A GIS-based disaster situation awareness and assessment method

Abstract

This invention relates to the interdisciplinary field of geographic information engineering and disaster risk management, and discloses a GIS-based disaster situation perception and assessment method. The method involves collecting multi-source data of a disaster area and dividing it into measured and estimated grids to construct a spatial transmission and correlation network for the disaster. Subsequently, it integrates the disaster and geographical features of the measured grids to generate spatiotemporal coupling features, extracts environmental features of the estimated grids, and infers the situation of the estimated grids along the network extension path in conjunction with environmental intervention analysis. Next, it couples spatial chain triggering effects with temporal evolution trends to generate a global disaster development index. Then, based on the network topology and index-quantified transmission driving effects, it identifies key intervention nodes and simulates the overall situation differences before and after node blocking to assess prevention and control effectiveness. Finally, it weighs prevention and control effectiveness against environmental costs to generate an optimal prevention and control decision index, determines target nodes and resource deployment, and achieves dynamic simulation and precise prevention and control.

Description

Technical Field

[0001] This invention relates to the interdisciplinary field of geographic information engineering and disaster risk management, and more specifically to a GIS-based disaster situation perception and assessment method. Background Technology

[0002] Existing disaster situation awareness and assessment technologies mainly rely on the spatial analysis and visualization capabilities of Geographic Information Systems (GIS). By aggregating multi-source sensor networks, remote sensing images, and basic geographic information data, a spatial database covering disaster intensity, topography, and disaster-bearing body attributes is constructed. Spatial interpolation, layer overlay, exponential models, or empirical statistical methods are used to spatially map and statically assess the disaster impact range, hazard level, and vulnerability of disaster-bearing bodies in the areas covered by acquired monitoring data. At the same time, by combining historical disaster cases and disaster-causing factor thresholds, a digital expression of the current spatial pattern of the disaster and a quantitative classification of local risk status are achieved.

[0003] However, existing content technologies still have the following drawbacks:

[0004] Firstly, existing technologies mostly rely on spatial interpolation or static overlay methods to assess unknown areas, and only rely on spatial distance attenuation or empirical thresholds for estimation. They fail to incorporate geographical environmental characteristics such as topographic damping and land type vulnerability as dynamic intervention factors into the transmission calculation of disaster spread across grids. This results in the disaster situation assessment of the area to be assessed being detached from the constraints of the actual geographical environment, and the assessment results lack physical meaning and are not accurate enough.

[0005] Secondly, existing technologies typically treat disasters as discrete static sections for risk assessment, failing to construct a spatial transmission and correlation network that reflects the cross-grid topological diffusion path of disasters. They also cannot couple and extrapolate the cascading triggering effects between multiple grids in the spatial dimension with the evolution trend of monitoring data in the temporal dimension, making it difficult to dynamically quantify the overall development trend of the disaster situation.

[0006] Third, existing technologies typically treat disasters as discrete static sections for risk assessment, failing to construct a spatial transmission and correlation network that reflects the cross-grid topological diffusion path of disasters. They also cannot couple and extrapolate the cascading triggering effects between multiple grids in the spatial dimension with the evolution trend of monitoring data in the temporal dimension, making it difficult to dynamically quantify the overall development trend of the disaster situation.

[0007] Fourth, existing technologies often unilaterally pursue risk reduction effects when generating prevention and control decisions, failing to transform environmental characteristics such as terrain damping features and land vulnerability into prevention and control cost coefficients that reflect the difficulty of implementation and the risk of resource exposure. They also fail to comprehensively weigh prevention and control effectiveness against environmental costs, resulting in poor feasibility of the generated prevention and control decisions in actual complex geographical environments, making it difficult to achieve optimal deployment under limited resource constraints. Summary of the Invention

[0008] In order to overcome the above-mentioned deficiencies of the prior art, the present invention provides a GIS-based disaster situation perception and assessment method to solve the problems existing in the background art.

[0009] This invention provides the following technical solution: a GIS-based disaster situation awareness and assessment method, comprising:

[0010] S1: Collect time-series monitoring data and basic geographic information data of the disaster area, perform spatial grid division and data matching to divide the measured grid units and the grid units to be estimated, and construct a disaster spatial transmission correlation network;

[0011] S2: Based on the time-series monitoring data and basic geographic information data of the measured grid units, extract disaster state features and geographic environment features and fuse them to generate measured spatiotemporal coupling features. Based on the basic geographic information data of the grid units to be estimated, extract geographic environment features and generate environmental features to be estimated.

[0012] S3: Based on the spatial transmission and correlation network of disasters and the measured spatiotemporal coupling characteristics, analyze the spatial extension path of disaster impact, and combine the environmental characteristics to be estimated to conduct environmental intervention analysis on the disaster impact on the spatial extension path, and finally generate the disaster situation assessment results of the grid unit to be estimated.

[0013] S4: Based on the disaster situation assessment results and the disaster spatial transmission correlation network, analyze the chain triggering effect between the grid unit to be estimated and the measured grid unit, and combine the changing trend of time series monitoring data to extrapolate the evolution of the overall disaster situation and generate a disaster development index;

[0014] S5: Based on the disaster spatial transmission correlation network and disaster development index, analyze the transmission driving effect of each grid unit in the network on the overall disaster situation, generate a disaster prevention and control priority index, and identify key intervention nodes to block the spread of disaster;

[0015] S6: Based on the correlation network between key intervention nodes and disaster spatial transmission, analyze the differences in the overall disaster situation before and after the change of the status of key intervention nodes, and generate a disaster prevention and control effectiveness index;

[0016] S7: Based on the disaster prevention and control effectiveness index and the environmental characteristics to be estimated, conduct a comprehensive trade-off analysis on the prevention and control effectiveness and environmental characteristics of different key intervention nodes, generate the optimal prevention and control decision index, determine the corresponding target prevention and control nodes, and generate resource deployment identifiers for the target prevention and control nodes.

[0017] Preferably, S1 uses GIS spatial analysis technology to divide the target disaster area into regular grids, unifies the spatial coordinates and associates the acquired multi-source heterogeneous time-series monitoring data with basic geographic information data, so that grids covered by time-series monitoring data are defined as measured grid units, and grids without time-series monitoring data coverage but containing basic geographic information data are defined as grid units to be estimated.

[0018] The disaster element attributes and spatial adjacency relationships of each grid cell are extracted. The spatial transmission weights between adjacent and spatially similar grid cells are calculated based on the geographical environment attenuation characteristics and disaster spread dynamics. With each grid cell as a network node and the spatial transmission weights as directed edges, a disaster spatial transmission association network is constructed to characterize the cross-grid topological diffusion path of disaster impact.

