Intelligent comprehensive management and control system for forest farm

Abstract

The invention relates to the technical field of intelligent forestry management, and particularly discloses a forest farm intelligent comprehensive management and control system, which comprises a space-air-ground collaborative sensing module, a fire danger dynamic modeling module and an intelligent decision execution module. The method comprises the following steps: acquiring temperature field data through multispectral satellite remote sensing, constructing a high-precision terrain model through a laser radar, extracting vegetation features through hyperspectral imaging, extracting a thermal anomaly structure by adopting topological continuous coherence analysis and a Morse-Smal complex method, and establishing an adaptive fire danger model in combination with a quantum annealing optimizer. The system can generate a fire danger thermodynamic diagram in real time and divide risk levels, and drives the unmanned aerial vehicle to perform priority patrol and obstacle avoidance path planning. The problems of difficulty in pseudo hot area identification, low fire danger prediction precision, non-intelligent path planning and the like in a complex environment are solved, and the intelligent level of forest farm fire prevention and control is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart forestry management, and in particular to a smart integrated management and control system for a forest farm. Background Art

[0002] With the ongoing digital and intelligent transformation of forestry resource management, forest farms, as a vital component of the forest ecosystem, are gradually shifting their management model from traditional manual oversight to intelligent, integrated management and control. Modern forest farms not only shoulder the crucial responsibility of cultivating forest resources and protecting the ecosystem, but they also face multiple risks and threats, including forest fires, pests and diseases, and illegal logging. Especially in the context of intensifying global climate change and the frequent occurrence of extreme weather events, forest fires have become a primary threat to forest farm safety. To enhance forest farms' ability to perceive, warn, and respond to emergencies such as fire risks, there is an urgent need to develop a comprehensive intelligent forest farm management and control system that integrates multi-source data perception, intelligent analysis and modeling, and precise decision-making and execution. Leveraging advanced technologies such as remote sensing, artificial intelligence, quantum computing, and edge computing, this system enables all-weather, all-factor dynamic perception and closed-loop management of the forest environment. This system comprehensively elevates the intelligent capabilities of forest farms in disaster prevention and control, resource allocation, and ecological conservation, and promotes the modernization of forestry governance systems and capabilities.

[0003] The existing technology has the following deficiencies: Existing technologies inadequately model the impact of terrain effects and environmental noise, lacking in-depth analysis of the topological structure of thermal signals. Furthermore, in the process of fusion of multi-source data from space, air, and ground, they fail to fully explore the spatial persistence characteristics and temporal evolution of temperature fields. This paper introduces advanced methods such as topological persistence homology analysis, Morse-Smale complex construction, and quantum annealing optimization. By leveraging high-dimensional topological structures and physical mechanisms, this paper achieves the precise extraction of true fire risk signals and the effective elimination of false hot zones, resolving this long-standing but underappreciated technical challenge in complex forest environments. Summary of the Invention

[0004] The purpose of the present invention is to provide a smart integrated forest management and control system to solve the problems mentioned above.

[0005] The purpose of the present invention can be achieved through the following technical solutions: A forest farm intelligent integrated management and control system, including: An air-ground-air collaborative sensing module, which is used to obtain forest temperature distribution data through multispectral satellite remote sensing, obtain sub-meter-precision terrain point cloud data using lidar scanning, and collect vegetation reflectance spectral feature data using hyperspectral imaging technology; The forest fire risk dynamic model establishment module includes the following specific processes: Perform topological persistence homology analysis on temperature field distribution data, extract the annular connected components and high-dimensional void features in the thermal graph, and generate a topological risk coefficient; Construct a Morse-Smale complex for the terrain 3D point cloud data, eliminate the pseudo hot spots caused by geological electromagnetic interference by calculating the critical point index, and output the terrain correction coefficient; 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. An intelligent decision-making execution module outputs a forest fire risk level based on a dynamic forest fire risk model, dynamically divides patrol priority areas, and generates an optimal path for the drone that includes obstacle avoidance constraints.

[0006] As a further solution of the present invention: the topological persistence coherence analysis of the temperature field distribution data specifically includes: Construct a three-dimensional simplicial complex structure of the temperature field and capture the topological characteristics of the thermal distribution by calculating the barcode representation of the homology group in each dimension; A multi-scale analysis method based on persistent homology is used to first identify the global connectivity characteristics of the temperature field at a coarse-grained level, and then locate local abnormal hot areas at a fine-grained level; For the identified annular connected components, their persistence parameters in the temperature gradient field are calculated. When the persistence parameters exceed the preset threshold, they are identified as potential fire hazard channels. For the detected high-dimensional cavity features, the possible spread direction of the fire is predicted by analyzing the spatiotemporal variation trend of its boundary heat flux; Finally, the persistence and evolution trend of each topological feature are quantified into a unified topological risk coefficient.

[0007] As a further solution of the present invention, the extraction of annular connected components and high-dimensional hole features in the heat map specifically includes: A heat map dimensionality reduction method based on manifold learning is used to map the original temperature field into a low-dimensional feature space; A level set based on distance function is constructed in the feature space, 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 evaluation indicator of the fire hazard severity; Based on the high-dimensional cavity characteristics, a heat conduction model from its geometric center to the boundary is established, and the fire development risk is determined by solving the stability parameters of the model. The risk assessment results of the annular component and the stability analysis results of the cavity characteristics are weighted and fused to generate a comprehensive topological risk coefficient.

