Artificial intelligence-based complex disaster decision support system

Abstract

The present invention relates to the field of artificial intelligence-based disaster prediction and response technology. More specifically, it relates to an intelligent disaster management system that predicts the likelihood of disaster occurrence from various data such as weather, hydrology, environment, and social infrastructure, automatically issues warnings and alerts based on the prediction results, and automatically performs response measures by linking in real-time with administrative agencies, communication networks, and broadcasting networks. To this end, the present invention provides an artificial intelligence-based complex disaster decision support system comprising: a data collection and preprocessing module capable of collecting diverse data, including the Korea Meteorological Administration, the Korea Water Resources Corporation, CCTV, and citizen reports, in real time and standardizing the collected data; a data storage and management module capable of storing the standardized data in a database and controlling data access by user and agency by distinguishing access rights; an AI analysis and prediction module capable of calculating the probability of disaster occurrence, including flooding, inundation, and torrential rain, in real time from the data stored in the database; an early warning transmission and broadcast linkage module capable of converting the results analyzed by the AI ​​analysis and prediction module into standard warning messages and transmitting them through multiple channels including broadcasting, text, electronic display boards, and smartphones; and a learning and simulation support module capable of calculating the error by comparing the results predicted by the AI ​​analysis and prediction module with the actual damage results and performing re-learning if there is an error.

Description

Technology Field

[0001] The present invention relates to the field of artificial intelligence (AI)-based disaster prediction and response technology. More specifically, it relates to an intelligent disaster management system that predicts the likelihood of disaster occurrence from various data, such as weather, hydrology, environment, and social infrastructure, automatically issues warnings and alerts based on the prediction results, and automatically performs response measures by linking in real-time with administrative agencies, communication networks, and broadcasting networks. Background Technology

[0002] As urbanization and climate change progress rapidly in modern society, the frequency and intensity of complex disasters, such as localized torrential rains, typhoons, heavy rainfall, sea level rise, and ground subsidence, are continuously increasing. Particularly in densely populated cities with high infrastructure, even short-term rainfall triggers a chain reaction of secondary damages, including sewer overflow, flooding of underpasses, river overflows, traffic paralysis, and communication disruptions.

[0003] Unlike in the past, these disasters cannot be explained by a single factor and take on a non-linear form in which meteorological, topographical, facility, and administrative data interact in a complex manner. Nevertheless, most current disaster response systems remain passive, and there is a temporal disconnect between prediction and response. In other words, rainfall forecasts provided by agencies such as the Korea Meteorological Administration are not linked in real-time with on-site administration or citizen evacuation, and response procedures by agency are also fragmented, resulting in delays in information transmission and administrative gaps.

[0004] Existing disaster prediction systems have largely remained confined to single analysis models relying on specific data. For example, it was common practice to predict rainfall and water level changes based on simple regression or statistics, or to issue alerts by comparing sensor values ​​in specific sections with thresholds.

[0005] However, these models fail to reflect complex factors such as the structure of urban drainage networks, topographical slope, ground permeability, soil saturation, and the influence of artificial structures. Furthermore, the lack of data standardization results in varying formats across agencies, and the separation of communication, administrative, and broadcasting networks makes real-time integrated response difficult.

[0006] Meanwhile, with the proliferation of IoT sensors and the explosive increase in data diversity—including CCTV, satellite imagery, drones, and citizen reports—the amount of information available for disaster prediction has become much more vast than in the past.

[0007] However, the absence of a system capable of collecting, refining, and analyzing this vast amount of data in real time leads to a problem where data cannot be utilized immediately in actual disaster situations, even if it exists. Collected data is stored separately by agency, and interoperability is difficult due to differing formats and time zones, resulting in uneven data quality and reliability. This data fragmentation also acts as a major obstacle to the training and application of AI-based prediction models.

[0008] Furthermore, existing disaster warning systems often rely on manual operations during the issuance and notification processes, frequently leading to issues such as delayed warning timing or unclear target areas. When administrative agencies request broadcasts or text messages after going through internal procedures following a forecast announcement, delays ranging from a few minutes to tens of minutes occur, and the same information is transmitted asynchronously to citizens, the media, and administrative networks.

[0009] Because some systems rely solely on a single communication network, there have been instances where alerts were not transmitted in the event of communication failures or power outages. Ultimately, the reality is that the golden time for disaster response is lost due to a structure where prediction, alerting, and administrative response are separated.

[0010] Recently, research on predictive disaster management incorporating artificial intelligence (AI) technology is being actively pursued. Models that predict rainfall, water levels, and flood risk by analyzing meteorological and hydrological data using deep learning are emerging, but most remain limited to single institutions or specific regional units.

[0011] These models fail to comprehensively perform real-time data collection, multi-agency integration, automated warnings and alerts, and the linkage of administrative measures. Furthermore, due to a lack of capabilities to visualize prediction results in a human-understandable format or to form citizen-participatory feedback loops, they are difficult to immediately reflect in actual administrative and social decision-making.

[0012] Furthermore, ensuring data security, authentication, and integrity is essential for disaster management systems to operate in actual administrative environments. In a structure where public data networks and private communication networks are mixed, false alarms caused by malicious attacks or data tampering may occur if authentication systems are separated or encryption is insufficient. Therefore, securing data reliability and system safety is just as important as prediction accuracy. Existing single-server systems carry the risk of complete service interruption in the event of failure or overload, making them unsuitable for actual national-level disaster response.

[0013] Therefore, there is an urgent need to develop an intelligent integrated system that integrates and preprocesses diverse data in real time, predicts complex disasters through artificial intelligence models, and enables the automatic transmission of warnings and alerts as well as the immediate execution of administrative orders.

[0015] Prior Art: KR Registered Patent Publication No. 10-2350534 (Published Jan. 13, 2022) The problem to be solved

[0017] The present invention was devised to solve the aforementioned problems and aims to provide an AI-based complex disaster decision support system that integrates real-time data from various sources, such as sensors, CCTVs, IoT devices, satellites, and public data, to enable an AI model to predict the risk of complex disasters such as flooding, heavy rain, and landslides, and to immediately disseminate the results through multiple channels, including text messages, broadcasts, electronic display boards, and mobile applications, thereby minimizing time delays in disaster response and preventing damage. means of solving the problem

[0018] The artificial intelligence-based complex disaster decision support system according to the present invention, devised to achieve the above objective, includes: a data collection and preprocessing module capable of collecting diverse data including the Korea Meteorological Administration, Korea Water Resources Corporation, CCTV, and citizen reports in real time and standardizing the collected data; a data storage and management module capable of storing the standardized data in a database and controlling data access by user and agency by distinguishing access rights; an AI analysis and prediction module capable of calculating the probability of disaster occurrence, including flooding, inundation, and torrential rain, in real time from the data stored in the database; an early warning transmission and broadcast linkage module capable of converting the results analyzed by the AI ​​analysis and prediction module into standard warning messages and transmitting them through multiple channels including broadcasting, text, electronic display boards, and smartphones; and a learning and simulation support module capable of calculating the error by comparing the results predicted by the AI ​​analysis and prediction module with the actual damage results and performing re-learning if there is an error.

[0019] In addition, it further includes a user interface module that can visualize prediction results so that citizens and administrative agencies can intuitively check them on terminals they carry, and can display expected flood areas, evacuation routes, and risk levels on the terminal's display.

[0020] In addition, the AI ​​analysis and prediction module further includes a time series prediction engine unit that receives meteorological and environmental data changing over time, including rainfall, water level, flow rate, temperature, humidity, and wind speed, and calculates short- and medium-term disaster occurrence probabilities; a spatial analysis engine unit that combines time series prediction results with spatial data to calculate flood risk by region and grid; a disaster simulation engine unit that can spatially and temporally reproduce the progression of an actual disaster based on the results of the prediction model; a composite risk calculation unit that can calculate the total risk when multiple factors are combined beyond a single disaster element; and an optimal evacuation route recommendation unit that calculates the safest route for citizens to move in the event of a disaster based on spatial analysis results. Effects of the invention

[0021] According to the present invention, by integrating the entire process of disaster prediction, warning, and response centered on artificial intelligence into a single automated loop, it is possible to fundamentally improve the limitations of the existing manual disaster management system.

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[0024] In addition, by collecting and refining complex data in real time, AI can precisely predict risks such as flooding and inundation, convert the results into CAP standard alerts, and immediately transmit them through multiple channels such as broadcasts, text messages, and electronic display boards.

[0025] In addition, the administrative and operational support module automatically executes Electronic Standard Operating Procedures (eSOPs) based on prediction results to control the response of each agency in real time, while the user interface provides citizens with visualized evacuation information and voice guidance, and has the effect of collecting on-site reports to reflect in AI learning.

[0026] In addition, all data is securely managed through encryption and integrity verification, and the learning and simulation modules continuously correct prediction errors, which has the effect of improving performance as the system operates.

