Geophysical feature-based geological disaster early warning system and method

Abstract

The invention relates to the technical field of geological disaster early warning systems, in particular to a geological disaster early warning system and method based on geophysical characteristics, and the system comprises a data collection module which is used for obtaining basic attribute data of a target object; the data storage module is used for storing the basic attribute data; the data extraction module is used for acquiring basic attribute data of each time period corresponding to the target object; a data processing module; the monitoring value reading module is used for reading geological disaster data of the monitoring object in the time range corresponding to the target object; the first acquisition module is used for generating first estimation information of the target object according to the geomechanical model of the target object and the geological disaster data of the monitoring object in the time range corresponding to the target object; and the early warning generation module not only considers traditional monitoring indexes, but also introduces a geomechanical model, so that the early warning accuracy is improved fundamentally.

Description

Technical Field

[0001] The present invention relates to the technical field of geological disaster early warning systems, in particular to a geological disaster early warning system and method based on geophysical characteristics. Background Art

[0002] In recent years, with the impact of global climate change and human activities, the frequency and intensity of geological disasters have shown an upward trend. This situation has put forward higher requirements for geological disaster early warning systems. Traditional geological disaster early warning methods mainly rely on the threshold trigger mechanism of a single or a few indicators, such as rainfall, surface displacement, etc. However, this method has many shortcomings in practical applications.

[0003] First, a single indicator cannot fully reflect the complexity of the geological environment. The formation of geological disasters is the result of the combined effects of multiple factors, and relying on only one or two indicators often cannot accurately assess the risk. For example, warning of landslide disasters based only on rainfall may ignore the influence of important factors such as geological structure and terrain characteristics, resulting in insufficient accuracy of warnings.

[0004] Secondly, traditional methods lack dynamic adjustment capabilities. The geological environment is changing dynamically, and fixed thresholds are difficult to adapt to such changes. For example, in a region, the same rainfall may lead to completely different consequences under different geological conditions. Fixed thresholds cannot flexibly respond to such situations and are prone to false positives or false negatives.

[0005] Furthermore, existing technologies are insufficient in processing and integrating multi-source data. Geological disaster warning requires comprehensive consideration of data from multiple fields such as geology, hydrology, and meteorology, but traditional systems have limited capabilities in data fusion and rapid processing, making it difficult to use this information in a timely and effective manner.

[0006] In addition, existing early warning systems often lack in-depth analysis of historical data and similar cases. Geological disasters often have a certain degree of repetitiveness and similarity, but existing technologies have failed to make full use of this feature to improve the accuracy of early warning.

[0007] Finally, traditional methods are also insufficient in terms of the timeliness of early warning. Due to limited data processing and analysis capabilities, the time from the appearance of abnormal data to the generation of early warning signals is often long, which may have serious consequences when facing sudden geological disasters.

[0008] In view of the above problems, there is an urgent need for a geological disaster early warning system that can comprehensively consider multiple factors, has dynamic adjustment capabilities, can quickly process multi-source data, make full use of historical experience, and can generate early warning signals in a timely manner. The present invention is a solution to these technical problems. Summary of the invention

[0009] The geological disaster early warning system and method based on geophysical characteristics proposed in the present invention realizes comprehensive, dynamic and rapid early warning of geological disasters through the collaborative work of multiple modules. The system includes a data acquisition module, a data storage module, a data extraction module, a data processing module, a monitoring value reading module, a first acquisition module and an early warning generation module, etc. The organic combination of these modules solves many problems existing in the prior art.

[0010] The present invention proposes a geological disaster early warning system based on geophysical characteristics, the system comprising:

[0011] Data collection module, used to obtain basic attribute data of the target object;

[0012] A data storage module, used for storing the basic attribute data;

[0013] A data extraction module is used to obtain basic attribute data of each time period corresponding to the target object;

[0014] A data processing module is used to analyze the geomechanical model corresponding to the target object according to the basic attribute data corresponding to the target object;

[0015] A monitoring value reading module, used to read the geological disaster data of the monitoring object within the time range corresponding to the target object, wherein the geological disaster data includes the geological disaster frequency, geological disaster degree and geological disaster intensity corresponding to the monitoring object;

[0016] A first acquisition module is used to generate first estimated information of the target object according to the geomechanical model of the target object and the geological disaster data of the monitoring object within the time range corresponding to the target object; and

[0017] The early warning generation module is used to generate a first reminder signal according to the first estimated information of the target object and send the first reminder signal, wherein the first reminder signal is used to remind monitoring personnel to monitor the geological disaster data of the target object.

