Geological disaster intelligent management and control system and method, medium, program product and terminal

Abstract

The invention provides a geological disaster intelligent management and control system and method, a medium, a program product and a terminal, and the system comprises a monitoring and early warning module which is used for obtaining geological monitoring data of a to-be-monitored region, carrying out the processing and analysis of the geological monitoring data to obtain disaster early warning data, and carrying out the early warning based on the disaster early warning data; the dynamic evaluation module is used for calculating by adopting a risk evaluation algorithm based on the geological survey data of the to-be-detected area to obtain a risk evaluation result; and the disaster identification module is used for processing and analyzing the remote sensing data of the to-be-detected area to obtain disaster identification data. According to the system, the monitoring and early warning module, the dynamic evaluation module and the disaster identification module are combined, geological disaster early warning evaluation can be carried out from multiple angles, possible disasters can be found as early as possible, early warning can be carried out, and related emergency early warning schemes can be recommended.

Description

Technical Field

[0001] The present application relates to the field of disaster monitoring and early warning technology, and in particular to intelligent geological disaster management and control systems, methods, media, program products and terminals. Background Art

[0002] At present, geological disasters, such as landslides, mud-rock flows, and ground subsidence, pose a serious threat to human society, economic activities, and the ecological environment. With the continuous intensification of global climate change and human activities, the frequency and scope of geological disasters are showing an expanding trend. In order to reduce the losses caused by geological disasters, early warning and risk control have become particularly important.

[0003] At present, the geological disaster risk management and control system mainly relies on traditional monitoring and early warning methods, including geological surveys, ground monitoring stations, remote sensing technology, etc. However, the existing geological disaster risk management and control system has the following shortcomings:

[0004] Geological disaster risk management involves multiple departments such as natural resources, emergency management, meteorology, and water conservancy. Currently, the information sharing and collaborative working mechanisms among these departments are not perfect;

[0005] Existing monitoring and early warning technologies are limited in accuracy, real-time and intelligence, and cannot meet the rapidly changing needs of geological disaster risk assessment;

[0006] As the amount of monitoring data increases, existing systems face challenges in data processing and analysis, making it difficult to achieve efficient risk assessment and decision support.

[0007] Although some improvement plans have been proposed to address the above issues, such as enhancing the automation and intelligence of monitoring equipment, and using big data analysis and artificial intelligence technology for risk assessment, there is still a lack of a comprehensive solution that can integrate the resources of different departments, the technology of different experts, increase social participation, and comprehensively enhance the early warning and management capabilities of geological disaster risks. Summary of the invention

[0008] In view of the shortcomings of the prior art mentioned above, the purpose of this application is to provide a geological disaster intelligent management and control system, method, medium, program product and terminal, which are used to solve the problems of untimely geological disaster monitoring and early warning, and incomplete management and control in the prior art.

[0009] To achieve the above-mentioned purpose and other related purposes, the first aspect of the present application provides a geological disaster intelligent management and control system, including: a monitoring and early warning module, which is used to obtain geological monitoring data of the area to be tested, process and analyze it to obtain disaster warning data, and issue an early warning based on the disaster warning data; a dynamic assessment module, which is used to calculate and obtain risk assessment results based on the geological survey data of the area to be tested using a risk assessment algorithm; and a disaster identification module, which is used to obtain disaster identification data after processing and analyzing the remote sensing data of the area to be tested.

[0010] In some embodiments of the first aspect of the present application, the monitoring and early warning module includes: a monitoring equipment unit, which is used to store the monitoring equipment and collect geological monitoring data of the area to be measured based on the monitoring equipment; a monitoring data unit, which is connected to the monitoring equipment unit and is used to receive and store the geological monitoring data of the monitoring equipment unit; a criterion management unit, which is used to set the corresponding early warning threshold according to the type of geological disaster; a data analysis unit, which is respectively connected to the monitoring data unit and the criterion management unit, and is used to compare and judge the geological monitoring data with the early warning threshold to obtain disaster early warning data; a monitoring alarm unit, which is connected to the data analysis unit and is used to issue an early warning based on the disaster early warning data.

[0011] In some embodiments of the first aspect of the present application, the dynamic evaluation module includes: an indicator management unit, which is used to select multiple evaluation indicators based on the risk factors of geological disasters, and configure each of the evaluation indicators to generate indicator data; a model management unit, which is connected to the indicator management unit, and is used to construct an evaluation model of the area to be tested based on the indicator data of each of the evaluation indicators of the area to be tested; an indicator layer unit, which is connected to the model management unit, and is used to calculate and obtain the susceptibility information of the area to be tested based on the indicator data and the evaluation model of the area to be tested; and a dynamic evaluation unit, which is connected to the indicator layer unit, and is used to calculate based on the susceptibility information of the area to be tested to obtain the risk information of the area to be tested.

[0012] In some embodiments of the first aspect of the present application, the disaster identification module includes: a threshold management unit, used to set a corresponding risk threshold according to the risk level of geological disasters; a file identification unit, used to process the remote sensing data of the area to be measured, compare the processing results with the corresponding risk threshold to obtain early identification point information, and construct a data level layer based on the processing results; a disaster identification list unit, used to store the early identification point information.

[0013] In some embodiments of the first aspect of the present application, the system also includes an expert consultation module; the expert consultation module is connected to the monitoring and early warning module, and is used to perform expert analysis based on the disaster warning data of the monitoring module to obtain an emergency plan.

