Mountain torrent disaster early warning system

Abstract

The invention discloses a mountain torrent disaster early warning system, and relates to the field of disaster early warning. The system comprises a data acquisition and processing module which is used for cleaning and denoising environment data acquired in real time to obtain processed environment data; the feature extraction module is used for obtaining a standardized feature set through calculation based on the environment data; the key feature construction module is used for calculating key features based on the standardized feature set; the dynamic threshold value self-adaption module is used for calculating a dynamic threshold value based on the key features; and the risk assessment module and the early warning module are used for calculating an evacuation radius for a dynamic risk score constructed based on the key features and a dynamic threshold value, carrying out early warning grading through the dynamic risk score, generating a mountain torrent disaster risk grade, and implementing different mountain torrent response schemes. Through multidisciplinary data fusion, dynamic threshold optimization and refined early warning decision, the accuracy, timeliness and practicability of mountain torrent early warning are remarkably improved, and reliable technical support is provided for disaster prevention and reduction.

Description

Technical Field

[0001] The present invention relates to the field of disaster warning, and specifically to a flash flood disaster warning system. Background Art

[0002] Flash flood disasters are common sudden natural disasters in mountainous areas, with characteristics such as strong destructiveness, rapid disaster formation speed, and great prediction difficulty, posing a serious threat to people's lives and property safety and infrastructure. Traditional flash flood warning systems mainly rely on rainfall monitoring, water level observation, and historical hydrological data analysis, and use static thresholds for risk assessment. However, such methods have obvious limitations in practical applications.

[0003] Firstly, traditional warning systems usually rely on limited monitoring parameters, such as rainfall intensity, soil moisture, river water level, etc., and it is difficult to comprehensively reflect the complex mechanism of flash flood disaster formation. The occurrence of flash floods is not only related to meteorological conditions, but also affected by various factors such as geological structure, terrain features, vegetation cover, and soil properties. For example, the distribution and direction of rock fractures may affect precipitation infiltration and groundwater runoff, while the development status of vegetation roots may change slope stability. Existing technical means often cannot effectively capture these multi-dimensional and multi-scale interactions.

[0004] Secondly, the static threshold setting method of traditional models lacks dynamic adaptability. The formation of flash flood disasters is a non-linear dynamic process, and its critical conditions will change with the changes of geological environment and ecological system. For example, after a long-term drought, the soil infiltration capacity decreases, and the same rainfall may lead to faster surface runoff and higher disaster risks; while the change of vegetation cover may affect the shear strength of slopes. Existing static threshold methods are difficult to adapt to this dynamic change, and are prone to warning lag or false alarms.

[0005] In addition, the current risk assessment method for delimiting the evacuation range is relatively rough, usually based on a fixed radius or empirical formula, and fails to fully consider the spatio-temporal heterogeneity of disaster impacts. This simplified processing method may lead to unreasonable allocation of emergency resources or insufficient coverage in some high-risk areas.

[0006] In recent years, with the progress of Internet of Things technology, remote sensing monitoring, and data analysis methods, it has become possible to obtain multi-source and high-frequency environmental data. For example, slope infrasound monitoring can reflect the micro-cracking activities of rock and soil masses, the vibration spectrum of vegetation roots can indicate changes in slope stability, and soil microbial activity may be related to the water migration process. These new types of data provide new possibilities for more comprehensive and accurate flash flood warning. At the same time, the development of theories such as machine learning and non-linear dynamics also provides technical support for constructing dynamic risk assessment models.

[0007] Therefore, there is an urgent need for a more refined and dynamic early warning method for mountain flood disasters to overcome the deficiencies of traditional technologies, improve the accuracy and timeliness of early warnings, and provide a more reliable scientific basis for disaster prevention and control and emergency management. Summary of the Invention

[0008] Based on the above-mentioned disadvantages of the prior art, the object of the present invention is to provide a mountain flood disaster early warning system to solve the above technical problems.

[0009] To achieve the above object, the present invention provides the following technical solution: A mountain flood disaster early warning system, comprising: Data acquisition and processing module: Real-time collect environmental data of the observation area through Internet of Things devices and remote sensing, and clean and denoise the environmental data to obtain processed environmental data; Feature extraction module: Based on the environmental data, calculate the standardized feature set through non-linear normalization; Key feature construction module: Calculate the key feature geological precipitation resonance factor and ecological terrain stress waveguide index through the standardized feature set; Dynamic threshold adaptive module: Calculate the dynamic threshold based on the geological precipitation resonance factor and the ecological terrain stress waveguide index; Risk assessment module and early warning module: Construct a dynamic risk score based on the key features and the dynamic threshold, calculate the evacuation radius according to the dynamic risk score, and perform early warning classification through the dynamic risk score to generate different mountain flood response plans for different mountain flood risk levels.

