Forest fuel load assessment and intelligent matching decision method for fungus consumption scheme

Abstract

The present application provides a kind of forest combustible load assessment and fungus consumption scheme intelligent matching decision method, it is related to forest fire prevention and ecological restoration technical field, including by remote sensing image vertical section identification combustible and constructs layered load distribution, future load distribution is predicted in combination with meteorological data.Through matching environment condition screening adaptation fungus, forest area is divided into grid unit, in combination with load prediction and fungus degradation data, for each grid generates the multiple fungus collaborative delivery configuration that satisfies time limit requirement.The present application realizes the fine prediction of combustible load and the automatic matching decision of efficient biological degradation scheme.

Description

Technical Field

[0001] This invention relates to the field of forest fire prevention and ecological restoration technology, and in particular to a method for intelligent matching and decision-making of forest combustible load assessment and fungal disposal schemes. Background Technology

[0002] Assessing forest combustible load and fungal depletion are important technical directions in forest fire prevention and ecological management. Current technologies typically rely on manual ground surveys or horizontal two-dimensional analysis based on remote sensing imagery to assess forest surface combustible load. Ground surveys, through on-site sampling and measurement using quadrats, can obtain relatively accurate data, but are time-consuming and labor-intensive, making large-scale, high-frequency dynamic monitoring difficult. Horizontal analysis based on remote sensing imagery mainly uses spectral information from satellite or aerial imagery to invert surface vegetation cover or biomass, thereby estimating combustible load. This method has a wide coverage area, but primarily reflects comprehensive information about the surface or canopy.

[0003] However, most existing remote sensing analysis methods focus on extracting information in the horizontal dimension, treating forest vegetation as a whole or a single layer. Forest combustibles exhibit a significant stratified structure in vertical space, with substantial differences in combustible types, densities, moisture content, and fire hazard levels across different layers. Traditional two-dimensional horizontal analysis results in overly generalized load assessments, failing to accurately reflect the true distribution and accumulation of combustibles in vertical space, thus affecting the accuracy of subsequent fire risk warnings and resource allocation. Regarding the decision-making process for utilizing fungi for combustible biodegradation, existing methods typically formulate deployment plans based on static, historically averaged environmental data and fixed fungal degradation parameters, often selecting fungal species based on experience or simple regional matching, and employing a uniform deployment strategy. This approach ignores the dynamic evolution of the forest environment, particularly key meteorological factors such as temperature, humidity, and precipitation, and their temporal impact on litter formation and fungal activity.

[0004] This static decision-making model has inherent flaws; it fails to effectively link the future dynamic changes in combustible load with the timeliness requirements of fungal degradation. The accumulation of forest litter is a dynamic process driven by climate cycles (such as alternations between drought and wet periods), and the degradation efficiency of different fungi is highly dependent on specific environmental conditions. Due to a lack of in-depth analysis and prediction of meteorological evolution patterns, it is impossible to proactively estimate the increase in combustible load in various regions during specific future periods. This results in a lack of foresight in the time dimension of the fungal release program, potentially leading to insufficient fungal activity during the degradation window or a mismatch with the peak period of combustible accumulation. Consequently, it becomes difficult to achieve effective disposal targets within the preset timeframe, affecting the overall controllability and execution efficiency of the program. Summary of the Invention

[0005] This invention provides a method and system for intelligent matching and decision-making of forest combustible load assessment and fungal disposal scheme, which can solve the problems in the prior art.

[0006] A first aspect of this invention provides a method for intelligent matching and decision-making between forest combustible load assessment and fungal disposal schemes, comprising:

[0007] Acquire remote sensing imagery and meteorological monitoring data of the target forest area;

[0008] By vertically slicing remote sensing image data according to the height dimension and identifying the distribution of combustible pixels by spectral feature matching, a vertically layered load spatial representation is constructed based on geometric boundary features and cross-layer vertical propagation features, generating layered load distribution data.

[0009] By extracting meteorological evolution characteristics from meteorological monitoring data, identifying the alternating patterns of drought stress cycles and wet recovery cycles, generating the future litter increment distribution, and spatiotemporally fusing it with stratified load distribution data, the load prediction distribution for future periods is obtained.

[0010] Degradation rate data and environmental condition data of candidate bacteria were obtained. By adaptively matching the environmental condition data with meteorological monitoring data, environmentally suitable bacteria were screened out.

[0011] The target forest area is divided into multiple spatial grid units;

[0012] For each spatial grid cell, by combining the load prediction distribution with the degradation rate data of environmentally adaptable bacteria, bacteria are screened to construct a multi-bacterial collaborative degradation scheme that meets the preset time limit constraints, and a bacteria deployment configuration is generated.

[0013] Output the fungal deployment configuration for each spatial grid unit.

[0014] In one optional embodiment, by vertically slicing remote sensing image data according to the height dimension, and combining spectral feature matching to identify the distribution of combustible material pixels, a vertically layered load spatial representation is constructed based on geometric boundary features and cross-layer vertical propagation features to generate layered load distribution data, including:

[0015] The remote sensing image data is vertically sliced ​​at preset height intervals. The multi-band spectral reflectance of each height slice is extracted. The multi-band spectral reflectance is matched with a preset vegetation spectral feature library for similarity. Pixels belonging to the combustible material category in each height slice are identified, and a combustible material pixel distribution map of each height slice is generated.

[0016] Spatial neighborhood connectivity detection is performed on the combustible pixel distribution map, and spatially adjacent combustible pixels are merged into candidate patches. The biomass density coefficient is generated by combining the geometric boundary features and cross-layer vertical propagation features of the candidate patches.

[0017] By coupling the patch area of ​​each candidate patch with the corresponding biomass density coefficient, the load value of each candidate patch is obtained. The load value is then bound to the height level and planar coordinates of each candidate patch in three-dimensional space to construct a load spatial representation structure containing vertical stratification information and horizontal spatial location information, thereby generating stratified load distribution data.

[0018] In one optional embodiment, spatial neighborhood connectivity detection is performed on the combustible pixel distribution map, spatially adjacent combustible pixels are merged into candidate patches, and the biomass density coefficient is generated by combining the geometric boundary features and cross-layer vertical propagation features of the candidate patches, including:

[0019] Perform same-layer spatial neighborhood connectivity detection on the combustible pixel points in the combustible pixel distribution map, identify spatially adjacent combustible pixel clusters within the same height slice, mark each combustible pixel cluster as a candidate patch, extract the geometric boundary of each candidate patch and calculate the boundary complexity index.

[0020] Extract the spatial overlap area of ​​candidate patches between adjacent height slices on the horizontal projection plane, calculate the overlap area ratio of each spatial overlap area, and extract the spectral reflectance difference between upper and lower candidate patches in each spatial overlap area. Perform weighted fusion calculation on the overlap area ratio and spectral reflectance difference to identify cross-layer connected patch groups with the potential for vertical fire spread. Calculate the corresponding height layer number and total vertical span for each cross-layer connected patch group to generate a vertical spread risk index.

[0021] The vertical propagation risk index and the boundary complexity index are weighted together to generate a comprehensive fire risk coefficient. The comprehensive fire risk coefficient is then used as a correction factor to perform risk-weighted transformation on the spectral statistical characteristics of each candidate patch, generating a biomass density coefficient that considers spatial morphological characteristics and vertical propagation characteristics.

[0022] In one optional embodiment, by extracting meteorological evolution characteristics from meteorological monitoring data, identifying the alternating pattern of drought stress cycles and wet recovery cycles, generating the future litter increment distribution, and spatiotemporally fusing it with stratified litter load distribution data, the predicted litter load distribution for future periods is obtained, including:

[0023] The temperature variation range, humidity variation range, and precipitation fluctuation range between adjacent time periods are extracted from meteorological monitoring data and combined into a meteorological evolution feature vector;

[0024] The temporal phase relationship between temperature change amplitude and precipitation fluctuation amplitude in the meteorological evolution feature vector was identified, the alternation pattern of drought stress cycle and wet recovery cycle was determined, the duration of vegetation water deficit in the drought stress cycle was extracted, and the vegetation leaf fall intensity value was determined. The soil moisture recovery rate in the wet recovery cycle was extracted, and the degree of inhibition of litter microbial decomposition was determined. The difference between the vegetation leaf fall intensity value and the degree of inhibition of litter microbial decomposition value was superimposed to generate the litter net accumulation rate distribution at different height levels.

[0025] Extract the combustible space connectivity paths of each height slice in the stratified load distribution data, identify the height slice locations where the combustible space connectivity paths are interrupted in the vertical direction, determine the future fallow increment values ​​to be preferentially allocated to the height slices corresponding to the interruption locations based on the net cumulative rate distribution of fallen debris and the load compensation capacity, generate the future fallow increment distribution, and overlay the future fallow increment distribution with the stratified load distribution data in three-dimensional space to generate the load prediction distribution for future periods.

[0026] In one optional embodiment, extracting the combustible space connectivity path of each height slice in the stratified load distribution data, and identifying the height slice location where the combustible space connectivity path is interrupted in the vertical direction includes:

[0027] Vertical projection overlap detection is performed on the combustible pixels between adjacent height slices in the layered load distribution data, and the overlap area ratio of combustible pixels of the upper and lower height slices on the horizontal projection plane is calculated.

[0028] When the overlap area ratio is lower than the preset connectivity determination threshold, the location between the upper height slice and the lower height slice is marked as the interruption position of the combustible space connectivity path. The load density value of the lower height slice corresponding to the interruption position is extracted, the difference between the load density value and the preset load saturation threshold is calculated, and the load compensation capacity is determined.

[0029] Extract the net cumulative rate value of litter corresponding to the lower height slice from the net cumulative rate distribution of litter. Calculate the natural increment of litter based on a preset time window. When the natural increment of litter does not exceed the load compensation capacity, the natural increment of litter is used as the priority allocation value. When the natural increment of litter exceeds the load compensation capacity, the load compensation capacity is used as the priority allocation value. Generate future litter increment values ​​that are prioritized for allocation to the lower height slice.

