Hydropower station natural disaster early warning method and device

Abstract

The invention provides a hydropower station natural disaster early warning method and device. The method comprises the steps that an instability induction factor model of a hydropower station is acquired; carrying out disaster susceptibility analysis on the environment data collected in real time, and determining a plurality of risk factors of natural disasters of the hydropower station; and performing evolution calculation on the plurality of risk factors according to the instability induction factor model to obtain a plurality of risk assessment values, and performing natural disaster early warning on the hydropower station according to the risk assessment values. According to the method, disaster susceptibility analysis is carried out on environment data collected in real time, risk factors of natural disasters of the hydropower station are determined, evolution calculation is carried out on the risk factors based on an obtained instability induction factor model of the hydropower station, and a risk assessment value used for carrying out natural disaster early warning on the hydropower station is determined. The timeliness, accuracy and comprehensiveness of early warning are greatly improved, the safe operation capability of the hydropower station is improved, and the method has wide application value.

Description

Technical Field

[0001] The present application relates to the technical field of disaster early warning, and in particular to a method and device for early warning of natural disasters in hydropower stations. Background Art

[0002] Hydropower stations are located in mountainous and canyon areas and usually face severe challenges from natural disasters. For example, heavy rains and floods may damage the equipment of hydropower stations; mudslides pose a serious threat to the operation of power generation equipment and personnel safety of hydropower stations; and when hydropower stations discharge floodwaters, the water level downstream rises rapidly, which has an adverse impact on production and life in downstream areas. However, traditional monitoring and early warning methods have many shortcomings in responding to natural disasters, such as lag, low accuracy, and difficulty in comprehensively monitoring a variety of natural disasters. These problems cause hydropower stations to often be in a passive state of response when facing natural disasters, and they are unable to take preventive measures in a timely and effective manner, which brings huge hidden dangers to the safe operation of hydropower stations.

[0003] It should be noted that the above introduction to the technical background is only for the convenience of providing a clear and complete description of the technical solutions of the present application and for the convenience of understanding by those skilled in the art. It cannot be considered that the above technical solutions are well known to those skilled in the art simply because they are described in the background technology section of the present application. Summary of the invention

[0004] The embodiments of the present application provide a method and device for early warning of natural disasters in a hydropower station.

[0005] The first embodiment of the present application proposes a natural disaster early warning method for a hydropower station, comprising:

[0006] Obtain the instability inducing factor model of the hydropower station;

[0007] Based on the disaster susceptibility analysis of real-time collected environmental data, multiple risk factors for natural disasters in hydropower stations are determined;

[0008] According to the instability inducing factor model, the evolution calculation of the plurality of risk factors is performed to obtain a plurality of risk assessment values, and according to the risk assessment values, a natural disaster early warning is issued to the hydropower station.

[0009] The second embodiment of the present application proposes a natural disaster early warning device for a hydropower station, comprising:

[0010] A first acquisition module, the first acquisition module is used to obtain an instability inducing factor model of a hydropower station;

[0011] A second acquisition module, the second acquisition module is used to determine multiple risk factors of natural disasters in the hydropower station based on disaster susceptibility analysis of environmental data collected in real time;

[0012] An early warning module is used to calculate the evolution of multiple risk factors according to the instability inducing factor model, obtain multiple risk assessment values, and issue natural disaster early warnings to the hydropower station based on the risk assessment values.

[0013] The third aspect embodiment of the present application proposes an electronic device, comprising: a processor; a memory for storing instructions executable by the processor; wherein the processor is configured to execute the instructions to implement the hydropower station natural disaster warning method proposed in the first aspect embodiment of the present application.

[0014] The fourth aspect embodiment of the present application proposes a non-temporary computer-readable storage medium. When the instructions in the storage medium are executed by the processor of an electronic device, the electronic device can execute the method proposed in the first aspect embodiment of the present application.

[0015] The fifth aspect embodiment of the present application proposes a computer program product, including a computer program, which implements the method proposed in the first aspect embodiment of the present application when executed by a processor in a communication device.

[0016] The technical solution provided by the embodiments of the present application brings at least the following beneficial effects:

[0017] By analyzing the susceptibility of disasters to environmental data collected in real time, the risk factors of natural disasters in hydropower stations are determined. Based on the acquired instability-inducing factor model of the hydropower station, the evolution of the risk factors is calculated to determine the risk assessment value used for natural disaster early warning of the hydropower station. This greatly improves the timeliness, accuracy and comprehensiveness of early warnings, enhances the ability of hydropower stations to operate safely, and has a wide range of application value.

