An artificial intelligence-based flood prevention and drought resistance method for water conservancy projects

Abstract

The application provides a flood prevention and drought resistance method for water conservancy projects based on artificial intelligence, and relates to the technical field of data processing.The method comprises the following steps: collecting air samples under different water conservancy disaster scenarios, analyzing characteristic odor components in the samples, combining corresponding disaster types, time, place, weather and hydrological information, and constructing a classified and managed odor fingerprint database; based on the detection requirements of the characteristic odor components in the constructed odor fingerprint database, selecting a micro odor sensor and performing performance testing and calibration, and deploying the sensor to form a sensor network for real-time collection of odor data according to the topography, water flow and disaster history records of the water conservancy region.The application improves the timeliness and comprehensiveness of flood prevention and drought resistance decision-making.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence and machine learning, and in particular to an artificial intelligence-based method for flood control and drought relief in water conservancy projects. Background Technology

[0002] Related Concepts and Existing Technologies: Water conservancy projects play a crucial role in ensuring the rational use of water resources and resisting floods and droughts. In the traditional field of flood control and drought relief, water level and flow data obtained from hydrological monitoring stations, as well as manual on-site surveys, are mainly relied upon to judge the development trend of floods and droughts. With the development of science and technology, satellite remote sensing technology has also been applied to monitor large-scale water body changes and precipitation. Traditional methods can, to a certain extent, monitor and warn of water-related disasters, providing support for flood control and drought relief decision-making.

[0003] However, existing methods for flood control and drought relief in water conservancy projects have many shortcomings. On the one hand, relying solely on hydrological data and remote sensing monitoring makes it difficult to capture some early, subtle changes, resulting in a certain lag in early warning. For example, in the early stages of water pollution, conventional hydrological indicators such as water level and flow may not have shown significant changes, making early and accurate warnings difficult. On the other hand, manual on-site surveys are not only inefficient but also limited by manpower, material resources, and geographical conditions, making comprehensive and real-time monitoring impossible. Furthermore, traditional methods mostly analyze only single types of disasters or data, lacking the ability to fuse and analyze multi-source data, making it difficult to cope with complex and ever-changing water disaster situations. The accuracy and reliability of comprehensive early warnings need to be improved. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide an artificial intelligence-based method for flood control and drought relief in water conservancy projects, so as to improve the timeliness and comprehensiveness of flood control and drought relief decision-making.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] A first aspect is a method for flood control and drought relief in water conservancy projects based on artificial intelligence, the method comprising:

[0007] Step 1: By collecting air samples under different water disaster scenarios, analyze the characteristic odor components in the samples, and combine them with the corresponding disaster type, time, location, meteorological and hydrological information to construct a classified odor fingerprint database;

[0008] Step 2: Based on the detection requirements of characteristic odor components in the constructed odor fingerprint database, select miniature odor sensors and conduct performance tests and calibrations. According to the topography, water flow and disaster history of the water conservancy area, deploy the sensors to form a sensor network for real-time acquisition of odor data.

[0009] Step 3: Extract odor fingerprint database data and historical meteorological and hydrological data to form a training set. After preprocessing, input the data into the long short-term memory network model for training. Evaluate and optimize the model through the test set to establish a correlation model between odor characteristics and disaster types and occurrence probabilities.

[0010] Step 4: Collect odor data from sensor networks, hydrological sensor data, and satellite remote sensing data in real time. After time synchronization and alignment, use a data fusion algorithm to fuse them. Input the fused data into the trained odor-disaster correlation model and output disaster early warning results. Dynamically adjust the early warning level based on the consistency between odor early warning and early warning from other data sources. In case of conflict, activate the expert verification mechanism to generate the final early warning.

[0011] Furthermore, by collecting air samples under different water disaster scenarios, analyzing the characteristic odor components in the samples, and combining this with the corresponding disaster type, time, location, meteorological and hydrological information, a classified odor fingerprint database is constructed, including:

[0012] Step 1.1: Based on the analysis of water conservancy disaster types and historical data, receive different disaster scenario definitions and key time node information input by the expert system, and output the determined disaster scenario classification rules;

[0013] Step 1.2: Based on the output disaster scenario classification rules and combined with geographic information system data, generate a spatial distribution scheme of sampling points covering multiple representative water conservancy areas;

[0014] Step 1.3: Receive air sample data packets uploaded by the gas sampling equipment deployed at the sampling point. Each data packet contains the sample number, collection time, geographical location, and synchronously collected meteorological and hydrological data.

[0015] Step 1.4: Transmit the received sample data packet to the gas chromatography-mass spectrometry analysis system and output the structured analysis results containing the types and concentrations of volatile organic compounds and the markers of characteristic odor components;

[0016] Step 1.5: Integrate disaster scenario rules, sampling point spatial information, and structured analysis results to construct an odor fingerprint database indexed by disaster type and geographical region.

[0017] Furthermore, based on the detection requirements of characteristic odor components in the constructed odor fingerprint database, miniature odor sensors are selected and their performance is tested and calibrated. According to the topography, water flow, and historical disaster records of the water conservancy area, the sensors are deployed to form a sensor network for real-time odor data acquisition, including:

[0018] Step 2.1: Analyze the physicochemical properties of characteristic odor components in the constructed odor fingerprint database to generate a list of detection targets and performance parameter requirements for the miniature odor sensor;

[0019] Step 2.2: Based on the performance parameter requirements, perform any of the following operations: match a commercially available sensor model that meets the requirements from the pre-set sensor library, issue a nanomaterial modification instruction to the R&D system, trigger the customized sensor R&D process, and output the final selected sensor model or customized sensor technical specifications.

[0020] Step 2.3: Construct a digital twin test field containing water environment parameters, import the output sensor specifications into the test field, simulate different concentrations of target VOCs and temperature and humidity environments, output calibration parameter sets and store them in the sensor configuration library;

[0021] Step 2.4: Call the topographic data of the water conservancy area, the spatial distribution map of historical disasters and the spatial distribution plan of sampling points, calculate the final monitoring grid through the odor diffusion mathematical model, and generate the sensor deployment coordinate set and network topology instructions;

[0022] Step 2.5: Send the calibration parameter set and deployment instructions to the on-site IoT gateway to control the installed equipment to complete the following operations: fix the sensor hardware according to the coordinate set, load the calibration parameters into the sensor firmware, establish a multi-hop communication link according to the network topology instructions, and receive sensor network status feedback in real time.

[0023] Furthermore, odor fingerprint database data and historical meteorological and hydrological data were extracted to form a training set. After preprocessing, the data were input into a long short-term memory network model for training. The model was evaluated and optimized using a test set to establish a correlation model between odor characteristics and disaster types and probabilities of occurrence, including:

[0024] Step 3.1: Initiate a query request to the odor fingerprint database to extract the characteristic odor component dataset, and synchronously call the historical meteorological and hydrological database to generate a spatiotemporally aligned multi-source training dataset;

[0025] Step 3.2: Perform automated cleaning on the scheduled multi-source training dataset to output a standardized structured dataset;

[0026] Step 3.3: Based on the data scale and model complexity, automatically allocate resources and deploy a containerized LSTM training platform, and load the standardized dataset from Step 3.2;

[0027] Step 3.4: Based on the standardized dataset, perform iterative training on the training platform: initialize the number of LSTM network layers, the number of hidden units and the learning rate parameters, segment the sequence data based on the time sliding window, dynamically adjust the weight matrix through backpropagation, monitor the convergence status of the loss function in real time and output candidate models;

[0028] Step 3.5: Validate the performance of the candidate model using the test dataset. When the accuracy is lower than the preset threshold, the following optimization operations will be automatically triggered: add an attention mechanism layer to improve feature extraction capability, expand the training samples for specific disaster scenarios, and adjust the learning rate decay strategy. Repeat steps 3.4-3.5 until the final validated odor-disaster association model is generated.

