A geological disaster monitoring and early warning method and system

Abstract

This application relates to the field of geological disaster warning technologies, and particularly to a geological disaster monitoring and warning method and system. The method includes: obtaining monitoring information of a snow mountain, analyzing the monitoring information to determine an environmental information set and equipment status; analyzing the monitoring information to obtain terrain information, environmental temperature, snowfall information, current snowmelt information, and infrasound information; analyzing the monitoring information, the environmental information set, and the current snowmelt information to determine continuous snowmelt information; determining an avalanche risk based on the continuous snowmelt information, the snowfall information, and the infrasound information; determining an avalanche level based on the terrain information, the continuous snowmelt information, and the snowfall information; and determining and sending an avalanche signal based on the equipment status, the avalanche risk, and the avalanche level. This application determines the avalanche risk and avalanche level through the analysis of multi-dimensional data, and sends an avalanche signal through an error detection mechanism, realizing accurate avalanche warning and accurate notification.

Description

Technical Field

[0001] This application relates to the technical field of geological disaster early warning, and particularly to a geological disaster monitoring and early warning method and system. Background Art

[0002] Geological disasters refer to geological processes that are formed under the action of natural or human factors and cause damage and losses to human life, property, and the environment. In order to reduce the losses caused by geological disasters, it is necessary to give early warnings of geological disasters. Existing geological disaster monitoring and early warning can already achieve automatic monitoring of data, data transmission, and data analysis for professional personnel to refer to.

[0003] However, restricted by the geographical environment and infrastructure conditions, the geological disaster monitoring data in high-altitude areas is affected by environmental factors and will have large fluctuations in a short period of time. By the time it is detected, it may already be too late, and people cannot correctly judge whether there is a risk, resulting in missed or misjudged early warnings of geological disasters, seriously threatening people's life and property safety. Summary of the Invention

[0004] This application provides a geological disaster monitoring and early warning method and system to solve the above technical problems.

[0005] In a first aspect, this application provides a geological disaster monitoring and early warning method, which includes: obtaining monitoring information of a snow mountain, analyzing the monitoring information to determine an environmental information set and equipment status; analyzing the monitoring information to obtain terrain information, environmental temperature, snowfall information, current snowmelt information, and infrasound information; analyzing the monitoring information, the environmental information set, and the current snowmelt information to determine continuous snowmelt information; determining an avalanche risk based on the continuous snowmelt information, the snowfall information, and the infrasound information; determining an avalanche level based on the terrain information, the continuous snowmelt information, and the snowfall information; determining an avalanche signal based on the equipment status, the avalanche risk, and the avalanche level, and sending the avalanche signal using an error detection mechanism.

[0006] Through the above technical solutions, first, the monitoring information of the snow mountain is obtained, and the monitoring information is analyzed to obtain an environmental information set, equipment status, geological information, environmental temperature, snowfall information, current snowmelt information, and infrasound information, providing a data basis for subsequent avalanche situation judgment. Moreover, by obtaining the equipment status, it can be ensured that all data is accurate and error-free, and any equipment anomalies can be detected immediately. Then, by analyzing the monitoring information, environmental information set, and current snowmelt information, the continuous snowmelt information is obtained, providing necessary data for subsequent determination of avalanche risk and avalanche level, and the snow mountain status can also be reflected from the continuous snowmelt information. After that, the avalanche risk is determined through the continuous snowmelt information, snowfall information, and infrasound information, and the avalanche level is determined through the terrain information, continuous snowmelt information, and snowfall information. The determination of avalanche risk and avalanche level is the most important step in early warning. Finally, an avalanche signal is formulated based on the equipment status, avalanche risk, and avalanche level, and an error monitoring mechanism is used to ensure the correct transmission of the avalanche signal. The formulation and transmission of the avalanche signal can provide timely avalanche early warning.

[0007] Optionally, the analysis of the monitoring information to determine the environmental information set includes: obtaining the weather forecast information of the snow mountain location, analyzing the weather forecast information to determine cloud information, light information, wind speed information, and humidity information; analyzing the light information and the cloud information to determine the light intensity information; and determining the cloud information, the light intensity information, the wind speed information, and the humidity information as the environmental information set.

[0008] Through the above technical solutions, the weather forecast information is obtained, and multiple key factors such as cloud information, light information, wind speed information, and humidity information are obtained from the weather forecast information, ensuring the comprehensiveness and meticulousness of the analysis. By combining the cloud information and the light information to obtain the light intensity information, this step ensures the accuracy of the obtained light intensity information. Finally, adding multiple pieces of information to the environmental information set can provide more accurate information reflecting the snow mountain environment for subsequent snow mountain early warning means.

[0009] Optionally, the analysis of the monitoring information to determine the equipment status includes: determining the equipment information according to the monitoring information; determining the equipment working voltage according to the equipment information, and determining the power supply status according to the equipment working voltage; determining the equipment type according to the equipment information, and screening out the image sensor according to the equipment type; determining the image content outside the equipment according to the image sensor; determining the environmental status outside the equipment according to the image content; and determining the equipment status according to the power supply status and the environmental status.

[0010] Through the above technical solution, device information is obtained from the monitoring information, and the operating voltage is determined according to the device information to judge the status of the power supply. In this step, the basic situation of the device is determined by analyzing the condition of the operating voltage. If the operating voltage is abnormal, the content monitored by the device is unreliable. Then, according to the device information, after determining the device type, an image sensor is selected, the image content is obtained, and the image content is analyzed to determine whether the environment has an impact on the device. Through this step, it is determined whether the external environment will affect the device monitoring, and the device status can be evaluated more comprehensively to ensure the accuracy of the monitoring data.

[0011] Optionally, analyzing the monitoring information, the environmental information set, and the current snowmelt information to determine the continuous snowmelt information includes: determining the snow mountain location information and time information according to the monitoring information; determining the change information of the light illumination direction according to the time information; determining the environmental temperature according to the environmental information set; analyzing the snow mountain location information, the change information of the light illumination direction, the light intensity information, and the time information to determine the snow mountain light illumination information; analyzing the environmental temperature and the snow mountain light illumination information to determine the temperature change trend; and analyzing the temperature change trend based on the current snowmelt information and the environmental information set to determine the continuous snowmelt information.

[0012] Through the above technical solution, first, the snow mountain location information and time information are obtained according to the monitoring information, and these two pieces of information provide the necessary data for the subsequent snow mountain light illumination. Then, through the time information, the change information of the light illumination direction is determined. This step determines at what time the light shines on which direction of the snow mountain, ensuring the accuracy of the subsequent analysis of the snow mountain light illumination. After that, analyzing the snow mountain location information, the change information of the light illumination direction, the light intensity information, and the time information to obtain the snow mountain light illumination information provides the most direct basis for determining the temperature change trend subsequently. The environmental temperature is obtained from the environmental information set, and the temperature change trend is determined by combining the snow mountain light illumination information. Based on the current snowmelt information and the environmental information set, the temperature change trend is analyzed to determine the continuous snowmelt information. In addition to considering the direct factor of temperature change, other information existing in the environment is also comprehensively considered. The multi-dimensional analysis makes the determination of the continuous snowmelt more accurate.

[0013] Optionally, analyzing the environmental temperature and the snow mountain sunlight information to determine the temperature change trend includes: analyzing the snow mountain sunlight information to determine the snow mountain sunlight time information and the snow mountain sunlight intensity; obtaining the sunlight time and the non-sunlight time of the snow mountain according to the snow mountain sunlight time information; determining the warming trend according to the snow mountain sunlight intensity, the environmental temperature, and the sunlight time; determining the temperature after warming when entering the non-sunlight time according to the warming trend and the sunlight time; obtaining the overall temperature information according to the weather forecast information; determining the cooling trend according to the non-sunlight time, the temperature after warming, and the overall temperature information; and combining the warming trend and the cooling trend to determine the temperature change trend.

[0014] Through the above technical solution, by analyzing the snow mountain sunlight information, the sunlight time information and the snow mountain sunlight intensity are determined. Then, from the snow mountain sunlight time, the sunlight time and the non-sunlight time are determined. This step combines the sunlight and time factors and considers the situations of sunlight and non-sunlight separately to ensure the rigor of predicting the temperature change trend. By comprehensively analyzing the snow mountain sunlight intensity, the environmental temperature, and the sunlight time, the warming trend is obtained. The temperature at the last moment is obtained from the warming trend as the initial temperature of the non-sunlight time, that is, the temperature after warming. Then, the temperature information is obtained from the weather forecast information. The non-sunlight time, the temperature after warming, and the overall temperature information are combined to determine the cooling trend. The warming trend and the cooling trend are combined to obtain the temperature change trend. By comprehensively considering multi-dimensional factors to obtain the warming trend and the cooling trend, the prediction result is more objective and credible. Moreover, the determination of the cooling trend is also closely linked to the warming trend, determining the integrity and continuity of the temperature change trend.