[0018] Preferably, the data processing module is further used to obtain the geological disaster information corresponding to the target object according to the geomechanical model corresponding to the target object and the geological disaster data of the monitoring object within the time range corresponding to the target object;

[0019] The monitoring value reading module is also used to read the geological disaster monitoring and early warning parameters corresponding to the target object;

[0020] The first acquisition module is also used to generate second estimated information of the target object according to the geological disaster information of the target object and the geological disaster monitoring and early warning parameters of the target object;

[0021] The warning generation module is also used to generate a second reminder signal according to the second estimated information of the target object, wherein the second reminder signal is used to remind the monitoring personnel that the target object has a geological disaster omen. Preferably, the system also includes:

[0022] A second acquisition module is used to acquire second disaster occurrence information corresponding to the target object according to the second estimated information of the target object, wherein the second disaster occurrence information includes monitoring object information of geological disasters similar to the target object, second disaster occurrence frequency, and second disaster occurrence time period information;

[0023] A third acquisition module, used for acquiring third estimated information corresponding to the target object according to the second disaster occurrence information corresponding to the target object;

[0024] Among them, the warning generation module is also used to generate a third reminder signal according to the third estimated information corresponding to the target object, and the third reminder signal is used to generate the warning degree classification and warning level information of the geological disaster corresponding to the third estimated information according to the third estimated information corresponding to the target object.

[0025] Preferably, the second acquisition module is further used to determine at least one monitoring object that produces similar geological disasters according to the second estimated information of the target object;

[0026] The data extraction module is also used to extract the geomechanical models of the target object and at least one monitored object that produces similar geological hazards, and generate a geomechanical model list;

[0027] The second acquisition module is also used to acquire the geological disaster data of each monitoring object in the geomechanical model list;

[0028] The second acquisition module is also used to obtain the geological disaster data of each monitored object in the geomechanical model list of the target object by querying the geomechanical model list of each monitored object according to the indicator parameters of the target object.

[0029] Preferably, the first acquisition module is also used to calculate the geological disaster risk index of the target object according to the following formula:

[0030] RI=∑(W i ·F i H i )

[0031] Among them, RI is the geological disaster risk index, W i is the weight coefficient of the i-th geological disaster type, F i is the occurrence frequency of the i-th geological disaster, H i is the degree of hazard of the i-th geological disaster; the weight coefficient W iDetermined based on historical data and expert evaluation, and meeting ΣW i =1; the occurrence frequency F i It is obtained by statistical historical data, and F i ∈[0,1]; the degree of harm H i According to the possible loss caused by geological disasters, H i ∈[0, 1];

[0032] Subsequently, the first acquisition module generates first estimation information according to the calculated geological disaster risk index RI, combined with the geological force coefficient and geological hazard coefficient of the target object, the geological force data and geological hazard data of the target object.

[0033] Preferably, the warning generation module is further used to calculate the warning level according to the following formula:

[0034] WL=α·RI+β·SI+γ·TI

[0035] Among them, WL is the warning level, α, β, γ are weight coefficients and α+β+γ=1, RI is the geological hazard risk index, SI is the geological sensitivity index, and TI is the trigger factor index;

[0036] The geological sensitivity index SI is calculated by evaluating geological structure and terrain characteristics, and SI∈[0,1];

[0037] The trigger factor index TI considers external factors such as rainfall and seismic activity, and TI∈[0,1];

[0038] Subsequently, the warning generation module generates and sends a corresponding reminder signal based on the calculated warning level WL in combination with the first estimated information, the second estimated information and the third estimated information.

[0039] A geological disaster early warning method based on geophysical characteristics, the method comprising the following steps:

[0040] Get the basic attribute data of the target object;

[0041] Saving the basic attribute data;

[0042] Read the basic attribute data of the target object for each time period;

[0043] Analyze the geomechanical model corresponding to the target object according to the basic attribute data corresponding to the target object;

[0044] Reading geological disaster data of the monitored object within a time range corresponding to the target object, wherein the geological disaster data includes the geological disaster frequency, the geological disaster degree and the geological disaster intensity corresponding to the monitored object; generating first estimated information of the target object according to the geomechanical model of the target object and the geological disaster data of the monitored object within the time range corresponding to the target object;

[0045] A first reminder signal is generated according to first estimated information of the target object, and the first reminder signal is sent, wherein the first reminder signal is used to remind monitoring personnel to monitor the geological disaster data of the target object.

[0046] Preferably, the method further comprises the following steps:

[0047] According to the geomechanical model corresponding to the target object and the geological disaster data of the monitored object within the time range corresponding to the target object, the geological disaster information corresponding to the target object is obtained;

[0048] Read the geological disaster monitoring and early warning parameters corresponding to the target object;

[0049] Generating second estimated information of the target object according to the geological disaster information of the target object and the geological disaster monitoring and early warning parameters of the target object;

[0050] A second reminder signal is generated according to the second estimated information of the target object, wherein the second reminder signal is used to remind monitoring personnel that there are signs of geological disasters in the target object.

[0051] Preferably, the method further comprises the following steps:

[0052] According to the second estimated information of the target object, second disaster occurrence information corresponding to the target object is obtained, wherein the second disaster occurrence information includes monitoring object information of geological disasters similar to the target object, second disaster occurrence frequency, and second disaster occurrence time period information;

[0053] Acquire third estimated information corresponding to the target object according to the second disaster occurrence information corresponding to the target object;

[0054] A third reminder signal is generated according to the third estimated information corresponding to the target object, and the third reminder signal is used to generate the warning degree classification and warning level information of the geological disaster corresponding to the third estimated information according to the third estimated information corresponding to the target object.

