Hydropower station natural disaster intelligent early warning and emergency decision-making system based on multi-source data fusion and dynamic threshold optimization

Abstract

The invention provides a hydropower station natural disaster intelligent early warning and emergency decision-making method and system based on multi-source data fusion and dynamic threshold optimization, and the method comprises the steps: collecting the water regimen data of a hydropower station and a drainage basin in real time through the deployment of a water level gauge, a rain gauge, a wind speed sensor and a thunder and lightning monitoring device, and combining weather forecast data and historical hydrological data, thereby achieving the intelligent early warning and emergency decision-making of the hydropower station. And dynamically calculating a disaster threshold value, and generating a graded early warning signal. The system comprises a data acquisition module, a dynamic threshold matching module, an early warning generation module, a case retrieval module, an information issuing module and a response tracking module. The early warning threshold is dynamically adjusted through multi-source data fusion and a machine learning technology, and the early warning accuracy is improved; a historical case library is constructed by using a knowledge graph technology, and intelligent recommendation of a disaster disposal scheme is realized; and early warning information and disposal suggestions are directionally pushed through multiple channels, so that the emergency response efficiency is improved. The conversion from passive response to active early warning is realized, and the influence of natural disasters on the operation of the hydropower station is obviously reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy projects and disaster emergency management, and in particular to an intelligent early warning and emergency decision-making system for natural disasters in hydropower stations based on multi-source data fusion and dynamic threshold optimization. Background Art

[0002] Traditional hydropower stations rely primarily on manual monitoring and fixed thresholds for natural disaster warnings, resulting in slow response, inefficient information transmission, and untargeted response measures. Manual inspections and static sensor networks struggle to obtain comprehensive data in real time during extreme weather conditions, while fixed warning standards are unable to adapt to dynamic environmental changes, leading to false or missed alerts. Furthermore, historical experience is difficult to quickly draw upon when disasters occur, and emergency decision-making often relies on personal experience, lacking scientific and systematic support.

[0003] To address these issues, it is necessary to build an intelligent early warning system that integrates real-time monitoring data, dynamic threshold models, and a historical case library. This system should be capable of integrating multi-source data, leveraging the Internet of Things and edge computing for efficient data collection; employing machine learning to dynamically adjust warning thresholds for improved accuracy; and establishing a structured case library to aid decision-making through knowledge graphs. Furthermore, the system should support multi-level linkage, automatically generating and disseminating action recommendations in real time, improving emergency response efficiency and enabling a shift from passive response to proactive early warning. Summary of the Invention

[0004] This paper proposes an intelligent early warning and emergency decision-making system for hydropower station natural disasters based on multi-source data fusion and dynamic threshold optimization. This system addresses the aforementioned issues, aiming to solve the problems of insufficient power consumption, severe power curtailment, and the difficulty of scheduling optimization in complex, random environments in multi-energy complementary systems. By constructing a long-term scheduling optimization model with an annual cycle, this system incorporates the randomness, uncertainty, and time correlation of wind power, photovoltaic power, and hydropower, and implements intelligent scheduling decisions based on reinforcement learning techniques.

[0005] In order to achieve the above technical features, the purpose of the present invention is to achieve the following: a method for intelligent early warning and emergency decision-making of natural disasters in hydropower stations based on multi-source data fusion and dynamic threshold optimization, comprising:

[0006] Step 1: Data collection:

[0007] Collect relevant data on hydropower stations and river basins;

[0008] Step 2: Dynamic threshold matching:

[0009] Input the relevant data collected in step 1, as well as meteorological forecast data and historical hydrological data, into the multi-hazard dynamic early warning model, dynamically calculate the disaster threshold, and generate a graded early warning signal;

[0010] Step 3: Warning generation:

[0011] Automatically generate standardized warning information based on the graded warning signals obtained in step 2;

[0012] Step 4: Case search:

[0013] Establish a structured historical case library in advance; based on the standardized warning information in step 3, construct the current disaster feature vector, and combine it with the real-time meteorological vector. Calculate the case matching degree through a similarity matching algorithm, and extract the handling processes of the top N highly matching cases;

[0014] Step 5: Information release:

[0015] Push the standardized warning information in step 3 and the related handling procedures obtained in step 4 to the responsible departments through multiple channels;

[0016] Step 6: Response tracking:

[0017] Collect response status data through IoT devices and generate execution feedback report feedback.

