Cesium-based geological disaster emergency visualization integration method and system

Abstract

The invention discloses a Cesium-based geological disaster emergency visualization integration method and system, and relates to the technical field of image processing, and the method comprises the steps: collecting real-time multi-source geological disaster data of a geological disaster region, and carrying out the geological disaster prototype matching with a geological disaster prototype library, and obtaining a target geological disaster prototype; displaying an interference geological disaster area and a geological disaster area; obtaining a target disaster deduction model; time sequence monitoring analysis is carried out on the geological disaster area and the interference geological disaster area, a time sequence monitoring result of the geological disaster area and a time sequence monitoring result of the interference geological disaster area are determined, deduction analysis is carried out, a target deduction analysis result is obtained, and visual integrated display is carried out through a Cesium platform. The technical problem that in the prior art, the geological disaster real-time monitoring and disaster evolution dynamic deduction visualization integration capability is insufficient is solved, and the technical effect of integrated linkage of geological disaster real-time sensing, dynamic deduction and three-dimensional visualization is achieved.

Description

Technical Field

[0001] The present invention relates to the field of image processing technology, and particularly to a method and system for geological disaster emergency visualization integration based on Cesium. Background Art

[0002] In geological disaster monitoring and emergency treatment, ground sensors, remote sensing observations, etc. are usually used to obtain monitoring data such as rainfall, surface displacement, pore water pressure, ground motion, etc. However, limited by heterogeneous data sources, untimely updates, and limited coverage, it is difficult to achieve continuous real-time dynamic monitoring of geological disaster areas and interference areas. Disaster evolution analysis is mostly based on empirical formulas or single-factor driving models, and fails to fully combine multi-source time-series data for comprehensive deduction, resulting in insufficient accuracy and timeliness of predicting the development trend of disasters. In terms of visualization, disaster information is mostly displayed in the form of static map superposition, lacking real-time presentation and interactive analysis of dynamic processes such as the spatial expansion of disasters and changes in risk levels. There is data fragmentation between the monitoring, deduction, and visualization links, and information flow is sluggish, unable to form a complete closed-loop of disaster perception, evolution deduction, and auxiliary decision-making, and it is difficult to meet the application requirements of rapid response and efficient early warning for geological disasters. Summary of the Invention

[0003] The present application provides a method and system for geological disaster emergency visualization integration based on Cesium, which is used to solve the technical problem of insufficient visualization integration ability of the existing technology in real-time monitoring of geological disasters and dynamic deduction of disaster evolution.

[0004] In view of the above problems, the present application provides a method and system for geological disaster emergency visualization integration based on Cesium.

[0005] In the first aspect of the present application, a method for geological disaster emergency visualization integration based on Cesium is provided, and the method includes:

[0006] Collect real-time multi-source geological disaster data in the geological disaster area, perform geological disaster prototype matching with the geological disaster prototype library to obtain the target geological disaster prototype, where the target geological disaster prototype includes an interference bandwidth identifier; transmit the target geological disaster prototype, real-time multi-source geological disaster data, and the geological disaster area to the Cesium platform, and combine the interference bandwidth identifier to display the interference geological disaster area and the geological disaster area; perform matching in the disaster deduction model library based on the target geological disaster prototype to obtain the target disaster deduction model; according to the set of target geological disaster influencing factors corresponding to the target geological disaster prototype, perform time-series monitoring and analysis on the geological disaster area and the interference geological disaster area to determine the time-series monitoring results of the geological disaster area and the time-series monitoring results of the interference geological disaster area; use the target disaster deduction model to perform deduction analysis on the time-series monitoring results of the geological disaster area and the time-series monitoring results of the interference geological disaster area to obtain the target deduction analysis result; perform visual integration display of the target deduction analysis result through the Cesium platform.

[0007] In the second aspect of the present application, a geological disaster emergency visualization integration system based on Cesium is provided, and the system includes:

[0008] The first matching module is used to collect real-time multi-source geological disaster data in the geological disaster area, perform geological disaster prototype matching with the geological disaster prototype library to obtain the target geological disaster prototype, where the target geological disaster prototype includes an interference bandwidth identifier; the area display module is used to transmit the target geological disaster prototype, real-time multi-source geological disaster data, and the geological disaster area to the Cesium platform, and combine the interference bandwidth identifier to display the interference geological disaster area and the geological disaster area; the second matching module is used to perform matching in the disaster deduction model library based on the target geological disaster prototype to obtain the target disaster deduction model; the time-series monitoring and analysis module is used to perform time-series monitoring and analysis on the geological disaster area and the interference geological disaster area according to the set of target geological disaster influencing factors corresponding to the target geological disaster prototype to determine the time-series monitoring results of the geological disaster area and the time-series monitoring results of the interference geological disaster area; the deduction analysis module is used to use the target disaster deduction model to perform deduction analysis on the time-series monitoring results of the geological disaster area and the time-series monitoring results of the interference geological disaster area to obtain the target deduction analysis result; the visual integration display module is used to perform visual integration display of the target deduction analysis result through the Cesium platform.

[0009] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0010] This application collects real-time multi-source geological disaster data in the geological disaster area, performs geological disaster prototype matching with the geological disaster prototype library to obtain the target geological disaster prototype, where the target geological disaster prototype includes an interference bandwidth identifier; transmits the target geological disaster prototype, the real-time multi-source geological disaster data, and the geological disaster area to the Cesium platform, and combines the interference bandwidth identifier to display the interference geological disaster area and the geological disaster area; performs matching in the disaster deduction model library based on the target geological disaster prototype to obtain the target disaster deduction model; according to the set of target geological disaster influencing factors corresponding to the target geological disaster prototype, performs time-series monitoring and analysis on the geological disaster area and the interference geological disaster area to determine the time-series monitoring results of the geological disaster area and the time-series monitoring results of the interference geological disaster area; uses the target disaster deduction model to perform deduction analysis on the time-series monitoring results of the geological disaster area and the time-series monitoring results of the interference geological disaster area to obtain the target deduction analysis result; and performs visual integration display of the target deduction analysis result through the Cesium platform. The present invention solves the technical problem of the insufficient visualization integration ability of the prior art in real-time monitoring of geological disasters and dynamic deduction of disaster evolution. Through the integrated process of collecting real-time multi-source geological disaster data, prototype matching, disaster deduction model deduction analysis, and dynamic visualization display on the Cesium platform, the technical effect of realizing the integration of real-time perception, dynamic deduction, and three-dimensional visualization of geological disasters is achieved. Brief Description of the Drawings

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0012] Figure 1 It is a schematic flowchart of a method for visual integration of geological disaster emergency based on Cesium provided by an embodiment of this application;

[0013] Figure 2 It is a schematic structural diagram of a system for visual integration of geological disaster emergency based on Cesium provided by an embodiment of this application.