[0019] Preferably, in step S2, for the measured grid cell, the monitoring value of its time-series monitoring data at the current moment is calculated as the disaster intensity value, and the change difference of the time-series monitoring data of adjacent time steps is calculated as the evolution trend value. The disaster intensity value and the evolution trend value constitute the disaster state characteristics. Based on the digital elevation model in its basic geographic information data, the grid slope is calculated, and the grid slope and the elevation difference between adjacent grids are substituted into the negative exponential decay function to calculate the terrain damping coefficient. The land use type in the basic geographic information data is calculated by looking up the table and assigning values ​​according to the preset vulnerability mapping table to obtain the land type vulnerability. The terrain damping coefficient and the land type vulnerability constitute the geographic environment characteristics. The disaster state characteristics and geographic environment characteristics of the same grid are mapped to the same multi-dimensional vector space for fusion by feature splicing and normalization operations to generate measured spatiotemporal coupling characteristics.

[0020] For the grid cell to be estimated, the topographic damping coefficient and land type vulnerability are obtained using the same calculation method as the measured grid cell based on its basic geographic information data to form the environmental features to be estimated. The values ​​of the dimensions corresponding to the disaster state features in the environmental features to be estimated are set to zero or missing to maintain the dimensional alignment with the measured spatiotemporal coupling features in the vector space.

[0021] Preferably, step S3 uses the measured grid cell as the search starting point, searches for connected paths along the directed edges of the disaster spatial transmission association network, determines the maximum topological span of the disaster's cross-grid spread, and generates the spatial extension path of the disaster's impact.

[0022] Along the spatial extension path, extract the measured spatiotemporal coupling characteristics of the upstream grid cells or the actual disaster impact value obtained from prior iterative calculations, and weight them with the spatial transmission weight of the directed edge they cross to obtain the initial transmission impact value transmitted to the current grid cell to be estimated.

[0023] The initial transmission impact value is interactively calculated with the environmental characteristics of the current grid cell to be estimated. Specifically, the initial transmission impact value is multiplied by the terrain damping coefficient for attenuation, and then the attenuated result is multiplied by the land type vulnerability for correction and amplification to calculate the actual disaster impact value of the current grid cell to be estimated.

[0024] The above weighted calculation and interactive calculation process is iterated until the actual disaster impact value is calculated for all grid cells to be estimated on the spatial extension path. The actual disaster impact value is compared with the preset disaster risk classification threshold range to determine the risk level of each grid cell to be estimated and generate a disaster situation assessment result that includes spatial location and risk level identifier.

[0025] Preferably, S4 is based on the disaster spatial transmission association network, extracts the spatial transmission weight between all adjacent grid units, takes the grid units with risk level marked as high risk or above as the trigger source, calculates the product of the spatial transmission weight of the trigger source to the adjacent grid unit along the directed edge and the risk level value of the trigger source as the cascade trigger probability, sums up the cascade trigger probability of all transmission paths in the network, and generates a global chain susceptibility.

[0026] Extract the evolution trend value of the measured grid cell, calculate the product of the global chain susceptibility and the evolution trend value, couple the spatial chain susceptibility with the disaster development trend in the time dimension, and calculate the disaster development index.

[0027] Preferably, S5 is based on the disaster spatial transmission and correlation network, and calculates the sum of the cascading trigger probabilities of the network loss after disconnecting each grid cell and its associated directed edges, which is used as the structural criticality.

[0028] Extract the actual disaster impact value of all downstream grid cells along the directed edge of each grid cell, and calculate the sum of the actual disaster impact values ​​of the downstream grid cells as the downstream threat amplitude.

[0029] The product of structural criticality, downstream threat magnitude, and disaster development index is calculated and used as the disaster prevention and control priority index for each grid cell. The grid cell with the highest disaster prevention and control priority index is identified as the key intervention node to block the spread of disaster.

[0030] Preferably, step S6 is based on a disaster spatial transmission and correlation network, removing key intervention nodes and their associated directed edges from the network to simulate a state change to a blocking state, and calculating the sum of the remaining cascading trigger probabilities in the network after removal as the residual chain susceptibility.

[0031] Extract the evolution trend value of the measured grid cell corresponding to the key intervention node, and calculate the product of the residual susceptibility and the evolution trend value as the residual disaster development index;

[0032] The difference between the disaster development index and the residual disaster development index is calculated, and the ratio of this difference to the disaster development index is calculated as the disaster prevention and control effectiveness index.

[0033] Preferably, step S7 extracts the topographic damping coefficient and land type vulnerability from the environmental features to be estimated corresponding to each key intervention node, and calculates the product of the topographic damping coefficient and land type vulnerability as the cost coefficient for prevention and control implementation.

[0034] The ratio of the disaster prevention and control effectiveness index of each key intervention node to the prevention and control implementation cost coefficient is used as the optimal prevention and control decision index.

[0035] The key intervention node with the largest optimal prevention and control decision index is identified as the target prevention and control node, and the spatial location of the target prevention and control node is extracted and combined with the risk level identifier generated in step S3 to generate a resource deployment identifier.

[0036] The technical effects and advantages of this invention are as follows:

[0037] (1) By extracting the topographic damping coefficient and land vulnerability of the grid cell to be estimated as the environmental features to be estimated, and multiplying the initial transmission influence value with the topographic damping coefficient along the spatial extension path for attenuation operation, and then multiplying it with the land vulnerability for correction and amplification operation, the geographical environmental features are incorporated as dynamic intervention factors into the transmission calculation of disaster spread across grids. This makes the disaster situation assessment of the area to be estimated closely fit the actual geographical environment constraints, effectively improving the physical meaning and accuracy of the assessment results.

[0038] (2) By constructing a disaster spatial transmission correlation network that characterizes the cross-grid topological diffusion path of disaster impact, extracting the spatial transmission weight between adjacent grids and the risk level of the triggering source to calculate the cascade triggering probability to generate global chain susceptibility, and calculating the product of global chain susceptibility and evolution trend value, the cascade triggering effect between multiple grids in the spatial dimension and the disaster development trend in the time dimension are coupled and deduced, so as to dynamically quantify the overall development trend of the disaster situation and generate a disaster development index.

[0039] (3) The sum of the cascading trigger probabilities of the loss after disconnecting each grid unit is calculated based on the disaster spatial transmission correlation network as the structural criticality. The disaster prevention and control priority index is generated by combining the downstream threat amplitude and the disaster development index to identify key intervention nodes. The relative difference between the disaster development index and the residual disaster development index is calculated by simulating the state change of key intervention nodes to generate the disaster prevention and control effectiveness index. This realizes the quantification of node transmission driving effect and intervention effect by combining network topology and global situation evolution, thereby ensuring that the identification of key nodes has topological globality and realizing accurate quantitative evaluation of prevention and control effectiveness.

[0040] (4) By extracting the topographic damping coefficient and land vulnerability from the environmental features to be estimated at key intervention nodes, the product of these factors is calculated as the prevention and control implementation cost coefficient. The ratio of the disaster prevention and control effectiveness index to the prevention and control implementation cost coefficient is calculated to generate the optimal prevention and control decision index. This achieves a comprehensive trade-off analysis between prevention and control effectiveness and environmental costs, thereby making the generated prevention and control decisions highly feasible in the actual complex geographical environment and effectively realizing the optimal deployment under the constraints of limited resources. Attached Figure Description

[0041] Figure 1 This is a diagram illustrating the method steps of the present invention.