[0018] Preferably, the data collection in step 1 specifically includes:

[0019] Water level gauges, rain gauges, wind speed sensors and lightning monitoring devices are deployed to collect real-time data on the water level H(t), flow Q(t), rainfall intensity R(t), wind speed V(t) and lightning intensity L(t) of the hydropower station and the river basin, and transmit them to the cloud server through the Internet of Things protocol.

[0020] Preferably, the dynamic threshold matching in step 2 specifically includes:

[0021] The real-time data {H(t), Q(t), R(t), V(t), L(t)} collected in step 1, as well as the weather forecast data F(t) and the historical hydrological data D hist , input into the multi-hazard dynamic early warning model, and dynamically calculate the disaster threshold T k , k represents the disaster type, and generates a graded warning signal W m , blue, yellow, orange, red m∈{blue, yellow, orange, red};

[0022] The dynamic threshold T k The calculation formula is:

[0023] T k =α·T hist +β·ΔF(t)+γ·G geo ;

[0024] Among them, T histis the historical statistical threshold, ΔF(t) is the weather forecast deviation correction term, G geo is the geological stability coefficient, α, β, γ are dynamic weight coefficients, and α+β+γ=1.

[0025] Preferably, the warning generation in step 3 specifically includes:

[0026] According to the graded warning signal W m Automatically generate standardized warning information I(W m ), the standardized warning information includes disaster type k, impact range A, expected duration τ and response measure template S(W m ).

[0027] Preferably, the case search in step 4 specifically includes:

[0028] Pre-establish a structured historical case library C = {c1, c2, ..., c n}, based on the current disaster feature vector X=[k,W m ,H(t),Q(t)] and real-time weather vector Y=[R(t),V(t),L(t)], and calculate the case matching degree Sim(c i ), and extract the disposal process P(c i );

[0029] The matching degree Sim(c i ) is calculated as:

[0030]

[0031] Among them, DTW is the dynamic time warping algorithm, cos is the similarity, and λ∈[0,1] is the weight coefficient.

[0032] Preferably, the information release in step 5 specifically includes:

[0033] The standardized warning information I(W m ) and the associated disposal process P(c i ) Push to responsible departments through multiple channels, with a push frequency f meeting the following requirements:

[0034] f(W m )=f 基准 2 m ;

[0035] Among them, the benchmark f 基准 : Blue warning, the base frequency when m=1, such as once a day; m∈{1,2,3,4} corresponds to the warning level: blue, yellow, orange, red, and the frequency increases exponentially.

[0036] Preferably, the response tracking in step 6 specifically includes:

[0037] Collect response status data S={s1,s2,...,s p}, and generate an execution feedback report feedback R 反馈 ;

[0038] Dynamically update the model parameters θ = {α, β, γ, λ} by optimizing the objective function J(θ);

[0039]

[0040] Among them, η is the regularization coefficient, the actual loss i is the actual loss value caused by historical disasters, and the predicted loss i (θ) is the model prediction value.

[0041] Preferably, in the multi-disaster dynamic early warning model in step 2:

[0042] Flood disaster threshold T 洪水 The calculation further satisfies:

[0043]

[0044] Among them, Q 历史 (T r ) is T r The historical flow rate of one return year, ΔR(t) is the predicted rainfall increment in the next Δt hours, σ 地质 is the geological landslide risk coefficient, 0≤σ 地质 <1;

[0045] Heavy rain disaster threshold T 暴雨 The calculation further satisfies:

[0046]

[0047] Among them, R 临界 is the critical rainfall value corresponding to the regional drainage capacity, S 土壤 is the real-time soil moisture, S 饱和 is the soil saturation moisture, V 排水 is the real-time drainage rate, V 设计 is the design drainage rate;

[0048] Lightning disaster threshold T 雷电 The calculation further satisfies:

[0049]

[0050] Among them, L 设备 is the lightning protection level threshold of the equipment, V安全 Provides safe wind speed for equipment resistance.