[0029] Furthermore, real-time acquisition of odor data from sensor networks, hydrological sensor data, and satellite remote sensing data, after time synchronization and alignment, is followed by data fusion algorithms, including:

[0030] Step 4.1: Receive real-time odor data streams, hydrological sensor data streams, and satellite remote sensing data streams uploaded from the deployed sensor network, unify the data acquisition frequency through the timestamp alignment engine, and generate a spatiotemporally synchronized multimodal data cube;

[0031] Step 4.2: Perform parallel feature engineering on the multimodal data cube: extract the concentration gradient of characteristic odor components from the odor data stream, analyze the water level mutation rate and water quality anomaly index from the hydrological data stream, identify precipitation intensity heat zones and vegetation water stress indicators from the satellite data stream, and output a standardized feature vector set;

[0032] Step 4.3: Based on the correlation analysis results of the feature vector set, dynamically select the fusion algorithm: when the linear correlation between features is strong, use principal component analysis for dimensionality reduction fusion; when the features are nonlinearly correlated, switch to neural network weighted fusion to generate a comprehensive disaster risk scoring matrix.

[0033] Furthermore, the fused data will be input into the trained odor-hazard correlation model to output hazard warning results, including:

[0034] Step 5.1: Receive the output disaster risk comprehensive score matrix, input it into the trained odor-disaster association model, parse the original prediction data output by the model, and generate a structured intermediate result containing the following elements: disaster type probability distribution vector, confidence score of each disaster type, and disaster key feature contribution analysis report;

[0035] Step 5.2: Execute a tiered strategy based on the intermediate results: when the probability of a single disaster type is >70% and the confidence level is >80%, generate a deterministic early warning instruction; when the probabilities of multiple disaster types are all <50% but the confidence level is >60%, generate a risk monitoring instruction; when the confidence level is <50%, trigger a cross-source verification request.

[0036] Step 5.3: Integrate disaster type probabilities with multimodal data cube spatial information to generate a dynamic water conservancy disaster risk heat map;

[0037] Step 5.4: Integrate early warning instructions and risk heat maps, encapsulate them into machine-readable early warning data packages and visual alarm reports, and transmit them to the flood control and drought relief decision-making terminal.

[0038] Furthermore, the alert level is dynamically adjusted based on the consistency between odor alerts and alerts from other data sources. In case of conflict, an expert verification mechanism is activated to generate a final alert, including:

[0039] Step 6.1: Receive the encapsulated early warning data packet, synchronously acquire the water level exceeding warning output by the independent hydrological early warning system and the drought index abnormality warning issued by the satellite remote sensing system, and perform spatiotemporal matching of the three sources of signals;

[0040] Step 6.2: When there is a conflict between multiple early warning signals, perform the following operations: automatically retrieve the historical similar case library for pattern matching, initiate a multi-party video conference request to connect with water conservancy and meteorological expert terminals, and integrate expert decision-making opinions with machine prediction results to generate the final early warning.

[0041] Secondly, an artificial intelligence-based flood control and drought relief system for water conservancy projects includes:

[0042] The acquisition module is used to collect air samples under different water disaster scenarios, analyze the characteristic odor components in the samples, and construct a classified odor fingerprint database by combining the corresponding disaster type, time, location, meteorological and hydrological information. Based on the detection requirements of characteristic odor components in the constructed odor fingerprint database, a miniature odor sensor is selected and its performance is tested and calibrated. According to the topography, water flow and disaster history of the water conservancy area, the sensor is deployed to form a sensor network for real-time collection of odor data.

[0043] The extraction module is used to extract odor fingerprint database data and historical meteorological and hydrological data to form a training set. After preprocessing, the data is input into the long short-term memory network model for training. The model is evaluated and optimized through the test set to establish a correlation model between odor characteristics and disaster types and occurrence probabilities.

[0044] The computing module is used to collect odor data from sensor networks, hydrological sensor data, and satellite remote sensing data in real time. After time synchronization and alignment, the data is fused using a data fusion algorithm. The fused data is then input into a trained odor-disaster correlation model, which outputs disaster early warning results. The warning level is dynamically adjusted based on the consistency between odor warnings and warnings from other data sources. In case of conflict, an expert verification mechanism is activated to generate the final warning.

[0045] Thirdly, a computing device, comprising:

[0046] One or more processors;

[0047] A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method.

[0048] Fourthly, a computer-readable storage medium storing a program that, when executed by a processor, implements the method.

[0049] The above-described solution of the present invention has at least the following beneficial effects:

[0050] By introducing odor sensing technology to construct an odor fingerprint database, characteristic odor components can be captured in the early stages of disasters to achieve early warning, making up for the lag of traditional methods; deploying a network of miniature odor sensors combined with geographic information and diffusion models enables real-time monitoring of the entire region, breaking through the limitations of manual surveys; using a long short-term memory network model to train and optimize multi-source data improves the accuracy and intelligence of disaster prediction; through multi-modal data fusion algorithms and conflict handling mechanisms, the early warning level is dynamically adjusted by integrating multi-source information, reducing false alarms and missed alarms, comprehensively improving the timeliness, comprehensiveness, and intelligence of flood control and drought relief decision-making, reducing human and material costs, and effectively responding to complex water conservancy disaster scenarios. Attached Figure Description

[0051] Figure 1 This is a flowchart illustrating an artificial intelligence-based flood control and drought relief method for water conservancy projects, provided by an embodiment of the present invention.

[0052] Figure 2 This is a schematic diagram of an artificial intelligence-based flood control and drought relief system for water conservancy projects provided by an embodiment of the present invention. Detailed Implementation

[0053] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0054] like Figure 1 As shown, an embodiment of the present invention proposes a method for flood control and drought relief in water conservancy projects based on artificial intelligence, the method comprising the following steps:

[0055] Step 1: By collecting air samples under different water disaster scenarios, analyze the characteristic odor components in the samples, and combine them with the corresponding disaster type, time, location, meteorological and hydrological information to construct a classified odor fingerprint database;

[0056] Step 2: Based on the detection requirements of characteristic odor components in the constructed odor fingerprint database, select miniature odor sensors and conduct performance tests and calibrations. According to the topography, water flow and disaster history of the water conservancy area, deploy the sensors to form a sensor network for real-time acquisition of odor data.

[0057] Step 3: Extract odor fingerprint database data and historical meteorological and hydrological data to form a training set. After preprocessing, input the data into the long short-term memory network model for training. Evaluate and optimize the model through the test set to establish a correlation model between odor characteristics and disaster types and occurrence probabilities.

[0058] Step 4: Collect odor data from sensor networks, hydrological sensor data, and satellite remote sensing data in real time. After time synchronization and alignment, use a data fusion algorithm to fuse them. Input the fused data into the trained odor-disaster correlation model and output disaster early warning results. Dynamically adjust the early warning level based on the consistency between odor early warning and early warning from other data sources. In case of conflict, activate the expert verification mechanism to generate the final early warning.

