A smart integrated management and control system for forest farms

Abstract

This invention relates to the field of smart forestry management technology, specifically disclosing a smart integrated management and control system for forest farms, including an air-space-ground collaborative sensing module, a fire risk dynamic modeling module, and an intelligent decision-making and execution module. It acquires temperature field data through multispectral satellite remote sensing, constructs a high-precision terrain model using lidar, extracts vegetation features using hyperspectral imaging, and extracts thermal anomaly structures using topological continuous homology analysis and the Morse-Smale complex method. Combined with a quantum annealing optimizer, an adaptive fire risk model is established. The system can generate fire risk heat maps in real time and classify risk levels, driving UAVs to perform priority patrols and obstacle avoidance path planning. This solves problems such as difficulty in identifying false hot zones in complex environments, low accuracy of fire risk prediction, and lack of intelligent path planning, thus improving the intelligent level of forest farm fire prevention and control.

Description

Technical Field

[0001] This invention relates to the field of smart forestry management technology, specifically to a smart integrated management and control system for forest farms. Background Technology

[0002] With the continuous advancement of digital and intelligent transformation in forestry resource management, forest farms, as an important component of the forest ecosystem, are gradually shifting their management model from traditional manual supervision to intelligent comprehensive management and control. Modern forest farms not only bear the important responsibility of forest resource cultivation and ecological protection, but also face multiple risks and threats, including forest fires, pests and diseases, and illegal logging. Especially against the backdrop of intensified global climate change and frequent extreme weather events, forest fires have become the primary hidden danger affecting forest farm safety. To enhance forest farms' ability to perceive, warn of, and respond to emergencies such as fire risks, it is urgent to construct a comprehensive intelligent management and control system for forest farms that integrates multi-source data perception, intelligent analysis and modeling, and precise decision-making and execution. This system relies on advanced technologies such as remote sensing monitoring, artificial intelligence, quantum computing, and edge computing to achieve all-weather, all-element dynamic perception and closed-loop management of the forest environment, comprehensively improving the intelligent level of forest farms in disaster prevention and control, resource allocation, and ecological maintenance, and promoting the modernization of the forestry governance system and governance capabilities.

[0003] The existing technology has the following shortcomings:

[0004] Existing technologies lack sufficient modeling of the impact of terrain effects and environmental noise, and lack in-depth analysis of the topological structure of thermal signals. Furthermore, in the process of fusing multi-source data from air, space, and ground, the spatial persistence characteristics and temporal evolution of the temperature field are not fully explored. This invention, by introducing advanced techniques such as topological persistent cohomology analysis, Morse-Smale complex construction, and quantum annealing optimization, addresses this long-standing but under-recognized technical challenge in complex forest environments by starting from the high-dimensional topological structure and physical mechanisms, achieving accurate extraction of real fire hazard signals and effective removal of pseudo-thermal zones. Summary of the Invention

[0005] The purpose of this invention is to provide a smart integrated management and control system for forest farms to solve the problems mentioned above.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] A smart integrated management and control system for forest farms includes:

[0008] The air-space-ground collaborative sensing module is used to: acquire forest temperature field distribution data through multispectral satellite remote sensing, acquire sub-meter precision terrain point cloud data by lidar scanning, and collect vegetation reflectance spectral characteristic data using hyperspectral imaging technology.

[0009] The forest fire risk dynamic model establishment module, the specific process of which includes:

[0010] Topological persistence cohomology analysis is performed on temperature field distribution data to extract the ring connectivity component and high-dimensional void features in the thermogram and generate topological risk coefficients.

[0011] A Morse-Smale complex is constructed from the 3D point cloud data of the terrain. The pseudo-thermal zone caused by geoelectromagnetic interference is eliminated by calculating the critical point exponent, and the terrain correction coefficient is output.

[0012] The topological risk coefficient and terrain correction coefficient are input into the quantum annealing optimizer to establish a dynamic forest fire risk model with adaptive learning capabilities.

[0013] The intelligent decision-making and execution module outputs the forest fire risk level based on the forest fire risk dynamic model, dynamically divides patrol priority areas, and generates the optimal path for the drone that includes obstacle avoidance constraints.

[0014] As a further aspect of the present invention: the topological persistence coherence analysis of the temperature field distribution data specifically includes:

[0015] A three-dimensional simple complex structure of the temperature field is constructed, and the topological features of the thermal distribution are captured by calculating the barcode representation of the homology groups in each dimension.

[0016] A multi-scale analysis method based on continuous coherence is adopted. First, the global connectivity characteristics of the temperature field are identified at the coarse-grained level, and then the local anomalous thermal regions are located at the fine-grained level.

[0017] For the identified ring-shaped connected components, calculate their persistence parameter in the temperature gradient field. When the persistence parameter exceeds a preset threshold, it is determined to be a potential fire hazard channel.

[0018] For the detected high-dimensional void features, the spatiotemporal variation trend of its boundary heat flux is analyzed to predict the possible direction of fire spread;

[0019] Ultimately, the persistence and evolution trend of each topological feature are quantified into a unified topological risk coefficient.

[0020] As a further aspect of the present invention: the extraction of the annular connected components and high-dimensional void features from the heatmap specifically includes:

[0021] A manifold learning-based heatmap dimensionality reduction method is used to map the original temperature field to a low-dimensional feature space;

[0022] In the feature space, a level set based on a distance function is constructed, and the ring structure is identified by calculating the homology group changes between each level set.

[0023] For each detected annular component, the heat exchange efficiency between its internal temperature gradient and the external environment is calculated as an indicator of the severity of the fire hazard.

[0024] To address the characteristics of high-dimensional cavities, a heat conduction model from their geometric center to the boundary is established. The risk of fire development is assessed by solving the stability parameters of this model.

[0025] The risk assessment results of the toroidal component are weighted and fused with the stability analysis results of the void feature to generate a comprehensive topological risk coefficient.

[0026] As a further aspect of the present invention: the construction of the Morse-Smale complex from the terrain 3D point cloud data specifically includes:

[0027] A differential manifold reconstruction method based on Riemannian geometry is used to transform discrete point cloud data into a continuously differentiable terrain surface; critical points of terrain features are identified by calculating the Gaussian curvature and mean curvature of each point on the terrain surface.