[0018] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0020] Figure 1 A schematic diagram of a process flow of a natural disaster early warning method for a hydropower station provided in an embodiment of the present application;

[0021] Figure 2 A schematic diagram of another method for early warning of natural disasters in hydropower stations provided in an embodiment of the present application;

[0022] Figure 3 A schematic diagram of the structure of a natural disaster warning device for a hydropower station provided in an embodiment of the present application;

[0023] Figure 4 A schematic diagram of the structure of an electronic device provided according to an embodiment of the present application;

[0024] Figure 5 It is a schematic diagram of the structure of another electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0025] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the appended claims.

[0026] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the embodiments of the present application. The singular forms of "a" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0027] It should be understood that, although the terms first, second, third, etc. may be used to describe various information in the embodiments of the present application, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the embodiments of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein may be interpreted as "at..." or "when..." or "in response to determination".

[0028] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0029] The following describes a natural disaster early warning method and device for a hydropower station according to an embodiment of the present application with reference to the accompanying drawings.

[0030] Figure 1 A schematic diagram of a method for early warning of natural disasters in a hydropower station provided in an embodiment of the present application. Figure 1 As shown, the method includes but is not limited to the following steps:

[0031] S101, obtaining a model of instability inducing factors of a hydropower station.

[0032] In a feasible implementation, since the hydropower station is located in a high mountain valley area with frequent natural disasters, the instability inducing factor model can be constructed by comprehensively considering internal and external factors. Among them, the internal factors usually include the slope morphology in the area where the hydropower station is located (the slope, height, shape, etc. of the slope, these factors will affect the stability of the slope), the stratum lithology (the type, strength, degree of joint development of the rock mass, etc. will affect the stability of the slope), the geological structure (such as faults, folds, cracks and other geological structures have an important influence on the stability of the slope), the rock structure (the integrity, block size, weathering degree, etc. of the rock mass are also important factors affecting the stability of the slope), the ground stress (the magnitude and direction of the ground stress will affect the stability of the rock mass of the slope), etc. External factors usually include surface water and groundwater (scouring and soaking of surface water and infiltration of groundwater will reduce the stability of slopes), earthquake effects (vibration and shear force generated by earthquakes may cause slope instability), weathering (long-term physical and chemical weathering will reduce the strength and stability of the rock mass), artificial excavation and blasting (excavation and blasting operations during construction will destroy the integrity of the rock mass and reduce the stability of the slope), engineering loads (the weight of hydropower station buildings and equipment and the loads during operation will also affect the stability of the slope), reservoir water level changes (changes in the water level of the hydropower station reservoir, especially sudden rises and falls, will have a significant impact on the slopes in the reservoir area and may cause slope instability), etc.

[0033] Based on the above internal and external factors, identify the key factors that have the greatest impact on the stability of the hydropower station slope. As an example, the key factors include: slope and slope height, which directly affect the gravity effect and stability of the slope; soil moisture, which affects the mechanical properties and bearing capacity of the soil; civil material properties, such as specific gravity, porosity, compressive strength, shear strength, etc., which directly determine the shear strength and deformation performance of the slope; vegetation coverage, which affects the erosion resistance and hydraulic scouring force of the slope; and also includes precipitation, debris flow, flood discharge, earthquake, etc. Using relevant mathematical and mechanical principles, establish a mathematical model that describes the relationship between slope stability and key factors. Specifically, as an example, it is assumed that the slope material is an ideal material that is homogeneous, continuous, and linearly elastic; mechanical principles such as the Mohr-Coulomb failure criterion or the Drucker-Prager yield criterion are applied to describe the failure conditions of the slope material; numerical methods such as the finite element method and the limit equilibrium method are used to calculate the bearing capacity, failure mode, and stability of the slope; mathematical expressions for slope stability, such as safety factor, failure probability, etc., are established based on mechanical principles; key factors are introduced as variables into mathematical expressions to establish a quantitative relationship between slope stability and key factors.

[0034] After determining the mathematical model of the relationship between slope stability and key factors, numerical simulation methods, such as finite element analysis and discrete element analysis, can be used to simulate and analyze the stability of the slope and evaluate the stability of the slope under different factors. Based on the simulation analysis results, the risk level of slope instability can be evaluated and corresponding prevention and control measures can be formulated.