[0008] As a further solution of the present invention, the construction of a Morse-Smale complex for the terrain three-dimensional point cloud data specifically includes: The method of differential manifold reconstruction based on Riemannian geometry is used to transform discrete point cloud data into a continuously differentiable terrain surface. The 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 of key terrain including peaks, saddles and valley bottoms; By using the intersection characteristics of stable and unstable manifolds, a Morse-Smale complex reflecting the macroscopic structure of the terrain is generated. An adaptive smoothing algorithm is introduced in the construction process of the Morse-Smale complex to eliminate local distortion caused by uneven point cloud density.

[0009] As a further solution of the present invention, the construction of a Morse-Smale complex for the terrain three-dimensional point cloud data specifically includes: The method of differential manifold reconstruction based on Riemannian geometry is used to transform discrete point cloud data into a continuously differentiable terrain surface. The 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 of key terrain including peaks, saddles and valley bottoms; By using the intersection characteristics of stable and unstable manifolds, a Morse-Smale complex reflecting the macroscopic structure of the terrain is generated. An adaptive smoothing algorithm is introduced in the construction process of the Morse-Smale complex to eliminate local distortion caused by uneven point cloud density.

[0010] As a further solution of the present invention: the elimination of pseudo hot zones by calculating the critical point index specifically includes: A mapping relationship model between the critical point index and the electromagnetic interference characteristics is established, where the peak critical point corresponds to a positive index and the valley critical point corresponds to a negative index; Acquire real-time geological electromagnetic field strength data through quantum sensing networks; perform convolution operations on the electromagnetic field strength data and the critical point index to calculate the electromagnetic interference weight of each critical point; The areas where the critical points with weights exceeding the threshold belong are marked as pseudo hot spots; the energy minimization method based on topology preservation is used to correct the terrain curvature of the marked areas.

[0011] As a further solution of the present invention: the process of outputting the terrain correction coefficient specifically includes: Based on the corrected Morse-Smale complex, the terrain complexity parameter of each grid cell is calculated; combined with the vegetation cover density data, the terrain and vegetation coupling influencing factor is constructed; Through time-space sequence analysis, the blocking effect coefficient of terrain on thermal radiation propagation is extracted; The complexity parameters, coupling influence factors and blocking effect coefficients are input into the pre-trained terrain correction model to output terrain correction coefficients with physical meaning; The terrain correction coefficient is updated in real time and fed back to the fire risk dynamic model to form a closed-loop optimization system.

[0012] As a further solution of the present invention, the inputting of the topological risk coefficient and the terrain correction coefficient into the quantum annealing optimizer specifically includes: Using a heterogeneous computing architecture based on superconducting quantum bits, a two-dimensional Ising model with topological risk coefficients and terrain correction coefficients was constructed; Through the designed quantum gate operation sequence, the fire risk optimization problem is mapped into a ground state solution problem; A dynamic tuning mechanism is introduced during the quantum annealing process to automatically adjust the annealing rate and quantum tunneling intensity according to the real-time input coefficient changes; Leveraging quantum parallel computing capabilities, it simultaneously evaluates millions of possible fire risk evolution paths; The final output optimization solution with the lowest energy is taken as the optimal fire risk distribution pattern.

[0013] As a further solution of the present invention, the establishment of a dynamic forest fire risk model with adaptive learning capabilities specifically includes: Build a dual-channel deep learning network with spatiotemporal convolution kernels and quantum attention mechanism; The spatiotemporal convolution channel is used to extract the local spatiotemporal features of the fire risk coefficient, and the quantum attention channel is used to capture long-range dependencies; Design an online update mechanism for model parameters based on reinforcement learning to continuously optimize prediction accuracy through interaction with the environment; Introducing a generative adversarial network to construct a virtual fire risk scenario, enhancing the model's generalization ability in extreme situations; 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.

[0014] As a further solution of the present invention, the dynamic division of patrol priority areas and generation of optimal drone paths specifically include: 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, patrol priority is determined; a multivariate constraint database including terrain obstacles, electromagnetic interference areas, and no-fly zones is constructed; Design a hybrid path planner that combines 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 the ability to be dynamically replanned. When changes in the fire risk situation are detected, the optimal patrol route can be recalculated within 30 seconds, while ensuring that the operational constraints of the drone's flight time and sensor coverage range are met.