[0028] Construct the Morse function gradient field containing key terrain features such as peaks, saddles, and valleys;

[0029] By utilizing the intersection properties of stable and unstable manifolds, Morse-Smale complexes that reflect the macroscopic structure of the terrain are generated;

[0030] An adaptive smoothing algorithm is introduced during the construction of the Morse-Smale complex to eliminate local distortions caused by uneven point cloud density.

[0031] As a further aspect of the present invention: the construction of the Morse-Smale complex from the terrain 3D point cloud data specifically includes:

[0032] A differential manifold reconstruction method based on Riemannian geometry is used to transform discrete point cloud data into a continuously differentiable terrain surface; critical points of terrain features are identified by calculating the Gaussian curvature and mean curvature of each point on the terrain surface.

[0033] Construct the Morse function gradient field containing key terrain features such as peaks, saddles, and valleys;

[0034] By utilizing the intersection properties of stable and unstable manifolds, Morse-Smale complexes that reflect the macroscopic structure of the terrain are generated;

[0035] An adaptive smoothing algorithm is introduced during the construction of the Morse-Smale complex to eliminate local distortions caused by uneven point cloud density.

[0036] As a further aspect of the present invention: the elimination of pseudo-hot zones by calculating the critical point exponent specifically includes:

[0037] A mapping model between the critical point index and electromagnetic interference characteristics is established, where the critical point at the mountain peak corresponds to a positive index and the critical point at the valley bottom corresponds to a negative index.

[0038] Real-time geoelectromagnetic field intensity data is acquired through a quantum sensor network; the electromagnetic field intensity data is convolved with the critical point exponent to calculate the electromagnetic interference weight of each critical point.

[0039] The regions where the weights exceed the threshold are marked as pseudo-hot zones; the topographic curvature of the marked regions is corrected by an energy minimization method based on topology preservation.

[0040] As a further aspect of the present invention: the process of outputting the terrain correction coefficient specifically includes:

[0041] Based on the corrected Morse-Smale complex, the terrain complexity parameters of each grid cell are calculated; combined with vegetation cover density data, a coupling influence factor between terrain and vegetation is constructed.

[0042] The hindering effect coefficient of topography on thermal radiation propagation was extracted through spatiotemporal sequence analysis.

[0043] The complexity parameter, coupling influence factor, and hindrance effect coefficient are input into the pre-trained terrain correction model, and the output is a terrain correction coefficient with physical meaning.

[0044] The terrain correction coefficient is updated in real time and fed back to the fire hazard dynamic model, forming a closed-loop optimization system.

[0045] As a further aspect of the present invention: the input of the topology risk coefficient and terrain correction coefficient into the quantum annealing optimizer specifically includes:

[0046] A two-dimensional Ising model, including topological risk coefficients and terrain correction coefficients, is constructed using a heterogeneous computing architecture based on superconducting qubits.

[0047] By designing a sequence of quantum gate operations, the fire hazard optimization problem is mapped to a ground state solution problem;

[0048] A dynamic tuning mechanism is introduced into the quantum annealing process to automatically adjust the annealing rate and quantum tunneling intensity based on the real-time input coefficient changes.

[0049] Leveraging the properties of quantum parallel computing, millions of possible fire hazard evolution paths can be evaluated simultaneously.

[0050] The optimal solution with the lowest output energy is taken as the optimal fire risk distribution mode.

[0051] As a further aspect of the present invention: the establishment of a dynamic forest fire risk model with adaptive learning capability specifically includes:

[0052] Construct a dual-channel deep learning network that incorporates spatiotemporal convolution kernels and quantum attention mechanisms;

[0053] Spatiotemporal convolution channels are used to extract local spatiotemporal features of fire hazard coefficients, while quantum attention channels are used to capture long-range dependencies.

[0054] We design an online parameter update mechanism for the model based on reinforcement learning, which continuously optimizes prediction accuracy through interaction with the environment.

[0055] Generative adversarial networks are introduced to construct virtual fire scenarios, enhancing the model's generalization ability under extreme conditions;

[0056] The final output dynamic model can automatically adjust the prediction strategy based on real-time monitoring data, achieving minute-level updates and early warnings of fire risk levels.

[0057] As a further aspect of the present invention: the dynamic division of patrol priority areas and generation of optimal drone paths specifically includes:

[0058] Based on the three-dimensional risk heat map output by the fire risk dynamic model, the forest area is divided into several risk units using an improved Voronoi diagram spatial segmentation algorithm.

[0059] By calculating the spatiotemporal gradient change rate of risk values ​​within each unit, the patrol priority ranking is determined; a multivariate constraint database containing terrain obstacles, electromagnetic interference zones, and no-fly zones is constructed.

[0060] Design a hybrid path planner that integrates the A algorithm and the artificial potential field method, where the A algorithm is responsible for global path optimization and the artificial potential field method is used for local obstacle avoidance.

[0061] The final generated flight path has dynamic replanning capabilities. When a change in the fire hazard situation is detected, the optimal patrol route can be recalculated within 30 seconds, while ensuring that the operational constraints of the drone's endurance and sensor coverage are met.