[0051] Preferably, in the case search in step 4:

[0052] The structured historical case database C is constructed into a graph structure G = (V, E) through knowledge graph technology, where the node V represents the disaster factor and the edge E represents the relationship between the treatment action and the effect;

[0053] Semantic similarity calculation uses embedding representation based on the BERT model:

[0054]

[0055] Among them, E(·) is the semantic vector generated by the BERT model.

[0056] Preferably, the differentiated push strategy adopted for information release in step 5 includes:

[0057] Frequency of water information transmission by reservoir dispatching departments:

[0058]

[0059] Inspection frequency published by the equipment department:

[0060] n 巡检 (W m )=n 巡检基准 ·m;

[0061] Among them, n 巡检基准 This is the benchmark frequency for yellow warning.

[0062] Preferably, the optimization algorithm for response tracking in step 6 adopts stochastic gradient descent, and the parameter update formula is:

[0063]

[0064] Among them, ∈ is the learning rate, is the objective function gradient, and iterates until |J(θ t+1 )-J(θ t )|<δ, δ is the convergence threshold.

[0065] Another aspect of the present invention provides a hydropower station natural disaster intelligent early warning and emergency decision-making system based on multi-source data fusion and dynamic threshold optimization, the system is used to implement the hydropower station natural disaster intelligent early warning and emergency decision-making method, including:

[0066] The data acquisition module is equipped with sensors to collect relevant data of the hydropower station and the watershed, and transmit it to the cloud server through the Internet of Things protocol;

[0067] A dynamic threshold matching module is connected to the data acquisition module and inputs data into the built-in multi-disaster dynamic early warning model to dynamically calculate the disaster threshold and generate a graded early warning signal;

[0068] An early warning generation module, connected to the dynamic threshold matching module, automatically generates standardized early warning information according to the graded early warning signal;

[0069] The case retrieval module has a built-in structured historical case library. Based on standardized warning information, it constructs the current disaster feature vector and combines it with the real-time meteorological vector. It calculates the case matching degree through a similarity matching algorithm and extracts the handling procedures of the top N highly matching cases.

[0070] An information release module connects the warning generation module and the case retrieval module to push standardized warning information and related handling processes to the responsible departments through multiple channels;

[0071] The response tracking module collects response status data through IoT devices and generates execution feedback report feedback.

[0072] The present invention has the following beneficial effects:

[0073] The present invention significantly improves the accuracy and timeliness of natural disaster warnings through dynamic threshold matching and real-time data fusion, avoiding the false alarm and omission problems of traditional fixed threshold methods. The system intelligently recommends historical disposal plans based on knowledge graphs and similarity algorithms, enhancing the scientific nature and pertinence of decision-making. The multi-channel targeted push mechanism achieves efficient collaboration between departments and shortens emergency response time. The closed-loop feedback function continuously optimizes model parameters to ensure adaptive improvement of the system. It is applicable to all types of hydropower stations and river basins, especially those in extreme weather and geologically complex areas, providing reliable technical support for disaster prevention and control, effectively reducing disaster losses, and ensuring the safe operation of hydropower stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] The present invention will be further described below with reference to the accompanying drawings and examples.

[0075] Figure 1 This is a diagram of the overall system architecture of the intelligent early warning and emergency decision-making system for natural disasters in hydropower stations based on multi-source data fusion and dynamic threshold optimization provided by the present invention.

[0076] Figure 2 This is a data processing flow chart of the dynamic threshold matching module of the intelligent early warning and emergency decision-making system for natural disasters in hydropower stations based on multi-source data fusion and dynamic threshold optimization provided by the present invention.

[0077] Figure 3 This is a workflow diagram of the case retrieval module of the intelligent early warning and emergency decision-making system for natural disasters in hydropower stations based on multi-source data fusion and dynamic threshold optimization provided by the present invention.

[0078] Figure 4 A closed-loop flow chart of information release and response tracking for the intelligent early warning and emergency decision-making system for natural disasters in hydropower stations based on multi-source data fusion and dynamic threshold optimization provided by the present invention. DETAILED DESCRIPTION

[0079] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0080] Example 1:

[0081] A method for intelligent early warning and emergency decision-making of natural disasters in hydropower stations based on multi-source data fusion and dynamic threshold optimization, comprising:

[0082] Step 1: Data collection: Collect relevant data on the hydropower station and the river basin; specifically, including:

[0083] Water level gauges, rain gauges, wind speed sensors, and lightning monitoring devices are deployed in advance at the hydropower station to collect real-time data on the water level H(t), flow Q(t), rainfall intensity R(t), wind speed V(t), and lightning intensity L(t) of the hydropower station and the basin, and transmit the data to the cloud server through the Internet of Things protocol.