[0014] In some embodiments of the first aspect of the present application, the expert consultation module includes: an expert configuration unit, used to configure corresponding experts and expert information based on the geological disaster type; a judgment rule unit, used to configure corresponding expert judgment rules based on the geological disaster type; an expert consultation unit, used to perform judgment based on the disaster warning data to obtain a judgment result; and a judgment result unit, used to make a judgment based on the judgment result to obtain an emergency plan.

[0015] To achieve the above-mentioned purpose and other related purposes, the second aspect of the present application provides a method for intelligent management and control of geological disasters, which is applied to the intelligent management and control system of geological disasters as described above, the system including a monitoring and early warning module, a dynamic assessment module and a disaster identification module; the method includes: obtaining disaster warning data through the monitoring and early warning module; obtaining risk assessment results through the dynamic assessment module; obtaining disaster identification data through the disaster identification module; and performing analysis based on the disaster warning data, risk assessment results and disaster identification data to identify and control geological disasters in the test area according to the analysis results.

[0016] To achieve the above-mentioned purpose and other related purposes, the third aspect of the present application provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the method for intelligent management and control of geological disasters is implemented.

[0017] To achieve the above-mentioned purpose and other related purposes, the fourth aspect of the present application provides a computer program product, which includes a computer program code. When the computer program code runs on a computer, the computer implements the method for intelligent management and control of geological disasters.

[0018] To achieve the above-mentioned purpose and other related purposes, the fifth aspect of the present application provides an electronic terminal, including a memory, a processor and a computer program stored in the memory; the processor executes the computer program to implement the intelligent management method of geological disasters.

[0019] As described above, the intelligent geological disaster management and control system, method, medium, program product and terminal of the present application have the following beneficial effects: the present invention combines the monitoring and early warning module, the dynamic assessment module and the disaster identification module, and can perform geological disaster early warning assessment from multiple angles. The present invention can detect possible disasters as early as possible, make early warnings and recommend relevant emergency warning plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 Shown is a structural schematic diagram of a geological disaster intelligent management and control system in one embodiment of the present application.

[0021] Figure 2 Shown is a structural schematic diagram of a monitoring and early warning module in one embodiment of the present application.

[0022] Figure 3 Shown is a structural schematic diagram of a dynamic evaluation module in one embodiment of the present application.

[0023] Figure 4 Shown is a structural schematic diagram of a disaster identification module in one embodiment of the present application.

[0024] Figure 5 Shown is another structural schematic diagram of a geological disaster intelligent management and control system in one embodiment of the present application.

[0025] Figure 6 Shown is a structural diagram of an expert consultation module in one embodiment of the present application.

[0026] Figure 7 Shown is a flow chart of a method for intelligent management and control of geological disasters in one embodiment of the present application.

[0027] Figure 8 Shown is a schematic diagram of the structure of an electronic terminal in one embodiment of the present application. DETAILED DESCRIPTION

[0028] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0029] The technical solution of the embodiment of the present application is applied to the field of geological disaster monitoring and early warning, which includes surface deformation monitoring, landslide warning, crack monitoring, etc., and can use InSAR technology, GNSS displacement monitors and other intelligent devices to realize automatic data collection.

[0030] It should be noted that the technical solutions of the embodiments of the present application can be widely used in many other fields in addition to being applied to the field of geological disaster monitoring and early warning, so as to improve the ability of disaster monitoring, early warning, response and recovery in various fields. Including but not limited to environmental monitoring and protection, urban emergency management, mine safety management, forest fire prevention, earthquake disaster reduction, chemical and hazardous chemical accident prevention, emergency communication and command and dispatch, public safety management, risk assessment and governance, etc. Among them, environmental monitoring and protection include environmental change analysis, pollution source monitoring, ecological impact assessment, etc., and big data analysis and AI technology can be used for environmental risk identification and early warning. Urban emergency management includes urban safety monitoring, emergency resource management, intelligent decision-making assistance, etc., and can integrate a variety of perception monitoring and data collection technologies, combined with big data analysis to improve risk identification and early warning capabilities. Mine safety management includes monitoring, early warning and comprehensive prevention and control of disasters such as gas, fire, and water disasters, etc., and can build an intelligent system for comprehensive disaster prevention and control to achieve precise management. Forest fire prevention includes fire risk level analysis, automatic identification and alarm of fire points, combustion situation analysis, etc., to achieve early warning and emergency command of forest fire risks. Earthquake disaster reduction includes earthquake monitoring and prediction, disaster analysis, emergency rescue auxiliary decision-making, etc., integrating the Internet of Things, GIS and big data technology to improve earthquake disaster defense capabilities. Chemical and hazardous chemical accident prevention includes real-time monitoring technology, accident warning technology, emergency disposal technology, etc., and a comprehensive emergency rescue system can be established to improve the active protection and control capabilities of chemical accidents. Emergency communication and command and dispatch include on-duty, plan management, command drills, coordination and consultation, etc., which can support multiple communication methods and realize the collaboration of on-site personnel, command centers, and experts. Public safety management includes urban safety problem monitoring, early warning, alarm, emergency rescue, etc., which can integrate various urban resources and provide a comprehensive command and dispatch management system. Risk assessment and governance include risk point management, risk inspection list management, risk level management, etc., and control measures can be formulated to implement hierarchical control. The technical solution of the embodiment of the present application has wide applicability and flexibility, and can provide customized solutions according to the needs of different fields to improve the efficiency and effectiveness of disaster management.

[0031] To facilitate understanding of the embodiments of the present application, first Figure 1 Detailed description. Figure 1 The schematic diagram of the structure of a geological disaster intelligent management and control system in an embodiment of the present invention is shown. The geological disaster intelligent management and control system in this embodiment mainly includes: a monitoring and early warning module 110, a dynamic evaluation module 120 and a disaster identification module 130.