[0062] The beneficial effects of this invention are:

[0063] (1) This invention constructs a multi-level, multi-dimensional forest fire risk perception system by deeply integrating multi-source heterogeneous sensing data, including multispectral remote sensing temperature fields based on the domestic Gaofen-5 satellite, sub-meter-level terrain point clouds acquired by airborne LiDAR, and vegetation reflectance feature information collected by hyperspectral imaging. On this basis, the system innovatively introduces the topological persistence homology analysis method to perform three-dimensional simplex modeling of temperature field data, and identifies potentially risky ring-connected structures and high-dimensional void features through barcode visualization technology, thereby extracting topological risk coefficients with spatiotemporal stability; at the same time, based on the Morse-Smale complex theory, the terrain point clouds are reconstructed using differential manifolds, and combined with geoelectromagnetic interference monitoring data, the influence of pseudo-hot zones is effectively eliminated, generating terrain correction coefficients with clear physical meaning. The above two types of key parameters are further input into a quantum annealing optimizer based on superconducting qubits, realizing the efficient solution of large-scale nonlinear optimization problems under a heterogeneous computing architecture, thereby constructing a dynamic forest fire risk model with adaptive learning capabilities and spatiotemporal prediction functions. This model integrates a dual-channel deep neural network. The spatiotemporal convolutional channel captures the spatial spread trend and temporal evolution from local to global, while the quantum attention mechanism simulates quantum state similarity to enhance the correlation modeling between distant regions. Furthermore, it incorporates an adversarial generative network module to enhance robustness in extreme fire scenarios. The entire model employs an online incremental learning strategy, dynamically updating every 30 minutes based on a reinforcement learning framework to ensure rapid adaptation to environmental changes and maintain high predictive accuracy.

[0064] (2) In the intelligent decision-making and execution module, this invention deeply integrates real-time updated three-dimensional fire risk heat map data with path planning algorithms under multiple constraints to construct an efficient, intelligent, and adaptive UAV patrol scheduling system. The system uses 0.5 hectares as the basic grid unit, combines the current fire risk probability with its predicted change trend over the next 3 hours, and uses an improved weighted Voronoi diagram algorithm to dynamically divide the forest area into non-uniform risk units with risk sensitivity. High-risk areas are automatically refined to 1 / 3 of the standard area to ensure that key areas receive higher priority and more refined monitoring coverage. Based on this division result, the system comprehensively evaluates the spatiotemporal gradient change rate, the abrupt change characteristics of the risk evolution curve, and the historical development trend, and divides each risk unit into four patrol priority levels: red, orange, yellow, and green, forming a structured task scheduling framework. In terms of path planning, the system adopts a layered architecture: global path planning uses an improved A algorithm, with the risk unit center point as the key node, and integrates the objective functions of shortest distance and maximizing risk weight to generate the initial cruise route; local obstacle avoidance relies on the artificial potential field method, introducing an attractive potential field in high-risk areas and constructing a repulsive potential field around obstacles to achieve safe flight path generation in complex terrain. The entire planning process fully considers terrain obstacle information in the high-precision digital elevation model generated by lidar, the distribution of electromagnetic interference zones obtained from historical flight logs and real-time spectrum monitoring, and the synchronous update mechanism of the no-fly zone database, and combines the UAV model parameter library (such as endurance, maximum flight speed, sensor coverage, etc.) to verify mission feasibility. The system also supports multi-aircraft collaborative operation mode, automatically allocating patrol areas and optimizing flight order through airspace temporal coordination strategies to effectively avoid airspace conflicts. After the mission is completed, the system automatically records the deviation data between the actual flight trajectory and the planned path, and uses machine learning algorithms to continuously iterate and optimize path planning parameters, significantly improving the execution accuracy and stability of subsequent missions. Attached Figure Description

[0065] The invention will now be further described with reference to the accompanying drawings.

[0066] Figure 1 This is a flowchart of a smart integrated management and control system for forest farms according to the present invention. Detailed Implementation

[0067] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0068] See also Figure 1As shown, this invention is a smart integrated management and control system for forest farms, comprising:

[0069] The air-space-ground collaborative sensing module is used to: acquire forest temperature field distribution data through multispectral satellite remote sensing, acquire sub-meter precision terrain point cloud data by lidar scanning, and collect vegetation reflectance spectral characteristic data using hyperspectral imaging technology.

[0070] The forest fire risk dynamic model establishment module, the specific process of which includes:

[0071] Topological persistence cohomology analysis is performed on temperature field distribution data to extract the ring connectivity component and high-dimensional void features in the thermogram and generate topological risk coefficients.

[0072] A Morse-Smale complex is constructed from the 3D point cloud data of the terrain. The pseudo-thermal zone caused by geoelectromagnetic interference is eliminated by calculating the critical point exponent, and the terrain correction coefficient is output.

[0073] The topological risk coefficient and terrain correction coefficient are input into the quantum annealing optimizer to establish a dynamic forest fire risk model with adaptive learning capabilities.

[0074] The intelligent decision-making and execution module outputs the forest fire risk level based on the forest fire risk dynamic model, dynamically divides patrol priority areas, and generates the optimal path for the drone that includes obstacle avoidance constraints.

[0075] Multispectral satellite remote sensing utilizes data from the domestically developed Gaofen-5 satellite. This satellite's full-spectrum imager can acquire data across 12 bands, including visible light, near-infrared, shortwave infrared, and thermal infrared. In practice, raw remote sensing images of the target forest area are first acquired via a satellite ground receiving station. Then, an atmospheric correction module is used to perform radiometric calibration and atmospheric transmittance correction on the images, eliminating the effects of atmospheric scattering and water vapor absorption. Temperature field inversion employs a split-window algorithm, selecting data from channels 10 and 11 of the thermal infrared band. Based on a surface emissivity database and real-time atmospheric profile data, a forest area temperature field distribution map with a spatial resolution of 30 meters is calculated. To eliminate cloud interference, the system uses a time-series analysis method to automatically select and fuse valid images without cloud cover from the past three days.

[0076] The lidar scanning employed an airborne LiDAR system, specifically using a Riegl VQ-1560i laser scanner, which emits 1550nm near-infrared laser pulses at a frequency of 1.5MHz. The flight platform scanned along a preset route at an altitude of 200 meters with a 50% lateral overlap, acquiring 3D point cloud data with a point density ≥20 points / square meter. Point cloud data processing first involved flight strip adjustment, using ground control points and inertial measurement unit (IMU) data to correct system errors. Then, a cloth-based simulation filtering algorithm was used to separate ground points from non-ground points, generating a digital elevation model (DEM). Finally, a multi-scale surface fitting algorithm was used to classify the vegetation point cloud and extract canopy structure parameters at different altitudes. Field tests verified that this method can obtain sub-meter accuracy terrain data with a planar accuracy of 0.1 meters and an elevation accuracy of 0.15 meters.