[0015] Beneficial effects of the present invention: (1) This paper constructs a multi-level and multi-dimensional forest fire risk perception system by deeply integrating multi-source heterogeneous perception data, including multispectral remote sensing temperature fields based on the domestically produced Gaofen-5 satellite, sub-meter-level terrain point clouds obtained 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 complex modeling on the temperature field data, and uses barcode visualization technology to identify ring-connected structures and high-dimensional void features with potential risks, thereby extracting topological risk coefficients with spatiotemporal stability; at the same time, based on the Morse-Smale complex theory, the terrain point cloud is reconstructed by differential manifolds, combined with geological electromagnetic interference monitoring data, effectively eliminating the influence of pseudo-hot zones and 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 quantum bits, realizing efficient solution of large-scale nonlinear optimization problems under a heterogeneous computing architecture, thereby constructing a forest fire risk dynamic model with adaptive learning capabilities and spatiotemporal prediction functions. The model integrates a dual-channel deep neural network. A spatiotemporal convolutional channel captures local-to-global spatial spread trends and temporal evolution, while a quantum attention mechanism simulates quantum state similarity to enhance correlation modeling between distant regions. A generative adversarial network module is introduced to enhance robustness in extreme fire scenarios. The entire model utilizes an online incremental learning strategy and is dynamically updated every 30 minutes based on a reinforcement learning framework, ensuring rapid adaptation to environmental changes while maintaining high predictive accuracy.

[0016] (2) In the intelligent decision-making execution module, the present invention deeply integrates the real-time updated three-dimensional fire risk heat map data with the path planning algorithm under multiple constraints to build an efficient, intelligent and adaptive drone patrol scheduling system. The system uses 0.5 hectares as the basic grid unit, combines the current fire risk probability and its predicted change trend in the next 3 hours, and uses an improved weighted Voronoi diagram algorithm to dynamically divide the forest area into different spaces, generating risk-sensitive non-uniform risk units. The high-risk area is automatically refined to 1 / 3 of the standard area to ensure that key areas receive higher priority and more detailed monitoring coverage. Based on this division result, the system divides each risk unit into four patrol priority levels: red, orange, yellow and green, by comprehensively evaluating the spatiotemporal gradient change rate, the mutation characteristics of the risk evolution curve and the historical development trend, forming a structured task scheduling framework. The system employs a layered architecture for path planning. Global path planning utilizes a modified A algorithm, with the center of the risk unit as the key node. The system integrates the objectives of minimizing distance and maximizing risk weight to generate an initial patrol route. Local obstacle avoidance relies on an artificial potential field method, introducing an attractive potential field in high-risk areas and a repulsive potential field around obstacles to generate a safe flight path in complex terrain. The entire planning process fully considers terrain obstacle information from a high-precision digital elevation model generated by lidar, the distribution of electromagnetic interference zones derived from historical flight logs and real-time spectrum monitoring, and a synchronized update mechanism for a no-fly zone database. Mission feasibility is verified by integrating a database of drone model parameters (such as flight time, maximum speed, and sensor coverage). The system also supports multi-drone collaborative operations, automatically allocating patrol areas and optimizing flight sequences through an airspace timing coordination strategy to effectively avoid airspace conflicts. Upon mission completion, the system automatically records deviations between the actual flight trajectory and the planned path. Using machine learning algorithms, it continuously iteratively optimizes path planning parameters, significantly improving the accuracy and stability of subsequent missions. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention will be further described below with reference to the accompanying drawings.

[0018] Figure 1 It is a flow chart of a smart integrated management and control system for a forest farm according to the present invention. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0020] See also Figure 1As shown, the present invention is a forest farm intelligent comprehensive management and control system, including: An air-ground-air collaborative sensing module, which is used to obtain forest temperature distribution data through multispectral satellite remote sensing, obtain sub-meter-precision terrain point cloud data using lidar scanning, and collect vegetation reflectance spectral feature data using hyperspectral imaging technology; The forest fire risk dynamic model establishment module includes the following specific processes: Perform topological persistence homology analysis on temperature field distribution data, extract the annular connected components and high-dimensional void features in the thermal graph, and generate a topological risk coefficient; Construct a Morse-Smale complex for the terrain 3D point cloud data, eliminate the pseudo hot spots caused by geological electromagnetic interference by calculating the critical point index, and output the terrain correction coefficient; 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. An intelligent decision-making execution module outputs a forest fire risk level based on a dynamic forest fire risk model, dynamically divides patrol priority areas, and generates an optimal path for the drone that includes obstacle avoidance constraints.

[0021] Multispectral satellite remote sensing uses data from the domestically produced Gaofen-5 satellite, which carries a full-spectrum spectral imager that can acquire data from 12 bands, including visible light, near-infrared, short-wave infrared, and thermal infrared. In specific implementation, the original remote sensing image of the target forest area is first obtained through the satellite ground receiving station. The atmospheric correction module is then used to perform radiation calibration and atmospheric transmittance correction on the image to eliminate the effects of atmospheric scattering and water vapor absorption. The temperature field inversion uses a split window algorithm, selecting the 10th and 11th channel data of the thermal infrared band. Based on the surface emissivity database and real-time atmospheric profile data, a forest 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 valid images without cloud cover in the past three days for fusion processing.

[0022] LiDAR scanning was performed using an airborne LiDAR system, specifically 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 pre-set route at an altitude of 200 meters and a 50% lateral overlap, acquiring three-dimensional point cloud data with a density of ≥20 points per square meter. Point cloud data processing began with flight-strip adjustment, correcting for system errors using ground control points and inertial measurement unit (IMU) data. A fabric-simulation filtering algorithm was then used to separate ground and 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 structural parameters at different altitudes. Field measurements have verified that this method can produce sub-meter terrain data with a planar accuracy of 0.1 meter and an elevation accuracy of 0.15 meter.