[0042] Figure 2 This is a system structure block diagram of the present invention. Detailed Implementation

[0043] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. In addition, the forms of the various structures described in the following embodiments are merely illustrative. The GIS-based disaster situation perception and assessment method involved in the present invention is not limited to the structures described in the following embodiments. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] like Figure 1 The embodiment shown provides a GIS-based disaster situation awareness and assessment method, including:

[0045] S1: Collect time-series monitoring data and basic geographic information data of the disaster area, perform spatial grid division and data matching to divide the measured grid units and the grid units to be estimated, and construct a disaster spatial transmission correlation network.

[0046] In this embodiment, S1 divides the target disaster area into regular grids based on GIS spatial analysis technology, unifies the spatial coordinates and associates the acquired multi-source heterogeneous time-series monitoring data with basic geographic information data, so as to define the grids covered by time-series monitoring data as measured grid units, and define the grids without time-series monitoring data coverage but containing basic geographic information data as grid units to be estimated.

[0047] The disaster element attributes and spatial adjacency relationships of each grid cell are extracted. The spatial transmission weights between adjacent and spatially similar grid cells are calculated based on the geographical environment attenuation characteristics and disaster spread dynamics. With each grid cell as a network node and the spatial transmission weights as directed edges, a disaster spatial transmission association network is constructed to characterize the cross-grid topological diffusion path of disaster impact.

[0048] It should be specifically explained that, through an IoT sensor network deployed in the disaster area, real-time time-series monitoring data such as precipitation, temperature, humidity, and water level are collected, and basic geographic information data such as digital elevation models, land use types, and water system distribution are retrieved from the geographic information system database. Subsequently, based on GIS spatial analysis technology, the target disaster area is divided into regular grids. Spatial interpolation and projection transformation are used to unify the spatial coordinates of the multi-source heterogeneous time-series monitoring data and basic geographic information data. Based on spatial overlay analysis, the data with unified coordinates is assigned to the corresponding grids to complete attribute association. On this data matching basis, grids with spatial coverage of time-series monitoring data are defined as measured grid units, and grids without time-series monitoring data coverage but containing basic geographic information data are defined as grid units to be estimated. When constructing a disaster spatial transmission and correlation network, the disaster element attributes of each grid unit are extracted. The spatial adjacency and water flow and wind direction relationships between grids are extracted by combining the digital elevation model and water system distribution. The spatial transmission weights between adjacent and spatially close grid units are calculated based on the geographical environment attenuation characteristics and disaster spread dynamics. The geographical environment attenuation characteristics are quantified by elevation difference and friction distance. The disaster spread dynamics are calculated by using fluid or diffusion models according to the disaster type to calculate the migration probability. With each grid unit as a network node and the calculated spatial transmission weights as directed edges, a disaster spatial transmission and correlation network that characterizes the cross-grid topological diffusion path of disaster impact is constructed.

[0049] Specifically, the regular grid division adopts a regular square geographic grid. The grid size is adaptively determined based on the spatial scale characteristics of disaster spread and the spatial resolution of the basic geographic information data. Specifically, the minimum spatial resolution of the basic geographic information data can be used as the benchmark grid size, and adjusted by multiples based on the effective impact radius of disaster spread to ensure the consistency of geographic environmental attributes within a single grid. Disaster element attributes are defined as multi-dimensional feature vectors. The attribute vector of the measured grid unit is composed of the time-series monitoring data sequence and the basic geographic information data, while the attribute vector of the grid unit to be estimated is composed of the basic geographic information data and lacks the time-series dimension. When calculating the spatial transmission weight, the spatial transmission weight of adjacent and spatially close grid units i and j is calculated. The calculation formula is: ;in, The geographical environment attenuation coefficient is determined by... The calculation yielded, where This represents the absolute value of the elevation difference between the two grid cells in the digital elevation model. The distance between the geometric centers of the two grid cells is the Euclidean distance. The surface friction resistance coefficient is obtained by looking up a table based on the land use type of the two grids. This constitutes the friction distance. and The preset terrain damping coefficient and distance attenuation coefficient can be empirically calibrated based on historical disaster case data of the target disaster area, or adaptively obtained through cross-validation methods. The migration probability is calculated for disaster spread dynamics. The calculation process uses the disaster element attribute vector as the driving input: when the disaster type is flood, the measured water level and flow rate of grid i are used as initial conditions input into the one-dimensional hydrodynamic model. The proportion of the flow rate overflowing the shared boundary between grid i and grid j to the total outflow flow rate of grid i is calculated as... When the disaster type is forest fire, the temperature and humidity monitoring data of grid i are used as ignite conditions, combined with wind direction and slope aspect data, and input into the cellular automata model to calculate the probability of grid j being ignited. Finally, based on the above calculation results, the network construction operation is performed: First, all the measured grid cells and the grid cells to be estimated are directly mapped to a set of nodes in the graph structure; then, for any node i in the network and its topologically adjacent and spatially similar nodes j, the calculated... Assign the initial weight to the directed edge connecting node i and node j; then, set the initial weight... The comparison and selection are performed against a preset blocking threshold, which can be empirically determined based on historical disaster case data of the target disaster area. If the value is below the preset blocking threshold, the disaster propagation path from node i to node j is determined to be physically blocked, and the directed edge is removed from the network. If the weighted directed edge is greater than or equal to the preset blocking threshold, the directed edge is retained. Finally, the retained set of weighted directed edges and nodes is used to generate a disaster spatial transmission association network stored in the format of a sparse adjacency matrix or edge list. Only the topological paths that the disaster can actually cross for transmission are retained in the network.

[0050] S2: Based on the time-series monitoring data and basic geographic information data of the measured grid units, disaster state features and geographic environment features are extracted and fused to generate measured spatiotemporal coupling features. Based on the basic geographic information data of the grid units to be estimated, geographic environment features are extracted to generate environmental features to be estimated.

[0051] In this embodiment, S2 calculates the disaster intensity value of the time-series monitoring data of the measured grid cell at the current moment, and calculates the change difference of the time-series monitoring data of adjacent time steps as the evolution trend value. The disaster state feature is composed of the disaster intensity value and the evolution trend value. The grid slope is calculated based on the digital elevation model in the basic geographic information data, and the grid slope and the elevation difference between adjacent grids are substituted into the negative exponential decay function to calculate the terrain damping coefficient. The land use type in the basic geographic information data is calculated by looking up the table and assigning values ​​according to the preset vulnerability mapping table to obtain the land type vulnerability. The terrain damping coefficient and the land type vulnerability constitute the geographic environment feature. The disaster state feature and geographic environment feature of the same grid are mapped to the same multi-dimensional vector space for fusion by feature splicing and normalization operations to generate measured spatiotemporal coupling features.