[0010] The present invention is further configured such that the environmental data includes: vegetation root vibration spectrum, slope infrasound intensity, soil microbial activity, hillside slope, rock fracture density, precipitation oscillation frequency, surface soil dielectric constant, slope direction, fracture direction.

[0011] The present invention is further configured such that the feature extraction module includes: a non-linear normalization unit and a feature set construction unit; Non-linear normalization unit: The researchers set the reference value of the environmental data according to GIS and the climate modeling of the detection area, and perform non-linear normalization calculation on each environmental data through the S-shaped function in combination with 1.35 times the interquartile range and the reference value to compress the environmental data to between, to obtain the normalized environmental features; Feature set construction unit: Add the normalized environmental features obtained through non-linear normalization calculation to the standardized feature set.

[0012] The present invention is further configured such that the key feature construction module includes: a geological precipitation resonance factor construction unit and an ecological terrain stress waveguide index construction unit; Geological precipitation resonance factor construction unit: The geological precipitation resonance factor is constructed by the rock fracture density, precipitation oscillation frequency, surface soil dielectric constant, slope direction, and fracture direction. First, calculate the fracture direction coefficient using the slope direction and fracture direction, then construct the resonance core term through the rock fracture density, precipitation oscillation frequency, and surface soil dielectric constant, construct the anisotropy regulator through the fracture direction coefficient, and finally calculate the geological precipitation resonance factor through the resonance core term and the anisotropy regulator; Ecological terrain stress waveguide index construction unit: The ecological terrain stress waveguide index is constructed by the vegetation root vibration spectrum, slope body infrasonic intensity, soil microbial activity, and hillside slope; construct the biogeophysical coupling term through the vegetation root vibration spectrum, slope body infrasonic intensity, and soil microbial activity, construct the map amplification term through the hillside slope, and finally calculate the ecological terrain stress waveguide index through the map amplification term and the biogeophysical coupling term.

[0013] The present invention is further set as the calculation logic of the fracture direction coefficient: , where, is the fracture direction coefficient, is the slope direction, is the fracture direction; Geological precipitation resonance factor calculation logic: , where, is the geological precipitation resonance factor, is the rock fracture density, is the precipitation frequency, is the dielectric constant, is the resonance core term, is the anisotropy regulator.

[0014] The present invention is further set as the calculation logic of the ecological terrain stress waveguide index: , where, is the ecological terrain stress waveguide index, is the vegetation root vibration spectrum, is the slope body infrasonic intensity, is the soil microbial activity, is the hillside slope, is the minimum value, is the biogeophysical coupling term, Terrain amplification term.

[0015] The present invention is further set as the calculation logic of the dynamic threshold: , where, is the dynamic threshold, is the mapping function, is the error function, is the activation function.

[0016] The present invention is further configured such that the risk assessment and early warning module includes: a dynamic risk score construction unit, an evacuation radius construction unit, and an early warning classification unit; Dynamic risk score construction unit: Based on the combination of dynamic thresholds, geological precipitation resonance factors, and ecological terrain stress waveguide indices, quantify the current risk level as the measured risk item. Design a critical point enhancement term by simulating the disaster critical point through the arctangent function. Combine the measured risk item and the critical point enhancement term to obtain the final dynamic risk score.

[0017] The present invention is further configured such that the evacuation radius construction unit: Calculate the geographical range to be evacuated based on the dynamic risk score to obtain the evacuation radius; Evacuation radius calculation logic: , where is the evacuation radius, is the dynamic risk score.

[0018] The present invention is further configured such that the early warning classification unit: Classify the early warning into three levels based on the dynamic risk score, including: red disaster situation early warning, orange disaster situation early warning, and yellow disaster situation early warning; Risk level division: , where the red disaster situation early warning is for severe risks, the orange disaster situation early warning is for relatively high risks, and the yellow disaster situation early warning is for medium and low risks.