[0027] Furthermore, by simultaneously enhancing the accuracy of disaster prediction, the speed of warnings, the efficiency of administrative response, and the effectiveness of citizen participation, it has the effect of realizing an autonomous intelligent disaster management system that enables cities and local communities to detect risks on their own and respond preemptively. Brief explanation of the drawing

[0028] FIG. 1 is a diagram illustrating the entire process of an artificial intelligence-based complex disaster decision support system according to a preferred embodiment of the present invention. FIG. 2 is a diagram illustrating the propagation process when a disaster occurs in an artificial intelligence-based complex disaster decision support system according to a preferred embodiment of the present invention. FIG. 3 is a diagram illustrating the overall components of an artificial intelligence-based complex disaster decision support system according to a preferred embodiment of the present invention. Specific details for implementing the invention

[0029] Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. First, it should be noted that in assigning reference numerals to the components of each drawing, the same components are given the same reference numeral whenever possible, even if they are shown in different drawings. Furthermore, in describing the present invention, if it is determined that a detailed description of related known components or functions may obscure the essence of the present invention, such detailed description is omitted. Additionally, while preferred embodiments of the present invention will be described below, the technical concept of the present invention is not limited or restricted thereto and can be modified and implemented in various ways by those skilled in the art.

[0031] FIG. 1 is a diagram illustrating the overall process of an artificial intelligence-based complex disaster decision support system according to a preferred embodiment of the present invention, FIG. 2 is a diagram illustrating the propagation process when a disaster occurs in an artificial intelligence-based complex disaster decision support system according to a preferred embodiment of the present invention, and FIG. 3 is a diagram illustrating the overall components of an artificial intelligence-based complex disaster decision support system according to a preferred embodiment of the present invention.

[0033] Hereinafter, with reference to FIGS. 1 to 3, the components of an artificial intelligence-based complex disaster decision support system according to a preferred embodiment of the present invention will be described in detail.

[0035] An artificial intelligence-based complex disaster decision support system according to a preferred embodiment of the present invention comprises a data collection and preprocessing module (100), a data storage and management module (200), an AI analysis and prediction module (300), an early warning and alert transmission and broadcasting linkage module (400), a user interface module (500), an administration and operation support module (600), an external linkage module (700), a learning and simulation support module (800), and a security, authentication, and performance management module (900).

[0037] The data collection and preprocessing module (100) can collect, refine, and standardize various forms of data flowing in from the external environment and convert them into an integrated data structure that can be directly utilized by the analysis and prediction engine in the subsequent stage.

[0039] The data collection and preprocessing module (100) comprises a public data linkage unit (110), a spatial information input unit (120), an IoT sensing information collection unit (130), a data validation unit (140), a data standardization and alignment unit (150), a metadata management unit (160), a data warehouse linkage unit (170), a flood risk variable extraction unit (180), and a rainfall scenario generation unit (190).

[0041] The public data linkage unit (110) is linked with disaster-related data networks operated by the national and local governments to collect meteorological, hydrological, geographical, and administrative information in real time. Specifically, it includes precipitation and wind speed data from the Korea Meteorological Administration, river and sewage water levels from the Korea Water Resources Corporation, disaster text message dispatch history from the Ministry of the Interior and Safety, drainage pump station operation rates from local governments, and road control information.

[0043] Since each institution uses different API formats and unit systems, the public data linkage unit of the present invention converts them into a unified data format and distinguishes between the collection time and the observation time to store them together as metadata. In addition, to prevent duplicate data collection, a unique hash value is assigned to each item, and data can be accumulated stably even in situations of temporary communication failure or congestion through a streaming queuing structure. The data collected in this way is immediately transmitted to the data validation unit (140) for a verification procedure.

[0045] The spatial information input unit (120) performs the role of integrating and managing basic spatial information for analysis based on geographical location.

[0047] The spatial information input unit (120) collects spatial information such as road networks, river networks, terrain elevation (DEM), administrative boundaries, land cover maps, drainage facility networks, and shelter locations, unifies the coordinate system and unit system, and removes overlapping areas. The collected spatial information is converted into a graph structure composed of nodes and links, configured to be compatible with the evacuation path search algorithm in subsequent stages.

[0049] In addition, derived indicators necessary for AI learning, such as slope, watershed area, curvature, and imperviousness ratio, are automatically calculated based on DEM data. The data refined in the spatial information input unit is then transmitted to the standardization and alignment unit (150) and combined with public data.

[0051] The IoT sensing information collection unit (130) collects real-time data from various sensors and video equipment installed at the site. The sensors include rain gauges, water level gauges, wind speed gauges, temperature and humidity gauges, soil moisture sensors, radiation sensors, etc., and the video equipment consists of CCTV cameras in rivers and major urban areas.

[0053] The IoT sensing information collection unit (130) standardizes the communication protocol of each sensor and integrates it into an MQTT or HTTP-based data stream, and in the event of transmission failure, temporarily stores it in a local buffer and retransmits it. In the case of video data, to improve transmission efficiency, it extracts only the key scenes that have been compressed frame by frame and transmits them to the video analysis engine. When an abnormal signal is detected from a sensor or equipment, it automatically transmits an error signal to the data validation unit (140) and immediately sends a notification to the administrator.

[0055] The data validation unit (140) is a quality assurance step that performs formal and statistical verification of all collected data. It checks for the presence of essential items in the input data, consistency of units, consistency of the time sequence, and rapid variability of values, and detects outliers by comparing with data from adjacent sensors or nearby administrative districts.

[0057] In addition, missing intervals are compensated for by methods such as recent value correction, spatiotemporal interpolation, and adjacent sensor averaging, and each data point is assigned a quality flag which is used to adjust weights in the subsequent analysis stage. The results of the quality verification are recorded together in the metadata management unit (160), and data below a certain level is automatically masked so that it is not input into the AI ​​prediction module.

[0059] The data standardization and integration unit (150) integrates data structures and time axes from various sources into a single standard system.

[0060] The data standardization and alignment unit (150) converts input formats such as JSON, XML, and CSV into a standard table structure and performs time and coordinate alignment between collected public data, spatial information, and sensor information. Through this, the correlation between multiple data that occurred in the same area at the same time can be secured.

[0062] For example, rainfall data and water level data are matched based on the observation time and coordinates, allowing real-time tracking of whether an increase in rainfall in a specific area leads to a rise in water level. Additionally, during the standardization process, derived indicators required for the prediction model, such as imperviousness, slope coefficient, and effective drainage volume, are automatically calculated and transmitted to the AI ​​analysis module (300).

[0064] The metadata management unit (160) acts as a structure that manages the source, creation time, quality evaluation, transformation history, disaster type, etc. of all data.

[0066] The metadata management unit (160) records Provenance information for all data, enabling the tracking of the basis for prediction results or the identification of the cause of errors. It also assigns a quality grade and reliability to each data, so that data with low reliability is automatically excluded when the AI ​​model learns.

[0068] The logs stored in the metadata management unit (160) are also used in the security and authentication module and are utilized to verify the data integrity of the entire system.

[0070] The data warehouse linkage unit (170) loads data that has been refined and verified into a central repository and provides an optimized structure so that the analysis and prediction module (300) can access it quickly.

[0072] The data warehouse linkage unit (170) manages data by classifying it into a time series database (TSDB), a spatial database (GIS DB), and an unstructured data storage (image and log data) according to the data type. Data is backed up and version-controlled at regular intervals, and sample sets for AI training are automatically generated and stored.

[0074] The flood risk variable extraction unit (180) calculates input variables necessary for flood prediction based on spatial information and weather information. It extracts slope and watershed area from DEM data, calculates imperviousness by analyzing land cover maps, and derives hourly runoff, flood depth, drainage efficiency, etc. by combining them with rainfall scenarios. The variables generated in this way are directly used as input features for the AI ​​prediction model.

[0076] The rainfall scenario generation unit (190) generates a probabilistic rainfall scenario by fusing short-term forecast data from the meteorological agency with real-time rainfall observation values. The generated scenario consists of three forms: conservative, standard, and aggressive, and executes a prediction model according to different risk conditions for each.

[0078] This scenario data is transmitted to the AI ​​prediction module to predict flood areas and depths, and the prediction results are immediately sent to the warning and alert transmission and broadcast integration module.

[0080] The data collection and preprocessing module of the present invention collects data from various sources, verifies its quality, and constructs a highly reliable integrated dataset through standardization and alignment processes. This data is sequentially transmitted to the AI ​​analysis and prediction module, the simulation support module, and the early warning transmission module, thereby dramatically improving the accuracy of disaster prediction and the speed of response. Ultimately, acting as the nervous system of the entire system, this module is capable of transforming complex and irregular external data into a systematic and predictable form.

[0082] The data storage and management module (200) serves as a central data hub for the complex disaster prediction system and can perform the function of systematically storing information refined and standardized in the data collection and preprocessing module (100), and providing data necessary for artificial intelligence analysis and prediction, visualization, and administrative decision-making quickly and stably.

[0084] The data storage and management module (200) comprises a data warehouse unit (210), a metadata management unit (220), a log and history management unit (230), an indexing and search optimization unit (240), a version control and backup unit (250), a data access control unit (260), and a performance monitoring unit (270).

[0086] The data warehouse section (210) forms the central axis of the present invention and stores all structured and unstructured data related to disasters in an integrated manner.

[0088] The data warehouse section (210) is divided into three sub-layers: a time-series database, a spatial database, and an unstructured data storage. The time-series database stores continuous data that changes over time, such as rainfall, water level, temperature, and wind speed, and adopts a compression and partitioning structure to maintain high-resolution time-series data in seconds. The spatial database stores data including spatial coordinates, such as DEMs, river networks, road networks, and administrative boundaries, and has a built-in coordinate system matching verification function to ensure the consistency of geographic information. The unstructured data storage stores unstructured data such as images, audio, and logs, and automatically distributes and stores data according to its size and format.

[0090] Through such a multi-layered structure, the access speed and processing efficiency of large-scale data can be maximized. In addition, the data warehouse unit (210) is directly connected to the AI ​​analysis and prediction module (300) and provides time series data or spatial parameters required for prediction in real time.

