Geological disaster monitoring and early warning method

Abstract

The invention discloses a geological disaster monitoring and early warning method, and relates to the technical field of geological disaster monitoring, and the method comprises the steps: collecting the soil water content and soil bulk density of a soil body, and inputting the data into a processing module; according to the method, factors such as moisture distribution in the soil, soil particle characteristics and interaction between the soil and water can be comprehensively considered, accurate quantification of the soil moisture condition is achieved, more reliable basic data is provided for landslide risk assessment, the phenomenon of early warning missing report caused by inaccurate soil moisture monitoring can be reduced, and the early warning effect is improved. The method considers a plurality of key influence factors such as rainfall intensity, a soil permeability coefficient and soil physical properties, so that the method can accurately predict the occurrence probability and the potential scale of the landslide, is helpful for decision makers to formulate more effective disaster prevention measures, can also give out a plurality of early warning alarms, and has a good application prospect. Therefore, decision makers can be reminded to take emergency measures to reduce disaster loss, and more intelligent and more efficient monitoring and early warning effects can be achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of geological disaster monitoring, and in particular to a geological disaster monitoring and early warning method. Background Art

[0002] Geological disasters refer to geological phenomena caused by natural factors or human activities that pose a threat to the safety of people's lives and property. Among them, landslides, as a common geological disaster, are characterized by suddenness and great destructive power. The occurrence of landslides is often closely related to multiple factors such as soil moisture content and physical properties. Therefore, accurate and timely monitoring and early warning of landslides are of great significance for reducing disaster losses and protecting people's lives and property.

[0003] However, the existing geological disaster monitoring and early warning methods may only focus on soil rainfall or soil surface moisture content, and may ignore the moisture distribution inside the soil and the physical properties of the soil, such as soil particle density and porosity changes, resulting in inaccurate assessment of soil effective saturation, which will affect the prediction and assessment of landslides. In addition, the existing monitoring and early warning methods may only focus on single and static parameters, ignoring the changing trend of parameters over time. Summary of the invention

[0004] The purpose of the present invention is to provide a geological disaster monitoring and early warning method to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a geological disaster monitoring and early warning method, comprising the following monitoring and early warning steps: Step I: collect the soil moisture content, soil bulk density and rainfall per unit time of the target soil through the acquisition module, and input the collected data into the processing module; Step II: Remove outliers, fill in missing values ​​and sort out soil moisture content, soil bulk density and rainfall per unit time through the processing module, and output soil moisture content SOPD, soil bulk density SOPA and rainfall intensity LPLA; Step III: Input soil water content SOPD, soil bulk density SOPA and rainfall intensity LPLA into the early warning analysis module, and the early warning analysis module outputs soil effective saturation SOP, landslide assessment index LPL and landslide early warning index WBG; Step IV: Input the effective soil saturation SOP, landslide assessment index LPL and landslide warning index WBG into the alarm response module. The alarm response module outputs green warning, yellow warning and red warning based on the landslide warning index WBG, and implements corresponding response measures.

[0006] Optionally, the early warning analysis module includes: a soil saturation submodule, a soil landslide assessment submodule and a landslide early warning analysis submodule.

[0007] Optionally, the calculation formula of the soil saturation submodule is as follows: ; in: SOP refers to soil effective saturation; SOPA refers to soil bulk density, SOPB refers to soil particle density, SOPC refers to water density, SOPD refers to soil water content, SOPS refers to soil porosity, A1 refers to adjustment factor, and FA refers to influencing parameter; The processing process of the soil saturation submodule is as follows: the soil water content SOPD and the soil bulk density SOPA are input into the soil landslide assessment submodule, and the soil effective saturation SOP is output based on the soil particle density SOPB and the water density SOPC.

[0008] Optionally, the calculation formula of the soil landslide assessment submodule is as follows: ; in: LPL refers to the Landslide Assessment Index; B1 refers to the weight adjustable coefficient of LPLA, B2 refers to the weight adjustable coefficient of LPLB, LPLA refers to rainfall intensity, LPLB refers to soil permeability coefficient, and α refers to the empirical coefficient; The processing process of the soil landslide assessment submodule is as follows: the soil effective saturation SOP and rainfall intensity LPLA are input into the landslide early warning analysis submodule, and the landslide assessment index LPL is output based on the soil permeability coefficient LPLB.