[0023] Hyperspectral imaging uses the Headwall Nano-Hyperspec airborne sensor, which has a spectral range of 400-1000nm and a spectral resolution of 5nm, collecting a total of 270 continuous bands of data. The flight altitude during implementation was set to 300 meters, and the ground resolution reached 0.3 meters. Data preprocessing includes three steps: radiation correction, geometric correction, and stripe removal. The continuous wavelet transform method is used to extract the characteristics of vegetation reflectance spectra. First, the original spectral curve is denoised and smoothed, and then a 6-layer decomposition is performed using the db5 wavelet basis function to extract characteristic bands reflecting biochemical parameters such as leaf moisture content and chlorophyll concentration. In order to eliminate the influence of lighting conditions, the system simultaneously collects solar irradiance data and converts all reflectance data into relative values ​​under standard lighting conditions.

[0024] The spatiotemporal registration protocol utilizes a modified SIFT feature matching algorithm. The specific implementation process is as follows: Scale-invariant feature points are first extracted from satellite and aerial imagery; feature matching is then performed using a nearest neighbor search algorithm accelerated by a KD tree; and finally, the RANSAC algorithm is used to eliminate mismatched points and calculate the optimal spatial transformation matrix. To achieve sub-pixel registration accuracy, the system further utilizes phase correlation for fine correction after completing the coarse matching. For time synchronization, all sensing devices are connected to the Beidou satellite timing system, ensuring that the data acquisition timestamp error is less than 1 millisecond.

[0025] Multi-source data fusion utilizes an evidence-based framework. The specific implementation involves establishing a unified geographic coordinate system and resampling all data to a 1-meter spatial resolution. Basic probability distribution functions are defined for three types of evidence: temperature, topography, and spectrum. The joint probability of each grid cell is calculated using the Dempster combination rule. Finally, the maximum a posteriori probability criterion is used to determine the optimal fusion result. To address conflicting evidence, the system introduces a discount factor to downgrade unreliable data. Field validation has shown that this fusion method can improve the overall accuracy of fire risk monitoring by over 35%.

[0026] Quality control is implemented through a three-level verification mechanism: the first level is equipment self-checking, where each sensor monitors its operating status in real time and reports any anomalies; the second level is data verification, which filters out abnormal values ​​using pre-set reasonableness thresholds; and the third level is manual spot checks, which regularly visually verify the automated processing results. The system also has a comprehensive data traceability system, documenting the entire processing chain from raw data to final product, ensuring that any problems can be quickly located and corrected.

[0027] The real-time transmission system utilizes a dual-channel design combining 5G and Beidou. Specifically, 5G base stations are deployed at fixed monitoring stations, providing data transmission coverage within a 3-kilometer radius. Beidou short message communication modules are integrated into mobile monitoring equipment to ensure emergency communications in areas without network coverage. All transmitted data is encrypted using the national SM4 algorithm and supplemented with a CRC checksum to ensure data integrity. The system incorporates a transmission priority strategy, giving fire warning data the highest transmission privileges and enabling it to preempt bandwidth resources to ensure timely delivery.

[0028] The data preprocessing pipeline includes seven standardized steps: radiometric correction to eliminate sensor variations; 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 valid information; normalization to unify dimensions; and quality assessment to annotate data reliability. Each processing step offers a variety of algorithms to choose from, and the system automatically selects the optimal processing chain based on the characteristics of the input data.

[0029] The collaborative sensing scheduler uses a reinforcement learning algorithm to optimize resource allocation. Specifically, it models constraints such as satellite transit time, drone endurance, and ground station deployment locations as a Markov decision process; designs a multi-objective reward function that incorporates data timeliness, spatial coverage, and energy costs; and uses Q-learning training to determine the optimal scheduling strategy. The system automatically generates a new observation task list every six hours and dynamically adjusts the operating parameters of each sensing device.

[0030] The forest fire risk dynamic modeling module first acquires real-time temperature field distribution data collected by the air-ground-air collaborative sensing module. This data is stored in a regular grid format, with each grid point containing precise geographic coordinates and temperature values. The raw temperature data is then fed into a 3D simplicial complex construction unit, which uses an improved Delaunay triangulation algorithm to generate a tetrahedral mesh structure in the 3D space composed of longitude, latitude, and temperature values, forming a simplicial complex with topological characteristics.

[0031] During the construction process, a dynamic gradient threshold is set, establishing topological connections when the temperature difference between adjacent grid points exceeds 0.5°C, ensuring that significant temperature variation characteristics are captured. A continuous homology analysis is performed on the constructed three-dimensional simplicial complex, gradually increasing the filter value in steps of 0.1°C starting from the lowest temperature, and tracking the generation and disappearance of homology groups in each dimension in real time. The H0-dimensional homology group reflects the changes in the number of connected regions in the temperature field, the H1-dimensional homology group records the evolution of the annular hot zone structure, and the H2-dimensional homology group characterizes the dynamic characteristics of high-dimensional void features.