[0055] Preferably, the method further comprises the following steps:

[0056] The method further comprises the following steps:

[0057] The geological disaster risk index of the target object is calculated according to the following formula:

[0058] RI=∑(W i ·F i ·H i )

[0059] Among them, RI is the geological disaster risk index, W i is the weight coefficient of the i-th geological disaster type, F i is the occurrence frequency of the i-th geological disaster, H i is the hazard level of the i-th geological disaster;

[0060] The weight coefficient W i Determined based on historical data and expert evaluation, and meeting ΣW i =1;

[0061] The occurrence frequency F i It is obtained by statistical historical data, and F i ∈[0, 1];

[0062] The hazard level H i According to the possible loss caused by geological disasters, H i ∈[0, 1]; the warning level is calculated according to the following formula:

[0063] WL=α·RI+β·SI+γ·TI

[0064] Among them, WL is the warning level, α, β, γ are weight coefficients and α+β+γ=1, RI is the geological hazard risk index, SI is the geological sensitivity index, and TI is the trigger factor index;

[0065] The geological sensitivity index SI is calculated by evaluating geological structure and terrain characteristics, and SI∈[0,1];

[0066] The trigger factor index TI takes into account external factors such as rainfall and seismic activity, and TI∈[0,1]

[0067] According to the calculated geological disaster risk index RI and warning level WL, combined with the first estimated information, the second estimated information and the third estimated information, a corresponding reminder signal is generated and sent.

[0068] The beneficial effects of the present invention are mainly reflected in the following aspects:

[0069] First, the present invention realizes the comprehensive analysis of multi-dimensional data through the collaborative work of the data acquisition module and the data processing module. The system not only considers the traditional monitoring indicators, but also introduces the geomechanical model, which fundamentally improves the accuracy of early warning. This multi-dimensional analysis method effectively solves the problem that a single indicator is difficult to fully reflect the complexity of the geological environment.

[0070] Secondly, the data processing module and the first acquisition module of the present invention jointly realize dynamic risk assessment. By continuously updating and analyzing real-time data, the system can adjust the risk assessment results in a timely manner, effectively overcoming the limitations of the traditional fixed threshold method. This dynamic adjustment capability enables the system to better adapt to different geological conditions and environmental changes.

[0071] Furthermore, the information transfer and processing mechanism between the modules of the present invention, especially the design of the data extraction module and the monitoring value reading module, greatly improves the system's ability to process and integrate multi-source data. This not only improves the comprehensiveness of the early warning, but also significantly improves the system's response speed.

[0072] In addition, the present invention innovatively introduces a similar case analysis mechanism. Through the design of the second acquisition module and the third acquisition module, the system can automatically identify and analyze historical cases similar to the target object, thereby more accurately assessing potential risks. This method makes full use of the repeatability and similarity characteristics of geological disasters and effectively improves the accuracy of early warning.

[0073] Finally, the present invention has made innovations in algorithm design and introduced calculation formulas for geological hazard risk index (RI) and warning level (WL). These algorithms not only take into account multiple influencing factors, but also achieve flexible adjustment through the setting of weight coefficients. This algorithm mechanism solves the problem that the influence of different factors in traditional methods is difficult to quantify, making the warning results more objective and reliable.

[0074] In general, the present invention effectively solves the problems existing in the prior art through the synergy between the modules of the system, the innovative design of the algorithm mechanism, and the comprehensive utilization of multi-dimensional data. It not only improves the accuracy and timeliness of geological disaster warning, but also enhances the adaptability and reliability of the system. This comprehensive technological innovation provides a more powerful tool for geological disaster prevention and control, and has important practical significance for protecting people's lives and property and reducing the losses caused by geological disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 It is the overall logic block diagram of the system of the present invention.

[0076] Figure 2 This is the logic flow of the data processing module of the present invention.

[0077] Figure 3 This is the logic flow of the first acquisition module of the present invention.

[0078] Figure 4 This is the logical flow of the early warning generation module of the present invention.

[0079] Figure 5This is the logical flow of the monitoring value reading module of the present invention.

[0080] Figure 6 This is the logical flow of the data extraction module of the present invention. DETAILED DESCRIPTION

[0081] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects thereof are described in detail below in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0082] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0083] Example 1

[0084] See also Figure 1-6 First, the present invention proposes a geological disaster early warning system based on geophysical characteristics. The system includes a data acquisition module 1, a data storage module 2, a data extraction module 3, a data processing module 4, a monitoring value reading module 5, a first acquisition module 6 and an early warning generation module 7. These modules work together to form a complete geological disaster early warning system.

[0085] Specifically, the data acquisition module 1 is used to obtain basic attribute data of the target object. In one embodiment of the present invention, the target object may be a specific geological area, such as a hillside or a river bank. The basic attribute data may include but is not limited to information such as the geological structure, rock and soil characteristics, topography and landforms of the area. These data provide a basis for subsequent analysis.

[0086] Next, the data storage module 2 is responsible for storing these basic attribute data. Preferably, the data storage module 2 can adopt a distributed storage system to ensure the security and reliability of the data. This can not only improve the access efficiency of the data, but also ensure the integrity of the data when a storage node fails.