[0030] In an optional embodiment, for each spatial grid cell, by combining the load prediction distribution with the degradation rate data of environmentally adapted bacteria, a multi-bacterial collaborative degradation scheme that meets preset time constraints is selected to generate a bacterial deployment configuration, including:

[0031] The load value and litter matrix component ratio of each spatial grid cell are extracted from the load prediction distribution. The target degradation rate threshold of the spatial grid cell is calculated by combining the preset time limit constraint with the load value. The litter matrix component ratio is cross-matched with the matrix degradation preference spectrum of each fungus in the degradation rate data of environmentally adapted fungi to calculate the contribution value of each fungus to the degradation efficiency of the spatial grid cell.

[0032] The environmentally adaptable bacteria are sorted in descending order according to their degradation efficiency contribution value to generate a candidate sequence of bacteria. Bacteria are selected from the candidate sequence in turn, and the marginal efficiency is calculated based on the competition relationship between bacteria to screen the cumulative degradation rate value of bacteria. The selected bacterial combinations whose cumulative degradation rate value reaches the target degradation rate threshold are recorded as a synergistic degradation scheme.

[0033] The degradation rate values ​​of different matrix types in the proportion of litter matrix components of each fungus in the collaborative degradation scheme are extracted. The degradation task sharing ratio of each fungus in the spatial grid cell is calculated. Based on the degradation task sharing ratio and load value, the release density requirement of each fungus is calculated. The release density requirement is bound to the spatial coordinates of the spatial grid cell to generate the fungus release configuration of the spatial grid cell.

[0034] In one optional embodiment, environmentally adaptable bacteria are sorted in descending order according to their degradation efficiency contribution value to generate a bacterial candidate sequence. Bacteria are then selected sequentially from the candidate sequence, and the marginal efficiency is calculated based on the competition relationship between bacteria to screen for cumulative degradation rate values. Selected bacterial combinations whose cumulative degradation rate values ​​reach the target degradation rate threshold are recorded as collaborative degradation schemes.

[0035] Select the fungus that ranks first in the candidate fungal sequence and record its corresponding degradation rate as the current cumulative degradation rate and the previous cumulative degradation rate.

[0036] Select the next candidate bacteria, add the degradation rate of the next candidate bacteria to the current cumulative degradation rate to obtain the superimposed degradation rate, identify the competing bacteria in the selected bacteria combination that have a matrix competition relationship with the next candidate bacteria, count the number of competing bacteria, obtain the corresponding competition inhibition coefficient from the preset competition inhibition curve based on the number of competing bacteria, and use the competition inhibition coefficient to subtract the superimposed degradation rate to obtain the updated cumulative degradation rate.

[0037] The difference between the updated cumulative degradation rate and the previous cumulative degradation rate is calculated as the marginal degradation contribution. The marginal efficiency is obtained by calculating the ratio of the marginal degradation contribution to the degradation rate of the next candidate bacteria.

[0038] When the marginal efficiency is lower than the preset marginal admission threshold, skip the next candidate bacteria; when the marginal efficiency is not lower than the preset marginal admission threshold and the updated cumulative degradation rate has not reached the target degradation rate threshold, include the next candidate bacteria in the selected bacteria combination, update the previous round of cumulative degradation rate to the updated cumulative degradation rate, and continue to select the next candidate bacteria until the target degradation rate threshold is reached. Record the selected bacteria combination as a collaborative degradation scheme.

[0039] A second aspect of this invention provides an intelligent matching decision-making system for assessing forest combustible load and fungal disposal schemes, comprising:

[0040] The data acquisition unit is used to acquire remote sensing image data and meteorological monitoring data of the target forest area;

[0041] The layered load unit is used to identify the distribution of combustible pixels by vertically slicing remote sensing image data according to the height dimension, combined with spectral feature matching, and to construct a vertically layered load spatial representation based on geometric boundary features and cross-layer vertical propagation features, thereby generating layered load distribution data.

[0042] The litter load prediction unit is used to extract meteorological evolution characteristics from meteorological monitoring data, identify the alternating patterns of drought stress cycles and wet recovery cycles, generate the future litter load increment distribution, and perform spatiotemporal fusion with stratified litter load distribution data to obtain the litter load prediction distribution for future periods.

[0043] The fungal screening unit is used to acquire degradation rate data and environmental condition data of candidate fungi. By performing adaptive matching verification between environmental condition data and meteorological monitoring data, environmentally suitable fungi are screened out.

[0044] Grid division unit is used to divide the target forest area into multiple spatial grid units;

[0045] The scheme generation unit is used to select fungi for each spatial grid cell by combining the load prediction distribution and the degradation rate data of environmentally adaptable fungi, construct a multi-fungus collaborative degradation scheme that meets the preset time limit constraints, and generate a fungi deployment configuration.

[0046] Configure the output unit to output the fungal deployment configuration for each spatial grid unit.

[0047] A third aspect of the present invention provides an electronic device, comprising:

[0048] processor;

[0049] Memory used to store processor-executable instructions;

[0050] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0051] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0052] In this embodiment of the invention, the vertical spatial distribution characteristics of forest combustible load are combined with meteorologically driven litter increase prediction to achieve a refined and forward-looking assessment of future fire hazards. Through vertical slicing and spectral feature recognition technology, the system can penetrate the canopy surface and effectively identify combustible pixels at different vertical heights, overcoming the limitations of traditional horizontal remote sensing assessments. This constructs a layered spatial representation of the load that more closely resembles the actual three-dimensional distribution, significantly improving the dimensionality and accuracy of the load assessment. Furthermore, the introduction of meteorological evolution feature analysis identifies the alternation patterns of drought and wet cycles, thereby predicting the spatiotemporal increase of future litter, achieving a leap from static status quo assessment to dynamic trend prediction, enabling the predicted load distribution to reflect… The accumulation process of combustibles is driven by meteorological conditions. By matching and verifying the environmental requirements of candidate fungi with actual and predicted meteorological data of forest areas, it is ensured that the selected environmentally suitable fungi have the potential to survive and perform degradation functions in the target area. By coupling the load prediction distribution, gridded spatial units and fungal degradation rate, a multi-fungus collaborative scheme that meets specific cleanup time requirements can be constructed in each grid, realizing the accurate and efficient allocation of degradation resources and load disposal needs. The final output gridded fungal deployment and configuration scheme has both spatial targeting and temporal predictability. It not only considers the current three-dimensional distribution of combustibles, but also incorporates future incremental predictions and matches the dynamic capabilities of biodegradation. Attached Figure Description

[0053] Figure 1 A flowchart illustrating the intelligent matching decision-making method for forest combustible load assessment and fungal disposal schemes;

[0054] Figure 2 This is a flowchart of meteorological evolution and load prediction. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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.

[0056] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0057] Figure 1 This is a flowchart illustrating the intelligent matching decision-making method for forest combustible load assessment and fungal disposal schemes according to an embodiment of the present invention. Figure 1 As shown, the intelligent matching decision-making method for forest combustible load assessment and fungal disposal schemes includes:

[0058] Acquire remote sensing imagery and meteorological monitoring data of the target forest area;

[0059] By vertically slicing remote sensing image data according to the height dimension and identifying the distribution of combustible pixels by spectral feature matching, a vertically layered load spatial representation is constructed based on geometric boundary features and cross-layer vertical propagation features, generating layered load distribution data.

[0060] By extracting meteorological evolution characteristics from meteorological monitoring data, identifying the alternating patterns of drought stress cycles and wet recovery cycles, generating the future litter increment distribution, and spatiotemporally fusing it with stratified load distribution data, the load prediction distribution for future periods is obtained.

[0061] Degradation rate data and environmental condition data of candidate bacteria were obtained. By adaptively matching the environmental condition data with meteorological monitoring data, environmentally suitable bacteria were screened out.

[0062] The target forest area is divided into multiple spatial grid units;

[0063] For each spatial grid cell, by combining the load prediction distribution with the degradation rate data of environmentally adaptable bacteria, bacteria are screened to construct a multi-bacterial collaborative degradation scheme that meets the preset time limit constraints, and a bacteria deployment configuration is generated.

[0064] Output the fungal deployment configuration for each spatial grid unit.

[0065] In one optional embodiment, by vertically slicing remote sensing image data according to the height dimension, and combining spectral feature matching to identify the distribution of combustible material pixels, a vertically layered load spatial representation is constructed based on geometric boundary features and cross-layer vertical propagation features to generate layered load distribution data, including:

[0066] The remote sensing image data is vertically sliced ​​at preset height intervals. The multi-band spectral reflectance of each height slice is extracted. The multi-band spectral reflectance is matched with a preset vegetation spectral feature library for similarity. Pixels belonging to the combustible material category in each height slice are identified, and a combustible material pixel distribution map of each height slice is generated.

[0067] Spatial neighborhood connectivity detection is performed on the combustible pixel distribution map, and spatially adjacent combustible pixels are merged into candidate patches. The biomass density coefficient is generated by combining the geometric boundary features and cross-layer vertical propagation features of the candidate patches.

[0068] By coupling the patch area of ​​each candidate patch with the corresponding biomass density coefficient, the load value of each candidate patch is obtained. The load value is then bound to the height level and planar coordinates of each candidate patch in three-dimensional space to construct a load spatial representation structure containing vertical stratification information and horizontal spatial location information, thereby generating stratified load distribution data.