[0052] For the grid cell to be estimated, the topographic damping coefficient and land type vulnerability are obtained using the same calculation method as the measured grid cell based on its basic geographic information data to form the environmental features to be estimated. The values ​​of the dimensions corresponding to the disaster state features in the environmental features to be estimated are set to zero or missing to maintain the dimensional alignment with the measured spatiotemporal coupling features in the vector space.

[0053] It should be specifically noted that, for the measured grid unit, the absolute monitoring value at the current moment is extracted from its time-series monitoring data as the disaster intensity value. Simultaneously, the difference between the current monitoring value and the monitoring value at the previous adjacent moment is calculated, and this difference is used as the evolution trend value. The disaster state characteristics of the grid are constituted by the disaster intensity value and the evolution trend value. The time-series monitoring data and difference calculation are specific to the disaster type: when the disaster is flooding, the real-time water level is extracted as the disaster intensity value, and the water level rise over adjacent time steps is calculated as the evolution trend value; when the disaster is forest fire, the real-time temperature is extracted as the disaster intensity value, and the temperature rise over adjacent time steps is calculated as the evolution trend value; when the disaster is landslide, the current cumulative displacement is extracted as the disaster intensity value, and the displacement increase over adjacent time steps is calculated as the evolution trend value. For the measured grid unit, digital elevation model and land use type data are extracted from the basic geographic information data. First, the slope value of the grid center point is calculated based on the digital elevation model, and the elevation difference between this grid and the adjacent target grid is obtained. Then, the elevation difference and slope value are input into the terrain resistance assessment logic. The logic follows a negative exponential decay law, that is, the larger the elevation difference and the steeper the slope, the smaller the calculated terrain damping coefficient. The terrain damping coefficient is calculated using the following formula: ,in This is the terrain attenuation coefficient. The elevation difference between the grid and its adjacent target grids is represented by s, where s is the slope value at the grid center point. A stronger topographic damping coefficient indicates a stronger barrier effect of the geographical environment on the spread of disasters. Conversely, a smaller elevation difference and a gentler terrain indicate a larger topographic damping coefficient. Next, land use types are matched against a pre-defined vulnerability mapping table to convert qualitative land types into quantitative land vulnerability values. For example, construction land and water areas are assigned higher vulnerability values, while forest land and grassland are assigned lower vulnerability values. Higher vulnerability values ​​indicate that the land type is more sensitive to disasters and more easily transmits them. The topographic damping coefficient and land vulnerability values ​​together constitute the geographical environmental characteristics of the grid. A feature splicing operation is used to concatenate the disaster state characteristics and geographical environmental characteristics calculated from the same measured grid in dimensional order to form a joint feature sequence. Subsequently, a max-min normalization method is used to linearly scale and map the values ​​of each dimension in the joint feature sequence to the desired value. Within the standardized range, numerical differences between different physical dimensions are eliminated to generate the final measured spatiotemporal coupling characteristics.

[0054] For the grid cells to be estimated, since they lack time-series monitoring data coverage from IoT sensors, it is impossible to calculate disaster intensity and evolution trend values. Therefore, based solely on their basic geographic information data, the same terrain resistance assessment logic and table lookup matching method as the measured grid are used to calculate the terrain damping coefficient and land type vulnerability to constitute the environmental features to be estimated. At the same time, in order to maintain strict alignment with the measured spatiotemporal coupling features in the feature space, zero values ​​or missing markers are filled in the dimension positions corresponding to the disaster state features in the environmental features to be estimated, so that the environmental features to be estimated and the measured spatiotemporal coupling features maintain the same dimensional structure for unified calculation by the subsequent network propagation inference model.

[0055] S3: Based on the spatial transmission and correlation network of disasters and the measured spatiotemporal coupling characteristics, analyze the spatial extension path of disaster impact, and combine the environmental characteristics to be estimated to conduct environmental intervention analysis on the disaster impact along the spatial extension path, and finally generate the disaster situation assessment results of the grid cells to be estimated.

[0056] In this embodiment, S3 takes the measured grid cell as the search starting point, searches for connected paths along the directed edges of the disaster spatial transmission association network, determines the maximum topological span of the disaster spreading across the grid, and generates the spatial extension path of the disaster impact.

[0057] Along the spatial extension path, extract the measured spatiotemporal coupling characteristics of the upstream grid cells or the actual disaster impact value obtained from prior iterative calculations, and weight them with the spatial transmission weight of the directed edge they cross to obtain the initial transmission impact value transmitted to the current grid cell to be estimated.

[0058] The initial transmission impact value is interactively calculated with the environmental characteristics of the current grid cell to be estimated. Specifically, the initial transmission impact value is multiplied by the terrain damping coefficient for attenuation, and then the attenuated result is multiplied by the land type vulnerability for correction and amplification to calculate the actual disaster impact value of the current grid cell to be estimated.

[0059] The above weighted calculation and interactive calculation process is iterated until the actual disaster impact value is calculated for all grid cells to be estimated on the spatial extension path. The actual disaster impact value is compared with the preset disaster risk classification threshold range to determine the risk level of each grid cell to be estimated and generate a disaster situation assessment result that includes spatial location and risk level identifier.

[0060] It should be specifically explained that generating the spatial extension path of disaster impact includes: taking the measured grid cell with available time-series monitoring data as the search starting point, and searching for connected paths along the direction of the directed edges in the disaster spatial transmission association network constructed in step S1 (i.e., the direction of physical spread of the disaster, such as the downstream direction of water flow or the downwind direction of fire); the maximum topological span refers to the topological distance of the farthest grid cell to be estimated that can be continuously reached from the starting point along the directed edges in the association network, and all the connected nodes and edges searched constitute the spatial extension path of the disaster that may spread across the grid.

[0061] The calculation of the initial transmission impact value includes: along the spatial extension path, the disaster impact is transmitted outward layer by layer. Disaster characterization data of upstream grid cells are extracted. If the current grid to be estimated is directly adjacent to the measured grid, the measured spatiotemporal coupling characteristics of the measured grid are extracted as the upstream input; if the current grid to be estimated is located in a more outer layer, the actual disaster impact value of the adjacent grid to be estimated obtained in previous iterations is extracted as the upstream input. The upstream input is weighted by the spatial transmission weight of the directed edge it crosses. Specifically, the disaster value of the upstream input is multiplied by the spatial transmission weight to obtain the initial transmission impact value without intervention from the current grid environment. This spatial transmission weight characterizes the attenuation effect of spatial distance and dynamic factors (such as gravity and wind direction) on the purely physical transmission of the disaster across grids.