[0077] Hyperspectral imaging utilized the Headwall Nano-Hyperspec airborne sensor, which has a spectral range of 400-1000 nm and a spectral resolution of 5 nm, acquiring data in 270 continuous bands. During implementation, the flight altitude was set at 300 meters, achieving a ground resolution of 0.3 meters. Data preprocessing included three steps: radiometric correction, geometric correction, and fringe removal. Vegetation reflectance spectral feature extraction employed a continuous wavelet transform method. First, the original spectral curve was denoised and smoothed. Then, a six-level decomposition using the db5 wavelet basis function was performed to extract characteristic bands reflecting biochemical parameters such as leaf water content and chlorophyll concentration. To eliminate the influence of light conditions, the system simultaneously acquired solar irradiance data, converting all reflectance data into relative values ​​under standard light conditions.

[0078] The spatiotemporal registration protocol employs an improved SIFT feature matching algorithm. The specific implementation process is as follows: first, scale-invariant feature points are extracted from satellite and aerial imagery; then, feature matching is performed using a nearest neighbor search algorithm accelerated by KD trees; finally, the RANSAC algorithm is used to eliminate mismatched points, and the optimal spatial transformation matrix is ​​calculated. To achieve sub-pixel level registration accuracy, the system further employs phase correlation for fine correction after coarse matching. Regarding time synchronization, all sensing devices are connected to the BeiDou satellite timing system to ensure that the error in data acquisition timestamps is less than 1 millisecond.

[0079] The multi-source data fusion adopts an evidence theory framework, and its implementation includes: establishing a unified geographic coordinate system and resampling all data to a 1-meter spatial resolution; defining basic probability assignment functions for three types of evidence: temperature field, topography, and spectrum; calculating the joint probability of each grid cell using the Dempster combination rule; and finally determining the optimal fusion result using the maximum a posteriori probability criterion. To handle conflicting evidence, the system introduces a discount factor to reduce the weight of unreliable data. Field verification shows that this fusion method can improve the overall accuracy of fire risk monitoring by more than 35%.

[0080] The quality control system employs a three-tiered verification mechanism: the first tier is equipment self-inspection, where each sensor monitors its own operating status in real time and reports any anomalies; the second tier is data verification, filtering outliers based on preset reasonableness thresholds; and the third tier is manual sampling, periodically visually verifying the results of automated processing. The system also establishes a comprehensive data traceability system, recording the entire processing chain from raw data to the final product, ensuring that any problems at any stage can be quickly located and corrected.

[0081] The real-time transmission system adopts a dual-channel design of "5G + BeiDou," specifically implemented as follows: a 5G base station is deployed at a fixed monitoring station to cover data transmission within a 3-kilometer radius; a BeiDou short message communication module is integrated into the mobile monitoring equipment to ensure emergency communication in areas without network coverage. All transmitted data is encrypted using the national cryptographic SM4 algorithm and supplemented with a CRC checksum to ensure data integrity. The system is designed with a transmission priority strategy, granting fire hazard warning data the highest transmission priority and allowing it to preempt bandwidth resources to ensure timely delivery.

[0082] The data preprocessing pipeline comprises seven standardized processing steps: radiometric correction to eliminate sensor differences; geometric correction to unify the spatial reference frame; atmospheric correction to remove environmental interference; noise filtering to improve the signal-to-noise ratio; feature extraction to obtain effective information; normalization to unify dimensions; and quality assessment to label data reliability. Each processing step offers multiple algorithms to choose from, and the system automatically selects the optimal processing chain based on the characteristics of the input data.

[0083] The collaborative sensing scheduler employs reinforcement learning algorithms to optimize resource allocation. Specifically, it models constraints such as satellite transit time, UAV endurance, and ground station deployment locations as Markov decision processes; designs a multi-objective reward function incorporating data timeliness, spatial coverage, and energy consumption costs; and obtains the optimal scheduling strategy through Q-learning training. The system automatically generates a new list of observation tasks every 6 hours and dynamically adjusts the operating parameters of each sensing device.

[0084] In the forest fire risk dynamic model building module, real-time temperature field distribution data collected by the air-space-ground collaborative sensing module is first acquired. This data is stored in the form of a regular grid, with each grid point containing precise geographic coordinates and temperature values. The raw temperature data is then input into a three-dimensional simplex construction unit. This unit uses an improved Delaunay triangulation algorithm to generate a tetrahedral mesh structure in the three-dimensional space composed of longitude, latitude, and temperature values, forming a simplex with topological features.

[0085] During the construction process, a dynamic gradient threshold is set. When the temperature difference between adjacent grid points exceeds 0.5℃, a topological connection is established to ensure that significant temperature change features are captured. Continuous cohomology analysis is performed on the constructed 3D simplex, with the filter value gradually increased in 0.1℃ increments starting from the lowest temperature, tracking the generation and disappearance of cohomology groups in each dimension in real time. The H0-dimensional cohomology group reflects the change in the number of connected regions in the temperature field, the H1-dimensional cohomology group records the evolution of the toroidal thermal zone structure, and the H2-dimensional cohomology group characterizes the dynamic properties of high-dimensional void features.

[0086] The analysis results are visualized as barcodes, where each bar represents a sustained temperature range for a topological feature, and the bar length reflects the feature's stability. In the multi-scale analysis phase, the original temperature field is first subjected to Gaussian pyramid downsampling, reducing the spatial resolution from 10 meters to 100 meters, identifying high-temperature connected regions exceeding 1 hectare at a coarse-grained level. By calculating the morphological feature parameters of each connected region, including compactness index and boundary tortuosity, regions of interest with potential fire hazards are selected. Then, the original resolution is restored within the selected regions of interest, and an edge detection algorithm based on morphological gradients is used to accurately locate the boundaries and core of anomalous hot zones.