[0069] In one specific implementation, after acquiring remote sensing image data of the target forest area, the data is vertically sliced ​​according to the height dimension. The remote sensing image data is typically acquired using LiDAR or airborne synthetic aperture radar (SAR) and includes reflection signals from different altitude layers above the ground. A preset height interval is set as... This value is determined based on the vegetation stratification characteristics of the target forest area. For temperate deciduous broad-leaved forests, The standard height is typically set between 2 and 5 meters. However, due to the complex vertical structure of tropical rainforests... It can be set from 1 meter to 3 meters. Height slices are generated sequentially from the ground surface upwards according to this height interval, with each slice corresponding to a height range.

[0070] For each height slice, its multi-band spectral reflectance data is extracted. Multi-band spectral reflectance includes reflectance values ​​in the visible light band, near-infrared band, and short-wave infrared band. Specifically, for each pixel within the slice, the reflectance values ​​in the blue light band (wavelength approximately 450 nm to 495 nm), green light band (wavelength approximately 495 nm to 570 nm), red light band (wavelength approximately 620 nm to 750 nm), near-infrared band (wavelength approximately 750 nm to 1400 nm), and short-wave infrared band (wavelength approximately 1400 nm to 3000 nm) are read to form the multi-band spectral vector for that pixel.

[0071] The extracted multi-band spectral reflectance is matched against a pre-defined vegetation spectral feature library for similarity. This library stores standard spectral feature curves for different vegetation types and combustible material types, including spectral features of combustible material categories such as dead branches, dead leaves, dry grass, and shrubs. Similarity matching is achieved using either the spectral angle mapping (SAM) algorithm or the Euclidean distance algorithm. For each pixel... spectral vector ,in This indicates the number of bands, relative to the types of combustibles in the feature library. Standard spectral vector Calculate spectral angles :

[0072] ;

[0073] When the spectral angle Less than the preset threshold When this happens, the pixel is determined to belong to the flammable material category. (Preset threshold) The value is set according to the required recognition accuracy, typically ranging from 0.1 radians to 0.3 radians. After matching and determining all pixels, a pixel distribution map of combustible materials for each height slice is generated. This distribution map is stored in the form of a binary matrix, where a pixel value of 1 represents combustible material and a pixel value of 0 represents non-combustible material.

[0074] Spatial neighborhood connectivity detection is performed on the generated combustible material pixel distribution map. Spatial neighborhood connectivity detection is based on the eight-neighbor connectivity criterion, which determines the connectivity between each combustible material pixel and its eight neighboring pixels. A connected component labeling algorithm is used to scan the combustible material pixel distribution map, marking spatially adjacent combustible material pixels as the same connected component. After connected component labeling, each connected component constitutes a candidate patch. Candidate patches represent spatially continuous combustible material accumulation areas.

[0075] For the identified candidate patches, geometric boundary features and cross-layer vertical propagation features are extracted. Geometric boundary features include the patch's perimeter, area, shape index, and boundary complexity. Patch perimeter... The patch area is obtained by counting the number of pixels at the patch boundary and multiplying it by the pixel resolution. Shape index is obtained by counting the total number of pixels within a patch and multiplying it by the area per unit pixel. The formula used to describe the regularity of patch shape is:

[0076] ;

[0077] Shape Index The closer the value is to 1, the closer the patch shape is to a circle; the larger the value, the more irregular the patch shape. Boundary complexity The fractal dimension is used to reflect the tortuosity of the patch boundary. The box counting method is used in the calculation to count the minimum number of boxes required to cover the patch boundary at different scales.

[0078] Cross-layer vertical propagation characteristics describe the continuity and propagation ability of patches in the vertical direction. For patches located in high-altitude layers... The patches were detected at adjacent height levels. and Are there any spatially overlapping patches? Calculate the horizontal projection overlap rate of patches in adjacent layers. :

[0079] ;

[0080] in This represents the area of ​​overlap between the current layer patch and the adjacent layer patch on the horizontal projection. This represents the area of ​​the patch in the current layer. Overlap rate. The higher the value, the stronger the vertical continuity of the combustible material, and the greater the risk of vertical fire spread.

[0081] Based on the extracted geometric boundary features and cross-layer vertical propagation features, a biomass density coefficient is generated. The biomass density coefficient reflects the actual combustible load per unit area and is closely related to the patch's geometric characteristics and vertical spread characteristics. The calculation of the biomass density coefficient comprehensively considers the effects of shape index, boundary complexity, and overlap rate.

[0082] ;

[0083] in As a baseline biomass density, it was obtained from an empirical database based on vegetation type. For coniferous forest litter, Typically, the amount is 1.2 kg to 2.5 kg per square meter for broadleaf forest litter. Typically, it ranges from 0.8 kg to 1.8 kg per square meter. Parameters , , The weighting coefficients reflect the influence of shape index, boundary complexity, and overlap rate on biomass density, respectively. They were determined by regression analysis based on field quadrat survey data, with typical value ranges of 0.05 to 0.15, 0.10 to 0.20, and 0.20 to 0.40.

[0084] After obtaining the biomass density coefficient of each candidate patch, the patch area of ​​the candidate patch is coupled with the corresponding biomass density coefficient to calculate the carrying capacity of each candidate patch. :

[0085] ;

[0086] The load value represents the total mass of combustible material within a patch. After calculating the load for all patches, the load values ​​are spatially bound to the height hierarchy and planar coordinates of each candidate patch in three dimensions. The height hierarchy is represented by the height range of the slice in which the patch is located, and the planar coordinates are represented by the latitude and longitude of the patch's centroid or by the coordinates in the projected coordinate system. The three-dimensional spatial binding constructs a load spatial representation structure containing vertical hierarchical information and horizontal spatial location information. This structure is stored in the form of a three-dimensional voxel mesh or point cloud, with each data unit recording its spatial location coordinates. and the corresponding load capacity value .

[0087] After the spatial characterization structure of forest fuel load is constructed, hierarchical fuel load distribution data is generated. This data clearly shows the vertical and horizontal distribution of combustible materials in the target forest area, providing accurate spatial basis data for subsequent fuel load prediction and fungal disposal scheme formulation. The hierarchical fuel load distribution data is stored in a standard format, supporting visualization and spatial analysis operations in Geographic Information System (GIS) software, making it easy for decision-makers to intuitively understand the spatial distribution characteristics of forest combustible materials and high-risk areas.

[0088] In one optional embodiment, spatial neighborhood connectivity detection is performed on the combustible pixel distribution map, spatially adjacent combustible pixels are merged into candidate patches, and the biomass density coefficient is generated by combining the geometric boundary features and cross-layer vertical propagation features of the candidate patches, including:

[0089] Perform same-layer spatial neighborhood connectivity detection on the combustible pixel points in the combustible pixel distribution map, identify spatially adjacent combustible pixel clusters within the same height slice, mark each combustible pixel cluster as a candidate patch, extract the geometric boundary of each candidate patch and calculate the boundary complexity index.

[0090] Extract the spatial overlap area of ​​candidate patches between adjacent height slices on the horizontal projection plane, calculate the overlap area ratio of each spatial overlap area, and extract the spectral reflectance difference between upper and lower candidate patches in each spatial overlap area. Perform weighted fusion calculation on the overlap area ratio and spectral reflectance difference to identify cross-layer connected patch groups with the potential for vertical fire spread. Calculate the corresponding height layer number and total vertical span for each cross-layer connected patch group to generate a vertical spread risk index.

[0091] The vertical propagation risk index and the boundary complexity index are weighted together to generate a comprehensive fire risk coefficient. The comprehensive fire risk coefficient is then used as a correction factor to perform risk-weighted transformation on the spectral statistical characteristics of each candidate patch, generating a biomass density coefficient that considers spatial morphological characteristics and vertical propagation characteristics.

[0092] In one specific implementation, after obtaining the combustible pixel distribution map, spatial clustering processing is required to form a patch structure with clear boundaries from the discrete combustible pixels. For each combustible pixel in the combustible pixel distribution map, a spatial neighborhood search range is established with that pixel as the center. The neighborhood search range can use a 3×3 pixel window or a 5×5 pixel window. Within the same height slice, the connectivity between the current pixel and other combustible pixels in its spatial neighborhood is detected. An eight-neighborhood connectivity determination rule is used, that is, two combustible pixels are determined to be spatially adjacent when they are directly adjacent in the horizontal, vertical, or diagonal directions. Using a depth-first search algorithm or a breadth-first search algorithm, all spatially adjacent combustible pixels within the same height slice are merged into a combustible pixel cluster. A unique patch identifier is assigned to each combustible pixel cluster, marking it as a candidate patch, and the height slice layer number to which the candidate patch belongs and the set of pixel coordinates contained therein are recorded.

[0093] For each candidate patch, its geometric boundary is extracted to quantify its spatial morphological features. All pixels within the candidate patch are traversed, and the eight-neighborhood of each pixel is detected. If a neighboring location does not belong to the candidate patch, the current pixel is marked as a boundary pixel. All boundary pixels are connected in spatial topological order to form a closed geometric boundary contour. The perimeter of the geometric boundary is calculated. The area of ​​the candidate patch is obtained by accumulating the Euclidean distances between adjacent pixels on the boundary contour. The boundary complexity index is obtained by counting the total number of pixels within a candidate patch and multiplying it by the actual area of ​​a single pixel. The boundary complexity index is calculated based on the relationship between perimeter and area. Using the shape index formula This index reflects the irregularity of candidate patch boundaries; a higher boundary complexity index indicates a more complex boundary morphology. For candidate patches with jagged or fractal boundaries, the boundary complexity index is typically greater than 1.5, while the boundary complexity index of regular circular or square patches is close to 1.0.

[0094] To identify potential vertical propagation paths of fire, it is necessary to analyze the spatial correspondence between candidate patches between adjacent height slices. Two vertically adjacent height slices are selected, denoted as the lower slice and the upper slice, respectively. For each candidate patch in the lower slice, the horizontal coordinates of all its pixels are projected onto a horizontal projection plane to form a projection region. Similarly, each candidate patch in the upper slice is horizontally projected. The intersection region of the lower and upper candidate patch projection regions on the horizontal projection plane is calculated; this intersection region is the spatial overlap region. The number of pixels in the spatial overlap region is counted, and the overlap area percentage is calculated. Using formula ,in The area of ​​the spatially overlapping region. The area of ​​the candidate patches in the lower layer. The area of ​​the candidate patch in the upper layer.