[0032] The analysis results are visualized in the form of barcodes, where each bar represents a continuous temperature interval of a topological feature, and the length of the bar reflects the stability of the feature. In the multi-scale analysis stage, the original temperature field is first downsampled using a Gaussian pyramid to reduce the spatial resolution from 10 meters to 100 meters, and high-temperature connected areas with an area of ​​more than 1 hectare are identified at a coarse-grained level. By calculating the morphological characteristic parameters of each connected area, including the compactness index and boundary tortuosity, areas of interest with potential fire hazard characteristics are screened out. The original resolution is then restored within the selected area of ​​interest, and an edge detection algorithm based on morphological gradients is used to accurately locate the boundaries and core positions of abnormal hot zones.

[0033] For each identified annular connected component, a two-dimensional temperature gradient field is constructed and the circulation index is calculated. The morphological stability of the structure in continuous time frames is analyzed, and 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 according to the vegetation type, it is determined to be a potential fire channel. For the detected high-dimensional void features, the α-shape algorithm is first used to accurately determine its geometric boundaries. Then, based on the law of heat conduction, the heat flux vectors at each point on the boundary are calculated. Principal component analysis is used to determine the dominant direction of the heat flux, and the trend of the divergence value is calculated to predict the possible spread direction and intensity of the fire.

[0034] During the feature extraction phase, the t-SNE manifold learning algorithm was used to reduce the original temperature field data from a three-dimensional to a two-dimensional feature space. The perplexity parameter was set to 30 to ensure that the reduced features retained the original data structure. Level sets based on Euclidean distance were constructed in the feature space. The homology group changes between each level set were calculated using the fast marching method to identify ring structures with significant topological features. Each valid ring component was divided into an inner core region and an outer buffer region. The heat exchange efficiency coefficient between the two regions was calculated based on Newton's law of cooling. This coefficient, corrected by wind speed data, was used as the primary assessment indicator of fire severity.

[0035] To address high-dimensional void characteristics, a discrete heat conduction model based on the finite volume method was established. Thermodynamic stability was determined by analyzing the eigenvalues ​​of the system matrix. When the real part of the eigenvalue exceeded -0.05, the system was deemed unstable. The heat exchange efficiency coefficient of the annular component and the stability parameter of the void characteristics were normalized and linearly combined according to preset weights to generate a comprehensive topological risk coefficient. During the risk coefficient generation process, the system queries a database of historical fire cases and uses a dynamic time warping algorithm to calculate the similarity between the current topological characteristics and historical disaster patterns. The spatial distribution density and temporal duration of the characteristics were analyzed to calculate the cumulative risk effect index.

[0036] A long-short-term memory network model is introduced to predict the future evolution trend of topological features. The analysis results of the three dimensions of spatial distribution, temporal evolution, and historical matching are integrated through a fuzzy logic system, and finally a dynamic topological risk coefficient with spatiotemporal prediction capabilities is output. This coefficient is updated every minute and displayed in real time on the monitoring platform through color coding, providing a quantitative basis for forest fire prevention decisions. The entire analysis process is implemented using a distributed computing architecture, and various computing tasks are executed in parallel on a GPU cluster to ensure that the entire analysis process is completed within 5 minutes, meeting the time requirements of real-time warning. The system also has a complete quality control mechanism to automatically verify the intermediate results generated by each processing link to ensure that the final output topological risk coefficient has reliable accuracy and stability.

[0037] The specific implementation process for processing 3D terrain point cloud data is as follows: First, raw point cloud data collected by an airborne lidar system is acquired. This data contains the 3D coordinates and reflection intensity information of each sampling point, with a point density of no less than 20 points per square meter. The raw point cloud is then fed into a preprocessing unit, which first performs outlier filtering to remove noise points that significantly deviate from the surface. Then, a flight track adjustment is performed, using ground control points and inertial measurement unit data to correct for systematic errors.

[0038] The preprocessed point cloud data is fed into the differential manifold reconstruction module, which uses an interpolation method based on radial basis functions to convert discrete point clouds into continuous differential manifold surfaces. During the reconstruction process, the support radius of the basis function is dynamically adjusted according to the point cloud density, increasing the interpolation range in sparse areas and reducing it in dense areas to ensure the smoothness and accuracy of the reconstructed surface. After completing the manifold reconstruction, the system calculates the Gaussian curvature and mean curvature of each point on the surface and identifies the critical points of the terrain features by finding the extreme points of curvature. The peak critical point corresponds to the local maximum curvature, the valley critical point corresponds to the local minimum curvature, and the saddle critical point is manifested as a saddle-shaped distribution of curvature. The spatial distribution of these critical points constitutes the basic topological skeleton of the terrain.

