invention provides a three-dimensional data acquisition management system and method, aiming to improve the existing methods that usually focus on a single goal and ignore how to balance multiple optimization goals, which leads to insufficient acquisition coverage or excessive energy consumption, and cannot provide the optimal solution after comprehensive consideration of various factors.

[0005] In a first aspect, the present invention provides the following technical solution, a method for three-dimensional data acquisition and management, comprising the following steps:

[0006] S1. Obtain a three-dimensional spatial model of the area to be collected, and discretize the space into multiple sampling units to form a voxel grid;

[0007] S2. Identify the blocked area in the three-dimensional space model and construct an effectively accessible three-dimensional acquisition space;

[0008] S3. Generate initial layout points based on the device field of view parameters and establish the acquisition path control function for each device;

[0009] S4. Based on the acquisition parameters of the device and the sampling unit positions, a view coverage relationship is established to determine whether each sampling unit is effectively covered by the device;

[0010] S5. Construct a joint optimization objective function that simultaneously considers the acquisition coverage, path energy consumption cost, and spatial conflict penalty between the path and the occlusion area;

[0011] S6. Under the constraints, the optimal equipment layout points and path control functions are solved through optimization methods, and the optimal results are output to guide the layout and scheduling of actual three-dimensional data acquisition equipment.

[0012] Preferably, in S2, the effective reachable space is obtained by voxel modeling of the occluded area and excluding the points where occlusion exists in any acquisition direction based on ray analysis, thereby obtaining the area where the device can be deployed.

[0013] Preferably, in S3, the device path control function is established through a kinematic model, which is a time-continuous function whose speed input is constrained by the maximum speed and acceleration of the device, and takes the initial layout point as the path starting point.

[0014] Preferably, in S4, the established view coverage relationship is based on the following conditions:

[0015] The distance between the device and the sampling unit does not exceed the effective collection radius of the device, the device is pointing towards the unit, and there are no obstructed voxels between the line of sight and the unit;

[0016] It also includes constructing a support graph structure between device views, where each node in the support graph corresponds to a device view. If the views of two devices overlap in space, a connecting edge is established in the graph to form a continuously nested view chain structure.

[0017] Preferably, the constructed joint optimization objective function includes the following three sub-objectives:

[0018] S501. The proportion of sampling units not covered by any equipment in the total sampling units;

[0019] S502: All devices collect the total energy cost of the path, where the energy cost is represented by the integral of the path speed function over the path interval;

[0020] S503 : Penalty values ​​generated when all device paths cross the occlusion area during execution, where the penalty values ​​are determined by a spatial intersection measure between the path trajectory and the occlusion voxel set.

[0021] Preferably, in S6, the optimization method includes an alternating direction multiplier method or a mixed integer programming method, by jointly optimizing and solving the layout point variables, the path variables, and the coverage relationship variables, so that the joint objective function value is minimized;

[0022] Path optimization is modeled based on optimal control theory. By constructing a Hamiltonian function and combining it with the Lagrange multiplier method to determine the optimal control variables, the energy consumption of the equipment path is minimized.

[0023] Preferably, the initial layout point generation in the method adopts a heuristic sampling method, which generates multiple candidate points according to spatial boundary conditions and obstacle distribution, and selects the point with the highest visible coverage as the initial layout position.

[0024] In a second aspect, the present invention provides the following technical solution: a three-dimensional data acquisition and management system, the system comprising:

[0025] The spatial modeling module is used to perform grid discrete modeling on the acquisition area and identify obstacle areas to form the acquisition space;

[0026] The layout generation module is used to generate the initial layout points according to the equipment parameters and establish the equipment path control function;

[0027] Viewshed modeling module, used to build the viewshed coverage relationship between devices and spatial sampling units and construct the supporting graph structure;

[0028] The optimization solution module is used to establish a joint optimization objective for coverage, path energy consumption, and occlusion penalty, and solve the optimal device layout plan and path;

[0029] The scheduling control module is used to send the optimal result to the three-dimensional data acquisition equipment and schedule and control the equipment layout and trajectory execution tasks.