[0095] For candidate patch pairs with spatially overlapping regions, spectral reflectance differences are further extracted to assess the continuity of combustible material types. Within the spatially overlapping regions, the spectral reflectance values ​​of corresponding pixels in the lower and upper candidate patches are extracted separately. The average spectral reflectance of all pixels in the lower patch within the overlapping region is calculated. Calculate the average spectral reflectance of all pixels in the overlapping region of the upper patch. Calculate the difference in spectral reflectance. The normalized difference formula is used. This index reflects the similarity of the upper and lower combustibles in terms of spectral characteristics. The smaller the difference in spectral reflectance, the closer the types of upper and lower combustibles are, and the stronger the continuity of vertical fire propagation.

[0096] Vertical connectivity is comprehensively evaluated by weighting and fusing the overlap area ratio and spectral reflectance difference. Spatial overlap weights are set. and spectral continuity weight Both satisfy Calculate cross-layer connectivity strength. A weighted fusion formula is adopted. The connectivity strength increases when the difference in spectral reflectance is inverted to enhance spectral similarity. A threshold for cross-layer connectivity strength is set. The value is usually 0.6. When a candidate patch is determined to have the potential for vertical fire propagation, it is included in the interlayer connected patch group.

[0097] For each cross-layer connected patch group, count the number of height layers it contains. The height slice layer number is obtained by traversing all candidate patches within the patch group and deduplicating the count. The total vertical span is then calculated. The vertical propagation risk index is obtained by calculating the difference between the highest and lowest layer numbers within a patch group and multiplying it by the thickness of a single slice. The index is calculated based on the number of height layers and the total vertical span. The total vertical span is normalized, and a reference span value is set. (Typically, a typical height of the forest's vertical structure is taken, such as 30 meters), calculate the normalized span. This makes it a dimensionless parameter, and then the product weighted summation formula is used. ,in and These are weighting coefficients, reflecting the contribution of the number of layers and the normalized span to the propagation risk, respectively. Both are dimensionless parameters and are typically taken as... , The higher the vertical spread risk index, the stronger the potential for vertical fire spread in that patch group.

[0098] After obtaining the boundary complexity index and the vertical propagation risk index, they need to be weighted together to generate a comprehensive fire risk coefficient. For each candidate patch, its cross-layer connected patch group is retrieved, and the corresponding vertical propagation risk index is extracted. For isolated candidate patches that do not belong to any cross-layer connected patch group, their vertical propagation risk index is set to 0. Boundary complexity weights are then defined. and vertical propagation weights Both satisfy Calculate the comprehensive fire hazard coefficient. Using the weighted summation formula This coefficient comprehensively considers the spatial morphological characteristics and vertical propagation properties of patches, and can fully reflect the potential danger level of candidate patches in forest fires.

[0099] For each candidate patch, its spectral statistical properties are extracted as the basic features for biomass estimation. The spectral reflectance of all pixels within the candidate patch in the near-infrared band is statistically analyzed, and the mean near-infrared reflectance is calculated. and standard deviation The spectral reflectance of all pixels within the candidate patch in the red light band is statistically analyzed, and the mean red light band reflectance is calculated. Normalized Difference Vegetation Index (NDVI) was calculated based on near-infrared and red light reflectance. Using formula The normalized difference vegetation index (NDVI) is positively correlated with vegetation biomass, but it does not take into account the impact of spatial morphology and vertical propagation characteristics on actual fire risk.

[0100] The comprehensive fire hazard coefficient is used as a correction factor to perform risk-weighted transformation on the spectral statistical characteristics. For each candidate patch, its normalized vegetation index is multiplied by the comprehensive fire hazard coefficient to generate a risk-weighted vegetation index. Using formula ,in The fire hazard enhancement factor, typically set to 0.3, is used to adjust the impact of the overall fire hazard factor on biomass estimation. An empirical regression model is established between the risk-weighted vegetation index and actual biomass density to convert the risk-weighted vegetation index into a biomass density coefficient. This biomass density coefficient can simultaneously reflect the spectral characteristics, spatial morphology, and vertical propagation potential of combustibles, providing a quantitative indicator that considers multiple fire hazard factors for subsequent load assessment.

[0101] like Figure 2As shown, a flowchart illustrating the meteorological evolution and load prediction process is presented.

[0102] In one optional embodiment, by extracting meteorological evolution characteristics from meteorological monitoring data, identifying the alternating pattern of drought stress cycles and wet recovery cycles, generating the future litter increment distribution, and spatiotemporally fusing it with stratified litter load distribution data, the predicted litter load distribution for future periods is obtained, including:

[0103] The temperature variation range, humidity variation range, and precipitation fluctuation range between adjacent time periods are extracted from meteorological monitoring data and combined into a meteorological evolution feature vector;

[0104] The temporal phase relationship between temperature change amplitude and precipitation fluctuation amplitude in the meteorological evolution feature vector was identified, the alternation pattern of drought stress cycle and wet recovery cycle was determined, the duration of vegetation water deficit in the drought stress cycle was extracted, and the vegetation leaf fall intensity value was determined. The soil moisture recovery rate in the wet recovery cycle was extracted, and the degree of inhibition of litter microbial decomposition was determined. The difference between the vegetation leaf fall intensity value and the degree of inhibition of litter microbial decomposition value was superimposed to generate the litter net accumulation rate distribution at different height levels.

[0105] Extract the combustible space connectivity paths of each height slice in the stratified load distribution data, identify the height slice locations where the combustible space connectivity paths are interrupted in the vertical direction, determine the future fallow increment values ​​to be preferentially allocated to the height slices corresponding to the interruption locations based on the net cumulative rate distribution of fallen debris and the load compensation capacity, generate the future fallow increment distribution, and overlay the future fallow increment distribution with the stratified load distribution data in three-dimensional space to generate the load prediction distribution for future periods.

[0106] In one specific implementation, temperature, humidity, and precipitation data recorded on a continuous time series by several meteorological monitoring stations within the target forest area are acquired. The time axis is segmented according to fixed time intervals, for example, in 6-hour units. For each time interval, the temperature difference between this time interval and the previous time interval is extracted as the temperature variation amplitude, the humidity difference is extracted as the humidity variation amplitude, and the precipitation difference is extracted as the precipitation fluctuation amplitude. The temperature variation amplitude, humidity variation amplitude, and precipitation fluctuation amplitude within the same time interval are arranged in a fixed order to form a three-dimensional vector, which is the meteorological evolution feature vector.

[0107] When performing time-series analysis on meteorological evolution feature vectors, it is necessary to pay attention to the phase relationship between the amplitude of temperature change and the amplitude of precipitation fluctuation on the time axis. Specifically, within a continuous time window, when the amplitude of temperature change shows a continuous upward trend and the amplitude of precipitation fluctuation remains at a low level, it indicates that the time window corresponds to a drought stress cycle; when the amplitude of temperature change falls from a high level and the amplitude of precipitation fluctuation shows a significant increase, it indicates that the time window corresponds to a wet recovery cycle. By judging the sign and numerical trends of the amplitude of temperature change and the amplitude of precipitation fluctuation within adjacent time windows, the alternation pattern of drought stress cycles and wet recovery cycles can be identified.

[0108] Within the identified drought stress cycle, the start and end times of the cycle are extracted, and the time difference between them is calculated. This time difference represents the duration of vegetation water deficit. The vegetation leaf drop intensity is determined based on the duration of vegetation water deficit, specifically using a piecewise linear mapping method. For example, when the duration is less than 48 hours, the vegetation leaf drop intensity is 0.2; when the duration is between 48 and 120 hours, the vegetation leaf drop intensity increases linearly between 0.2 and 0.6; and when the duration exceeds 120 hours, the vegetation leaf drop intensity is 0.8.

[0109] Within the identified moist recovery cycle, soil moisture monitoring data is extracted, and the time required for soil moisture to recover from its initial level to normal levels is calculated. The reciprocal of this time is the soil moisture recovery rate. The degree of inhibition of litter microbial decomposition is determined based on the soil moisture recovery rate. A higher recovery rate indicates a rapid establishment of a moist environment, faster recovery of microbial activity, and a lower degree of inhibition. Conversely, a lower recovery rate indicates a slow establishment of a moist environment, delayed recovery of microbial activity, and a higher degree of inhibition.

[0110] The difference between the vegetation leaf litter intensity value and the value of inhibited microbial decomposition of litter is summed, i.e., the vegetation leaf litter intensity value minus the value of inhibited microbial decomposition of litter is calculated, to obtain a comprehensive value. This comprehensive value reflects the net accumulation rate of litter. This comprehensive value is then distributed according to different height layers to generate a distribution of the net accumulation rate of litter at different height layers. Specifically, the rate value is higher in the surface layer; the rate value gradually decreases in the middle and upper layers. This distribution method reflects the differences in litter accumulation in vertical space.

[0111] Combustible material pixels are extracted from each height slice in the hierarchical load distribution data. Within each height slice, a spatial connectivity path for combustible materials is constructed based on the spatial adjacency relationship of the pixels. Specifically, combustible material pixels within the same height slice are treated as nodes in a graph structure. When two pixels are adjacent horizontally or within a preset threshold distance, a connecting edge is established between these two nodes, thus forming a spatial connectivity path for combustible materials. Vertically, the distribution of combustible material connectivity paths in adjacent height slices is compared. When the coverage area of ​​a combustible material connectivity path within a certain height slice is significantly smaller than the coverage area of ​​its adjacent height slices above and below, or when the number of combustible material pixels within that height slice is lower than a preset threshold, the height slice is determined to be the location where the spatial connectivity path for combustible materials is interrupted in the vertical direction.