[0035] The key factors will be identified, mathematical models will be established, simulation analysis will be conducted, and prevention and control measures will be summarized to complete the establishment of the instability inducing factor model.

[0036] S102, based on the disaster susceptibility analysis of the environmental data collected in real time, determine multiple risk factors for natural disasters in the hydropower station.

[0037] In a feasible implementation, the disaster susceptibility analysis of hydropower stations is a complex but crucial process. Sensor networks, remote sensing technology, drone monitoring and other means can be used to collect environmental data in real time, such as geological structure information, seismic activity data, rainfall data, surface deformation data, etc. At the same time, integrated historical environmental data, including disaster type, occurrence time, location, impact range, etc., are used for comparative analysis. Before conducting comparative analysis, the real-time collected environmental data is usually preprocessed, including removing outliers and missing values ​​to ensure data quality, and formatting the environmental data for subsequent analysis.

[0038] Comparative analysis operations include: using geographic information system technology to analyze the topography, geological structure, rock and soil types around the hydropower station, and combining the instability inducing factor model corresponding to the historical environmental data to update and verify the real-time collected environmental data, and evaluate the current risk level of geological disasters. On the other hand, analyze real-time meteorological data such as rainfall, temperature, wind speed, etc., evaluate the risks of meteorological disasters such as heavy rain, floods, and lightning, and use meteorological forecasting tools to predict the possibility and intensity of meteorological disasters in the future. In addition, monitor hydrological data such as river flow and water level, evaluate the risks of hydrological disasters such as floods and dam breaches, and use hydrological monitoring tools to predict the evolution of floods and provide decision support for flood control scheduling.

[0039] The results of the susceptibility analysis of geological disasters, meteorological disasters and hydrological disasters are comprehensively evaluated to identify the risk factors that pose a threat to the safety of hydropower stations; according to the nature and source of the risk factors, they are divided into natural risk factors (such as earthquakes, rainfall, river flow, etc.) and man-made risk factors (such as improper construction management, design defects, etc.), and then each risk factor is described in detail and quantitatively analyzed.

[0040] S103, calculating the evolution of multiple risk factors according to the instability inducing factor model, obtaining multiple risk assessment values, and issuing natural disaster warnings to the hydropower station according to the risk assessment values.

[0041] In a feasible implementation, the instability inducing factor model is used to perform evolution calculations on the risk factors. During the calculation process, the temporal changes, spatial distribution and interactions of the risk factors should be considered to obtain the evolution trend and possible impact of each risk factor. Based on the evolution calculation results of the risk factors, indicators for assessing the natural disaster risks of hydropower stations are determined. These indicators may include the probability of risk occurrence, possible losses, duration of risk impact, etc. The risk assessment value of each risk factor is calculated using the assessment indicators. The risk assessment value should be able to reflect the contribution of the risk factor to the natural disaster risk of the hydropower station.

[0042] In the process of natural disaster early warning, the threshold of natural disaster early warning is set according to historical data and expert experience. When the risk assessment value reaches or exceeds the threshold, the early warning mechanism is triggered. When the early warning mechanism is triggered, the early warning information is released in a timely manner. The early warning information should include the name of the risk factor, risk assessment value, early warning level, possible impact range and duration, etc. The early warning information should be released through multiple channels such as radio, television, and mobile phone text messages to ensure that hydropower station employees and surrounding residents can obtain it in time. According to the early warning level and risk assessment value, formulate corresponding emergency response measures, such as organizing employees to evacuate dangerous areas, launching emergency plans, and deploying emergency resources, etc., to ensure the effectiveness and timeliness of emergency response measures.

[0043] It should be noted that it is necessary to update the data related to risk factors in real time and continuously optimize the instability inducing factor model to improve the accuracy and reliability of the model. The early warning effect should be evaluated, the accuracy and timeliness of the early warning information should be analyzed, and the early warning mechanism should be improved and optimized based on the evaluation results. The specific update and optimization process will not be described here.

[0044] In summary, by analyzing the disaster susceptibility of real-time collected environmental data, the risk factors of natural disasters in hydropower stations are determined, and the evolution of the risk factors is calculated based on the instability inducing factor model of the hydropower station, and the risk assessment value used for natural disaster early warning of hydropower stations is determined. This greatly improves the timeliness, accuracy and comprehensiveness of early warnings, enhances the ability of hydropower stations to operate safely, and has a wide range of application value.