[0087] Then, the data extraction module 3 obtains the basic attribute data of each time period corresponding to the target object. The time period here can be hourly, daily or weekly, depending on the monitoring needs and the characteristics of the target object. For example, for a hillside where landslides may occur, we may need to obtain data once an hour; while for a relatively stable area, obtaining data once a day or a week may be sufficient.

[0088] Secondly, the data processing module 4 analyzes the geomechanical model corresponding to the target object according to the basic attribute data corresponding to the target object. This step is one of the cores of the entire system. The geomechanical model can help us understand the mechanical properties of the target object and predict its behavior under different conditions. For example, for a hillside, we can establish a mechanical model that includes factors such as slope, rock and soil properties, and groundwater level to predict its stability.

[0089] Again, the monitoring value reading module 5 is responsible for reading the geological disaster data of the monitoring object within the time range corresponding to the target object. These data include the frequency, degree and intensity of geological disasters corresponding to the monitoring object. For example, for an area where landslides may occur, we may monitor indicators such as soil displacement, groundwater level changes, and rainfall. The frequency of these data may be hourly or daily, the degree may be expressed in displacement or water level change, and the intensity may be measured by the potential impact range or the possible loss.

[0090] Next, the first acquisition module 6 generates the first estimated information of the target object based on the geomechanical model of the target object and the geological hazard data of the monitored object within the time range corresponding to the target object. This step combines the model prediction with the actual monitoring data to obtain a preliminary risk assessment. For example, if the model predicts that a slope may be unstable under certain conditions, and the monitoring data shows that these conditions are forming, then the system will generate a first estimated information with a higher risk.

[0091] Finally, the early warning generation module 7 generates a first reminder signal according to the first estimated information of the target object, and sends the signal. This signal is used to remind the monitoring personnel to pay attention to the geological disaster data of the target object. In practical applications, this signal may be an email, a text message or an alarm on the monitoring system.

[0092] In one embodiment of the present invention, the data processing module 4 can also obtain the geological disaster information corresponding to the target object according to the geomechanical model corresponding to the target object and the geological disaster data of the monitoring object within the time range corresponding to the target object. This information is more detailed and specific than the first estimated information, and may include the type, scope, intensity, etc. of possible geological disasters.

[0093] At the same time, the monitoring value reading module 5 can also read the geological disaster monitoring and early warning parameters corresponding to the target object. These parameters may include various thresholds, such as the warning value of soil displacement rate, the warning value of rainfall, etc. These parameters are usually set by experts based on experience and historical data. For example, for a slope where landslide may occur, the warning value of soil displacement rate may be set to 5mm / day.

[0094] Then, the first acquisition module 6 can also generate second estimated information of the target object according to the geological disaster information of the target object and the geological disaster monitoring and early warning parameters of the target object. This second estimated information is more accurate than the first estimated information because it considers more factors and more specific early warning parameters.

[0095] Based on this, the early warning generation module 7 can also generate a second reminder signal according to the second estimated information of the target object. This signal is used to remind the monitoring personnel that the target object has a sign of geological disaster. Compared with the first reminder signal, the second reminder signal may have a higher degree of urgency and may require the monitoring personnel to take more active preventive measures.

[0096] In another embodiment of the present invention, the system further includes a second acquisition module 8 and a third acquisition module 9. The second acquisition module 8 is used to acquire the second disaster occurrence information corresponding to the target object based on the second estimated information of the target object. This information includes the monitoring object information of similar geological disasters as the target object, the frequency of the second disaster occurrence, and the time period of the second disaster occurrence. For example, if the target object is a slope where a landslide may occur, the system will search for similar slopes where landslides have occurred in history, and analyze the frequency and time characteristics of these landslides.

[0097] The third acquisition module 9 acquires third estimated information corresponding to the target object according to the second disaster occurrence information corresponding to the target object. This estimated information comprehensively considers the characteristics of the target object itself and the historical data of similar objects, and therefore may be more accurate.

[0098] Based on the third estimated information, the warning generation module 7 can also generate a third reminder signal. This signal is used to generate the warning level classification and warning level information of the geological disaster corresponding to the third estimated information according to the third estimated information corresponding to the target object. For example, the warning level may be divided into three levels: low, medium, and high, and each level corresponds to different response measures.

[0099] In another embodiment of the present invention, the second acquisition module 8 can also determine at least one monitoring object that produces similar geological disasters based on the second estimated information of the target object. This method can help us better understand the risks that the target object may face. For example, if we find that the characteristics of a slope are similar to those of a slope where a large-scale landslide has occurred in history, we need to be particularly vigilant.

[0100] At the same time, the data extraction module 3 can also extract the geomechanical model of the target object and at least one monitoring object that produces similar geological hazards, and generate a geomechanical model list. This list can help us compare the characteristics of different objects more systematically.

[0101] The second acquisition module 8 can also obtain the geological hazard data of each monitored object in the geomechanical model list, and obtain the geological hazard data of each monitored object in the geomechanical model list of the target object by querying the geomechanical model list of each monitored object according to the indicator parameters of the target object. This method can help us assess the risk of the target object more accurately.