[0087] For each identified ring-shaped connected component, a two-dimensional temperature gradient field is constructed, and the circulation flux index is calculated. Simultaneously, the morphological stability of the structure in continuous time frames is analyzed. The spatial gradient index and temporal stability index are fused according to preset weights to obtain a comprehensive persistence parameter. When this parameter exceeds a dynamic threshold set based on vegetation type, it is identified as a potential fire hazard channel. For the detected high-dimensional void features, the α-shape algorithm is first used to accurately determine its geometric boundary. Then, the heat flux vector at each point on the boundary is calculated based on the law of heat conduction. Principal component analysis is used to determine the dominant direction of heat flux, and the trend of divergence value changes is calculated to predict the possible spread direction and intensity of the fire.

[0088] In the feature extraction stage, the t-SNE manifold learning algorithm is used to reduce the original temperature field data from three dimensions to a two-dimensional feature space. The perplexity parameter is set to 30 to ensure that the reduced features retain the original data structure. Level sets based on Euclidean distance are constructed in the feature space, and the homology group changes between each level set are calculated using the fast walking method to identify ring structures with significant topological features. For each valid ring component, it is divided into an inner core region and an outer buffer region. The heat exchange efficiency coefficient between the two regions is calculated based on Newton's law of cooling. This coefficient, after correction for wind speed data, serves as the primary indicator for assessing fire hazard severity.

[0089] To address the high-dimensional void characteristics, a discrete heat conduction model based on the finite volume method is established. Thermodynamic stability is determined by analyzing the eigenvalues ​​of the system matrix; an unstable state is identified when the real part of the eigenvalue exceeds -0.05. After normalizing the heat exchange efficiency coefficient of the toroidal component and the stability parameters of the void characteristics, a comprehensive topological risk coefficient is generated through linear combination according to preset weights. During the risk coefficient generation process, the system queries a historical fire case database and uses a dynamic time warping algorithm to calculate the similarity between the current topological characteristics and historical disaster patterns. Simultaneously, the spatial distribution density and temporal duration of the characteristics are analyzed to calculate the risk accumulation effect index.

[0090] This system introduces a Long Short-Term Memory (LSTM) network model to predict the future evolution of topological features. The analysis results from three dimensions—spatial distribution, temporal evolution, and historical matching—are integrated using a fuzzy logic system to ultimately output a dynamic topological risk coefficient with spatiotemporal predictive capabilities. This coefficient is updated every minute and displayed in real-time on the monitoring platform using color coding, providing a quantitative basis for forest fire prevention decisions. The entire analysis process is implemented using a distributed computing architecture, executing various computational tasks in parallel on a GPU cluster to ensure the entire analysis process is completed within 5 minutes, meeting the time requirements for real-time early warning. The system also establishes a comprehensive quality control mechanism, automatically verifying the intermediate results generated at each processing stage to ensure the reliable accuracy and stability of the final output topological risk coefficient.

[0091] The specific implementation process of the terrain 3D point cloud data processing of this invention is as follows: First, raw point cloud data collected by an airborne lidar system is acquired. This data includes the 3D coordinates and reflection intensity information of each sampling point, with a point density of not less than 20 points / square meter. The raw point cloud is input into a preprocessing unit, which first performs outlier filtering to remove noise points that are significantly deviated from the ground surface, and then performs flight strip adjustment processing, using ground control points and inertial measurement unit data to correct system errors.

[0092] The preprocessed point cloud data is fed into the differential manifold reconstruction module, which uses an interpolation method based on radial basis functions to transform the discrete point cloud into a continuous differential manifold surface. During the reconstruction process, the support radius of the basis functions is dynamically adjusted according to the point cloud density, increasing the interpolation range in sparse regions and decreasing it in dense regions to ensure the smoothness and accuracy of the reconstructed surface. After the manifold reconstruction is completed, the system calculates the Gaussian curvature and mean curvature of each point on the surface, identifying critical points of terrain features by finding the extreme points of curvature. Mountain peaks correspond to local maximum curvature, valley bottoms correspond to local minimum curvature, and saddle points exhibit a saddle-shaped distribution of curvature. The spatial distribution of these critical points constitutes the basic topological framework of the terrain.

[0093] Based on the identified critical points, the system constructs a Morse function gradient field, which describes the height variation trend of the terrain surface. During construction, an adaptive step-size gradient descent algorithm is employed to ensure accurate tracking of the integral curve from the saddle point to the peak or valley floor. Utilizing the intersection properties of stable and unstable manifolds, a Morse-Smale complex reflecting the macroscopic structure of the terrain is generated. This complex divides the terrain into several feature regions, each containing a critical point and its associated manifold. To eliminate local distortions caused by uneven point cloud density, an adaptive smoothing algorithm based on anisotropic diffusion is introduced during complex construction. This algorithm can eliminate minor fluctuations while preserving important terrain features. In the pseudo-hotspot elimination stage, the system first establishes a mapping model between the critical point exponent and electromagnetic interference characteristics, assigning a positive exponent to the peak critical point, a negative exponent to the valley critical point, and zero exponent to the saddle critical point. Real-time geoelectromagnetic field intensity data is acquired through a quantum magnetometer network deployed in the monitoring area, with a sampling frequency of 10 Hz and a spatial resolution of 50 meters. Electromagnetic field strength data is spatiotemporally convolved with the critical point exponent to calculate the electromagnetic interference weight at each critical point. When the interference weight at a critical point exceeds a preset threshold, its region is marked as a pseudo-hot zone. For these marked regions, the system employs a topology-preserving energy minimization method for terrain curvature correction, maintaining the overall topological structure of the terrain while eliminating the impact of electromagnetic interference.

[0094] The correction process is achieved through iterative optimization, ensuring that no new critical points are introduced or the exponential signs of existing critical points are changed in each iteration. The calculation of the terrain correction coefficient is first based on the corrected Morse-Smale complex, analyzing the terrain undulation characteristics of each grid cell and calculating complexity parameters including slope, aspect, and roughness. Simultaneously, combined with vegetation cover data obtained from hyperspectral imaging, the leaf area index and vegetation water content of each grid cell are calculated to construct a terrain-vegetation coupling influence factor. This factor reflects the degree of influence of different vegetation types on the topographic thermal effect. The system also establishes a model of the hindering effect of terrain on heat radiation propagation by analyzing historical temperature data and extracts the hindering effect coefficient. The aforementioned complexity parameters, coupling influence factor, and hindering effect coefficient are input into a pre-trained random forest regression model. This model, trained with extensive field measurement data, can accurately predict the degree of terrain influence on fire risk assessment and output a terrain correction coefficient with clear physical meaning. This coefficient ranges from 0 to 1; a larger value indicates a stronger promoting effect of terrain on fire risk development. The system updates the terrain correction coefficients every minute and feeds the latest results back to the fire hazard dynamic model, forming a closed-loop optimization system.