[0045] Figure 2 This is a flow chart of another hydropower station natural disaster early warning method provided by the embodiment of the present application. Figure 2 As shown, the method includes but is not limited to the following steps:

[0046] S201, obtaining historical environmental data of the hydropower station.

[0047] It should be noted that historical environmental data can be obtained through the official website of the hydropower station, government environmental protection departments, academic research institutions, environmental data platforms, hydropower industry databases, etc. When obtaining historical environmental data of the hydropower station, the accuracy and reliability of the data should be ensured. At the same time, since the environmental data of different hydropower stations and regions may be different, it is necessary to select and screen according to the specific situation.

[0048] For further details on step S201, please refer to the relevant contents in the above embodiment, which will not be repeated here.

[0049] S202, based on the preprocessing of historical environmental data, obtaining an instability inducing factor model of the hydropower station.

[0050] Optionally, as an example, based on the preprocessing of historical environmental data (including removing default values ​​and outliers, and data formatting, etc.), the first intermediate data is obtained; based on the time series analysis of the first intermediate data, the second intermediate data is obtained. It should be noted that time series analysis is used to analyze the long-term changes, periodic changes, and random changes of the first intermediate data based on the time series. Commonly used time series analysis methods include moving average, difference, autoregressive integrated moving average, etc., and the appropriate time series analysis method should be selected according to actual needs, which will not be repeated here. Specifically, the first intermediate data is periodically decomposed to obtain the characteristics of the trend, period, and random components of the first intermediate data. Based on fitting the above characteristics, the second intermediate data is obtained.

[0051] According to the structural stability requirements of the hydropower station and the operating conditions of the hydropower station, the second intermediate data is subjected to regression analysis to obtain the instability inducing factor model of the hydropower station. It should be noted that regression analysis is to describe the dependency between independent variables (predictor variables or input variables) and dependent variables (response variables or output variables) by establishing a mathematical model. This relationship is usually represented by the function Y=f(X), where X is the input variable that affects the prediction result, Y is the output variable to be predicted, and the function f represents the regression model. The basic principle of regression analysis is to use the statistical principle of data to perform mathematical processing on a large amount of statistical data, determine the correlation between variables, and establish a regression equation (or function expression) with good correlation to predict or explain the change of the dependent variable. In this embodiment, according to the structural stability requirements of the hydropower station, multiple measurement indicators are determined as dependent variables; according to the operating conditions of the hydropower station, multiple instability inducing factors are determined as independent variables; based on the dependent variable and the independent variable, the second intermediate data is subjected to regression analysis to obtain the instability inducing factor model of the hydropower station.

[0052] It should be noted that the second intermediate data usually includes data related to the structural stability of the hydropower station extracted from the time series analysis, such as trend components, seasonal components, outliers, etc.; the selection of independent variables is usually based on assumptions, and possible independent variables are selected, such as water level fluctuation amplitude, maximum flow, temperature fluctuation, earthquake frequency, material aging index, etc. According to the type of dependent variable and the distribution of data, select the appropriate regression type, such as linear regression, nonlinear regression, etc. According to the selected regression type and specific model, establish a regression equation; use this regression equation as the main framework to determine the instability inducing factor model of the hydropower station.

[0053] For further details on step S202, please refer to the relevant contents in the above embodiment, which will not be repeated here.

[0054] S203, based on the disaster susceptibility analysis of the environmental data collected in real time, determine multiple risk factors for natural disasters in the hydropower station.

[0055] Optionally, as an example, according to the geological conditions, hydrological conditions, meteorological conditions and human factors of the hydropower station, the environmental data collected in real time are integrated to obtain the first data warehouse. It should be noted that the architecture and model of the first data warehouse are designed according to the needs and data characteristics of the hydropower station; the subject domain and dimensions of the first data warehouse, such as geological conditions, hydrological conditions, meteorological conditions, etc., are determined; the table structure and index strategy of the first data warehouse are designed to improve query performance. The first data warehouse can provide a convenient data query interface to facilitate users to query and analyze data. It can use data mining and machine learning technology to conduct in-depth analysis of the data, discover potential risks and laws, and provide data support for the safe operation, optimized scheduling and decision support of the hydropower station.

[0056] According to the type of natural disaster, GIS spatial analysis is performed on the first data warehouse to obtain the change trend of the hydropower station under the influence of environmental parameters. It should be noted that statistical analysis, machine learning models, etc. can also be used to perform spatial analysis on the first data warehouse and obtain the change trend of the hydropower station under the influence of environmental parameters. Then, based on the disaster susceptibility analysis of the change trend, multiple risk factors for natural disasters in the hydropower station are determined.