[0092] The metadata management unit (220) can manage the context and history of the data and records attributes such as the creation date and time, source organization, processing stage, quality index, coordinate system, unit system, person in charge, and applied algorithm for all datasets.

[0094] In particular, the results of the validation performed in the data collection and preprocessing module (100) are reflected together, and a 'Quality Flag' is automatically assigned to each dataset. This flag acts as a data weight during the AI ​​learning and prediction process, and data with low reliability is automatically excluded.

[0096] In addition, the metadata management unit (220) manages the cross-reference structure between datasets so that, for example, it can track whether 'rainfall data A' was used as the basis for 'flood depth prediction result B'. Thanks to this structure, the basis for the prediction result can be transparently provided to administrative agencies or broadcasting stations.

[0098] The log and history management unit (230) records all data flows and change history occurring in the system. Actions ranging from input, modification, deletion, conversion, and retrieval of each data are automatically stored as logs, and for each step, the user ID, time, processing module, and request parameters are recorded together.

[0100] This serves not only as a basis for future performance verification and security audits but also as a benchmark for data recovery in the event of a failure. Additionally, log data is encrypted and backed up to a separate secure storage, and access control policies are applied to prevent external access.

[0102] The indexing and search optimization unit (240) enables rapid searching and querying of large datasets. The indexing and search optimization unit (240) creates multiple indexes based on meta-information such as data type, location, time, disaster type, and quality grade, and returns fast results even when searching for complex conditions.

[0104] For example, complex queries such as “predicted flood depth of Haeundae-gu, Busan Metropolitan City as of 6:00 PM on August 10, 2025” can also be processed with a single call. To achieve this, a memory-based cache structure and a parallel indexing engine are introduced, and delay is minimized when making real-time calls from the analysis module (300).

[0106] The version control and backup unit (250) manages the change history of the dataset and creates and stores snapshots at regular intervals. A version number is assigned whenever the data is changed, and restoration to a previous version is possible.

[0108] For example, if the Korea Meteorological Administration's data format changes or the units of specific variables are adjusted, the version control and backup department maintains past versions to ensure that the same environment can be reproduced during retraining or verification. In addition, backups are performed on physically separated dual storage systems, and integrity verification is conducted at regular intervals.

[0110] The data access control unit (260) is a security layer that manages user authority and access scope. The range of accessible data is distinguished according to the user's authority level, such as administrative agencies, broadcasting companies, research institutions, and general citizens, and each call is tracked through an authentication token and log records.

[0112] For example, users in administrative agencies can view the entire real-time forecast results and metadata, while general users can only access summarized forecast information. Access control is integrated with security, authentication, and performance management modules to prevent unauthorized access or data leakage in advance.

[0114] The performance monitoring unit (270) monitors the processing speed, response delay, data load, and error rate of the data storage and retrieval process in real time. The performance monitoring unit calculates the average processing time for each stage of collection, loading, and retrieval, and sends an alert to the administrator if the threshold is exceeded. In addition, by analyzing the data call patterns of the artificial intelligence prediction module (300) and automatically caching frequently used datasets, the response speed of the entire system is improved.

[0116] The data storage and management module (200) has a clear connection with the upper and lower modules. In the upper stage, it receives data that has been verified for quality from the data collection and preprocessing module and loads it into the warehouse, and in the lower stage, it supplies the data to the AI ​​analysis and prediction module (300).

[0118] In addition, the prediction results are stored again in the data storage and management module (200) and used as a dataset for future retraining. At the same time, the warning and alert transmission module (400) and the administrative and operational support module (600) retrieve the prediction results stored in the data storage and management module (200) and directly utilize them for creating warning messages, evacuation instructions, administrative reports, etc.

[0120] The data storage and management module (200) of the present invention goes beyond the concept of a simple storage facility and operates as an integrated data hub that comprehensively manages data quality, history, security, and availability. Through this, the reliability and consistency of the entire complex disaster prediction system are maintained, and the reproducibility and verifiability of the prediction results are secured. Furthermore, this data management structure is designed to be reusable as is when expanding the system to other regions or other types of disasters in the future, thereby demonstrating excellent effects in terms of scalability and maintainability.

[0122] The AI ​​analysis and prediction module (300) is a component that serves as the brain of the complex disaster response system. It analyzes various time-series, spatial, and environmental data accumulated in the data storage and management module to predict the likelihood of occurrence of complex disasters such as flooding, landslides, heavy rain, and fire in real time, and transmits the results to the warning and alert transmission and broadcast linkage module (400).

[0124] The AI ​​analysis and prediction module (300) comprises a time series prediction engine unit (310), a spatial analysis engine unit (320), a disaster simulation engine unit (330), a complex risk calculation unit (340), an optimal evacuation route recommendation unit (350), a scenario generation and evaluation unit (360), an AI model management and learning control unit (370), and a result verification and visualization unit (380).

[0126] The time series prediction engine unit (310) receives weather and environmental data that changes over time, such as rainfall, water level, flow rate, temperature, humidity, and wind speed, and calculates the probability of short-term and medium-term disaster occurrence. In the present invention, a Long Short-Term Memory (LSTM) and a Transformer-based deep learning model are used in parallel to learn the patterns of past time series and reflect non-linear change characteristics.

[0128] The prediction engine receives rainfall scenarios that are updated in real time, predicts flood depth and flood area in 30-minute, 1-hour, 3-hour, and 6-hour intervals, and calculates a reliability index for each hourly prediction result. If the reliability falls below a certain threshold, an ensemble correction algorithm is automatically invoked to adjust the prediction value based on recent observations and similar past patterns. As a result, the system can maintain a certain level of prediction stability despite the variability of short-term forecasts.

[0130] The spatial analysis engine unit (320) combines time series prediction results with spatial data to calculate the flood risk level by region and grid. The spatial analysis engine unit (320) simulates the flow and collection direction of water based on spatial data such as a DEM (Digital Elevation Model), sewer network, land cover map, location of drainage facilities, and road network, and automatically identifies a risk zone by considering the elevation difference and drainage system of a specific area.

[0132] For example, even with the same rainfall amount, areas with gentle slopes and high imperviousness are predicted to have deeper flooding, and sections with clogged sewer networks are calculated to have delayed runoff. These spatial analysis results are then transmitted to the composite risk calculation unit and visualization unit in subsequent stages.

[0134] The disaster simulation engine unit (330) reproduces the progression of an actual disaster in space and time based on the results of the prediction model.

[0136] The disaster simulation engine unit (330) receives rainfall scenarios divided into time units and simulates water flow, ground saturation, river flooding, etc. in three dimensions. Each simulation is performed by CPU and GPU parallel processing, and the results are calculated as indicators such as flooded area, flood depth, and spread rate per hour.

[0138] In addition, when actual observation data is available, the prediction results and simulation results are automatically compared to calculate the error rate and correct the model parameters. Thanks to this self-calibration process, the prediction engine of the present invention continuously learns and improves accuracy.

[0140] The composite risk calculation unit (340) calculates the total risk when multiple factors are combined, beyond a single disaster element. The composite risk calculation unit (340) converts flood risk, landslide risk, traffic disruption risk, power supply risk, etc., into normalized scores and calculates the overall risk for each region through weighted sum.

[0142] Weights are automatically determined by an AI model learning from past disaster data; for example, even during heavy rainfall, areas with high terrain and good drainage are assessed as having a low risk. Additionally, since the composite risk is updated over time, zones where risk increases rapidly at a specific point in time can be detected in real time.

[0144] The optimal evacuation route recommendation unit (350) calculates the route through which citizens can move most safely in the event of a disaster based on the spatial analysis results. The optimal evacuation route recommendation unit (350) converts road network data into a graph structure, sets the expected flood area as a weight, and searches for the shortest and safest route using the Dijkstra or A* algorithm.

[0146] In addition, attributes such as traffic volume, road width, gradient, and illumination are considered for each node, and different routes are presented depending on the mode of transportation (walking or vehicle). The calculated evacuation routes are visualized on the map of the user interface module and provided as real-time guidance.

[0148] The scenario generation and evaluation unit (360) automatically generates multiple disaster response scenarios by assuming various weather conditions and administrative response conditions. The scenario generation and evaluation unit (360) constructs hundreds of response scenarios by combining input conditions such as rainfall scenarios, drainage facility status, and availability of personnel and vehicles, and compares and evaluates the prediction results for each scenario.

[0150] The evaluation results are provided to policymakers and used as data for improving actual disaster response manuals (eSOPs). In addition, prediction results for each scenario are automatically converted into CAP format and used to generate alert messages that match the corresponding conditions when broadcasting or sending text messages.

[0152] The AI ​​model management and learning control unit (370) comprehensively manages the artificial intelligence learning lifecycle of the present invention. The AI ​​model management and learning control unit (370) systematically records the model's learning data, hyperparameters, version, learning date, and verification results, and automatically performs retraining when performance falls below a certain standard.

[0154] During retraining, past disaster cases and recent observation data stored in the data storage and management module (200) are used together, and when a new model is created, it is replaced with the optimal model through parallel testing with the existing model. In addition, the learning control unit (370) automatically distributes GPU resource usage and maintains a continuous learning system so that the model can evolve without system interruption.

[0156] The result verification and visualization unit (380) is responsible for evaluating the accuracy of the prediction results and intuitively showing the results to the manager and citizens.