[0014] Description of the reference numerals: The first matching module 11, the area display module 12, the second matching module 13, the time-series monitoring and analysis module 14, the deduction analysis module 15, the visual integration display module 16. Detailed Embodiments

[0015] This application provides a method and system for visual integration of geological disaster emergency based on Cesium, aiming to solve the technical problem of insufficient visualization integration ability in real-time monitoring of geological disasters and dynamic deduction of disaster evolution in the prior art. Through the integration process of collecting real-time multi-source geological disaster data, prototype matching, disaster deduction model deduction analysis, and dynamic visualization display on the Cesium platform, the technical effect of realizing the integration of real-time perception, dynamic deduction, and three-dimensional visualization of geological disasters is achieved.

[0016] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0017] It should be noted that any variations of the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or server that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products, or devices.

[0018] Embodiment 1, as Figure 1 shown, this application provides a method for visual integration of geological disaster emergency based on Cesium, and the method includes:

[0019] Step S100: Collect real-time multi-source geological disaster data in the geological disaster area, perform geological disaster prototype matching with the geological disaster prototype library, and obtain the target geological disaster prototype, where the target geological disaster prototype includes an interference bandwidth identifier.

[0020] In the embodiments of the present application, first, real-time multi-source geological disaster data in the geological disaster area is collected, that is, information from multiple data sources such as remote sensing monitoring, meteorological perception, hydrological observation, and seismic detection is integrated to form a full-scale data set reflecting the state of geological disasters at the current moment. Subsequently, the real-time data is compared with the pre-constructed geological disaster prototype library, which is formed by aggregating similar samples of multi-source geological disaster data sets and sample interference bandwidth sets. Each prototype contains a set of benchmark multi-source geological disaster data and the corresponding interference bandwidth identifier.

[0021] In the matching stage, approximate matching calculations are performed on the real-time data and each benchmark data in the prototype library, and the proximity of each prototype to the real-time data is evaluated based on a set similarity metric (such as Euclidean distance). Finally, the prototype with the highest matching degree is selected as the target geological disaster prototype in the current situation, and the target geological disaster prototype includes an interference bandwidth identifier.

[0022] Further, in the method provided by the application embodiment, collecting real-time multi-source geological disaster data in the geological disaster area, performing geological disaster prototype matching with the geological disaster prototype library to obtain the target geological disaster prototype, further includes:

[0023] Obtaining a sample multi-source geological disaster data set and a corresponding sample interference bandwidth set, performing homogeneous aggregation on them to construct a geological disaster prototype, obtaining a geological disaster prototype library, wherein each geological disaster prototype includes benchmark multi-source geological disaster data; respectively performing approximate matching on the real-time multi-source geological disaster data with the benchmark multi-source geological disaster data of each geological disaster prototype in the geological disaster prototype library, and taking the geological disaster prototype corresponding to the benchmark multi-source geological disaster data with the highest matching degree as the target geological disaster prototype.

[0024] In the embodiment of the present application, first, a sample multi-source geological disaster data set and a corresponding sample interference bandwidth set are collected from historical cases. The multi-source data includes topographic and geomorphic features, rock and soil structure parameters, historical landslide records, meteorological data (such as rainfall, rainfall intensity), seismic wave intensity, epicenter distance, water level fluctuations, etc. The interference bandwidth set reflects the influence area width of each sample disaster event in the spatial or temporal dimension, usually derived from post-disaster remote sensing mapping or three-dimensional simulation data.

[0025] Then, M aggregation punctuation marks are randomly selected from the sample data set as representative central points. Here, the K-means initial central point random selection method is adopted to ensure that the clustering centers have coverage and representativeness, where M is a positive integer not less than 3. Subsequently, based on the selected M punctuation marks, K-means homogeneous aggregation analysis is performed on the entire sample data set, and the data is clustered into M clusters to obtain M aggregated sample multi-source geological disaster data sets. The samples within each cluster represent a set of typical disaster characteristics of a certain type. Then, mean shift analysis is performed on each aggregated sample set to extract the distribution center of the samples in this class as its typical representative, thereby obtaining M benchmark multi-source geological disaster data, that is, the core data representation of each disaster prototype. After determining the benchmark data, the mean values of the sample interference bandwidth sets within each aggregated cluster are calculated to obtain M interference bandwidth values, respectively representing the average perturbation range of this type of disaster in the spatial or temporal dimension. Finally, each benchmark data is combined with the corresponding interference bandwidth to form a geological disaster prototype including benchmark multi-source geological disaster data and interference bandwidth identification, and a complete geological disaster prototype library is constructed.

[0026] Next, collect real-time multi-source geological disaster data in the target area, and perform Euclidean distance similarity matching between the real-time multi-source geological disaster data and the benchmark data of each geological disaster prototype in the prototype library. Calculate the Euclidean distance between the real-time data and each prototype. The smaller the distance, the higher the matching degree. Finally, select the prototype with the smallest Euclidean distance as the target geological disaster prototype in the current scenario.

[0027] Further, in the method provided by the application embodiment, after obtaining the sample multi-source geological disaster data set and the corresponding sample interference bandwidth set and constructing geological disaster prototypes through homogeneous aggregation to obtain a geological disaster prototype library, it further includes:

[0028] Randomly extract M aggregation punctuation marks from the sample multi-source geological disaster data set, where M is a positive integer greater than or equal to 3; perform homogeneous aggregation on the sample multi-source geological disaster data set based on the M aggregation punctuation marks to obtain M aggregated sample multi-source geological disaster data sets; perform mean shift analysis on the M aggregated sample multi-source geological disaster data sets to determine M benchmark multi-source geological disaster data; perform interference bandwidth identification by combining the M aggregated sample multi-source geological disaster data sets and the sample interference bandwidth set to determine M interference bandwidths; use the M interference bandwidths to label the M benchmark multi-source geological disaster data to obtain the geological disaster prototype library.