[0084] Step 2: Dynamic threshold matching: The relevant data collected in Step 1, as well as meteorological forecast data and historical hydrological data, are input into the multi-hazard dynamic early warning model to dynamically calculate the disaster threshold and generate a graded early warning signal. This includes:

[0085] The real-time data {H(t), Q(t), R(t), V(t), L(t)} collected in step 1, as well as the weather forecast data F(t) and the historical hydrological data D hist , input into the multi-hazard dynamic early warning model, and dynamically calculate the disaster threshold T k , k represents the disaster type, and generates a graded warning signal W m , (blue yellow orange red m∈{blue, yellow, orange, red});

[0086] The dynamic threshold T k The calculation formula is:

[0087] T k =α·T hist +β·ΔF(t)+γ·G geo ;

[0088] Among them, T hist is the historical statistical threshold, ΔF(t) is the weather forecast deviation correction term, G geo is the geological stability coefficient, α, β, γ are dynamic weight coefficients, and α+β+γ=1.

[0089] Step 3: Warning generation: Automatically generate standardized warning information based on the graded warning signals obtained in step 2; specifically,

[0090] According to the graded warning signal W m Automatically generate standardized warning information I(W m ), the standardized warning information includes disaster type k, impact range A, expected duration τ and response measure template S(W m ).

[0091] Step 4: Case retrieval: Pre-establish a structured historical case library; Based on the standardized warning information in step 3, construct the current disaster feature vector, and combine it with the real-time meteorological vector. Calculate the case matching degree through a similarity matching algorithm, and extract the handling processes of the top N highly matching cases; specifically, include:

[0092] Pre-establish a structured historical case library C = {c1, c2, ..., c n}, based on the current disaster feature vector X=[k,W m ,H(t),Q(t)] and real-time weather vector Y=[R(t),V(t),L(t)], and calculate the case matching degree Sim(c i ), and extract the disposal process P(c i );

[0093] The matching degree Sim(c i ) is calculated as:

[0094]

[0095] Among them, DTW is the dynamic time warping algorithm, cos is the similarity, and λ∈[0,1] is the weight coefficient.

[0096] Step 5: Information release: The standardized warning information from step 3 and the associated handling procedures obtained from step 4 are pushed to the responsible departments through multiple channels; specifically:

[0097] The standardized warning information I(W m ) and the associated disposal process P(c i ) Push to responsible departments through multi-channel targeted push, with push frequency f meeting the following requirements:

[0098] f(W m )=f 基准 2 m ;

[0099] Among them, the benchmark f 基准: The base frequency for a blue alert (m = 1) (e.g., once a day); m∈{1,2,3,4} corresponds to the alert level (blue, yellow, orange, red), and the frequency increases exponentially (e.g., the base frequency for a red alert is f = 8·f 基准 ).

[0100] Step 6: Response tracking: Collect response status data through IoT devices and generate execution feedback reports; specifically,

[0101] Collect response status data S={s1,s2,...,s p}, and generate an execution feedback report feedback R 反馈 ;

[0102] Dynamically update the model parameters θ = {α, β, γ, λ} by optimizing the objective function J(θ);

[0103]

[0104] Among them, η is the regularization coefficient, the actual loss i is the actual loss value caused by historical disasters, and the predicted loss i (θ) is the model prediction value.

[0105] Furthermore, in the multi-disaster dynamic early warning model in step 2:

[0106] Flood disaster threshold T 洪水 The calculation further satisfies:

[0107]

[0108] Among them, Q 历史 (T r ) is T r The historical flow rate of one return year, ΔR(t) is the predicted rainfall increment in the next Δt hours, σ 地质 is the geological landslide risk coefficient, 0≤σ 地质 <1;

[0109] Heavy rain disaster threshold T 暴雨 The calculation further satisfies:

[0110]

[0111] Among them, R 临界 is the critical rainfall value corresponding to the regional drainage capacity, S 土壤 is the real-time soil moisture, S 饱和 is the soil saturation moisture, V 排水 is the real-time drainage rate, V 设计 is the design drainage rate;

[0112] Lightning disaster threshold T 雷电 The calculation further satisfies:

[0113]

[0114] Among them, L 设备 is the lightning protection level threshold of the equipment, V 安全 Provides safe wind speed for equipment resistance.