[0112] The numerical values ​​of the height slices corresponding to the interruption locations in the net accumulation rate distribution of litter are preferentially allocated. That is, at these height slice locations, the net accumulation rate values ​​of litter are filled according to spatial location, increasing the amount of litter accumulated at those locations. This preferential allocation mechanism simulates the supplementary effect of litter in vertical space, especially at locations where combustible material connectivity is interrupted, where the accumulation of litter can compensate for the spatial discontinuity of the original combustible material. After the preferential allocation is completed, the future incremental distribution of litter is generated.

[0113] The future litter increase distribution and stratified load distribution data are overlaid in three dimensions. Specifically, for each height slice, the value of the corresponding height slice in the future litter increase distribution is added pixel-by-pixel to the value of the corresponding height slice in the stratified load distribution data. The summed value represents the predicted load value for that height slice in the future period. All the predicted load values ​​of all height slices are stacked vertically to form a three-dimensional spatial distribution structure, which represents the predicted load distribution for the future period.

[0114] For example, in a subtropical forest region, meteorological monitoring data showed that over a 14-day period, the first 7 days saw a sustained rise in temperature and almost zero precipitation, indicating a drought stress cycle with a vegetation water deficit duration of 168 hours and a leaf fall intensity of 0.75. The following 7 days saw a significant increase in precipitation and a drop in temperature, indicating a moist recovery cycle with a soil moisture recovery rate of 0.03 units per hour and a litter microbial decomposition inhibition level of 0.25. The difference was summed to obtain a net litter accumulation rate of 0.5. In the stratified load distribution data, a break in combustible material connectivity was identified in slices at heights of 8 to 10 meters. The net litter accumulation rate was preferentially allocated to this height range, significantly increasing the litter increase in these slices. Finally, the future litter increase distribution was overlaid with the stratified load distribution data in three dimensions to generate a predicted load distribution for the next 30 days, providing a spatiotemporal basis for subsequent fungal release decisions.

[0115] In one optional embodiment, extracting the combustible space connectivity path of each height slice in the stratified load distribution data, and identifying the height slice location where the combustible space connectivity path is interrupted in the vertical direction includes:

[0116] Vertical projection overlap detection is performed on the combustible pixels between adjacent height slices in the layered load distribution data, and the overlap area ratio of combustible pixels of the upper and lower height slices on the horizontal projection plane is calculated.

[0117] When the overlap area ratio is lower than the preset connectivity determination threshold, the location between the upper height slice and the lower height slice is marked as the interruption position of the combustible space connectivity path. The load density value of the lower height slice corresponding to the interruption position is extracted, the difference between the load density value and the preset load saturation threshold is calculated, and the load compensation capacity is determined.

[0118] Extract the net cumulative rate value of litter corresponding to the lower height slice from the net cumulative rate distribution of litter. Calculate the natural increment of litter based on a preset time window. When the natural increment of litter does not exceed the load compensation capacity, the natural increment of litter is used as the priority allocation value. When the natural increment of litter exceeds the load compensation capacity, the load compensation capacity is used as the priority allocation value. Generate future litter increment values ​​that are prioritized for allocation to the lower height slice.

[0119] In one specific implementation, when identifying the interruption features of combustible spatial connectivity paths in a vertically layered structure, it is necessary to analyze each height slice in the layered load distribution data layer by layer. The combustible pixels in each height slice are vertically projected, forming a planar projection area of ​​combustible distribution on a horizontal projection plane. By calculating the overlap area of ​​combustible pixels between adjacent height slices on the horizontal projection plane, the vertical connectivity is quantitatively characterized. Specifically, for the first... Layer height slice and the first Slice the data at each layer height, extract the set of combustible pixels for each layer, project the horizontal coordinates of these pixels onto a unified reference plane, and calculate the intersection area of ​​the two projected regions. Total area of ​​the lower projection area The ratio of the two values ​​yields the percentage of overlapping area. This percentage directly reflects the degree of spatial continuity between the upper and lower layers of combustibles. A higher percentage indicates a continuous distribution of combustibles in the vertical direction, forming an effective fire propagation channel; a lower percentage indicates a significant spatial interruption in the vertical direction.

[0120] To determine the percentage of overlapping area, a preset connectivity threshold is set. This threshold is typically determined based on a physical model of forest fire spread, and generally ranges from 0.3 to 0.5. When the calculated overlap area ratio... Below the preset connectivity threshold When this occurs, the location between adjacent height slices is determined to be the point where the spatial connectivity path of combustibles is interrupted. In practical applications, interruptions in the spatial connectivity path of combustibles often correspond to natural stratification phenomena in the vertical structure of forests, such as the gap layer between the tree layer and the shrub layer, or the transition zone between the shrub layer and the herb layer. Identifying these interruption locations is of great significance for assessing the difficulty of fire propagation in the vertical direction, and also provides key information for the spatial layout of fungal degradation.

[0121] After marking the interruption location, the current load density value needs to be extracted from the lower-level height slice of the interruption location. The load density value reflects the mass of combustible material per unit area within a slice of that height, typically expressed in kilograms per square meter. This load density value is compared with a preset load saturation threshold. Compare and calculate the difference between the two. This difference is defined as the load compensation capacity. Preset load saturation threshold. The threshold is set based on the vegetation type and ecological carrying capacity of the height slice. For shrubland, the threshold is typically between 2 and 4 kg / m²; for herbaceous layer, it is typically between 1 and 2 kg / m². The calculation of the load compensation capacity reflects the amount of combustible material that the height slice can still accommodate under the current conditions, providing a quantitative basis for the spatial allocation of subsequent litter increments.

[0122] Extract the net accumulation rate of litter corresponding to the lower height slice from the net accumulation rate distribution of litter. This value represents the natural accumulation rate of litter on a slice of this height per unit time, after deducting losses due to natural decomposition and wind erosion. The net accumulation rate of litter is then expressed as... With load compensation capacity When performing proportional allocation, first calculate within the preset time window. Within this height slice, the potential increase in litter volume at the natural accumulation rate. Compare the relationship between this natural increment and the load compensation capacity. When the natural accumulation of litter in a given height slice within a preset time window does not exceed its carrying capacity, the future litter increment value is preferentially allocated to the lower height slice. Directly equal to ;when When this occurs, it indicates that the natural accumulation will exceed the carrying capacity. At this point, the excess needs to be truncated and prioritized for allocation to the future litter increment values ​​of the lower-level slices. The excess portion is allocated to lower-level height slices according to the vertical gravity settlement model.

[0123] In the proportional allocation process, the vertical settling characteristics of litter are considered. For adjacent height slices marked as interrupted locations, due to the weak spatial connectivity between the upper and lower layers, the litter in the upper layer settles to the lower layer more efficiently under gravity. According to the physical model of litter settling, the settling rate is related to the particle size, density, and height slice spacing of the litter. When allocating the net cumulative rate of litter according to the settling weight, a weighted allocation formula is used. ,in This is a settlement weighting factor, with a value ranging from 0.6 to 0.9. This weighting factor is set to comprehensively consider the physical characteristics and spatial unevenness of litter. For coniferous litter, The value is too high because its mass is relatively small and it settles easily; for broadleaf litter, The value is too low because its volume is large and its settling speed is slow.

[0124] After generating the future litter increment values ​​that are prioritized for allocation to lower-level height slices, these values ​​are superimposed on the original load density values ​​of the lower-level height slices to update the future load prediction values ​​for that height slice. This update process needs to be performed one by one at the scale of spatial grid cells to ensure that the load prediction values ​​of each height slice in each spatial grid cell accurately reflect the vertical distribution characteristics of litter. For vertical profiles with multiple interruption points, each interruption point needs to be processed sequentially from top to bottom, accumulating the future litter increment values ​​for each layer. When processing multiple interruptions, a layer-by-layer recursive approach is adopted, continuing to pass the unallocated litter increments from the upper layers to the lower layers until all litter increments are reasonably allocated or reach the lowest-level height slice.

[0125] The calculation methods for carrying capacity compensation differ depending on the forest type. In coniferous forests, due to their relatively simple vertical structure, there are typically only two main layers: the tree layer and the litter layer. Discontinuities are mainly concentrated between the tree canopy layer and the surface litter layer. In this case, the calculation of carrying capacity compensation focuses on the carrying capacity of the surface litter layer. In broadleaf forests, due to their complex vertical structure, there are multiple layers such as the tree layer, shrub layer, and herb layer. Discontinuities may occur at multiple interlayer interfaces. In this case, it is necessary to calculate the carrying capacity compensation corresponding to each discontinuity location separately and allocate the litter increment across multiple layers according to a vertical settlement model. In mixed forests, due to the mixed distribution of coniferous and broadleaf species, the complexity of the vertical structure further increases. It is necessary to calculate the carrying capacity compensation by weighted averaging, taking into account the species composition ratio and spatial distribution pattern, to ensure that the allocation result reflects the actual ecological characteristics of the mixed forest.

[0126] In an optional embodiment, for each spatial grid cell, by combining the load prediction distribution with the degradation rate data of environmentally adapted bacteria, a multi-bacterial collaborative degradation scheme that meets preset time constraints is selected to generate a bacterial deployment configuration, including:

[0127] The load value and litter matrix component ratio of each spatial grid cell are extracted from the load prediction distribution. The target degradation rate threshold of the spatial grid cell is calculated by combining the preset time limit constraint with the load value. The litter matrix component ratio is cross-matched with the matrix degradation preference spectrum of each fungus in the degradation rate data of environmentally adapted fungi to calculate the contribution value of each fungus to the degradation efficiency of the spatial grid cell.