[0062] Environmental intervention analysis involves interactive calculations between the initial transmitted impact value and the environmental characteristics of the current grid cell to be estimated. This includes attenuation and correction / amplification operations: First, the initial transmitted impact value is multiplied by the topographic damping coefficient for attenuation. The topographic damping coefficient is a value between 0 and 1, calculated based on topographic elevation and slope. The greater the elevation difference and the steeper the slope, the smaller the damping coefficient. This multiplication significantly reduces the transmitted disaster impact value, simulating the physical barrier effect of terrain on disaster spread. Next, the attenuated result is multiplied by land type vulnerability for correction / amplification. Land type vulnerability is also a value between 0 and 1. Vulnerable land types (such as cultivated land and construction land) have a vulnerability value close to 1, while disaster-resistant land types (such as forest land) have a lower vulnerability value. This multiplication differentiates the remaining disaster impact based on the underlying surface attributes, simulating the sensitivity of land cover to disaster destructive power. Finally, the actual disaster impact value of the current grid cell after environmental intervention is calculated.

[0063] The iterative process for generating assessment results includes: iteratively executing the aforementioned weighted calculation and interactive calculation process, advancing layer by layer from the inside out along the spatial extension path until the actual disaster impact value is calculated for all grid cells to be estimated along the path. Finally, the actual disaster impact value is compared with a preset disaster risk classification threshold range. For example, when the actual disaster impact value is greater than 0.8, it is determined to be extremely high risk; between 0.5 and 0.8, it is high risk; between 0.2 and 0.5, it is medium risk; and below 0.2, it is low risk. Based on the comparison results, the risk level of each grid cell to be estimated is determined. Combined with GIS spatial coordinates, vector surface or raster data containing spatial location and risk level identifiers (such as extremely high risk, high risk, medium risk, and low risk) is generated as the final disaster situation assessment result, realizing a complete closed loop from data quantification calculation to risk spatial visualization of disaster impact.

[0064] S4: Based on the disaster situation assessment results and the disaster spatial transmission correlation network, analyze the chain triggering effect between the grid unit to be estimated and the measured grid unit, and combine the changing trend of time series monitoring data to extrapolate the evolution of the overall disaster situation and generate a disaster development index.

[0065] In this embodiment, S4 is based on the disaster spatial transmission association network, extracts the spatial transmission weight between all adjacent grid units, takes the grid units with risk level marked as high risk or above as the trigger source, calculates the product of the spatial transmission weight of the trigger source along the directed edge to the adjacent grid unit and the risk level value of the trigger source as the cascade trigger probability, sums up the cascade trigger probability of all transmission paths in the network, and generates a global chain susceptibility.

[0066] Extract the evolution trend value of the measured grid cell, calculate the product of the global chain susceptibility and the evolution trend value, couple the spatial chain susceptibility with the disaster development trend in the time dimension, and calculate the disaster development index.

[0067] It should be specifically explained that, based on the disaster spatial transmission correlation network constructed in step S1, the spatial transmission weights between all adjacent grid cells are extracted; according to the disaster situation assessment results generated in step S3, grid cells with risk levels marked as high risk and extremely high risk are defined as trigger sources (i.e., grids where disasters have already occurred and have the ability to output destructive power), and the risk level value corresponding to the trigger source is extracted (for example, assigning low, medium, high, and extremely high risks to 1, 2, 3, and 4 respectively). The product of the spatial transmission weight of the trigger source along the directed edge to the adjacent grid cells and the risk level value of the trigger source is calculated, and this product is used as the cascade trigger probability; the cascade trigger probability comprehensively considers the severity of the upstream disaster and the unobstructedness of the spatial channel. The larger the value, the higher the possibility that adjacent grids will be triggered into disasters. For the entire disaster spatial transmission and correlation network, all directed edges that propagate outward from the triggering source are traversed, and all calculated cascading trigger probabilities are summed. That is, the triggering probability of all potential transmission paths in the network is accumulated to generate a global chain susceptibility. The global chain susceptibility is a comprehensive spatial index that reflects the domino-like chain spread reaction of the entire region under the current disaster spatial pattern. The higher the value, the stronger the spatial connectivity of the regional disaster and the easier it is to form a large-scale contiguous disaster. Then, extract the evolution trend value (such as the rate of rise in water level or the rate of rise in temperature) calculated from the measured grid cells in step S2, calculate the product of the global chain susceptibility and the evolution trend value, complete the coupled inference, and calculate the disaster development index. The coupled inference is to jointly consider the chain spread potential in the spatial dimension (global chain susceptibility) and the disaster aggravation dynamic in the time dimension (evolution trend value). If the evolution trend value is large (indicating that the disaster is deteriorating rapidly) and the global chain susceptibility is high (indicating that it is very easy to chain spread in space), the disaster development index calculated by the inference will be exponentially amplified, intuitively reflecting the intensity and risk of loss of control of the future evolution of the disaster situation.

[0068] S5: Based on the disaster spatial transmission correlation network and disaster development index, analyze the transmission driving effect of each grid unit in the network on the overall disaster situation, generate a disaster prevention and control priority index, and identify key intervention nodes to block the spread of disaster.

[0069] In this embodiment, S5 is based on the disaster spatial transmission and correlation network. After traversing and calculating the sum of the cascading trigger probabilities lost in the network after disconnecting each grid cell and its associated directed edges, it serves as the structural criticality.

[0070] Extract the actual disaster impact value of all downstream grid cells along the directed edge of each grid cell, and calculate the sum of the actual disaster impact values ​​of the downstream grid cells as the downstream threat amplitude.

[0071] The product of structural criticality, downstream threat magnitude, and disaster development index is calculated and used as the disaster prevention and control priority index for each grid cell. The grid cell with the highest disaster prevention and control priority index is identified as the key intervention node to block the spread of disaster.

[0072] It should be specifically explained that, based on the disaster spatial transmission and correlation network constructed in step S1, the node removal analysis method in graph theory is used to traverse each grid cell in the network. Assuming the currently traversed grid cell and all its associated directed edges are disconnected, the sum of the cascading trigger probabilities corresponding to the unreachable state of previously connected node pairs (especially between the trigger source node and the downstream estimated grid node) after disconnection is calculated. This sum of the cascading trigger probabilities of network loss is used as the structural criticality of the grid cell. The structural criticality reflects the importance of the grid cell as a "bridge" for disaster transmission; the larger the value, the more disaster transmission paths are blocked after removing the node, and the stronger the driving effect of the node on maintaining the network's disaster spread topology. The actual disaster impact values ​​calculated in step S3 for all downstream grid cells along the directed edges of each grid cell are extracted, and the actual disaster impact values ​​of the downstream grid cells are summed to obtain the downstream threat amplitude. The downstream threat amplitude reflects the potential damage scale caused by the disaster spreading downwards through the node if the current grid cell's defense line fails. The product of structural criticality, downstream threat magnitude, and disaster development index obtained in step S4 is calculated as the disaster prevention priority index for each grid cell. The disaster prevention priority index integrates spatial topological pivotality (structural criticality), potential destructive consequences (downstream threat magnitude), and temporal urgency (disaster development index). Finally, the grid cell with the largest disaster prevention priority index is identified as the key intervention node to block the spread of disaster.