[0029] In the embodiment of the present application, first, use the uniform random sampling method to randomly extract M aggregation punctuation marks from the sample multi-source geological disaster data set, where M is a positive integer not less than 3.

[0030] Next, based on the above M aggregation punctuation marks, perform the K-means clustering algorithm on the entire sample data set. By calculating the Euclidean distance between each sample and the current aggregation center, the samples are classified into the clusters represented by the nearest center, and finally M aggregated sample multi-source geological disaster data sets are generated, and each set represents a sample subset of a similar disaster form.

[0031] Subsequently, use the mean shift analysis method for each of the M aggregated sample multi-source geological disaster data sets respectively. By iteratively searching for the maximum density point in the feature space, the identification of the internal feature center of the cluster is realized, so as to extract the benchmark multi-source geological disaster data of each cluster and determine M benchmark multi-source geological disaster data.

[0032] Next, the interference bandwidth identification is carried out by combining the M aggregated sample multi-source geological disaster data sets and the sample interference bandwidth set. In this process, according to the corresponding relationship between the multi-source geological disaster data and the interference bandwidth, the sample interference bandwidth set is classified and aggregated according to the M aggregated sample multi-source geological disaster data sets, forming M aggregated sample interference bandwidth sets. Subsequently, the mean value calculation is performed on each set to extract the average disturbance range of this type of disaster in the historical samples, thereby obtaining M interference bandwidths.

[0033] Finally, the M interference bandwidths are respectively combined with the corresponding M benchmark multi-source geological disaster data in a structured manner, and M complete geological disaster prototypes are constructed by using the identification mapping method, and a geological disaster prototype library is summarized and formed.

[0034] Furthermore, in the method provided by the application embodiment, when the interference bandwidth identification is carried out by combining the M aggregated sample multi-source geological disaster data sets and the sample interference bandwidth set to determine M interference bandwidths, it further includes:

[0035] According to the one-to-one correspondence between the multi-source geological disaster data and the interference bandwidth, the sample interference bandwidth set is aggregated by combining the determined M aggregated sample multi-source geological disaster data sets to obtain M aggregated sample interference bandwidth sets; the mean value calculation is performed on the M aggregated sample interference bandwidth sets to obtain M interference bandwidths.

[0036] Furthermore, the method provided by the application embodiment further includes:

[0037] The similarity between any two of the M aggregated punctuation marks is less than or equal to the preset similarity threshold.

[0038] In the embodiment of the present application, first, according to the M aggregated sample multi-source geological disaster data sets obtained by the previous K-means clustering, combined with the one-to-one correspondence between the multi-source data and the interference bandwidth, the label index mapping algorithm is used to classify and organize the interference bandwidth set of the original samples. Specifically, the cluster label of each multi-source geological disaster sample data in the clustering is used as an index, and its corresponding interference bandwidth value is assigned to the same aggregated category, thereby forming M aggregated sample interference bandwidth sets. Each set corresponds to an interference feature data group of a disaster form, such as the landslide and collapse range, the lateral impact width of debris flow, etc.

[0039] After the aggregation is completed, the arithmetic mean statistical method is applied to each interference bandwidth set to calculate the average value of all bandwidth values in the set, obtaining M interference bandwidths. These average bandwidth values represent the typical disturbance ranges shown by various disasters in the historical samples and are used as interference bandwidth identifiers for the description of the subsequent prototype space influence range and the boundary control of the visualization model.

[0040] Meanwhile, to ensure that the extracted M aggregated punctuation marks have sufficient discriminability, a cosine similarity measurement strategy is adopted in the punctuation selection stage. A preset similarity threshold (for example, not higher than 0.8) is set, and only the point pairs with similarity less than or equal to the threshold are retained, avoiding the overlap of prototypes caused by the clustering centers being too close, and ensuring the independence and reliability of the disaster prototype library in subsequent deduction and analysis.

[0041] Step S200: Transmit the target geological disaster prototype, real-time multi-source geological disaster data, and the geological disaster area to the Cesium platform, and combine the interference bandwidth identifier to display the interference geological disaster area and the geological disaster area.

[0042] In the embodiment of the present application, the target geological disaster prototype, real-time multi-source geological disaster data, and the geological disaster area are jointly transmitted to the Cesium platform. First, a preset display template is called based on the prototype type, and then the original monitoring data is converted into the Cesium object format by using a format conversion algorithm to ensure its renderability on the platform. During the visualization process, using the interference bandwidth identifier in the target prototype, through the spatial buffer extension method, the core geological disaster area and the interference geological disaster area affected by the disturbance are respectively displayed on the Cesium platform, realizing the hierarchical visualization expression of the main disaster area and the extended influence area.

[0043] Furthermore, in the method provided by the embodiment of the application, when transmitting the target geological disaster prototype, real-time multi-source geological disaster data, and the geological disaster area to the Cesium platform and combining the interference bandwidth identifier to display the interference geological disaster area and the geological disaster area, it further includes:

[0044] Calling the display template of the Cesium platform based on the target geological disaster prototype; performing format conversion on the real-time multi-source geological disaster data to obtain the converted real-time multi-source geological disaster data in the object format conforming to Cesium; based on the display template, displaying the geological disaster area on the Cesium platform according to the converted real-time multi-source geological disaster data, and displaying the interference geological disaster area determined based on the interference bandwidth identifier.

[0045] In the embodiment of the present application, first, a display template of the Cesium platform is called based on the target geological disaster prototype. This display template is a pre-defined disaster type mapping configuration file, which includes the three-dimensional symbol expression, layer style, coloring rule, and label binding logic of different geological disaster categories such as landslides, debris flows, surges, and earthquakes in the Cesium platform, and is used to uniformly control the presentation methods of different disaster events in the visualization environment. According to the disaster type field recorded in the target geological disaster prototype, through the template indexing mechanism, the content of the matching display template is loaded to ensure that the disaster display conforms to its physical characteristics and spatial representation.

[0046] Subsequently, data structure processing is performed on the real-time multi-source geological disaster data in the geological disaster area collected. Using the GeoJSON-to-Cesium Entity structure conversion method, multi-source data such as original longitude and latitude coordinates, altitude information, rainfall, surface deformation, and magnitude parameters are converted into real-time multi-source geological disaster data that conforms to the object format of Cesium, that is, converted into the CZML format or Entity JSON format supported by the Cesium platform to meet its technical requirements for three-dimensional rendering, timeline control, and data interaction. The converted data retains all spatial features and attribute information and can be directly used for geospatial loading and layer binding.

