A Smart Control Method for Forestry Operations Based on Laser Point Clouds

Abstract

This application provides an intelligent control method for forestry operations based on laser point clouds, relating to the field of multi-mobile intelligent agent cooperative control and task scheduling technology. By constructing and initializing cumulative data including a first cumulative coverage state quantity and a second cumulative coverage state quantity, and adopting a unified state representation framework as a cooperative benchmark, the task progress of heterogeneous operation units (such as ground platforms and aerial platforms) is independently and separably quantified under the same reference system. This solves the problems of mixed operation states of multi-source heterogeneous platforms, inability to distinguish their respective contribution progress, and inability to dynamically compare and coordinate in the prior art. Furthermore, it integrates the urgency of its own needs with the complementarity of the other party's contribution into a unique cooperative control logic quantity, thereby solving the decision conflict problem in existing cooperative operations that relies on global path planning or fixed priorities and cannot dynamically respond to changes in real-time task requirements and information value.

Description

Technical Field

[0001] This application relates to the field of multi-mobile intelligent agent cooperative control and task scheduling technology, and particularly to an intelligent control method for dynamically allocating tasks to multiple heterogeneous operational units and data acquisition units under conditions of limited communication and insufficient prior environmental information. This method can be widely applied in fields such as geospatial information acquisition, facility inspection, and environmental monitoring. Among these applications, air-ground collaborative laser point cloud acquisition in complex forest scenarios is a crucial and highly challenging application scenario for this invention. Background Technology

[0002] In the field of multi-mobile intelligent agent collaborative control, existing technologies mainly adopt two approaches to achieve regional coverage or data acquisition: centralized control and distributed negotiation. Centralized control typically involves a central control unit that aggregates the status and environmental information of each agent, generates a unified work plan, and issues it for execution. Distributed negotiation, on the other hand, relies on each agent to make autonomous decisions based on preset rules or simple negotiation mechanisms.

[0003] However, in real-world operational scenarios where communication is limited and prior environmental information is insufficient, the above-mentioned technical approaches all have significant limitations. Centralized control methods are highly dependent on communication stability and bandwidth, making them difficult to adapt to complex environments such as forests, and the failure of the central node will lead to the loss of overall control capabilities. Although distributed negotiation methods reduce communication dependence, their decision-making mechanisms are mostly based on simple local rules, making it difficult to characterize the task coupling relationships of multiple agents in the operation process, resulting in limited collaborative efficiency.

[0004] Specifically, in forestry laser point cloud acquisition applications, existing technologies typically employ an independent data acquisition method where aerial and ground platforms operate along preset paths. Due to the lack of unified modeling and dynamic correlation of the operational status of aerial and ground platforms, this method cannot reflect changes in acquisition blind spots caused by environmental occlusion and other factors in real time, nor can it dynamically adjust task allocation based on the complementary relationship between platform operations. Overall data integrity and acquisition efficiency depend on post-acquisition supplementary acquisition and redundant operations, resulting in low operational efficiency and low resource utilization.

[0005] Therefore, there is an urgent need for an intelligent control method that can uniformly represent the operational status of multiple heterogeneous acquisition platforms under limited communication conditions, dynamically evaluate the task coupling relationship between platforms, and generate collaborative optimization task scheduling instructions, so as to improve the autonomy and overall operational efficiency of laser point cloud acquisition in complex forestry scenarios. Summary of the Invention

[0006] This application provides a method for intelligent control of forestry operations based on laser point clouds, the method comprising:

[0007] Construct and initialize cumulative data, which includes a first cumulative coverage state quantity and a second cumulative coverage state quantity;

[0008] Within the current synchronization control cycle, the ground platform and the air platform are controlled to perform laser point cloud acquisition operations to obtain first information data and second information data.

[0009] Based on the first information data and the second information data, the cumulative data is updated to generate an updated first cumulative coverage status value and an updated second cumulative coverage status value.

[0010] Based on the updated first cumulative coverage state quantity, the complementary relationship between the ground platform and the air platform is analyzed, and a first control guidance quantity is generated;

[0011] Based on the updated second cumulative coverage state quantity, the complementary relationship between the air platform and the ground platform is analyzed, and a second control guidance quantity is generated;

[0012] The consistency between the first control guide quantity and the second control guide quantity is determined. If they are determined to be consistent, a cooperative control logic quantity is generated.

[0013] If a conflict is determined, the conflict is adjudicated based on the updated first cumulative coverage state quantity and the updated second cumulative coverage state quantity, and a cooperative control logic quantity is generated.

[0014] Based on the collaborative control logic, a set of operation control instructions is generated, and the ground platform and the aerial platform are collaboratively controlled according to the set of operation control instructions to guide the platform to perform the corresponding laser point cloud acquisition operation.

[0015] As can be seen from the above technical solution, this application has the following beneficial effects:

[0016] 1. This application constructs and initializes cumulative data containing a first cumulative coverage state quantity and a second cumulative coverage state quantity, and adopts a unified state representation framework as a collaborative benchmark to independently and separably quantify the task progress of heterogeneous operation units (such as ground platforms and air platforms) under the same reference system. This solves the problem in the prior art that the operation states of multi-source heterogeneous platforms are mixed, and it is impossible to distinguish their respective contribution progress and to dynamically compare and coordinate them.

[0017] 2. This application analyzes the complementary relationship between platforms based on the updated cumulative coverage state quantity and generates a first control guidance quantity and a second control guidance quantity. By using a two-way coupling degree calculation and conflict resolution mechanism, it integrates the urgency of its own needs and the complementarity of the other party's contributions into a unique collaborative control logic quantity. This solves the decision conflict problem in existing collaborative operations that relies on global path planning or fixed priorities and cannot dynamically respond to changes in real-time task requirements and information value. Attached Figure Description

[0018] The present application will be further described below with reference to the accompanying drawings.

[0019] Figure 1 A flowchart of the first intelligent control method for forestry operations based on laser point clouds provided in this application;

[0020] Figure 2 A flowchart of a second intelligent control method for forestry operations based on laser point clouds provided in this application. Detailed Implementation

[0021] The terms "first," "second," and "third," etc., used in this application specification, claims, and drawings are used to distinguish different objects, not to limit a specific order.

[0022] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0023] To facilitate understanding of the technical solution of this application, the following embodiments will be described using forestry operation scenarios as the main application object. It should be noted that the multi-acquisition platform collaborative control and task scheduling method involved in this application belongs to a general intelligent control technology, which does not depend on a specific application field or operation object. The forestry operation scenario is chosen for description because forestry laser point cloud acquisition operations simultaneously possess typical characteristics such as insufficient prior environmental information, limited communication conditions, diverse acquisition platform types, and complex operation coverage relationships. This fully demonstrates the technical advantages and applicability of the technical solution of this application in terms of multi-acquisition platform collaborative control capabilities under complex operation conditions.

[0024] To ensure clarity and conciseness in the description of the following embodiments, a brief introduction to the related technologies is given first:

[0025] In forestry operations, laser point cloud data acquisition typically relies on the collaborative efforts of ground-based and aerial acquisition platforms. Ground-based platforms primarily acquire information about the understory spatial structure, while aerial platforms acquire information about the canopy and overall topography. Current forestry laser point cloud acquisition operations generally rely on pre-defined work area divisions, work sequence arrangements, and fixed work paths. By pre-planning the work area and time periods for each acquisition platform, redundant acquisition and platform waiting time are reduced. Tasks are assigned to acquisition platforms based on empirical rules or established procedures; for example, after an aerial acquisition platform completes scanning along a designated route, a ground-based acquisition platform enters the corresponding area for supplementary acquisition. This type of scheduling prioritizes coverage and operational efficiency.

[0026] Meanwhile, forestry operation safety and operation quality assessment is usually achieved by monitoring and evaluating key parameters during the data collection process. For example, by monitoring the coverage density, occlusion degree, feature integrity, and operation status of the laser point cloud, operation quality evaluation results or risk warning information are generated based on preset thresholds or experience standards, thereby providing reference for operators.

[0027] Research has revealed that existing forestry laser point cloud acquisition mechanisms lack direct correlation in terms of indicator selection, data representation, and decision-making logic, resulting in low coupling between the two. Specifically, even if a scheduling scheme based on a pre-set work path or area can improve acquisition efficiency locally, it struggles to reflect in real-time the impact of factors such as forest shading changes and differences in aerial and ground perspectives on the overall point cloud coverage integrity. Furthermore, work quality assessments or risk warnings are often based on discrete thresholds, lacking comprehensive consideration of the operational sequence of multiple acquisition platforms, overlapping work areas, and complementary relationships between platforms.