[0128] The environmentally adaptable bacteria are sorted in descending order according to their degradation efficiency contribution value to generate a candidate sequence of bacteria. Bacteria are selected from the candidate sequence in turn, and the marginal efficiency is calculated based on the competition relationship between bacteria to screen the cumulative degradation rate value of bacteria. The selected bacterial combinations whose cumulative degradation rate value reaches the target degradation rate threshold are recorded as a synergistic degradation scheme.

[0129] The degradation rate values ​​of different matrix types in the proportion of litter matrix components of each fungus in the collaborative degradation scheme are extracted. The degradation task sharing ratio of each fungus in the spatial grid cell is calculated. Based on the degradation task sharing ratio and load value, the release density requirement of each fungus is calculated. The release density requirement is bound to the spatial coordinates of the spatial grid cell to generate the fungus release configuration of the spatial grid cell.

[0130] In one specific implementation, after obtaining the predicted load distribution data for each spatial grid cell, a precise fungal deployment configuration scheme needs to be generated for each spatial grid cell. The predicted load distribution data includes load values. The composition of the litter matrix, including its components such as cellulose, hemicellulose, lignin, and soluble carbohydrates, is also considered. The composition of the litter matrix can be expressed as a vector. ,in Indicates the total number of matrix types. Indicates the first The mass percentage of different substrate types in litter. Set preset time limits based on forest fire risk management needs. This time limit constraint is usually determined based on the regional fire prevention cycle, requiring the amount of combustible material to be reduced to below the safety threshold within this time limit.

[0131] Target degradation rate threshold of spatial grid cells The calculation is obtained by applying the load value to the preset time limit constraint. The calculation formula is as follows: ,in This is the load reduction ratio coefficient, typically ranging from 0.6 to 0.8, representing the proportion of combustibles that need to be eliminated through microbial degradation. This threshold defines the minimum degradation rate requirement that a microbial co-degradation scheme must achieve within a spatial grid cell.

[0132] The degradation rate data for environmentally adapted microorganisms includes substrate degradation preference profiles for each microorganism. These profiles describe the differences in degradation ability of specific microorganisms to different litter substrate types and can be represented as a matrix. , where matrix elements Indicates the first The first type of bacteria affects the first The degradation rate per unit time for each substrate type, expressed in grams per square meter per day. This is determined by the proportion of litter substrate components. By cross-matching with the matrix degradation preference spectrum, the contribution of each fungal species to the degradation efficiency of the spatial grid cell can be calculated. The calculation formula is: ,in For the first The weighting coefficients for different matrix types reflect the importance of that matrix type in contributing to fire hazard. Lignin and cellulose are typically assigned higher weights due to their high calorific value and resistance to natural decomposition, while soluble carbohydrates are assigned lower weights due to their easy decomposition and relatively low contribution to combustion.

[0133] Based on the contribution value of degradation efficiency The environmentally adaptable fungi were sorted in descending order to generate candidate fungal sequences. ,in To adapt the total number of bacteria to the environment, A synergistic degradation scheme was constructed by sequentially selecting fungi from the candidate fungal sequences. During the selection process, the competitive relationships between fungi need to be considered, as different fungi in a coexisting environment may experience a decrease in actual degradation capacity due to competition for nutrient resources, space occupation, and inhibition by secondary metabolites. The competitive relationships between fungi can be represented by a competition coefficient matrix. Description, in which elements Indicates the first The first type of bacteria affects the first The inhibition coefficient of the degradation ability of each fungus ranges from 0 to 1, with a smaller value indicating a stronger inhibitory effect.

[0134] When the selected fungal collection is At that time, the newly added number Marginal efficiency of individual fungi The calculation formula is This marginal efficiency reflects the actual degradation efficiency that newly added bacteria can achieve in the presence of existing bacteria. Only when the marginal efficiency exceeds a set marginal contribution threshold... Only then were these fungi included in the synergistic degradation program, in which... The standard value is typically set at 10% to 20% of the average degradation efficiency of the selected bacteria to avoid introducing bacteria with low contribution rates, which would increase management complexity.

[0135] The cumulative degradation rate is calculated in real time during the selection process. The calculation method is as follows When the cumulative degradation rate value first reaches or exceeds the target degradation rate threshold. If the selection of fungi stops, the currently selected fungal combinations are recorded as a collaborative degradation scheme for the spatial grid unit. If the cumulative degradation rate value still does not reach the target degradation rate threshold after traversing the entire fungal candidate sequence, the spatial grid unit is marked as a region with insufficient degradation capacity, and the preset time limit constraint needs to be adjusted or manual intervention measures need to be taken.

[0136] After determining the synergistic degradation scheme, it is necessary to calculate the required deployment density of each microbial species in the scheme. The degradation rate values ​​of each microbial species for different substrate types in the litter substrate composition of the synergistic degradation scheme are extracted to construct a degradation rate contribution matrix. , of which elements Indicates the first The first type of bacteria affects the first The effective degradation rate of each substrate type after considering competition. For each substrate type... Calculate the total degradation rate of the substrate by all bacteria in the synergistic degradation scheme. , and then calculate the first Individual bacterial species in substrate type Degradation task sharing ratio .

[0137] No. The proportion of the overall degradation task borne by individual fungi within a spatial grid cell The weighted average of the contribution ratios for each matrix type is obtained, and the calculation formula is as follows: This overall workload sharing ratio reflects the proportion of the workload undertaken by this fungus in the overall litter degradation task. Based on the workload sharing ratio and load value, the required deployment density for each fungus species is calculated. The calculation formula is: ,in For the first The daily degradation capacity per unit biomass of each fungal species, expressed as grams of litter per gram of mycelium per day, was obtained through laboratory culture and measurement. Release density requirements. The unit is grams of mycelium per square meter, providing direct guidance for on-site application.

[0138] The required release density of each type of fungus is compared with the spatial coordinates of the spatial grid unit. The data is bound together to generate a fungal release configuration data structure containing fungal species identifiers, release density values, and spatial location information. The fungal release configuration is stored in a structured data format, including fields such as: grid cell number, center coordinates, fungal species name, release density, release time window, and environmental monitoring requirements. The release time window is determined based on the optimal growth conditions for the fungi and humidity and temperature forecasts from meteorological monitoring data, ensuring that the fungi can quickly establish a mycelial network after release. The environmental monitoring requirements specify the key environmental parameters to be monitored after release and the monitoring frequency, used to verify the fungal colonization effect and degradation process.

[0139] For boundary grid cells, the synergistic effect with adjacent grid cells needs to be considered when generating fungal deployment configurations. If adjacent grid cells deploy the same fungi, a synergistic enhancement effect may occur in the boundary area due to mycelial expansion. In this case, the deployment density requirement of the boundary grid cells can be appropriately reduced, with the reduction determined based on the fungal expansion rate and grid cell size, generally ranging from 5% to 15%. If the fungi deployed in adjacent grid cells have a strong competitive relationship, it is necessary to set up an isolation zone or adjust the deployment density distribution in the boundary area to avoid fungal competition leading to a decrease in degradation efficiency. By performing the above calculation process on all spatial grid cells, a refined fungal deployment configuration scheme covering the entire target forest area is finally generated, providing a scientific basis and operational guidelines for actual forest combustible microbial disposal operations.

[0140] In one optional embodiment, environmentally adaptable bacteria are sorted in descending order according to their degradation efficiency contribution value to generate a bacterial candidate sequence. Bacteria are then selected sequentially from the candidate sequence, and the marginal efficiency is calculated based on the competition relationship between bacteria to screen for cumulative degradation rate values. Selected bacterial combinations whose cumulative degradation rate values ​​reach the target degradation rate threshold are recorded as collaborative degradation schemes.

[0141] Select the fungus that ranks first in the candidate fungal sequence and record its corresponding degradation rate as the current cumulative degradation rate and the previous cumulative degradation rate.

[0142] Select the next candidate bacteria, and add the degradation rate of the next candidate bacteria to the current cumulative degradation rate to obtain the superimposed degradation rate. Identify the competing bacteria in the selected bacterial combination that have a matrix competition relationship with the next candidate bacteria, count the number of competing bacteria, obtain the corresponding competition inhibition coefficient from the preset competition inhibition curve based on the number of competing bacteria, and use the competition inhibition coefficient to subtract the superimposed degradation rate to obtain the updated cumulative degradation rate.

[0143] The difference between the updated cumulative degradation rate and the previous cumulative degradation rate is calculated as the marginal degradation contribution. The marginal efficiency is obtained by calculating the ratio of the marginal degradation contribution to the degradation rate of the next candidate bacteria.

[0144] When the marginal efficiency is lower than the preset marginal admission threshold, skip the next candidate fungi.

[0145] When the marginal efficiency is not lower than the preset marginal admission threshold and the updated cumulative degradation rate has not reached the target degradation rate threshold, the next candidate bacteria are included in the selected bacteria combination, the previous round of cumulative degradation rate is updated to the updated cumulative degradation rate, and the next candidate bacteria are selected until the target degradation rate threshold is reached. The selected bacteria combination is then recorded as a collaborative degradation scheme.

[0146] In one optional embodiment, after completing the load prediction distribution and screening of environmentally suitable fungi, it is necessary to construct a multi-fungus synergistic degradation scheme for specific spatial grid units from the environmentally suitable fungi. Since different fungi exhibit differences in degradation rates, matrix competition, and synergistic or antagonistic effects when degrading forest fuels, a scientific fungi combination screening mechanism needs to be established to avoid resource waste and ecological imbalance caused by over-exploitation while ensuring degradation efficiency.

[0147] For a specific spatial grid cell, the predicted combustible load is first obtained based on the predicted load distribution. This predicted load value comprehensively considers the spatiotemporal fusion result of the current layered load distribution data and the future litter increment distribution. According to the preset time constraints set by forest fire prevention management requirements, such as needing to reduce the combustible load to below a safe threshold within 180 days, the target degradation rate threshold is calculated using the predicted combustible load value and the preset time constraints. Assume the predicted combustible load value for a certain grid cell is... The preset time limit constraint is Days, then the target degradation rate threshold Calculated as The target degradation rate threshold represents the minimum overall degradation rate that the fungal synergistic degradation scheme needs to achieve to realize the load control target within a preset time limit.