[0047] After completing the data conversion, based on the display template, the geological disaster area is displayed in the Cesium platform according to the converted real-time multi-source geological disaster data. Specifically, by calling the layer rendering interface of the Cesium platform, the geological disaster area is superimposed on the three-dimensional map in the form of a planar entity or a dynamic boundary, and label visualization is displayed in cooperation with attributes such as disaster intensity value and timestamp to achieve real-time visual expression of the state of the geological disaster area.

[0048] At the same time, the interference bandwidth identifier in the target geological disaster prototype is parsed, and a spatial expansion layer is automatically generated outside the boundary of the geological disaster area using the buffer generation algorithm, and the interference geological disaster area determined based on the interference bandwidth identifier is displayed. This area is rendered in a semi-transparent style or a dashed boundary to indicate the extended impact area that the current disaster may affect, thereby assisting users in intuitively identifying the relationship between the disaster body and the potential interference range in the Cesium platform.

[0049] Step S300: Match based on the target geological disaster prototype in the disaster deduction model library to obtain the target disaster deduction model.

[0050] In the embodiment of the present application, first, a match is made based on the target geological disaster prototype in the disaster deduction model library to obtain the target disaster deduction model. Specifically, first, the benchmark multi-source geological disaster data included in the target geological disaster prototype is extracted as the standardized feature set of the current disaster scenario. The benchmark multi-source geological disaster data includes key parameters such as terrain slope, geological type, soil moisture content, cumulative rainfall, and earthquake intensity.

[0051] Next, the feature vector construction method is used to standardize the benchmark multi-source geological disaster data into feature vectors of a unified scale. The unified format facilitates direct matching with the model template. The dimension of each feature vector corresponds to the key environmental or physical parameters required for disaster evolution, such as slope, rainfall rate, magnitude, etc.

[0052] Subsequently, the eigenvector similarity matching method is used, and the cosine similarity is adopted as the similarity measurement criterion. The constructed benchmark eigenvectors are compared with the input feature templates of each preset model in the disaster deduction model library in turn. Each model presets a corresponding feature template when building the library, which describes the disaster types, feature conditions, and trigger mechanism ranges applicable to the model.

[0053] By calculating the similarity scores, the model closest to the current benchmark multi-source geological disaster data features is selected. To ensure that the selected deduction model has the best adaptability in practical applications, the similarity threshold screening method is adopted. Only when the cosine similarity is higher than the preset threshold (e.g., 0.85), the corresponding model is listed as a candidate.

[0054] Finally, among all the candidate models that meet the similarity requirements, an optimal matching deduction model is directly determined according to the highest similarity score, which is used as the target disaster deduction model in the current disaster scenario.

[0055] Furthermore, the method provided by the application embodiment further includes:

[0056] The disaster deduction model library includes a landslide-tsunami intelligent deduction model, an earthquake-rainfall-landslide intelligent deduction model, an earthquake-rainfall-debris flow intelligent deduction model, and a dam-break intelligent deduction model.

[0057] In the embodiment of the present application, the disaster deduction model library includes a landslide-tsunami intelligent deduction model, an earthquake-rainfall-landslide intelligent deduction model, an earthquake-rainfall-debris flow intelligent deduction model, and a dam-break intelligent deduction model.

[0058] For the landslide-tsunami intelligent deduction model, the training data used during training are historical landslide-into-water event data, including physical parameters such as landslide volume, landslide entry speed, entry angle, and water depth. The data are sourced from on-site geological surveys and post-disaster monitoring records. The training method adopts the shallow water equation numerical simulation method. By solving the changes in water body movement under the disturbance of landslide entry into water, a dynamic process model of landslide entry-induced tsunami is established. The input data are landslide volume, entry speed, and water depth, and the output data are the maximum wave height of the tsunami, wave propagation speed, and tsunami diffusion path.

[0059] For the earthquake-rainfall-landslide intelligent deduction model, the training data used during training are historical earthquake and heavy rainfall-induced landslide event data, including environmental and geological information such as magnitude, epicentral distance, surface rainfall, rainfall duration, terrain slope, and rock and soil types. The data are sourced from seismic networks, meteorological stations, and geological surveys. The training method adopts the support vector machine (SVM) classification method to establish the classification boundary between earthquakes, rainfall, and landslide occurrence. The input data are magnitude, rainfall, slope, and rock and soil types, and the output data are the probability of landslide occurrence, the boundary of potential landslide areas, and the initial sliding direction.

[0060] For the intelligent deduction model of earthquake rainfall - debris flow, the training data used during training are the debris flow event data that occurred under historical earthquake and rainfall conditions, including information such as the distribution of geological fracture zones, cumulative rainfall, basin slope, soil moisture content, and earthquake intensity. The data are sourced from geological disaster investigations and meteorological and hydrological monitoring data. The training method uses the numerical simulation method of debris flow dynamics to simulate the evolution of debris flow by calculating the initiation, flow, and deposition processes of the materials. The input data are the positions of geological fracture zones, rainfall amounts, slopes, and soil moisture contents, and the output data are the flow trajectories of debris flow, velocity changes, and the scope of deposition areas.

[0061] By means of this way with standard training data, using a single method at each step, and clear corresponding input and output, the establishment of the disaster deduction model library is completed.

[0062] Furthermore, the method provided by the application embodiment further includes:

[0063] Collect water level, rainfall, earthquake data, and dam body monitoring data of the same time series multiple times to obtain a sample dam body stability monitoring data set and the corresponding dam body breach risk data set; use the meshless numerical simulation method to construct a dam body breach intelligent deduction model according to the sample dam body stability monitoring data set and the dam body breach risk data set.

[0064] In the embodiment of the present application, first, the time - synchronous data acquisition method is adopted. By arranging an automated monitoring system on the dam body, including devices such as water level gauges, rain gauges, seismic acceleration meters, displacement meters, piezometers, and strain gauges, the water level, rainfall, seismic motion parameters, and the structural response data inside and on the surface of the dam body are collected multiple times at the same moment. Through high - frequency periodic synchronous sampling, it is ensured that all data points have a unified time stamp, forming a structurally complete time - series sample, and constructing a sample dam body stability monitoring data set.