[0158] The result verification and visualization unit (380) calculates performance indicators such as RMSE, MAE, and F1-score between the predicted value and the actual observed value, and expresses the predicted flood area in terms of color, depth, and time axes through a map-based 2D / 3D visualization engine. In addition, the prediction result is linked with a CAP conversion module and automatically converted into a forecast text, voice broadcast, and electronic display.

[0160] These components operate in an organically interconnected manner. The time series prediction engine unit (310) and the spatial analysis engine unit (320) receive data in real time from the data storage and management module, and when the analysis is completed, transmit the results to the composite risk calculation unit (340) and the evacuation route recommendation unit (350).

[0162] The generated results are stored in a data warehouse for future re-learning and are simultaneously transmitted to the warning and alert transmission and broadcast linkage module (400) to lead to the citizen notification system. When a new risk pattern is detected in the scenario generation and evaluation unit (360), the results are transmitted to the administration and operation support module (600) so that the response manual is automatically updated.

[0164] The AI ​​analysis and prediction module (300) of the present invention operates not as a simple data analysis system, but as an intelligent prediction platform that continuously learns and self-corrects. By compensating for the limitations of existing rule-based disaster prediction systems that issue warnings only for a single variable or a limited area, the present invention significantly improves the precision and speed of prediction through a complex AI prediction structure that integrates time series, spatial, and simulation information.

[0166] In particular, by reflecting the topographical characteristics and infrastructure structure of each region in real time, it is possible to go beyond simply stating that "heavy rain is dangerous" and quantitatively present "at what time, in which area, and at what depth flooding is expected."

[0168] The warning and alert transmission and broadcast linkage module (400) can quickly and accurately transmit warning and alert information to the public and relevant agencies based on the disaster prediction results calculated by the AI ​​analysis and prediction module (300), and is composed of a forecast data conversion unit (410), a CAP (Common Alerting Protocol) generation unit (420), a multilingual broadcast script generation unit (430), a text and mobile notification unit (440), a broadcast linkage control unit (450), an ARS voice notification unit (460), an electronic display board and loudspeaker transmission device linkage unit (470), and a warning status monitoring unit (480).

[0170] Each component of the warning and alert transmission and broadcast linkage module (400) operates organically to simultaneously transmit risk information such as flooding, heavy rain, and landslides calculated by AI to administrative agencies, broadcasting stations, mobile carriers, and citizen terminals, and can minimize radio blind spots through a multilingual and multi-media integrated structure.

[0172] The forecast data conversion unit (410) operates as an intermediate step for processing the prediction results transmitted from the AI ​​analysis and prediction module (300) into an alarm message. The forecast data conversion unit (410) converts numerical data such as flood depth, flood area, time of occurrence, duration, and range of impact into a language that humans can understand.

[0174] For example, it is automatically generated with specific expressions such as “Expected flood depth of 30cm or more within the next 30 minutes in the area of ​​○○-gu ○○-dong.” During conversion, the standard template of public institutions, such as administrative districts, geographical coordinates, and damage scale, is adhered to, and three warning levels of ‘advisory’, ‘warning’, and ‘severe’ are automatically assigned according to the urgency of the message. The converted forecast data is transmitted to the CAP generation unit (420).

[0176] The CAP (Common Alerting Protocol) generation unit (420) is a core component that converts the broadcast and text transmission of the present invention into a standardized format. CAP is an international disaster alert standard (XML-based) and plays a role in ensuring connectivity between broadcasting companies, telecommunication companies, and local government systems.

[0178] In the present invention, AI prediction results are automatically converted into a CAP structure, and the main fields of the CAP are <identifier> , <sender> , <sent> , <status> , <msgtype> , <scope>, <area> The back is automatically filled. For example <area> The latitude and longitude coordinates of administrative districts predicted to be at risk of flooding are automatically inserted into the field, and <description>The field contains a forecast text converted into natural language. Additionally, the CAP generation unit (420) is designed with a multi-topic structure to simultaneously process multiple forecast types (flooding, fire, earthquake, radiation, etc.).

[0180] The multilingual broadcast script generation unit (430) automatically translates the body of the CAP message into multiple languages, such as English, Chinese, and Vietnamese, in addition to Korean, so that the alert can be delivered to foreign residents and tourists. The multilingual broadcast script generation unit (430) combines an AI translation engine and Text-To-Speech (TTS) technology to reconstruct the sentence structure into a broadcast speech sentence.

[0182] For example, the sentence “A flood warning has been issued for the ○○ area. Please evacuate immediately.” is converted into the English voice “A flood warning has been issued for your area. Please evacuate immediately.” The generated voice file is saved in WAV or MP3 format and transmitted to the broadcast interlock control unit.

[0184] The text and mobile notification unit (440) is configured to directly deliver information to citizens through disaster text messages (CBS) and mobile push notifications. Among the CAP messages <headline>class <description>SMS or app push notifications are automatically generated based on the field content, and only alerts related to the corresponding region are delivered based on the recipient's location information (GPS coordinates).

[0186] In addition, in the event of a communication failure or retransmission error, it is automatically re-transmitted via a secondary route (another carrier or satellite network). The notification includes the estimated time of flooding, shelter locations, and safety guidelines, and touching it leads to a map-based evacuation route guidance screen.

[0188] The broadcast linkage control unit (450) is linked with multiple media such as a public broadcasting station, cable broadcasting, internet broadcasting, radio, and YouTube live streaming, and plays the role of automatically transmitting disaster information.

[0190] The broadcast linkage control unit (450) is linked to the disaster broadcasting standard API of the Korea Communications Commission, and when a CAP message is sent to a broadcasting operator server, it is immediately transmitted in the form of a bottom subtitle, voice guide, and screen banner of the corresponding broadcast. In addition, when linked to a digital platform such as YouTube or IPTV, video subtitles and TTS voice are automatically inserted to maintain broadcast quality.

[0192] The ARS voice notification unit (460) is a device that makes an automatic voice call to citizens who are unable to receive text messages or who have not been able to check disaster messages, and delivers the warning content.

[0194] The ARS voice notification unit (460) uses TTS voice generated by the broadcast script generation unit to sequentially make calls to each phone number. The voice notification is repeated three times until the recipient answers, and upon answering, an announcement saying "A flood warning has been issued for ○○-gu ○○-dong. Please move to the nearest shelter" is automatically played. In addition, whether the call was successful and the reception result are recorded in the system log, allowing for post-monitoring by the administrative agency.

[0196] The electronic display board and loudspeaker transmission device linkage unit (470) is responsible for real-time warnings in offline spaces. It is linked with disaster electronic display boards, municipal road electronic display boards, loudspeakers around parks and rivers, and village broadcasting systems to simultaneously transmit warning messages. A summary of the CAP message is displayed in a scrolling form on the electronic display board, and multilingual voice is played sequentially on the loudspeaker.

[0198] In addition, the present invention is configured to periodically check the communication status of each device and automatically activate a backup device (a nearby electronic display or broadcast speaker) in the event of a signal failure. This prevents information gaps caused by the failure of specific equipment.

[0200] The alarm status monitoring unit (480) monitors the transmission status and reception rate in real time after the warning and alarm are transmitted. It collects transmission success rates, delay times, number of retransmissions, and citizen reception feedback for each medium and displays them on a dashboard. For example, if the text reception rate in a specific area drops below 80%, the system automatically readjusts to strengthen ARS notifications and electronic display transmissions.

[0202] In addition, the transmission log is stored in the data storage and management module (200) and is used for future performance verification and administrative report writing.

[0204] The warning and alert transmission and broadcast linkage module (400) is connected in real time with the AI ​​analysis and prediction module (300), and CAP conversion is performed as soon as the prediction result is confirmed.

[0206] When a CAP message is generated, it is simultaneously transmitted to all media, such as broadcast, text, voice, and electronic display boards, and the status of each media is monitored by the monitoring unit (480). At the same time, the results of the warning and alarm transmission are fed back to the data storage and management module (200) and accumulated again as learning data for the system.

[0208] Furthermore, the present invention goes beyond merely "issuing" an alarm and realizes "an alarm reaching everyone" through a multi-path, multi-language, and multi-format transmission structure. Information is transmitted via electronic display boards and loudspeakers even when mobile communication is interrupted, and foreign residents can understand the alarm without language barriers. Since this structure is fully compatible with national disaster management standard systems (CAP, EDXL, etc.), it can be expanded to the same format when linked with international disaster response systems in the future.

[0210] The user interface module (500) can intuitively provide the results of the complex disaster prediction and response system to various users, such as citizens, administrative agencies, and broadcasters, and at the same time collect field information from users to improve the prediction accuracy and response speed within the system.

[0212] The user interface module (500) is configured to include a mobile application unit for citizens (510), a web-based dashboard unit (520), an integrated management interface unit for the control center (530), a citizen reporting and two-way communication unit (540), a multilingual user environment management unit (550), and a visualization and notification UI engine unit (560).

[0214] Each component is closely linked with the AI ​​analysis and prediction module (300), the warning and alert transmission module (400), and the administration and operation support module (500) to implement the entire process of prediction-warning-response in a form that humans can understand.

[0216] The mobile application unit (510) for citizens is the core user contact point of the present invention and provides real-time guidance on the entire process before, during, and after a disaster through a smartphone terminal.

[0218] The mobile application unit for citizens (510) recognizes the user's GPS coordinates in real time using location-based services, and if the location is included in a danger zone calculated by an AI prediction model, it immediately displays an alarm screen.