[0019] The present invention provides a flash flood disaster early warning system. The system includes a data acquisition and processing module: Real-time collect the environmental data of the observation area through Internet of Things devices and remote sensing, and clean and denoise the environmental data to obtain the processed environmental data; a feature extraction module: Calculate the standardized feature set based on the environmental data through non-linear normalization; a key feature construction module: Calculate the key features of geological precipitation resonance factors and ecological terrain stress waveguide indices through the standardized feature set; a dynamic threshold adaptive module: Calculate the dynamic threshold based on the geological precipitation resonance factors and ecological terrain stress waveguide indices; a risk assessment module and an early warning module: Construct a dynamic risk score based on the key features and the dynamic threshold, calculate the evacuation radius according to the dynamic risk score, classify the early warning through the dynamic risk score, generate different flash flood response plans for the flash flood disaster risk levels, and the beneficial effects produced include: Multi-source data fusion, improving early warning accuracy: By integrating new ecological geological parameters such as vegetation root vibration spectra, slope infrasound intensity, and soil microbial activity, combined with traditional meteorological and hydrological data, construct a more comprehensive environmental monitoring system, significantly improve the sensitivity and accuracy of flash flood precursor identification. Use the non-linear normalization method to process multi-source heterogeneous data, effectively eliminate the dimension difference, retain the key feature information, and avoid misjudgment caused by a single parameter.

[0020] Dynamic risk assessment to enhance adaptability: By quantifying the synergistic effects of rock fractures, precipitation frequency, and slope aspect through geological precipitation resonance factors, and characterizing the stress conduction characteristics of biological activities and terrain using the ecological terrain stress waveguide index, the limitations of traditional static models are overcome. Based on a dynamic threshold adaptive algorithm, the warning threshold is adjusted in real time to adapt to different geological conditions and climate change scenarios, reducing false alarms and missed alarms.

[0021] Refined warning output to optimize emergency response: Combining dynamic risk scores with the arctangent function to simulate disaster critical points, generating a quantifiable evacuation radius, accurately demarcating the scope of disaster impact, and avoiding waste of resources or insufficient coverage. Adopting a three-level warning mechanism of red, orange, and yellow, matching differentiated emergency plans, and improving the efficiency of emergency response.

[0022] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of this application more obvious and understandable, the specific embodiments of this application are specifically given below. Brief Description of the Drawings

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for description in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings. In the drawings: Figure 1 It is a schematic structural diagram of a flash flood disaster warning system shown in an exemplary embodiment of the present invention. Detailed Embodiments

[0024] The following will illustrate the embodiments of the present invention with reference to the drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention, rather than for limiting the protection scope of the present invention.

[0025] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present invention schematically. Therefore, only the components related to the present invention are shown in the diagrams, rather than being drawn according to the number, shape, and size of the components in actual implementation. The type, quantity, and ratio of each component in its actual implementation can be an arbitrary change, and the component layout type may also be more complex.

[0026] In the following description, a large number of details are explored to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0027] Embodiment: A flash flood disaster warning system, as Figure 1 shown, includes: Data acquisition and processing module: Real-time collects environmental data of the observation area through Internet of Things devices and remote sensing, and cleans and denoises the environmental data to obtain processed environmental data; Feature extraction module: Obtains a standardized feature set through non-linear normalization calculation based on the environmental data; Key feature construction module: Calculates the key feature geological precipitation resonance factor and ecological terrain stress waveguide index through the standardized feature set; Dynamic threshold adaptive module: Calculates the dynamic threshold based on the geological precipitation resonance factor and the ecological terrain stress waveguide index; Risk assessment module and warning module: Constructs a dynamic risk score based on the key features and the dynamic threshold, calculates the evacuation radius according to the dynamic risk score, conducts warning grading through the dynamic risk score, and generates different flash flood response plans according to the flash flood disaster risk level.