[0015] Optionally, based on the current snowmelt information and the environmental information set, analyzing the temperature change trend to determine the continuous snowmelt information includes: analyzing the temperature change trend, dividing the temperature range, and determining the division result; determining the above-zero temperature range information according to the division result; determining the start time and the end time of the above-zero temperature range according to the above-zero temperature range information; when the temperature in the temperature change trend is greater than zero degree, analyzing the humidity information and the wind speed information to obtain the humidity change trend and the wind speed change trend; constructing the temperature change curve, the humidity change curve, and the wind speed change curve in the above-zero temperature range according to the temperature change trend, the humidity change trend, and the wind speed change trend; and determining the continuous snowmelt amount according to the temperature change curve, the humidity change curve, and the wind speed change curve, referring to the following formula:

[0016]

[0017] Wherein, M represents the continuous snowmelt volume, t represents the preset analysis time, t1 represents the start time of the above-zero temperature range, t2 represents the end time of the above-zero temperature range, T(t) represents the temperature change curve, v(t) represents the wind speed change curve, H(t) represents the humidity change curve, k represents the preset temperature change empirical coefficient, a represents the preset wind speed change empirical coefficient, and b represents the preset humidity change empirical coefficient.

[0018] Through the above technical solution, the temperature range is first divided according to the temperature change trend, and the start time and end time of the above-zero temperature are determined, which is crucial for the subsequent analysis of the continuous snowmelt volume, because the snowmelt volume changes significantly when the temperature is greater than zero. Then, based on the humidity information and wind speed information, the humidity change trend and wind speed change trend are obtained, and the humidity change curve and wind speed change curve are obtained. At the same time, the temperature change curve is also obtained through the temperature change trend. This step comprehensively considers the influence of humidity and wind speed, because the influence of humidity and wind speed on snowmelt is also significant when the temperature is greater than zero. By comprehensively analyzing these factors, the snowmelt situation can be evaluated more comprehensively. Then, calculations are carried out through mathematical formulas. The formulas not only consider the direct influence of temperature, but also consider the indirect influence of humidity and wind speed. Through the calculation of the formulas, the snowmelt situation can be understood more intuitively and comprehensively, providing a scientific basis for the calculation of the snowmelt volume. And the introduction of empirical coefficients makes the calculation results more in line with the actual situation.

[0019] Optionally, determining the avalanche risk according to the continuous snowmelt volume information, the snowfall volume information and the infrasound information includes: obtaining the continuous snowmelt volume, the snowfall volume and the snowmelt-snowfall cycle according to the continuous snowmelt volume information and the snowfall volume information; determining the infrasound intensity according to the infrasound information; determining the avalanche risk according to the continuous snowmelt volume, the snowfall volume, the snowmelt-snowfall cycle and the infrasound intensity, referring to the following formula:

[0020]

[0021] Wherein, R represents the avalanche risk, M represents the continuous snowmelt volume, S represents the snowfall volume, P represents the snowmelt-snowfall cycle, I represents the infrasound intensity, w1 represents the preset continuous snowmelt volume weight coefficient, w2 represents the preset snowfall volume weight coefficient, and w3 represents the preset infrasound intensity weight coefficient.

[0022] Through the above technical solution, the numerical values of continuous snowmelt and snowfall and the snowmelt and snowfall cycle are obtained based on the continuous snowmelt information and snowfall information, and the infrasonic wave intensity is obtained from the infrasonic wave information. The avalanche risk is calculated by means of a mathematical formula. The solution considers multiple factors related to avalanche risk, including meteorological conditions (continuous snowmelt, snowfall) and physical phenomena (infrasonic wave), improves the comprehensiveness and accuracy of the assessment, and provides calculations through mathematical formulas, providing a scientific assessment method for avalanche risk assessment, quantifying the risk, and intuitively understanding the avalanche risk.

[0023] Optionally, determining the avalanche level according to the terrain information, the continuous snowmelt information and the snowfall information includes: determining the slope type and slope size according to the terrain information; when the slope type is a concave slope, obtaining the snow depth information according to the monitoring information; according to the snowfall information, the snow depth information and the continuous snowmelt information, determining the change in snow depth on the concave slope; determining the snow water content according to the continuous snowmelt information; dividing the avalanche type according to the snow water content; determining the avalanche level according to the change in snow depth and the avalanche type; when the slope type is a convex slope, obtaining the slope image information according to the monitoring information; analyzing the slope image information and the slope size to construct a slope convex surface model; determining the stress distribution and snow distribution change of the slope convex surface according to the slope convex surface model and the snowfall information; determining the avalanche level according to the stress distribution and snow distribution change of the slope convex surface.

[0024] Through the above technical solution, the slope type and slope size are first determined according to the terrain conditions, and the avalanche level is discussed in different cases, making the analysis of the avalanche level more accurate and targeted. When the slope is a concave slope, the snow water content and the change in snow depth are analyzed according to the characteristics of the concave slope to judge what the avalanche level will be in the case of a concave slope if an avalanche occurs. This step can obtain the snow depth information by combining the monitoring information, and based on the snowfall, snow depth and continuous snowmelt, accurately analyze the change trend of the snow depth and divide the avalanche type according to the snow water content, providing multi-dimensional data for the avalanche level assessment. When it is a convex slope, a slope convex surface model is constructed through the image information and slope size of the snow mountain, and then the stress distribution and snow distribution are analyzed, providing an intuitive perspective for the avalanche level assessment of the convex slope. The overall solution significantly improves the avalanche warning ability by comprehensively using a variety of monitoring information and analysis methods. Whether it is a concave slope or a convex slope, more accurate risk assessment and level division can be carried out.

[0025] Optionally, determining the avalanche level according to the terrain information, the continuous snowmelt information, and the snowfall information further includes: analyzing the slope type and the slope size to determine the avalanche direction; obtaining the human activity area, analyzing the avalanche direction and the human activity area to determine the impact degree of the avalanche on humans; determining the time node when the avalanche occurs according to the snowmelt and snowfall cycle; and adjusting the avalanche level according to the time node, the impact degree, and the avalanche level.

[0026] Through the above technical solution, by analyzing the slope type and the slope size, the potential direction of the avalanche can be judged more accurately, providing key data for subsequent analysis of the impact on humans. Obtaining the human activity area information and analyzing it in combination with the avalanche direction can scientifically evaluate the impact degree of the avalanche on the human activity area. According to the analysis of the snowmelt and snowfall cycle, the time node when the avalanche may occur can be predicted more accurately, and the impact on humans can also be analyzed from the time perspective. Combining the time node, the impact degree, and the initial avalanche level can dynamically adjust the avalanche level, making the evaluation result more in line with the actual situation and improving the pertinence and effectiveness of the emergency response.

[0027] In a second aspect, the present application provides a geological disaster monitoring and early warning system, which includes:

[0028] An information acquisition module, configured to acquire the monitoring information of the snow mountain, analyze the monitoring information, and determine the environmental information set and the equipment status; an information analysis module, configured to analyze the monitoring information to obtain the terrain information, the environmental temperature, the snowfall information, the current snowmelt information, and the infrasound information; a snowmelt amount determination module, configured to analyze the monitoring information, the environmental information set, and the current snowmelt information to determine the continuous snowmelt amount information; a risk determination module, configured to determine the avalanche risk according to the continuous snowmelt amount information, the snowfall information, and the infrasound information; a level determination module, configured to determine the avalanche level according to the terrain information, the continuous snowmelt amount information, and the snowfall information; and a signal sending module, configured to determine the avalanche signal according to the equipment status, the avalanche risk, and the avalanche level, and send the avalanche signal using an error detection mechanism. Description of the Drawings

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0030] Figure 1 It is a schematic diagram of an application scenario provided by an embodiment of the present application;

[0031] Figure 2 Flow chart of a geological disaster monitoring and early warning method provided by an embodiment of the present application;

[0032] Figure 3 Schematic structural diagram of a geological disaster monitoring and early warning system provided by an embodiment of the present application. Detailed implementation manners

[0033] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Apparently, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without making creative efforts shall fall within the protection scope of the present application.

[0034] In addition, the term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after, unless otherwise specified.