[0057] For further details on step S203, please refer to the relevant contents in the above embodiment, which will not be repeated here.

[0058] S204, calculating the evolution of multiple risk factors according to the instability inducing factor model, obtaining multiple risk assessment values, and issuing a natural disaster warning to the hydropower station according to the risk assessment values.

[0059] Optionally, as an example, based on the principal component analysis of multiple risk factors, several main risk factors of natural disasters occurring in hydropower stations are determined. Specifically, according to the correlation between the multiple risk factors, the principal component analysis is performed on the multiple risk factors to obtain third intermediate data; according to the level of natural disasters, the third intermediate data is sorted and prioritized to determine several main risk factors of natural disasters occurring in hydropower stations.

[0060] After obtaining the main risk factors, the probability and impact of the main risk factors are obtained by simulating their evolution based on the instability-inducing factor model. Specifically, multiple input parameters are obtained based on parameter combinations and condition changes of several main risk factors; probability analysis and impact matrix analysis are performed on multiple input parameters based on the instability-inducing factor model, and the evolution process of the input parameters is simulated to obtain the probability and impact of the main risk factors.

[0061] Then, based on the multiplication calculation of the probability and the impact degree, the corresponding risk assessment value is obtained. Further, according to the risk assessment value, the natural disaster is graded and the duration is predicted; according to the results of the grade classification and duration prediction, the corresponding risk response plan is specified, and a natural disaster warning is carried out.

[0062] For further details on step S204, please refer to the relevant contents in the above embodiments, which will not be repeated here.

[0063] In summary, by analyzing the disaster susceptibility of real-time collected environmental data, the risk factors of natural disasters in hydropower stations are determined, and the evolution of the risk factors is calculated based on the instability inducing factor model of the hydropower station, and the risk assessment value used for natural disaster early warning of hydropower stations is determined. This greatly improves the timeliness, accuracy and comprehensiveness of early warnings, enhances the ability of hydropower stations to operate safely, and has a wide range of application value.

[0064] Figure 3 This is a schematic diagram of the structure of a natural disaster warning device for a hydropower station provided in an embodiment of the present application. Figure 3 As shown, the hydropower station natural disaster early warning device 300 includes:

[0065] A first acquisition module 301, the first acquisition module 301 is used to obtain an instability inducing factor model of a hydropower station;

[0066] A second acquisition module 302, the second acquisition module 302 is used to perform a disaster susceptibility analysis on the environmental data collected in real time, and determine a plurality of risk factors for natural disasters in the hydropower station;

[0067] The early warning module 303 is used to calculate the evolution of multiple risk factors according to the instability inducing factor model, obtain multiple risk assessment values, and issue natural disaster early warnings to the hydropower station according to the risk assessment values.

[0068] Figure 4 The present invention is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. Figure 4 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0069] like Figure 4 As shown, the electronic device 400 includes a processor 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the memory 1006 to the random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device 400 are also stored. The processor 401, the ROM 402 and the RAM 403 are connected to each other through a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0070] The following components are connected to the I / O interface 405: a memory 406 including a hard disk, etc.; and a communication part 407 including a network interface card such as a LAN (Local Area Network) card, a modem, etc., and the communication part 407 performs communication processing via a network such as the Internet; a drive 408 is also connected to the I / O interface 405 as needed.

[0071] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 407. When the computer program is executed by the processor 401, the above-mentioned functions defined in the method of the present application are executed.

[0072] In an exemplary embodiment, a storage medium including instructions is also provided, such as a memory including instructions, and the instructions can be executed by the processor 401 of the electronic device 400 to complete the above method. Optionally, the storage medium can be a non-transitory computer-readable storage medium, for example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0073] In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0074] Figure 5 It is a schematic diagram of the structure of another electronic device provided according to an embodiment of the present application. Figure 5 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present application. Figure 5 As shown, the electronic device 500 includes a processor 501 and a memory 502. The memory 502 is used to store program codes, and the processor 501 is connected to the memory 502 to read the program codes from the memory 502 to implement the hydropower station natural disaster early warning method in the above embodiment.

[0075] Optionally, the number of processors 501 may be one or more.

[0076] Optionally, the electronic device may further include an interface 503, and the number of the interface 503 may be multiple. The interface 503 may be connected to an application program and may receive data from an external device such as a sensor.