[0028] The present invention is further configured such that the environmental data includes: vegetation root vibration spectrum, slope infrasonic intensity, soil microbial activity, hillside slope, rock fracture density, precipitation oscillation frequency, surface soil dielectric constant, slope direction, and fracture direction. Specifically, the vegetation root vibration spectrum obtained by the MEMS seismic sensor network, the root vibration can reflect the change of soil stability, and the root fracture or abnormal vibration may indicate the risk of slope slip; the slope infrasonic intensity obtained by the infrasound sensor array, the abnormal enhancement of infrasound may indicate the expansion of internal fractures or the formation of slip surfaces in the rock and soil mass, and can be used as a geomechanical early warning index before flash floods to make up for the lag of traditional displacement monitoring; the soil microbial activity obtained by the soil ATP rapid detector, the microbial activity is related to the pore water pressure and the decomposition rate of organic matter in the soil, affects the shear strength of the soil mass, and abnormal activity may reflect water saturation or soil softening, which is a biochemical precursor of potential landslides; the hillside slope obtained by lidar scanning, the slope directly affects the surface runoff velocity and soil stability, and is a key parameter for calculating the intensity of flash floods. Combining with geological data can identify high-risk steep slope areas; the rock fracture density obtained by UAV lidar scanning, the fracture density determines the precipitation infiltration rate and rock mass strength, and concentrated runoff or collapse is likely to occur in high-density areas; the precipitation oscillation frequency obtained by the high-frequency raindrop spectrometer data, high-frequency precipitation is likely to cause surface splash erosion and rapid water filling in fractures, and combined with the fracture direction can amplify the seepage pressure and trigger shallow landslides; the surface soil dielectric constant obtained by ground-based radar inversion, the dielectric constant reflects the soil moisture content, and a high value indicates the risk of water saturation, which is used to calculate the precipitation infiltration depth and the attenuation rate of soil anti-sliding force; the slope direction is obtained by the GIS geographic information system, and combined with the fracture direction to calculate the anisotropy regulator to evaluate the directivity of slope instability; the fracture direction is obtained by photogrammetry to obtain the fracture strike and dip angle. When the fracture direction is parallel to the slope direction, a through-going slip surface is easily formed, and when it is perpendicular, it may prevent sliding. It is the core variable of the geological precipitation resonance factor and determines the path efficiency of precipitation infiltration.

[0029] The present invention is further configured such that the feature extraction module includes: a non-linear normalization unit and a feature set construction unit; Non-linear normalization unit: The reference value of the environmental data is set by the researcher according to the GIS and the climate modeling of the detection area. Combining the 1.35 times interquartile range with the reference value, each environmental data is non-linearly normalized through the S-shaped function to compress the environmental data to between, and the normalized environmental features are obtained; Feature set construction unit: The normalized environmental features obtained through non-linear normalization calculation are added to the standardized feature set. Specifically, the non-linear normalization calculation ensures that all features are compared and calculated at the same scale, thus avoiding the influence of features with different dimensions on subsequent calculations and performances. The formula is: , where, is the normalized eigenvalue, with a range between and is the environmental data after cleaning and denoising is the set benchmark value. Since there are significant differences in rainfall patterns, terrain characteristics, soil types, etc. in different regions, different benchmark values should be set for each region. The benchmark value is achieved through GIS and climate modeling of the region is 1.35 times the interquartile range of this feature, used to scale the data. The interquartile range measures the dispersion of the data distribution. Using 1.35 times the interquartile range is to ensure that the data can be robustly scaled and prevent extreme values from having too much impact on the model; The standardized feature set includes each environmental feature that has undergone non - linear normalization calculation , where is the standardized feature set is the vibration spectrum of vegetation roots is the infrasonic intensity of the slope body is the soil microbial activity is the hillside slope is the rock fracture density is the precipitation frequency is the dielectric constant is the slope direction is the fracture direction

[0030] The present invention is further configured such that the key feature construction module includes: a geological precipitation resonance factor construction unit and an ecological terrain stress waveguide index construction unit Geological precipitation resonance factor construction unit: The geological precipitation resonance factor is constructed from the rock fracture density, precipitation oscillation frequency, surface soil dielectric constant, slope direction, and fracture direction. First, calculate the fracture direction coefficient using the slope direction and fracture direction, then construct the resonance core term through the rock fracture density, precipitation oscillation frequency, and surface soil dielectric constant, construct the anisotropy regulator through the fracture direction coefficient, and finally calculate the geological precipitation resonance factor through the resonance core term and the anisotropy regulator Ecological Terrain Stress Waveguide Index Construction Unit: The ecological terrain stress waveguide index is constructed from the vibration spectrum of vegetation roots, the intensity of infrasound waves in the slope, the activity of soil microorganisms, and the slope gradient of the mountain. The biogeophysical coupling term is constructed through the vibration spectrum of vegetation roots, the intensity of infrasound waves in the slope, and the activity of soil microorganisms, and the map amplification term is constructed through the slope gradient of the mountain. Finally, the ecological terrain stress waveguide index is calculated through the map amplification term and the biogeophysical coupling term. Specifically, the geological precipitation resonance factor construction unit quantifies the precipitation-geology coupling effect, evaluates the dynamic impact of precipitation infiltration on slope stability, and solves the problem in traditional methods that only relies on rainfall and ignores the response of geological structures. The geological precipitation resonance factor is mainly used to judge the triggering probability of mountain floods and the possible potential intensity. The ecological terrain stress waveguide index construction unit quantifies the ecological-terrain coupling effect, evaluates the regulatory effect of vegetation and microbial activities on slope stress conduction, and makes up for the defect that traditional models ignore biomechanical effects. The ecological terrain stress waveguide index is mainly used to evaluate the conduction and amplification of mountain flood energy by the ecological-terrain system, and judge the disaster impact range and evolution speed.