[0059] In this embodiment of the invention, by constructing an odor fingerprint database, early warnings can be achieved through characteristic odor components in the early stages of water conservancy disasters, compensating for the lag in capturing subtle early changes by traditional methods. Deploying a network of miniature odor sensors, with optimized layout based on the characteristics of water conservancy areas, enables real-time and comprehensive collection of odor data, overcoming the bottlenecks of low efficiency and limited coverage of manual surveys. Utilizing a long short-term memory network model to train and optimize multi-source data can uncover the complex correlation between odor characteristics and disasters, improving the accuracy and intelligence of disaster prediction. Through multi-modal data fusion and a dynamic early warning adjustment mechanism, more reliable early warning results are generated by integrating multi-source information, reducing misjudgments from single data sources. In case of conflicts, expert verification ensures the credibility of the early warning, thereby comprehensively improving the timeliness, accuracy, comprehensiveness, and decision-making efficiency of flood and drought prevention early warnings for water conservancy projects, reducing human and material costs, and effectively responding to complex and ever-changing disaster scenarios.

[0060] In a preferred embodiment of the present invention, step 1 above may include:

[0061] Step 1.1: Based on the analysis of water conservancy disaster types and historical data, receive different disaster scenario definitions and key time node information input by the expert system, and output the determined disaster scenario classification rules;

[0062] Step 1.2: Based on the output disaster scenario classification rules and combined with geographic information system data, generate a spatial distribution scheme of sampling points covering multiple representative water conservancy areas;

[0063] Step 1.3: Receive air sample data packets uploaded by the gas sampling equipment deployed at the sampling point. Each data packet contains the sample number, collection time, geographical location, and synchronously collected meteorological and hydrological data.

[0064] Step 1.4: Transmit the received sample data packet to the gas chromatography-mass spectrometry analysis system and output the structured analysis results containing the types and concentrations of volatile organic compounds and the markers of characteristic odor components;

[0065] Step 1.5: Integrate disaster scenario rules, sampling point spatial information, and structured analysis results to construct an odor fingerprint database indexed by disaster type and geographical region.

[0066] In this embodiment of the invention, the disaster scenario classification rules based on expert systems and historical data ensure the scientific rigor and relevance of sample collection, covering different stages of various types of disasters such as floods, droughts, and pollution, thus solving the problem of insufficient early-stage subtle changes captured by traditional methods. Combined with the spatial distribution scheme of sampling points generated by a geographic information system, fully considering spatial factors such as topography and water flow, the samples cover representative areas such as the upper, middle, and lower reaches of large rivers and different types of lakes / reservoirs, ensuring the spatial balance of the data and the integrity of the disaster scenarios. Simultaneous collection of meteorological and hydrological data and correlation with air samples provides fundamental information for spatiotemporal alignment in subsequent multi-source data fusion analysis. High-precision analysis using gas chromatography-mass spectrometry and machine learning to label characteristic odor components ensure the accuracy of volatile organic compound data and its correlation with disasters in the database. Finally, the constructed two-dimensional indexed odor fingerprint database not only achieves structured data management and rapid retrieval but also provides high-quality training data for the odor-disaster correlation model, making the database a core hub connecting odor perception and disaster early warning.

[0067] In this embodiment of the invention, the specific steps include:

[0068] Step 1.1: Based on the analysis of water disaster types (such as floods, droughts, and water pollution) and historical data, the system inputs the definitions of different disaster scenarios (such as the initial stage of rainfall before a flood, the early stage of river rise, and different degrees of drought) and key time node information, and outputs standardized disaster scenario classification rules to guide the timing of sample collection and scenario matching.

[0069] Step 1.2: Based on the established disaster scenario classification rules and combined with Geographic Information System (GIS) data (such as topography, river and lake distribution), plan the location of sampling points in multiple representative water conservancy areas (such as the upper, middle and lower reaches of large rivers, freshwater / saltwater lakes, irrigation / power generation reservoirs, etc.), and generate a spatial distribution scheme of sampling points covering different geographical features and disaster risk areas to ensure that the samples are regionally representative.

[0070] Step 1.3: Using gas sampling equipment (such as sampling tubes equipped with adsorbents or air sampling pumps) deployed at sampling points, air samples are collected at fixed time intervals, and the sample number, collection time, geographical location (latitude and longitude), real-time meteorological data (temperature, humidity, wind speed, wind direction, etc.) and hydrological data (water level, flow rate, water quality indicators, etc.) are recorded simultaneously to form an air sample data package containing multi-dimensional information, providing spatiotemporal correlation data for subsequent analysis.

[0071] Step 1.4: Transmit the air sample data package to the gas chromatography-mass spectrometry (GC-MS) analysis system to perform qualitative and quantitative analysis of volatile organic compounds (VOCs) in the sample, determine the types and concentrations of VOCs, and identify characteristic odor components (such as specific VOCs related to industrial pollution) under different disaster scenarios by comparing with a known chemical library and using machine learning algorithms (such as principal component analysis, PCA), and output structured analysis results.

[0072] Step 1.5: Integrate disaster scenario classification rules, sampling point spatial information (geographic location, region type) and structured analysis results (characteristic odor components, concentration, associated environmental data), establish a two-dimensional index according to disaster type (flood, drought, pollution, etc.) and geographical region (river basin, lake area, etc.), construct a classified odor fingerprint database, and realize structured storage and rapid retrieval of data.

[0073] In a preferred embodiment of the present invention, step 2 above may include:

[0074] Step 2.1: Analyze the physicochemical properties of characteristic odor components in the constructed odor fingerprint database to generate a list of detection targets and performance parameter requirements for the miniature odor sensor;

[0075] Step 2.2: Based on the performance parameter requirements, perform any of the following operations: match a commercially available sensor model that meets the requirements from the pre-set sensor library, issue a nanomaterial modification instruction to the R&D system, trigger the customized sensor R&D process, and output the final selected sensor model or customized sensor technical specifications.

[0076] Step 2.3: Construct a digital twin test field containing water environment parameters, import the output sensor specifications into the test field, simulate different concentrations of target VOCs and temperature and humidity environments, output calibration parameter sets and store them in the sensor configuration library;

[0077] Step 2.4: Call the topographic data of the water conservancy area, the spatial distribution map of historical disasters and the spatial distribution plan of sampling points, calculate the final monitoring grid through the odor diffusion mathematical model, and generate the sensor deployment coordinate set and network topology instructions;

[0078] Step 2.5: Send the calibration parameter set and deployment instructions to the on-site IoT gateway to control the installed equipment to complete the following operations: fix the sensor hardware according to the coordinate set, load the calibration parameters into the sensor firmware, establish a multi-hop communication link according to the network topology instructions, and receive sensor network status feedback in real time.

[0079] In this embodiment of the invention, based on the analysis of characteristic odor components from an odor fingerprint database, sensor detection targets and performance parameters are generated in a targeted manner. This ensures the sensor's accurate capture of characteristic odors associated with water-related disasters, solving the problem of traditional monitoring methods being insensitive to early trace signals. A dual-mode selection approach, combining commercial matching with customized R&D, and nanomaterial modification technology, is adopted to ensure both the efficiency of technology implementation and breakthroughs in existing sensor performance bottlenecks. By simulating water-related environmental parameters through a digital twin test field, dynamic generation and storage of sensor calibration parameters are achieved, improving the sensor's detection stability in complex field environments and avoiding false alarms caused by environmental interference. The sensor deployment grid is optimized using terrain data, historical disaster distribution, and odor diffusion models, forming a precise coverage and multi-hop communication intelligent network at key locations such as river cross-sections and reservoir inlets. This eliminates the blindness of manual deployment and enables real-time data transmission and status monitoring through wireless networking technology. Ultimately, a low-cost, high-reliability real-time odor monitoring system is constructed, providing continuous and comprehensive data source support for disaster early warning.