[0073] Assume a disaster spatial transmission network includes measured grid cell O, intermediate grid cells A and B, and downstream grid cells C and D. The cascading trigger probability of directed edge O→A is 0.8, A→C is 0.7, A→D is 0.6, O→B is 0.5, and B→C is 0.4; the actual disaster impact values ​​of grids C and D are 3 and 2, respectively; the current disaster development index is 1.5. For grid A: assuming grid A and its associated edges are disconnected, the paths from O to C (via A, probability 0.7) and from O to D (via A, probability 0.6) become unreachable. The sum of the cascading trigger probabilities of network loss is 0.7 + 0.6 = 1.3, so the structural criticality of A is 1.3; the downstream of grid A are C and D, with a downstream threat amplitude of 3 + 2 = 5; therefore, the disaster prevention priority index of grid A = 1.3 (structural criticality) × 5 (downstream threat amplitude) × 1.5 (disaster development index) = 9.75. Similarly, for grid B: assuming that disconnecting grid B only results in the loss of the path from O to C (via B, probability 0.4), the structural criticality is 0.4; B's downstream is only C, with a downstream threat amplitude of 3; then the disaster prevention priority index of grid B = 0.4 × 3 × 1.5 = 1.8. In comparison, the prevention priority index of grid A (9.75) is much greater than that of grid B (1.8). Therefore, the system identifies grid A as the key intervention node for blocking disaster spread, because blocking A can cut off the most disaster transmission paths and protect the most downstream areas.

[0074] S6: Based on the correlation network between key intervention nodes and disaster spatial transmission, analyze the differences in the overall disaster situation before and after the change of the status of key intervention nodes, and generate a disaster prevention and control effectiveness index.

[0075] In this embodiment, S6 is based on the disaster spatial transmission and correlation network. Key intervention nodes and their associated directed edges are removed from the network to simulate a change in state to a blocking state. The sum of the remaining cascading trigger probabilities in the network after removal is calculated as the residual chain susceptibility.

[0076] Extract the evolution trend value of the measured grid cell corresponding to the key intervention node, and calculate the product of the residual susceptibility and the evolution trend value as the residual disaster development index;

[0077] The difference between the disaster development index and the residual disaster development index is calculated, and the ratio of this difference to the disaster development index is calculated as the disaster prevention and control effectiveness index.

[0078] It should be specifically explained that, based on the disaster spatial transmission and correlation network constructed in step S1, the simulation implements physical blocking measures (such as building dikes to block water and creating isolation zones) on key intervention nodes. At the algorithm level, the key intervention nodes and all their associated directed edges are removed from the network topology, so that they no longer have the function of transmitting disasters. For the residual network after removal, the cascading trigger probability of all remaining connected paths in the network is re-accumulated according to the same calculation logic as in step S4 to generate residual chain susceptibility. The residual chain susceptibility reflects the potential chain spread space base that still exists in the entire area after blocking the key node. The evolution trend value of the measured grid cell corresponding to the key intervention node (i.e., the disaster deterioration dynamic in the time dimension, which does not immediately disappear due to single-point blocking) is extracted, and the product of the residual chain susceptibility and the evolution trend value is calculated to generate the residual disaster development index. The residual disaster development index reflects the minimum residual intensity that the disaster situation may evolve in the future after the key node is physically blocked. The difference between the disaster development index (i.e., the original evolution intensity without intervention) generated in step S4 and the residual disaster development index is calculated. This difference represents the absolute amount by which the disaster evolution intensity is reduced after intervention. Since the absolute reduction amounts of disasters of different scales lack horizontal comparability, the ratio of this difference to the disaster development index is further calculated as the disaster prevention and control effectiveness index. The disaster prevention and control effectiveness index is essentially the relative decline rate of disaster evolution intensity, and its value is between 0 and 1. It eliminates the influence of the size of the disaster base and objectively reflects the contribution of key intervention nodes to curbing the overall disaster situation.

[0079] Assuming the disaster development index calculated in step S4 before intervention is 80; the system identifies grid K as a key intervention node in step S5, and the evolution trend value of the measured area corresponding to grid K is 2.0. Simulate blocking grid K: After removing grid K and its associated edges from the network, recalculate the sum of the remaining cascading trigger probabilities in the network, and obtain a residual chain susceptibility of 15; calculate the residual disaster development index = residual chain susceptibility (15) × evolution trend value (2.0) = 30; then calculate the prevention and control effectiveness: first calculate the absolute difference in the reduction of disaster evolution intensity = disaster development index before intervention (80) - residual disaster development index (30) = 50; then calculate the relative reduction rate = difference (50) ÷ disaster development index before intervention (80) = 0.625; then the disaster prevention and control effectiveness index is 0.625. This shows that blocking intervention for grid K can reduce the future evolution intensity of the overall disaster situation by 62.5%, verifying the high efficiency of this key intervention node.

[0080] S7: Based on the disaster prevention and control effectiveness index and the environmental characteristics to be estimated, conduct a comprehensive trade-off analysis on the prevention and control effectiveness and environmental characteristics of different key intervention nodes, generate the optimal prevention and control decision index, determine the corresponding target prevention and control nodes, and generate resource deployment identifiers for the target prevention and control nodes.

[0081] In this embodiment, step S7 extracts the topographic damping coefficient and land type vulnerability from the environmental features to be estimated corresponding to each key intervention node, and calculates the product of the topographic damping coefficient and land type vulnerability as the cost coefficient for prevention and control implementation.

[0082] The ratio of the disaster prevention and control effectiveness index of each key intervention node to the prevention and control implementation cost coefficient is used as the optimal prevention and control decision index.

[0083] The key intervention node with the largest optimal prevention and control decision index is identified as the target prevention and control node, and the spatial location of the target prevention and control node is extracted and combined with the risk level identifier generated in step S3 to generate a resource deployment identifier.