[0028] Since the acquisition sequence, work area allocation, and platform collaboration methods directly affect point cloud coverage and the formation of potential acquisition blind spots, existing job quality assessment or risk warning mechanisms only make post-event judgments on the results and cannot provide quantitative and feedback-based collaborative information to the job scheduling process. As a result, operators still need to rely on experience-based judgment when adjusting the job sequence or reallocating work areas, making it difficult to fully improve acquisition efficiency while ensuring data integrity. At the same time, acquisition blind spots or redundant acquisition are often only discovered after the job is completed, resulting in delayed problem detection and resource response.

[0029] The above analysis shows that in existing forestry laser point cloud acquisition operations, the operation scheduling mechanism and the operation status assessment mechanism lack effective coupling, resulting in a lack of intrinsic connection between acquisition efficiency optimization and data integrity assurance. This makes it difficult to detect and control potential acquisition problems in complex forest environments in a timely manner, thus restricting the improvement of the overall efficiency and intelligence level of forestry laser point cloud acquisition operations.

[0030] Example 1

[0031] To address the aforementioned problems, this application provides an intelligent control method for forestry operations based on laser point clouds. Please refer to [link / reference]. Figure 1 .

[0032] S1, construct and initialize the cumulative data.

[0033] Obtain the spatial boundary coordinates of the preset work area, set the grid cell size (0.5m×0.5m in this embodiment) according to the effective coverage resolution of the laser point cloud, discretize the work area into several grid cells, assign a unique geographic index identifier to each grid cell, and form a standard coverage grid cell set, which serves as a unified spatial index benchmark for all subsequent coverage status records.

[0034] A raster structure and geographic index identical to the standard coverage grid cell set are adopted. Three fields are established for each raster cell: a coverage count counter, a recent coverage timestamp, and a cumulative point cloud density value. After instantiating this data structure, the coverage count counters of all raster cells are set to 0, the recent coverage timestamp is set to null, and the cumulative point cloud density value is set to 0, completing the construction and initialization of the first cumulative coverage state quantity. The first cumulative coverage state quantity is used to independently record the cumulative effective coverage of the ground platform cluster from the start of the operation to the current time.

[0035] The second cumulative coverage state variable uses the same raster partitioning rules, data structure, and field definitions as the first cumulative coverage state variable, and allocates storage space independently. All raster cell coverage count counters are set to 0, the most recent coverage timestamp is set to empty, and the cumulative point cloud density value is set to 0, thus completing the construction and initialization of the second cumulative coverage state variable. The second cumulative coverage state variable is used to independently record the cumulative effective coverage of the aerial platform cluster from the start of the operation to the current time.

[0036] The first and second cumulative coverage status variables, after initialization, are associated together with the current synchronization control cycle number 0 and encapsulated as cumulative data, which is then stored in the coverage status management unit. Simultaneously, effective coverage determination thresholds (point cloud density ≥ 10 points / grid, point spacing ≤ 0.1 m, registration error ≤ 0.05 m) and preset coverage interval division thresholds (first interval [0, 300), second interval [300, 600), third interval [600, ∞)) are pre-configured for use in subsequent synchronization control cycles.

[0037] Through the above processing, the cumulative coverage status of the ground platform and the air platform are independently quantified under a unified spatial grid reference, and the first cumulative coverage status quantity and the second cumulative coverage status quantity are given a clear zero value starting point, providing accumulative and comparable basic data for subsequent cycles to perform status updates, coupling degree calculations and conflict adjudication based on the first information data and the second information data.

[0038] To ensure clarity and conciseness in the description of the following embodiments, a brief introduction to the relevant terms is given first:

[0039] Cumulative data refers to a structured data set containing the first and second cumulative coverage status quantities. Using the standard coverage grid cell set as a spatial index, it records the cumulative effective coverage status of the ground platform group and the air platform group from the start of the operation to the current moment, and is the core status input for all subsequent collaborative decisions.

[0040] The first cumulative coverage status quantity refers to a data entity that characterizes the cumulative effective coverage of the ground platform group within the operational area. In this embodiment, it is implemented in the form of a raster map. Each raster cell stores a coverage count counter, a recent coverage timestamp, and a cumulative point cloud density value, which is used to quantitatively reflect the operational progress and coverage integrity from a ground perspective.

[0041] The second cumulative coverage status quantity refers to a data entity that characterizes the cumulative effective coverage of the aerial platform group within the operational area. Its data structure is exactly the same as that of the first cumulative coverage status quantity, including the same grid division rules and field definitions, but its storage space is independent. It is used to quantitatively reflect the operational progress and coverage integrity from an aerial perspective.

[0042] A standard coverage grid cell set refers to the complete set of grid cells formed by regularly discretizing a pre-defined work area according to fixed geographic dimensions (0.5m × 0.5m in this embodiment). Each grid cell has a unique geographic index identifier and serves as a unified spatial reference for recording the coverage status of ground and aerial platforms. This set is constructed once during the initialization phase and remains unchanged throughout the entire work cycle.

[0043] The coverage count counter is an integer variable that records the number of valid coverage events in each grid cell. The counter increments by 1 for each valid coverage event, and is initialized to 0.

[0044] The most recent coverage timestamp is the time identifier of the most recent valid coverage time recorded in each grid cell. It is associated with the synchronization control cycle number and is used to determine the freshness of the coverage data. It is set to empty during initialization.

[0045] The cumulative point cloud density value refers to the cumulative variable that records the sum of all effective covered point cloud densities within each raster cell. It can be used to calculate the average point cloud density or the weighted fusion density, and is uniformly assigned a value of 0 during initialization.

[0046] Effective coverage refers to an event where laser point cloud data meets a preset quality threshold and can be considered as having generated effective information collection on the target area. In this embodiment, the criteria for determining effective coverage are: the number of point clouds in a single grid is ≥10, the point cloud spacing is ≤0.1 m, and the point cloud registration error is ≤0.05 m. Only point cloud data that meets these conditions can be included in the update of the cumulative coverage status.

[0047] The overlay state management unit is a logical management module used to store, maintain, and schedule accumulated data. It can be implemented using shared memory, distributed cache, or a central database to ensure the consistency, atomicity, and efficient access of the first and second accumulated overlay state variables in cross-cycle calls.

[0048] The synchronous control cycle number is an integer sequence number used by this method to identify continuous control cycles. The initial cycle number is 0, and it increments by 1 after each complete data acquisition-state update-decision-instruction issuance cycle. The corresponding cycle number is recorded for each update of the cumulative covered state quantity to support state traceability in the time dimension.

[0049] The preset coverage interval division rule refers to the conversion rule that maps continuous coverage reference values ​​to discrete interval identifiers. This embodiment uses a fixed threshold division: a first preset coverage interval [0, 300), a second preset coverage interval [300, 600), and a third preset coverage interval [600, ∞). This rule is preset during the initialization phase, and the coverage interval identifiers need to be remapped after each update of the coverage reference value in subsequent cycles.

[0050] In some possible implementations, forestry laser point cloud acquisition requires the collaborative work of ground and aerial platforms. However, the two types of platforms differ significantly in their operational perspectives, coverage efficiency, and environmental adaptability: ground platforms, being close to the ground surface, can acquire near-ground details such as forest trunks, fallen trees, and shrubs, but their coverage is limited by canopy shading; aerial platforms, operating from above, can achieve rapid, wide-area coverage of the top of the canopy and open areas, but struggle to penetrate the closed canopy to obtain understory information. If point clouds collected in each cycle are simply superimposed without constructing cumulative coverage state quantities from different perspectives, it is impossible to distinguish the respective contribution progress of the two types of platforms, let alone quantify their task coupling relationship. This results in a lack of calculable data foundation for subsequent complementary relationship analysis, control guidance quantity generation, and conflict resolution.

[0051] By constructing a first and second cumulative coverage state variable and using a unified set of standard coverage grid cells as a spatial reference, the operational progress of ground and aerial platforms can be quantitatively expressed in a separable and comparable manner under the same geographic reference system. The initialization operation clears all raster values ​​for both types of state variables, providing a clear and unbiased starting point for all subsequent incremental updates, ensuring that the effective coverage contribution of each cycle is accurately accumulated into the corresponding platform view state. The input to the first cumulative coverage state variable is the effective coverage point cloud and corresponding pose of the ground platform for each cycle; the processing involves raster projection and incremental accumulation; the output is a cumulative coverage distribution map from the ground view. The input to the second cumulative coverage state variable is the effective coverage point cloud and corresponding pose of the aerial platform for each cycle; the output is a cumulative coverage distribution map from the aerial view.

[0052] The two cumulative coverage state variables evolve independently, and spatial overlay analysis can reveal the complementarity and redundancy of the two types of platforms in the coverage area in real time. The cumulative coverage state variable is not only a recorder of the operation progress, but also the core input for subsequent calculations of the first and second coverage coupling degrees, as well as a key criterion for determining which platform group is lagging behind in the conflict resolution stage. The initial zero coverage state establishes traceable and verifiable initial conditions for the entire closed-loop control process, fundamentally solving the problem of mixed coverage states of multi-source heterogeneous platforms and the inability to dynamically compare and coordinate them in existing technologies.