[0080] In this embodiment of the invention, the specific steps include:

[0081] Step 2.1: Analyze the physicochemical properties (e.g., molecular structure, volatility, concentration range) of the characteristic odor components (e.g., specific VOCs) already marked in the odor fingerprint database to clarify the list of target substances that the sensor needs to detect (e.g., benzene compounds, sulfides, etc.). Combined with the environmental requirements of water conservancy monitoring (e.g., long-term field operation, complex meteorological conditions), generate sensor performance parameter requirements, including: low power consumption (≤100mW), high sensitivity (detection limit down to ppb level), anti-interference capability (e.g., stable detection in 90% humidity environment), and response time (≤10 seconds), as the basis for sensor selection or development.

[0082] Step 2.2: Commercial sensor matching: Search the pre-built sensor library for commercial models that meet the performance parameter requirements (such as micro sensors based on graphene gas sensing elements), compare their detection range, power consumption, reliability and other indicators, and select the final product.

[0083] Customized R&D Trigger: If commercial sensors cannot meet the requirements (such as insufficient detection accuracy), a nanomaterial modification instruction (such as functional modification of graphene surface) is issued to the R&D system to initiate the customized R&D process. Through material selection, circuit design, prototype manufacturing and other steps, sensor technical specifications that meet specific needs are output (such as a dedicated gas-sensitive layer design for a certain type of industrial pollutant).

[0084] Step 2.3: Construct a digital twin test field containing water conservancy and environmental parameters to simulate the target VOCs concentration gradient (e.g., 0.1ppb to 10ppm), temperature and humidity range (temperature 0-40℃, humidity 20%-90%), and wind speed conditions (0-10m / s) in real-world scenarios. Import the selected or developed sensor specifications into the test field, conduct multi-scenario tests through a gas mixing system and environmental simulation device, record the sensor's response curves to target substances of different concentrations, generate a calibration parameter set (e.g., sensitivity coefficient, drift compensation coefficient), and store it in the sensor configuration library to ensure that the sensor has accurate detection capabilities before actual deployment.

[0085] Step 2.4: Utilize topographic data (e.g., elevation, slope), historical disaster spatial distribution maps (e.g., flood-prone river sections, pollution-prone areas), and sampling point spatial distribution schemes for the water conservancy area. Combine this with a mathematical model of odor diffusion (e.g., a model based on Fick's diffusion law), and apply Fick's first law (steady-state diffusion): ,in, It is the diffusion coefficient, which is related to the properties of VOCs, ambient temperature, humidity, and the intensity of air turbulence. It is a concentration gradient, representing the rate of change of odor concentration in space, according to Fick's second law (unsteady-state diffusion): ,in, It is time, It is the source item. Indicates the release rate of the odor source. It refers to odor concentration, which is the amount of odorant substance per unit volume. It is the diffusion coefficient. , , The spatial coordinates correspond to different directions in three-dimensional space, calculating the propagation path and coverage of odors under different terrain and water flow conditions. Based on the calculation results, the final monitoring grid is generated, the sensor deployment coordinate set is determined (such as key locations such as the middle and lower reaches of river sections, reservoir inlets and outlets, and downwind directions of industrial pollution sources), and the network topology (such as star or mesh networking) is designed to ensure that the sensor network covers the entire area and that the communication links between nodes are stable.

[0086] Step 2.5: Receive the calibration parameter set and deployment instructions via the on-site IoT gateway, and control the installed equipment to perform the following operations:

[0087] Hardware fixation: Install sensors at designated locations according to the coordinate set, and equip them with waterproof and dustproof housings to ensure mechanical stability;

[0088] Firmware loading: The calibration parameters are written to the sensor firmware to complete hardware initialization;

[0089] Network communication: Multi-hop communication links are established through wireless communication technologies such as ZigBee and LoRa to achieve real-time connection between sensor nodes and data acquisition center, while monitoring parameters such as signal strength and data transmission status of sensor network in real time to ensure normal network operation.

[0090] In a preferred embodiment of the present invention, step 3 above may include:

[0091] Step 3.1: Initiate a query request to the odor fingerprint database to extract the characteristic odor component dataset, and synchronously call the historical meteorological and hydrological database to generate a spatiotemporally aligned multi-source training dataset;

[0092] Step 3.2: Perform automated cleaning on the scheduled multi-source training dataset to output a standardized structured dataset;

[0093] Step 3.3: Based on the data scale and model complexity, automatically allocate resources and deploy a containerized LSTM training platform, and load the standardized dataset from Step 3.2;

[0094] Step 3.4: Based on the standardized dataset, perform iterative training on the training platform: initialize the number of LSTM network layers, the number of hidden units and the learning rate parameters, segment the sequence data based on the time sliding window, dynamically adjust the weight matrix through backpropagation, monitor the convergence status of the loss function in real time and output candidate models;

[0095] Step 3.5: Validate the performance of the candidate model using the test dataset. When the accuracy is lower than the preset threshold, the following optimization operations will be automatically triggered: add an attention mechanism layer to improve feature extraction capability, expand the training samples for specific disaster scenarios, and adjust the learning rate decay strategy. Repeat steps 3.4-3.5 until the final validated odor-disaster association model is generated.

[0096] In this embodiment of the invention, the spatiotemporal alignment mechanism of the multi-source training dataset ensures the temporal correlation between odor features and meteorological and hydrological data, solving the one-sidedness problem of traditional single-data modeling; automated data cleaning and standardization effectively remove noise and fill in missing values, improving data quality and laying a reliable foundation for model training; the containerized LSTM training platform dynamically allocates resources based on data scale, achieving high efficiency and scalability of model training, adapting to the data complexity of different water conservancy areas; the combination of time sliding window and backpropagation algorithm enables the LSTM model to capture the time series features of odor data and its long-term dependence on disasters, improving the accuracy of feature extraction; and the automatic optimization mechanism dynamically adjusts the model performance bottlenecks to avoid overfitting or underfitting, ensuring the model's generalization ability in multi-disaster scenarios. The resulting odor-disaster correlation model has high accuracy and strong robustness, providing core algorithmic support for intelligent prediction of water conservancy disasters and improving the reliability and timeliness of early warning.

[0097] In this embodiment of the invention, the specific steps include:

[0098] Step 3.1: Generation of multi-source training dataset and data extraction: Send a query request to the odor fingerprint database, filter characteristic odor component data (such as VOCs types and concentrations) by disaster type (such as floods, droughts, water pollution) and time range, and call the historical meteorological and hydrological database to obtain meteorological data (rainfall, temperature, wind speed, etc.) and hydrological data (water level, flow, water quality indicators, etc.) for the same period.

[0099] Spatiotemporal alignment: Based on timestamps (e.g., minutes / hours), odor data is mapped one-to-one with meteorological and hydrological data to ensure the association of multi-source data at the same point in time, generating a unified training dataset containing odor features, environmental parameters, and disaster labels.

[0100] Step 3.2: Improve data quality by preprocessing the training dataset. ,in, It is raw numerical data. and These are the minimum and maximum values ​​of the feature in the entire dataset. Numerical data (such as concentration and water level) are normalized (normalized to the [0,1] interval), and categorical data (such as disaster type labels) are one-hot encoded to unify the data format for model input.

[0101] Step 3.3: Deploy the containerized LSTM training platform and allocate resources: Based on the size of the training dataset (such as sample size and feature dimension) and the complexity of the LSTM model (such as the number of layers and the number of parameters), automatically allocate resources (such as the number of CPU cores and GPU memory) to ensure training efficiency.

[0102] Containerized deployment: Deploy the LSTM training platform using container technology (such as Docker), and encapsulate the software environment required for model training (such as Python framework, TensorFlow / PyTorch library) to achieve environment consistency and portability.

[0103] Data loading: Import the standardized training dataset into the training platform and divide the data into batches to facilitate iterative model training.