[0084] It should be specifically explained that by extracting the topographic damping coefficient and land vulnerability from the environmental characteristics to be estimated for each key intervention node, the product of the two is calculated as the prevention and control implementation cost coefficient. This cost coefficient reflects the engineering difficulty and environmental risk cost of implementing physical blocking measures at that node. A larger topographic damping coefficient indicates steeper terrain, making it more difficult for machinery and personnel to reach the site; a larger land vulnerability indicates more fragile surface cover, making forced construction more likely to trigger secondary disasters such as landslides. The product of the two couples spatial physical barriers with ecological vulnerability barriers; a larger product indicates a higher cost for implementing intervention measures. Then, the ratio of the disaster prevention and control effectiveness index of each key intervention node to the prevention and control implementation cost coefficient is calculated as the optimal prevention and control decision index. This optimal prevention and control decision index is essentially the cost-effectiveness ratio of disaster prevention intervention. The disaster prevention and control effectiveness index represents the benefits of implementing blocking measures, while the prevention and control implementation cost coefficient represents the costs incurred in implementing blocking measures. Using division to calculate the ratio of benefits to costs eliminates the blind spot of simply pursuing high benefits while ignoring engineering feasibility. A larger index indicates a higher prevention and control benefit per unit cost. By comparing the optimal prevention and control decision index of all key intervention nodes, the node with the largest ratio is determined as the target prevention and control node, that is, the disaster prevention interception point with the highest global cost-effectiveness. Subsequently, the spatial location coordinates of the target prevention and control node and the risk level identifier generated in step S3 are extracted, and the spatial location coordinates and risk level identifier are bound to a data structure to generate a resource deployment identifier. The resource deployment identifier is an instruction data packet containing precise positioning and urgency attributes, which is used to directly drive the scheduling decision and visualization rendering of downstream business systems. The spatial coordinates of the target prevention and control node are mapped to the corresponding layer of the electronic map. A preset graphic symbol matching the risk level is overlaid on the coordinate point for highlighting. At the same time, the boundary of the downstream impact range protected by the node after blocking a disaster is drawn. In addition, a corresponding resource scheduling list card is generated in the sidebar of the map interface. The card displays the coordinates, risk level, and recommended disaster prevention resource types and quantities for the target prevention and control node. The recommended disaster prevention resource types are determined based on the current disaster type and land vulnerability, and the recommended quantity is calculated based on the ratio of the downstream threat amplitude of the target prevention and control node to the preset unit resource disaster prevention effectiveness threshold. Through the above visualization, the abstract decision index is transformed into map graphics and scheduling information that can be intuitively interpreted by the command personnel.

[0085] Suppose that two key intervention nodes, A and B, are identified through the aforementioned steps. Node A has a disaster prevention and control effectiveness index of 0.6, a terrain damping coefficient of 0.8, and a land vulnerability of 0.5. Therefore, the prevention and control implementation cost coefficient for node A is 0.8 × 0.5 = 0.4, and the optimal prevention and control decision index is 0.6 ÷ 0.4 = 1.5. Node B has a disaster prevention and control effectiveness index as high as 0.9, but it is located in a steep mountainous area with a terrain damping coefficient of 0.95 and a land vulnerability of 0.8. Therefore, the prevention and control implementation cost coefficient for node B is 0.95 × 0.8 = 0.76, and the optimal prevention and control decision index is 0.9 ÷ 0.76 ≈ 1.18. The comparison shows that although node B has the highest interception effectiveness, its implementation cost is too high, resulting in a low cost-effectiveness ratio. The cost-effectiveness ratio of node A (1.5) is greater than that of node B (1.18), and the system ultimately identifies node A as the target prevention and control node. Subsequently, the latitude and longitude coordinates of node A (e.g., 118.5°E, 31.2°N) are extracted and combined with the "extremely high risk" flag determined in S3 to generate a resource deployment flag. After the GIS rendering engine reads the flag, it renders a flashing red blocking icon at the coordinates of 118.5°E, 31.2°N on the electronic map and pops up a dispatch card in the sidebar: "Target point A (extremely high risk), it is recommended to deploy 30 engineering machines and 5,000 sandbags to build a dam for interception," thus completing the closed loop of the entire process from data calculation to visual command and dispatch.

[0086] like Figure 2 This embodiment provides an implementation system for a GIS-based disaster situation awareness and assessment method, including a network construction module, a feature extraction module, an environmental intervention analysis module, an evolutionary deduction analysis module, a key intervention node identification module, a disaster prevention and control effectiveness index analysis module, and a result generation module. The network construction module is connected to the feature extraction module. The network construction module and the feature extraction module are both connected to the environmental intervention analysis module. The network construction module and the environmental intervention analysis module are both connected to the evolutionary deduction analysis module. The network construction module and the evolutionary deduction analysis module are both connected to the key intervention node identification module. The network construction module and the key intervention node identification module are both connected to the disaster prevention and control effectiveness index analysis module. The feature extraction module and the disaster prevention and control effectiveness index analysis module are both connected to the result generation module.

[0087] The associated network construction module collects time-series monitoring data and basic geographic information data of the disaster area, performs spatial grid division and data matching to divide the measured grid units and the grid units to be estimated, and constructs a disaster spatial transmission associated network;

[0088] The feature extraction module extracts disaster state features and geographic environment features based on the time-series monitoring data and basic geographic information data of the measured grid cells, and fuses them to generate measured spatiotemporal coupling features. Based on the basic geographic information data of the grid cells to be estimated, it extracts geographic environment features to generate environmental features to be estimated.

[0089] The environmental intervention analysis module analyzes the spatial extension path of disaster impact based on the disaster spatial transmission correlation network and measured spatiotemporal coupling characteristics, and performs environmental intervention analysis on the disaster impact along the spatial extension path in combination with the environmental characteristics to be estimated, and finally generates the disaster situation assessment results of the grid unit to be estimated.

[0090] The evolutionary deduction and analysis module analyzes the chain triggering effect between the grid unit to be estimated and the measured grid unit based on the disaster situation assessment results and the disaster spatial transmission correlation network, and combines the changing trend of time series monitoring data to perform the evolutionary deduction of the overall disaster situation and generate a disaster development index.

[0091] The key intervention node identification module analyzes the transmission and driving effect of each grid unit in the disaster spatial transmission correlation network and the disaster development index on the overall disaster situation, and generates a disaster prevention and control priority index to identify key intervention nodes that block the spread of disaster.

[0092] The disaster prevention and control effectiveness index analysis module analyzes the differences in the overall disaster situation before and after the change of the status of key intervention nodes based on the correlation network between key intervention nodes and disaster spatial transmission, and generates a disaster prevention and control effectiveness index.

[0093] The result generation module performs a comprehensive weighting analysis on the prevention and control effectiveness and environmental characteristics of different key intervention nodes based on the disaster prevention and control effectiveness index and the environmental characteristics to be estimated, generates the optimal prevention and control decision index, determines the corresponding target prevention and control node, and generates the resource deployment identifier of the target prevention and control node.