[0148] In the set of environmentally adapted fungi, each fungus possesses its own degradation rate data. This degradation rate data is based on the degradation ability of a single fungus under standard environmental conditions, obtained through laboratory measurements or field experiments. To establish a candidate fungal sequence, it is necessary to evaluate the degradation efficiency contribution value of the environmentally adapted fungi. The degradation efficiency contribution value comprehensively considers the degradation rate value of the fungus and its activity expression coefficient under the current grid cell environmental conditions. The activity expression coefficient reflects the influence of environmental factors such as temperature and humidity in meteorological monitoring data on the degradation activity of the fungus. [A specific fungus is mentioned here, but the context is unclear.] Degradation efficiency contribution value Calculated as ,in This represents the degradation rate of this type of fungus. This represents the activity expression coefficient of the fungus under the current environmental conditions. The activity expression coefficient is calculated by matching the temperature and humidity parameters in meteorological monitoring data with the optimal temperature and humidity ranges of the fungus. When the environmental parameters are within the optimal range, the activity expression coefficient is close to 1, and the coefficient decreases when the environmental parameters deviate from the optimal range.

[0149] All environmentally adaptable bacteria are sorted in descending order based on their degradation efficiency contribution value to generate candidate bacterial sequences. Bacteria ranking higher in the candidate sequences have higher monomeric degradation efficiency contributions and are given priority consideration for inclusion in the synergistic degradation scheme. The top-ranked bacterium in the candidate sequence is selected as the initial choice, as it has the highest degradation efficiency contribution value. The degradation rate value corresponding to this top-ranked bacterium is recorded as the current cumulative degradation rate. At the same time, initialize the cumulative degradation rate of the previous round. This is also equal to the degradation rate value. The selected bacterial combination is initialized as a set containing only the first bacterial species.

[0150] The iterative selection process begins, sequentially selecting the next candidate fungi from the candidate fungal sequence for evaluation. For each next candidate fungi, the additive effect between it and the currently selected fungal combinations is first calculated. The degradation rate of the next candidate fungi is then calculated. Compared with the current cumulative degradation rate By superimposing the values, the ideal superimposed degradation rate can be obtained. , calculated as This superposition of degradation rates assumes that there is no mutual influence between different types of bacteria, and that the degradation rates can be linearly superimposed.

[0151] In real forest environments, substrate competition occurs when multiple fungi coexist. Substrate competition refers to the competition among different fungi for the same nutrient substrate or growth space, leading to the inhibition of degradation activities in each fungi. This study identifies competing fungi in the selected fungal ensemble that have a substrate-competing relationship with the next candidate fungi. The substrate competition relationship is determined using an inter-fungus interaction matrix recorded in a fungal ecology database. This matrix identifies whether a substrate-competing, synergistic, or neutral relationship exists between any two fungi. The process involves iterating through each fungi in the selected ensemble, querying its interaction relationship with the next candidate fungi, counting the fungi exhibiting substrate competition (recording them as competing fungi), and then counting the number of competing fungi. .

[0152] The corresponding competition inhibition coefficient is obtained from the preset competition inhibition curve based on the number of competing bacteria. The pre-defined competition inhibition curve is an empirical curve obtained by fitting multi-microbial degradation experimental data, describing the relationship between the number of competing bacteria and the degree of degradation rate reduction. This curve typically shows a trend where the degree of degradation rate reduction gradually increases with the increase of the number of competing bacteria, but the rate of reduction gradually slows down. The competition inhibition curve can be expressed in the form of an exponential decay function. ,in The competitive inhibition strength parameter was determined through fitting experimental data. When the number of competing bacteria is 0, the competitive inhibition coefficient is 1, indicating no inhibition; as the number of competing bacteria increases, the competitive inhibition coefficient decreases, indicating that the degradation rate is inhibited.

[0153] The cumulative degradation rate is reduced by the competition inhibition coefficient to obtain the updated cumulative degradation rate considering the competition effect. , calculated as This updated cumulative degradation rate reflects the overall degradation rate that can actually be achieved under the influence of matrix competition after incorporating the next candidate bacteria into the selected bacterial combination.

[0154] To evaluate the actual contribution of the next candidate bacterial species to the synergistic degradation scheme, the marginal degradation contribution is calculated. The marginal degradation contribution is defined as the difference between the updated cumulative degradation rate and the previous cumulative degradation rate, and is calculated as follows: The marginal degradation contribution represents the actual increase in the degradation rate of the synergistic degradation scheme compared to the previous round after incorporating the next candidate bacterial species. If the marginal degradation contribution is small, it indicates that the candidate bacterial species is significantly affected by competitive inhibition, and its actual contribution is limited.

[0155] Further calculations of the marginal efficiency, defined as the ratio of the marginal degradation contribution to the degradation rate of the next candidate bacterial species, are performed. Marginal efficiency reflects the actual extent to which the degradation capacity of the next candidate bacteria is utilized in the synergistic degradation scheme. When the marginal efficiency is close to 1, it indicates that the degradation capacity of the candidate bacteria is almost fully utilized and is not significantly inhibited by competition; when the marginal efficiency is low, it indicates that the candidate bacteria is severely inhibited by competition from the selected bacteria, and its actual contribution is far lower than its theoretical degradation rate.

[0156] The calculated marginal efficiency is compared with the preset marginal admission threshold. A comparison is then made. The preset marginal admission threshold is a minimum marginal efficiency standard set based on resource input benefit analysis, typically between 0.3 and 0.5, to ensure that each included fungus can generate sufficient actual degradation contribution and avoid resource waste caused by introducing fungi with excessively low degradation efficiency. When the marginal efficiency is lower than the preset marginal admission threshold, it is determined that the actual contribution of the next candidate fungus is insufficient to compensate for its introduction cost and ecological intervention risk. A skip operation is then performed, and the candidate fungus is not included in the selected fungus combination. Instead, the next candidate fungus is directly selected from the fungus candidate sequence for further evaluation.

[0157] When the marginal efficiency is not lower than the preset marginal admission threshold, the next candidate fungus is determined to have sufficient marginal contribution value, and it is further determined whether the updated cumulative degradation rate has reached the target degradation rate threshold. If the updated cumulative degradation rate has not reached the target degradation rate threshold, i.e. This indicates that the overall degradation rate of the synergistic degradation scheme is insufficient to meet the load control target within the preset time limit, and more fungal species need to be included. The next candidate fungal species is formally included in the selected fungal combination, and the member list of the selected fungal combination is updated. The cumulative degradation rate from the previous round is updated. Accumulate degradation rate for the current update At the same time, update the current cumulative degradation rate. Also for This will serve as the benchmark value for the next round of iterative evaluation.

[0158] A second aspect of this invention provides an intelligent matching decision-making system for assessing forest combustible load and fungal disposal schemes, comprising:

[0159] The data acquisition unit is used to acquire remote sensing image data and meteorological monitoring data of the target forest area;

[0160] The layered load unit is used to identify the distribution of combustible pixels by vertically slicing remote sensing image data according to the height dimension, combined with spectral feature matching, and to construct a vertically layered load spatial representation based on geometric boundary features and cross-layer vertical propagation features, thereby generating layered load distribution data.

[0161] The litter load prediction unit is used to extract meteorological evolution characteristics from meteorological monitoring data, identify the alternating patterns of drought stress cycles and wet recovery cycles, generate the future litter load increment distribution, and perform spatiotemporal fusion with stratified litter load distribution data to obtain the litter load prediction distribution for future periods.

[0162] The fungal screening unit is used to acquire degradation rate data and environmental condition data of candidate fungi. By performing adaptive matching verification between environmental condition data and meteorological monitoring data, environmentally suitable fungi are screened out.

[0163] Grid division unit is used to divide the target forest area into multiple spatial grid units;

[0164] The scheme generation unit is used to select fungi for each spatial grid cell by combining the load prediction distribution and the degradation rate data of environmentally adaptable fungi, construct a multi-fungus collaborative degradation scheme that meets the preset time limit constraints, and generate a fungi deployment configuration.

[0165] Configure the output unit to output the fungal deployment configuration for each spatial grid unit.

[0166] A third aspect of the present invention provides an electronic device, comprising:

[0167] processor;

[0168] Memory used to store processor-executable instructions;

[0169] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0170] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0171] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0172] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligent matching and decision-making of forest combustible load assessment and fungal disposal schemes, characterized in that, include: Acquire remote sensing imagery and meteorological monitoring data of the target forest area; By vertically slicing remote sensing image data according to the height dimension and identifying the distribution of combustible pixels by spectral feature matching, a vertically layered load spatial representation is constructed based on geometric boundary features and cross-layer vertical propagation features, generating layered load distribution data. By extracting meteorological evolution characteristics from meteorological monitoring data, identifying the alternating patterns of drought stress cycles and wet recovery cycles, generating the future litter increment distribution, and spatiotemporally fusing it with stratified load distribution data, the load prediction distribution for future periods is obtained. Degradation rate data and environmental condition data of candidate bacteria were obtained. By adaptively matching the environmental condition data with meteorological monitoring data, environmentally suitable bacteria were screened out. The target forest area is divided into multiple spatial grid units; For each spatial grid cell, by combining the load prediction distribution with the degradation rate data of environmentally adaptable bacteria, bacteria are screened to construct a multi-bacterial collaborative degradation scheme that meets the preset time limit constraints, and a bacteria deployment configuration is generated. Output the fungal deployment configuration for each spatial grid unit.