[0104] Step 3.4: Iterative training of the LSTM model and parameter initialization: Set the LSTM network structure parameters, such as the number of hidden layers (e.g., 2-3 layers), the number of hidden units per layer (e.g., 64-256), the learning rate (e.g., 0.001), the number of iterations (e.g., 100-200 rounds), and initialize the weight matrix and bias terms.

[0105] Sequence data segmentation: Using a time sliding window (e.g., window size of 12 hours and step size of 1 hour) to segment continuous time series data into input sequences (e.g., odor / hydrological data of the first 12 hours) and output labels (e.g., disaster type / probability of the 13th hour) to capture the temporal dependencies of the data.

[0106] Backpropagation training: via forward propagation ,in, For sample size, For model predicted values, For real labels, The loss function is used to calculate the model's predicted values. The loss function (such as cross-entropy loss or mean squared error) is used to measure the difference between the prediction and the true label. The model parameters (such as weights and biases) are updated through the backpropagation algorithm to gradually reduce the value of the loss function.

[0107] Real-time monitoring: During training, the loss function curve is plotted in real time to monitor the convergence status. If the loss value no longer decreases after multiple iterations, the model is determined to have converged and a candidate model is output.

[0108] Step 3.5: Model evaluation and optimization, test set validation: Input the reserved test dataset (usually accounting for 20%-30% of the total data) into the candidate model, calculate the accuracy (the ratio of correctly predicted samples to the total number of samples), recall (the ratio of correctly predicted samples of a certain class to the actual number of samples of that class), F1 score and other indicators (the harmonic mean of accuracy and recall) to evaluate the model's ability to generalize to unknown data.

[0109] Automatic optimization trigger: If the model performance does not reach the preset threshold (e.g., accuracy < 85%), the following optimization operations will be automatically executed:

[0110] Add an attention mechanism: Add an attention layer after the LSTM layer to enhance the model's attention to key features (such as changes in the concentration of specific VOCs) and improve feature extraction capabilities.

[0111] Expanding training samples: For disaster scenarios with low accuracy (such as drought), supplement similar samples from the database or generate virtual samples through data augmentation techniques (such as time series shifting and noise addition) to balance the data distribution.

[0112] Adjust the learning rate decay: reduce the learning rate or adopt a dynamic decay strategy (such as exponential decay) to avoid the model getting stuck in local finality or overfitting. Repeat steps 3.4-3.5 until the model's accuracy, recall and other metrics on the test set reach the expected standards (such as ≥90%), and generate the final usable odor-disaster association model.

[0113] In a preferred embodiment of the present invention, step 4 above may include:

[0114] Step 4.1: Receive real-time odor data streams, hydrological sensor data streams, and satellite remote sensing data streams uploaded from the deployed sensor network, unify the data acquisition frequency through the timestamp alignment engine, and generate a spatiotemporally synchronized multimodal data cube;

[0115] Step 4.2: Perform parallel feature engineering on the multimodal data cube: extract the concentration gradient of characteristic odor components from the odor data stream, analyze the water level mutation rate and water quality anomaly index from the hydrological data stream, identify precipitation intensity heat zones and vegetation water stress indicators from the satellite data stream, and output a standardized feature vector set;

[0116] Step 4.3: Based on the correlation analysis results of the feature vector set, dynamically select the fusion algorithm: when the linear correlation between features is strong, use principal component analysis for dimensionality reduction fusion; when the features are nonlinearly correlated, switch to neural network weighted fusion to generate a comprehensive disaster risk scoring matrix.

[0117] In this embodiment of the invention, a timestamp alignment engine is used to unify the frequency and synchronize the spatiotemporal data streams of real-time odor, hydrology, and satellite remote sensing data. This solves the problems of inconsistent acquisition frequencies and spatiotemporal misalignment of multi-source data, ensuring that the data can be fused and analyzed on the same time scale. Parallel feature engineering extracts key indicators such as concentration gradient, water level change rate, and precipitation heat zone from the multimodal data cube, and combines them with standardized processing to generate a unified feature vector set. This effectively filters redundant information and highlights disaster-sensitive features, improving data representation capabilities. The dynamic fusion algorithm selection mechanism based on feature correlation analysis avoids the limitations of a single algorithm, making the fusion process more closely aligned with data characteristics. The generated comprehensive disaster risk scoring matrix can comprehensively reflect the collaborative early warning information of multi-source data, improving the accuracy and reliability of disaster early warning.

[0118] In this embodiment of the invention, the specific steps include:

[0119] Step 4.1: Real-time acquisition of three types of data streams:

[0120] Odor data stream: from a deployed network of miniature odor sensors, including concentration values ​​of characteristic odor components and information on acquisition time and location;

[0121] Hydrological data stream: from devices such as water level gauges, flow meters, and water quality sensors, including real-time monitoring data such as water level, flow rate, pH value, and dissolved oxygen;

[0122] Satellite remote sensing data stream: acquire remote sensing monitoring data such as precipitation intensity, surface temperature, vegetation index (such as NDVI), and water distribution through satellite data interface.

[0123] Data from different acquisition frequencies are processed uniformly:

[0124] For high-frequency data (such as odor sensors collecting data once per minute), it is converted to a low-frequency frequency (such as once per hour) by downsampling (such as taking the mean or maximum value).

[0125] For low-frequency data (such as data acquired daily by satellite remote sensing), it is upscaled to the target frequency using interpolation methods (such as linear interpolation).

[0126] Align the three types of data based on timestamps (accurate to the second) to ensure that multi-source data at the same time point (e.g., 10:00 on June 6, 2025) correspond one-to-one, and generate a multimodal data cube (e.g., a three-dimensional matrix structure) containing time (T), space (X,Y,Z), and data type (odor / hydrology / remote sensing).

[0127] Step 4.2: Odor Data Stream Feature Extraction: For the time series data of characteristic odor components, calculate the concentration change rate between adjacent time points to reflect the odor diffusion or decay trend, compare with the historical thresholds in the odor fingerprint database, and mark abnormal points where the concentration exceeds the normal range.

[0128] Hydrological data stream feature extraction:

[0129] It calculates the change in water level per unit time, identifies rapid rising or falling trends, and generates a comprehensive anomaly index by measuring the deviation of water quality indicators (such as pollutant concentration) from standard values.

[0130] Feature extraction from satellite remote sensing data stream:

[0131] Based on satellite precipitation data, hotspots with precipitation intensity higher than the regional average can be identified (e.g., through threshold segmentation or clustering algorithms).

[0132] Vegetation water stress index: The rate of change of vegetation index (NDVI) is used to assess the degree of vegetation drought.

[0133] Standardization process:

[0134] The extracted features (such as concentration gradient and water level mutation rate) are normalized in step 3.2 to eliminate dimensional differences and generate a standardized feature vector set in a unified format.

[0135] In a preferred embodiment of the present invention, step 5 above may include:

[0136] Step 5.1: Receive the output disaster risk comprehensive score matrix, input it into the trained odor-disaster association model, parse the original prediction data output by the model, and generate a structured intermediate result containing the following elements: disaster type probability distribution vector, confidence score of each disaster type, and disaster key feature contribution analysis report;

[0137] Step 5.2: Execute a tiered strategy based on the intermediate results: when the probability of a single disaster type is >70% and the confidence level is >80%, generate a deterministic early warning instruction; when the probabilities of multiple disaster types are all <50% but the confidence level is >60%, generate a risk monitoring instruction; when the confidence level is <50%, trigger a cross-source verification request.