[0053] S2 controls the ground platform and the air platform to perform laser point cloud acquisition operations within the current synchronization control cycle, and acquires first information data and second information data.

[0054] During the current synchronization control cycle, the coverage status management unit sends a set of operation control instructions to multiple ground platforms, instructing them to perform laser point cloud acquisition according to preset travel paths, preset scanning frequencies, and preset scanning angles; at the same time, it sends flight path control instructions and scan trigger instructions to multiple air platforms, controlling them to perform laser point cloud acquisition according to planned flight paths and altitude parameters.

[0055] During the acquisition process, first raw laser point cloud data output from multiple ground platforms and second raw laser point cloud data output from multiple aerial platforms are acquired respectively; ground pose information of multiple ground platforms and aerial pose information of multiple aerial platforms within the current synchronization control cycle are acquired simultaneously.

[0056] The first raw laser point cloud data is preprocessed, including denoising, outlier removal, and coordinate registration based on ground pose information, to generate the first effective coverage point cloud data. The first effective coverage point cloud data is projected onto a standard coverage grid cell set, and the number of points, point spacing, and registration error in each grid cell are counted. Effective coverage grid cells are selected based on the effective coverage judgment threshold. The number of newly added coverage grid cells in this cycle is counted based on the effective coverage grid cells and recorded as the first point cloud coverage density increment. A pre-trained target recognition model is called on the first effective coverage point cloud data to identify target instances such as fallen trees, shrubs, bare ground, stumps, and saplings. The number of target instances identified for the first time in this cycle is counted and recorded as the first new feature discovery rate. The first point cloud coverage density increment, the first new feature discovery rate, and the ground pose information are structured and encapsulated, and the current synchronization control cycle number is appended to generate the first information data.

[0057] The second raw laser point cloud data undergoes the same preprocessing procedure as the first raw laser point cloud data to generate the second effective coverage point cloud data. The second effective coverage point cloud data is projected onto a standard coverage grid cell set. The point cloud distribution characteristics within each grid cell are statistically analyzed, and effective coverage grid cells are selected based on the effective coverage threshold. The number of newly added coverage grid cells in this cycle is counted based on the effective coverage grid cells and recorded as the second point cloud coverage density increment. A pre-trained target recognition model is called on the second effective coverage point cloud data to identify target instances such as the top of the canopy, forest gaps, tree canopy boundaries, and dead standing trees. The number of target instances identified for the first time in this cycle is counted and recorded as the second new feature discovery rate. The second point cloud coverage density increment, the second new feature discovery rate, and the aerial pose information are structured and encapsulated, and the current synchronization control cycle number is appended to generate the second information data.

[0058] To ensure clarity and conciseness in the description of the following embodiments, a brief introduction to the relevant terms is given first:

[0059] The first raw laser point cloud data refers to the unprocessed raw point cloud data set that is collected and transmitted back in real time by multiple ground platforms during the current synchronous control cycle, including information such as three-dimensional coordinates, reflection intensity, and echo count.

[0060] The second raw laser point cloud data refers to the unprocessed raw point cloud data set that is collected and transmitted back in real time by multiple aerial platforms during the current synchronization control cycle. Its data format is consistent with that of the first raw laser point cloud data.

[0061] Ground pose information refers to the set of spatial position and attitude parameters of the ground platform within the current synchronization control cycle, including three-dimensional geographic coordinates, heading angle, pitch angle, and roll angle, which are used for motion distortion correction of the original point cloud, coordinate transformation, and multi-platform point cloud registration.

[0062] Airborne attitude information refers to the set of spatial position and flight attitude parameters of an airborne platform within the current synchronization control cycle, including latitude and longitude, absolute altitude, yaw angle, pitch angle, and roll angle, which are used for the absolute geolocation and trajectory inversion of airborne point clouds.

[0063] Preprocessing refers to a series of standardized operations performed to transform raw laser point cloud data into effectively covered point cloud data, including denoising, motion distortion correction, coordinate transformation, point cloud registration, and redundancy removal, to ensure the spatial consistency and quality of the point cloud data.

[0064] The first effective coverage point cloud data refers to the subset of point clouds that, after preprocessing, meets the effective coverage determination threshold and is determined to be effective coverage for the first time in this period.

[0065] The second effective coverage point cloud data refers to the subset of point clouds that, after preprocessing, meets the effective coverage determination threshold and is determined to be effective coverage for the first time in this period.

[0066] The first point cloud coverage density increment refers to the total number of new effective coverage grids added by multiple ground platforms within this synchronization control cycle. It is calculated by projecting the first effective coverage point cloud data onto the standard coverage grid cell set and spatially comparing it with the first cumulative coverage status before the update.

[0067] The second point cloud coverage density increment refers to the total number of effective coverage grids added by multiple airborne platforms within this synchronization control cycle, and its calculation method is the same as that of the first point cloud coverage density increment.

[0068] The first new feature discovery rate refers to the total number of target instances first identified by multiple ground platforms through target identification of the first effective coverage point cloud data within the current synchronization control period. Target instance types include fallen trees under forest canopy, shrubs and grass, bare ground, stumps, and saplings.

[0069] The second new feature discovery rate refers to the total number of target instances first identified by multiple aerial platforms through target identification of the second effective coverage point cloud data within the current synchronization control period. Target instance types include canopy tops, forest gaps, tree canopy boundaries, and dead standing trees.

[0070] The first information data refers to the data unit generated by structuring and encapsulating the first point cloud coverage density increment, the first new feature discovery rate and ground pose information, and attaching the current synchronization control cycle number.

[0071] The second information data refers to the data unit generated by structuring and encapsulating the second point cloud coverage density increment, the second new feature discovery rate, and the aerial pose information, with the current synchronization control cycle number appended.

[0072] In some possible implementations, ground-based and aerial platforms simultaneously collect data within the same operational area. However, their sensor characteristics, operational postures, and coverage perspectives are drastically different: aerial platforms can scan tens of thousands of points per second, completing canopy coverage of several hectares within minutes, but struggle to penetrate dense forests; ground-based platforms move slowly and have a narrow scanning range, yet can precisely reconstruct the three-dimensional structure of the forest understory. If the two types of point clouds are simply merged and stored without extracting incremental metrics, it becomes impossible to distinguish the respective contribution progress of the two platforms, let alone quantify the task coupling relationship between them.

[0073] By executing standardized acquisition, processing, and quantization processes in each synchronization control cycle, the raw point cloud is unified into a standard coverage grid cell set and transformed into two orthogonal metrics: coverage density increment and new feature discovery rate. The coverage density increment is used to update the cumulative coverage state, enabling the system to monitor the operational progress of both types of platforms in real time. The new feature discovery rate expresses the perceived value of a platform for a specific area, providing a complementary criterion for subsequent coupling degree calculations. The independent encapsulation of the first and second information data preserves the perspective differences between ground and air platforms while achieving data alignment through unified cycle numbering and spatial indexing. This allows the real-time acquisition contributions of both types of platforms to participate in subsequent complementary relationship analysis and conflict resolution in a unified metric format.

[0074] It should be noted that the "first new feature discovery rate" and "second new feature discovery rate" mentioned in this embodiment are functional terms. Their specific numerical representation is quantified by the absolute number of target instances first identified in this period, in order to simplify the expression and intuitively reflect the absolute value of new discoveries in this period. In other embodiments, the discovery rate can also be defined as the ratio of the number of target instances first identified in this period to the total number of target instances identified in this period, or the ratio to the total number of target instances identified historically. Such variations are simple substitutions by those skilled in the art based on this embodiment and still fall within the protection scope of this invention.

[0075] S3, update the cumulative data based on the first information data and the second information data.

[0076] Read the first and second cumulative coverage status values ​​before the start of the current synchronization control cycle from the coverage status management unit; parse the first information data and extract the list of newly added coverage grid indexes corresponding to the first point cloud coverage density increment, the measured point cloud density values ​​of each newly added grid, the identifiers of new feature instances identified in each newly added grid, and the current synchronization control cycle number.

[0077] For each newly added coverage raster in the first point cloud coverage density increment, locate the raster unit with the same geographic index in the first cumulative coverage status quantity, increment the coverage count counter of the raster by 1, add the cumulative point cloud density value to the measured point cloud density value collected in the current period of the raster, update the most recent coverage timestamp to the current synchronization control period number, and set the new feature identifier of the raster to be discovered according to the new feature instance identifier carried in the first new feature discovery rate.

[0078] After updating all newly added graticles, based on the coverage count counter or point cloud density accumulation value of each graticle in the updated first cumulative coverage status, recalculate the preset coverage range of each graticle and refresh the coverage range identifier of each graticle to obtain the updated first cumulative coverage status.