[0035] The embodiments of the present application will be further described in detail below with reference to the accompanying drawings of the specification.

[0036] In the snow mountain area, the weather conditions are relatively complex, with frequent large fluctuations in a short period of time. Moreover, the snow mountain area has a high altitude, strong sunlight, and rapid cloud changes. These factors will all affect the snow mountain environment and lead to avalanches. The current technical means only issue early warnings for avalanches by obtaining snow mountain monitoring data in real time, often too late, and there is no time to deal with it when the early warning is issued, seriously threatening people's lives and property safety.

[0037] Based on this, the present application provides a geological disaster monitoring and early warning method and system. By obtaining the monitoring information of the snow mountain, the environmental information set and the equipment status are determined from the monitoring information. By determining the equipment status, it is ensured that the monitoring information is accurate. Then, by analyzing the monitoring information, the terrain information, environmental temperature, snowfall information, current snowmelt information, infrasound information and environmental information set are obtained, and the continuous snowmelt information is determined. By using multiple data, the continuous snowmelt information is accurately obtained, providing important information data for predicting the avalanche situation in the next period of time. Then, according to the three parameters of the continuous snowmelt information, snowfall information and infrasound information, the probability of an avalanche occurring, that is, the avalanche risk, is determined. The assessment of the avalanche risk is the basis of avalanche early warning. Then, through the terrain information, continuous snowmelt information and snowfall information, the avalanche level is determined. The avalanche level reflects the severity of the avalanche if it occurs, which is also crucial for avalanche early warning. Finally, by comprehensively considering the equipment status, avalanche risk and avalanche level, the avalanche signal is determined, and the error detection mechanism is used to ensure accurate transmission to achieve avalanche early warning.

[0038] Figure 1 FIG. is a schematic diagram of an application scenario provided by the present application. In the process of avalanche early warning, the method provided by the present application is applied to accurately predict the avalanche risk and avalanche level, thereby realizing avalanche early warning.

[0039] Specifically, the method provided by the present application is applied to any server. The server communicates with several sensors and communicates with the avalanche signal management system.

[0040] The server obtains the monitoring information of the snow mountain from several sensors, analyzes the monitoring information to determine the environmental information set and the equipment status, and obtains the terrain information, environmental temperature, snowfall information, current snowmelt information and infrasound information. By analyzing the environmental information set and the current snowmelt information, the continuous snowmelt information is determined. Then, by using the continuous snowmelt information, snowfall information and infrasound information, the avalanche risk can be determined. After obtaining the avalanche risk, the avalanche level is determined by using the geological information, continuous snowmelt information and snowfall information. According to the equipment status, avalanche risk and avalanche level, the avalanche signal is formulated and sent to the avalanche signal management system to realize the prediction of the avalanche situation in the next period of time and carry out early warning.

[0041] The specific implementation manner can refer to the following embodiments.

[0042] Figure 2 FIG. is a flowchart of a geological disaster monitoring and early warning method provided by an embodiment of the present application. The method of this embodiment can be applied to the server in the above scenario. As Figure 2 shown, the method includes:

[0043] S201. Obtain the monitoring information of the snow mountain, analyze the monitoring information, and determine the environmental information set and the equipment status.

[0044] The monitoring information can be the information obtained by monitoring the snow mountain environment, weather, etc. through several sensor devices. The monitoring information can include sensor device information, weather information, environmental temperature information, and light information. The environmental information set can be the information set of the snow mountain environment, and the environmental information set includes cloud information, light intensity information, wind speed information, and humidity information. The device status can be the status of several sensor devices for snow mountain monitoring, and the device status can include power status, working status, and environmental status.

[0045] Specifically, after receiving the monitoring information of several sensor devices and obtaining the monitoring information of the snow mountain, the monitoring information is analyzed through computer technologies such as natural language processing technology and computer vision technology to obtain the environmental information set and the device status. The determination of the device status ensures the accuracy of the obtained monitoring information. If the status of a certain device is abnormal, then the information obtained from this device with abnormal status is for reference only rather than completely correct data. The content in the environmental information set is a set of information that indirectly affects the situation of the snow mountain. The cloud information affects the light intensity information by absorbing and refracting light, and the light intensity information directly affects the environmental temperature and thus affects the snow melting situation. The wind speed information and humidity information also have an impact on the snow melting.

[0046] S202. Analyze the monitoring information to obtain terrain information, environmental temperature, snowfall amount information, current snowmelt amount information, and infrasound information.

[0047] The terrain information can be the geological information of the snow mountain, and the geological information can include slope type and slope size. The environmental temperature can be the actual temperature of the area of the snow mountain being monitored. The snowfall amount information can be the information on the amount of snowfall in a certain period of time on the snow mountain being monitored, and the snowfall amount information can be obtained through an ultrasonic snow amount sensor. The current snowmelt amount information can be the information on how much snow has melted on the snow mountain being monitored, and the current snowmelt amount information can be obtained through a weighing-type snow water equivalent sensor. The infrasound information can be the infrasound information monitored on the snow mountain being monitored, and the infrasound information can include infrasound frequency and infrasound intensity, and the infrasound information can be obtained through a capacitive infrasound sensor.

[0048] Specifically, natural language processing technology is used to analyze the content of the monitoring information to obtain the terrain information, environmental temperature, snowfall amount information, current snowmelt amount information, and infrasound information of the monitored snow mountain. The slope type in the terrain information determines the characteristics of an avalanche. The greater the slope, the stronger the tendency for the snow to slide, and the faster the avalanche speed when an avalanche occurs. The environmental temperature affects the stability of the snow on the snow mountain. When the temperature is high, the snow is prone to melting, leading to sliding and fracture, and then causing an avalanche. For example, when the temperature rises in spring, a large amount of snow accumulated in winter begins to melt due to the temperature increase, and the avalanche risk increases significantly. The snowfall amount information can directly affect the thickness of the snow cover. When there is continuous or heavy snowfall, the snow accumulates continuously, and the avalanche risk will also increase significantly. The current snowmelt amount is the basic data for determining the continuous snowmelt amount, and the continuous snowmelt amount is one of the key factors affecting avalanches; infrasound can directly damage the stability of the snow on the snow mountain and cause an avalanche due to its weak attenuation and energy transmission ability. At the same time, a large amount of infrasound is also generated when an avalanche occurs. If there is very strong infrasound, it may indicate that an avalanche has occurred in a higher altitude area outside the monitoring range, triggering a series of avalanches.

[0049] In summary, terrain information, environmental temperature, snowfall amount information, current snowmelt amount information, and infrasound information are obtained from the monitoring information, providing necessary information for assessing avalanche risk.

[0050] S203. Analyze the monitoring information, environmental information set, and current snowmelt amount information to determine the continuous snowmelt amount information.

[0051] The continuous snowmelt amount information can be the information on how much snow will melt in total on the monitored snow mountain in a future period of time.

[0052] Specifically, through the named entity recognition technology in natural language processing technology, the environmental temperature and current snowmelt amount information are obtained from the monitoring information, and the wind speed information, humidity information, and light information are obtained from the environmental information set. The factors affecting the continuous snowmelt amount information are determined based on the environmental temperature, current snowmelt amount information, wind speed information, humidity information, and light information, and the continuous snowmelt amount information is calculated through mathematical analysis means. The continuous snowmelt amount is one of the key factors for an avalanche to occur. When the snowmelt amount increases continuously, the risk of an avalanche occurring and the grade of the avalanche when it occurs will also increase.

[0053] S204. Determine the avalanche risk based on the continuous snowmelt amount information, snowfall amount information, and infrasound information.

[0054] The avalanche risk can be the risk of an avalanche occurring on the monitored snow mountain in a future period of time, and the avalanche risk can be a numerical value.

[0055] Specifically, based on the continuous snowmelt information, snowfall information, and infrasound information, the avalanche risk value is calculated through mathematical analysis. By quantifying the avalanche risk numerically, it is possible to more quickly determine the magnitude of the avalanche risk.

[0056] S205. Determine the avalanche level based on the terrain information, continuous snowmelt information, and snowfall information.

[0057] The avalanche level can be the severity level of an avalanche if it occurs on a snow-capped mountain being monitored within a certain period in the future.

[0058] Specifically, starting from the terrain information, terrain data is obtained from the terrain information. Different types of terrains can be divided through the terrain data. By combining the terrain with the continuous snowmelt information and snowfall information, the avalanche level is determined through mathematical analysis and physical analysis means.

[0059] S206. Determine the avalanche signal based on the device status, avalanche risk, and avalanche level, and use an error detection mechanism to send the avalanche signal.