[0102] An innovative point of the present invention is that the first acquisition module 6 can also calculate the geological disaster risk index of the target object according to the following formula:

[0103] RI=Σ(W i ·F i ·H i )

[0104] Among them, RI is the geological disaster risk index, W i is the weight coefficient of the i-th geological disaster type, F i is the occurrence frequency of the i-th geological disaster, H i is the degree of hazard of the i-th geological disaster; the weight coefficient W i Determined based on historical data and expert evaluation, and meeting ΣW i =1; the occurrence frequency F i It is obtained by statistical historical data, and F i ∈[0,1]; the degree of harm H i According to the possible loss caused by geological disasters, H i ∈[0, 1];

[0105] Subsequently, the first acquisition module generates first estimation information according to the calculated geological disaster risk index RI, combined with the geological force coefficient and geological hazard coefficient of the target object, the geological force data and geological hazard data of the target object.

[0106] Weight coefficient W i Determined based on historical data and expert evaluation, and meeting ∑W i = 1. For example, for an area where landslides and debris flows are likely to occur, we might assign a weight of 0.6 to landslides and a weight of 0.4 to debris flows.

[0107] Frequency of occurrence F i It is obtained by statistical historical data, and F i ∈[0, 1]. For example, if a certain geological disaster has occurred 10 times in the past 100 years, then F i May be set to 0.1.

[0108] Hazard level i According to the possible loss caused by geological disasters, H i∈[0, 1]. For example, for a disaster that may cause heavy casualties and property losses, its H i value may be close to 1.

[0109] Subsequently, the first acquisition module 6 generates first prediction information based on the calculated geological disaster risk index RI, in combination with the geological force coefficient and geological hazard coefficient of the target object, and the geological force data and geological hazard data of the target object.

[0110] Another innovation point of the present invention is that the early warning generation module 7 can also calculate the early warning level according to the following formula:

[0111] WL = α·RI + β·SI + γ·TI

[0112] Where, WL is the early warning level, α, β, and γ are weight coefficients and α + β + γ = l, RI is the geological disaster risk index, SI is the geological sensitivity index, and TI is the trigger factor index;

[0113] The geological sensitivity index SI is calculated by evaluating geological structures, terrain features and other factors, and SI ∈ [0, 1];

[0114] The trigger factor index TI takes into account external factors such as rainfall and seismic activity, and TI ∈ [0, 1]

[0115] Subsequently, the early warning generation module generates a corresponding reminder signal and sends it according to the calculated early warning level WL, in combination with the first prediction information, the second prediction information and the third prediction information.

[0116] The geological sensitivity index SI is calculated by evaluating geological structures, terrain features and other factors, and SI ∈ [0, 1]. For example, for an area with loose rock structures and large slopes, its SI value may be relatively high.

[0117] The trigger factor index TI takes into account external factors such as rainfall and seismic activity, and TI ∈ [0, 1]. For example, during the heavy rain season or in earthquake-prone areas, the TI value may increase significantly.

[0118] The selection of the weight coefficients α, β, and γ needs to be determined according to specific circumstances. For example, in a high-incidence area where rainfall triggers geological disasters, we may assign a relatively high value to γ, such as 0.4, while α and β are each 0.3.

[0119] Subsequently, the early warning generation module 7 generates a corresponding reminder signal and sends it according to the calculated early warning level WL, in combination with the first prediction information, the second prediction information and the third prediction information. For example, when WL > 0.7, it may trigger a high-level alarm; when 0.4 < WL ≤ 0.7, it triggers a medium-level alarm; when WL ≤ 0.4, it triggers a low-level alarm.

[0120] The present invention also proposes a geological disaster early warning method based on geophysical characteristics. The steps of the method correspond one-to-one to the functional modules of the above system.

[0121] The method comprises the following steps:

[0122] Get the basic attribute data of the target object;

[0123] Saving the basic attribute data;

[0124] Read the basic attribute data of the target object for each time period;

[0125] Analyze the geomechanical model corresponding to the target object according to the basic attribute data corresponding to the target object;

[0126] Reading geological disaster data of the monitored object within a time range corresponding to the target object, wherein the geological disaster data includes the geological disaster frequency, the geological disaster degree and the geological disaster intensity corresponding to the monitored object; generating first estimated information of the target object according to the geomechanical model of the target object and the geological disaster data of the monitored object within the time range corresponding to the target object;

[0127] A first reminder signal is generated according to first estimated information of the target object, and the first reminder signal is sent, wherein the first reminder signal is used to remind monitoring personnel to monitor the geological disaster data of the target object.

[0128] The method further comprises the following steps:

[0129] According to the geomechanical model corresponding to the target object and the geological disaster data of the monitored object within the time range corresponding to the target object, the geological disaster information corresponding to the target object is obtained;

[0130] Read the geological disaster monitoring and early warning parameters corresponding to the target object;

[0131] Generating second estimated information of the target object according to the geological disaster information of the target object and the geological disaster monitoring and early warning parameters of the target object;

[0132] A second reminder signal is generated according to the second estimated information of the target object, wherein the second reminder signal is used to remind monitoring personnel that there are signs of geological disasters in the target object.