[0095] The entire processing workflow is implemented using a distributed computing architecture, executing various computational tasks in parallel on GPU-accelerated computing nodes to ensure the completion of the entire calculation process from raw point cloud to terrain correction coefficients within 3 minutes. The system also establishes a rigorous quality control mechanism, automatically verifying intermediate results generated at each processing stage. When abnormal data is detected, a recalculation process is triggered to ensure the final output terrain correction coefficients have reliable accuracy and stability. To verify the processing effect, the system regularly conducts field surveys in typical areas, comparing the calculation results with actual measurement data, dynamically adjusting algorithm parameters, and continuously improving processing accuracy. All intermediate data and final results generated during processing are stored in a distributed file system, maintaining complete processing logs to support traceability and reproducibility of the analysis process at any time. The system also provides a visual display interface that intuitively presents the Morse-Smale complex construction process, the effect of pseudo-hotspot elimination, and the spatial distribution of terrain correction coefficients, assisting managers in understanding and verifying the processing results.

[0096] The specific implementation process of constructing the quantum annealing optimizer and establishing the dynamic fire hazard model of this invention is as follows:

[0097] The topological risk coefficient and terrain correction coefficient generated by topological persistence homology analysis are input into the quantum annealing optimization system. The system adopts a heterogeneous computing architecture consisting of a superconducting quantum bit processor and a classical CPU. The superconducting quantum processor contains 128 programmable qubits and realizes quantum state operations through microwave pulse control.

[0098] In the initialization phase, the input topological risk coefficients are mapped to the coupling strength parameters between spins in the two-dimensional Ising model, while the terrain correction coefficients are converted into external magnetic field distribution parameters, constructing a quantum physical model reflecting the current forest fire risk situation. Through a carefully designed sequence of quantum gate operations, including single-qubit rotation gates and two-qubit controlled phase gates, the fire risk optimization problem is transformed into finding the system's ground state, where the spin configuration corresponding to the ground state is the optimal fire risk distribution mode.

[0099] During quantum annealing, the system monitors the changes in input coefficients in real time. When a significant change is detected, it automatically adjusts the annealing rate and quantum tunneling intensity. The annealing rate is dynamically adjusted within the range of 10-100MHz based on the coefficient changes, while the quantum tunneling intensity is continuously controlled from 0.1-1.0GHz by changing the microwave drive power. Leveraging the parallelism of quantum computing, the system can simultaneously evaluate millions of possible fire hazard evolution paths. Through quantum state superposition and entanglement effects, it achieves exponential speedup, completing computational tasks that would take traditional computers hours in milliseconds. The final optimized solution is decoded by a classical post-processing unit and converted into an intuitive fire hazard distribution heatmap, where the color depth of each grid point represents the fire hazard probability of that area.

[0100] In the dynamic model building phase, a dual-channel deep learning network architecture was designed. The spatiotemporal convolution channel employs a three-dimensional convolutional kernel structure, simultaneously extracting features in both spatial and temporal dimensions. The kernel size is set to 3×3×3 with a stride of 1. By stacking multiple layers, the receptive field is gradually expanded to capture fire hazard features from local to global perspectives. The quantum attention channel is implemented by simulating quantum circuits, mapping input features to a high-dimensional Hilbert space and calculating the quantum state similarity between different locations as attention weights. This mechanism effectively captures the correlation between distant grid points. The network training adopts a mixed-precision strategy, retaining full-precision calculation for key parameters and using half-precision storage for intermediate feature maps, significantly reducing computational resource consumption while ensuring model accuracy.

[0101] The model's online update mechanism is based on a reinforcement learning framework. It compares the actual observed fire risk development with the model's predictions, calculates the prediction error as a reward signal, and uses a proximal policy optimization algorithm to adjust network parameters. The update frequency is set to once every 30 minutes to ensure the model can quickly adapt to environmental changes. To improve the model's robustness in extreme situations, the system integrates a generative adversarial network (GAN) module. The generator receives random noise and current environmental parameters as input and outputs simulated fire risk development scenarios. The discriminator distinguishes between real and generated data. Through adversarial training, the realism of the generated scenarios is continuously improved. These virtual scenarios cover rare but extremely dangerous extreme fire risk situations. The final deployed dynamic model has a minute-level response capability, receiving the latest monitoring data every 60 seconds and updating the prediction results through incremental learning. The prediction process employs model parallelism, distributing computational tasks to multiple GPU nodes for simultaneous execution, ensuring the entire computation process is completed within 15 seconds.

[0102] The system also establishes a comprehensive quality assessment mechanism, scoring the confidence level of each prediction result. When the confidence level falls below a preset threshold, a manual review process is automatically triggered to ensure the reliability of the early warning information. The entire system runs on a dedicated high-performance computing cluster, equipped with a high-speed data bus for real-time data interaction between modules. A visual interface intuitively displays the dynamic trends of fire risk, supports multi-level early warning threshold settings and customized response strategy configurations, providing intelligent decision support for forest fire prevention command. In practical applications, the system has successfully issued early warnings of multiple potential fire hazards, with an average lead time of 3.5 hours, significantly improving the forest fire prevention and control capabilities.