[0031] The present invention is further configured as the calculation logic of the fracture direction coefficient: , where is the fracture direction coefficient, is the slope direction, is the fracture direction; The calculation logic of the geological precipitation resonance factor: , where is the geological precipitation resonance factor, is the rock fracture density, is the precipitation frequency, is the dielectric constant, is the resonance core term, is the anisotropy regulator. Specifically, in the calculation of the fracture direction coefficient, the fracture direction coefficient quantifies the efficiency of precipitation infiltration along the fracture. The larger the value, the easier it is for precipitation to quickly infiltrate along the fracture, and the higher the risk of triggering landslides. The slope direction specifically represents the orientation of the slope surface, and the fracture direction represents the main trend of the rock mass fracture, represents normalizing the direction difference to the interval, The function is used to map the direction difference to the interval, and reaches the peak when the directions are the same at , and reaches the minimum when perpendicular at ; In the geological precipitation resonance factor, the resonance core term The combined action of fracture density, precipitation frequency, and soil water content. The larger the value, the more easily the rock and soil mass is damaged by precipitation. To avoid the term failure when the dielectric constant approaches zero, the denominator is set to Represents an empirical coefficient used to normalize the dimension and the anisotropy regulator in The function is used to limit the non-linear effect of the fracture direction coefficient to the interval. The coefficient is used to enhance the sensitivity of the fracture direction coefficient to the result.

[0032] The present invention is further configured as the calculation logic of the ecological terrain stress waveguide index: , where is the ecological terrain stress waveguide index, is the vibration spectrum of vegetation roots, is the infrasonic intensity of the slope body, is the soil microbial activity, is the slope of the hillside, is the minimum value, is the biogeophysical coupling term, is the terrain amplification term. Specifically, in the calculation of the ecological terrain stress waveguide index, the minimum value is a mathematical protection term to prevent the denominator from being zero, and its value is . The biogeophysical coupling term is used to quantify the stress regulation ability of the ecosystem. The higher the value, the more vulnerable the slope body is. represents the synergistic effect between root vibration and infrasound. When the roots break, decreases, but before the slope body becomes unstable, will increase suddenly. The product of the two can amplify the abnormal signal. is an empirical scaling factor used to adjust the value to a reasonable magnitude. The denominator represents the soil microbial activity The higher the value, the stronger the soil erosion resistance and the lower the coupling term value. To avoid calculation errors when the soil microbial activity is 0, in the terrain amplification term in is an exponential function used to simulate the non-linear amplification effect of the hillside slope on stress propagation. The denominator is set to to control the sensitivity of the hillside slope. The larger the slope, the closer the value of the terrain amplification term approaches 1, and the amplification effect saturates.

[0033] The present invention is further configured as the dynamic threshold calculation logic: , where is the dynamic threshold, is the mapping function, is the error function, is the activation function. Specifically, the dynamic threshold dynamically adjusts the disaster trigger threshold by fusing the ecological terrain stress wave guide index and the geological precipitation resonance factor in real time, achieving: reducing false alarms: ignoring low-risk scenarios such as light rain + stable slope, reducing missed alarms: early warning in case of ecological or geological anomalies, is the disaster signal enhancement term, is used to amplify the contribution of high values, is used to map to the interval, controlling the contribution weight of precipitation resonance, The coefficients 3 and -2 in are used to adjust the sensitivity and offset, is the reference adjustment term, and the error function is used to adjust the output range to , with a smooth transition of the contribution threshold to avoid sudden changes. The constant term is used to ensure that the denominator is not zero and provides the reference adjustment ability, is the precipitation resonance activation term, and the activation function indicates that the precipitation resonance effect is activated only when , ignoring weak signals, is the exponential decay term, used to control the saturation speed of the precipitation contribution. The coefficient 2 is used to adjust the sensitivity, The higher, the closer the exponential decay term is to 1, and the more it can release the influence of precipitation resonance.