2. The method according to claim 1, characterized in that, By vertically slicing remote sensing image data according to the height dimension and identifying the distribution of combustible material pixels through spectral feature matching, a vertically stratified load spatial representation is constructed based on geometric boundary features and cross-layer vertical propagation features, generating stratified load distribution data including: The remote sensing image data is vertically sliced ​​at preset height intervals. The multi-band spectral reflectance of each height slice is extracted. The multi-band spectral reflectance is matched with a preset vegetation spectral feature library for similarity. Pixels belonging to the combustible material category in each height slice are identified, and a combustible material pixel distribution map of each height slice is generated. Spatial neighborhood connectivity detection is performed on the combustible pixel distribution map, and spatially adjacent combustible pixels are merged into candidate patches. The biomass density coefficient is generated by combining the geometric boundary features and cross-layer vertical propagation features of the candidate patches. By coupling the patch area of ​​each candidate patch with the corresponding biomass density coefficient, the load value of each candidate patch is obtained. The load value is then bound to the height level and planar coordinates of each candidate patch in three-dimensional space to construct a load spatial representation structure containing vertical stratification information and horizontal spatial location information, thereby generating stratified load distribution data.

3. The method according to claim 2, characterized in that, Spatial neighborhood connectivity detection is performed on the combustible pixel distribution map, and spatially adjacent combustible pixels are merged into candidate patches. Combining the geometric boundary features and cross-layer vertical propagation features of the candidate patches, a biomass density coefficient is generated, including: Perform same-layer spatial neighborhood connectivity detection on the combustible pixel points in the combustible pixel distribution map, identify spatially adjacent combustible pixel clusters within the same height slice, mark each combustible pixel cluster as a candidate patch, extract the geometric boundary of each candidate patch and calculate the boundary complexity index. Extract the spatial overlap area of ​​candidate patches between adjacent height slices on the horizontal projection plane, calculate the overlap area ratio of each spatial overlap area, and extract the spectral reflectance difference between upper and lower candidate patches in each spatial overlap area. Perform weighted fusion calculation on the overlap area ratio and spectral reflectance difference to identify cross-layer connected patch groups with the potential for vertical fire spread. Calculate the corresponding height layer number and total vertical span for each cross-layer connected patch group to generate a vertical spread risk index. The vertical propagation risk index and the boundary complexity index are weighted together to generate a comprehensive fire risk coefficient. The comprehensive fire risk coefficient is then used as a correction factor to perform risk-weighted transformation on the spectral statistical characteristics of each candidate patch, generating a biomass density coefficient that considers spatial morphological characteristics and vertical propagation characteristics.

4. The method according to claim 1, characterized in that, By extracting meteorological evolution characteristics from meteorological monitoring data, identifying the alternating patterns of drought stress cycles and wet recovery cycles, and generating the future litter increment distribution, this data is spatiotemporally fused with stratified litter load distribution data to obtain the predicted litter load distribution for future periods, including: The temperature variation range, humidity variation range, and precipitation fluctuation range between adjacent time periods are extracted from meteorological monitoring data and combined into a meteorological evolution feature vector; The temporal phase relationship between temperature change amplitude and precipitation fluctuation amplitude in the meteorological evolution feature vector was identified, the alternation pattern of drought stress cycle and wet recovery cycle was determined, the duration of vegetation water deficit in the drought stress cycle was extracted, and the vegetation leaf fall intensity value was determined. The soil moisture recovery rate in the wet recovery cycle was extracted, and the degree of inhibition of litter microbial decomposition was determined. The difference between the vegetation leaf fall intensity value and the degree of inhibition of litter microbial decomposition value was superimposed to generate the litter net accumulation rate distribution at different height levels. Extract the combustible space connectivity paths of each height slice in the stratified load distribution data, identify the height slice locations where the combustible space connectivity paths are interrupted in the vertical direction, determine the future fallow increment values ​​to be preferentially allocated to the height slices corresponding to the interruption locations based on the net cumulative rate distribution of fallen debris and the load compensation capacity, generate the future fallow increment distribution, and overlay the future fallow increment distribution with the stratified load distribution data in three-dimensional space to generate the load prediction distribution for future periods.

5. The method according to claim 4, characterized in that, Extract the combustible space connectivity paths of each height slice in the stratified load distribution data, and identify the height slice locations where the combustible space connectivity paths are interrupted in the vertical direction, including: Vertical projection overlap detection is performed on the combustible pixels between adjacent height slices in the layered load distribution data, and the overlap area ratio of combustible pixels of the upper and lower height slices on the horizontal projection plane is calculated. When the overlap area ratio is lower than the preset connectivity determination threshold, the location between the upper height slice and the lower height slice is marked as the interruption position of the combustible space connectivity path. The load density value of the lower height slice corresponding to the interruption position is extracted, the difference between the load density value and the preset load saturation threshold is calculated, and the load compensation capacity is determined. Extract the net cumulative rate value of litter corresponding to the lower height slice from the net cumulative rate distribution of litter. Calculate the natural increment of litter based on a preset time window. When the natural increment of litter does not exceed the load compensation capacity, the natural increment of litter is used as the priority allocation value. When the natural increment of litter exceeds the load compensation capacity, the load compensation capacity is used as the priority allocation value. Generate future litter increment values ​​that are prioritized for allocation to the lower height slice.

6. The method according to claim 1, characterized in that, For each spatial grid cell, by combining the predicted load distribution with the degradation rate data of environmentally adapted bacteria, a multi-bacterial collaborative degradation scheme that meets the preset time constraints is constructed, generating a bacterial deployment configuration including: The load value and litter matrix component ratio of each spatial grid cell are extracted from the load prediction distribution. The target degradation rate threshold of the spatial grid cell is calculated by combining the preset time limit constraint with the load value. The litter matrix component ratio is cross-matched with the matrix degradation preference spectrum of each fungus in the degradation rate data of environmentally adapted fungi to calculate the contribution value of each fungus to the degradation efficiency of the spatial grid cell. The environmentally adaptable bacteria are sorted in descending order according to their degradation efficiency contribution value to generate a candidate sequence of bacteria. Bacteria are selected from the candidate sequence in turn, and the marginal efficiency is calculated based on the competition relationship between bacteria to screen the cumulative degradation rate value of bacteria. The selected bacterial combinations whose cumulative degradation rate value reaches the target degradation rate threshold are recorded as a synergistic degradation scheme. The degradation rate values ​​of different matrix types in the proportion of litter matrix components of each fungus in the collaborative degradation scheme are extracted. The degradation task sharing ratio of each fungus in the spatial grid cell is calculated. Based on the degradation task sharing ratio and load value, the release density requirement of each fungus is calculated. The release density requirement is bound to the spatial coordinates of the spatial grid cell to generate the fungus release configuration of the spatial grid cell.

7. The method according to claim 6, characterized in that, Environmentally adaptable bacteria are sorted in descending order according to their degradation efficiency contribution value to generate a candidate bacterial sequence. Bacteria are then selected sequentially from the candidate sequence, and the marginal efficiency is calculated based on the competition relationship between bacteria to screen the cumulative degradation rate values. The selected bacterial combinations whose cumulative degradation rate values ​​reach the target degradation rate threshold are recorded as synergistic degradation schemes, including: Select the fungus that ranks first in the candidate fungal sequence and record its corresponding degradation rate as the current cumulative degradation rate and the previous cumulative degradation rate. Select the next candidate bacteria, add the degradation rate of the next candidate bacteria to the current cumulative degradation rate to obtain the superimposed degradation rate, identify the competing bacteria in the selected bacteria combination that have a matrix competition relationship with the next candidate bacteria, count the number of competing bacteria, obtain the corresponding competition inhibition coefficient from the preset competition inhibition curve based on the number of competing bacteria, and use the competition inhibition coefficient to subtract the superimposed degradation rate to obtain the updated cumulative degradation rate. The difference between the updated cumulative degradation rate and the previous cumulative degradation rate is calculated as the marginal degradation contribution. The marginal efficiency is obtained by calculating the ratio of the marginal degradation contribution to the degradation rate of the next candidate bacteria. When the marginal efficiency is lower than the preset marginal admission threshold, skip the next candidate bacteria; when the marginal efficiency is not lower than the preset marginal admission threshold and the updated cumulative degradation rate has not reached the target degradation rate threshold, include the next candidate bacteria in the selected bacteria combination, update the previous round of cumulative degradation rate to the updated cumulative degradation rate, and continue to select the next candidate bacteria until the target degradation rate threshold is reached. Record the selected bacteria combination as a collaborative degradation scheme.

8. A forest combustible load assessment and fungal disposal scheme intelligent matching decision system, used to implement the method as described in any one of claims 1-7, characterized in that, include: The data acquisition unit is used to acquire remote sensing image data and meteorological monitoring data of the target forest area; The layered load unit is used to identify the distribution of combustible pixels by vertically slicing remote sensing image data according to the height dimension, combined with spectral feature matching, and to construct a vertically layered load spatial representation based on geometric boundary features and cross-layer vertical propagation features, thereby generating layered load distribution data. The litter load prediction unit is used to extract meteorological evolution characteristics from meteorological monitoring data, identify the alternating patterns of drought stress cycles and wet recovery cycles, generate the future litter load increment distribution, and perform spatiotemporal fusion with stratified litter load distribution data to obtain the litter load prediction distribution for future periods. The fungal screening unit is used to acquire degradation rate data and environmental condition data of candidate fungi. By performing adaptive matching verification between environmental condition data and meteorological monitoring data, environmentally suitable fungi are screened out. Grid division unit is used to divide the target forest area into multiple spatial grid units; The scheme generation unit is used to select fungi for each spatial grid cell by combining the load prediction distribution and the degradation rate data of environmentally adaptable fungi, construct a multi-fungus collaborative degradation scheme that meets the preset time limit constraints, and generate a fungi deployment configuration. Configure the output unit to output the fungal deployment configuration for each spatial grid unit.

9. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 7.

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