[0079] The second information data is parsed to extract the list of newly added coverage raster indexes corresponding to the second point cloud coverage density increment, the measured point cloud density values ​​of each newly added raster, the identifiers of new feature instances identified in each newly added raster, and the current synchronization control cycle number; the second cumulative coverage state quantity is updated in exactly the same way as the first cumulative coverage state quantity to obtain the updated second cumulative coverage state quantity.

[0080] Write the updated first cumulative coverage status value and the updated second cumulative coverage status value back to the coverage status management unit to overwrite the status data of the previous period and store them in association with the current synchronization control period number.

[0081] Through the above processing, the first cumulative coverage status quantity and the second cumulative coverage status quantity respectively absorb the new coverage contribution of the ground platform and the air platform in this cycle, realizing the independent progressive accumulation of the operation progress of the two types of platforms, and providing real-time and accurate status basis for subsequent complementary relationship analysis and conflict adjudication.

[0082] To ensure clarity and conciseness in the description of the following embodiments, a brief introduction to the relevant terms is given first:

[0083] The newly added coverage raster index list refers to the set of geographic indexes of raster cells in the first or second information data that correspond to the first or second point cloud coverage density increment in this period and that have reached effective coverage conditions for the first time in this period.

[0084] The measured point cloud density refers to the calculated point density of all laser point clouds that meet the effective coverage determination threshold in a newly added coverage grid within the current synchronization control cycle. The unit is points / grid. This value is statistically obtained by S2 when the effective coverage point cloud is projected onto the grid and encapsulated in the first information data or the second information data.

[0085] The new feature instance identifier refers to the unique identifier of each new target instance identified from the first effective coverage point cloud data or the second effective coverage point cloud data by the target recognition model within the current synchronization control cycle. This identifier is associated with the target type and spatial location and is encapsulated in the first information data or the second information data.

[0086] The coverage count counter is an integer field stored in each grid cell of the first and second cumulative coverage status quantities. It is used to record the total number of times the grid is effectively covered by a ground platform or an air platform. It is initialized to 0 and incremented by 1 for each effective coverage.

[0087] The cumulative point cloud density value refers to the floating-point field stored in each grid cell of the first and second cumulative coverage states. It is used to record the sum of the effective coverage point cloud densities of the grid in all historical periods. It is initialized to 0 and is accumulated in each period based on the measured point cloud density value.

[0088] The most recent coverage timestamp refers to the integer field stored in each grid cell of the first and second cumulative coverage status variables. It is used to record the synchronization control cycle number corresponding to the most recent effective coverage of the grid. It is set to empty during initialization and updated to the current cycle number each time a effective coverage occurs.

[0089] The new feature identifier refers to a Boolean field stored in each raster cell of the first and second cumulative coverage status quantities. It is used to mark whether the raster has been identified as having forestry target features in any historical period. It is initialized to false and is set to true and permanently retained once a new feature instance is found in the raster in a certain period.

[0090] Coverage interval remapping refers to the process of recalculating the coverage interval identifier of the raster cell whose value has changed according to the preset coverage interval division rules after the first or second cumulative coverage status value is updated. This is used to ensure the real-time correspondence between the interval identifier and the coverage reference value.

[0091] In some possible implementations, the first and second information data have already completed the quantification and encapsulation of the incremental contribution for the current cycle. However, these incremental data only reflect the instantaneous acquisition results of the ground platform and the air platform within a single cycle. If the newly added coverage grids in each cycle are simply discarded or used only for temporary display, without being continuously accumulated into the state container across cycles, the system cannot form a global understanding of the overall coverage progress of the work area, let alone determine in subsequent cycles which areas have been fully covered, which areas still have acquisition blind spots, and which type of platform is progressing faster or slower.

[0092] By executing a standardized state update process in each synchronization control cycle, the newly added covered rasters, measured point cloud density values, and new feature instance identifiers carried in the first and second information data are sequentially injected into the first and second cumulative coverage state quantities. A coverage count counter records the frequency of repeated coverage of a raster, reflecting the importance of the operation in that area; the cumulative point cloud density value records the level of detail in the raster coverage, providing a basis for subsequent coverage quality evaluation; the most recent coverage timestamp indicates the freshness of the coverage data, supporting the calculation of time decay weights; and the new feature identifier permanently marks discovered targets, avoiding resource waste caused by repeated mining. After completing the numerical update, the coverage interval remapping discretizes the continuously changing coverage baseline value into a finite number of interval levels, enabling the system to quickly determine the intensity level of operation adjustment based on the interval identifier.

[0093] The updated first and second cumulative coverage state variables are written back to the coverage state management unit, replacing the historical state of the previous cycle and becoming the starting benchmark for the state update of the next cycle. This state accumulation mechanism enables the system to have cross-cycle operational memory capabilities: the coverage contributions of ground platforms and air platforms are no longer disordered, discrete, and untraceable instantaneous events, but rather a continuously accumulating, comparable, and traceable quantified state sequence under a unified spatial benchmark. The updated first cumulative coverage state variable will serve as the core input for calculating the first coverage coupling degree in S4, used to evaluate the complementary value of air platforms to the ground from a ground perspective; the updated second cumulative coverage state variable will be used to calculate the second coverage coupling degree, evaluating the complementary value of ground platforms to the air from an air perspective; simultaneously, the two state variables will directly serve as key criteria for comparing the operational progress of the two types of platforms in the S5 conflict resolution phase. Without the cross-cycle accumulation and state maintenance of incremental contributions in S3, the coupling degree calculation in S4 would lose its historical benchmark, and the conflict resolution in S5 would become an empirical judgment without any state to rely on.

[0094] S4, generate a first control guidance quantity and a second control guidance quantity based on the updated first cumulative coverage state quantity and the updated second cumulative coverage state quantity, respectively.

[0095] In existing technologies, collaborative operations of multi-source heterogeneous platforms typically employ a single central control node, issuing task instructions to each platform through global path planning or fixed priority allocation. In this mode, the coverage contributions of ground and aerial platforms are merged, making it impossible to separately maintain the cumulative coverage status from their respective perspectives. Consequently, it is also impossible to quantify the complementary value and redundancy of the coverage areas between the two types of platforms in real time. This invention, by constructing independent, grid-based unified first and second cumulative coverage status quantities, enables for the first time a separable, comparable, and weighted fusion quantitative expression of the operational progress from both ground and aerial perspectives, providing a state basis for subsequent coupling degree calculations and conflict resolution.

[0096] Read the updated first cumulative coverage status and the updated second cumulative coverage status from the coverage status management unit; parse the first information data and the second information data corresponding to the current synchronization control cycle, and extract the first point cloud coverage density increment, the first new feature discovery rate, the second point cloud coverage density increment, and the second new feature discovery rate;

[0097] For the calculation of the first coverage coupling degree: taking the updated first cumulative coverage state quantity as the benchmark, and the second point cloud coverage density increment and the second new feature discovery rate in the second information data as references; traversing the newly added coverage raster index list in the second information data, counting the number of "uncovered" or "covered but point cloud density lower than a preset threshold" rasters in the first cumulative coverage state quantity before the update; if the proportion of this number to the second point cloud coverage density increment exceeds the complementarity relationship determination threshold (30% in this embodiment), then it is determined that there is a complementary relationship between the ground platform and the air platform, and a positive value is assigned to the first coverage coupling degree; otherwise, a negative value or zero value is assigned; calculating the comparison difference index according to the size and trend relationship between the updated first cumulative coverage state quantity and the second point cloud coverage density increment and the second new feature discovery rate, mapping the comparison difference index to the basic absolute value range, and obtaining the basic absolute value of the first coverage coupling degree; determining the corresponding weight level according to the preset coverage range in which the updated first cumulative coverage state quantity is located, and adjusting the basic absolute value with the weight level to generate the first coverage coupling degree.

[0098] For the generation of the first control guidance quantity: the operation guidance of the ground platform in the next synchronization control cycle is determined according to the numerical sign of the first coverage coupling degree—if it is positive, it moves closer to the operation area of ​​the air platform in this cycle; if it is negative, it moves away from the operation area of ​​the air platform in this cycle. It should be noted that the "operation guidance" mentioned in this embodiment refers to the platform's collaborative tendency at the task level, and its specific manifestations include, but are not limited to, the direction of spatial position movement, the time sequence of task execution, or the priority of sensor resource allocation. This embodiment uses spatial position movement as an instance of "operation guidance" to facilitate intuitive explanation of the technical effect of the present invention in a forestry scenario, rather than limiting the meaning of "operation guidance". The corresponding operation adjustment intensity level is determined according to the preset coverage interval where the updated first cumulative coverage state quantity is located (the first interval is high level, the second interval is medium level, and the third interval is low level), and the intensity level is mapped to the priority value (high level corresponds to 3, medium level corresponds to 2, and low level corresponds to 1); the operation guidance, operation adjustment intensity level, priority value, and the current synchronization control cycle number are structurally encapsulated to generate the first control guidance quantity;

[0099] For the generation of the second coverage coupling degree and the second control guidance quantity: the same assignment rules and generation logic as above are adopted. The updated second cumulative coverage state quantity is used as the benchmark, and the first point cloud coverage density increment and the first new feature discovery rate in the first information data are used as references to calculate the second coverage coupling degree and generate the second control guidance quantity.