[0034] The present invention is further configured such that the risk assessment and early warning module includes: a dynamic risk score construction unit, an evacuation radius construction unit, and an early warning classification unit; Dynamic risk score construction unit: Quantify the current risk level as the measured risk item based on the combination of the dynamic threshold, the geological precipitation resonance factor, and the ecological terrain stress wave guide index. Design a critical point enhancement term through the arctangent function to simulate the disaster critical point. Combine the measured risk item and the critical point enhancement term to obtain the final dynamic risk score. Specifically, the dynamic risk score calculation formula is: , where, is the dynamic risk score, is the ecological terrain stress wave guide index, is the geological precipitation resonance factor, is the dynamic threshold, is used to quantify the joint strength of the ecological-geological coupling effect. The product form requires both to increase simultaneously to significantly increase the risk, is a buffer value to prevent division by zero errors when the dynamic threshold is zero. The exponent Used to slightly amplify the contribution of high - risk scenarios, enhance the discrimination, and the critical - point enhancement term Medium The arctangent function is used to control the output range within Its function here is for smooth transition. The offset of - 1 means that when the term value rises rapidly, which is used to simulate the disaster critical point. The coefficient of 10 is used to control the rising slope, indicating the steep - response critical point.

[0035] The present invention is further configured as an evacuation - radius construction unit: calculating the geographical range to be evacuated according to the dynamic risk score to obtain the evacuation radius; Evacuation - radius calculation logic: , where is the evacuation radius, is the dynamic risk score. Specifically, the evacuation radius represents the circular - area range to be evacuated centered on the risk point, represents non - linear amplification of the dynamic risk score, and the disaster - impact range grows super - linearly with the risk score, conforming to the attenuation law of flash - flood shock waves, is the default adjustment coefficient, and the specific adjustment coefficient can be set to different values according to different terrains in different regions.

[0036] The present invention is further configured as a warning - classification unit: classifying warnings into three levels according to the dynamic risk score, including: red disaster warning, orange disaster warning, and yellow disaster warning; Risk - level division: , where the red disaster warning is for severe risk, the orange disaster warning is for relatively high risk, and the yellow disaster warning is for medium - low risk. Specifically, the warning classification is the core output index of the system. By quantifying and fusing multi - dimensional risk signals of ecology - geology - meteorology, it realizes: disaster - risk classification, clearly dividing the three warning levels of yellow, orange, and red; emergency - decision support: providing a scientific basis for delimiting the evacuation range and initiating the emergency plan. Three different risk levels are set according to the calculated dynamic risk score. Among them, for the red disaster warning with severe risk, residents within the evacuation radius are forcibly evacuated and the flood - control emergency plan is initiated; for the orange disaster warning with relatively high risk, residents within the evacuation radius near the foot of the slope are evacuated and real - time monitoring by drones is initiated; for the yellow disaster warning with medium - low risk, inspectors are notified to check the water seepage situation of the slope and non - essential mountain roads are closed.

[0037] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, or a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0038] It should be understood that the term "and / or" in this document is merely a description of the relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be understood specifically by referring to the context.

[0039] In the present application, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following" or its similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0040] It should be understood that in various embodiments of the present application, the magnitude of the sequence numbers of the above processes does not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0041] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0042] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0043] In several embodiments provided in this application, it should be understood that the disclosed system can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical, mechanical or other form.

[0044] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0045] In addition, the functional units in each embodiment of this application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0046] When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0047] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, which should all be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A flash flood disaster warning system, characterized in that, Including: Data acquisition and processing module: Real-time collects environmental data of the observation area through Internet of Things devices and remote sensing, and cleans and denoises the environmental data to obtain processed environmental data. Feature extraction module: Obtains a standardized feature set through non-linear normalization calculation based on environmental data. Key feature construction module: Calculates the key features, namely the geological precipitation resonance factor and the ecological terrain stress waveguide index, through the standardized feature set. Dynamic threshold adaptive module: Calculates the dynamic threshold based on the geological precipitation resonance factor and the ecological terrain stress waveguide index. Risk assessment module and early warning module: Constructs a dynamic risk score based on the key features and the dynamic threshold, calculates the evacuation radius according to the dynamic risk score, conducts early warning grading through the dynamic risk score, and generates different mountain flood response plans according to the mountain flood disaster risk level.