[0032] The monitoring and early warning module 110 is used to obtain geological monitoring data of the area to be monitored, process and analyze it to obtain disaster early warning data, and issue an early warning based on the disaster early warning data;

[0033] In one embodiment, in combination Figure 2It is noted that the monitoring and early warning module 110 includes: a monitoring device unit 111, a monitoring data unit 112, a criterion management unit 113, a data analysis unit 114 and a monitoring alarm unit 115.

[0034] In some examples, the monitoring equipment unit 111 is used to store monitoring equipment and collect geological monitoring data of the area to be monitored based on the monitoring equipment.

[0035] It should be noted that several monitoring points are set up in the area to be tested, and automated monitoring equipment is buried at the monitoring points. The monitoring equipment includes but is not limited to rain gauges, strain displacement sensors, water level gauges, pressure sensors, GNSS displacement monitors, temperature sensors, humidity sensors, etc. The monitoring equipment collects a variety of data from the area to be tested in real time, including rainfall, displacement changes of the surface or structure, groundwater level changes, surface pressure, temperature changes, humidity changes, etc. The various data constitute the geological monitoring data of the area to be tested. Through geological monitoring data, effective early warning and risk assessment of geological disasters can be carried out, so that corresponding preventive measures can be taken.

[0036] In some examples, the monitoring data unit 112 is connected to the monitoring device unit 111 to receive and store the geological monitoring data of the monitoring device unit 111; the geological monitoring data collected by the monitoring device unit 111 is stored in the monitoring data unit 112 to facilitate subsequent retrieval of data for analysis. The monitoring data unit may have data management functions, such as data classification, indexing, and secure storage. The monitoring device unit 111 is responsible for collecting data, while the monitoring data unit 112 is responsible for receiving, storing, and managing work, and the two work together.

[0037] In some examples, the criterion management unit 113 is used to set a corresponding warning threshold according to the type of geological disaster; the criterion management unit 113 sets the warning threshold according to different types of geological disasters, and the warning threshold is used to determine whether the geological conditions have reached the level that may cause disasters. Different warning thresholds need to be set for each type of geological disaster according to different parameters. The warning threshold can be dynamically adjusted according to the latest geological research, historical disaster data or real-time monitoring results to improve the accuracy and effectiveness of the early warning system.

[0038] The types of geological disasters generally include but are not limited to: landslide monitoring, debris flow monitoring, ground subsidence monitoring, earthquake monitoring, karst collapse monitoring, ground fissure development monitoring, ground deformation monitoring, volcanic activity monitoring, coastal erosion monitoring, groundwater level monitoring, surface water monitoring, soil erosion monitoring, vegetation change monitoring, building and infrastructure monitoring, etc.

[0039] Among them, landslide monitoring mainly monitors the stability of hillsides, including surface displacement, crack development, etc. Debris flow monitoring mainly monitors the hydrogeological conditions in areas where debris flow may occur, such as rainfall, water flow velocity and sediment content. Ground subsidence monitoring mainly monitors ground subsidence caused by groundwater extraction or underground mining activities. Earthquake monitoring mainly monitors earthquake activities, including the frequency, intensity and distribution of earthquakes. Karst collapse monitoring mainly monitors ground collapse caused by groundwater erosion in karst areas. Ground fissure development monitoring mainly monitors ground fissure development caused by crustal movement or geological tectonic activities. Ground deformation monitoring mainly monitors horizontal or vertical deformation of the ground caused by geological tectonic activities or other factors. Volcanic activity monitoring mainly monitors signs of volcanic activity such as earthquake activity, surface deformation, and gas emissions in volcanic areas. Coastal erosion monitoring mainly monitors the erosion of the coastline caused by natural factors or human activities. Groundwater level monitoring mainly monitors changes in groundwater levels, which is crucial for assessing geological disaster risks and groundwater resource management. Surface water monitoring mainly monitors the water level, flow rate and water quality of surface water bodies such as rivers and lakes to prevent disasters such as floods. Soil erosion monitoring focuses on monitoring soil erosion caused by rainfall, wind or water flow. Vegetation change monitoring focuses on monitoring changes in vegetation cover, which may be related to surface stability and erosion. Building and infrastructure monitoring focuses on monitoring the structural integrity of buildings and infrastructure to prevent damage caused by geological hazards.

[0040] In some examples, the data analysis unit 114 is connected to the monitoring data unit 112 and the criterion management unit 113, respectively, and is used to compare and judge the geological monitoring data with the warning threshold to obtain disaster warning data.

[0041] The data analysis unit 114 retrieves the geological monitoring data from the monitoring data unit 112 and the warning threshold from the judgment management unit 113, compares and judges various types of monitoring data in the geological monitoring data with their corresponding preset thresholds, and obtains disaster warning data, which is geological monitoring data that reaches or exceeds the warning threshold.

[0042] In some examples, the monitoring and alarm unit 115 is connected to the data analysis unit 114 and is used to issue an early warning based on the disaster warning data. The monitoring and alarm unit 115 is used to store geological monitoring data that reaches or exceeds the early warning threshold and to issue an automatic early warning. The early warning process also requires judgment, and the disaster early warning data is divided into different levels, for example, the first early warning level, the second early warning level and the third early warning level in sequence. If the current disaster early warning data is the first early warning level, only an automatic early warning is issued. If the disaster early warning data is the second early warning level or the third early warning level, the expert judgment process is triggered synchronously, and the relevant responsible persons are notified in the form of text messages and emails. After the early warning approval process is passed, the early warning information will be synchronized to the early warning release interface.