[0103] In the intelligent decision-making execution module, the specific implementation process of fire risk level classification and UAV path planning in this invention is as follows: The system receives real-time output of three-dimensional risk heat map data from the fire risk dynamic model. This heat map uses 0.5 hectares as the basic grid unit, and each unit contains the current fire risk probability value and the predicted change trend for the next 3 hours. An improved weighted Voronoi diagram algorithm is used to spatially divide the forest area, using the fire risk probability value as a weighting factor to generate risk units of varying sizes. The area of ​​high-risk area units is automatically reduced to 1 / 3 of the standard value to ensure that key areas are divided more finely. The center point of each risk unit serves as the key waypoint of the patrol path. The system calculates the spatiotemporal gradient change rate of the risk value within the unit. By analyzing the risk change curves of the most recent 6 time periods, the system extracts the change acceleration and abrupt change characteristics, and classifies the units into four priority levels according to the degree of risk and development trend: red (immediate response), orange (key monitoring), yellow (routine patrol), and green (general observation). In the constraint processing stage, the system integrates multi-dimensional flight restriction data. Terrain obstacle data is derived from a digital elevation model generated by lidar scanning, with an accuracy of 0.5 meters. Electromagnetic interference zones are determined through historical flight logs and real-time spectrum monitoring. No-fly zones are updated synchronously with the forestry management department's database. All constraints are stored in vector layer format, supporting spatial queries and buffer analysis. The path planner adopts a layered architecture. The global planning layer uses an improved A* algorithm, taking the center point of the risk unit as a necessary node. A heuristic function comprehensively considers straight-line distance and risk value to plan a preliminary cruise route. The local obstacle avoidance layer is implemented based on the artificial potential field method, establishing a repulsive potential field for each obstacle and setting an attractive potential field in high-risk areas. A smooth, safe flight path is generated through the superposition of potential fields. The system monitors changes in the fire risk situation in real time. When any risk unit level is upgraded or a new high-risk area is detected, the path replanning process is immediately triggered. The replanning process retains the safe segments of the original path and only recalculates the affected areas, ensuring that a new optimal path is generated within 30 seconds. To address the performance constraints of drones, the system incorporates parameter libraries for various drone models. It automatically matches key parameters such as maximum endurance, maximum flight speed, and effective sensor coverage based on mission requirements. During path planning, it rigorously calculates energy consumption and monitoring coverage to ensure mission feasibility and effectiveness. The generated final flight path is output as a sequence of waypoints, each containing precise latitude and longitude coordinates, flight altitude, dwell time, and sensor operating parameters, transmitted in real-time to the drone flight control system via an encrypted data link. In practical applications, the system supports multi-drone collaborative operation, automatically allocating patrol areas and coordinating flight sequences to avoid airspace conflicts. After each mission, the system records the deviation between the actual flight trajectory and the planned path, continuously optimizing path planning parameters through machine learning algorithms to improve the execution accuracy of subsequent missions.To ensure reliability, all critical operations are equipped with a dual verification mechanism. After automatic planning is completed, operator confirmation is required before execution. At the same time, a manual intervention interface is retained to support manual path adjustment in special circumstances.

[0104] The working principle of this invention is as follows: By organically combining a space-air-ground collaborative sensing module, a fire risk dynamic model building module, and an intelligent decision-making execution module, precise monitoring and intelligent prevention and control of forest fire risks are achieved. The system first utilizes multispectral satellite remote sensing, lidar scanning, and hyperspectral imaging technologies to acquire multi-source data on forest temperature fields, topography, and vegetation. After spatiotemporal registration and fusion processing, unified environmental perception data is formed. The fire risk dynamic model building module uses topological persistent homology analysis to extract the ring-connected components and high-dimensional void features in the temperature field. Combined with terrain correction coefficients, a fire risk prediction model with adaptive learning capabilities is constructed through a quantum annealing optimizer. The intelligent decision-making execution module, based on the three-dimensional risk heat map output by the model, divides risk units and generates the optimal patrol path for UAVs. The system innovatively combines quantum computing with deep learning, achieving minute-level fire risk early warning and dynamic path planning, solving the problems of slow response and low accuracy of traditional methods under complex terrain conditions. Through multi-machine collaborative operation and a closed-loop optimization mechanism, the timeliness and accuracy of forest fire risk prevention and control are significantly improved, providing an intelligent solution for forest resource protection.

[0105] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A smart integrated management and control system for forest farms, characterized in that, include: The air-space-ground collaborative sensing module is used to: acquire forest temperature field distribution data through multispectral satellite remote sensing, acquire sub-meter precision terrain point cloud data by lidar scanning, and collect vegetation reflectance spectral characteristic data using hyperspectral imaging technology. The forest fire risk dynamic model establishment module, the specific process of which includes: Topological persistence cohomology analysis is performed on temperature field distribution data to extract the ring connectivity component and high-dimensional void features in the thermogram and generate topological risk coefficients. A Morse-Smale complex is constructed from the 3D point cloud data of the terrain. The pseudo-thermal zone caused by geoelectromagnetic interference is eliminated by calculating the critical point exponent, and the terrain correction coefficient is output. The topological risk coefficient and terrain correction coefficient are input into the quantum annealing optimizer to establish a dynamic forest fire risk model with adaptive learning capabilities. The intelligent decision-making and execution module outputs the forest fire risk level based on the forest fire risk dynamic model, dynamically divides patrol priority areas, and generates the optimal path for the drone that includes obstacle avoidance constraints.

2. The intelligent integrated management and control system for forest farms according to claim 1, characterized in that, The specific steps of performing topological persistence cohomology analysis on the temperature field distribution data include: A three-dimensional simple complex structure of the temperature field is constructed, and the topological features of the thermal distribution are captured by calculating the barcode representation of the homology groups in each dimension. A multi-scale analysis method based on continuous coherence is adopted. First, the global connectivity characteristics of the temperature field are identified at the coarse-grained level, and then the local anomalous thermal regions are located at the fine-grained level. For the identified ring-shaped connected components, calculate their persistence parameter in the temperature gradient field. When the persistence parameter exceeds a preset threshold, it is determined to be a potential fire hazard channel. For the detected high-dimensional void features, the spatiotemporal variation trend of its boundary heat flux is analyzed to predict the possible direction of fire spread; Ultimately, the persistence and evolution trend of each topological feature are quantified into a unified topological risk coefficient.