[0030] In the third aspect, the invention provides the following technical solution: a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for three-dimensional data acquisition and management when executing the computer program.

[0031] In a fourth aspect, the present invention provides the following technical solution: a readable storage medium having a computer program stored thereon, wherein the computer program implements the above-mentioned method for three-dimensional data acquisition and management when executed by a processor.

[0032] The present invention has the following beneficial effects:

[0033] 1. The present invention constructs a three-dimensional spatial acquisition model and adopts a joint optimization objective function to achieve dynamic optimization of equipment layout points and paths. It takes into account multiple factors such as acquisition coverage, energy consumption, and occlusion area penalties to obtain the optimal equipment layout plan and path control strategy, effectively improving acquisition efficiency and the rationality of path planning.

[0034] 2. In the present invention, by establishing the relationship between the field of view of the device and the spatial voxel coverage, the acquisition coverage of each voxel can be comprehensively evaluated. Combined with the optimal control and path optimization algorithms, higher spatial acquisition coverage and accuracy are achieved, the uncovered area is reduced, and the integrity and accuracy of the acquisition task are improved.

[0035] 3. In the present invention, by introducing Hamiltonian optimal control theory and path energy consumption model, while optimizing the equipment acquisition path, it can effectively reduce the energy consumption of the equipment during the path execution process, minimize the energy consumption of the equipment operation and path conflict, and improve the operating efficiency and sustainability of the entire acquisition system. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A flow chart of a method for three-dimensional data acquisition and management proposed by the present invention;

[0037] Figure 2 This is a system module relationship architecture diagram for a three-dimensional data acquisition and management system proposed by the present invention. DETAILED DESCRIPTION

[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0039] Example 1

[0040] Reference Figure 1 In a first embodiment of the present invention, the present invention provides a method for three-dimensional data acquisition and management, comprising the following steps:

[0041] S1. Obtain a three-dimensional spatial model of the area to be collected, and discretize the space into multiple sampling units to form a voxel grid;

[0042] S2, identify the occluded area in the 3D space model and construct an effectively accessible 3D acquisition space;

[0043] S3. Generate initial layout points based on the device field of view parameters and establish the acquisition path control function for each device;

[0044] S4. Based on the acquisition parameters of the device and the sampling unit positions, a view coverage relationship is established to determine whether each sampling unit is effectively covered by the device;

[0045] S5. Construct a joint optimization objective function that simultaneously considers the acquisition coverage, path energy consumption cost, and spatial conflict penalty between the path and the occlusion area;

[0046] S6. Under the constraints, the optimal equipment layout points and path control functions are solved through optimization methods, and the optimal results are output to guide the layout and scheduling of actual three-dimensional data acquisition equipment.

[0047] In S2, the effective reachable space is obtained by voxel modeling of the occluded area and excluding the points that are blocked in any acquisition direction based on ray analysis, so as to obtain the area where the equipment can be deployed.

[0048] Specifically, the construction of effective reachable space relies on the voxel modeling results of the occluded area and combines the multi-directional ray projection analysis method to achieve accurate identification of the equipment deployment location. The system first discretizes all known obstacles in the three-dimensional space and maps them into a voxel grid to form an obstacle voxel set. Each voxel unit has a volume boundary definition, and its voxel center is recorded as The obstacle area is no longer processed continuously in the form of surface or volume, but is converted into a point set with uniform spatial scale. This conversion is direction-independent and convenient for subsequent algorithm processing. Next, the key processing link is based on the directional projection model (ray-tracing based visibility check) to determine whether there is any obstruction in each direction. This processing is based on a certain point to be deployed. As the starting point, along multiple preset directions Perform spatial path simulation:

[0049] ;

[0050] in, is the maximum projection distance (usually set to the upper limit of the device's maximum perception range), vector Can be evenly distributed on the sphere to ensure uniform coverage of directions. , discretize it into steps of A continuous point sequence, check point by point whether it passes through the occluding voxel set .