[0043] The dynamic assessment module 120 is used to calculate the risk assessment result by using the risk assessment algorithm based on the geological survey data of the area to be tested. Specifically, the risk assessment algorithm mainly refers to configuring indicators, configuring weights for the indicators, constructing a calculation formula and then calculating, wherein the indicator configuration, indicator weight configuration and calculation formula construction are all set according to the actual situation of the geological disaster. For example, the indicator weight configuration of different types of geological disasters is different, and the weight configuration of different indicators is also different, which is not limited in the embodiment of the present invention.

[0044] In one embodiment, in combination Figure 3 It is explained that the dynamic evaluation module 120 includes: an indicator management unit 121, a model management unit 122, an indicator layer unit 123 and a dynamic evaluation unit 124.

[0045] In some examples, the indicator management unit 121 is used to select multiple evaluation indicators based on the risk factors of geological disasters, and configure each of the evaluation indicators to generate indicator data; wherein, multi-source data fusion processing is performed on the geological survey data of the area to be measured, and indicator management is performed based on the data after the multi-source data fusion processing to generate indicator data.

[0046] It needs to be explained that before generating the indicator data, multi-source data fusion processing is performed to integrate data from different sources such as remote sensing data, geographic information system (GIS) data, historical disaster records, etc., in order to improve the accuracy and comprehensiveness of the indicators.

[0047] Specifically, the multi-source data fusion processing methods include any one or more of the following:

[0048] Method a. Use middleware or adapters to merge data from various sources with different formats, resolutions, and accuracies. Middleware or adapters can use OGC standards (such as GML, KML) to standardize geographic data. For example, GeoServer can be used as a GIS data server to convert geographic data in different formats into a standardized format for client use.

[0049] Method b. Use open source data quality tools, such as OpenRefine, to clean and standardize data. For example, OpenRefine can be used to identify and correct inconsistencies in the data, such as unifying address formats or standardizing date formats.

[0050] Method c. Use remote sensing data interpolation technology, such as Kriging, to improve the spatial resolution of the data. For example, MODIS satellite data can be interpolated using the Kriging method to obtain higher-resolution surface temperature data.

[0051] Method d. Ensure the privacy and security of multi-source data through data encryption, access control, audit logs, and data desensitization. Data encryption refers to the use of strong encryption algorithms, such as AES (Advanced Encryption Standard), to encrypt data in storage and transmission. For example, before storing sensitive data in the database, use the AES-256 encryption algorithm to encrypt it. During data transmission, use the SSL / TLS protocol to ensure the security of data in the network. Access control refers to the implementation of role-based access control (RBAC) or attribute-based access control (ABAC) to ensure that only authorized users can access specific data. Audit logs refer to detailed logs that record all data access and operations so that they can be tracked and audited when security incidents occur. For example, use log management tools such as ELK Stack (Elasticsearch, Logstash, Kibana) to collect, store, and analyze audit logs. Data desensitization refers to the desensitization of sensitive information when sharing or displaying data, such as blurring geographic tags that may expose residents' personal information on public geological disaster maps.

[0052] It should be noted that in the present invention, evaluation indicators are selected according to the risk factors of geological disasters. For example, slope unit data in geological disasters, based on which evaluation indicators such as slope, slope direction, and elevation difference can be selected. Each evaluation indicator configuration includes indicator data such as indicator name, classification range information, and corresponding assignment.

[0053] In some examples, the model management unit 122 is connected to the indicator management unit 121 and is used to build an evaluation model of the area to be tested based on the indicator data of each evaluation indicator of the area to be tested.

[0054] Specifically, different model data are configured according to different areas to be tested, that is, different evaluation models are constructed. For example, the weights corresponding to the indicator data of the current project are configured to form the evaluation model of the project. The evaluation model can collect and manage the data of different indicators. At the same time, it can also be input by the user. For example, the user fills in the name of the current evaluation model, selects the project to which it belongs, selects the corresponding required indicator data, and configures the indicator weight. Through this manual input method of the user, the configuration management of the evaluation models of different projects is realized, which is convenient for the subsequent use of the indicator data. At the same time, the indicator evaluation standard is provided, which can also ensure the correct calculation.

[0055] In some examples, the indicator layer unit 123 is connected to the model management unit 122 and is used to calculate and obtain the susceptibility information of the area to be tested based on the indicator data of the area to be tested and the evaluation model.

[0056] It should be noted that the indicator layer unit 123 includes a susceptible zoning layer and a risk assessment layer, and each corresponding layer includes a layer name and an assessment model name. Taking the multiple slope units contained in each layer in the process of geological disaster prevention and control as an example, the slope unit contains the layer name, the assessment model name and the indicator data configured by the model. The susceptibility index of the slope unit is obtained by constructing a calculation formula, and the corresponding susceptibility level is matched according to the susceptibility index. The susceptibility index and the corresponding susceptibility level are the geological disaster susceptibility level information.

[0057] Furthermore, the data of slope units, such as susceptibility index, susceptibility level and other information, are integrated and displayed in the prone zoning layer, which can intuitively display the geological disaster susceptibility level information.

[0058] In some examples, the dynamic evaluation unit 124 is connected to the indicator layer unit 123 and is used to perform calculations based on the susceptibility information of the area to be tested to obtain risk information of the area to be tested.