[0115] Furthermore, in the case search in step 4:

[0116] The structured historical case database C is constructed as a graph structure G = (V, E) using knowledge graph technology. Node V represents a disaster factor (e.g., "sharp increase in flow" or "landslide"), and edge E represents the relationship between the treatment action and the effect (e.g., "slope reinforcement → landslide risk reduced by 0.8").

[0117] Semantic similarity calculation uses embedding representation based on the BERT model:

[0118]

[0119] Among them, E(·) is the semantic vector generated by the BERT model.

[0120] Furthermore, the differentiated push strategy adopted for information release in step 5 includes:

[0121] Frequency of water information transmission by reservoir dispatching departments:

[0122]

[0123] Inspection frequency published by the equipment department:

[0124] n 巡检 (W m )=n 巡检基准 ·m;

[0125] Among them, n 巡检基准 : Yellow warning (m = 2) when the base frequency (such as 2 times a day); Red warning frequency is the inspection base 4·n 巡检基准 (e.g. 8 times a day).

[0126] Furthermore, the optimization algorithm for response tracking in step 6 adopts stochastic gradient descent, and the parameter update formula is:

[0127]

[0128] Among them, ∈ is the learning rate, is the objective function gradient, and iterates until |J(θ t+1 )-J(θ t )|<δ, δ is the convergence threshold.

[0129] Example 2:

[0130] Another aspect of the present invention provides a hydropower station natural disaster intelligent early warning and emergency decision-making system based on multi-source data fusion and dynamic threshold optimization, the system is used to implement the hydropower station natural disaster intelligent early warning and emergency decision-making method, including:

[0131] The data acquisition module is equipped with sensors to collect relevant data of the hydropower station and the watershed, and transmit it to the cloud server through the Internet of Things protocol;

[0132] A dynamic threshold matching module is connected to the data acquisition module and inputs data into the built-in multi-disaster dynamic early warning model to dynamically calculate the disaster threshold and generate a graded early warning signal;

[0133] An early warning generation module, connected to the dynamic threshold matching module, automatically generates standardized early warning information according to the graded early warning signal;

[0134] The case retrieval module has a built-in structured historical case library. Based on standardized warning information, it constructs the current disaster feature vector and combines it with the real-time meteorological vector. It calculates the case matching degree through a similarity matching algorithm and extracts the handling procedures of the top N highly matching cases.

[0135] An information release module connects the warning generation module and the case retrieval module to push standardized warning information and related handling processes to the responsible departments through multiple channels;

[0136] The response tracking module collects response status data through IoT devices and generates execution feedback report feedback.

[0137] Example 3:

[0138] This embodiment provides a specific deployment and implementation process of the method and system of the present invention:

[0139] 1. System architecture and module deployment:

[0140] The implementation of this system mainly includes the deployment and coordination of the following hardware and software components:

[0141] (1) Hardware layer:

[0142] Water level gauges (pressure or ultrasonic), radar flow meters, tipping bucket rain gauges, ultrasonic anemometers, and lightning monitoring devices are deployed in key areas of the hydropower station (such as dams, spillways, and substations) to collect data H(t), Q(t), R(t), V(t), and L(t) in real time.

[0143] IoT gateways are deployed upstream and downstream of the river basin to transmit data to cloud servers via LoRa or NB-IoT protocols.

[0144] (2) Software layer:

[0145] Dynamic threshold matching engine, case retrieval engine and information publishing platform are deployed in the cloud to support distributed computing and high-concurrency data processing.

[0146] The front-end development visual command platform integrates map display, early warning dashboard, case recommendation interface and response progress tracking module.

[0147] 2. Dynamic threshold matching module implementation details:

[0148] Step 1: Data preprocessing and feature extraction;

[0149] Clean the original data {H(t), Q(t), R(t), V(t), L(t)} and remove sensor abnormal values ​​(e.g., using the Z-score method: if |x i -μ|>3σ, it is considered an outlier).