[0100] The generated first and second control guidance variables are temporarily stored in the collaborative decision buffer, with the current synchronization control cycle number appended, for use in subsequent consistency determination and conflict resolution steps.

[0101] Through the above processing, ground platforms and air platforms generate quantitative control guidelines that include the operational tendency and urgency of the next cycle, based on their own cumulative coverage status and each other's contribution in the current cycle. This provides comparable and adjudicable decision inputs for the collaborative operations of the two types of platforms in the next cycle.

[0102] To ensure clarity and conciseness in the description of the following embodiments, a brief introduction to the relevant terms is given first:

[0103] The first coverage coupling degree refers to a comprehensive indicator that, from the perspective of the ground platform, takes the updated first cumulative coverage status as a benchmark, integrates the coverage density increment of the air platform in this cycle and the new feature discovery rate, and quantitatively evaluates the complementary value and urgency of the air platform's operating area to the unfinished area of ​​the ground platform in this cycle. Its numerical sign represents the complementary or redundant relationship, and the absolute value represents the degree of complementarity. The final coupling degree is formed after weighting the cumulative coverage status intervals.

[0104] The second coverage coupling degree refers to a comprehensive index that, from the perspective of the air platform, takes the updated second cumulative coverage status as a benchmark, and integrates the increase in coverage density of the ground platform in this cycle with the new feature discovery rate, to quantitatively evaluate the complementary value and urgency of the ground platform's operational area to the unfinished area of ​​the air platform in this cycle; its calculation rules are exactly the same as the first coverage coupling degree.

[0105] Operation guidance refers to the directional instruction in the control guidance quantity that indicates the platform's movement trend in the next cycle. It is determined by the numerical sign of the coverage coupling degree. Positive coupling degree corresponds to moving closer to the other party's operation area in the current cycle, while negative coupling degree corresponds to moving away from the other party's operation area in the current cycle. It can be expressed as the geographical coordinates, azimuth angle, or relative position vector of the specific target area.

[0106] The task adjustment intensity level refers to the urgency level divided according to the preset coverage interval where the platform's current cumulative coverage status is located. It is divided into three levels from low to high: the first interval corresponds to the low level, the second interval corresponds to the medium level, and the third interval corresponds to the high level. In this embodiment, the first interval [0, 300) is the high level, the second interval [300, 600) is the medium level, and the third interval [600, ∞) is the low level. This level is used to characterize the urgency of the platform's task adjustment for the next cycle.

[0107] Priority values ​​refer to discrete integer values ​​that are tied to the intensity level of job adjustments. They are used to compare the decision weights of the control guidance quantities of both parties during the conflict adjudication phase. In this embodiment, high priority corresponds to priority value 3, medium priority corresponds to 2, and low priority corresponds to 1.

[0108] The first control guidance quantity refers to the structured decision unit jointly generated by the first coverage coupling degree and the updated first cumulative coverage state quantity, which includes the operation guidance, operation adjustment intensity level, priority value and associated cycle number in the next synchronous control cycle of the ground platform.

[0109] The second control guidance quantity refers to the structured decision unit jointly generated by the second coverage coupling degree and the updated second cumulative coverage state quantity, which includes the operation guidance, operation adjustment intensity level, priority value and associated cycle number in the next synchronization control cycle of the air platform.

[0110] The complementarity determination threshold refers to the percentage threshold for determining whether the newly added coverage grids of the other platform in this cycle effectively supplement the one's own. In this embodiment, it is set to 30%, that is, more than 30% of the newly added grids of the other platform in this cycle must be located in the one's own cumulative coverage blind zone or low quality zone in order to determine that there is a complementary relationship and assign a positive value to the coverage coupling degree.

[0111] The comparison difference index is an intermediate calculation used to quantify the relative difference between one's own cumulative coverage status and the other party's coverage density increment and new feature discovery rate in the current period. It reflects the significance of the other party's contribution relative to one's own contribution; the more significant the difference, the larger the basic absolute value of the coverage coupling degree.

[0112] In some possible implementations, the updated first and second cumulative coverage status variables accurately record the operational progress of the ground and air platforms, but these status variables are merely "historical records" and cannot directly answer the core decision-making question of "where the platform should go in the next cycle and why." In existing technologies, multi-aircraft collaborative task allocation typically relies on global path planning or fixed priority negotiation, making it difficult to dynamically respond to the constantly changing coverage blind spots and information value during real-time data acquisition.

[0113] By constructing a two-way coupling mechanism, each type of platform can quantify the positive and negative value of the other platform's operational area to its own uncompleted areas, based on its own cumulative status and the other platform's contribution in the current cycle. When the first coverage coupling degree is positive, it means that the aerial platform has just covered an area with significant supplementary value to the ground—perhaps a forest gap edge or a sparse canopy area—and the ground should promptly follow up with additional sweeping of understory details. When it is negative, it means that the aerial platform has covered a large area already completed by the ground, and the ground should proactively avoid and redirect to other blind spots. The second coverage coupling degree grants the aerial platform the same reverse evaluation capability. When a ground platform discovers new high-value features (such as rare fallen trees or sapling groves) in a certain area, the aerial platform can be guided to conduct multi-angle supplementary sampling in that area to improve feature recognition accuracy.

[0114] The task orientation is directly driven by the coverage coupling degree symbol, realizing an intuitive decision-making logic of moving closer to complementary areas and moving away from redundant ones. The intensity level of task adjustment is determined by the platform's own cumulative coverage status—the lower the coverage progress, the higher the urgency of adjustment, and the system allocates resources with higher priority to avoid some areas being uncovered for a long time. This design integrates the two orthogonal dimensions of the urgency of one's own needs and the complementarity of the other's contributions into a single control orientation variable, enabling both types of platforms to independently generate task tendencies that conform to their own interests and the overall goals in each cycle.

[0115] The independent generation of the first and second control guidance quantities does not presuppose any master-slave relationship between platforms, but rather endows ground and air with equal decision-making expression capabilities. This equality provides a fair dialogue basis for subsequent consistency determination and conflict resolution: when both guidance quantities are consistent, they can be directly merged and executed; when conflicts occur, the resolution is based on objective indicators such as the cumulative coverage state difference and adjustment intensity level, rather than simply fixing the priority of one platform as higher. This enables global collaboration to be driven simply by periodically exchanging incremental information in forestry scenarios with limited communication and insufficient environmental prior knowledge.

[0116] It should be noted that, in this embodiment, the comparison between the updated first cumulative coverage state quantity and the updated second cumulative coverage state quantity is based on the sum of the coverage count counters of each grid cell in the corresponding cumulative coverage state quantity. The larger the sum of the coverage count counters, the more grid cells the platform group has cumulatively covered since the start of the operation, i.e., the higher the operation completion progress.

[0117] In other embodiments, the sum of cumulative point cloud density values ​​can also be used as a comparison benchmark, or the weighted score of coverage interval level mapping can be used as a comparison benchmark. In this embodiment, the sum of coverage count counters is used as the core comparison indicator for conflict resolution. This indicator has a clear physical meaning, is simple to calculate, and has strong real-time performance, and can effectively reflect the cumulative operational coverage breadth of the platform group.

[0118] S5, perform consistency determination on the first control guide quantity and the second control guide quantity, and generate cooperative control logic quantity.

[0119] Read the first and second control guidance quantities generated in the current synchronization control cycle from the collaborative decision buffer; parse the first and second control guidance quantities and extract the operation guidance, operation adjustment intensity level, priority value and associated cycle number of the ground platform and the air platform in the next synchronization control cycle respectively.

[0120] The consistency of the first control guidance quantity and the second control guidance quantity is determined. If there is no conflict between the two in terms of operation sequence, operation priority, or operation area allocation, they are determined to be consistent. The consistent operation guidance attributes of the two are retained, and the higher of the operation guidance, the geographical range of the collaborative area, and the adjustment intensity level of both parties is structurally encapsulated and directly merged to generate a collaborative control logic quantity. The "operation area allocation" mentioned here is a specific form of task resource allocation, which in this embodiment is manifested as the allocation of spatial resources. In other application scenarios, this allocation can be extended to time window allocation, computing resource allocation, or spectrum resource allocation.