[0065] Then, through the manual annotation method, combined with the existing records of dam body breach accidents or danger situations, the working condition segments highly relevant to the actual breach process in the historical monitoring data are analyzed. According to key indicators (such as sudden strain changes, sharp increases in pore water pressure, etc.), the risk levels are manually screened and marked to construct a dam body breach risk data set. This set serves as the output label in supervised learning and provides a reference under real working conditions for model training.

[0066] During the model construction process, the meshless numerical simulation method, specifically the smoothed particle hydrodynamics method (SPH), is used to discretize the dam structure into multiple particle units. The stress-strain evolution relationship is calculated between the particles according to physical rules. In the simulation, the sample dam stability monitoring data set is used as the simulation input boundary condition, and the water pressure, seismic acceleration, and seepage load at different time nodes are applied to simulate the dynamic response behavior between the particle groups and reconstruct the structural evolution process of the dam under multi-source disturbances.

[0067] Subsequently, the supervised learning training method is adopted to compare the structural response results output by the numerical simulation (such as local displacement, principal strain, critical slip band range, etc.) with the aforementioned dam breach risk data set to construct an input-output mapping relationship. During the training process, a fixed loss function is used to evaluate the error, and the physical parameters and material response control values in the simulation model are gradually updated, so that the model has the ability to distinguish and predict the breach risk.

[0068] After the training is finally completed, a dam breach intelligent deduction model that can perform rapid response simulation and breach risk judgment driven by new monitoring data is constructed.

[0069] Step S400: According to the set of target geological disaster influencing factors corresponding to the target geological disaster prototype, perform time-series monitoring and analysis on the geological disaster area and the interfering geological disaster area to determine the time-series monitoring results of the geological disaster area and the time-series monitoring results of the interfering geological disaster area.

[0070] In the embodiment of the present application, according to the set of target geological disaster influencing factors corresponding to the target geological disaster prototype, time-series monitoring and analysis are respectively performed on the geological disaster area and the interfering geological disaster area within a preset monitoring window, and the corresponding influencing factor set sequences are collected and generated. Subsequently, fluctuation variance analysis is performed on the influencing factor set sequences of these two types of areas, and the fluctuation coefficients of the geological disaster area and the interfering geological disaster area are respectively calculated, and the respective convolution scales are determined based on this fluctuation characteristic.

[0071] On this basis, further convolution analysis is respectively performed on the two types of sequences to extract the change trend and disturbance pattern from the multi-scale time characteristics, and finally the time-series monitoring results of the geological disaster area and the time-series monitoring results of the interfering geological disaster area are obtained.

[0072] Furthermore, in the method provided by the application embodiment, performing time-series monitoring and analysis on the geological disaster area and the interfering geological disaster area according to the set of target geological disaster influencing factors corresponding to the target geological disaster prototype to determine the time-series monitoring results of the geological disaster area and the time-series monitoring results of the interfering geological disaster area further includes:

[0073] According to the set of target geological disaster influencing factors corresponding to the target geological disaster prototype, temporal monitoring and analysis are carried out on the geological disaster area and the interfering geological disaster area within a preset monitoring window to obtain a sequence of geological disaster area influencing factor sets and a sequence of interfering geological disaster area influencing factor sets; perform fluctuation variance analysis on the sequence of geological disaster area influencing factor sets and the sequence of interfering geological disaster area influencing factor sets to determine the geological disaster area fluctuation coefficient and the interfering geological disaster area fluctuation coefficient; determine the convolution scale according to the geological disaster area fluctuation coefficient and the interfering geological disaster area fluctuation coefficient respectively, and perform convolution analysis on the sequence of geological disaster area influencing factor sets and the sequence of interfering geological disaster area influencing factor sets to obtain the temporal monitoring results of the geological disaster area and the temporal monitoring results of the interfering geological disaster area.

[0074] In the embodiment of the present application, first, a data acquisition method based on the feature mapping of the target geological disaster prototype is adopted. According to the set of target geological disaster influencing factors corresponding to the target geological disaster prototype, continuous high-frequency temporal data acquisition is carried out for the geological disaster area and the interfering geological disaster area respectively within a preset monitoring window. The set of influencing factors is preset according to the disaster prototype. For example, for the landslide prototype, rainfall, pore water pressure, and ground surface displacement data are collected, and for the debris flow prototype, cumulative rainfall, runoff, and slope change rate are collected. Through the integration of automated monitoring sensors, remote sensing observations, and ground station data, a complete and continuous sequence of geological disaster area influencing factor sets and a sequence of interfering geological disaster area influencing factor sets are formed under the unified time axis.

[0075] After data collection is complete, a sliding time window fluctuation variance calculation method is used to extract short-term fluctuation characteristics for the set of factors affecting the geohazard region and the set of factors affecting the interfering geohazard region. The specific process involves setting a sliding window of fixed length (e.g., 24 hours). Within each sliding window, the mean of the sampled data is first calculated. Based on the mean, the sum of squared deviations of each sampling point from the mean is then calculated to obtain the variance within the window, completing the variance sequence extraction. This method obtains the local fluctuation amplitude of each influencing factor within each monitoring window, providing a quantitative description of the dynamic level within the region. After obtaining the local fluctuation variance of each influencing factor, a weighted synthesis method based on preset weights is used to determine the fluctuation coefficients for the geohazard region and the interfering geohazard region. During the initialization phase, a corresponding weight is preset for each influencing factor corresponding to each hazard prototype. For example, in the landslide prototype, the weight for pore water pressure is set to 0.5, the weight for rainfall is set to 0.3, and the weight for surface displacement is set to 0.2. These weights are fixed and match the hazard mechanism. Then, for the geological hazard area, a weighted summation method is used to multiply the variance values of each influencing factor by the corresponding weight and then sum them up to finally determine the geological hazard area fluctuation coefficient. Simultaneously, the same method is used to perform weighted summation on the variance series of the influencing factors of the interference geological hazard area to determine the interference geological hazard area fluctuation coefficient.