[0051] If any point in a path in a certain direction falls into the range of an obstacle voxel, the field of view in that direction is considered to be limited. This judgment depends not only on whether the center points overlap, but also on the voxel boundary buffer value (which can be introduced -Nearest neighbor judgment mechanism), to prevent occlusion judgment from being too harsh. Finally, only points that meet the following conditions are retained as possible locations:

[0052] ;

[0053] This means that starting from this point, there is at least one unobstructed direction in which the device can perform the perception task. If there are obstacles blocking all directions, the point will be eliminated from the candidate set for deployment. It is worth noting that this voxel-based occlusion judgment method is more robust than the traditional geometric model or triangulated network-based occlusion judgment, and can effectively avoid boundary geometry calculation errors. By unifying the voxel scale, the consistency of occlusion analysis in different spatial areas can be guaranteed, which facilitates unified processing. Occlusion judgment supports parallel execution, and the path judgment method is naturally suitable for GPU or spatial index optimization. After the construction is completed, the system outputs the voxel set As the actual deployment area of ​​the equipment, the spatial distribution of this set is stable and does not fluctuate drastically with modeling errors, which ensures repeatability and engineering feasibility.

[0054] In S3, the device path control function is established through the kinematic model. The kinematic model is a time-continuous function, whose speed input is constrained by the maximum speed and acceleration of the device, and the initial layout point is used as the starting point of the path.

[0055] Specifically, in the equipment scheduling system, the generation of the path is not an isolated process, it relies on dynamic behavior modeling based on the deployable area. After completing the determination of the candidate point set for equipment deployment, the present invention enters the next stage - constructing the equipment path control function. The path is not only a connecting line in the geometric sense, but also a time-series continuous action plan that is subject to motion laws and execution constraints. Therefore, the introduction of kinematic modeling becomes the key. The equipment path control function adopts the continuous time domain expression form, and uses a function curve to describe the position change process of the equipment in three-dimensional space. Let the equipment be in time The path function inside is denoted as:

[0056] ;

[0057] in , indicating that at time The initial condition is that the equipment is located at the layout point. Departure, terminal time It can be regarded as a planning cycle or the moment of task completion. The change of the path control function is limited by the actual motion capability of the equipment, including the maximum linear velocity and maximum acceleration. For this purpose, the speed function is defined as:

[0058] ;

[0059] And the acceleration function:

[0060] ;

[0061] The following constraints are imposed in the present invention: - Maximum speed constraint:

[0062] ;

[0063] -Maximum acceleration constraint:

[0064] ;

[0065] in, and These are upper limits set in advance in the system based on the physical structure of the device or platform specifications. These constraints prevent non-physical motion in the path, such as sudden acceleration and reversals.

[0066] The specific construction of the path function can vary depending on the device type. For example, for flying devices (such as drones), it is preferable to use spline curves (such as cubic B-splines) to interpolate and model the path because they naturally meet the continuity requirements of position and velocity. The number of spline parameter nodes can be dynamically adjusted according to the complexity of the sampling area, preserving the expressive power of the path while avoiding excessive redundancy. The path initial value constraint ensures that the path starts from the device deployment point and has no drift error. That is:

[0067] ;

[0068] During the path control modeling process, to avoid problems such as excessive path curvature and frequent direction changes, a curvature constraint or soft constraint penalty term is introduced, applying a regulatory factor to the second-order derivative of the path function. This term serves as a proxy for energy consumption in subsequent optimization models and also helps ensure natural, smooth, and executable path generation. The path function not only maintains temporal continuity in data representation, but its modeling process also fully reflects the dynamic constraints faced by equipment in actual operation. Establishing a path control function within the deployable area is both an expression of spatial freedom and the rationality of the path function determines the feasibility of the final scheduling solution and the ability to ensure high-quality data collection.