[0077] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed in this application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0078] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A natural disaster early warning method for a hydropower station, characterized in that: include: Obtain the instability inducing factor model of the hydropower station; Based on the disaster susceptibility analysis of real-time collected environmental data, multiple risk factors for natural disasters in hydropower stations are determined; According to the instability inducing factor model, the evolution calculation of the plurality of risk factors is performed to obtain a plurality of risk assessment values, and according to the risk assessment values, a natural disaster early warning is issued to the hydropower station.

2. The method according to claim 1, characterized in that The method of obtaining the instability inducing factor model of the hydropower station includes: Obtain historical environmental data of hydropower stations; Based on the preprocessing of the historical environmental data, obtaining first intermediate data; Obtaining second intermediate data based on time series analysis of the first intermediate data; According to the structural stability requirements of the hydropower station and the operating conditions of the hydropower station, a regression analysis is performed on the second intermediate data to obtain an instability inducing factor model of the hydropower station.

3. The method according to claim 2, characterized in that The step of performing regression analysis on the second intermediate data according to the structural stability requirements of the hydropower station and the operating conditions of the hydropower station to obtain an instability inducing factor model of the hydropower station includes: According to the structural stability requirements of the hydropower station, several measurement indicators are determined as dependent variables; According to the operating conditions of the hydropower station, several instability inducing factors are determined as independent variables; Based on the dependent variable and the independent variable, a regression analysis is performed on the second intermediate data to obtain an instability inducing factor model of the hydropower station.

4. The method according to any one of claims 1 to 3, characterized in that The method of conducting disaster susceptibility analysis based on real-time collected environmental data to determine multiple risk factors for natural disasters in hydropower stations includes: According to the geological conditions, hydrological conditions, meteorological conditions and human factors of the hydropower station, the environmental data collected in real time are integrated to obtain the first data warehouse; According to the type of natural disaster, GIS spatial analysis is performed on the first data warehouse to obtain the change trend of the hydropower station under the influence of the environmental parameters; Based on the disaster susceptibility analysis of the change trend, multiple risk factors for natural disasters in hydropower stations are determined.

5. The method according to claim 1, characterized in that The step of calculating the evolution of the plurality of risk factors according to the instability inducing factor model to obtain corresponding risk assessment values ​​includes: Based on the principal component analysis of the plurality of risk factors, several main risk factors for natural disasters occurring in the hydropower station are determined; Based on the simulated evolution of the main risk factors by the instability inducing factor model, the probability of occurrence and the degree of impact of the main risk factors are obtained; Based on the multiplication calculation of the probability and the impact degree, a corresponding risk assessment value is obtained.

6. The method according to claim 5, characterized in that The principal component analysis of the plurality of risk factors is performed to determine several main risk factors for natural disasters in hydropower stations, including: performing principal component analysis on the plurality of risk factors according to the correlations between the plurality of risk factors to obtain third intermediate data; According to the natural disaster level, the third intermediate data is sorted and prioritized to determine several major risk factors for natural disasters in hydropower stations.

7. The method according to claim 5, characterized in that The simulating evolution of the main risk factors based on the instability inducing factor model to obtain the probability of occurrence and the degree of influence of the main risk factors includes: Based on parameter combination and condition change of several of the main risk factors, a plurality of input parameters are obtained; Based on the instability inducing factor model, probability analysis and influence matrix analysis are performed on the multiple input parameters, the evolution process of the input parameters is simulated, and the probability of occurrence and impact degree of the main risk factors are obtained.

8. The method according to claim 1, characterized in that The method of providing a natural disaster warning to a hydropower station according to the risk assessment value includes: Classify the natural disasters and predict their duration according to the risk assessment value; Based on the results of the classification and duration prediction, a corresponding risk response plan is designated and a natural disaster warning is issued.

9. A natural disaster early warning device for a hydropower station, characterized in that: include: A first acquisition module, the first acquisition module is used to obtain an instability inducing factor model of a hydropower station; A second acquisition module, the second acquisition module is used to determine multiple risk factors of natural disasters in the hydropower station based on disaster susceptibility analysis of environmental data collected in real time; An early warning module is used to calculate the evolution of multiple risk factors according to the instability inducing factor model, obtain multiple risk assessment values, and issue natural disaster early warnings to the hydropower station based on the risk assessment values.

10. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the method according to any one of claims 1 to 8.

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