[0039] Based on the identified critical points, the system constructs a Morse function gradient field, which describes the elevation trend of the terrain surface. During this construction, an adaptive step-size gradient descent algorithm is used to accurately trace the integral curve from saddle points to peaks or valley bottoms. Leveraging the intersection of stable and unstable manifolds, a Morse-Smale complex is generated, reflecting the terrain's macroscopic structure. This complex divides the terrain into several characteristic regions, each containing a critical point and its associated manifold. To eliminate local distortion caused by uneven point cloud density, an adaptive smoothing algorithm based on anisotropic diffusion is introduced during complex construction. This algorithm maintains important terrain features while eliminating minor fluctuations. During the pseudo-hotspot elimination phase, the system first establishes a mapping model between critical point indices and electromagnetic interference characteristics, assigning positive indices to peak critical points, negative indices to valley bottom critical points, and zero indices to saddle critical points. Real-time geomagnetic field intensity data is acquired through a network of quantum magnetometers deployed throughout the monitoring area, with a sampling frequency of 10 Hz and a spatial resolution of 50 meters. The electromagnetic field intensity data is convolved with the critical point index in a spatiotemporal manner to calculate the electromagnetic interference weight of each critical point. When the interference weight of a critical point exceeds a preset threshold, the area it belongs to is marked as a pseudo-hotspot. For these marked areas, the system uses a topology-preserving energy minimization method to correct terrain curvature, eliminating the effects of electromagnetic interference while maintaining the overall topological structure of the terrain.

[0040] The correction process is achieved through iterative optimization, with each iteration ensuring that no new critical points are introduced or the signs of existing ones are changed. The terrain correction coefficient is calculated by first analyzing the topographic relief characteristics of each grid cell based on the corrected Morse-Smale complex and calculating complexity parameters such as slope, aspect, and roughness. Simultaneously, combined with vegetation cover data acquired through hyperspectral imaging, the leaf area index and vegetation moisture content are calculated for each grid cell, constructing a terrain-vegetation coupling factor. This factor reflects the degree to which different vegetation types influence the terrain thermal effect. The system also analyzes historical temperature data to establish a model for the retardation effect of terrain on thermal radiation propagation and extract the retardation coefficient. These complexity parameters, coupling factors, and retardation coefficients are input into a pretrained random forest regression model. This model, trained with extensive field measurement data, accurately predicts the impact of terrain on fire risk assessment and outputs a terrain correction coefficient with clear physical meaning. This coefficient ranges from 0 to 1, with larger values ​​indicating a greater contribution of terrain to fire risk development. The system updates the terrain correction coefficient every minute and feeds the latest results back to the fire risk dynamic model to form a closed-loop optimization system.

[0041] The entire processing process utilizes a distributed computing architecture, executing various computational tasks in parallel on GPU-accelerated computing nodes. This ensures that the entire process, from raw point cloud to terrain correction coefficients, is completed within three minutes. The system also incorporates a rigorous quality control mechanism that automatically verifies intermediate results generated at each processing step. Detecting abnormal data triggers a recalculation process, ensuring the final output of terrain correction coefficients is reliably accurate and stable. To verify processing effectiveness, the system regularly conducts field surveys in representative areas, comparing the calculated results with actual measured 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 a complete processing log, enabling traceability and reproducibility of the analysis process at any time. The system also provides a visual display interface that intuitively illustrates the construction of the Morse-Smale complex, the elimination of pseudo-hot spots, and the spatial distribution of terrain correction coefficients, helping managers understand and verify processing results.

[0042] The specific implementation process of the quantum annealing optimizer construction and fire risk dynamic model establishment of the present invention is as follows: The topological risk coefficient and topographic correction coefficient generated by topological persistence coherence analysis are input into the quantum annealing optimization system, which adopts a heterogeneous computing architecture consisting of a superconducting quantum bit processor and a classical CPU. The superconducting quantum processor contains 128 programmable quantum bits and realizes quantum state operation through microwave pulse control.

[0043] During the initialization phase, the input topological risk coefficient is mapped to the coupling strength parameter between spins in the two-dimensional Ising model, while the terrain correction coefficient is converted into the external magnetic field distribution parameter. This constructs a quantum physics model that reflects 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 the problem of finding the system's ground state. The spin configuration corresponding to the ground state is the optimal fire risk distribution pattern.

[0044] During the quantum annealing process, the system monitors changes in the input coefficients in real time. When significant changes are detected, the annealing rate and quantum tunneling intensity are automatically adjusted. The annealing rate is dynamically adjusted within the range of 10-100 MHz according to the magnitude of the coefficient change, and the quantum tunneling intensity is continuously controlled from 0.1 to 1.0 GHz by varying the microwave drive power. Leveraging the parallelism of quantum computing, the system can simultaneously evaluate millions of possible fire risk evolution paths, achieving exponential acceleration through quantum state superposition and entanglement effects, completing computational tasks that would take traditional computers hours in milliseconds. The final output of the optimized solution is decoded by a classical post-processing unit and converted into an intuitive heat map of the fire risk distribution, where the color depth of each grid point represents the fire risk probability in that area.

[0045] During the dynamic model construction phase, a dual-channel deep learning network architecture was designed. The spatiotemporal convolution channel utilizes a three-dimensional convolution kernel structure to extract features simultaneously in both spatial and temporal dimensions. The convolution kernel size is set to 3×3×3 with a stride of 1. By stacking multiple layers, the receptive field is gradually expanded, capturing fire hazard characteristics from local to global perspectives. The quantum attention channel is implemented by simulating quantum circuits, mapping input features into a high-dimensional Hilbert space and calculating the quantum state similarity between different locations as attention weights. This mechanism effectively captures correlations between distant grid points. Network training utilizes a mixed-precision strategy, with key parameters computed at full precision and intermediate feature maps stored at half precision. This significantly reduces computational resource consumption while maintaining model accuracy.