[0009] Optionally, the calculation formula of the landslide early warning analysis submodule is as follows: ; in: WBG refers to the landslide warning index; refers to the rate of change of effective soil saturation, C1 refers to adjustment parameter 1, C2 refers to adjustment parameter 2, C1 and C2 are used to adjust the sensitivity of the warning level, WBGA refers to soil shear strength; MBB refers to landslide scale factor, e refers to the base of natural logarithm; The processing process of the landslide early warning analysis submodule is as follows: the soil effective saturation SOP and the landslide assessment index LPL are input into the landslide early warning analysis submodule, and based on the soil effective saturation change rate Output landslide warning index WBG.

[0010] Optionally, the processing process of the alarm response module is as follows: Input the landslide warning index WBG into the alarm response module; When WBG is less than 0.3, it is at the green warning level, which is a low-level warning; At this time, the likelihood of a landslide is low, but vigilance is still required; When 0.3≤WBG<0.6, it is at the yellow warning level, which is a medium-level warning; At this time, there is a possibility of landslide, so preventive measures need to be taken; When WBG ≥ 0.6, it is at the red warning level, which is a high-level warning; At this time, the possibility of landslide is high and emergency measures need to be taken immediately.

[0011] Optionally, the alarm response module is specifically: When at green alert level; Alarm: Issue warning information via SMS, radio and television to alert the public to landslide risks; Measures: Strengthen daily monitoring, pay close attention to changes in rainfall, soil saturation, etc., inspect potential geological disaster sites, and promptly identify and report problems; When at yellow alert level; Alarm: On the basis of green warning, increase the release channels such as TV weather forecast column and electronic terminal display screen to ensure the wide dissemination of information; Measures: On-duty personnel are on duty for all-day emergency duty, and strengthen the monitoring and inspection of geological disaster risk points, especially real-time monitoring during rainfall, and then notify residents and enterprises and institutions in geological disaster-prone areas to suspend outdoor work and make emergency preparations; When at red alert level; Alarm: Emergency warning information is released through multiple channels, including radio, television, mobile phone text messages and electronic display screens to ensure that the information covers every corner; Measures: Immediately activate the geological disaster emergency plan, organize professional teams to conduct on-site verification and monitoring, urgently evacuate people in and around geological disaster-prone areas to ensure their safety, close relevant roads to prevent vehicles and pedestrians from entering dangerous areas, and organize rescue teams to assemble and be on standby to be ready to carry out emergency rescue work at any time.

[0012] Optionally, the acquisition module uses a soil moisture sensor, a densitometer, a soil bulk density meter, a rainfall intensity meter and a landslide monitoring device.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention outputs soil effective saturation through the soil saturation submodule. The soil effective saturation comprehensively considers multiple factors such as soil moisture content, soil bulk density, soil particle density and soil porosity, and can accurately reflect the impact of soil moisture content on soil stability. This submodule provides a comprehensive indicator to evaluate the moisture status of the soil. By continuously monitoring the changes in soil effective saturation, the abnormal increase in soil saturation can be discovered in time. The soil effective saturation makes the assessment of soil moisture status more accurate and comprehensive. The soil effective saturation considers multiple factors such as soil physical properties and moisture content, thereby truly reflecting the moisture status of the soil, thereby enabling geological disaster monitoring and early warning to more accurately capture abnormal changes in soil saturation, providing a more reliable basis for early warning of geological disasters such as landslides.

[0014] 2. The present invention outputs a landslide assessment index through a soil landslide assessment submodule. By calculating the landslide assessment index, the landslide risk of a certain area under specific rainfall conditions can be evaluated. When the landslide assessment index value is high, it means that the landslide risk in the area is relatively high and corresponding preventive measures need to be taken. Therefore, the landslide assessment index provides a more accurate and comprehensive scientific basis for geological disaster monitoring and early warning. The landslide assessment index combines multiple factors that affect the occurrence of landslides to form a comprehensive assessment index, so that geological disaster monitoring and early warning can more comprehensively evaluate landslide risks and improve the accuracy and reliability of early warnings. The calculation of the landslide assessment index takes into account external factors such as rainfall intensity, so that the early warning system can adapt to different climatic conditions more flexibly. The introduction of the landslide assessment index makes the assessment of landslide risks more comprehensive and accurate.

[0015] 3. The present invention outputs a landslide warning index through a landslide warning analysis submodule. The calculation of the landslide warning index further refines the landslide risk into different warning levels, namely low, medium and high, thereby more intuitively reflecting the severity of the landslide risk. By real-time monitoring and calculating the landslide warning index value, the trend of increasing landslide risk can be discovered in time and corresponding emergency response measures can be taken. This is of great significance for improving the accuracy and timeliness of geological disaster monitoring and early warning. At the same time, the calculation of the landslide warning index takes into account factors such as the change rate of soil shear strength, so that the early warning system can more accurately evaluate the stability of the soil.