[0094] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0095] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A GIS-based disaster situation awareness and assessment method, characterized in that, include: S1: Collect time-series monitoring data and basic geographic information data of the disaster area, perform spatial grid division and data matching to divide the measured grid units and the grid units to be estimated, and construct a disaster spatial transmission correlation network; S2: Based on the time-series monitoring data and basic geographic information data of the measured grid units, extract disaster state features and geographic environment features and fuse them to generate measured spatiotemporal coupling features. Based on the basic geographic information data of the grid units to be estimated, extract geographic environment features and generate environmental features to be estimated. S3: Based on the spatial transmission and correlation network of disasters and the measured spatiotemporal coupling characteristics, analyze the spatial extension path of disaster impact, and combine the environmental characteristics to be estimated to conduct environmental intervention analysis on the disaster impact on the spatial extension path, and finally generate the disaster situation assessment results of the grid unit to be estimated. S4: Based on the disaster situation assessment results and the disaster spatial transmission correlation network, analyze the chain triggering effect between the grid unit to be estimated and the measured grid unit, and combine the changing trend of time series monitoring data to extrapolate the evolution of the overall disaster situation and generate a disaster development index; S5: Based on the disaster spatial transmission correlation network and disaster development index, analyze the transmission driving effect of each grid unit in the network on the overall disaster situation, generate a disaster prevention and control priority index, and identify key intervention nodes to block the spread of disaster; S6: Based on the correlation network between key intervention nodes and disaster spatial transmission, analyze the differences in the overall disaster situation before and after the change of the status of key intervention nodes, and generate a disaster prevention and control effectiveness index; S7: Based on the disaster prevention and control effectiveness index and the environmental characteristics to be estimated, conduct a comprehensive trade-off analysis on the prevention and control effectiveness and environmental characteristics of different key intervention nodes, generate the optimal prevention and control decision index, determine the corresponding target prevention and control nodes, and generate resource deployment identifiers for the target prevention and control nodes. 2.The GIS-based disaster situation awareness and assessment method according to claim 1, characterized in that, S1 uses GIS spatial analysis technology to divide the target disaster area into regular grids, and unifies the spatial coordinates and attributes of the acquired multi-source heterogeneous time-series monitoring data and basic geographic information data. In order to define the grids covered by time-series monitoring data as measured grid units, and the grids without time-series monitoring data coverage but containing basic geographic information data as grid units to be estimated. The disaster element attributes and spatial adjacency relationships of each grid cell are extracted. The spatial transmission weights between adjacent and spatially similar grid cells are calculated based on the geographical environment attenuation characteristics and disaster spread dynamics. With each grid cell as a network node and the spatial transmission weights as directed edges, a disaster spatial transmission association network is constructed to characterize the cross-grid topological diffusion path of disaster impact. 3.The GIS-based disaster situation awareness and assessment method according to claim 2, characterized in that, S2 calculates the disaster intensity value of the time series monitoring data of the measured grid cell at the current moment, and calculates the change difference of the time series monitoring data of adjacent time steps as the evolution trend value. The disaster state characteristics are composed of the disaster intensity value and the evolution trend value. The grid slope is calculated based on the digital elevation model in the basic geographic information data, and the terrain damping coefficient is obtained by substituting the grid slope and the elevation difference between adjacent grids into the negative exponential decay function. The land use type in the basic geographic information data is calculated by looking up the table and assigning values ​​according to the preset vulnerability mapping table. The terrain damping coefficient and the land use vulnerability constitute the geographic environment characteristics. By employing feature splicing and normalization operations, the disaster state features and geographical environment features of the same grid are mapped to the same multi-dimensional vector space for fusion, generating measured spatiotemporal coupled features; For the grid cell to be estimated, the topographic damping coefficient and land type vulnerability are obtained using the same calculation method as the measured grid cell based on its basic geographic information data to form the environmental features to be estimated. The values ​​of the dimensions corresponding to the disaster state features in the environmental features to be estimated are set to zero or missing to maintain the dimensional alignment with the measured spatiotemporal coupling features in the vector space. 4.The GIS-based disaster situation awareness and assessment method according to claim 3, characterized in that, S3 takes the measured grid cell as the search starting point, searches for connected paths along the directed edges of the disaster spatial transmission association network, determines the maximum topological span of the disaster spread across the grid, and generates the spatial extension path of the disaster impact. Along the spatial extension path, extract the measured spatiotemporal coupling characteristics of the upstream grid cells or the actual disaster impact value obtained from prior iterative calculations, and weight them with the spatial transmission weight of the directed edge they cross to obtain the initial transmission impact value transmitted to the current grid cell to be estimated. The initial transmission impact value is interactively calculated with the environmental characteristics of the current grid cell to be estimated. Specifically, the initial transmission impact value is multiplied by the terrain damping coefficient for attenuation, and then the attenuated result is multiplied by the land type vulnerability for correction and amplification to calculate the actual disaster impact value of the current grid cell to be estimated. The above weighted calculation and interactive calculation process is iterated until the actual disaster impact value is calculated for all grid cells to be estimated on the spatial extension path. The actual disaster impact value is compared with the preset disaster risk classification threshold range to determine the risk level of each grid cell to be estimated and generate a disaster situation assessment result that includes spatial location and risk level identifier.

5. The GIS-based disaster situation awareness and assessment method according to claim 4, characterized in that, The S4 method is based on the disaster spatial transmission association network. It extracts the spatial transmission weight between all adjacent grid cells, identifies grid cells with a risk level of high risk or above as trigger sources, calculates the product of the spatial transmission weight of the trigger source along the directed edge to the adjacent grid cells and the risk level value of the trigger source, and uses it as the cascade trigger probability. The cascade trigger probabilities of all transmission paths in the network are summed to generate a global chain susceptibility. Extract the evolution trend value of the measured grid cell, calculate the product of the global chain susceptibility and the evolution trend value, couple the spatial chain susceptibility with the disaster development trend in the time dimension, and calculate the disaster development index.

6. The GIS-based disaster situation awareness and assessment method according to claim 5, characterized in that, The S5 is based on the disaster spatial transmission and correlation network. After traversing and calculating the sum of the cascading trigger probabilities of the network loss after disconnecting each grid cell and its associated directed edges, it serves as the structural criticality. Extract the actual disaster impact value of all downstream grid cells along the directed edge of each grid cell, and calculate the sum of the actual disaster impact values ​​of the downstream grid cells as the downstream threat amplitude. The product of structural criticality, downstream threat magnitude, and disaster development index is calculated and used as the disaster prevention and control priority index for each grid cell. The grid cell with the highest disaster prevention and control priority index is identified as the key intervention node to block the spread of disaster.

7. A GIS-based disaster situation awareness and assessment method according to claim 6, characterized in that, The S6 method is based on a disaster spatial transmission and correlation network. Key intervention nodes and their associated directed edges are removed from the network to simulate a change in state to a blocking state. The sum of the remaining cascading trigger probabilities in the network after removal is calculated as the residual chain susceptibility. Extract the evolution trend value of the measured grid cell corresponding to the key intervention node, and calculate the product of the residual susceptibility and the evolution trend value as the residual disaster development index; The difference between the disaster development index and the residual disaster development index is calculated, and the ratio of this difference to the disaster development index is calculated as the disaster prevention and control effectiveness index.

8. The GIS-based disaster situation awareness and assessment method according to claim 7, characterized in that, The S7 extracts the topographic damping coefficient and land type vulnerability from the environmental features to be estimated corresponding to each key intervention node, and calculates the product of the topographic damping coefficient and land type vulnerability as the cost coefficient for prevention and control implementation. The ratio of the disaster prevention and control effectiveness index of each key intervention node to the prevention and control implementation cost coefficient is used as the optimal prevention and control decision index. The key intervention node with the largest optimal prevention and control decision index is identified as the target prevention and control node, and the spatial location of the target prevention and control node is extracted and combined with the risk level identifier generated in step S3 to generate a resource deployment identifier.

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