[0069] In S4, the viewshed coverage relationship established is based on the following conditions:

[0070] The distance between the device and the sampling unit does not exceed the effective collection radius of the device, the device is pointing towards the unit, and there are no obstructed voxels between the line of sight and the unit;

[0071] It also includes building a support graph structure between device views, where each node in the support graph corresponds to a device view. If the views of two devices overlap in space, a connecting edge is established in the graph to form a continuously nested view chain structure.

[0072] Specifically, the first is the distance condition. The straight-line distance between the device and the target point must not exceed its preset maximum acquisition radius. This constraint reflects the physical limit of the device's perception capabilities. In practical applications, the perception radius can be flexibly set based on the device type, resolution requirements, or optical conditions.

[0073] The second is directional judgment. Not all voxels within the radius are perceptible. The device must be facing the point, and the target voxel must fall within its field of view. In other words, starting from the current orientation of the device, the angle between the voxel position and it must be within the fan-shaped area where collection is allowed. If the back of the device is facing the voxel, it will be considered invalid even if the distance is closer. Third, there is no obstruction of vision. Even if the distance is right and the direction is right, if the line of sight passes through an obstacle (previously marked as an occluding voxel) in the middle, then this point is still considered invisible. The system will determine whether the line between the device and the voxel is penetrated by any obstacle. As long as there is occlusion, it is judged as "obscured";

[0074] Only voxel units that meet all three conditions will be marked as effectively covered by the device. Such mapping results have a direct impact on subsequent calculations of acquisition coverage, path benefit evaluation, etc. The system will uniformly define the set of all voxels that the device can cover as its field of view support set, which serves as the effective range of perception under the current path control state;

[0075] We also introduce a support graph structure between device views. This is an abstract data structure used to describe the spatial coupling relationship between the views of multiple devices.

[0076] The field of view of each device at any time along the path is considered a node in the graph. If the fields of view of two devices intersect, that is, there is at least one voxel that can be perceived by both devices simultaneously, then an undirected edge is established between the two nodes. This connection represents the information exchangeability or coverage overlap between markets. The construction of this support graph not only helps to determine coverage redundancy, but also provides structured input for the subsequent establishment of a joint optimization model. It can reveal which devices are locally collaborative, which areas have islands of view, and can also discover paths with high overlap, thereby assisting in the dynamic adjustment of path planning and point allocation. In addition, the support graph is inherently scalable. As the number of devices increases or the path changes, the graph structure can be updated in real time to ensure the timeliness and accuracy of the field of view topology of the entire system.

[0077] The constructed joint optimization objective function includes the following three sub-objectives:

[0078] S501. The proportion of sampling units not covered by any equipment in the total sampling units;

[0079] S502: All devices collect the total energy consumption cost of the path, where the energy consumption cost is represented by the integral of the path speed function over the path interval;

[0080] S503 : Penalty values ​​generated when all device paths cross the occlusion area during execution. The penalty values ​​are determined by the spatial intersection measure between the path trajectory and the occlusion voxel set.

[0081] Specifically, to achieve efficient, complete, and stable data collection in three-dimensional space, the system must integrate multiple objectives. Given the deployable area and path control function, a joint optimization model is solved to obtain a scheduling solution with optimal coverage, reasonable energy consumption, and safe obstacle avoidance. To this end, the following joint objective function is constructed:

[0082] ;

[0083] in:- :Uncovered voxel ratio- :Path Energy Consumption- :Occlusion conflict penalty- : Weight coefficient, used to adjust the importance of the three

[0084] Secondly, this item measures how many target voxels are not covered by any device perception. The target sampling voxel set is defined as , if a voxel If it is not covered by any device, it is marked as "not collected". Define the binary function ,when 1 if overwritten, 0 otherwise:

[0085] ;