[0150] Extract time series features, such as traffic change rate Accumulated rainfall

[0151] Step 2: Dynamic threshold calculation;

[0152] Flood threshold calculation:

[0153] According to the formula flood history benchmark geology Dynamically adjust the threshold:

[0154] Q 历史 (T r ) obtained through the historical hydrological database (e.g. the 20-year return flow of the Three Gorges Reservoir is 72,300m 3 / s).

[0155] ΔR(t) is based on the rainfall increment in the next 6 hours predicted by the weather forecast and accessed through the China Meteorological Administration data through the API.

[0156] σ 地质 The slope stability coefficient is obtained in real time through the geological monitoring system (for example, when the landslide risk coefficient of a certain area is 0.2, the threshold is magnified by 1.25 times).

[0157] Heavy rain threshold calculation:

[0158] Critical soil saturation drainage design using the formula for storm water Specific parameters:

[0159] R 临界 It is set according to the regional drainage design standard (for example, the critical rainfall corresponding to the drainage capacity of a city is 50 mm / h).

[0160] S 土壤 The soil moisture sensor is used to collect the saturated S 饱和 It is the soil saturation value determined in the laboratory.

[0161] V 排水 Based on the real-time feedback of pump station operation data, V 设计 Rated discharge capacity of the pumping station.

[0162] Step 3: Early warning signal generation;

[0163] Flood warning level mapping rules:

[0164]

[0165] 3. Case retrieval and disposal suggestion generation:

[0166] Step 1: Construction of historical case library;

[0167] Structured case storage:

[0168] Convert historical disaster records (such as the 2020 Yangtze River Basin catastrophic flood) into knowledge graph nodes, including:

[0169] Disaster characteristics: Peak flow Q peak , rainfall duration τ rain , wind speed V max .

[0170] Disposal actions: flood discharge scheduling plan, strengthening water situation monitoring and message sending frequency, and increasing the inspection frequency of key equipment.

[0171] Effect indicator: economic loss reduction rate η loss , response time t response .

[0172] Example: The associated edge of a case node is "flood discharge increases by 10% → downstream water level drops by 2m".

[0173] Step 2: Similarity matching and recommendation;

[0174] Using a hybrid similarity algorithm:

[0175]

[0176] Dynamic Time Warping (DTW): Align the current traffic sequence Q(t) with the historical traffic curves in the case library and calculate the minimum path distance.

[0177] Semantic similarity: Use the pre-trained BERT model to encode meteorological description text (such as "short-term heavy rainfall accompanied by thunderstorms") into vectors and calculate cosine similarity.

[0178] Output the top three high-matching cases and extract their disposal plans (such as "the equipment department strengthens the frequency of outdoor equipment inspections" and "increases the number of flood discharge gates opened").

[0179] 4. Information release and response tracking:

[0180] Step 1: Targeted push strategy;

[0181] Reservoir dispatching department:

[0182] Dynamically adjust the frequency of daily reservoir message sending based on the warning level:

[0183]

[0184] According to the actual water conditions, the dynamic time warping (DTW) algorithm is used to match historical similar cases, and the 2020 Yangtze River Basin flood dispatch records are screened out with a similarity of Sim = 0.88. The corresponding disposal plans for highly matched cases are output (the reservoir dispatch department needs to report the water conditions four times a day, open two flood discharge gates, and increase the discharge flow by 4000m 3 / s).

[0185] Equipment Department:

[0186] Dynamically adjust inspection frequency based on warning level:

[0187]

[0188] According to the results of natural disaster judgment, the dynamic time warping (DTW) algorithm is used to match historical similar cases, and the records of outdoor main transformer fault tripping caused by thunderstorm weather in 2022 are screened out with a similarity of Sim = 0.91. The corresponding disposal plan for high-matching cases is output (strengthen the inspection frequency of outdoor equipment such as main transformers in the plant station, and conduct equipment inspection every 3 hours).

[0189] Step 2: Closed-loop feedback and model optimization;

[0190] Data collection:

[0191] Monitor equipment inspection completion rates through IoT devices (such as RFID check-in records) and check water information reporting status through the water situation system.