[0220] Quantitative information such as "Flooding expected within 500m of current location, 8-minute walk to shelter" is presented on the alert screen, and the expected flood zone (red shading) and evacuation route (blue line) are displayed on the map. Users can record their movement by pressing the "Start Evacuation" button immediately upon receiving the alert, and this information is fed back into the system to generate real-time data such as actual evacuation speed, traffic congestion, and route blockage.

[0222] The application for citizens also supports a multilingual interface, automatically switching to major languages ​​such as Korean, English, Chinese, and Vietnamese. The language is recognized based on smartphone settings, and the system is synchronized so that the same data is displayed across all language versions at the same time. The screen layout consists of tabs for alarm notifications, map view, shelter guidance, real-time reporting, and safety guidelines, and an "Return to Safety" notification is automatically displayed when an alarm is cleared.

[0224] The web-based dashboard section (520) is a visualization and control interface for public officials, such as administrative agencies, local governments, and broadcasting companies. This dashboard comprehensively displays the results of the AI ​​prediction module (300) and the status of the prediction and warning transmissions, and allows for real-time viewing of flood-predicted areas, warning levels, transmission status, and citizen reports at a glance.

[0226] The map screen is designed based on GIS, and changes at each predicted point in time can be checked by shifting the time axis. The person in charge can manually adjust whether to issue an alert by selecting a specific area, or individually control whether to transmit through specific media (text, broadcast, electronic display, etc.).

[0228] In addition, the web-based dashboard section (520) allows for comparative analysis with past disaster history, enabling an evaluation of whether the current response is appropriate compared to similar cases. This function is linked to the eSOP system of the administrative and operational support module and is structured to automatically call response procedures for each person in charge.

[0230] The integrated management interface section (530) for the control center refers to a high-resolution display screen used in the national / local government integrated disaster safety center or broadcasting integrated control room.

[0232] The integrated management interface unit (530) for the control center is configured to monitor the status of thousands of sensors, CCTVs, and broadcasting equipment on one screen, and simultaneously displays AI prediction results and actual field footage.

[0234] For example, if river water levels across Busan Metropolitan City exceed a certain threshold, CCTV footage of the relevant area is automatically zoomed in, and flood depth values ​​predicted by forecasting models are overlaid on the video. This allows control center operators to make response decisions based on visual evidence rather than simple numerical data. Additionally, the control screen supports multi-user access, and displayed information is applied differentially based on departmental authority.

[0236] The citizen reporting and two-way communication unit (540) is configured to allow citizens to transmit situations (photos, videos, text, location) they have directly observed at the scene to the system in real time. When a user photographs and transmits a situation such as flooding, collapse, or fire through the "On-site Report" function within the application, the data is automatically converted into coordinates and displayed on the administrator dashboard.

[0238] After verifying the authenticity of the report, the administrator can transmit the information to the warning / alert transmission module for re-transmission via broadcast or text message upon approval. This structure forms a "citizen-participatory real-time disaster information ecosystem" and serves to complement actual risk factors missed by the AI ​​prediction model (300). Additionally, citizen report data is automatically stored in the data storage and management module (200) and reused as training data in the future.

[0240] The multilingual user environment management unit (550) performs the function of automatically translating and localizing all warnings, maps, and guidance sentences, taking into account users of various language and culture regions.

[0242] The multilingual user environment management unit (550) performs context-preserving translation by combining an AI translation engine and a sentence structure template, and standardizes specialized terms such as alert levels and action guidelines according to a predefined standard glossary. For example, "Severe Alert" is automatically converted into "Severe Alert" in English. In addition, it supports multilingual text-to-speech (TTS) and subtitles simultaneously, so that users with visual or hearing impairments can also perceive the same information.

[0244] The visualization and notification UI engine unit (560) is a core technology that generates and updates graphic elements in real time throughout the user interface of the present invention. The visualization and notification UI engine unit (560) displays flood-predicted areas on a map in the form of contour lines and implements animation effects that gradually change according to the level of risk.

[0246] In addition, the user's location, shelters, and traffic control zones are displayed in an overlaid layered format to intuitively represent complex information. The notification UI engine is directly linked with the warning and alert transmission module, ensuring that alert windows are displayed on both mobile and web platforms using the same visual and color specifications when an alert is issued. For example, "Advisories" are unified with a yellow background, "Warnings" with an orange background, and "Severe" with a red background.

[0248] This user interface module (500) is linked in real-time with the upper AI analysis and prediction module (300) and the warning / alarm transmission and broadcast linkage module (400). When a prediction result is generated by the AI ​​module (300), it is immediately transmitted to the UI engine (560) via the warning / alarm module (400) and simultaneously reflected on the citizen terminal and the administrative dashboard.

[0250] Conversely, citizen reports or on-site photos are transmitted into the system through the user interface module (500) and reflected in the AI ​​training data. That is, the user interface module (500) operates as an "integrated interface in which the output and input of information flow bidirectionally."

[0252] Furthermore, the present invention goes beyond simple alert delivery to implement a real-time, participatory disaster management environment. Users can manually check alerts or submit reports, and in this process, the AI ​​prediction model learns actual citizen responses to become increasingly sophisticated.

[0254] Administrative agencies can monitor evacuation situations and citizen activities in each region in real time through dashboards, and manually adjust alert levels or issue additional alerts if necessary. Unlike existing disaster information systems centered on one-way broadcasting, this structure can complete a "participatory intelligent disaster system" where data flows interact.

[0256] The administrative and operational support module (600) is responsible for the management and control functions that serve as the operational center of the complex disaster response system, and is configured to automatically execute response procedures of relevant agencies according to predicted disaster situations and to efficiently perform administrative instructions, reporting, and resource allocation.

[0258] The administrative and operational support module (600) is composed of an electronic standard operating procedure (eSOP) management unit, an automatic response situation instruction unit (610), an administrative reporting and statistics generation unit (620), a resource allocation and personnel management unit (630), an integrated control unit for linking ministries and local governments (640), a document automation and official document dispatch unit (650), and a real-time monitoring and situation judgment assistance unit (660), and each component is organically linked with an AI analysis and prediction module (300), a data storage and management module (200), an early warning and alert transmission module (400), and a user interface module (500).

[0260] The electronic standard operating procedure (eSOP) management unit (610) is the core of the administrative automation of the present invention and systematically executes predefined response procedures according to the type of disaster and the level of risk. For example, when the AI ​​analysis module calculates "flood depth of 30 cm or more, risk level 2," the eSOP management unit immediately calls the contact network of linked agencies such as the relevant local government official, fire department, police station, and environmental corporation, and automatically deploys the response manual for each agency.

[0262] eSOP consists of XML-based procedural templates, with sequential procedures such as 'alert issuance → evacuation guidance → road control → recovery support' codified. Responsible personnel can monitor the procedure flow in real-time on a dashboard and make manual adjustments if necessary. This ensures that disaster response is carried out automatically according to standardized system procedures, rather than relying on the experience of specific individuals.

[0264] The response situation automatic instruction unit (620) is configured to automatically issue actual instruction commands during the procedure execution phase of the eSOP. The response situation automatic instruction unit (620) receives risk levels from the AI ​​prediction module (300) and the warning / alarm transmission module (400), and generates specific action commands according to the role of each agency. For example, if the river water level exceeds a threshold, commands such as "immediately activate the drainage pump station near ○○ River" or "control the road in the downstream section" are automatically issued, and these are transmitted in real time via the administrative agency's business system or text / messenger API.

[0266] In addition, the execution status of a command is fed back from IoT sensors or the control center interface, and the procedure automatically proceeds to the next step when a "action completed" signal is received. This structure prevents delays and omissions in administrative directives and significantly improves the execution rate of disaster response.

[0268] The administrative reporting and statistics generation unit (630) automatically records all data generated during the disaster response process and performs the function of generating real-time statistics and reports. The administrative reporting and statistics generation unit (630) integrates AI prediction results, the time of issuance of warnings, details of measures taken by response agencies, citizen reports, damage reports, etc., and automatically edits them into daily reports, weekly reports, and situation reports.

[0270] The report can be output in various formats such as PDF, HWP, and DOCX, and is automatically transmitted to a higher authority linked to the approval system. In addition, the statistics generation unit (630) visualizes the frequency of occurrence by disaster type, alarm accuracy, response time, and resource input amount, thereby supporting policymakers in easily analyzing the data. The statistical data accumulated in this way is loaded back into the data storage and management module (200) and reused as training data for the disaster prediction AI in the future.

[0272] The resource allocation and personnel management department (640) automatically calculates the deployment plan for equipment, personnel, vehicles, medical resources, etc., required for each disaster stage. The resource allocation and personnel management department (640) databases the resource list of each local government and affiliated institution and calculates the "demand for necessary resources relative to the expected scale of damage" based on the AI ​​analysis results.

[0274] For example, if the flooded area exceeds a certain threshold, the deployment ratio of drainage pump vehicles and personnel is automatically calculated, and orders are issued to requisition insufficient resources from adjacent areas. Additionally, personnel deployment tables and equipment dispatch status are visualized on a dashboard, allowing for the immediate tracking of resource movements between the field and headquarters.

[0276] The integrated control unit (650) for inter-departmental and local government linkages is responsible for linking information between the central government, local governments, and related agencies such as fire, police, environment, and traffic. The integrated control unit (640) for inter-departmental and local government linkages is linked at the API level with the Ministry of Public Safety and Security’s Disaster Safety Management System (SAFER), the Korea Meteorological Administration, the National Police Agency’s Integrated Control System, and the Korea Water Resources Corporation’s Hydrological Information Network, and exchanges data and alerts from each agency.