[0121] If the first control guidance quantity and the second control guidance quantity conflict in terms of operation sequence, operation priority, or operation area allocation, a conflict resolution process will be initiated. The resolution is based on the difference between the first coverage progress index and the second coverage progress index as the core criterion. The first coverage progress index is calculated based on the sum of the coverage count counters of each grid cell in the updated first cumulative coverage status quantity, and the second coverage progress index is calculated based on the sum of the coverage count counters of each grid cell in the updated second cumulative coverage status quantity. The two indicators respectively represent the cumulative coverage breadth of the ground platform group and the air platform group from the start of the operation to the current time.

[0122] First, the absolute value of the difference between the first coverage progress indicator and the second coverage progress indicator is calculated. If the first coverage progress indicator is greater than the second coverage progress indicator, and the difference reaches or exceeds the first preset threshold (500 grids in this embodiment), then the ground platform group is determined to have a significantly leading cumulative coverage progress, and the air platform group is the lagging party, and the first control guidance value is adopted first. If the second coverage progress indicator is greater than the first coverage progress indicator, and the difference reaches or exceeds the first preset threshold, then the air platform group is determined to have a significantly leading cumulative coverage progress, and the ground platform group is the lagging party, and the second control guidance value is adopted first.

[0123] If the above difference does not reach the first preset threshold, but reaches or exceeds the second preset threshold (two hundred grids in this embodiment), it is determined that the difference in the cumulative coverage progress of the two parties is not significant. In this case, the control guidance quantity corresponding to the party with the higher job adjustment intensity level is adopted first (the higher the level, the higher the priority; if the levels are the same, the preset priority is used for adjudication).

[0124] If the above difference does not reach the second preset threshold, it is determined that the cumulative coverage progress of both parties is basically the same. At this time, the decision is made according to the preset operation priority order of the ground platform and the air platform. In this embodiment, the preset priority is that the air platform has a higher priority than the ground platform.

[0125] The operation orientation, geographical scope of the collaborative area, associated operation adjustment intensity level and priority value in the control orientation quantity that is ultimately adopted are extracted and encapsulated, and the current synchronization control cycle number is added to generate the collaborative control logic quantity.

[0126] It should be noted that this embodiment uses "operation area allocation" as the object of conflict resolution because spatial resources are the most competitive resource in forestry laser point cloud acquisition. However, the conflict resolution mechanism of this invention is universal, and its resolution logic (based on cumulative state quantity difference, preset threshold, and intensity level comparison) can be applied to any type of resource conflict.

[0127] To ensure clarity and conciseness in the description of the following embodiments, a brief introduction to the relevant terms is given first:

[0128] The first coverage progress index refers to a single scalar value obtained by numerically aggregating the updated first cumulative coverage status quantity, which is used to characterize the overall coverage progress of the ground platform group from the start of operation to the current moment; in this embodiment, the sum of the coverage count counters of each grid cell is used as the first coverage progress index.

[0129] The second coverage progress index refers to a single scalar value obtained by numerically aggregating the updated second cumulative coverage status quantity, which is used to characterize the overall coverage progress of the aerial platform group from the start of the operation to the current moment; in this embodiment, the sum of the coverage count counters of each grid cell is used as the second coverage progress index.

[0130] The first preset threshold is the progress difference threshold used in conflict adjudication to determine whether one party's cumulative coverage progress is significantly ahead of the other party; in this embodiment, the value is set to five hundred grids.

[0131] The second preset threshold is the progress difference threshold used in conflict resolution to determine whether the cumulative coverage progress of both parties is not significantly different; in this embodiment, the value is two hundred grids; the second preset threshold is less than the first preset threshold.

[0132] In some possible implementations, the updated first and second cumulative coverage status variables accurately record the operational progress of the ground and air platforms, but these status variables are merely "historical records" and cannot directly answer the core decision-making question of "where the platform should go in the next cycle and why." In existing technologies, multi-aircraft collaborative task allocation typically relies on global path planning or fixed priority negotiation, making it difficult to dynamically respond to the constantly changing coverage blind spots and information value during real-time data acquisition.

[0133] By constructing a two-way coupling mechanism, each type of platform can quantify the positive and negative value of the other platform's operational area to its own uncompleted areas, based on its own cumulative status and the other platform's contribution in the current cycle. When the first coverage coupling degree is positive, it means that the aerial platform has just covered an area with significant supplementary value to the ground—perhaps a forest gap edge or a sparse canopy area—and the ground should promptly follow up with additional sweeping of understory details. When it is negative, it means that the aerial platform has covered a large area already completed by the ground, and the ground should proactively avoid and redirect to other blind spots. The second coverage coupling degree grants the aerial platform the same reverse evaluation capability. When a ground platform discovers new high-value features (such as rare fallen trees or sapling groves) in a certain area, the aerial platform can be guided to conduct multi-angle supplementary sampling in that area to improve feature recognition accuracy.

[0134] The task orientation is directly driven by the coverage coupling degree symbol, realizing an intuitive decision-making logic of moving closer to complementary areas and moving away from redundant ones. The intensity level of task adjustment is determined by the platform's own cumulative coverage status—the lower the coverage progress, the higher the urgency of adjustment, and the system allocates resources with higher priority to avoid some areas being uncovered for a long time. This design integrates the two orthogonal dimensions of the urgency of one's own needs and the complementarity of the other's contributions into a single control orientation variable, enabling both types of platforms to independently generate task tendencies that conform to their own interests and the overall goals in each cycle.

[0135] The independent generation of the first and second control guidance quantities does not presuppose any master-slave relationship between platforms, but rather endows ground and air with equal decision-making expression capabilities. This equality provides a fair dialogue basis for consistency determination and conflict resolution.

[0136] By establishing a standardized conflict resolution and collaborative logic generation mechanism, the operational tendencies, urgency, and schedule differences carried in the first and second control guidance quantities are transformed into a unique collaborative decision output. The comparison of the differences between the first and second coverage schedule indicators, the tiered resolution of the first and second preset thresholds, and the step-by-step supplementation of operational adjustment intensity levels and preset priorities together constitute a multi-level, quantifiable, and dynamically adjustable conflict resolution system. This system does not rely on the pre-planning of the central planner but rather on the status data accumulated from the actual operations of the platform cluster, autonomously generating collaborative solutions adapted to the current distribution of coverage blind spots and schedule differences in each cycle.

[0137] The collaborative control logic quantity generated after the adjudication will serve as the core input of S6, and will be further parsed into specific control commands to drive the ground platform and the air platform to execute the next cycle of operations. Therefore, S5 is both the decision endpoint for coupling analysis and control guidance quantity generation, and the decision starting point for the transformation of collaborative control logic quantity into operational commands. It is the core hub connecting the platform's autonomous decision-making and the system's unified adjudication in the entire methodology.

[0138] S6 generates a set of job control instructions based on cooperative control logic quantities and performs cooperative control. (See also...) Figure 2 .

[0139] Read the collaborative control logic quantity generated in the current synchronization control cycle from the coverage status management unit; parse the collaborative control logic quantity and extract the geographical range of the collaborative area, the collaborative priority value, the associated job adjustment intensity level, and the current synchronization control cycle number determined after consistency judgment or conflict adjudication.

[0140] In this embodiment, the "geographical scope of the collaborative region" is an instantiation of the collaborative operation scope in the spatial dimension. In other application scenarios, the collaborative operation scope can be represented in different forms such as time intervals, task lists, or resource quotas.

[0141] Based on the spatial index mapping relationship between the geographical range of the collaborative area and the standard coverage grid cell set, the boundary of the collaborative area is solved into several target operation grid cell sets; based on the current pose information of each platform, the platform kinematic model and operation capability parameters, the corresponding ground platform and air platform individuals are assigned to the target operation grid cell sets;

[0142] For each ground platform assigned a task, a corresponding task control instruction is generated, including the target waypoint coordinate sequence, travel path planning, lidar scanning frequency setting, scanning angle range, scanning overlap rate requirement, point cloud acquisition triggering time, effective coverage judgment threshold parameter, and the current cycle number identifier. The above instructions are structured and encapsulated according to the platform communication protocol to form a subset of ground platform task control instructions.

[0143] For each airborne platform assigned a task, a corresponding task control instruction is generated, including the flight path node coordinate sequence, flight altitude, flight speed, heading angle, lidar scan trigger parameters, scan bandwidth setting, lateral overlap rate, point cloud density target value, and the current cycle number identifier. The above instructions are structured and encapsulated according to the platform communication protocol to form a subset of airborne platform task control instructions.

[0144] Merge the subset of ground platform operation control commands with the subset of air platform operation control commands, and add the current synchronization control cycle number and command effective timestamp to generate a complete set of operation control commands;

[0145] Through the communication link established by the overlay status management unit, the operation control command set is sent to the corresponding ground platform and air platform respectively; after receiving the command, each platform parses and loads it into the local controller, and performs laser point cloud acquisition operation in the next synchronization control cycle according to the command parameters;

[0146] Meanwhile, each platform encapsulates the instruction receiving status, instruction parsing status, and platform readiness status into execution status feedback information and sends it back to the overlay status management unit for the generation of collaborative control logic quantities and closed-loop monitoring in the next cycle.