[0060] The avalanche signal can be a signal that can express the device status, avalanche risk, and avalanche level.

[0061] Specifically, according to the respective contents of the device status, avalanche risk, and avalanche level, digital signals are generated to transmit information respectively, and the signals of these three are spliced together, and an error detection mechanism is used to ensure correct transmission.

[0062] Through the method provided in this embodiment, first, the monitoring information of the snow-capped mountain is obtained, and through analysis of the monitoring information, an environmental information set, device status, geological information, environmental temperature, snowfall information, current snowmelt information, and infrasound information are obtained, providing a data basis for subsequent avalanche situation judgment. And by obtaining the device status, it is determined that there is an abnormality in the device in the first time, ensuring that all data is accurate. Then, through analysis of the monitoring information, environmental information set, and current snowmelt information, continuous snowmelt information is obtained, providing necessary data for subsequent determination of avalanche risk and avalanche level, and the snow-capped mountain status can also be reflected from the continuous snowmelt. After that, the avalanche risk is determined through the continuous snowmelt information, snowfall information, and infrasound information, and the avalanche level is determined through the terrain information, continuous snowmelt information, and snowfall information. The determination of avalanche risk and avalanche level is the most important step in early warning. Finally, an avalanche signal is formulated based on the device status, avalanche risk, and avalanche level, and an error monitoring mechanism is used to ensure correct transmission of the avalanche signal. The formulation and transmission of the avalanche signal can provide timely avalanche early warning.

[0063] In some embodiments, obtain the weather forecast information of the snow mountain location, analyze the weather forecast information, and determine cloud information, light information, wind speed information, and humidity information; analyze the light information and cloud information to determine the light intensity information; and determine the cloud information, light intensity information, wind speed information, and humidity information as the environmental information set.

[0064] The weather forecast information can be the snow mountain weather information obtained from the meteorological monitoring of the snow mountain location by the meteorological bureau or some other institutions. The cloud information can be the cloud information given in the weather forecast, and the cloud information can include cloud thickness information and cloud type information. The light information can be the light information given in the weather forecast, and the light information can include light intensity information and light azimuth information. The humidity information can be the humidity information given in the weather forecast, and the humidity information can include humidity percentage and humidity change information. The light intensity information can be the information reflecting the strength of light, and the light intensity information can include infrared intensity information and ultraviolet intensity information.

[0065] Specifically, obtain the weather forecast from the meteorological bureau or other third-party institutions, and use natural language processing technology to determine cloud information, light information, wind speed information, and humidity information. In addition to the light itself, the light information is also affected by clouds. Obtain the time information from the light information and cloud information, determine the time corresponding to the light information and cloud information, obtain the cloud thickness and cloud type according to the cloud information, and use a mathematical model to analyze the absorption, refraction, etc. of the cloud thickness and cloud type on light. Finally, obtain the light intensity. The available models include the Lambert model (diffuse reflection model), Phong model (simple lighting model), etc. Finally, determine the cloud information, light intensity information, wind speed information, and humidity information as the environmental information set.

[0066] Through the method provided in this embodiment, obtain the weather forecast information, and obtain multiple key factors such as cloud information, light information, wind speed information, and humidity information from the weather forecast information, ensuring the comprehensiveness and meticulousness of the analysis. Combine the cloud information and light information to obtain the light intensity information, which ensures the accuracy of the obtained light intensity information. Finally, add multiple pieces of information to the environmental information set, which can provide more accurate information reflecting the snow mountain environment for subsequent snow mountain warning means.

[0067] In some embodiments, determine the device information according to the monitoring information; determine the device operating voltage according to the device information, and determine the power supply state according to the device operating voltage; determine the device type according to the device information, and screen out the image sensor according to the device type; determine the image content outside the device according to the image sensor; determine the environmental state outside the device according to the image content; and determine the device state according to the power supply state and the environmental state.

[0068] The device information can be the information of all devices participating in the snow mountain monitoring work. The device information can include the device name and device type. The device operating voltage can be the voltage of the device that is currently working. The power supply status can be the status of the power supply that provides power to the device. The device type can be the type of the device for monitoring. The device type can be an image sensor or a non-image sensor. An image sensor can be a sensor that can collect digital image signals and output images after conversion. An image sensor can be a surveillance camera. The image content can be a partial content of the image obtained from the image sensor. The environmental status can be the impact status of the monitored snow mountain environment on the device.

[0069] Specifically, determine the device information from the monitoring information. Use natural language processing technology to obtain the device operating voltage from the device information. Analyze whether the operating voltage is stable and whether it reaches the required operating voltage of the device. If the operating voltage is stable and reaches the operating voltage, the power supply status is normal. If there is an abnormality in either item, the power supply status is abnormal. Obtain the device type from the device information, filter out the image sensors, obtain the image content of the image sensors, and use computer vision analysis technology to analyze whether the environment affects the operation of the device. Determine the environmental status according to the analysis result. If the environment affects the device operation, the environmental status is abnormal. As shown in the image content, there is snow accumulation blocking the front of the camera. This situation can be considered that the environment affects the device operation. Determine the device status according to the power supply status and the environmental status. The device status is normal only when both are normal.

[0070] Through the method provided in this embodiment, obtain the device information from the monitoring information, determine the operating voltage according to the device information to judge the status of the power supply. This step determines the basic situation of the device by analyzing the condition of the operating voltage. If the operating voltage is abnormal, the content monitored by the device is unreliable. Then, according to the device information, after determining the device type, filter out the image sensors, obtain the image content, and analyze the image content to determine whether the environment has an impact on the device. Through this step, it is determined whether the external environment will affect the device monitoring, and the device status can be evaluated more comprehensively to ensure the accuracy of the monitoring data.

[0071] In some embodiments, determine the snow mountain location information and time information according to the monitoring information; determine the illumination azimuth change information according to the time information; determine the environmental temperature according to the environmental information set; analyze the snow mountain location information, illumination azimuth change information, illumination intensity information and time information to determine the snow mountain illumination information; analyze the environmental temperature and the snow mountain illumination information to determine the temperature change trend; based on the current snowmelt information and the environmental information set, analyze the temperature change trend to determine the continuous snowmelt information.

[0072] The snow mountain location information can be information revealing the location of the snow mountain and the monitoring orientation. The illumination azimuth change information can be information revealing the change process of the illumination azimuth of sunlight. The snow mountain illumination information can be the illumination information received by the monitored snow mountain, and the snow mountain illumination information can include illumination time and illumination intensity. The temperature change trend can be the change information of the environmental temperature of the snow mountain within a certain time period.

[0073] Specifically, through natural language processing technology, location information can be obtained from the monitoring information to obtain the snow mountain location information. Time information is obtained according to the time stamp in the monitoring information. The season and time in the time information are analyzed. The change information of the illumination azimuth is determined through the natural evolution law. The environmental temperature is obtained from the environmental information set. This temperature is the temperature at the current time. According to the geographical location of the snow mountain and the orientation of the mountain, combined with the illumination azimuth information, it is judged when the snow mountain has illumination. Furthermore, when there is illumination, which ranges of the snow mountain have illumination is obtained. And according to the illumination intensity information, the illumination intensity when the snow mountain has illumination is obtained, so as to determine the snow mountain illumination information. Through the environmental temperature and the obtained snow mountain illumination information, a calculation model is used to predict the temperature change, that is, the temperature change trend is obtained. The model can be an ARIMA (Autoregressive Integrated Moving Average Model) or an empirical model obtained according to historical situations. Then, based on the current snowmelt volume information and the environmental information set, according to the temperature change trend, through mathematical analysis means, the continuous snowmelt volume information is determined.

[0074] Through the method provided in this embodiment, first, the snow mountain location information and time information are obtained according to the monitoring information. These two pieces of information provide the necessary data for the subsequent illumination of the snow mountain. Then, through the time information, the illumination azimuth change information is determined, and it is determined at what time the illumination shines on which azimuth of the snow mountain, ensuring the accuracy of the subsequent analysis of the snow mountain illumination. After that, the snow mountain location information, illumination azimuth change information, illumination intensity information, and time information are analyzed to obtain the snow mountain illumination information, providing the most direct basis for determining the temperature change trend subsequently. The environmental temperature is obtained from the environmental information set. Combining the snow mountain illumination information to determine the temperature change trend, based on the current snowmelt volume information and the environmental information set, analyzing the temperature change trend to determine the continuous snowmelt volume information. In addition to considering the direct factor of temperature change, other information existing in the environment is also comprehensively considered. The multi-dimensional analysis makes the determination of the continuous snowmelt volume more accurate.