[0046] The model's online update mechanism is implemented based on a reinforcement learning framework. It compares actual observed fire risk developments with model 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 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 module. The generator receives random noise and current environmental parameters as input and outputs simulated fire risk development scenarios. The discriminator is responsible for distinguishing between real data 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 predictions through incremental learning. The prediction process uses model parallelism to distribute computing tasks to multiple GPU nodes for simultaneous execution, ensuring that the entire computational process is completed within 15 seconds.

[0047] The system also has a comprehensive quality assessment mechanism, assigning a confidence score to each prediction result. When the confidence score falls below a preset threshold, a manual review process is automatically triggered to ensure the reliability of the warning information. The entire system runs on a dedicated high-performance computing cluster, equipped with a high-speed data bus for real-time data exchange between modules. A visual interface displays dynamic trends in fire risk, supports multi-level warning threshold settings and custom response strategy configuration, and provides intelligent decision-making support for forestry fire prevention and control. In actual use, the system has successfully issued warnings of potential fires multiple times, with an average lead time of 3.5 hours, significantly improving the forestry fire prevention and control capabilities.

[0048] In the intelligent decision-making execution module, the specific implementation process of the present invention's fire risk classification and drone path planning is as follows: The system receives three-dimensional risk heat map data output in real time by the fire risk dynamic model. This heat map uses 0.5-hectare grid cells as the basic unit, and each cell contains the current fire risk probability value and the predicted change trend for the next three hours. An improved weighted Voronoi diagram algorithm is used to spatially partition the forest area, using the fire risk probability value as a weight factor to generate risk cells of varying sizes. The area of ​​cells in high-risk areas is automatically reduced to 1 / 3 of the standard value, ensuring more refined classification of key areas. The center point of each risk cell serves as a key waypoint for the patrol route. The system calculates the spatiotemporal gradient change rate of the risk value within the cell. By analyzing the risk change curves for the last six time periods, the system extracts the acceleration of change and mutation characteristics. Cells are then prioritized according to risk level and development trend: red (immediate action), orange (key monitoring), yellow (routine inspection), and green (general observation). In the constraint processing phase, 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 synchronized with the forestry management database. All constraints are stored as vector layers, supporting spatial queries and buffer analysis. The path planner adopts a layered architecture. The global planning layer uses a modified A* algorithm, using the center of the risk unit as the required 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 using an artificial potential field method, establishing a repulsive potential field for each obstacle and an attractive potential field in high-risk areas. This superposition of potential fields creates a smooth, safe flight path. The system monitors changes in the fire risk situation in real time. If any risk unit level increases 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 recalculates only the affected areas, ensuring that a new optimal path is generated within 30 seconds. In response to the performance constraints of drones, the system has a built-in parameter library for multiple models. It automatically matches key parameters such as the maximum flight time, maximum flight speed, and effective sensor coverage range according to mission requirements. It strictly calculates energy consumption and monitoring coverage during path planning to ensure the feasibility and effectiveness of the mission. The final flight path generated is output as a sequence of waypoints. Each waypoint contains precise latitude and longitude coordinates, flight altitude, dwell time, and sensor operating parameters, which are transmitted to the drone flight control system in real time via an encrypted data link. In actual applications, the system supports multi-machine collaborative operation mode, can automatically allocate patrol areas and coordinate flight timing to avoid airspace conflicts. After each mission is executed, the system will record the deviation data between the actual flight trajectory and the planned path, and continuously optimize the path planning parameters through machine learning algorithms to improve the execution accuracy of subsequent tasks.To ensure reliability, all key operations are equipped with a double 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.

[0049] The present invention operates by integrating an air-space-ground collaborative perception module, a dynamic fire risk modeling module, and an intelligent decision-making and execution module to achieve precise monitoring and intelligent prevention and control of forest fire risks. The system first utilizes multispectral satellite remote sensing, lidar scanning, and hyperspectral imaging technologies to acquire multi-source data on the forest's temperature field, terrain, and vegetation. After spatiotemporal registration and fusion, unified environmental perception data is generated. The dynamic fire risk modeling module uses topological persistence homology analysis to extract circularly connected components and high-dimensional void features in the temperature field. Combined with terrain correction coefficients, it constructs a fire risk prediction model with adaptive learning capabilities using a quantum annealing optimizer. The intelligent decision-making and execution module divides risk units based on the three-dimensional risk heat map output by the model and generates optimal drone patrol routes. The system innovatively combines quantum computing with deep learning to achieve minute-by-minute fire risk warnings and dynamic path planning, addressing the slow response and low accuracy of traditional methods in complex terrain. Through multi-machine collaborative operation and a closed-loop optimization mechanism, the system significantly improves the timeliness and accuracy of forest fire risk prevention and control, providing an intelligent solution for forest resource protection.