[0138] Step 5.3: Integrate disaster type probabilities with multimodal data cube spatial information to generate a dynamic water conservancy disaster risk heat map;

[0139] Step 5.4: Integrate early warning instructions and risk heat maps, encapsulate them into machine-readable early warning data packages and visual alarm reports, and transmit them to the flood control and drought relief decision-making terminal.

[0140] In this embodiment of the invention, the generation of structured intermediate results makes the basis for early warning traceable and interpretable, improving the transparency and credibility of model predictions and facilitating decision-makers to quickly grasp the probability of disasters and key influencing factors. The implementation of the hierarchical strategy enables refined management of early warning response, ensuring timely handling of emergency disasters through high-probability, high-confidence deterministic early warnings, and avoiding misjudgments in low-confidence scenarios through risk monitoring and cross-source verification mechanisms, significantly reducing the risk of missed and false alarms. The dynamic water conservancy disaster risk heat map combines abstract probability data with spatial information, presenting risk distribution intuitively in a visual form, assisting decision-makers in quickly locating high-risk areas and formulating spatially targeted prevention and control strategies. The encapsulation and transmission of early warning data packets and visual reports ensure the standardization and real-time nature of multimodal early warning information, providing flood control and drought relief decision-making terminals with comprehensive decision-making basis that is both machine-readable and human-judgeable, comprehensively improving the scientific nature, accuracy, and timeliness of emergency response.

[0141] In this embodiment of the invention, the specific steps include:

[0142] Step 5.1: Data Input: Receive the disaster risk comprehensive score matrix (containing risk scores after multi-source data fusion, organized by time and spatial dimensions) output from Step 4.3 as input to the trained odor-disaster association model (such as LSTM neural network).

[0143] Model prediction:

[0144] Through step 3.4 above, the model calculates and outputs the original prediction data via forward propagation, including:

[0145] Disaster type probability distribution vector: For each time point / spatial area, output the probability of occurrence of various disasters (such as floods, droughts, pollution) (e.g., flood probability 65%, drought probability 20%, pollution probability 15%).

[0146] Confidence score: An assessment of the model's reliability in predicting results (e.g., 0-100 points, reflecting the stability of the prediction).

[0147] Key Feature Contribution Analysis Report: Quantifies the degree of influence of each input feature (such as odor concentration gradient, water level change rate) on the prediction results (e.g., a change in the concentration of a certain VOCs contributes 40% to the flood prediction probability).

[0148] Structured processing:

[0149] The original prediction data is parsed into a standardized format to generate structured intermediate results containing the above three elements, which facilitates subsequent hierarchical processing and visualization.

[0150] Step 5.2: Deterministic Early Warning Triggering Conditions:

[0151] When the probability of a single disaster type is greater than 70% and the confidence level is greater than 80%, it is judged as a high-confidence prediction, and a deterministic early warning instruction is generated, which clarifies the disaster type, the probability of occurrence, and the recommended measures (e.g., flood warning: the probability of occurrence in the next 24 hours is 85%, and it is recommended to activate the flood prevention plan).

[0152] Risk monitoring instruction trigger conditions:

[0153] When the probability of multiple disaster types is less than 50% but the confidence level is greater than 60%, it is judged as a potential risk but the type is unclear. A risk monitoring instruction is generated to prompt continuous tracking and monitoring (e.g., multi-disaster risk warning: the probability of drought and pollution is 45%, please strengthen the monitoring of water sources and farmland).

[0154] Cross-origin verification request triggering conditions:

[0155] When the confidence level is less than 50%, the prediction is deemed unreliable, triggering a cross-source verification request. This automatically retrieves other data sources (such as backup sensors or historical similar cases) for cross-validation to avoid misjudgment by a single model.

[0156] Step 5.3: Data Fusion: Combine the probability of disaster types (such as the probability distribution of floods) with the spatial information (such as river location, reservoir coordinates, and administrative divisions) in the multimodal data cube to form spatiotemporal-risk three-dimensional data.

[0157] Visual rendering:

[0158] Using Geographic Information Systems (GIS) or data visualization tools, risk probabilities are mapped to color gradients (e.g., blue, yellow, and red represent low, medium, and high risks), generating dynamic water disaster risk heat maps that display the risk levels and evolution trends of different regions in real time (e.g., the dynamic process of a river section's risk level upgrading from yellow to red).

[0159] Interactive features:

[0160] It supports a click-to-search function. Clicking on a specific area in the heat map allows users to view the probability of specific disaster types, the contribution of key features, and historical data comparisons, helping decision-makers quickly locate high-risk areas.

[0161] Step 5.4: Machine-readable data package: The structured intermediate results (probability, confidence level, feature contribution), graded early warning instructions and heat map coordinate data are encapsulated into an early warning data package in JSON / XML format, which includes metadata such as timestamp, spatial coordinates, early warning level, and data source, so as to facilitate automatic parsing and linkage of the flood control and drought relief decision-making system (such as triggering automated processes such as gate scheduling and material allocation).

[0162] Visualized Alert Reports:

[0163] Generate human-readable visualization reports, including heat maps, probability distribution charts, key feature trend curves, and text descriptions (e.g., pollution-related odors have been detected around the XX Reservoir, and satellite remote sensing shows that the vegetation index in the area is abnormal, so it is recommended to immediately start emergency water quality monitoring).

[0164] Multi-terminal transmission:

[0165] Through IoT gateways, cloud computing platforms, or dedicated communication networks, early warning data packets and visual reports are synchronously transmitted to flood control and drought relief decision-making terminals (such as command center screens and mobile apps), supporting real-time access by multiple users and ensuring that decision-making information reaches water conservancy, meteorology, emergency management and other departments in a timely manner.

[0166] In a preferred embodiment of the present invention, step 6 above may include:

[0167] Step 6.1: Receive the encapsulated early warning data packet, synchronously acquire the water level exceeding warning output by the independent hydrological early warning system and the drought index abnormality warning issued by the satellite remote sensing system, and perform spatiotemporal matching of the three sources of signals;

[0168] Step 6.2: When there is a conflict between multiple early warning signals, perform the following operations: automatically retrieve the historical similar case library for pattern matching, initiate a multi-party video conference request to connect with water conservancy and meteorological expert terminals, and integrate expert decision-making opinions with machine prediction results to generate the final early warning.

[0169] In this embodiment of the invention, a three-source signal spatiotemporal matching mechanism ensures precise alignment of odor warning data packets with independent hydrological and satellite remote sensing warnings in terms of timestamps and spatial locations. This solves the problem of warning conflict judgment caused by asynchronous data from multiple systems and provides a unified benchmark for subsequent fusion analysis. When multiple source signals conflict, a historical similar case library is automatically retrieved for pattern matching, which can quickly locate the potential patterns of conflict scenarios and reduce the cost of repeated analysis. The multi-party video conferencing mechanism connects experts in fields such as water conservancy and meteorology, combining quantitative analysis of machine predictions with qualitative judgments based on human experience, effectively compensating for the decision-making blind spots of a single AI model in extremely complex scenarios. Finally, the final warning generated by integrating expert opinions and machine prediction results retains the real-time and data processing advantages of artificial intelligence while injecting in-depth analysis capabilities of domain knowledge. This significantly improves the accuracy, credibility, and scientific nature of warning results in conflict scenarios, avoiding emergency response deviations caused by misjudgments from a single data source or model limitations, and providing a more reliable final decision-making basis for flood control and drought relief command.

[0170] In this embodiment of the invention, the specific steps include:

[0171] Step 6.1: Data Reception and Type Resolution:

[0172] Warning data packet: Receive the data packet containing odor warning information encapsulated in step 5.4, including disaster type probability, confidence level, risk heat map coordinates, etc.