[0075] In some embodiments, the illumination information of the snow mountain is analyzed to determine the illumination time information and illumination intensity of the snow mountain; based on the illumination time information of the snow mountain, the illumination time and non-illumination time of the snow mountain are obtained; based on the illumination intensity, ambient temperature, and illumination time of the snow mountain, the temperature increase trend is determined; based on the temperature increase trend and illumination time, the temperature after temperature increase when entering the non-illumination time is determined; based on the weather forecast information, the overall temperature information is obtained; based on the non-illumination time, the temperature after temperature increase, and the overall temperature information, the temperature decrease trend is determined; by combining the temperature increase trend and the temperature decrease trend, the temperature change trend is determined.

[0076] The illumination time information of the snow mountain can be the time information indicating whether there is illumination or no illumination on the monitored snow mountain. The illumination intensity of the snow mountain can be the illumination intensity when there is illumination on the monitored snow mountain. The illumination time can be the time when there is illumination on the monitored snow mountain. The non-illumination time can be the time when there is no illumination on the monitored snow mountain. The temperature increase trend can be the temperature increase trend of the monitored snow mountain under illumination. The temperature after temperature increase can be the temperature at the last moment of illumination on the monitored snow mountain. The overall temperature information can be the regional temperature information provided by the weather forecast. The temperature decrease trend can be the temperature decrease trend of the monitored snow mountain under non-illumination.

[0077] Specifically, the illumination information of the snow mountain is analyzed through natural language processing technology to obtain the illumination time information and illumination intensity of the snow mountain. Within the illumination time information of the snow mountain, the time periods with illumination and without illumination on the snow mountain are obtained. When there is illumination, the intensity of infrared rays is obtained from the illumination intensity of the snow mountain. By using existing models such as ARIMA (Autoregressive Integrated Moving Average Model) or empirical models derived from local historical conditions, the temperature increase trend is calculated. Based on this temperature increase trend, the ambient temperature at the last moment of the illumination time, that is, the temperature after temperature increase, can be obtained. This temperature is used as the initial temperature for the non-illumination time. By combining the temperature information of the weather forecast and using the same method, the temperature decrease trend is obtained. The temperature increase trend and the temperature decrease trend are combined to obtain the complete temperature change trend.

[0078] Through the method provided in this embodiment, the sunlight information of the snow-capped mountain is analyzed to determine the sunlight duration information and the sunlight intensity of the snow-capped mountain. Then, the sunlight duration and the non-sunlight duration are determined from the sunlight duration of the snow-capped mountain. This step combines the sunlight and time factors, and considers the situations of sunlight and non-sunlight separately to ensure the rigor of predicting the temperature change trend. By comprehensively analyzing the sunlight intensity of the snow-capped mountain, the ambient temperature, and the sunlight duration, the warming trend is obtained. The temperature at the last moment is obtained from the warming trend as the initial temperature during the non-sunlight duration, that is, the temperature after warming. Then, the air temperature information is obtained from the weather forecast information. The non-sunlight duration, the temperature after warming, and the overall air temperature information are combined to determine the cooling trend. The warming trend and the cooling trend are combined to obtain the temperature change trend. By comprehensively considering multiple-dimensional factors to obtain the warming trend and the cooling trend, the prediction result is more objective and credible. Moreover, the determination of the cooling trend is also closely linked to the warming trend, determining the complete continuity of the temperature change trend.

[0079] In some embodiments, the temperature change trend is analyzed, the temperature range is divided, and the division result is determined; according to the division result, the above-zero temperature range information is determined; according to the above-zero temperature range information, the start time and the end time of the above-zero temperature range are determined; when the temperature in the temperature change trend is greater than zero degree, the humidity information and the wind speed information are analyzed to obtain the humidity change trend and the wind speed change trend; according to the temperature change trend, the humidity change trend, and the wind speed change trend, the temperature change curve, the humidity change curve, and the wind speed change curve during the above-zero temperature range are constructed; according to the temperature change curve, the humidity change curve, and the wind speed change curve, the continuous snowmelt amount is determined with reference to formula (1):

[0080]

[0081] where M represents the continuous snowmelt amount, t represents the preset analysis time, t1 represents the start time of the above-zero temperature range, t2 represents the end time of the above-zero temperature range, T(t) represents the temperature change curve, v(t) represents the wind speed change curve, H(t) represents the humidity change curve, k represents the preset temperature change empirical coefficient, a represents the preset wind speed change empirical coefficient, and b represents the preset humidity change empirical coefficient.

[0082] The temperature range can be a range divided according to the temperature values in the temperature change trend. The temperature can include the above-zero temperature range and the below-zero temperature range. The preset analysis time can be the time period for predicting the continuous snowmelt volume. The humidity change trend can be the trend of humidity change of the monitored snow mountain within the preset analysis time. The wind speed change trend can be the trend of wind speed change of the monitored snow mountain within the preset analysis time. The temperature change curve can be a curve showing the change of temperature over time constructed based on the temperature values in the temperature change trend and the time values in the preset analysis time. The humidity change curve can be a curve showing the change of humidity over time constructed based on the humidity values in the humidity change trend and the time values in the preset analysis time. The wind speed change curve can be a curve showing the change of wind speed over time constructed based on the wind speed values in the wind speed change trend and the time values in the preset analysis time. The preset temperature change empirical coefficient can be a coefficient representing the degree of influence of temperature on the snowmelt volume obtained based on historical experience. The preset wind speed change empirical coefficient can be a coefficient representing the degree of influence of wind speed on the snowmelt volume obtained based on historical experience. The preset humidity change empirical coefficient can be a coefficient representing the degree of influence of humidity on the snowmelt volume obtained based on historical experience.

[0083] Specifically, first, analyze the temperature change trend through natural language processing technology, and divide the range according to the temperature value. When the temperature is greater than zero degrees Celsius, it is the above-zero temperature range. When the temperature is less than zero degrees Celsius, it is the below-zero temperature range. When in the below-zero temperature range, since the temperature is too low to cause snow cover to melt, the below-zero temperature range does not need to be considered additionally. When in the above-zero temperature range, first determine the start time and end time of the above-zero temperature range, and then analyze the time period between the start time and the end time of the above-zero temperature range (hereinafter referred to as the above-zero time period). Select the humidity information and wind speed information within the above-zero time period from the humidity information and wind speed information, obtain the humidity change trend and wind speed change trend based on the data during this period, and then construct a unary function curve with the time axis as the x-axis and the corresponding values as the y-axis through the values and corresponding time nodes of the temperature change trend, humidity change trend, and wind speed change trend, that is, the temperature change curve, humidity change curve, and wind speed change curve. Considering that the influence degrees of temperature, humidity, and wind speed on the snowmelt volume are different, specific empirical coefficients need to be obtained based on historical experience, and then substitute each parameter into formula (1) for calculation to obtain the snowmelt volume from the start time to the end time of the above-zero temperature range, that is, the continuous snowmelt volume.

[0084] Through the method provided in this embodiment, first divide the temperature range according to the temperature change trend to determine the start time and end time of the above-zero temperature, which is crucial for subsequent continuous snowmelt volume analysis because the snowmelt volume changes significantly when the temperature is greater than zero degrees. Then, obtain the humidity change trend and wind speed change trend based on the humidity information and wind speed information, and obtain the humidity change curve and wind speed change curve. At the same time, obtain the temperature change curve through the temperature change trend. This step comprehensively considers the influence of humidity and wind speed because the influence of humidity and wind speed on snowmelt is also significant when the temperature is greater than zero degrees. By comprehensively analyzing these factors, the snowmelt situation can be evaluated more comprehensively. Then, calculate through a mathematical formula. The formula not only considers the direct influence of temperature but also the indirect influence of humidity and wind speed. Through the calculation of the formula, the snowmelt situation can be understood more intuitively and comprehensively, providing a scientific basis for snowmelt volume calculation. And the introduction of the empirical coefficient makes the calculation result more in line with the actual situation.

[0085] In some embodiments, according to the continuous snowmelt volume information and snowfall volume information, obtain the continuous snowmelt volume, snowfall volume, and snowmelt-snowfall cycle; according to the infrasound information, determine the infrasound intensity; according to the continuous snowmelt volume, snowfall volume, snowmelt-snowfall cycle, and infrasound intensity, determine the avalanche risk, referring to formula (2):

[0086]

[0087] Among them, R represents the avalanche risk, M represents the continuous snowmelt volume, S represents the snowfall volume, P represents the snowmelt-snowfall cycle, I represents the infrasound intensity, w1 represents the preset weight coefficient of the continuous snowmelt volume, w2 represents the preset weight coefficient of the snowfall volume, and w3 represents the preset weight coefficient of the infrasound intensity.