[0059] Similarly, taking the slope unit as an example, based on the susceptibility index of the slope unit, the rainfall evaluation index and the probability of danger are calculated. Further, the risk probability of the hazard-bearing body obtained in the hazard-bearing body management (i.e., the probability of danger of the current slope unit), the number of personnel or economic value, and the vulnerability of personnel or buildings are combined to calculate the casualty risk value and economic loss risk value of the slope unit, and the corresponding risk levels are matched based on the casualty risk value and the economic loss risk value, and the risk level of the slope unit is determined by comparing the risk level of the casualty risk value with the risk level of the economic loss risk value. At the same time, the risk level, risk value and other data based on the slope unit are integrated and displayed in the risk evaluation layer to intuitively display the geological disaster risk level information.

[0060] In one embodiment, the dynamic evaluation unit 124 includes: a spatiotemporal dynamic analysis subunit; the spatiotemporal dynamic analysis subunit is used to predict the development trend of geological disasters through time series analysis, geographic space analysis and dynamic simulation based on the time-varying characteristics of geological disasters.

[0061] In some examples, the time series analysis process of the spatiotemporal dynamic analysis subunit includes: using the autoregressive integrated moving average model and the seasonal decomposition time series prediction model to analyze the historical data of geological disasters and identify periodic and trend characteristics. For example, the frequency of earthquakes is analyzed in time series to predict possible future earthquake activities.

[0062] Specifically, the Autoregressive Integrated Moving Average Model (ARIMA) model and the seasonal decomposition time series prediction model are two statistical tools that can deeply analyze the historical data of geological disasters. First, by collecting and cleaning the historical records of geological disasters, including key information such as the time, location, and scale of the events. Secondly, exploratory data analysis is performed, and charts and descriptive statistics are used to preliminarily identify the periodicity and trend characteristics in the data. Subsequently, the stationarity of the time series is determined by the Augmented Dickey-Fuller (ADF) test. If the data is non-stationary, the difference operation will be applied to transform the data until it reaches a stationary state. For data with strong seasonality, such as some geological disasters that occur more frequently in a specific season, the seasonal ARIMA (SARIMA) model can be used to adjust the seasonal components in the data through seasonal decomposition. After determining the model parameters, the ARIMA or SARIMA model is fitted, and residual analysis is performed to ensure the model fit and the appropriateness of the diagnostic model. Through model validation, the model's predictive ability is evaluated, and the accuracy of the model is measured by calculating the prediction error index. Ultimately, the model's prediction results are converted into easy-to-understand reports to provide decision makers with in-depth insights into the periodicity and trends of geological hazards, thereby helping them to develop effective prevention and response strategies. In addition, new geological hazard data will be continuously monitored and the model will be updated regularly to ensure the accuracy of the prediction and the adaptability of the model.

[0063] In some examples, the geospatial analysis process of the spatiotemporal dynamic analysis subunit includes: applying GIS technology to manage and analyze spatial data, identifying the spatial distribution patterns and hotspots of geological hazards. For example, using GIS to perform spatial analysis of landslide-prone areas, combining data such as topography, soil type, and rainfall.

[0064] Specifically, historical data of geological disasters are first imported through the GIS platform, including information such as the geographic location, time, type and severity of the event. Then, spatial analysis tools of GIS, such as buffer analysis, overlay analysis and hotspot analysis, are used to explore the spatial distribution characteristics of geological disasters. For example, buffer analysis is used to determine potential risk areas around specific geological structures or infrastructure; overlay analysis is used to identify areas where multiple risk factors work together; and hotspot analysis is used to find high-risk areas where geological disasters frequently occur. In addition, the geocoding function of GIS can convert address or coordinate data into geographic locations, thereby more accurately locating geological disaster events. GIS spatial interpolation techniques, such as Kriging interpolation, can be used to estimate the risk of geological disasters in data-sparse areas. Through the visualization function of GIS, geological disaster risk maps can be created to intuitively display the risk levels of different regions and provide decision support for policymakers and emergency response teams. Finally, the database connection function of GIS can integrate real-time monitoring data, such as rainfall, seismic activity, etc., to dynamically update risk assessments to ensure the timeliness and accuracy of information. Through the comprehensive application of these GIS technologies, the spatial distribution patterns of geological disasters can be more comprehensively understood, and geological disaster risks can be effectively identified and managed.

[0065] In some examples, the dynamic simulation process of the spatiotemporal dynamic analysis subunit includes: developing a dynamic simulation model based on physical processes, such as a fluid dynamics model and a geotechnical mechanics model, to simulate the development process of geological disasters. For example, using finite element analysis (FEA) software to simulate the dynamic process of landslides and analyze stability under different conditions.

[0066] Furthermore, blockchain technology is introduced and used to ensure the security and immutability of data, especially in data sharing and cross-departmental collaboration, to improve the credibility of data.

[0067] In order to ensure the security and non-tamperability of the data in the technical solution, the embodiment of the present application introduces blockchain technology, and the specific operation process is as follows: First, after the indicator data generated by the indicator management unit is created and configured, the hash value of these data (such as a data fingerprint) is recorded on the blockchain to ensure the integrity and originality of the data; secondly, in the process of constructing the evaluation model by the model management unit, the parameters and configuration information of the model are also recorded through the blockchain to ensure the transparency and traceability of the model construction; then, when the indicator layer unit calculates the susceptibility information, the calculation result and its corresponding timestamp are stored together on the blockchain to ensure the non-tamperability of the calculation result; finally, when the dynamic evaluation unit calculates the risk information, the risk assessment results and related data are also recorded on the blockchain to achieve full-link security protection of the risk assessment process. In this way, the introduction of blockchain technology provides a decentralized, secure and reliable data storage and verification mechanism for the entire geological disaster assessment process, ensuring the security and authenticity of the data throughout the life cycle.