[0278] This invention is based on CAP, a standard for disaster information exchange, and since the message transmission method is performed through an API interface in XML or JSON format, it offers very high interoperability with systems of other agencies. When a disaster occurs, the reporting system between the central and local governments is automatically activated, and the same data is simultaneously displayed on the control screens of each agency. This structure automates the disaster administration system, which previously relied on manual reporting, thereby eliminating delays in information transmission.

[0280] The document automation and official document dispatch unit (660) automatically performs the creation and dispatch of official documents between administrative agencies. When the AI ​​analysis result exceeds a certain threshold, the document automation and official document dispatch unit (660) automatically creates an official document according to a predefined format and sends it via an electronic approval system or email API.

[0282] For example, an official document titled "Request to convene an emergency response meeting following the issuance of a flood warning in ○○ District" is automatically generated and sent to each department head. The document number, recipient, time of dispatch, and reply status of the sent document are automatically tracked, and all document history is recorded in the data storage and management module (200).

[0284] The real-time monitoring and situation judgment assistance unit (670) monitors the status of the entire administrative response process in real time and assists the manager in making situation judgments. The real-time monitoring and situation judgment assistance unit (670) comprehensively analyzes the response status of each agency, citizen reports, and the status of warning and alert transmissions, and automatically calculates the "action rate to date," "number of unimplemented instructions," "average response time," etc.

[0286] In addition, it is equipped with an AI-based decision assistance algorithm that displays judgment messages, such as "recommendation to raise response level" or "recommendation to maintain alert," on the manager screen if the situation becomes prolonged or the predicted results change. This feature enhances the objectivity of on-site judgments and has the effect of reducing unnecessary alert repetitions.

[0288] The administrative and operational support module (600) is closely linked with the higher-level AI analysis and prediction module (300), the warning and alert transmission module (400), and the user interface module (500). When the AI ​​prediction result is classified as a risk level, the eSOP management unit of this module immediately operates to execute response procedures, and the warning and alert transmission module (400) is responsible for public notification.

[0290] At the same time, the situation judgment support unit (670) monitors the speed and effectiveness of the on-site response, and all processes are recorded in the data storage and management module (200). Subsequently, the administrative reporting unit (630) automatically documents the results and submits them to policymakers, thereby fully automating the entire process of prediction → response → reporting.

[0292] The administrative and operational support module (600) of the present invention is characterized in that it is not merely a system that assists administrative functions, but forms a real-time link between AI prediction and administrative measures. While existing systems required manual administrative response after an alert was issued, the present invention has a structure in which response procedures are automatically prepared and executed at the prediction stage, thereby fundamentally resolving the delay in response immediately before a disaster occurs.

[0294] In addition, since all administrative records and the results of actions are automatically logged, they can be utilized as objective supporting data during post-audits and policy evaluations.

[0296] The external linkage module (700) enables real-time data exchange and control by interconnecting the complex disaster prediction and response system with external agencies, public data networks, broadcasting and telecommunications infrastructure, private platforms, etc.

[0298] The external linkage module (700) comprises a national agency linkage unit (710), a local government and public institution linkage unit (720), a weather and hydrological data linkage unit (730), a broadcasting and telecommunications linkage unit (740), an IoT and sensor network linkage unit (750), a private platform linkage unit (760), and a standard protocol management unit (770).

[0300] The National Agency Interconnection Department (710) is responsible for connecting with central administrative agencies and public disaster management systems. This department is directly connected via API and dedicated networks with the Ministry of Public Administration and Security's National Disaster Management System (NDMS), the Korea Meteorological Administration's weather data hub, the Ministry of Environment's water quality and river water level observation network, the National Police Agency's traffic situation integrated system, the National Fire Agency's disaster dispatch network, etc.

[0302] For example, when radar rainfall data from the meteorological agency is input in real time, the AI ​​analysis module (300) of the present invention immediately updates the weights of the flood prediction model based on this. In addition, the disaster level (caution, alert, severe) issued by the NDMS is automatically reflected in the eSOP procedure of the administrative and operational support module (600) so that there is no discrepancy in response orders between the central government and local governments.

[0304] The local government and public institution interfacing unit (720) is responsible for exchanging data with local disaster management systems, local public enterprises, public infrastructure management agencies, etc. at the city, county, and district levels. The local government and public institution interfacing unit (720) is connected to each local government's river management system, traffic control center, integrated water and sewage control network, urban facility management network, etc., and collects integrated sensor data by region (rainwater pipe water level, pump station operating status, road flood sensor, etc.).

[0306] In addition, it is linked with the alarm broadcasting system built by the local government, allowing CAP messages generated centrally to be automatically transmitted through the local broadcasting network. In this case, if the local government system uses proprietary formats (JSON, CSV, XML, etc.), the protocol management unit of the external integration module automatically converts the format to ensure compatibility without data loss.

[0308] The weather and hydrological data linkage unit (730) is configured to collect and normalize input data based on natural phenomena. The weather and hydrological data linkage unit (730) is linked with the Korea Meteorological Administration’s AWS (Automatic Weather Station), K-water water level observation station, hydrological radar, satellite image system, etc., to receive real-time values ​​such as rainfall amount, water level, flow rate, soil moisture, and groundwater level.

[0310] The collected data is stored not merely as numerical values ​​but also along with spatiotemporal coordinates, and is directly used as training data for the prediction model in the AI ​​analysis module. Additionally, it includes a time synchronization function to correct the time lag between satellite imagery and ground sensors, minimizing prediction errors caused by differences in data acquisition cycles.

[0312] The broadcasting and telecommunications interoperability unit (740) performs the function of distributing disaster information in real time to broadcasters, telecommunications companies, and portal platforms in cooperation with the warning and alert transmission and broadcasting interoperability module (400). The broadcasting and telecommunications interoperability unit (740) is connected to the Korea Communications Commission's disaster broadcasting integrated system, three mobile carriers (CBS warning network), cable broadcasting operators, radio and OTT platforms via standardized APIs.

[0314] For example, if the AI ​​prediction result is confirmed as a 'flood warning,' a CAP message is transmitted to the Korea Communications Commission server and simultaneously reflected in nationwide broadcast subtitles, radio audio, and portal disaster banners. In addition, it is directly linked with SMS and mobile app push servers to collect data such as text message transmission volume, success rate, and transmission delay time in real time. The broadcasting and telecommunications integration unit periodically monitors the transmission status and incorporates a fault avoidance algorithm that automatically activates an alternative route (third-party network, satellite network) if a failure is detected in a specific communication path.

[0316] The IoT and sensor network interoperability unit (750) communicates directly with equipment such as water level sensors, rain sensors, CCTVs, electronic display boards, and drones installed throughout the city. The IoT and sensor network interoperability unit (750) supports various communication technologies and protocols such as LoRa, NB-IoT, LTE-M, and 5G, and periodically checks the status information (power, signal strength, data transmission cycle) of each piece of equipment.

[0318] For example, if a river water level sensor does not transmit data for a certain period of time, the system automatically interpolates missing values ​​using data from nearby sensors or issues an inspection alert to the control center. CCTV footage is transmitted to an AI analysis module in the form of metadata and is used for learning actual flood footage and verifying prediction results. Additionally, when drone video data is received, it is automatically combined with coordinate information and converted into a 3D terrain model.

[0320] The private platform linkage unit (760) is responsible for connecting with private data sources such as portals, map services, social media, navigation, and traffic apps. The private platform linkage unit (760) is connected with external platforms such as Naver and Kakao Map APIs, Google Maps, Twitter (X), Instagram, and YouTube, and collects citizen reports, search trends, and real-time SNS posts.

[0322] For example, if keywords such as "○○-dong flooding" or "road closure" surge in a specific area, this information is entered into the system and used as a supplementary indicator when assessing risk levels. Additionally, through integration with map services, API calls are made to automatically avoid dangerous sections on navigation app routes when an alert is issued. This allows citizens to be guided along safe routes without any separate operation.

[0324] The standard protocol management unit (770) is a core component for ensuring compatibility of all the aforementioned interoperability functions. The standard protocol management unit (770) manages the data standard format (CAP, EDXL, GeoJSON, etc.) in the upper application layer and the transmission protocol (MQTT, RESTful API, etc.) in the lower communication layer in an integrated manner, thereby automatically converting the data structure and transmission method to match the format and interface specifications used by external organization systems.

[0326] For example, it converts JSON format data from the Korea Meteorological Administration into a CAP structure or reconstructs CSV data from local governments into GeoJSON format for use in map visualization. In addition, the headquarters includes encrypted communication (SSL / TLS) and digital signature verification functions to prevent data tampering. If an integrity error occurs during data transmission, it immediately performs retransmission and forwards the error log to the security management module.

[0328] This external linkage module (700) operates as an "input / output gateway" within the entire system. Data coming from the outside passes through the external linkage module (700), is normalized, and transmitted to the AI ​​analysis module, while prediction and alert results generated internally are distributed to external organizations and media. During this process, the status of each linkage unit is monitored in real time, and if delays, omissions, or format errors occur, they are automatically restored.

[0330] Furthermore, this invention adopts a bidirectional interlocking structure. This not only involves simply "receiving" data but also feeds back analysis results to external agencies to be reflected in administrative and policy decisions. For example, if a prediction model detects a flood risk in a specific river, the relevant data is immediately shared with the Korea Water Resources Corporation, enabling the automatic issuance of commands to operate on-site sluice gates. This structure is characterized by enhancing the efficiency of collaboration among national agencies and automating and standardizing disaster response decision-making.