[0088] The snowmelt-snowfall cycle can be the time period for monitoring the snowmelt volume and snowfall volume, and it is also the time period for predicting the avalanche risk in the future. For example, if the snowmelt-snowfall cycle is 2 hours, then the snowfall volume information and continuous snowmelt volume information are the snowfall volume information and continuous snowmelt volume information in the next 2 hours, and the obtained avalanche risk represents the risk of an avalanche in the next 2 hours.

[0089] The infrasound intensity can be the energy magnitude of the infrasound detected in the snow-capped mountains. The preset weight coefficient of the continuous snowmelt volume can be the coefficient obtained according to the historical situation of the snow-capped mountains regarding the influence degree of the continuous snowmelt volume on the avalanche risk. The preset weight coefficient of the snowfall volume can be the coefficient obtained according to the historical situation of the snow-capped mountains regarding the influence degree of the snowfall volume on the avalanche risk. The preset weight coefficient of the infrasound intensity can be the coefficient obtained according to the historical situation of the snow-capped mountains regarding the influence degree of the infrasound intensity on the avalanche risk.

[0090] Specifically, through natural language processing technology, specific values of continuous snowmelt and snowfall are obtained from continuous snowmelt information and snowfall information, and the snow avalanche risk within a certain period of time to be predicted, that is, the snowmelt and snowfall cycle, is determined according to the prediction requirements. Then, the infrasonic wave intensity is obtained from the infrasonic wave information and calculated through formula (2). Since the prediction of snow avalanche risk is for a period of time in the future, the snowmelt and snowfall cycle needs to be introduced when calculating the impact of snowfall and continuous snowmelt on snow avalanche risk. By introducing the snowmelt and snowfall cycle, the snow avalanche risk can be more uniformly divided. Without the snowmelt and snowfall cycle, this value will lose its reference value. For example, when predicting the snow avalanche risk within the next 2 hours and the snow avalanche risk within the next 6 hours, there is only snowfall and snowmelt in the first two hours. However, without the snowmelt and snowfall cycle, the snow avalanche risks within the next 2 hours and the next 6 hours will be the same. But in reality, through 2 hours of continuous snowfall and snowmelt, although no snow avalanche occurs within 2 hours, it may occur at the 3rd hour. There are obvious differences in the probabilities of snow avalanches occurring in these two time periods. After calculating the impact of snowfall and continuous snowmelt on snow avalanche risk, the values are added to the impact of infrasonic wave intensity to obtain the snow avalanche risk value, and the size of the snow avalanche risk is judged according to the value size.

[0091] Through the method provided in this embodiment, the values of the two and the snowmelt and snowfall cycle are obtained according to the continuous snowmelt information and snowfall information, and the infrasonic wave intensity is obtained from the infrasonic wave information. The snow avalanche risk is calculated by means of a mathematical formula. The solution considers multiple factors related to snow avalanche risk, including meteorological conditions (continuous snowmelt, snowfall) and physical phenomena (infrasonic wave), improving the comprehensiveness and accuracy of the assessment. And through the mathematical formula for calculation, a scientific assessment method for snow avalanche risk is provided, quantifying the risk and intuitively understanding the snow avalanche risk.

[0092] In some embodiments, according to the terrain information, the slope type and slope size are determined; when the slope type is a concave slope, according to the monitoring information, the snow depth information is obtained; according to the snowfall information, snow depth information and continuous snowmelt information, the change in snow depth on the concave slope is determined; according to the continuous snowmelt information, the snow water content is determined; according to the snow water content, the snow avalanche type is divided; according to the change in snow depth and snow avalanche type, the snow avalanche grade is determined; when the slope type is a convex slope, according to the monitoring information, the slope image information is obtained; the slope convex surface model is constructed by analyzing the slope image information and slope size; according to the slope convex surface model and snowfall information, the stress distribution on the slope convex surface and the change in snow distribution are determined; according to the stress distribution on the slope convex surface and the change in snow distribution, the snow avalanche grade is determined.

[0093] The slope type can be the snow mountain type determined by the snow mountain terrain. The slope type can include concave slopes and convex slopes. The slope magnitude can be the numerical value reflecting the steepness of the snow mountain. A concave slope can be a slope with a concave surface. The characteristic of a concave slope is that the upper slope is steep and the lower slope is relatively gentle. A convex slope can be a slope with a convex surface. The characteristic of a convex slope is that the upper slope is gentle and the lower slope is relatively steep. The snow water content can be the numerical value indicating the amount of water contained in the snow. The avalanche type can be the type of avalanche when it is monitored that an avalanche is about to occur on the snow mountain. The avalanche type can include wet avalanches and dry avalanches. The slope image information can be the image information reflecting the slope information obtained from the snow mountain image by the image sensor. The convex slope model of the slope can be the 3D model of the convex slope generated by the computer. The stress distribution of the convex slope of the slope can be the stress distribution of the convex part at the upper part of the convex slope. The change in snow accumulation distribution can be the situation of the change in snow accumulation distribution due to the influence of snowfall amount in the convex part at the upper part of the convex slope.

[0094] Specifically, analyze the terrain information according to natural language processing technology to obtain the slope type and slope magnitude. When the slope type is a concave slope, the snow accumulates at the lower part of the slope. Obtain the snow depth information from the monitoring information. Based on the snow depth information, judge the change in snow depth through the snowfall amount information and the continuous snowmelt amount information, which can be obtained through simple calculations of the snowfall amount value and the snowmelt amount value. For example, subtract the snowmelt amount value from the snowfall amount value. When the calculation result is positive, it means that the snowfall amount is more, the snowmelt amount is less, and the snow depth will increase. When the calculation result is negative, it means that the snowfall amount is less, the snowmelt amount is more, and the snow depth will decrease.

[0095] According to the continuous snowmelt amount information, calculate the snow water content. The snow water content can be a graded quantity. For example, divide the snow water content into level one, level two, and level three. When the continuous snowmelt amount is less than 1 mm, the snow water content is level one. When the continuous snowmelt amount is greater than 1 mm and less than 10 mm, it is level two. When the continuous snowmelt amount is greater than 10 mm, it is level three. According to the snow water content, determine whether the avalanche type is a wet avalanche or a dry avalanche. According to the above example, when the snow water content is level one, it can be considered a dry avalanche. When it is level three, it is considered a wet avalanche. When it is level two, it is classified as a dry avalanche or a wet avalanche according to the actual value. According to the change in snow depth and the avalanche type, when an avalanche occurs during the growth of snow depth, the snow volume of the avalanche will increase, and the avalanche level needs to be appropriately increased. When the avalanche type is a wet avalanche, the harm of the avalanche will be greater than that of a dry avalanche, and the avalanche level needs to be appropriately increased. On the contrary, if the snow is less, the avalanche level needs to be appropriately decreased, and it also needs to be appropriately decreased during a dry avalanche.

[0096] When the slope type is a convex slope, focus on studying the upper part of the slope, that is, the top of the slope. Obtain image information from the monitoring information. Using computer image processing technology, first perform denoising processing on the image information. The processing means can be Gaussian filtering or median filtering. Identify the snow-capped mountain through the recognition algorithm of the main body of the snow-capped mountain, and then perform enhancement processing on the snow-capped mountain image to make the main body of the snow-capped mountain more prominent. The recognition algorithm can use the YOLO (YOU ONLY LOOK ONCE) algorithm. Based on the enhancement processing of the main body of the snow-capped mountain, there are significant differences between the snow-capped mountain and other contents in the image, so the image can be segmented. Then, the image recognition algorithm in OpenCV (Open Source Computer Vision Library) can be used to extract the surface features of the snow-capped mountain (such as roughness and directionality). Finally, combined with the slope size, computer 3D modeling is performed, that is, a convex slope model of the mountain slope is constructed. According to the convex slope model of the mountain slope and the snowfall information, determine the stress distribution on the convex slope of the mountain slope and the change in snow accumulation distribution through physical force analysis means, and determine the avalanche level from the stress distribution and the change in snow accumulation distribution. For example, when the stress is concentrated in a certain area, when there is more snowfall, the change in snow accumulation distribution is likely to lead to imbalance and cause an avalanche. Adjust the avalanche level according to the stress magnitude and the snow accumulation distribution.