[0192] Model optimization:

[0193] Use stochastic gradient descent (SGD) to update the threshold weights α, β, γ:

[0194] (Similarly update β, γ);

[0195] Optimization goal: minimize the mean squared error (MSE) between the predicted loss and the actual loss.

[0196] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for intelligent early warning and emergency decision-making of natural disasters in hydropower stations based on multi-source data fusion and dynamic threshold optimization, characterized in that: include: Step 1: Data collection: Collect relevant data on hydropower stations and river basins; Step 2: Dynamic threshold matching: Input the relevant data collected in step 1, as well as meteorological forecast data and historical hydrological data, into the multi-hazard dynamic early warning model, dynamically calculate the disaster threshold, and generate a graded early warning signal; Step 3: Warning generation: Automatically generate standardized warning information based on the graded warning signals obtained in step 2; Step 4: Case search: Establish a structured historical case library in advance; based on the standardized warning information in step 3, construct the current disaster feature vector, and combine it with the real-time meteorological vector. Calculate the case matching degree through a similarity matching algorithm, and extract the handling processes of the top N highly matching cases; Step 5: Information release: Push the standardized warning information in step 3 and the related handling procedures obtained in step 4 to the responsible departments through multiple channels; Step 6: Response tracking: Collect response status data through IoT devices and generate execution feedback report feedback.

2. The method for intelligent early warning and emergency decision-making of natural disasters in hydropower stations based on multi-source data fusion and dynamic threshold optimization according to claim 1 is characterized in that: The data collection in step 1 specifically includes: Water level gauges, rain gauges, wind speed sensors and lightning monitoring devices are deployed to collect real-time data on the water level H(t), flow Q(t), rainfall intensity R(t), wind speed V(t) and lightning intensity L(t) of the hydropower station and the river basin, and transmit them to the cloud server through the Internet of Things protocol.

3. The method for intelligent early warning and emergency decision-making of natural disasters in hydropower stations based on multi-source data fusion and dynamic threshold optimization according to claim 2 is characterized in that: The dynamic threshold matching in step 2 specifically includes: The real-time data {H(t), Q(t), R(t), V(t), L(t)} collected in step 1, as well as the weather forecast data F(t) and the historical hydrological data D hist , input into the multi-hazard dynamic early warning model, and dynamically calculate the disaster threshold T k , k represents the disaster type, and generates a graded warning signal W m , blue, yellow, orange, red m∈{blue, yellow, orange, red}; The dynamic threshold T k The calculation formula is: T k =α·T hist +β·ΔF(t)+γ·G geo ; Among them, T hist is the historical statistical threshold, ΔF(t) is the weather forecast deviation correction term, G geo is the geological stability coefficient, α, β, γ are dynamic weight coefficients, and α+β+γ=1.

4. The method for intelligent early warning and emergency decision-making of natural disasters in hydropower stations based on multi-source data fusion and dynamic threshold optimization according to claim 3 is characterized in that: The generation of the early warning in step 3 specifically includes: According to the graded warning signal W m Automatically generate standardized warning information I(W m ), the standardized warning information includes disaster type k, impact range A, expected duration τ and response measure template S(W m ).

5. The method for intelligent early warning and emergency decision-making of natural disasters in hydropower stations based on multi-source data fusion and dynamic threshold optimization according to claim 4 is characterized in that: The case search in step 4 specifically includes: Pre-establish a structured historical case library C = {c1, c2, ..., c n }, based on the current disaster feature vector X=[k,W m ,H(t),Q(t)] and real-time weather vector Y=[R(t),V(t),L(t)], and calculate the case matching degree Sim(c i ), and extract the disposal process P(c i ); The matching degree Sim(c i ) is calculated as: Among them, DTW is the dynamic time warping algorithm, cos is the similarity, and λ∈[0,1] is the weight coefficient.

6. The method for intelligent early warning and emergency decision-making of natural disasters in hydropower stations based on multi-source data fusion and dynamic threshold optimization according to claim 4 is characterized in that: The information release in step 5 specifically includes: The standardized warning information I(W m ) and the associated disposal process P(c i ) Push to responsible departments through multiple channels, with a push frequency f meeting the following requirements: f(W m )=f 基准 ·2 m ; Among them, the benchmark f 基准 : Blue warning, the base frequency when m=1, such as once a day; m∈{1,2,3,4} corresponds to the warning level: blue, yellow, orange, red, and the frequency increases exponentially.