[0332] The learning and simulation support module (800) continuously improves the artificial intelligence (AI) model of the complex disaster prediction system and performs integrated data learning, evaluation, training, and virtual response functions to verify the reliability of the prediction algorithm.

[0334] The learning and simulation support module (800) is configured to include a learning data generation and refinement unit (810), a model learning and retraining unit (820), a scenario-based virtual disaster simulation unit (830), a performance evaluation and feedback unit (840), a model version management and optimization unit (850), and a policy training support unit (860), and can be interconnected in real time with a data storage and management module (200), an AI analysis and prediction module (300), an external linkage module (700), and an administration and operation support module (600).

[0336] The learning data generation and refinement unit (810) performs the function of converting all source data collected by the system (weather, hydrology, IoT sensors, citizen reports, administrative records, etc.) into a form suitable for AI learning and refining the quality.

[0338] The training data generation and refinement unit (810) performs processing such as outlier detection, missing value correction, coordinate synchronization, and time series alignment on large volume data periodically input from the data storage and management module (200). For example, missing values ​​caused by temporary communication errors of the rainfall sensor are reinforced using real-time data from adjacent observation stations, and time errors in CCTV footage are corrected using GPS reference time. The refined data is structured into a training dataset through a labeling procedure.

[0340] Labeling is automatically classified by categories such as 'flooding occurrence', 'alert level', and 'response success rate', and accuracy is improved by merging manual verification data from administrative agencies when necessary.

[0341] The model learning and retraining unit (820) is configured to periodically train the core artificial intelligence engine of the present invention. The model learning and retraining unit (820) supports various algorithm structures such as supervised learning, unsupervised learning, and deep learning (Deep Neural Network), and trains time series analysis models, spatial prediction models, rainfall-flow-flood linkage models, etc., in parallel.

[0343] For example, a probabilistic model is generated to predict the probability of flooding occurring within the next 3 hours by simultaneously inputting rainfall-flood depth data from the past 5 years and real-time water level information. In addition, the model learning and retraining unit automatically compares the difference between the actual prediction results and the actual occurrence results received as feedback from the AI ​​analysis and prediction module, and automatically performs a retraining procedure if the model's error rate exceeds a threshold.

[0345] These automatic retraining loops are designed to allow the model to improve on its own without human intervention, forming an autonomous learning structure where prediction accuracy cumulatively improves over the long term.

[0347] The scenario-based virtual disaster simulation unit (830) is configured to verify the AI ​​model's response and response procedures by setting virtual conditions before an actual disaster occurs. The scenario-based virtual disaster simulation unit (830) is linked with the eSOP procedure of the administrative and operational support module, and can simulate the entire process of "virtual disaster occurrence - prediction - warning - administrative response".

[0349] For example, if a scenario such as 'heavy rainfall of 80mm per hour, river basin flooding, and road closure delays' is entered, the system processes it as if it were a real occurrence, issues an alert, and sends automatic instructions to relevant departments. For each stage, the AI ​​model's predicted values, response time, and alert accuracy are recorded and used for evaluation.

[0351] The scenario-based virtual disaster simulation unit (830) can generate "city-scale virtual flood images" using 3D terrain information and real-time rendering technology, in addition to 2D map-based simulations. Through this, the person in charge can experimentally verify evacuation routes, resource allocation, and broadcasting timing in an environment close to the actual situation.

[0353] The performance evaluation and feedback unit (840) analyzes the results of learning and simulation to quantitatively measure the model's prediction accuracy, sensitivity, recall, response speed, etc. The performance evaluation and feedback unit (840) automatically calculates statistical indicators such as ROC Curve, RMSE, F1 Score, and Recall, and generates a comparative analysis report for each model.

[0355] For example, if Model A has a flood prediction accuracy of 91% and Model B has 88%, the system adopts Model A as the primary operational model and switches Model B to an auxiliary model. Additionally, the evaluation results are linked to the AI ​​analysis module, so variables with low reliability are automatically excluded or their weights are adjusted. The feedback unit is also connected to the training data refinement unit; if the cause of the error is a data quality issue, it immediately corrects outliers in the source data or requests re-collection. This completes a virtuous cycle structure for AI learning.

[0357] The model version management and optimization unit (850) manages the version of the AI ​​model created at each point in time and performs the function of distributing it in an optimized form. The model version management and optimization unit (850) automatically records the training data range, algorithm structure, weight matrix, evaluation results, application date and time, etc. for each model version. For example, when "Flood Prediction Model_v3.2" is newly trained, it automatically activates the new version by comparing the performance difference with the existing version, or operates it in parallel if necessary.

[0359] In addition, the optimization unit (850) monitors GPU usage, memory usage, response time, etc. in real time and automatically adjusts the model so that it does not fall into an overloaded state. The model managed in this way is applied equally to the simulation environment as well as the prediction module, thereby maintaining consistent performance.

[0361] The policy training support unit (860) is configured to support administrative agencies, local governments, and public institutions in conducting training and education using the system, in addition to technical learning functions. The policy training support unit (860) automatically generates "administrative response training scenarios based on AI prediction results" and enables the execution of roles, instructions, and reporting procedures for each department in a virtual environment. For example, if a flood risk is predicted in a specific area, the policy training support unit (860) simulates a virtual communication sequence between the district office's disaster safety division, fire station, police station, and environmental corporation.

[0363] When each agency receives virtual alert messages on the dashboard and inputs a response, the system evaluates response speed and collaboration efficiency through a process identical to a real-world scenario. The results of this training are transmitted to the administrative and operational support module and reflected as data for improving eSOPs. This leads to a substantial improvement in disaster response capabilities.

[0365] The learning and simulation support module (800) is closely linked with other modules. The data storage and management module (200) provides the original learning data, and the AI ​​analysis and prediction module (300) verifies the learning results in real time. The external linkage module (700) receives new datasets from the Korea Meteorological Administration, the Korea Water Resources Corporation, the Ministry of Environment, etc., and incorporates them into the learning, while the administration and operation support module (600) uses the simulation training results to improve administrative guidelines. In this way, each module is combined in a cyclical structure of learning, prediction, verification, and policy improvement, so that the entire system operates in a self-generating manner.

[0367] In particular, the present invention adopts a Real-time Adaptive Learning Architecture so that the model periodically checks data quality and prediction performance, and can automatically perform retraining if the prediction accuracy falls below a certain standard.

[0369] For example, if the accuracy of the existing model drops below 80% due to a change in heavy rainfall patterns compared to the past, the system automatically trains the latest three months of data to create a new model and immediately replaces it. The replacement process is carried out in a zero-downtime manner, ensuring that real-time prediction services are not interrupted.

[0371] Furthermore, the present invention holds significant technical importance in that it goes beyond simple technical learning to implement a learning-based decision-making structure for policy and administration. The AI ​​model does not remain merely a prediction tool but operates as a simulator for improving administrative procedures. By repeatedly training on actual response scenarios, it diagnoses weaknesses of the administrative organization (such as reporting delays, duplication of orders, and resource shortages) based on data and automatically suggests improvement plans. Therefore, the learning and simulation support module of the present invention can be described as an intelligent policy training platform that integrates technical learning and administrative learning.

[0373] The security, authentication, and performance management module (900) is a core infrastructure for ensuring the stable operation and data integrity of the complex disaster prediction and response system, and protects and monitors the operation of all modules, and can perform information security, access control, authentication management, performance diagnosis, and disaster recovery in an integrated manner.

[0375] The security, authentication, and performance management module (900) is composed of a user authentication and authorization management unit (910), a data encryption and integrity verification unit (920), a communication security and network protection unit (930), an access record and log monitoring unit (940), a system performance monitoring unit (950), a self-recovery and fault response unit (960), and an operational indicator management and optimization unit (970).

[0377] Each component of the security, authentication, and performance management module (900) is linked in real-time with the data storage and management module (200), the external linkage module (700), and the administration and operation support module (600) to maintain the security and reliability of the entire system.

[0379] The user authentication and authorization management unit (910) verifies the identity of all users (administrators, developers, citizen participants, external agency accounts, etc.) accessing the system of the present invention and restricts the scope of access according to their respective roles. The user authentication and authorization management unit (910) applies a multi-factor authentication (MFA) method and requires a combination of elements such as a password, OTP, fingerprint, or public institution certificate when authenticating a user.

[0381] After logging in, permissions for menus, data, reports, and command execution are granted differentially according to Role-Based Access Control (RBAC) policies. For example, ordinary citizens are limited to viewing alerts and registering reports, local government officials can execute resource allocation commands, and central agency administrators have full log access and system control. All records of authentication and permission changes are automatically logged and traceable.

[0383] The data encryption and integrity verification unit (920) encrypts and signs all data that passes in and out of the system to prevent forgery and alteration. The stored data is stored using an AES-256-based symmetric key encryption method, and the transmission section uses the TLS 1.3 protocol to ensure end-to-end encryption.

[0385] Furthermore, based on AI analysis results, SHA-256 hash values ​​are automatically assigned to key data, such as warning and alert messages and administrative reports, allowing for the determination of tampering based on hash matching during subsequent integrity verification. Digital signatures are also applied when data is exchanged between agencies via external integration modules, clearly identifying the source and authenticity of the data. Through this, all alerts and administrative directives are preserved in an "impossible to tamper with."