[0086] This item reflects the overall sampling accuracy. The lower the value, the more comprehensive the coverage. The system considers the motion energy consumption generated by each device along its path. It is not a simple path length measurement, but an integral modeling of energy consumption based on the speed function. The device path is a time-continuous function , the speed is , then its energy consumption can be approximated as the square integral of the velocity norm:

[0087] ;

[0088] in For the The model implicitly prefers a smooth path and avoids frequent acceleration / deceleration. If the device path crosses the occlusion area during execution, that is, the trajectory is If there is a non-empty intersection, a penalty is incurred. Here is the definition:

[0089] ;

[0090] in This is an indicator function for obstruction areas. It takes on a value of 1 when a pathpoint falls within an obstruction area and 0 otherwise. The integral represents the "length of time" the device spends interacting with the obstruction area during operation. A larger value indicates a greater number of paths with impassable crossings.

[0091] In S6, the optimization method includes an alternating direction multiplier method or a mixed integer programming method, which performs a joint optimization on the layout point variables, the path variables and the coverage relationship variables so as to minimize the value of the joint objective function;

[0092] Path optimization is modeled based on optimal control theory. By constructing a Hamiltonian function and combining it with the Lagrange multiplier method to determine the optimal control variables, the energy consumption of the equipment path is minimized.

[0093] Specifically, in order to minimize the energy consumption of the device path, static planning or heuristic path approximation is not used. Instead, the path optimization process is transformed into a typical optimal control problem for solution. This modeling method can finely control the system performance indicators while satisfying the motion constraints. The device path function It is defined as a state variable that changes with time, and its control variable is the instantaneous speed of the device The goal is to control how the device "walks" so that it can take an optimal trajectory from the starting point to the end point with the minimum energy consumption and satisfied constraints. The state control model is defined as: State variables: ;

[0094] Control variables: , the dynamic equation is written as:

[0095] ;

[0096] Boundary conditions: The initial position is the layout point: , if there is an end point constraint, specify , otherwise it is a free termination in space, and the objective function is the minimum energy consumption path, that is, the quadratic cost function of the control input:

[0097] ;

[0098] In order to introduce the optimal control theory framework, the present invention constructs the Hamiltonian function:

[0099] ;

[0100] in, is the co-state variable (i.e., Lagrange multiplier), representing the sensitivity of the system state to the objective function, and is optimally controlled according to the Pontryagin minimum principle. The Hamiltonian function must be minimized at every moment and satisfy the following set of necessary conditions:

[0101] ;

[0102] ;

[0103] ;

[0104] The above conditions indicate that there is a linear proportional relationship between the optimal speed and the co-state variable, which in this case is a constant vector. This means that the optimal path corresponds to a smooth trajectory with uniform energy distribution, and the control input varies linearly with time, which is consistent with the characteristics of the actual equipment dynamic model.

[0105] The initial deployment point generation in this method adopts a heuristic sampling method. The method generates multiple candidate points according to the spatial boundary conditions and obstacle distribution, and selects the point with the highest visible coverage as the initial deployment position.

[0106] Specifically, the initial layout point generation in the method adopts a heuristic sampling method. The method generates multiple candidate points according to the spatial boundary conditions and obstacle distribution, and selects the point with the highest visible coverage as the initial layout position. The generation area of ​​the candidate point is limited to the interior of the space area determined to be deployable in the previous step, that is, the voxel set On this basis, the system constructs a set of candidate points according to the following heuristic rules: Boundary constraints: All candidate points must meet the space closed boundary conditions to avoid the equipment being close to walls, site edges and other high-risk areas. By setting the edge buffer distance, critical points can be automatically eliminated. Obstacle avoidance: The minimum Euclidean distance between the candidate point and any obstacle voxel should not be less than the device volume safety radius and the perception field boundary to prevent initial crossing occlusion in subsequent paths. Direction distribution uniformity: To ensure comprehensive field of view coverage, the sampling direction is dispersed in space as much as possible to avoid candidate points being highly concentrated in a certain area, resulting in redundant layout;