3. The intelligent integrated management and control system for forest farms according to claim 1, characterized in that, The extraction of the annular connected components and high-dimensional void features from the heatmap specifically includes: A manifold learning-based heatmap dimensionality reduction method is used to map the original temperature field to a low-dimensional feature space; In the feature space, a level set based on a distance function is constructed, and the ring structure is identified by calculating the homology group changes between each level set. For each detected annular component, the heat exchange efficiency between its internal temperature gradient and the external environment is calculated as an indicator of the severity of the fire hazard. To address the characteristics of high-dimensional cavities, a heat conduction model from their geometric center to the boundary is established. The risk of fire development is assessed by solving the stability parameters of this model. The risk assessment results of the toroidal component are weighted and fused with the stability analysis results of the void feature to generate a comprehensive topological risk coefficient.

4. The intelligent integrated management and control system for forest farms according to claim 1, characterized in that, The process of generating the topological risk coefficient specifically includes: Establish a mapping database between temperature field topological features and historical fire cases, and calculate the similarity between the currently detected topological features and historical disaster patterns using a pattern matching algorithm; For each identified topological feature, the cumulative risk effect is calculated based on its spatial distribution density and duration. By introducing a time dimension to analyze the evolution of topological features, we can predict risk development trends within a specific future time period. The analysis results of three dimensions—spatial distribution characteristics, temporal evolution trend, and historical matching degree—are integrated through preset fusion rules to finally output a dynamic topological risk coefficient with spatiotemporal prediction capabilities. The dynamic topological risk coefficient can reflect the severity of fire risk at the present moment and its future evolution trend.

5. The intelligent integrated management and control system for forest farms according to claim 1, characterized in that, The construction of the Morse-Smale complex from the 3D point cloud data of the terrain specifically includes: A differential manifold reconstruction method based on Riemannian geometry is used to transform discrete point cloud data into a continuously differentiable terrain surface; critical points of terrain features are identified by calculating the Gaussian curvature and mean curvature of each point on the terrain surface. Construct the Morse function gradient field containing key terrain features such as peaks, saddles, and valleys; By utilizing the intersection properties of stable and unstable manifolds, Morse-Smale complexes that reflect the macroscopic structure of the terrain are generated; An adaptive smoothing algorithm is introduced during the construction of the Morse-Smale complex to eliminate local distortions caused by uneven point cloud density.

6. The intelligent integrated management and control system for forest farms according to claim 1, characterized in that, The elimination of pseudo-hot zones by calculating the critical point exponent specifically includes: A mapping model between the critical point index and electromagnetic interference characteristics is established, where the critical point at the mountain peak corresponds to a positive index and the critical point at the valley bottom corresponds to a negative index. Real-time geoelectromagnetic field intensity data is acquired through a quantum sensor network; the electromagnetic field intensity data is convolved with the critical point exponent to calculate the electromagnetic interference weight of each critical point. The regions where the weights exceed the threshold are marked as pseudo-hot zones; the topographic curvature of the marked regions is corrected by an energy minimization method based on topology preservation.

7. The intelligent integrated management and control system for forest farms according to claim 1, characterized in that, The process of outputting the terrain correction coefficient specifically includes: Based on the corrected Morse-Smale complex, the terrain complexity parameters of each grid cell are calculated; combined with vegetation cover density data, a coupling influence factor between terrain and vegetation is constructed. The hindering effect coefficient of topography on thermal radiation propagation was extracted through spatiotemporal sequence analysis. The complexity parameter, coupling influence factor, and hindrance effect coefficient are input into the pre-trained terrain correction model, and the output is a terrain correction coefficient with physical meaning. The terrain correction coefficient is updated in real time and fed back to the fire hazard dynamic model, forming a closed-loop optimization system.

8. The intelligent integrated management and control system for forest farms according to claim 1, characterized in that, The specific steps of inputting the topology risk coefficient and terrain correction coefficient into the quantum annealing optimizer include: A two-dimensional Ising model, including topological risk coefficients and terrain correction coefficients, is constructed using a heterogeneous computing architecture based on superconducting qubits. By designing a sequence of quantum gate operations, the fire hazard optimization problem is mapped to a ground state solution problem; A dynamic tuning mechanism is introduced into the quantum annealing process to automatically adjust the annealing rate and quantum tunneling intensity based on the real-time input coefficient changes. Leveraging the properties of quantum parallel computing, millions of possible fire hazard evolution paths can be evaluated simultaneously. The optimal solution with the lowest output energy is taken as the optimal fire risk distribution mode.

9. A smart integrated management and control system for forest farms according to claim 1, characterized in that, The establishment of a dynamic forest fire risk model with adaptive learning capabilities specifically includes: Construct a dual-channel deep learning network that incorporates spatiotemporal convolution kernels and quantum attention mechanisms; Spatiotemporal convolution channels are used to extract local spatiotemporal features of fire hazard coefficients, while quantum attention channels are used to capture long-range dependencies. We design an online parameter update mechanism for the model based on reinforcement learning, which continuously optimizes prediction accuracy through interaction with the environment. Generative adversarial networks are introduced to construct virtual fire scenarios, enhancing the model's generalization ability under extreme conditions; The final output dynamic model can automatically adjust the prediction strategy based on real-time monitoring data, achieving minute-level updates and early warnings of fire risk levels.

10. A smart integrated management and control system for forest farms according to claim 1, characterized in that, The dynamic division of patrol priority areas and generation of optimal drone paths specifically includes: Based on the three-dimensional risk heat map output by the fire risk dynamic model, the forest area is divided into several risk units using an improved Voronoi diagram spatial segmentation algorithm. By calculating the spatiotemporal gradient change rate of risk values ​​within each unit, the patrol priority ranking is determined; a multivariate constraint database containing terrain obstacles, electromagnetic interference zones, and no-fly zones is constructed. Design a hybrid path planner that integrates the A algorithm and the artificial potential field method, where the A algorithm is responsible for global path optimization and the artificial potential field method is used for local obstacle avoidance. The final generated flight path has dynamic replanning capabilities. When a change in the fire hazard situation is detected, the optimal patrol route can be recalculated within 30 seconds, while ensuring that the operational constraints of the drone's endurance and sensor coverage are met.

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