[0173] Independent hydrological early warning system: Simultaneously acquires alerts generated by the water level sensor network and hydrological model, such as the specific location, magnitude of exceedance, and expected duration of water levels exceeding warning thresholds.

[0174] Satellite remote sensing systems acquire drought index (such as the Standardized Precipitation Evapotranspiration Index, SPEI) anomaly data to identify the extent, severity, and evolution trend of drought-stricken areas.

[0175] Spatiotemporal alignment processing:

[0176] Time matching: Based on minute-level timestamps, the occurrence times of the three source signals are aligned to ensure that the analysis is of early warning information within the same time period (e.g., all data are from 14:00 to 14:10 on June 6, 2025).

[0177] Spatial matching: By using a geographic information system (GIS), the spatial coordinates of odor warnings (such as sensor deployment locations), the river cross-section locations of hydrological warnings, and the polygons of drought areas from satellite remote sensing are spatially overlaid to identify overlapping or conflicting areas (such as a situation where an abnormal odor warning and a normal water level are simultaneously detected in a certain river section, but the satellite shows that the vegetation is under drought stress).

[0178] Step 6.2: Compare whether the conclusions from the three source signals are consistent:

[0179] Consistent scenarios: such as odor warnings indicating pollution, hydrological alerts indicating abnormal water quality, and satellite remote sensing showing hot spots at sewage outlets, directly confirming the warnings.

[0180] Conflict scenario: For example, if an odor warning indicates a flood risk (based on historical odor patterns), but the hydrological system shows normal water levels and satellite remote sensing shows no abnormal precipitation, the conflict handling process is triggered.

[0181] Historical similar case database matching: Automatically retrieves historical case databases to find historical events similar to the current conflict pattern (such as cases where the odor was abnormal at a certain time in the past but the hydrological indicators were normal, and it was eventually confirmed that it was a local pollution source leak), extracts key features (such as odor components, hydrological lag time, and changes in satellite cloud images), and generates similar case matching reports to help determine the potential causes of the current conflict.

[0182] Multi-party video conferencing mechanism: The system automatically initiates video conference invitations, connecting the terminal devices of water conservancy engineering experts (analyzing hydrological data), meteorological experts (interpreting satellite cloud images), and environmental chemistry experts (analyzing odor components), sharing real-time data, historical case matching results, and machine prediction analysis reports, and conducting cross-domain discussions (such as analyzing whether abnormal odors are caused by sudden upstream pollution, and the lag effect that hydrological indicators have not yet reflected).

[0183] Integration of expert opinions and machine predictions: The system combines machine prediction results (e.g., a 65% probability of flooding output from an odor-hazard model) with expert assessments (e.g., meteorological experts indicating no rainfall in the next 24 hours, and hydrological experts confirming sufficient river flood discharge capacity), and generates a final early warning conclusion through weighted voting or a consensus algorithm.

[0184] If most experts believe that the abnormal odor is caused by non-hazardous factors (such as fertilizer volatilization in farmland), then the machine warning will be rejected and replaced with intensive monitoring.

[0185] If experts determine that there is a potential disaster risk (such as the premature release of abnormal odors due to hidden leakage from an upstream reservoir), the warning level will be raised, and a final warning instruction containing the basis for human assessment will be generated.

[0186] Final early warning output: The integrated results (such as pollution early warning: based on abnormal odor and expert consultation, it is determined that there is a risk of industrial wastewater leakage in the XX section of the river, and it is recommended to immediately start emergency monitoring) will be fed back to the flood control and drought relief decision-making terminal through the original transmission channel, and the risk heat map will be updated simultaneously, and the final conclusions and recommended measures after conflict resolution will be marked.

[0187] like Figure 2 As shown, embodiments of the present invention also provide an artificial intelligence-based flood control and drought relief system for water conservancy projects, comprising:

[0188] The acquisition module is used to collect air samples under different water disaster scenarios, analyze the characteristic odor components in the samples, and construct a classified odor fingerprint database by combining the corresponding disaster type, time, location, meteorological and hydrological information. Based on the detection requirements of characteristic odor components in the constructed odor fingerprint database, a miniature odor sensor is selected and its performance is tested and calibrated. According to the topography, water flow and disaster history of the water conservancy area, the sensor is deployed to form a sensor network for real-time collection of odor data.

[0189] The extraction module is used to extract odor fingerprint database data and historical meteorological and hydrological data to form a training set. After preprocessing, the data is input into the long short-term memory network model for training. The model is evaluated and optimized through the test set to establish a correlation model between odor characteristics and disaster types and occurrence probabilities.

[0190] The computing module is used to collect odor data from sensor networks, hydrological sensor data, and satellite remote sensing data in real time. After time synchronization and alignment, the data is fused using a data fusion algorithm. The fused data is then input into a trained odor-disaster correlation model, which outputs disaster early warning results. The warning level is dynamically adjusted based on the consistency between odor warnings and warnings from other data sources. In case of conflict, an expert verification mechanism is activated to generate the final warning.

[0191] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for flood control and drought relief in water conservancy projects based on artificial intelligence, characterized in that, The method includes: Step 1: By collecting air samples under different water disaster scenarios, analyze the characteristic odor components in the samples, and combine them with the corresponding disaster type, time, location, meteorological and hydrological information to construct a classified odor fingerprint database; Step 2: Based on the detection requirements of characteristic odor components in the constructed odor fingerprint database, select miniature odor sensors and perform performance testing and calibration. Based on the topography, water flow, and historical disaster records of the water conservancy area, deploy the sensors to form a sensor network for real-time odor data acquisition, including: Step 2.1: Analyze the physicochemical properties of characteristic odor components in the constructed odor fingerprint database to generate a list of detection targets and performance parameter requirements for the miniature odor sensors; Step 2.2: Based on the performance parameter requirements, perform any of the following operations: match a suitable commercial sensor model from the pre-set sensor library, or issue a nanomaterial modification instruction to the R&D system. Step 2.3: Trigger the customized sensor R&D process and output the final selected sensor model or customized sensor technical specifications; Step 2.4: Construct a digital twin test field containing water conservancy environmental parameters, import the output sensor specifications into the test field, simulate different concentrations of target VOCs and temperature and humidity environments, output calibration parameter sets and store them in the sensor configuration library; Step 2.5: Call the topographic data of the water conservancy area, historical disaster spatial distribution map and sampling point spatial distribution scheme, calculate the final monitoring grid through the odor diffusion mathematical model, generate sensor deployment coordinate set and network topology instructions. The odor diffusion mathematical model is a model based on Fick's diffusion law, using Fick's first law: ,in, It is the diffusion coefficient, which is related to the properties of VOCs, ambient temperature, humidity, and the intensity of air turbulence. It is a concentration gradient, representing the rate of change of odor concentration in space, according to Fick's second law: ,in, It is time, It is the source item. Indicates the release rate of the odor source. It refers to odor concentration, which is the amount of odorant substance per unit volume. It is the diffusion coefficient. , , The spatial coordinates correspond to different directions in three-dimensional space, and the propagation path and coverage of odor under different terrain and water flow conditions are calculated. Based on the calculation results, the final monitoring grid is generated, the sensor deployment coordinate set is determined, and the network topology is designed. Step 2.5: The calibration parameter set and deployment instructions are sent to the on-site IoT gateway to control the installed equipment to complete the following operations: fix the sensor hardware according to the coordinate set, load the calibration parameters into the sensor firmware, establish a multi-hop communication link according to the network topology instructions, and receive sensor network status feedback in real time. Step 3: Extract odor fingerprint database data and historical meteorological and hydrological data to form a training set. After preprocessing, input the data into the long short-term memory network model for training. Evaluate and optimize the model through the test set to establish a correlation model between odor characteristics and disaster types and occurrence probabilities. Step 4: Collect odor data from sensor networks, hydrological sensor data, and satellite remote sensing data in real time. After time synchronization and alignment, use a data fusion algorithm to fuse them. Input the fused data into the trained odor-disaster correlation model and output disaster early warning results. Dynamically adjust the early warning level based on the consistency between odor early warning and early warning from other data sources. In case of conflict, activate the expert verification mechanism to generate the final early warning.