[0068] At the same time, this application introduces cloud computing and edge computing technologies to achieve rapid data processing and real-time response, especially in areas with large data volumes or limited network conditions.

[0069] The specific operation process includes: first, using the powerful computing resources and storage capacity of cloud computing, a large amount of indicator data generated by the indicator management unit is uploaded to the cloud platform for centralized management and backup to ensure the security and accessibility of the data; second, when the model management unit builds the evaluation model, the high-performance computing service of the cloud computing platform is used to accelerate the processing of complex model construction and data analysis tasks to improve processing efficiency; then, when the indicator layer unit and the dynamic evaluation unit calculate the susceptibility information and risk information, the edge computing technology is used to sink the data processing and analysis tasks to a place closer to the data source, reduce data transmission delays, and achieve rapid response; in addition, in areas with limited network conditions, edge devices can perform local data processing and preliminary analysis, and only upload key information to the cloud, reducing the network burden while ensuring real-time performance; finally, through the collaborative work of cloud computing and edge computing, the intelligent allocation of data processing and analysis tasks is realized, resource utilization is optimized, and the response speed and reliability of the entire system are improved. Through this method of combining cloud computing and edge computing, the present application can efficiently process and analyze a large amount of data related to geological disasters, and even in an environment with limited network conditions, it can also achieve rapid assessment and real-time response to geological disaster risks.

[0070] It should be noted that in the dynamic assessment module 120, the corresponding layer is configured for the current area to be tested and the corresponding assessment model is selected. The geological disaster susceptibility level information and geological disaster risk level information obtained after calculation can be used to synchronously obtain the risk index of the current area to be tested in real time based on the geological disaster susceptibility level information and geological disaster risk level information. The dynamic assessment module 120 can determine the risk index of the current area to be tested in real time, and make early warnings and corresponding emergency plans in a timely manner. In one embodiment, combined with Figure 4 It is explained that the disaster identification module 130 is used to obtain disaster identification data after processing and analyzing the remote sensing data of the area to be detected. The disaster identification module 130 includes a threshold management unit 131, a file identification unit 132 and a disaster identification list unit 133.

[0071] In some examples, the threshold management unit 131 is used to set a corresponding risk threshold according to the risk level of the geological disaster.

[0072] It should be noted that when the disaster identification module 130 collects InSAR data and image data, different administrative division latitudes have different standard settings for the cumulative deformation variable and deformation rate of InSAR data. Then, based on the difference between the cumulative deformation variable and deformation rate of the InSAR data and the standard settings, there are different risk levels. Corresponding risk thresholds are set according to different risk levels, and the risk threshold is the specific difference from the standard setting.

[0073] In some examples, the file identification unit 132 is connected to the threshold management unit 131, and is used to process the remote sensing data of the area to be measured, compare the processing results with the corresponding risk thresholds to obtain early identification point information, and construct a data level layer based on the processing results.

[0074] The file identification unit 132 collects remote sensing data of the area to be tested, such as InSAR data (insar data file) and image data (tif file), analyzes and judges the data, and then compares the analyzed data with the corresponding risk threshold in the threshold management unit 131 to identify the excessively fluctuating data, and obtains the point information that exceeds the risk threshold, which is the early identification point information. At the same time, the data is displayed in the GIS data level layer to intuitively display the specific information of the disaster identification points in the current area to be tested.

[0075] Specifically, InSAR data and image data are used as the original data support for disaster identification. By making threshold judgments on the two dimensions of cumulative deformation and deformation rate of InSAR data, and performing intelligent algorithm recognition and result processing on image data, the early disaster identification point information that exceeds the threshold standard is integrated and displayed in a disaster identification map, providing data basis for subsequent manual investigation and completing the confirmation of hidden danger information.

[0076] In some examples, the disaster identification list unit 133 is used to store the early identification point information, store the early identification point information of the current area to be tested and synchronously display it in the disaster identification list to facilitate personnel to perform hidden danger confirmation operations on the early identification points.

[0077] In one embodiment, in combination Figure 5 and Figure 6 It is noted that the system further includes an expert consultation module 140; the expert consultation module 140 is connected to the monitoring and early warning module 110, and is used to perform expert judgment based on the disaster warning data of the monitoring module 110 to obtain an emergency plan. The expert consultation module 140 includes an expert configuration unit 141, a judgment rule unit 142, an expert consultation unit 143, and a judgment result unit 144.

[0078] In some examples, the expert configuration unit 141 is used to configure corresponding experts and expert information based on the type of geological disaster. In the process of identifying geological disasters, it is necessary to collect detailed information of experts in the corresponding geological disaster field, including but not limited to the professional background, work experience, research results, etc. of the experts. Based on the collected information, an expert configuration unit 141 is established to classify and organize the expert information according to professional fields, experience, etc.

[0079] In some examples, the judgment rule unit 142 is used to configure corresponding expert judgment rules based on the type of geological disaster; the expert judgment rule means that the number of experts required for judgment is different according to the type of geological disaster, and the number of experts required is different according to the level of geological disaster. The experience of the experts themselves leads to differences in judgment ability, and it is necessary to consider the professional background, experience, historical performance and other factors of the experts. Therefore, considering the comprehensive factors of different types, different levels, different abilities, etc., the corresponding number of experts is set, and the expert judgment rules are combined to make the judgment results more accurate.