[0097] Through the method provided in this embodiment, first determine the slope type and slope size according to the terrain conditions, and discuss the avalanche level in different cases, making the analysis of the avalanche level more accurate and targeted. When the slope is a concave slope, analyze the change in snow water content and snow accumulation thickness according to the characteristics of the concave slope, and judge what the avalanche level will be in the case of a concave slope if an avalanche occurs. By combining the monitoring information, the snow accumulation thickness information can be obtained, and based on the snowfall amount, snow accumulation thickness and continuous snowmelt amount, accurately analyze the change trend of the snow accumulation thickness and classify the avalanche type in combination with the snow water content to provide multi-dimensional data for avalanche level assessment. When it is a convex slope, a convex slope model of the mountain slope is constructed through the image information of the snow-capped mountain and the slope size, and then the stress distribution and snow accumulation distribution are analyzed, providing an intuitive perspective for the avalanche level assessment of the convex slope. The overall solution significantly improves the avalanche warning ability by comprehensively using a variety of monitoring information and analysis methods. Whether it is a concave slope or a convex slope, more accurate risk assessment and level classification can be carried out.

[0098] In some embodiments, analyze the slope type and slope size to determine the avalanche direction; obtain the human activity area, analyze the avalanche direction and the human activity area to determine the impact degree of the avalanche on humans; determine the time node when the avalanche occurs according to the snowmelt and snowfall cycle; adjust the avalanche level according to the time node, impact degree and avalanche level.

[0099] The avalanche direction can be the impact direction of the snow when an avalanche occurs.

[0100] The human activity area can be the human living area and the human working area near the snow-capped mountains.

[0101] Specifically, the falling direction of the avalanche can be determined according to the slope type, and the falling speed of the avalanche can be determined according to the slope gradient. Combining the two can determine the avalanche trend. The human activity area can be obtained using Geographic Information System (GIS) data. Through the map data and remote sensing images contained in the GIS data, buildings and human activity locations can be determined, thereby determining the human activity area. It can be judged from the avalanche trend whether the avalanche will eventually fall into the human activity area. In addition, according to the snowmelt and snowfall cycle, the time node when the avalanche occurs can be determined. For example, if the snowmelt and snowfall cycle is 2 hours, the occurrence time is within the next 2 hours.

[0102] Adjust the avalanche level according to the impact of the avalanche on humans and the time node. For example, if the avalanche will directly rush towards the place where humans live, then this avalanche can be considered to have a great impact on humans, and the time of occurrence is at night, when people are resting. Then the avalanche level needs to be increased in order to issue a more urgent notice.

[0103] Through the method provided in this embodiment, by analyzing the slope type and slope gradient, the potential trend of the avalanche can be judged more accurately, providing key data for subsequent analysis of the impact on humans. Obtaining the information of the human activity area and analyzing it in combination with the avalanche trend can scientifically evaluate the impact degree of the avalanche on the human activity area. According to the analysis of the snowmelt and snowfall cycle, the time node when the avalanche may occur can be predicted more accurately, and the impact on humans can also be analyzed from the time perspective. Combining the time node, the impact degree and the initial avalanche level can dynamically adjust the avalanche level, making the evaluation result more in line with the actual situation and improving the pertinence and effectiveness of the emergency response.

[0104] Figure 3 The following is a schematic structural diagram of a geological disaster monitoring and early warning system provided by an embodiment of the present application. As Figure 3 shown, the geological disaster monitoring and early warning system 300 of this embodiment includes: an information acquisition module 301, an information analysis module 302, a snowmelt amount determination module 303, a risk determination module 304, a level determination module 305, and a signal transmission module 306.

[0105] The information acquisition module 301 is used to acquire the monitoring information of the snow-capped mountains, analyze the monitoring information, and determine the environmental information set and the device status;

[0106] The information analysis module 302 is used to analyze the monitoring information to obtain the terrain information, environmental temperature, snowfall amount information, current snowmelt amount information, and infrasound information;

[0107] The snowmelt volume determination module 303 is configured to analyze the monitoring information, the environmental information set, and the current snowmelt volume information to determine the continuous snowmelt volume information;

[0108] The risk determination module 304 is configured to determine the avalanche risk according to the continuous snowmelt volume information, the snowfall information, and the infrasound information;

[0109] The level determination module 305 is configured to determine the avalanche level according to the terrain information, the continuous snowmelt volume information, and the snowfall information;

[0110] The signal sending module 306 is configured to determine the avalanche signal according to the device status, the avalanche risk, and the avalanche level, and send the avalanche signal using an error detection mechanism.

[0111] Optionally, the information acquisition module 301 is specifically configured to:

[0112] Obtain the weather forecast information of the snow mountain location, analyze the weather forecast information to determine the cloud information, the light information, the wind speed information, and the humidity information; analyze the light information and the cloud information to determine the light intensity information; and determine the cloud information, the light intensity information, the wind speed information, and the humidity information as the environmental information set.

[0113] Optionally, the information acquisition module 301 is specifically configured to:

[0114] Determine the device information according to the monitoring information; determine the device operating voltage according to the device information, and determine the power supply status according to the device operating voltage; determine the device type according to the device information, and screen out the image sensors according to the device type; determine the image content outside the device according to the image sensors; determine the environmental status outside the device according to the image content; and determine the device status according to the power supply status and the environmental status.

[0115] Optionally, the snowmelt volume determination module 303 is specifically configured to:

[0116] Determine the snow mountain location information and the time information according to the monitoring information; determine the light azimuth change information according to the time information; determine the environmental temperature according to the environmental information set; analyze the snow mountain location information, the light azimuth change information, the light intensity information, and the time information to determine the snow mountain light information; analyze the environmental temperature and the snow mountain light information to determine the temperature change trend; and analyze the temperature change trend based on the current snowmelt volume information and the environmental information set to determine the continuous snowmelt volume information.

[0117] Optionally, the snowmelt volume determination module 303 is specifically configured to:

[0118] Analyze the sunlight information of the snow mountain to determine the snow mountain sunlight duration information and the snow mountain sunlight intensity; based on the snow mountain sunlight duration information, obtain the sunlight duration and the non-sunlight duration of the snow mountain; based on the snow mountain sunlight intensity, the ambient temperature, and the sunlight duration, determine the warming trend; based on the warming trend and the sunlight duration, determine the temperature after warming when entering the non-sunlight duration; based on the weather forecast information, obtain the overall temperature information; based on the non-sunlight duration, the temperature after warming, and the overall temperature information, determine the cooling trend; combine the warming trend and the cooling trend to determine the temperature change trend.

[0119] Optionally, the snowmelt volume determination module 303 is specifically configured to:

[0120] Analyze the temperature change trend, divide the temperature range, and determine the division result; based on the division result, determine the above-zero temperature range information; based on the above-zero temperature range information, determine the start time and the end time of the above-zero temperature range; when the temperature in the temperature change trend is greater than zero degrees, analyze the humidity information and the wind speed information to obtain the humidity change trend and the wind speed change trend; based on the temperature change trend, the humidity change trend, and the wind speed change trend, construct the temperature change curve, the humidity change curve, and the wind speed change curve in the above-zero temperature range; based on the temperature change curve, the humidity change curve, and the wind speed change curve, determine the continuous snowmelt volume, referring to the following formula:

[0121]

[0122] where M represents the continuous snowmelt volume, t represents the preset analysis time, t1 represents the start time of the above-zero temperature range, t2 represents the end time of the above-zero temperature range, T(t) represents the temperature change curve, v(t) represents the wind speed change curve, H(t) represents the humidity change curve, k represents the preset temperature change experience coefficient, a represents the preset wind speed change experience coefficient, and b represents the preset humidity change experience coefficient.

[0123] Optionally, the risk determination module 304 is specifically configured to:

[0124] Based on the continuous snowmelt volume information and the snowfall amount information, obtain the continuous snowmelt volume, the snowfall amount, and the snowmelt-snowfall cycle; based on the infrasound information, determine the infrasound intensity; based on the continuous snowmelt volume, the snowfall amount, the snowmelt-snowfall cycle, and the infrasound intensity, determine the avalanche risk, referring to the following formula:

[0125]

[0126] Wherein, R represents the avalanche risk, M represents the continuous snowmelt volume, S represents the snowfall volume, P represents the snowmelt and snowfall cycle, I represents the infrasonic intensity, w1 represents the preset weight coefficient of the continuous snowmelt volume, w2 represents the preset weight coefficient of the snowfall volume, and w3 represents the preset weight coefficient of the infrasonic intensity.