[0076] After determining the fluctuation coefficients of the geological hazard area and the interference geological hazard area, a fluctuation amplitude-driven adaptive convolution scale setting method is used to set the convolution processing scale according to the fluctuation intensity of different areas. The setting standard is that when the fluctuation coefficient is greater than 15, the convolution scale is set to 3 hours to capture rapid and drastic changes; when the fluctuation coefficient is between 8 and 15, the convolution scale is set to 6 hours to adapt to the medium-speed evolution process; when the fluctuation coefficient is less than 8, the convolution scale is set to 12 hours to highlight the chronic change trend. For example, the fluctuation coefficient of the geological hazard area is 19.1, which is a high fluctuation level, so the convolution scale is set to 3 hours, while the fluctuation coefficient of the interference geological hazard area is 7.5, which is a low fluctuation level, so the convolution scale is set to 12 hours, realizing adaptive scale adjustment based on the frequency of change.

[0077] After the convolution scale is set, the multi-scale convolution feature extraction method is adopted to perform convolution analysis on the sequence of the influencing factor set of the geological disaster area and the sequence of the influencing factor set of the interfering geological disaster area respectively. According to the set convolution scale of each, local sliding weighted calculation is applied to perform local smoothing and change trend enhancement of the time series data. Through the convolution operation, important disaster evolution features such as sudden short-term increase in pore water pressure, sharp rise in rainfall intensity, and acceleration stage of displacement are extracted, and random noise interference is filtered out. In the geological disaster area with a convolution scale of 3 hours, the process of sharp rise in pore pressure within 24 hours is captured, while in the interfering geological disaster area with a 12-hour scale, the chronic displacement growth trend is highlighted. Finally, the time series monitoring results of the geological disaster area and the time series monitoring results of the interfering geological disaster area are obtained respectively.

[0078] Step S500: Use the target disaster deduction model to perform deduction analysis on the time series monitoring results of the geological disaster area and the time series monitoring results of the interfering geological disaster area to obtain the target deduction analysis result.

[0079] In the embodiment of the present application, after obtaining the time series monitoring results of the geological disaster area and the time series monitoring results of the interfering geological disaster area, use the target disaster deduction model to perform deduction analysis on the time series monitoring results of the geological disaster area and the time series monitoring results of the interfering geological disaster area to obtain a dynamic prediction of the development of the disaster.

[0080] Specifically, first call the target disaster deduction model that matches the current disaster prototype characteristics. The physical mechanism of disaster evolution or the data-driven evolution path is defined inside this model to ensure that the deduction conforms to the disaster cause logic. In the deduction analysis stage, the input feature standardization method is adopted to uniformly map the core influencing factor sequences in the time series monitoring results of the geological disaster area and the time series monitoring results of the interfering geological disaster area, such as rainfall intensity change, pore water pressure evolution, and surface displacement trend, into the standard input format required by the deduction model. After completing the input standardization, use the dynamic evolution process deduction method to recursively deduce the change trend of each influencing factor in the future time period and simulate the dynamic response process of the main disaster area and the interfering area evolving over time. During the deduction process, the geological disaster area is the main affected area of the disaster, and its input features dominate the core trend of disaster development. The interfering geological disaster area is the secondary response area, reflecting the surrounding disaster conduction effect. The overall disaster evolution trajectory is formed through coupled deduction. Finally, through the above deduction process, the target deduction analysis result is output, which comprehensively depicts the future disaster evolution trend and the expansion range.

[0081] Step S600: Visually integrate and display the target deduction analysis result through the Cesium platform.

[0082] In the embodiment of the present application, after obtaining the target deduction analysis result, the target deduction analysis result is visually integrated and displayed through the Cesium platform. Specifically, first, format conversion processing is performed on the target deduction analysis result to standardize data such as disaster evolution trends, spatial expansion boundaries, and risk level changes into a visual format supported by the Cesium platform, such as GeoJSON format, CZML format, or 3D Tiles format. Subsequently, the corresponding visual interface is called on the Cesium platform. Based on the spatial position information and time change information in the deduction analysis result, a three-dimensional visual entity of the geological disaster area is created, and different disaster evolution stages are distinguished by different colors, transparencies, or dynamic animation effects. At the same time, driven by the time axis control, the expansion process of the geological disaster area evolving over time is dynamically displayed. Finally, a spatial, dynamic, and interactive integrated display of the target deduction analysis result is achieved on the Cesium platform.

[0083] In the embodiment of the present application, in summary, the embodiment of the present application has at least the following technical effects:

[0084] The present application collects real-time multi-source geological disaster data in the geological disaster area, performs geological disaster prototype matching with the geological disaster prototype library to obtain the target geological disaster prototype, wherein the target geological disaster prototype includes an interference bandwidth identifier; the target geological disaster prototype, the real-time multi-source geological disaster data, and the geological disaster area are transmitted to the Cesium platform, and combined with the interference bandwidth identifier, the interference geological disaster area and the geological disaster area are displayed; based on the target geological disaster prototype, matching is performed in the disaster deduction model library to obtain the target disaster deduction model; according to the set of target geological disaster influencing factors corresponding to the target geological disaster prototype, time-series monitoring and analysis are performed on the geological disaster area and the interference geological disaster area to determine the time-series monitoring results of the geological disaster area and the time-series monitoring results of the interference geological disaster area; the target disaster deduction model is used to perform deduction analysis on the time-series monitoring results of the geological disaster area and the time-series monitoring results of the interference geological disaster area to obtain the target deduction analysis result; the target deduction analysis result is visually integrated and displayed through the Cesium platform. The present invention solves the technical problem of the insufficient visual integration ability of the prior art in the real-time monitoring of geological disasters and the dynamic deduction of disaster evolution. Through the integrated process of collecting real-time multi-source geological disaster data, prototype matching, disaster deduction model deduction analysis, and dynamic visual display on the Cesium platform, the technical effect of realizing the integrated linkage of real-time perception, dynamic deduction, and three-dimensional visualization of geological disasters is achieved.