[0107] After generating candidate points, the system will evaluate the field of view visibility of each point, taking that point as the virtual layout position of the device, and combining the device's viewing angle range and acquisition radius to detect how many valid sampling voxels can be covered from that position. This number is the visible coverage index of the point. During the field of view detection process, the occlusion culling mechanism described earlier is also applied to ensure that all counted voxels are unobstructed, reachable, and fall within the effective field of view of the device. The detection method can be ray casting or directional sampling beam method, depending on the performance of the device. After obtaining the visible coverage of all candidate points, the system will sort them according to the indicators and select a group of points with the highest coverage as the initial layout points. The goal is to make the initial layout have greater global field of view support capabilities and improve the search quality of the entire scheduling system.

[0108] Example 2:

[0109] Reference Figure 2 In a second embodiment of the present invention, the present invention provides a system for three-dimensional data acquisition and management, the system comprising:

[0110] The spatial modeling module is used to perform grid discrete modeling on the acquisition area and identify obstacle areas to form the acquisition space;

[0111] The layout generation module is used to generate the initial layout points according to the equipment parameters and establish the equipment path control function;

[0112] Viewshed modeling module, used to build the viewshed coverage relationship between devices and spatial sampling units and construct the supporting graph structure;

[0113] The optimization solution module is used to establish a joint optimization objective for coverage, path energy consumption, and occlusion penalty, and solve the optimal device layout plan and path;

[0114] The scheduling control module is used to send the optimal results to the three-dimensional data acquisition equipment and to schedule and control the equipment layout and trajectory execution tasks.

[0115] Specifically, the spatial modeling module is responsible for gridding the collection area and identifying obstacle areas, ultimately forming a complete collection space. The gridded space facilitates subsequent path optimization and visibility analysis. Obstacle area identification can be achieved based on known obstacle locations or through real-time sensor feedback.

[0116] Layout Generation Module: Based on device parameters (such as the device's maximum collection radius and sensing capabilities), this module generates multiple initial layout points and establishes the device's path control function. Initial layout points are generated using a heuristic sampling method to ensure that the selected layout points maximize coverage and avoid obstacles.

[0117] Viewshed Modeling Module: This module is responsible for constructing the viewshed coverage relationship between devices and spatial sampling units. The viewshed coverage relationship takes into account the device's perception range, directionality, and the impact of obstructions. Through viewshed modeling, the system can clearly determine the area that each device can cover and construct a support graph structure between devices, indicating the overlap of fields of view and the possibility of information exchange between devices.

[0118] Optimization Solution Module: This module constructs a joint optimization objective function, taking into account multiple factors such as coverage, path energy consumption, and occlusion penalties. By optimizing the objective function, it solves the optimal device layout plan and path, thereby achieving path planning with minimal energy consumption, maximum coverage, and optimal obstacle avoidance performance.

[0119] Scheduling and Control Module: Finally, the optimal result output by the optimization solution module is sent to the 3D data acquisition device through the scheduling and control module. The scheduling and control module is responsible for scheduling and controlling the equipment layout and trajectory execution tasks, ensuring that the equipment executes the path according to the optimal solution and completes the data acquisition task;

[0120] Example 3

[0121] The third embodiment of the present invention is based on the same inventive concept and proposes a computer-readable storage medium. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, it implements the steps of a three-dimensional data acquisition and management method of the above embodiment.

[0122] Example 4

[0123] The fourth embodiment of the present invention is based on the same inventive concept. The present invention proposes a computer device, including: a processor and a memory; the processor and the memory communicate with each other; the memory is used to store instructions; the processor is used to execute the instructions in the memory, and execute a method for three-dimensional data acquisition and management of the above embodiment.