[0147] Through the above processing, the collaborative control logic is transformed into low-level operation instructions that can be directly recognized and executed by heterogeneous platforms, enabling task allocation and collaborative operation between ground and air platforms in the next synchronization control cycle, and completing a complete closed-loop control process from state awareness, coupling analysis, conflict resolution to instruction execution.

[0148] To ensure clarity and conciseness in the description of the following embodiments, a brief introduction to the relevant terms is given first:

[0149] The operation control instruction set refers to a structured set of instructions generated by parsing and parameterizing the cooperative control logic quantities, which is directed to multiple ground platforms and multiple air platforms. It includes all motion control parameters and sensor control parameters required by each platform to perform laser point cloud acquisition operations in the next synchronization control cycle.

[0150] The target operation grid cell set refers to the set of grid cells that need to be prioritized for coverage or supplementary sampling during the next week, which are indexed from the standard coverage grid cell set based on the geographical range of the collaborative area determined in the collaborative control logic quantity. It serves as the spatial benchmark for the allocation of platform operation areas.

[0151] The target waypoint coordinate sequence refers to the sequence of geographical coordinates that the ground platform needs to pass through in the next synchronization control cycle. It is determined by the collaborative area boundary, the platform's current position, and the path planning algorithm, and is used to guide the platform to move to the target operation area.

[0152] The route node coordinate sequence refers to the sequence of three-dimensional spatial node coordinates that the airborne platform needs to fly over in the next synchronization control cycle. It includes longitude, latitude and altitude, and is jointly determined by the boundary of the cooperative area, the current position of the platform and the route planning algorithm.

[0153] Scan overlap rate refers to the ratio of the width of the overlapping area between adjacent scan strips to the width of the scan strip during continuous scanning operations on a ground platform or an aerial platform. In this embodiment, the lateral overlap rate of the ground platform is preset to 30%, and the lateral overlap rate of the aerial platform is preset to 60%, which is used to ensure the quality of point cloud stitching and coverage integrity.

[0154] Point cloud acquisition triggering timing refers to the time synchronization command for starting lidar scanning after the platform moves to the designated work position. It can be expressed as three modes: immediate triggering, delayed triggering, or pose arrival triggering, which is jointly determined by the priority value in the collaborative control logic quantity and the work adjustment intensity level.

[0155] The instruction effective timestamp refers to the time reference at which the job control instruction set begins execution. It is associated with the next synchronization control cycle number and is used to ensure that all platforms start the job under the same time reference, thereby achieving spatiotemporal alignment of data collected by multiple platforms.

[0156] Execution status feedback information refers to the structured data packets returned by each platform to the coverage status management unit after receiving the job control instruction set, including instruction reception confirmation, instruction parsing status, platform health status, and readiness status. These data packets are used for input correction and system monitoring for collaborative decision-making in the next cycle.

[0157] In some possible implementations, the collaborative control logic has already expressed the decision of "where the ground platform should go first and with what priority" for the air platform in the next cycle. However, this logic is essentially a high-level abstract instruction directed to the collaborative decision-making layer and cannot be directly recognized by the platform's underlying execution mechanisms. The ground platform's motion controller needs specific waypoint coordinates and speed, and the lidar scanner needs clear frequencies, angles, and trigger sequences; the air platform's flight control system needs precise flight path nodes, flight altitudes, and heading angles. Without a refined transformation from the collaborative logic to the underlying instruction set, all the results of previous state accumulation, coupling analysis, and conflict resolution will be impossible to implement.

[0158] By establishing a standardized instruction generation and distribution mechanism, the collaborative control logic quantities are parsed, mapped, and parameterized step by step, ultimately encapsulated into a set of job control instructions executable by the local controllers of each platform. Its processing flow comprises four levels:

[0159] The first layer is the spatial solution layer. The collaborative region in the collaborative control logic is usually expressed as a geographic polygon or a raster index range, which needs to be solved into continuous waypoints or route nodes. Ground platforms are constrained by kinematics (minimum turning radius, maximum climb angle) and cannot traverse the raster grid by grid. Path planning algorithms are needed to transform the raster set into a smooth and feasible sequence of waypoint coordinates. Air platforms need to couple the two-dimensional operating area with flight altitude to generate a complete sequence of route nodes that includes climb, level flight, and descent phases.

[0160] The second layer is the parameter mapping layer. The collaborative priority value and the operational adjustment intensity level need to be mapped to specific acquisition parameters. A high priority level (value 3) corresponds to an aggressive acquisition strategy: scanning frequency increased to 40kHz, scanning angle widened to 180°, and point cloud density target value ≥30 points / grid; a low priority level (value 1) corresponds to a conservative acquisition strategy: scanning frequency reduced to 20kHz, scanning angle narrowed to 120°, and point cloud density target value ≥10 points / grid. The overlap rate parameter is set differently depending on the platform type. For aerial platforms, due to higher positioning accuracy and more stable flight paths, the preset overlap rate is higher than that for ground platforms.

[0161] The third layer is the command encapsulation layer. Ground platforms and air platforms from different manufacturers and of different models have heterogeneous communication protocols and control interfaces. By maintaining the communication protocol template library for each platform through the overlay status management unit, standardized operation command parameters are adapted to the private format of each platform, ensuring that the command set can be correctly parsed. At the same time, a unified timestamp and cycle number are attached, enabling all platforms to start operations under the same time reference, ensuring the spatiotemporal consistency of data collected in the next cycle.

[0162] The fourth layer is the distribution and feedback layer. After an instruction is issued, the platform needs to return execution status feedback information. If a platform fails to receive the instruction due to communication interruption, insufficient power, or mechanical failure, the coverage status management unit needs to trigger a retransmission within this cycle or transfer the task of that platform to another homogeneous platform; if the platform successfully receives the instruction but is not expected to arrive at the work area on time, its feedback information will carry the expected arrival time deviation as an input correction item for the collaborative decision-making in the next cycle.

[0163] Through the four layers of processing described above, S6 completes the crucial leap from decision-making to execution. The collaborative control logic is no longer an abstract strategy symbol, but is transformed into a stream of physical commands that drive the movement, scanning, and data collection of the real platform. The point cloud data collected by the ground and air platforms in the next cycle will be transformed back into first and second information data via S2, triggering the state update in S3, the coupling degree calculation in S4, and the conflict resolution in S5, forming a cyclical and continuously evolving autonomous collaborative closed loop. Therefore, S6 is both the end point of the current cycle of collaborative control and the starting point of the next cycle of collaborative control, and is the only execution exit connecting the decision space and the physical space in the entire methodology.

[0164] It should be noted that this embodiment uses a forestry laser point cloud acquisition scenario as an example to provide a detailed description of the multi-platform collaborative control method provided in this application. Terms such as "operation guidance," "operation area allocation," "geographical range of collaborative area," and "waypoints / routes" are presented in a spatially relevant manner in this embodiment to intuitively illustrate the technical effects of the invention in the specific application scenario of forestry.

[0165] However, the meaning of these terms in this invention is not limited to spatial dimension. Specifically:

[0166] "Task orientation" refers to the platform's collaborative tendency at the task level, including but not limited to spatial movement direction, temporal sequence tendency, or resource usage tendency.

[0167] "Job area allocation" is a specific form of task resource allocation. Other forms include time window allocation, computing resource allocation, spectrum resource allocation, etc.

[0168] "Collaborative geographical scope" is an instantiation of the scope of collaborative operations in the spatial dimension. The scope of collaborative operations can also be represented as a time interval, a task list, or a resource quota.

[0169] The core of this invention lies in achieving dynamic scheduling of multi-platform collaborative tasks by constructing and updating dual-cumulative coverage state variables, combined with coupling degree calculation and conflict resolution mechanisms. This core method is universal and can be applied to any scenario requiring multi-agent collaborative task execution, including but not limited to collaborative inspection between ground robots and aerial drones in industrial environments, collaborative operations between agricultural drones and unmanned vehicles, collaborative transportation between logistics delivery vehicles and delivery drones in urban environments, task timing scheduling in time-sensitive networks, and spectrum resource allocation in multi-sensor collaborative detection. Therefore, the description of this embodiment should not be construed as limiting the scope of protection of this application.