[0050] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A forest farm intelligent integrated management and control system, characterized by: include: An air-ground-air collaborative perception module, which is used to obtain forest temperature distribution data through multispectral satellite remote sensing, obtain sub-meter-precision terrain point cloud data using lidar scanning, and collect vegetation reflectance spectral feature data using hyperspectral imaging technology; The forest fire risk dynamic model establishment module includes the following specific processes: Perform topological persistence homology analysis on temperature field distribution data, extract the annular connected components and high-dimensional void features in the thermal graph, and generate a topological risk coefficient; Construct a Morse-Smale complex for the terrain 3D point cloud data, eliminate the pseudo hot spots caused by geological electromagnetic interference by calculating the critical point index, and output the terrain correction coefficient; 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. An intelligent decision-making execution module outputs a forest fire risk level based on a dynamic forest fire risk model, dynamically divides patrol priority areas, and generates an optimal path for the drone that includes obstacle avoidance constraints.

2. A forest farm intelligent integrated management and control system according to claim 1, characterized in that: The topological persistence coherence analysis of the temperature field distribution data specifically includes: Construct a three-dimensional simplicial complex structure of the temperature field and capture the topological characteristics of the thermal distribution by calculating the barcode representation of the homology group in each dimension; A multi-scale analysis method based on persistent homology is used to first identify the global connectivity characteristics of the temperature field at a coarse-grained level, and then locate local abnormal hot areas at a fine-grained level; For the identified annular connected components, their persistence parameters in the temperature gradient field are calculated. When the persistence parameters exceed the preset threshold, they are identified as potential fire hazard channels. For the detected high-dimensional cavity features, the possible spread direction of the fire is predicted by analyzing the spatiotemporal variation trend of its boundary heat flux; Finally, 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 is characterized in that: The extraction of the annular connected components and high-dimensional hole features in the heat map specifically includes: A heat map dimensionality reduction method based on manifold learning is used to map the original temperature field into a low-dimensional feature space; A level set based on distance function is constructed in the feature space, 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 evaluation indicator of the fire hazard severity; Based on the high-dimensional cavity characteristics, a heat conduction model from its geometric center to the boundary is established, and the fire development risk is determined by solving the stability parameters of the model. The risk assessment results of the annular component and the stability analysis results of the cavity characteristics are weighted and fused to generate a comprehensive topological risk coefficient.

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

5. The intelligent integrated management and control system for forest farms according to claim 1 is characterized in that: The construction of the Morse-Smale complex for the terrain three-dimensional point cloud data specifically includes: The method of differential manifold reconstruction based on Riemannian geometry is used to transform discrete point cloud data into a continuously differentiable terrain surface. The 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 of key terrain including peaks, saddles and valley bottoms; By using the intersection characteristics of stable and unstable manifolds, a Morse-Smale complex reflecting the macroscopic structure of the terrain is generated. An adaptive smoothing algorithm is introduced in the construction process of the Morse-Smale complex to eliminate local distortion caused by uneven point cloud density.

6. The intelligent integrated management and control system for forest farms according to claim 1 is characterized in that: Eliminating the pseudo hot zone by calculating the critical point index specifically includes: A mapping relationship model between the critical point index and the electromagnetic interference characteristics is established, where the peak critical point corresponds to a positive index and the valley critical point corresponds to a negative index; Acquire real-time geological electromagnetic field strength data through quantum sensing networks; perform convolution operations on the electromagnetic field strength data and the critical point index to calculate the electromagnetic interference weight of each critical point; The areas where the critical points with weights exceeding the threshold belong are marked as pseudo hot spots; the energy minimization method based on topology preservation is used to correct the terrain curvature of the marked areas.

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

8. The intelligent integrated management and control system for forest farms according to claim 1 is characterized in that: The step of inputting the topological risk coefficient and the terrain correction coefficient into the quantum annealing optimizer specifically includes: Using a heterogeneous computing architecture based on superconducting quantum bits, a two-dimensional Ising model with topological risk coefficients and terrain correction coefficients was constructed; Through the designed quantum gate operation sequence, the fire risk optimization problem is mapped into a ground state solution problem; A dynamic tuning mechanism is introduced during the quantum annealing process to automatically adjust the annealing rate and quantum tunneling intensity according to the real-time input coefficient changes; Leveraging quantum parallel computing capabilities, it simultaneously evaluates millions of possible fire risk evolution paths; The final output optimization solution with the lowest energy is taken as the optimal fire risk distribution pattern.

9. The intelligent integrated management and control system for forest farms according to claim 1 is characterized in that: The establishment of a dynamic forest fire risk model with adaptive learning capabilities specifically includes: Build a dual-channel deep learning network with spatiotemporal convolution kernels and quantum attention mechanism; The spatiotemporal convolution channel is used to extract the local spatiotemporal features of the fire risk coefficient, and the quantum attention channel is used to capture long-range dependencies; Design an online update mechanism for model parameters based on reinforcement learning to continuously optimize prediction accuracy through interaction with the environment; Introducing a generative adversarial network to construct a virtual fire risk scenario, enhancing the model's generalization ability in extreme situations; 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. The intelligent 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 include: 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, patrol priority is determined; a multivariate constraint database including terrain obstacles, electromagnetic interference areas, and no-fly zones is constructed; Design a hybrid path planner that combines 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 the ability to be dynamically replanned. When changes in the fire risk situation are detected, the optimal patrol route can be recalculated within 30 seconds, while ensuring that the operational constraints of the drone's flight time and sensor coverage range are met.

Images