[0133] The method further comprises the following steps:

[0134] According to the second estimated information of the target object, second disaster occurrence information corresponding to the target object is obtained, wherein the second disaster occurrence information includes monitoring object information of geological disasters similar to the target object, second disaster occurrence frequency, and second disaster occurrence time period information;

[0135] Acquire third estimated information corresponding to the target object according to the second disaster occurrence information corresponding to the target object;

[0136] A third reminder signal is generated according to the third estimated information corresponding to the target object, and the third reminder signal is used to generate the warning degree classification and warning level information of the geological disaster corresponding to the third estimated information according to the third estimated information corresponding to the target object.

[0137] The method further comprises the following steps:

[0138] The geological disaster risk index of the target object is calculated according to the following formula:

[0139] RI=∑(W i ·F i H i )

[0140] Among them, RI is the geological disaster risk index, W i is the weight coefficient of the i-th geological disaster type, F i is the occurrence frequency of the i-th geological disaster, H i is the hazard level of the i-th geological disaster;

[0141] The weight coefficient W i Determined based on historical data and expert evaluation, and meeting ∑W i =1;

[0142] The occurrence frequency F i It is obtained by statistical historical data, and F i ∈[0, 1];

[0143] The hazard level H i According to the possible loss caused by geological disasters, H i ∈[0, 1]; the warning level is calculated according to the following formula:

[0144] WL=α·RI+β·SI+γ·TI

[0145] Among them, WK is the warning level, α, β, γ are weight coefficients and α+β+γ=1, RI is the geological hazard risk index, SI is the geological sensitivity index, and TI is the trigger factor index;

[0146] The geological sensitivity index SI is calculated by evaluating geological structure and terrain characteristics, and SI∈[0,1]

[0147] The trigger factor index TI considers external factors such as rainfall and seismic activity, and TI∈[0,1];

[0148] According to the calculated geological disaster risk index RI and warning level WL, combined with the first estimated information, the second estimated information and the third estimated information, a corresponding reminder signal is generated and sent.

[0149] The advantage of this method is that it takes into account a variety of factors, including historical data, real-time monitoring data, similar case analysis, etc., so that it can more accurately predict the occurrence of geological disasters.

[0150] In general, the geological disaster early warning system and method based on geophysical characteristics proposed in the present invention greatly improves the accuracy and timeliness of geological disaster early warning through multi-level data collection, analysis and early warning mechanisms. It not only takes into account the characteristics of the target object itself, but also refers to the historical data of similar objects, and introduces a quantitative risk assessment model, making the early warning results more objective and reliable. This system and method can effectively reduce casualties and property losses caused by geological disasters in practical applications, and is of great significance to the prevention and control of geological disasters.

[0151] In order to verify the superiority of the present invention, we designed a set of comparative experiments, using the geophysical characteristics-based geological disaster early warning system of the present invention (Example 1) and the traditional single indicator threshold early warning system (Comparative Example 1) for testing. The test site was selected in a mountainous area with a high incidence of geological disasters. The test time span was one year, covering different seasons and weather conditions.

[0152] Example 1 adopts the multi-level early warning mechanism proposed by the present invention, comprehensively considering the geomechanical model, historical data, real-time monitoring data and similar case analysis. Comparative Example 1 only relies on the threshold of a single indicator (such as rainfall) to trigger the early warning.

[0153] We selected the following key indicators for testing:

[0154] 1. Warning accuracy rate: the ratio of correct warnings to the total number of warnings.

[0155] 2. Underreporting rate: the proportion of disaster events that failed to receive warnings to the total number of disaster events.

[0156] 3. False alarm rate: the ratio of false alarms to the total number of alarms.

[0157] 4. Warning lead time: the time interval from the issuance of a warning to the actual occurrence of a disaster.

[0158] 5. System response time: the time from receiving abnormal data to issuing an early warning signal.

[0159] The detection method is as follows:

[0160] 1. Warning accuracy: record all warning signals issued by the system and compare them with actual geological disaster events.

[0161] 2. False negative rate: Count the actual geological disaster events that occurred and check whether there are any situations that have not been warned by the system.

[0162] 3. False alarm rate: Record all warning signals issued by the system to check whether there are any disasters that have not actually occurred.

[0163] 4. Warning lead time: record the time when the warning signal is issued and the time when the actual disaster occurs, and calculate the time difference.

[0164] 5. System response time: The system log records the time when abnormal data is received and the time when the warning signal is issued, and calculates the time difference.

[0165] After a year of testing, we obtained the following results:

[0166] index Example 1 Comparative Example 1 Early warning accuracy 92％ 75％ False negative rate 3％ 15％ False Positive Rate 8％ 25％ Average warning lead time 24 hours 6 hours Average system response time 5 minutes 15 minutes

[0167] It can be seen from the test results that the geological disaster early warning system based on geophysical characteristics of the present invention is significantly superior to the traditional single indicator threshold early warning system in all indicators.

[0168] First, in terms of early warning accuracy, the system of the present invention reached 92%, which is much higher than the 75% of the comparison system. This shows that the multi-level early warning mechanism of the present invention can more accurately identify potential geological disaster risks. The high accuracy rate can not only better protect the safety of life and property, but also improve the public's trust in the early warning system, thereby enhancing the effectiveness of disaster prevention and mitigation work.