[0170] The foregoing has shown and described the basic principles, main features, and advantages of this application. Those skilled in the art should understand that this application is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of this application. Various changes and modifications can be made to this application without departing from the spirit and scope thereof, and all such changes and modifications fall within the scope of this application as claimed. The scope of protection of this application is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent control of forestry operations based on laser point clouds, controlling multiple ground platforms and aerial platforms, characterized in that, The method includes: Construct and initialize cumulative data, which includes a first cumulative coverage state quantity and a second cumulative coverage state quantity; Within the current synchronization control cycle, acquire first raw laser point cloud data collected by multiple ground platforms and second raw laser point cloud data collected by multiple air platforms; Obtain the ground pose information of the ground platform and the air pose information of the air platform during the current synchronization control cycle; The first raw laser point cloud data is preprocessed to generate the first effective coverage point cloud data; The second original laser point cloud data is preprocessed to generate the second effective coverage point cloud data; Based on the first effective coverage point cloud data, the first point cloud coverage density increment and the first new feature discovery rate of multiple ground platforms in the current synchronization control cycle are calculated. The first new feature discovery rate refers to the total number of target instances first identified by multiple ground platforms through target identification of the first effective coverage point cloud data in the current synchronization control cycle. The target instance types include fallen trees under forest, shrubs and grass, bare ground, stumps, and saplings. The first point cloud coverage density increment, the first new feature discovery rate, and the ground pose information are encapsulated to generate first information data. The first information data refers to the data unit generated after the first point cloud coverage density increment, the first new feature discovery rate, and the ground pose information are encapsulated in a structured manner, with the current synchronization control cycle number appended. Based on the second effective coverage point cloud data, the second point cloud coverage density increment and the second new feature discovery rate of multiple aerial platforms in the current synchronization control period are calculated. The second new feature discovery rate refers to the total number of target instances first identified by multiple aerial platforms through target identification of the second effective coverage point cloud data in the current synchronization control period. The target instance types include canopy top, forest gap, tree canopy boundary, and dead standing trees. The second point cloud coverage density increment, the second new feature discovery rate, and the aerial pose information are encapsulated to generate second information data. The second information data refers to the data unit generated after the second point cloud coverage density increment, the second new feature discovery rate, and the aerial pose information are encapsulated in a structured manner, with the current synchronization control cycle number appended. Based on the first information data and the second information data, the cumulative data is updated to generate an updated first cumulative coverage status quantity and an updated second cumulative coverage status quantity. The first cumulative coverage status quantity refers to a data entity that characterizes the cumulative effective coverage of the ground platform group in the operating area. Its data structure is implemented in the form of a raster map. Each raster cell stores a coverage count counter, the most recent coverage timestamp, and the cumulative point cloud density value, which is used to quantitatively reflect the operation progress and coverage integrity from the ground perspective. The second cumulative coverage status quantity refers to a data entity that characterizes the cumulative effective coverage of the aerial platform group within the operating area. Its data structure is exactly the same as that of the first cumulative coverage status quantity, including the same grid division rules and field definitions, but the storage space is independent. It is used to quantitatively reflect the operation progress and coverage integrity from the aerial perspective. Based on the updated first cumulative coverage state quantity, the complementary relationship between the ground platform and the air platform is analyzed, and a first control guidance quantity is generated. The first control guidance quantity refers to a structured decision unit jointly generated by the first coverage coupling degree and the updated first cumulative coverage state quantity, which includes the operation guidance, operation adjustment intensity level, priority value and associated cycle number in the next synchronization control cycle of the ground platform. Based on the updated second cumulative coverage state quantity, the complementary relationship between the air platform and the ground platform is analyzed, and a second control guidance quantity is generated. The second control guidance quantity refers to a structured decision unit jointly generated by the second coverage coupling degree and the updated second cumulative coverage state quantity, which includes the operation guidance, operation adjustment intensity level, priority value and associated cycle number in the next synchronization control cycle of the air platform. The consistency between the first control guide quantity and the second control guide quantity is determined. If they are determined to be consistent, a cooperative control logic quantity is generated. If a conflict is determined, the conflict is adjudicated based on the updated first cumulative coverage state quantity and the updated second cumulative coverage state quantity, and a cooperative control logic quantity is generated. Based on the collaborative control logic, a set of operation control instructions is generated, and the ground platform and the aerial platform are collaboratively controlled according to the set of operation control instructions to guide the platform to perform the corresponding laser point cloud acquisition operation.

2. The method according to claim 1, characterized in that, The step of updating the cumulative data based on the first information data and the second information data to generate an updated first cumulative coverage status value and an updated second cumulative coverage status value includes: Based on the first point cloud coverage density increment, the first new feature discovery rate and the ground pose information in the first information data, the first cumulative coverage state quantity is updated to obtain the updated first cumulative coverage state quantity. Based on the second point cloud coverage density increment, the second new feature discovery rate, and the aerial pose information in the second information data, the second cumulative coverage state quantity is updated to obtain the updated second cumulative coverage state quantity.

3. The method according to claim 2, characterized in that, The step of analyzing the complementary relationship between the ground platform and the air platform based on the updated first cumulative coverage status quantity, and generating a first control guidance quantity, includes: A first coverage coupling degree is generated based on the updated first cumulative coverage state quantity and the second information data; A first control guidance quantity is generated based on the first coverage coupling degree and the updated first cumulative coverage state quantity; The step of analyzing the complementary relationship between the airborne platform and the ground platform based on the updated second cumulative coverage status quantity, and generating a second control guidance quantity, includes: A second coverage coupling degree is generated based on the updated second cumulative coverage state quantity and the first information data; A second control guidance quantity is generated based on the second coverage coupling degree and the updated second cumulative coverage state quantity.

4. The method according to claim 3, characterized in that, The assignment rules for the first coverage coupling degree include: When it is determined that there is a complementary relationship between point cloud acquisition between the ground platform and the air platform, a positive value is assigned to the first coverage coupling degree; When it is determined that the complementary relationship does not exist, a negative value or a zero value is assigned to the first coverage coupling degree; Based on the relationship and trend of the updated first cumulative coverage state quantity with the second point cloud coverage density increment and the second new feature discovery rate in the second information data, the basic absolute value of the first coverage coupling degree is determined, wherein the more significant the difference between the size relationship and the trend relationship, the larger the basic absolute value. Based on the weight level corresponding to the preset coverage interval where the updated first cumulative coverage state quantity is located, the basic absolute value is adjusted to generate the first coverage coupling degree. The second coverage coupling degree adopts the same assignment rule as the first coverage coupling degree and is generated based on the updated second cumulative coverage state quantity, the first point cloud coverage density increment in the first information data, and the first new feature discovery rate.

5. The method according to claim 3, characterized in that, The step of generating a first control guidance quantity based on the first coverage coupling degree and the updated first cumulative coverage state quantity includes: Based on the numerical sign of the first coverage coupling degree, the operational orientation of the ground platform in the next synchronization control cycle is determined; Based on the preset coverage range where the updated first cumulative coverage status is located, the corresponding job adjustment intensity level is determined; The operation guidance is combined with the operation adjustment intensity level to generate a first control guidance quantity that includes the operation priority or operation area allocation tendency of the ground platform in the next synchronization control cycle. The second control guide quantity is generated in the same way as the first control guide quantity, based on the second coverage coupling degree and the updated second cumulative coverage state quantity.

6. The method according to claim 5, characterized in that, The preset coverage range includes a first preset coverage range, a second preset coverage range, and a third preset coverage range, which are set sequentially from low to high according to their coverage status. When the updated first cumulative coverage status is within the first preset coverage range, the job adjustment intensity level is high. When the updated first cumulative coverage status is within the second preset coverage range, the job adjustment intensity level is medium. When the updated first cumulative coverage status is within the third preset coverage range, the job adjustment intensity level is low. The preset coverage interval division method corresponding to the second cumulative coverage status quantity is the same as that of the first cumulative coverage status quantity.

7. The method according to claim 1, characterized in that, If a conflict is determined, the conflict is adjudicated based on the updated first cumulative coverage state quantity and the updated second cumulative coverage state quantity, and a cooperative control logic quantity is generated, including: When it is determined that there is a conflict between the first control guidance quantity and the second control guidance quantity in terms of operation sequence, operation priority or operation area allocation, the updated first cumulative coverage state quantity and the updated second cumulative coverage state quantity are compared. If the updated first cumulative coverage state value is greater than the updated second cumulative coverage state value and the difference exceeds the first preset threshold, then the first control guidance value is adopted first. If the updated second cumulative coverage state value is greater than the updated first cumulative coverage state value and the difference exceeds the first preset threshold, then the second control guidance value is adopted first. If the difference does not exceed the first preset threshold but exceeds the second preset threshold, the control guidance amount corresponding to the one with the higher job adjustment intensity level shall be adopted first. If the difference does not exceed the second preset threshold, the decision will be made according to the preset operational priority order of the ground platform and the air platform. Write the preferred control guidance quantity into the collaborative control logic quantity.

8. The method according to claim 1, characterized in that, If the determination is consistent, the generation of collaborative control logic quantities includes: When it is determined that there is no conflict between the first control guidance quantity and the second control guidance quantity in terms of work sequence, work priority or work area allocation, the consistent work guidance attribute of the two is retained. Write the consistent job orientation attribute into the collaborative control logic quantity.

9. The method according to claim 1, characterized in that, There are multiple ground platforms and multiple aerial platforms.

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