[0124] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0125] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for three-dimensional data acquisition and management, characterized in that: The following steps are involved: S1. Obtain a three-dimensional spatial model of the area to be collected, and discretize the space into multiple sampling units to form a voxel grid; S2. Identify the blocked area in the three-dimensional space model and construct an effectively accessible three-dimensional acquisition space; S3. Generate initial layout points based on the device field of view parameters and establish the acquisition path control function for each device; S4. Based on the acquisition parameters of the device and the sampling unit positions, a view coverage relationship is established to determine whether each sampling unit is effectively covered by the device; S5. Construct a joint optimization objective function that simultaneously considers the acquisition coverage, path energy consumption cost, and spatial conflict penalty between the path and the occlusion area; S6. Under the constraints, the optimal equipment layout points and path control functions are solved through optimization methods, and the optimal results are output to guide the layout and scheduling of actual three-dimensional data acquisition equipment.

2. A method for three-dimensional data acquisition and management according to claim 1, characterized in that: In S2, the effective reachable space is obtained by performing voxel modeling on the blocked area and excluding the blocked points in any acquisition direction based on ray analysis, thereby obtaining the area where the device can be deployed.

3. The method for three-dimensional data acquisition and management according to claim 1, characterized in that: In S3, the device path control function is established through a kinematic model. The kinematic model is a time-continuous function whose speed input is constrained by the maximum speed and acceleration of the device, and the initial layout point is used as the path starting point.

4. The method for three-dimensional data acquisition and management according to claim 1, characterized in that: In S4, the established view coverage relationship is based on the following conditions: The distance between the device and the sampling unit does not exceed the effective collection radius of the device, the device is pointing towards the unit, and there are no obstructed voxels between the line of sight and the unit; It also includes constructing a support graph structure between device views, where each node in the support graph corresponds to a device view. If the views of two devices overlap in space, a connecting edge is established in the graph to form a continuously nested view chain structure.

5. The method for three-dimensional data acquisition and management according to claim 1, characterized in that: The constructed joint optimization objective function includes the following three sub-objectives: S501. The proportion of sampling units not covered by any equipment in the total sampling units; S502: All devices collect the total energy cost of the path, where the energy cost is represented by the integral of the path speed function over the path interval; S503 : Penalty values ​​generated when all device paths cross the occlusion area during execution, where the penalty values ​​are determined by a spatial intersection measure between the path trajectory and the occlusion voxel set.

6. The method for three-dimensional data acquisition and management according to claim 1, characterized in that: In S6, the optimization method includes an alternating direction multiplier method or a mixed integer programming method, which performs a joint optimization on the layout point variables, the path variables, and the coverage relationship variables so as to minimize the value of the joint objective function; Path optimization is modeled based on optimal control theory. By constructing a Hamiltonian function and combining it with the Lagrange multiplier method to determine the optimal control variables, the energy consumption of the equipment path is minimized.

7. The method for three-dimensional data acquisition and management according to claim 1, characterized in that: The initial layout point generation in the method adopts a heuristic sampling method, which generates multiple candidate points according to spatial boundary conditions and obstacle distribution, and selects the point with the highest visible coverage as the initial layout position.

8. A system for three-dimensional data acquisition and management, characterized in that: A method for three-dimensional data acquisition and management according to any one of claims 1 to 7, wherein the system comprises: The spatial modeling module is used to perform grid discrete modeling on the acquisition area and identify obstacle areas to form the acquisition space; The layout generation module is used to generate the initial layout points according to the equipment parameters and establish the equipment path control function; Viewshed modeling module, used to build the viewshed coverage relationship between devices and spatial sampling units and construct the supporting graph structure; The optimization solution module is used to establish a joint optimization objective for coverage, path energy consumption, and occlusion penalty, and solve the optimal device layout plan and path; The scheduling control module is used to send the optimal result to the three-dimensional data acquisition equipment and schedule and control the equipment layout and trajectory execution tasks.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for three-dimensional data acquisition and management according to any one of claims 1 to 7 is implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for three-dimensional data acquisition and management according to any one of claims 1 to 7 is implemented.

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