[0169] Secondly, in terms of missed alarm rate and false alarm rate, the system of the present invention also shows obvious advantages. The low missed alarm rate (3%) means that the system can capture most geological disaster events, greatly reducing the risk of loss caused by untimely warning. At the same time, the low false alarm rate (8%) avoids unnecessary evacuation and panic, saves social resources, and improves the credibility of the early warning system.

[0170] Thirdly, the system of the present invention also shows a significant advantage in the warning lead time. The average warning lead time of 24 hours provides a sufficient time window for evacuation and disaster prevention preparations, while the comparison system only has a 6-hour lead time, which may lead to hasty and insufficient response measures. This advantage comes from the comprehensive analysis of multiple factors by the system of the present invention, which can identify potential risks earlier.

[0171] Finally, in terms of system response time, the system of the present invention can respond to abnormal data and issue an early warning in just 5 minutes, while the comparison system takes 15 minutes. This rapid response capability is particularly important in the face of sudden geological disasters and may save more lives.

[0172] These test results fully demonstrate the superiority of the system of the present invention. Its core advantages are:

[0173] 1. Multi-dimensional data analysis: By integrating geomechanical models, historical data, real-time monitoring data and similar case analysis, this system can more comprehensively assess geological hazard risks.

[0174] 2. Dynamic risk assessment: This system not only considers static geological conditions, but also updates risk assessments in real time to adapt to rapidly changing environmental factors.

[0175] 3. Intelligent early warning mechanism: By introducing the calculation formula of geological disaster risk index (RI) and warning level (WL), this system achieves more accurate and objective early warning judgment.

[0176] 4. Rapid response capability: Thanks to the modular design of the system and efficient data processing algorithms, the system can complete the entire process from data reception to warning generation in a very short time.

[0177] In summary, the geological disaster early warning system based on geophysical characteristics of the present invention has shown significant advantages in practical applications. It can not only improve the accuracy and timeliness of early warning, but also provide more valuable information for decision makers, so as to better guide disaster prevention and mitigation work. The promotion and application of this system will be of great significance to improving the ability to prevent and control geological disasters and protecting people's lives and property.

[0178] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A geological disaster early warning system based on geophysical characteristics, characterized in that: The system includes: a data acquisition module for acquiring basic attribute data of a target object; A data storage module, used for storing the basic attribute data; A data extraction module is used to obtain basic attribute data of each time period corresponding to the target object; A data processing module is used to analyze the geomechanical model corresponding to the target object according to the basic attribute data corresponding to the target object; A monitoring value reading module, used to read the geological disaster data of the monitoring object within the time range corresponding to the target object, wherein the geological disaster data includes the geological disaster frequency, geological disaster degree and geological disaster intensity corresponding to the monitoring object; A first acquisition module is used to generate first estimated information of the target object according to the geomechanical model of the target object and the geological disaster data of the monitoring object within the time range corresponding to the target object; and The early warning generation module is used to generate a first reminder signal according to the first estimated information of the target object and send the first reminder signal, wherein the first reminder signal is used to remind monitoring personnel to monitor the geological disaster data of the target object.

2. The geological disaster early warning system based on geophysical characteristics according to claim 1 is characterized in that: The data processing module is also used to obtain the geological disaster information corresponding to the target object according to the geomechanical model corresponding to the target object and the geological disaster data of the monitored object within the time range corresponding to the target object; the monitoring value reading module is also used to read the geological disaster monitoring and early warning parameters corresponding to the target object; The first acquisition module is also used to generate second estimated information of the target object according to the geological disaster information of the target object and the geological disaster monitoring and early warning parameters of the target object; The early warning generation module is also used to generate a second reminder signal according to the second estimated information of the target object, wherein the second reminder signal is used to remind the monitoring personnel that there are signs of geological disasters in the target object.

3. The geological disaster early warning system based on geophysical characteristics according to claim 2 is characterized in that: The system further comprises: A second acquisition module is used to acquire second disaster occurrence information corresponding to the target object according to the second estimated information of the target object, wherein the second disaster occurrence information includes monitoring object information of geological disasters similar to the target object, second disaster occurrence frequency, and second disaster occurrence time period information; A third acquisition module, used for acquiring third estimated information corresponding to the target object according to the second disaster occurrence information corresponding to the target object; Among them, the warning generation module is also used to generate a third reminder signal according to the third estimated information corresponding to the target object, and the third reminder signal is used to generate the warning degree classification and warning level information of the geological disaster corresponding to the third estimated information according to the third estimated information corresponding to the target object.

4. The geological disaster early warning system based on geophysical characteristics according to claim 3 is characterized in that: The second acquisition module is also used to determine at least one monitoring object that produces similar geological disasters based on the second estimated information of the target object; The data extraction module is also used to extract the geomechanical models of the target object and at least one monitored object that produces similar geological hazards, and generate a geomechanical model list; The second acquisition module is also used to acquire the geological disaster data of each monitoring object in the geomechanical model list; The second acquisition module is also used to obtain the geological disaster data of each monitored object in the geomechanical model list of the target object by querying the geomechanical model list of each monitored object according to the indicator parameters of the target object.