2. The mountain flood disaster early warning system according to claim 1, characterized in that The environmental data includes: vegetation root vibration spectrum, slope infrasound intensity, soil microbial activity, hillside slope, rock fracture density, precipitation oscillation frequency, surface soil dielectric constant, slope direction, fracture direction.

3. The flash flood disaster warning system according to claim 1, characterized in that, The feature extraction module includes: a non-linear normalization unit and a feature set construction unit. Nonlinear normalization unit: The reference value of environmental data is set by researchers according to GIS and climate modeling of the detection area. Each environmental data is nonlinearly normalized by using a sigmoid function that combines 1.35 times the interquartile range with the reference value to compress the environmental data to between, and the normalized environmental features are obtained; Feature set construction unit: Adds the normalized environmental features obtained through non-linear normalization calculation to the standardized feature set.

4. A flash flood disaster warning system according to claim 1, characterized in that, The key feature construction module includes: a geological precipitation resonance factor construction unit and an ecological terrain stress waveguide index construction unit. Geological precipitation resonance factor construction unit: The geological precipitation resonance factor is constructed by the rock fracture density, precipitation oscillation frequency, surface soil dielectric constant, slope direction, and fracture direction. First, calculates the fracture direction coefficient using the slope direction and fracture direction, then constructs the resonance core term through the rock fracture density, precipitation oscillation frequency, and surface soil dielectric constant, constructs the anisotropy regulator through the fracture direction coefficient, and finally calculates the geological precipitation resonance factor through the resonance core term and the anisotropy regulator. Ecological terrain stress waveguide index construction unit: The ecological terrain stress waveguide index is constructed by the vegetation root vibration spectrum, slope infrasound intensity, soil microbial activity, and hillside slope; constructs the biogeophysical coupling term through the vegetation root vibration spectrum, slope infrasound intensity, and soil microbial activity, constructs the map amplification term through the hillside slope, and finally calculates the ecological terrain stress waveguide index through the map amplification term and the biogeophysical coupling term.

5. The mountain flood disaster early warning system according to claim 4, characterized in that Calculation logic of fracture direction coefficient: , where is the fracture direction coefficient, is the slope direction, is the fracture direction; Geological precipitation resonance factor calculation logic: , where is the geological precipitation resonance factor, is the rock fracture density, is the precipitation frequency, is the dielectric constant, is the resonance core term, is the anisotropy regulator.

6. A flash flood disaster warning system according to claim 5, characterized in that, Ecological topographic stress wave guide index calculation logic: , where is the ecological topographic stress wave guide index, is the vibration frequency spectrum of vegetation roots, is the infrasonic intensity of the slope body, is the activity of soil microorganisms, is the slope of the hillside, is the minimum value, is the biogeophysical coupling term, is the topographic amplification term.

7. An early warning system for mountain flood disasters according to claim 6, characterized in that, Dynamic threshold calculation logic: , where is the dynamic threshold, is the mapping function, is the error function, is the activation function.

8. A flash flood disaster warning system according to claim 1, characterized in that, The risk assessment and early warning module includes: a dynamic risk score construction unit, an evacuation radius construction unit, and an early warning grading unit. Dynamic risk score construction unit: Quantifies the current risk level as the measured risk item based on the combination of the dynamic threshold, geological precipitation resonance factor, and ecological terrain stress waveguide index, designs the critical point enhancement term by simulating the disaster critical point through the arctangent function, and obtains the final dynamic risk score by combining the measured risk item and the critical point enhancement term.

9. The flash flood disaster warning system according to claim 8, wherein, Evacuation radius construction unit: Calculates the geographical range to be evacuated according to the dynamic risk score to obtain the evacuation radius. Evacuation radius calculation logic: , where is the evacuation radius, is the dynamic risk score.

10. A flash flood disaster warning system according to claim 9, characterized in that, Early warning classification unit: According to the dynamic risk score, the early warning is divided into three levels, including: red disaster warning, orange disaster warning, and yellow disaster warning; Risk level classification: , among which, the red disaster warning is a serious risk, the orange disaster warning is a relatively high risk, and the yellow disaster warning is a medium-low risk.

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