2. The method for flood control and drought relief in water conservancy projects based on artificial intelligence according to claim 1, characterized in that, By collecting air samples under different water disaster scenarios, analyzing the characteristic odor components in the samples, and combining this with corresponding disaster type, time, location, meteorological and hydrological information, a classified and managed odor fingerprint database is constructed, including: Step 1.1: Based on the analysis of water conservancy disaster types and historical data, receive different disaster scenario definitions and key time node information input by the expert system, and output the determined disaster scenario classification rules; Step 1.2: Based on the output disaster scenario classification rules and combined with geographic information system data, generate a spatial distribution scheme of sampling points covering multiple representative water conservancy areas; Step 1.3: Receive air sample data packets uploaded by the gas sampling equipment deployed at the sampling point. Each data packet contains the sample number, collection time, geographical location, and synchronously collected meteorological and hydrological data. Step 1.4: Transmit the received sample data packet to the gas chromatography-mass spectrometry analysis system and output the structured analysis results containing the types and concentrations of volatile organic compounds and the markers of characteristic odor components; Step 1.5: Integrate disaster scenario rules, sampling point spatial information, and structured analysis results to construct an odor fingerprint database indexed by disaster type and geographical region.

3. The method for flood control and drought relief in water conservancy projects based on artificial intelligence according to claim 2, characterized in that, Odor fingerprint database data and historical meteorological and hydrological data were extracted to form a training set. After preprocessing, the data were input into a long short-term memory network model for training. The model was evaluated and optimized using a test set to establish a correlation model between odor characteristics and disaster types and probabilities of occurrence, including: Step 3.1: Initiate a query request to the odor fingerprint database to extract the characteristic odor component dataset, and synchronously call the historical meteorological and hydrological database to generate a spatiotemporally aligned multi-source training dataset; Step 3.2: Perform automated cleaning on the scheduled multi-source training dataset to output a standardized structured dataset; Step 3.3: Based on the data scale and model complexity, automatically allocate resources and deploy a containerized LSTM training platform, and load the standardized dataset from Step 3.2; Step 3.4: Based on the standardized dataset, perform iterative training on the training platform: initialize the number of LSTM network layers, the number of hidden units and the learning rate parameters, segment the sequence data based on the time sliding window, dynamically adjust the weight matrix through backpropagation, monitor the convergence status of the loss function in real time and output candidate models; Step 3.5: Validate the performance of the candidate model using the test dataset. When the accuracy is lower than the preset threshold, the following optimization operations will be automatically triggered: add an attention mechanism layer to improve feature extraction capability, expand the training samples for specific disaster scenarios, and adjust the learning rate decay strategy. Repeat steps 3.4-3.5 until the final validated odor-disaster association model is generated.

4. The method for flood control and drought relief in water conservancy projects based on artificial intelligence according to claim 3, characterized in that, Real-time acquisition of odor data from sensor networks, hydrological sensor data, and satellite remote sensing data, followed by time synchronization and alignment, is performed using a data fusion algorithm, including: Step 4.1: Receive real-time odor data streams, hydrological sensor data streams, and satellite remote sensing data streams uploaded from the deployed sensor network, unify the data acquisition frequency through the timestamp alignment engine, and generate a spatiotemporally synchronized multimodal data cube; Step 4.2: Perform parallel feature engineering on the multimodal data cube: extract the concentration gradient of characteristic odor components from the odor data stream, analyze the water level mutation rate and water quality anomaly index from the hydrological data stream, identify precipitation intensity heat zones and vegetation water stress indicators from the satellite data stream, and output a standardized feature vector set; Step 4.3: Based on the correlation analysis results of the feature vector set, dynamically select the fusion algorithm: when the linear correlation between features is strong, use principal component analysis for dimensionality reduction fusion; when the features are nonlinearly correlated, switch to neural network weighted fusion to generate a comprehensive disaster risk scoring matrix.

5. The method for flood control and drought relief in water conservancy projects based on artificial intelligence according to claim 4, characterized in that, The fused data is input into the trained odor-disaster correlation model, which outputs disaster early warning results, including: Step 5.1: Receive the output disaster risk comprehensive score matrix, input it into the trained odor-disaster association model, parse the original prediction data output by the model, and generate a structured intermediate result containing the following elements: disaster type probability distribution vector, confidence score of each disaster type, and disaster key feature contribution analysis report; Step 5.2: Execute a tiered strategy based on the intermediate results: when the probability of a single disaster type is >70% and the confidence level is >80%, generate a deterministic early warning instruction; when the probabilities of multiple disaster types are all <50% but the confidence level is >60%, generate a risk monitoring instruction; when the confidence level is <50%, trigger a cross-source verification request. Step 5.3: Integrate disaster type probabilities with multimodal data cube spatial information to generate a dynamic water conservancy disaster risk heat map; Step 5.4: Integrate early warning instructions and risk heat maps, encapsulate them into machine-readable early warning data packages and visual alarm reports, and transmit them to the flood control and drought relief decision-making terminal.

6. The method for flood control and drought relief in water conservancy projects based on artificial intelligence according to claim 5, characterized in that, The alert level is dynamically adjusted based on the consistency between odor alerts and alerts from other data sources. In case of conflict, an expert verification mechanism is activated to generate a final alert, including: Step 6.1: Receive the encapsulated early warning data packet, synchronously acquire the water level exceeding warning output by the independent hydrological early warning system and the drought index abnormality warning issued by the satellite remote sensing system, and perform spatiotemporal matching of the three sources of signals; Step 6.2: When there is a conflict between multiple early warning signals, perform the following operations: automatically retrieve the historical similar case library for pattern matching, initiate a multi-party video conference request to connect with water conservancy and meteorological expert terminals, and integrate expert decision-making opinions with machine prediction results to generate the final early warning.

7. An artificial intelligence-based flood control and drought relief system for water conservancy projects, wherein the system implements the method as described in any one of claims 1 to 6, characterized in that, include: The acquisition module is used to collect air samples under different water disaster scenarios, analyze the characteristic odor components in the samples, and construct a classified odor fingerprint database by combining the corresponding disaster type, time, location, meteorological and hydrological information. Based on the detection requirements of characteristic odor components in the constructed odor fingerprint database, a miniature odor sensor is selected and its performance is tested and calibrated. According to the topography, water flow and disaster history of the water conservancy area, the sensor is deployed to form a sensor network for real-time collection of odor data. The extraction module is used to extract odor fingerprint database data and historical meteorological and hydrological data to form a training set. After preprocessing, the data is input into the long short-term memory network model for training. The model is evaluated and optimized through the test set to establish a correlation model between odor characteristics and disaster types and occurrence probabilities. The computing module is used to collect odor data from sensor networks, hydrological sensor data, and satellite remote sensing data in real time. After time synchronization and alignment, the data is fused using a data fusion algorithm. The fused data is then input into a trained odor-disaster correlation model, which outputs disaster early warning results. The warning level is dynamically adjusted based on the consistency between odor warnings and warnings from other data sources. In case of conflict, an expert verification mechanism is activated to generate the final warning.

8. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, implements the method as described in any one of claims 1 to 6.

Images