[0387] The communication security and network protection unit (930) protects the point of contact between the system's data transmission path and the external network. The communication security and network protection unit (930) integrates a firewall, an intrusion detection system (IDS), an intrusion prevention system (IPS), and a VPN gateway to block external hacking, unauthorized access, and denial of service (DDoS) attacks.

[0389] In addition, internal communication between modules takes place in an independent virtual network (VLAN), and data exchange between modules is permitted only through authentication token-based RESTful API calls. If a network disconnection or abnormal traffic is detected, the headquarters immediately blocks the session and sends an alert to the administrator. The communication security level is evaluated periodically, and security patches and certificate renewals are also performed automatically.

[0391] The access record and log monitoring unit (940) is configured to record and analyze major events such as all user actions, system commands, data transmission, and model updates in the form of logs. The unit transmits activity records of each module to a central log server through a log collection agent and detects abnormal behavior through real-time stream analysis.

[0393] For example, automatic blocking measures are executed if the same account logs in from multiple regions within a short period or downloads an abnormally large amount of data. Logs are preserved in their original form for over five years and are designed to be impossible to delete or manipulate by assigning hash-based timestamps. Furthermore, log monitoring results are integrated with an AI analysis engine to learn security threat patterns and progressively improve detection accuracy.

[0395] The system performance monitoring unit (950) checks the status of the server, network, DB, and AI computation module in real time and automatically detects overload, delay, and abnormal resource usage. The system performance monitoring unit monitors about 50 items, such as CPU, GPU, memory, disk I / O, network bandwidth, and API response time, and issues a performance degradation warning if a threshold is exceeded.

[0397] For example, if the GPU utilization of the AI ​​prediction module exceeds 90% and persists for more than 10 minutes, it automatically unloads inactive models or switches the computational load to distributed servers. This automatic load balancing feature ensures that prediction and alert services are not interrupted and maintains the availability of the entire system.

[0399] The self-recovery and fault response unit (960) is responsible for an automatic recovery structure that immediately detects and recovers from hardware, network, and software errors. The self-recovery and fault response unit (960) periodically checks the response status of each module through a 'Health Check loop', and if an abnormal response is detected, it immediately restarts the process or switches traffic to an alternative node.

[0401] In the event of a database failure, immediate recovery is performed from mirrored storage, and the time, module, and cause of the failure are recorded in the logs. Furthermore, even after self-recovery, processes restarted without administrator approval must pass security and integrity verification to be fully restored. This structure enables the maintenance of a stable 24-hour disaster prediction service without human intervention.

[0403] The operational indicator management and optimization unit (970) comprehensively evaluates the overall performance and security level of the system and performs long-term optimization. The operational indicator management and optimization unit periodically analyzes the indicators of each module (average response speed, failure recovery time, log error rate, prediction delay time, etc.) and generates automatic reports. In addition, an AI-based performance prediction model is built in to automatically calculate future load prediction and resource allocation plans.

[0405] For example, if a surge in data input is expected during a specific period (such as the summer rainy season), the system scales up the number of GPU instances and adjusts network bandwidth in advance. This ensures both cost efficiency and stability.

[0407] The security, authentication, and performance management module (900) is organically linked with other modules to form a security-performance-recovery loop for the entire system.

[0409] All information stored in the data collection and management module (100) is encrypted and transmitted, and the external integration module performs electronic signature and SSL verification in parallel during inter-agency communication. Instruction commands from the administrative and operational support module (600) are also executed only after undergoing authentication token verification. When the learning and simulation support module (800) distributes a new model, it is reflected in the actual operation server only after hash verification and electronic signature are completed. This mutual verification structure significantly increases the overall reliability of the system and fundamentally blocks the possibility of forgery or tampering.

[0411] In particular, unlike existing simple security modules, the present invention implements an AI-driven autonomous security management architecture.

[0413] By analyzing data collected from the log monitoring unit (940) and the performance monitoring unit (950), the system determines the risk level and executes response scenarios. For example, if an attack pattern is repeated in a specific IP range, the system automatically modifies the firewall policy and immediately registers an IP blocking command.

[0415] Furthermore, if recurring abnormal traffic is concentrated during specific time periods, schedule-based defense rules are generated to establish a predictive defense system. This creates an active security environment where the system evolves autonomously without the need for manual intervention by administrators.

[0417] The security, authentication, and performance management module (900) of the present invention is a core component that technically guarantees the infrastructure stability of the national disaster response system, going beyond a mere level of defense that prevents intrusion. All processes of prediction, warning, and administrative instruction are executed only after passing the security verification of this module, and all data flows are maintained in an encrypted, recordable, and recoverable form.

[0419] Consequently, the present invention can simultaneously secure the three essential requirements of a disaster information system: safety, integrity, and continuity.

[0421] In this invention, nine modules operate in a single cyclic flow to predict and respond to complex disasters. A data collection and preprocessing module (100) collects information in real time from the Korea Meteorological Administration, Korea Water Resources Corporation, local government sensors, CCTVs, citizen reports, etc., removes outliers, and standardizes the format.

[0423] The refined data is transferred to a data storage and management module (200), encrypted and stored in a relational time-series DB, and can be searched and linked through a metadata index. The AI ​​analysis and prediction module receives this data and calculates the probability of flooding and the risk level using deep learning based on time-series and spatial learning.

[0425] The prediction results are transferred to the warning and alert transmission and broadcast linkage module (400), converted into a CAP-format warning message, and simultaneously transmitted through multiple channels such as text, broadcast, electronic display board, and app. The user interface module visualizes this in map, voice, and multiple languages, and when a citizen uploads a photo of the scene or a report, it is transmitted to the server along with the coordinates and time and reused as training data. The administration and operation support module (600) automatically executes the electronic standard operating procedure (eSOP) according to the prediction stage, issues instructions to relevant agencies, and records the results of the action in a log.

[0427] The external linkage module (700) is connected via API to external organizations such as the Ministry of the Interior and Safety, the Korea Meteorological Administration, and broadcasting and telecommunications companies to share prediction information and to ensure that instructions from higher-level organizations are reflected in the system's internal procedures in real time. The learning and simulation support module (800) analyzes errors by comparing prediction results with actual damage data, automatically retrains the model, and verifies response efficiency through virtual scenarios.

[0429] All processes are carried out under the supervision of the security, authentication, and performance management module (900), and data encryption, integrity verification, access control, and self-recovery are performed simultaneously. In this way, each module continuously exchanges data, and prediction → alert → administration → feedback → learning are connected in a single self-evolving loop, enabling preemptive response to disaster situations.

[0431] The foregoing description is merely an illustrative explanation of the technical concept of the present invention, and those skilled in the art to which the present invention pertains will be able to make various modifications, changes, and substitutions within the scope of the essential characteristics of the present invention. Accordingly, the embodiments disclosed in the present invention and the accompanying drawings are intended to explain, not limit, the technical concept of the present invention, and the scope of the technical concept of the present invention is not limited by such embodiments and accompanying drawings. The scope of protection of the present invention shall be interpreted by the claims below, and all technical concepts within an equivalent scope shall be interpreted as being included within the scope of rights of the present invention. Explanation of the symbols

[0432] 100 - Data Collection and Preprocessing Module 200 - Data Storage and Management Module 300 - AI Analysis and Prediction Module 400 - Early Warning / Alert Transmission and Broadcast Interconnection Module 500 - User Interface Module 600 - Administrative Operations Support Module 700 - External Integration Module 800 - Learning Simulation Support Module 900 - Security Authentication and Performance Management Module< / description> < / headline> < / description> < / scope> < / msgtype> < / status> < / sent> < / sender> < / identifier>

Claims

Claim 1 It includes a data collection and preprocessing module capable of collecting diverse data in real time, including the Korea Meteorological Administration, Korea Water Resources Corporation, CCTV, and citizen reports, and standardizing the collected data; a data storage and management module capable of storing standardized data in a database and controlling data access by user and agency by distinguishing access rights; an AI analysis and prediction module capable of calculating the probability of disaster occurrence, including flooding, inundation, and torrential rain, in real time from data stored in the database; an early warning transmission and broadcasting linkage module capable of converting results analyzed by the AI ​​analysis and prediction module into standard warning messages and transmitting them via multiple channels, including broadcasts, text messages, electronic display boards, and smartphones; and a learning and simulation support module capable of calculating errors by comparing the results predicted by the AI ​​analysis and prediction module with actual damage results and performing re-learning if errors exist. The AI ​​analysis and prediction module includes a time series prediction engine unit that receives meteorological and environmental data that changes over time, including rainfall, water level, flow rate, temperature, humidity, and wind speed, and calculates short- and medium-term disaster occurrence probabilities, and the time series prediction results and spatial data and An artificial intelligence-based complex disaster decision support system comprising: a spatial analysis engine unit capable of calculating flood risk by region and grid by combination; a disaster simulation engine unit capable of spatially and temporally reproducing the progression pattern of an actual disaster occurrence based on the results of a prediction model; a complex risk calculation unit capable of calculating the total risk when multiple factors are combined beyond a single disaster element; and an optimal evacuation route recommendation unit capable of calculating the safest route for citizens to move in the event of a disaster based on the spatial analysis results. Claim 2 An artificial intelligence-based complex disaster decision support system according to claim 1, further comprising a user interface module capable of visualizing prediction results so that they can be intuitively checked on a terminal that citizens and administrative agencies can possess, and capable of displaying predicted flood areas, evacuation routes, and risk levels on the terminal's display. Claim 3 delete

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