7. The method for intelligent early warning and emergency decision-making of natural disasters in hydropower stations based on multi-source data fusion and dynamic threshold optimization according to claim 6 is characterized in that: The response tracking in step 6 specifically includes: Collect response status data S={s1,s2,...,s p }, and generate an execution feedback report feedback R 反馈 ; Dynamically update the model parameters θ = {α, β, γ, λ} by optimizing the objective function J(θ); Among them, η is the regularization coefficient, the actual loss i is the actual loss value caused by historical disasters, and the predicted loss i (θ) is the model prediction value.

8. The method for intelligent early warning and emergency decision-making of natural disasters in hydropower stations based on multi-source data fusion and dynamic threshold optimization according to claim 2 is characterized in that: In the multi-disaster dynamic early warning model in step 2: Flood disaster threshold T 洪水 The calculation further satisfies: Among them, Q 历史 (T r ) is T r The historical flow rate of one return year, ΔR(t) is the predicted rainfall increment in the next Δt hours, σ 地质 is the geological landslide risk coefficient, 0≤σ 地质 <1; Heavy rain disaster threshold T 暴雨 The calculation further satisfies: Among them, R 临界 is the critical rainfall value corresponding to the regional drainage capacity, S 土壤 is the real-time soil moisture, S 饱和 is the soil saturation moisture, V 排水 is the real-time drainage rate, V 设计 is the design drainage rate; Lightning disaster threshold T 雷电 The calculation further satisfies: Among them, L 设备 is the lightning protection level threshold of the equipment, V 安全 Provides safe wind speed for equipment resistance.

9. The method for intelligent early warning and emergency decision-making of natural disasters in hydropower stations based on multi-source data fusion and dynamic threshold optimization according to claim 5 is characterized in that: In the case search in step 4: The structured historical case database C is constructed into a graph structure G = (V, E) through knowledge graph technology, where the node V represents the disaster factor and the edge E represents the relationship between the treatment action and the effect; Semantic similarity calculation uses embedding representation based on the BERT model: Among them, E(·) is the semantic vector generated by the BERT model.

10. The method for intelligent early warning and emergency decision-making of natural disasters in hydropower stations based on multi-source data fusion and dynamic threshold optimization according to claim 6, characterized in that: The differentiated push strategy adopted for information release in step 5 includes: Frequency of water information transmission by reservoir dispatching departments: Inspection frequency published by the equipment department: n 巡检 (W m )=n 巡检基准 ·m; Among them, n 巡检基准 This is the benchmark frequency for yellow warning.

11. The method for intelligent early warning and emergency decision-making of natural disasters in hydropower stations based on multi-source data fusion and dynamic threshold optimization according to claim 7, characterized in that: The optimization algorithm for response tracking in step 6 adopts stochastic gradient descent, and the parameter update formula is: i t+1 =θ t -∈·▽J(θ t ); Among them, ∈ is the learning rate, is the objective function gradient, and iterates until |J(θ t+1 )-J(θ t )|<δ, δ is the convergence threshold.

12. An intelligent early warning and emergency decision-making system for natural disasters in hydropower stations based on multi-source data fusion and dynamic threshold optimization, characterized by: The system is used to implement the intelligent early warning and emergency decision-making method for natural disasters in hydropower stations as described in any one of claims 1 to 11, comprising: The data acquisition module is equipped with sensors to collect relevant data of the hydropower station and the watershed, and transmit it to the cloud server through the Internet of Things protocol; A dynamic threshold matching module is connected to the data acquisition module and inputs data into the built-in multi-disaster dynamic early warning model to dynamically calculate the disaster threshold and generate a graded early warning signal; An early warning generation module, connected to the dynamic threshold matching module, automatically generates standardized early warning information according to the graded early warning signal; The case retrieval module has a built-in structured historical case library. Based on standardized warning information, it constructs the current disaster feature vector and combines it with the real-time meteorological vector. It calculates the case matching degree through a similarity matching algorithm and extracts the handling procedures of the top N highly matching cases. An information release module connects the warning generation module and the case retrieval module to push standardized warning information and related handling processes to the responsible departments through multiple channels; The response tracking module collects response status data through IoT devices and generates execution feedback report feedback.

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