[0127] Optionally, the level determination module 305 is specifically configured to:

[0128] Determine the hillside type and slope size according to the terrain information; when the hillside type is a concave slope, obtain the snow depth information according to the monitoring information; determine the change in snow depth on the concave slope according to the snowfall information, the snow depth information, and the continuous snowmelt volume information; determine the snow water content according to the continuous snowmelt volume information; classify the avalanche type according to the snow water content; determine the avalanche level according to the change in snow depth and the avalanche type; when the hillside type is a convex slope, obtain the hillside image information according to the monitoring information; analyze the hillside image information and the slope size to construct a convex hillside model; determine the stress distribution and snow distribution change of the convex hillside according to the convex hillside model and the snowfall information; determine the avalanche level according to the stress distribution and snow distribution change of the convex hillside.

[0129] Optionally, the geological disaster monitoring and early warning system 300 further includes a level adjustment module 307, which is specifically configured to: analyze the hillside type and the slope size to determine the avalanche direction; obtain the human activity area, analyze the avalanche direction and the human activity area to determine the impact degree of the avalanche on humans; determine the time node when the avalanche occurs according to the snowmelt and snowfall cycle; adjust the avalanche level according to the time node, the impact degree, and the avalanche level.

[0130] The system of this embodiment can be used to execute the method of any of the above embodiments, and its implementation principle and technical effects are similar, which will not be elaborated here.

Claims

1. A geological disaster monitoring and early warning method, characterized in that, Including: Obtain the monitoring information of the snow mountain, analyze the monitoring information, and determine the environmental information set and the device status; Analyze the monitoring information to obtain the terrain information, environmental temperature, snowfall information, current snowmelt information, and infrasound information; Analyze the monitoring information, the environmental information set, and the current snowmelt information to determine the continuous snowmelt information; Determine the avalanche risk based on the continuous snowmelt information, the snowfall information, and the infrasound information; Determine the avalanche level based on the terrain information, the continuous snowmelt information, and the snowfall information; Determine the avalanche signal based on the device status, the avalanche risk, and the avalanche level, and send the avalanche signal using an error detection mechanism.

2. The method according to claim 1, wherein The analysis of the monitoring information to determine the environmental information set includes: Obtain the weather forecast information of the snow mountain location, analyze the weather forecast information, and determine the cloud information, light information, wind speed information, and humidity information; Analyze the light information and the cloud information to determine the light intensity information; Determine the cloud information, the light intensity information, the wind speed information, and the humidity information as the environmental information set.

3. The method according to claim 1, characterized in that, The analysis of the monitoring information to determine the device status includes: Determine the device information based on the monitoring information; Determine the device operating voltage based on the device information, and determine the power supply status based on the device operating voltage; Determine the device type based on the device information, and screen out the image-type sensors based on the device type; Determine the image content outside the device based on the image-type sensor; Determine the environmental status outside the device based on the image content; Determine the device status based on the power supply status and the environmental status.

4. The method according to claim 2, wherein The analysis of the monitoring information, the environmental information set, and the current snowmelt information to determine the continuous snowmelt information includes: Determine the snow mountain location information and the time information based on the monitoring information; Determine the light azimuth change information based on the time information; Determine the environmental temperature based on the environmental information set; Analyze the snow mountain location information, the light azimuth change information, the light intensity information, and the time information to determine the snow mountain light information; Analyze the environmental temperature and the snow mountain light information to determine the temperature change trend; Analyze the temperature change trend based on the current snowmelt information and the environmental information set to determine the continuous snowmelt information.

5. The method according to claim 4, characterized in that, The analysis of the environmental temperature and the snow mountain light information to determine the temperature change trend includes: Analyze the snow mountain light information to determine the snow mountain light time information and the snow mountain light intensity; Obtain the light time and the non-light time of the snow mountain based on the snow mountain light time information; Determine the temperature increase trend based on the snow mountain light intensity, the environmental temperature, and the light time; Determine the temperature after temperature increase when entering the non-light time based on the temperature increase trend and the light time; Obtain the overall temperature information based on the weather forecast information; Determine the temperature decrease trend based on the non-light time, the temperature after temperature increase, and the overall temperature information; Combine the temperature increase trend and the temperature decrease trend to determine the temperature change trend.

6. The method according to claim 5, wherein Analyzing the temperature change trend based on the current snowmelt amount information and the environmental information set to determine the continuous snowmelt amount information, including: Analyzing the temperature change trend, dividing the temperature range, and determining the division result; Determining the above-zero temperature range information according to the division result; Determining the start time and end time of the above-zero temperature range according to the above-zero temperature range information; When the temperature in the temperature change trend is greater than zero degree, analyzing the humidity information and the wind speed information to obtain the humidity change trend and the wind speed change trend; Constructing a temperature change curve, a humidity change curve, and a wind speed change curve in the above-zero temperature range according to the temperature change trend, the humidity change trend, and the wind speed change trend; Determining the continuous snowmelt amount according to the temperature change curve, the humidity change curve, and the wind speed change curve, referring to the following formula: Wherein, M represents the continuous snowmelt amount, t represents the preset analysis time, t1 represents the start time of the above-zero temperature range, t2 represents the end time of the above-zero temperature range, T(t) represents the temperature change curve, v(t) represents the wind speed change curve, H(t) represents the humidity change curve, k represents the preset temperature change experience coefficient, a represents the preset wind speed change experience coefficient, and b represents the preset humidity change experience coefficient.

7. The method according to claim 6, wherein Determining the avalanche risk according to the continuous snowmelt amount information, the snowfall amount information, and the infrasound information, including: Obtaining the continuous snowmelt amount, the snowfall amount, and the snowmelt-snowfall cycle according to the continuous snowmelt amount information and the snowfall amount information; Determining the infrasound intensity according to the infrasound information; Determining the avalanche risk according to the continuous snowmelt amount, the snowfall amount, the snowmelt-snowfall cycle, and the infrasound intensity, referring to the following formula: Wherein, R represents the avalanche risk, M represents the continuous snowmelt amount, S represents the snowfall amount, P represents the snowmelt-snowfall cycle, I represents the infrasound intensity, w1 represents the preset continuous snowmelt amount weight coefficient, w2 represents the preset snowfall amount weight coefficient, and w3 represents the preset infrasound intensity weight coefficient.

8. The method according to claim 7, wherein Determining the avalanche grade according to the terrain information, the continuous snowmelt amount information, and the snowfall amount information, including: Determining the slope type and the slope size according to the terrain information; When the slope type is a concave slope, obtaining the snow depth information according to the monitoring information; Determining the change in the snow depth on the concave slope according to the snowfall amount information, the snow depth information, and the continuous snowmelt amount information; Determining the snow water content according to the continuous snowmelt amount information; Dividing the avalanche type according to the snow water content; Determining the avalanche grade according to the change in the snow depth and the avalanche type; When the slope type is a convex slope, obtaining the slope image information according to the monitoring information; Analyzing the slope image information and the slope size to construct a slope convex surface model; Determining the stress distribution on the slope convex surface and the change in the snow distribution according to the slope convex surface model and the snowfall amount information; Determine the avalanche level according to the stress distribution on the convex surface of the hillside and the change in snow accumulation distribution.

9. The method according to claim 8, wherein The determination of the avalanche level according to the terrain information, the continuous snowmelt amount information, and the snowfall amount information further includes: Analyze the hillside type and the slope size to determine the avalanche direction; Obtain the human activity area, analyze the avalanche direction and the human activity area, and determine the impact degree of the avalanche on humans; Determine the time node when the avalanche occurs according to the snowmelt and snowfall cycle; Adjust the avalanche level according to the time node, the impact degree, and the avalanche level.

10. A geological disaster monitoring and early warning system, characterized in that, It includes: An information acquisition module for acquiring the monitoring information of the snow mountain, analyzing the monitoring information, and determining the environmental information set and the equipment status; An information analysis module for analyzing the monitoring information to obtain terrain information, environmental temperature, snowfall amount information, current snowmelt amount information, and infrasound information; A snowmelt amount determination module for analyzing the monitoring information, the environmental information set, and the current snowmelt amount information to determine the continuous snowmelt amount information; A risk determination module for determining the avalanche risk according to the continuous snowmelt amount information, the snowfall amount information, and the infrasound information; A level determination module for determining the avalanche level according to the terrain information, the continuous snowmelt amount information, and the snowfall amount information; A signal sending module for determining the avalanche signal according to the equipment status, the avalanche risk, and the avalanche level, and sending the avalanche signal using an error detection mechanism.

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