[0085] Embodiment 2, based on the same inventive concept as the method for visual integration of geological disaster emergency based on Cesium in the foregoing embodiment, as Figure 2As shown, this application provides a geological disaster emergency visualization integration system based on Cesium. The system and method embodiments in this application are based on the same inventive concept. Among them, the system includes:

[0086] A first matching module 11, configured to collect real-time multi-source geological disaster data of a geological disaster area, perform geological disaster prototype matching with a geological disaster prototype library to obtain a target geological disaster prototype, where the target geological disaster prototype includes an interference bandwidth identifier; a regional display module 12, configured to transmit the target geological disaster prototype, real-time multi-source geological disaster data, and the geological disaster area to the Cesium platform, and combine with the interference bandwidth identifier to display the interference geological disaster area and the geological disaster area; a second matching module 13, configured to perform matching in a disaster deduction model library based on the target geological disaster prototype to obtain a target disaster deduction model; a time-series monitoring and analysis module 14, configured to perform time-series monitoring and analysis on the geological disaster area and the interference geological disaster area according to a set of target geological disaster influencing factors corresponding to the target geological disaster prototype to determine a time-series monitoring result of the geological disaster area and a time-series monitoring result of the interference geological disaster area; a deduction analysis module 15, configured to perform deduction analysis on the time-series monitoring result of the geological disaster area and the time-series monitoring result of the interference geological disaster area by using the target disaster deduction model to obtain a target deduction analysis result; a visualization integration display module 16, configured to perform visualization integration display on the target deduction analysis result through the Cesium platform.

[0087] Furthermore, the system is also used to implement the following functions:

[0088] Obtain a set of sample multi-source geological disaster data and a corresponding set of sample interference bandwidths, perform homogeneous aggregation on them to construct geological disaster prototypes, and obtain a geological disaster prototype library, where each geological disaster prototype includes reference multi-source geological disaster data; approximate-match the real-time multi-source geological disaster data with the reference multi-source geological disaster data of each geological disaster prototype in the geological disaster prototype library, and use the geological disaster prototype corresponding to the reference multi-source geological disaster data with the highest matching degree as the target geological disaster prototype.

[0089] Furthermore, the system is also used to implement the following functions:

[0090] Randomly extract M aggregated punctuation points from the sample multi-source geological disaster data set, where M is a positive integer greater than or equal to 3; perform homogeneous aggregation on the sample multi-source geological disaster data set based on the M aggregated punctuation points to obtain M aggregated sample multi-source geological disaster data sets; perform mean shift analysis on the M aggregated sample multi-source geological disaster data sets to determine M benchmark multi-source geological disaster data; combine the M aggregated sample multi-source geological disaster data sets and the sample interference bandwidth set to perform interference bandwidth identification to determine M interference bandwidths; use the M interference bandwidths to label the M benchmark multi-source geological disaster data to obtain the geological disaster prototype library.

[0091] Further, the system is also used to implement the following functions:

[0092] According to the one-to-one correspondence between the multi-source geological disaster data and the interference bandwidth, combine and aggregate the sample interference bandwidth set based on the determined M aggregated sample multi-source geological disaster data sets to obtain M aggregated sample interference bandwidth sets; perform mean calculation on the M aggregated sample interference bandwidth sets to obtain M interference bandwidths.

[0093] Further, the system is also used to implement the following functions:

[0094] The similarity between any two of the M aggregated punctuation points is less than or equal to a preset similarity threshold.

[0095] Further, the system is also used to implement the following functions:

[0096] According to the set of target geological disaster influencing factors corresponding to the target geological disaster prototype, perform time-series monitoring and analysis on the geological disaster area and the interfering geological disaster area within a preset monitoring window to obtain a sequence of sets of geological disaster area influencing factors and a sequence of sets of interfering geological disaster area influencing factors; perform fluctuation variance analysis on the sequence of sets of geological disaster area influencing factors and the sequence of sets of interfering geological disaster area influencing factors to determine the geological disaster area fluctuation coefficient and the interfering geological disaster area fluctuation coefficient; respectively determine the convolution scales according to the geological disaster area fluctuation coefficient and the interfering geological disaster area fluctuation coefficient, and perform convolution analysis on the sequence of sets of geological disaster area influencing factors and the sequence of sets of interfering geological disaster area influencing factors to obtain the time-series monitoring results of the geological disaster area and the time-series monitoring results of the interfering geological disaster area.

[0097] Further, the system is also used to implement the following functions:

[0098] The disaster deduction model library includes a landslide - surge intelligent deduction model, an earthquake - rainfall - landslide intelligent deduction model, an earthquake - rainfall - debris flow intelligent deduction model, and a dam break intelligent deduction model.

[0099] Further, the system is also used to implement the following functions:

[0100] Collect water level, rainfall, earthquake data, and dam body monitoring data at the same time series multiple times to obtain a sample dam body stability monitoring data set and a corresponding dam body breach risk data set; use the meshless numerical simulation method to construct a dam body breach intelligent deduction model based on the sample dam body stability monitoring data set and the dam body breach risk data set.

[0101] Further, the system is also used to implement the following functions:

[0102] Call the display template of the Cesium platform based on the target geological disaster prototype; perform format conversion on the real-time multi-source geological disaster data to obtain the converted real-time multi-source geological disaster data in the object format conforming to Cesium; based on the display template, display the geological disaster area in the Cesium platform according to the converted real-time multi-source geological disaster data, and display the interference geological disaster area determined based on the interference bandwidth identifier.

[0103] It should be noted that the above order of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above specific embodiments of this specification have been described. The processes depicted in the drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0104] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0105] This specification and the drawings are only exemplary descriptions of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A geological disaster emergency visualization integration method based on Cesium, characterized in that, The method includes: Collecting real-time multi-source geological disaster data of the geological disaster area, performing geological disaster prototype matching with the geological disaster prototype library to obtain a target geological disaster prototype, where the target geological disaster prototype includes an interference bandwidth identifier; Transmitting the target geological disaster prototype, the real-time multi-source geological disaster data, and the geological disaster area to the Cesium platform, and combining the interference bandwidth identifier to display the interference geological disaster area and the geological disaster area; Performing matching in the disaster deduction model library based on the target geological disaster prototype to obtain a target disaster deduction model; According to the set of target geological disaster influencing factors corresponding to the target geological disaster prototype, performing time-series monitoring and analysis on the geological disaster area and the interference geological disaster area to determine the time-series monitoring results of the geological disaster area and the time-series monitoring results of the interference geological disaster area; Using the target disaster deduction model to perform deduction analysis on the time-series monitoring results of the geological disaster area and the time-series monitoring results of the interference geological disaster area to obtain a target deduction analysis result; Visually integrating and displaying the target deduction analysis result through the Cesium platform.