[0080] In some examples, the expert consultation unit 143 is used to conduct research and judgment based on the disaster warning data to obtain the research and judgment results. Expert consultation is conducted based on the manually confirmed abnormal data, such as the alarm information of the monitoring and early warning module 110, to evaluate the emergency measures required. Specifically, when the equipment warning or inspection personnel find that there is an abnormal situation in the geological disaster body, they will notify the relevant personnel on site. At this time, the on-site staff needs to determine whether to enter the research and judgment link. If expert research and judgment is required, the expert consultation unit 143 will make an appointment for the research and judgment meeting, and call the expert notification related to the corresponding research and judgment rule of the research and judgment rule unit 142 to enter the meeting to start the discussion to obtain the research and judgment results.

[0081] In some examples, the judgment result unit 144 is used to make a judgment based on the judgment result to obtain an emergency plan. When the level of the judgment result does not match the current warning level, the system will cancel the current warning level and replace the current warning level with the expert judgment result level. If the judgment result is consistent with the current warning level, the corresponding emergency plan is matched.

[0082] It should be emphasized that the system also includes a mobile terminal, which can handle data anomalies, approvals, professional inspections, and the issuance of emergency instructions. The system can display various results on the mobile terminal. Through this system, possible disasters can be discovered early to reduce the impact of disasters on people, money, and materials. The present invention combines the monitoring and early warning module, the dynamic assessment module, and the disaster identification module to conduct geological disaster early warning assessments from multiple angles. The present invention can detect possible disasters as early as possible, make early warnings, and recommend relevant emergency warning plans.

[0083] like Figure 7 As shown, the present invention also provides a geological disaster intelligent management and control method, which is applied to the geological disaster intelligent management and control system as described above, wherein the system includes a monitoring and early warning module, a dynamic assessment module and a disaster identification module; the method includes:

[0084] Step S71: Obtain disaster warning data through the monitoring and warning module;

[0085] Step S72: Obtaining risk assessment results through the dynamic assessment module;

[0086] Step S73: Obtaining disaster identification data through the disaster identification module;

[0087] Step S74: Perform analysis based on the disaster warning data, risk assessment results and disaster identification data to identify and control geological disasters in the area to be tested according to the analysis results.

[0088] In the embodiments of the present application, words such as "first" and "second" are used to distinguish the same or similar items with substantially the same functions and effects, and do not limit their order. Those skilled in the art can understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit them to be different.

[0089] It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" represent examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0090] In the embodiments of the present application, "at least one" refers to one or more, and "plurality" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can represent: a, b, c, ab, ac, bc or abc, where a, b, c can be single or multiple.

[0091] It should also be understood that the division of modules in the embodiments of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional module in each embodiment of the present application may be integrated into a processor, or may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0092] Figure 8 is a schematic block diagram of an electronic terminal provided in an embodiment of the present application. Figure 8 As shown in FIG. 8 , the computer device includes: at least one processor 801, a memory 802, at least one network interface 803, and a user interface 805. The various components in the device are coupled together via a bus system 804. It can be understood that the bus system 804 is used to achieve connection and communication between these components. In addition to the data bus, the bus system 804 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, the bus system 804 is not described in detail. Figure 8 In the specification, various buses are labeled as bus systems.

[0093] The user interface 805 may include a display, a keyboard, a mouse, a trackball, a click gun, keys, buttons, a touch pad or a touch screen.

[0094] It is understood that the memory 802 can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM). The memory described in the embodiments of the present invention is intended to include but is not limited to these and any other suitable categories of memory.

[0095] The memory 802 in the embodiment of the present invention is used to store various categories of data to support the operation of the electronic terminal 800. Examples of these data include: any executable program for operating on the electronic terminal 800, such as an operating system 8021 and an application 8022; the operating system 8021 includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks. The application 8022 may include various applications, such as a media player (Media Player), a browser (Browser), etc., for implementing various application services. The method for intelligent management and control of geological disasters provided in the embodiment of the present invention may be included in the application 8022.

[0096] The method disclosed in the above embodiment of the present invention can be applied to the processor 801, or implemented by the processor 801. The processor 801 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the processor 801 or the instruction in the form of software. The above processor 801 may be a general processor, a digital signal processor (DSP, Digital Signal Processor), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor 801 can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiment of the present invention. The general processor 801 can be a microprocessor or any conventional processor, etc. In combination with the steps of the accessory optimization method provided in the embodiment of the present invention, it can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium, which is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0097] In an exemplary embodiment, the electronic terminal 800 may be implemented by one or more application specific integrated circuits (ASIC), DSP, programmable logic device (PLD), complex programmable logic device (CPLD) to execute the aforementioned method.

[0098] According to the method provided in the embodiments of the present application, the present application also provides a computer program product, which includes: computer program code, when the computer program code runs on a computer, the computer executes the intelligent geological disaster management method of any embodiment of the illustrated embodiments.

[0099] According to the method provided in the embodiments of the present application, the present application also provides a computer-readable storage medium, which stores a program code. When the program code runs on a computer, the computer executes the intelligent geological disaster management method of any embodiment of the illustrated embodiments.

[0100] The terms "component", "module", "system", etc. used in this specification are used to represent computer-related entities, hardware, firmware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program and / or a computer. By way of illustration, both applications running on a computing device and a computing device can be components. One or more components may reside in a process and / or an execution thread, and a component may be located on a computer and / or distributed between two or more computers. In addition, these components may be executed from various computer-readable media having various data structures stored thereon. Components may, for example, communicate through local and / or remote processes according to signals having one or more data packets (e.g., data from two components interacting with another component between a local system, a distributed system and / or a network, such as the Internet interacting with other systems through signals).