5. The geological disaster early warning system based on geophysical characteristics according to claim 4 is characterized in that: The first acquisition module is also used to calculate the geological disaster risk index of the target object according to the following formula: RI=∑(W i ·F i H i ) Among them, RI is the geological disaster risk index, W i is the weight coefficient of the i-th geological disaster type, F i is the occurrence frequency of the i-th geological disaster, H i is the degree of hazard of the i-th geological disaster; the weight coefficient W i Determined based on historical data and expert evaluation, and meeting ∑W i =1; the occurrence frequency F i It is obtained by statistical historical data, and F i ∈[0,1]; the degree of harm H i According to the possible loss caused by geological disasters, H i ∈[0, 1]; Subsequently, the first acquisition module generates first estimation information according to the calculated geological disaster risk index RI, in combination with the geological force coefficient and geological hazard coefficient of the target object, the geological force data and geological hazard data of the target object.

6. The geological disaster early warning system based on geophysical characteristics according to claim 5 is characterized in that: The warning generation module is also used to calculate the warning level according to the following formula: WL=α·RI+β·SI+γ·TI Among them, WL is the warning level, α, β, γ are weight coefficients and α+β+γ=1, RI is the geological hazard risk index, SI is the geological sensitivity index, and TI is the trigger factor index; The geological sensitivity index SI is calculated by evaluating geological structure and terrain characteristics, and SI∈[0,1]; The trigger factor index TI considers external factors such as rainfall and seismic activity, and TI∈[0,1]; Subsequently, the warning generation module generates and sends a corresponding reminder signal based on the calculated warning level WL in combination with the first estimated information, the second estimated information and the third estimated information.

7. A method for early warning of geological disasters based on geophysical characteristics according to the system of any one of claims 1 to 6, characterized in that: The method comprises the following steps: Get the basic attribute data of the target object; Saving the basic attribute data; Read the basic attribute data of the target object for each time period; Analyze the geomechanical model corresponding to the target object according to the basic attribute data corresponding to the target object; Reading geological disaster data of the monitored object within a time range corresponding to the target object, wherein the geological disaster data includes the geological disaster frequency, the geological disaster degree and the geological disaster intensity corresponding to the monitored object; generating first estimated information of the target object according to the geomechanical model of the target object and the geological disaster data of the monitored object within the time range corresponding to the target object; A first reminder signal is generated according to first estimated information of the target object, and the first reminder signal is sent, wherein the first reminder signal is used to remind monitoring personnel to monitor the geological disaster data of the target object.

8. The method for early warning of geological disasters based on geophysical characteristics according to claim 7, characterized in that: The method further comprises the following steps: According to the geomechanical model corresponding to the target object and the geological disaster data of the monitored object within the time range corresponding to the target object, the geological disaster information corresponding to the target object is obtained; Read the geological disaster monitoring and early warning parameters corresponding to the target object; Generating second estimated information of the target object according to the geological disaster information of the target object and the geological disaster monitoring and early warning parameters of the target object; A second reminder signal is generated according to the second estimated information of the target object, wherein the second reminder signal is used to remind monitoring personnel that there are signs of geological disasters in the target object.

9. The method for early warning of geological disasters based on geophysical characteristics according to claim 8, characterized in that: The method further comprises the following steps: According to the second estimated information of the target object, second disaster occurrence information corresponding to the target object is obtained, wherein the second disaster occurrence information includes monitoring object information of geological disasters similar to the target object, second disaster occurrence frequency, and second disaster occurrence time period information; According to the second disaster occurrence information corresponding to the target object, the third estimated information corresponding to the target object is obtained; according to the third estimated information corresponding to the target object, a third reminder signal is generated, and the third reminder signal is used to generate a warning degree classification and warning level information of the geological disaster corresponding to the third estimated information according to the third estimated information corresponding to the target object.

10. The method for early warning of geological disasters based on geophysical characteristics according to claim 9, characterized in that: The method further comprises the following steps: The geological disaster risk index of the target object is calculated according to the following formula: RI=∑(W i ·F i H i ) Among them, RI is the geological disaster risk index, W i is the weight coefficient of the i-th geological disaster type, F i is the occurrence frequency of the ith geological disaster, H i is the hazard level of the i-th geological disaster; The weight coefficient W i Determined based on historical data and expert evaluation, and meeting ∑W i =1; The occurrence frequency F i It is obtained by statistical historical data, and Fi∈[0,1]; The hazard level H i According to the possible loss caused by geological disasters, H i ∈[0, 1]; the warning level is calculated according to the following formula: WL=α·RI+β·SI+γ·TI Among them, WL is the warning level, α, β, γ are weight coefficients and α+β+γ=I, RI is the geological hazard risk index, SI is the geological sensitivity index, and TI is the trigger factor index; The geological sensitivity index SI is calculated by evaluating geological structure and terrain characteristics, and SI∈[0,1]; The trigger factor index TI considers external factors such as rainfall and seismic activity, and TI∈[0,1]; According to the calculated geological disaster risk index RI and warning level WL, combined with the first estimated information, the second estimated information and the third estimated information, a corresponding reminder signal is generated and sent.

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