2. The method for visual integration of geological disaster emergency based on Cesium according to claim 1, wherein Collecting real-time multi-source geological disaster data of the geological disaster area, performing geological disaster prototype matching with the geological disaster prototype library to obtain a target geological disaster prototype, including: Obtaining a set of sample multi-source geological disaster data and a corresponding set of sample interference bandwidths, performing homogeneous aggregation on them to construct geological disaster prototypes, and obtaining a geological disaster prototype library, where each geological disaster prototype includes reference multi-source geological disaster data; Approximately matching the real-time multi-source geological disaster data with the reference multi-source geological disaster data of each geological disaster prototype in the geological disaster prototype library, and taking the geological disaster prototype corresponding to the reference multi-source geological disaster data with the highest matching degree as the target geological disaster prototype.

3. The method for visual integration of geological disaster emergency based on Cesium according to claim 2, wherein, Obtaining a set of sample multi-source geological disaster data and a corresponding set of sample interference bandwidths, performing homogeneous aggregation on them to construct geological disaster prototypes, and obtaining a geological disaster prototype library, including: Randomly extracting M aggregation punctuation marks from the set of sample multi-source geological disaster data, where M is a positive integer greater than or equal to 3; Based on the M aggregation punctuation marks, performing homogeneous aggregation on the set of sample multi-source geological disaster data to obtain M sets of aggregated sample multi-source geological disaster data; Performing mean shift analysis on the M sets of aggregated sample multi-source geological disaster data to determine M sets of reference multi-source geological disaster data; Combining the M sets of aggregated sample multi-source geological disaster data and the set of sample interference bandwidths to perform interference bandwidth identification to determine M interference bandwidths; Using the M interference bandwidths to label the M sets of reference multi-source geological disaster data to obtain the geological disaster prototype library.

4. The method for visual integration of geological disaster emergency based on Cesium according to claim 3, characterized in that Combining the M sets of aggregated sample multi-source geological disaster data and the set of sample interference bandwidths to perform interference bandwidth identification to determine M interference bandwidths, including: According to the one-to-one correspondence between the multi-source geological disaster data and the interference bandwidth, combining and aggregating the set of sample interference bandwidths based on the determined M sets of aggregated sample multi-source geological disaster data to obtain M sets of aggregated sample interference bandwidths; Calculate the mean of the M aggregated sample interference bandwidth sets to obtain M interference bandwidths.

5. The method for visual integration of geological disaster emergency based on Cesium according to claim 3, wherein, The similarity between any two of the M aggregated punctuation marks is less than or equal to a preset similarity threshold.

6. The method for visual integration of geological disaster emergency based on Cesium according to claim 1, characterized in that, According to the set of target geological disaster influencing factors corresponding to the target geological disaster prototype, conduct time-series monitoring and analysis on the geological disaster area and the interfering geological disaster area to determine the time-series monitoring results of the geological disaster area and the interfering geological disaster area, including: According to the set of target geological disaster influencing factors corresponding to the target geological disaster prototype, conduct time-series monitoring and analysis on the geological disaster area and the interfering geological disaster area within a preset monitoring window to obtain a sequence of geological disaster area influencing factor sets and a sequence of interfering geological disaster area influencing factor sets; Conduct fluctuation variance analysis on the sequence of geological disaster area influencing factor sets and the sequence of interfering geological disaster area influencing factor sets to determine the fluctuation coefficient of the geological disaster area and the fluctuation coefficient of the interfering geological disaster area; Respectively determine the convolution scale according to the fluctuation coefficient of the geological disaster area and the fluctuation coefficient of the interfering geological disaster area, and conduct convolution analysis on the sequence of geological disaster area influencing factor sets and the sequence of interfering geological disaster area influencing factor sets to obtain the time-series monitoring results of the geological disaster area and the time-series monitoring results of the interfering geological disaster area.

7. The method for visual integration of geological disaster emergency based on Cesium according to claim 1, characterized in that The disaster deduction model library includes a landslide-tsunami intelligent deduction model, an earthquake rainfall-landslide intelligent deduction model, an earthquake rainfall-debris flow intelligent deduction model, and a dam breach intelligent deduction model.

8. The method for visual integration of geological disaster emergency based on Cesium according to claim 7, wherein Including: Collect water level, rainfall, earthquake data, and dam monitoring data of the same time series multiple times to obtain a set of sample dam stability monitoring data and a corresponding set of dam breach risk data; Use the meshless numerical simulation method to construct a dam breach intelligent deduction model based on the set of sample dam stability monitoring data and the set of dam breach risk data.

9. The method for visual integration of geological disaster emergency based on Cesium according to claim 1, wherein Transmit the target geological disaster prototype, real-time multi-source geological disaster data, and the geological disaster area to the Cesium platform, and combine the interference bandwidth identifier to display the interfering geological disaster area and the geological disaster area, including: Call the display template of the Cesium platform based on the target geological disaster prototype; Convert the format of the real-time multi-source geological disaster data to obtain converted real-time multi-source geological disaster data in an object format that conforms to Cesium; Based on the display template, display the geological disaster area in the Cesium platform according to the converted real-time multi-source geological disaster data, and display the interfering geological disaster area determined based on the interference bandwidth identifier.

10. The geological disaster emergency visualization integration system based on Cesium is characterized in that, The system includes: The first matching module is used to collect real-time multi-source geological disaster data of the geological disaster area, match the geological disaster prototype with the geological disaster prototype library to obtain a target geological disaster prototype, where the target geological disaster prototype includes an interference bandwidth identifier; The area display module is used to transmit the target geological disaster prototype, real-time multi-source geological disaster data, and the geological disaster area to the Cesium platform, and combine the interference bandwidth identifier to display the interfering geological disaster area and the geological disaster area; The second matching module is used to perform matching in the disaster deduction model library based on the target geological disaster prototype to obtain a target disaster deduction model; The time-series monitoring and analysis module is used to perform time-series monitoring and analysis on the geological disaster area and the interfering geological disaster area according to the set of target geological disaster influencing factors corresponding to the target geological disaster prototype, and determine the time-series monitoring results of the geological disaster area and the time-series monitoring results of the interfering geological disaster area; The deduction and analysis module is used to perform deduction and analysis on the time-series monitoring results of the geological disaster area and the time-series monitoring results of the interfering geological disaster area by using the target disaster deduction model to obtain a target deduction and analysis result; The visualization integration display module is used to perform visualization integration display on the target deduction and analysis result through the Cesium platform.

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