[0101] Those of ordinary skill in the art will appreciate that the various illustrative logical blocks and steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0102] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0103] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0104] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0105] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0106] In the above embodiments, the functions of each functional unit can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions (programs). When loading and executing computer program instructions (programs) on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media integrations. Available media may be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., high-density digital video discs (DVDs), or semiconductor media (e.g., solid state disks (SSDs), etc.).

[0107] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0108] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0109] In summary, the present application provides a geological disaster intelligent management and control system, method, medium, program product and terminal, including: a monitoring and early warning module, which is used to obtain geological monitoring data of the area to be tested for processing and analysis to obtain disaster warning data, and to issue an early warning based on the disaster warning data; a dynamic assessment module, which is used to calculate and obtain risk assessment results based on the geological survey data of the area to be tested using a risk assessment algorithm; a disaster identification module, which is used to obtain disaster identification data after processing and analysis based on remote sensing data of the area to be tested. The present invention combines the monitoring and early warning module, the dynamic assessment module and the disaster identification module, and can perform geological disaster early warning assessment from multiple angles. The present invention can detect possible disasters as early as possible, issue early warnings and recommend relevant emergency warning plans. Therefore, the present application effectively overcomes the various shortcomings of the prior art and has a high industrial utilization value.

[0110] The above embodiments are merely illustrative of the principles and effects of the present application and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A geological disaster intelligent management and control system, characterized in that: include: A monitoring and early warning module is used to obtain geological monitoring data of the area to be monitored, process and analyze it to obtain disaster early warning data, and issue an early warning based on the disaster early warning data; A dynamic assessment module is used to obtain risk assessment results by using a risk assessment algorithm based on the geological survey data of the area to be tested; The disaster identification module is used to obtain disaster identification data after processing and analyzing the remote sensing data of the area to be measured.

2. The intelligent geological disaster management and control system according to claim 1 is characterized in that: The monitoring and early warning module includes: A monitoring device unit, used for storing monitoring devices and collecting geological monitoring data of the area to be monitored based on the monitoring devices; A monitoring data unit, connected to the monitoring equipment unit, for receiving and storing geological monitoring data from the monitoring equipment unit; A judgment management unit is used to set corresponding warning thresholds according to the type of geological disasters; A data analysis unit, connected to the monitoring data unit and the judgment management unit respectively, for comparing and judging the geological monitoring data with the warning threshold value to obtain disaster warning data; The monitoring and warning unit is connected to the data analysis unit and is used for issuing warnings based on the disaster warning data.

3. The intelligent geological disaster management and control system according to claim 1 is characterized in that: The dynamic evaluation module includes: An indicator management unit is used to select multiple evaluation indicators based on the risk factors of geological disasters, and configure each of the evaluation indicators to generate indicator data; wherein, multi-source data fusion processing is performed on the geological survey data of the area to be measured, and indicator management is performed based on the data after the multi-source data fusion processing to generate indicator data; A model management unit, connected to the index management unit, and configured to construct an evaluation model of the area to be tested based on the index data of each evaluation index of the area to be tested; An indicator layer unit, connected to the model management unit, and used to calculate and obtain the susceptibility information of the area to be tested based on the indicator data of the area to be tested and the evaluation model; The dynamic evaluation unit is connected to the indicator layer unit and is used to perform calculations based on the susceptibility information of the area to be tested to obtain the risk information of the area to be tested.

4. The intelligent geological disaster management and control system according to claim 3 is characterized in that: The dynamic evaluation unit includes: a spatiotemporal dynamic analysis subunit; the spatiotemporal dynamic analysis subunit is used to predict the development trend of geological disasters through time series analysis, geographic space analysis and dynamic simulation based on the time-varying characteristics of geological disasters.

5. The geological disaster intelligent management and control system according to claim 1 is characterized in that: The disaster identification module includes: A threshold management unit is used to set corresponding risk thresholds according to the risk level of geological disasters; A file identification unit is used to process the remote sensing data of the area to be measured, compare the processing result with the corresponding risk threshold to obtain early identification point information, and construct a data level layer based on the processing result; The disaster identification list unit is used to store the early identification point information.

6. The geological disaster intelligent management and control system according to claim 1 is characterized in that: The system also includes an expert consultation module; the expert consultation module is connected to the monitoring and early warning module and is used to perform expert analysis and judgment based on the disaster early warning data of the monitoring module to obtain an emergency plan.

7. The intelligent geological disaster management and control system according to claim 6 is characterized in that: The expert consultation module includes: An expert configuration unit, used to configure corresponding experts and expert information based on the type of geological disasters; The judgment rule unit is used to configure corresponding expert judgment rules based on the geological disaster type; An expert consultation unit, used for conducting research and judgment based on the disaster warning data to obtain research and judgment results; The analysis and judgment result unit is used to make judgments based on the analysis and judgment results to obtain an emergency plan.

8. A method for intelligent management and control of geological disasters, characterized in that: The intelligent geological disaster management and control system applied to any one of claims 1 to 6 comprises a monitoring and early warning module, a dynamic assessment module and a disaster identification module; the method comprises: Obtain disaster warning data through the monitoring and early warning module; Obtaining risk assessment results through the dynamic assessment module; Obtaining disaster identification data through the disaster identification module; An analysis is performed based on the disaster warning data, risk assessment results and disaster identification data to identify and control geological disasters in the area to be tested according to the analysis results.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the intelligent geological disaster management method of claim 8 is implemented.

10. A computer program product, characterized in that The computer program product includes computer program code, and when the computer program code runs on a computer, the computer implements the intelligent geological disaster management method as claimed in claim 8.

11. An electronic terminal comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the intelligent geological disaster management method according to claim 8.

Images