[0016] 4. The present invention iterates the influencing parameters in the soil saturation submodule based on the landslide assessment index, so that the effective soil saturation and the landslide assessment index are continuously optimized in a cycle. This iterative method can realize real-time monitoring and dynamic adjustment. By continuously monitoring the changes in the geological environment and adjusting the influencing parameters in real time based on the landslide assessment index, it can be ensured that the early warning model is always synchronized with the current geological conditions, thereby improving the accuracy and timeliness of the early warning. By iteratively adjusting the influencing parameters, the calculation of the effective soil saturation can be indirectly affected, thereby accurately assessing the risk of landslides. By iteratively adjusting the influencing parameters, the early warning model can be better adapted to different geological and climatic conditions, thereby improving its wide applicability. In the landslide early warning, the soil moisture content and the effective soil saturation can be indirectly affected by real-time monitoring of the LPL value and adjusting the influencing parameters according to its changes, providing a more accurate and dynamic assessment for the landslide early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flowchart of the steps of the local geological disaster monitoring and early warning method; Figure 2 This is a schematic diagram of the overall structure of the local geological disaster monitoring and early warning method; Figure 3 This is a structural diagram of the early warning analysis module of the local geological disaster monitoring and early warning method. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Regarding this geological disaster monitoring and early warning method, it is different from the existing geological disaster monitoring and early warning methods. The existing geological disaster monitoring and early warning methods often only focus on a single monitoring parameter such as rainfall and soil moisture content, and may ignore the physical properties and permeability of the soil, which in turn leads to the accuracy and reliability of the early warning being limited. In addition, the existing soil landslide assessment may ignore the impact of soil particle density, porosity, etc. on landslide risk, as well as changes in dynamic factors such as rainfall intensity and soil permeability coefficient, which may lead to inaccurate soil landslide assessment and fail to fully reflect the size and changing trend of landslide risk.

[0020] The module of this geological disaster monitoring and early warning system method comprehensively considers the moisture distribution and dynamic changes inside the soil, realizes the accurate assessment of the soil moisture condition, and this method can accurately reflect the actual moisture condition of the soil, providing reliable basic data for the assessment of landslide risk. This method comprehensively considers multiple key influencing factors such as soil effective saturation, rainfall intensity, soil permeability coefficient, and the influence of soil physical properties on landslide risk, realizes the accurate calculation of the landslide potential index, and can also calculate the overall landslide warning index, and respond to multiple warning levels based on this index.

[0021] Example 1: Please refer to Figures 1 to 3 This implementation provides a geological disaster monitoring and early warning method, including the following monitoring and early warning steps: Step I: collect the soil moisture content, soil bulk density and rainfall per unit time of the target soil through the acquisition module, and input the collected data into the processing module; Step II: Remove outliers, fill in missing values ​​and sort out soil moisture content, soil bulk density and rainfall per unit time through the processing module, and output soil moisture content SOPD, soil bulk density SOPA and rainfall intensity LPLA; Step III: Input soil water content SOPD, soil bulk density SOPA and rainfall intensity LPLA into the early warning analysis module, and the early warning analysis module outputs soil effective saturation SOP, landslide assessment index LPL and landslide early warning index WBG; Step IV: Input the effective soil saturation SOP, the landslide assessment index LPL and the landslide warning index WBG into the alarm response module. The alarm response module outputs green warning, yellow warning and red warning based on the landslide warning index WBG, and implements corresponding response measures; The early warning analysis module includes: soil saturation submodule, soil landslide assessment submodule and landslide early warning analysis submodule.

[0022] In this embodiment: the effective soil saturation SOP, landslide assessment index LPL and landslide warning index WBG respectively calculated by the soil saturation submodule, soil landslide assessment submodule and landslide warning analysis submodule in this warning analysis module have the following functions; By calculating the effective soil saturation SOP, compared with the traditional soil moisture content monitoring method, SOP comprehensively considers multiple factors such as soil physical properties and moisture content, thereby more accurately reflecting the soil moisture status. This enables geological disaster monitoring and early warning to more accurately capture abnormal changes in soil saturation, improving the accuracy and reliability of early warning; Compared with the single factor evaluation method, the landslide assessment index LPL is calculated by combining multiple factors that affect the occurrence of landslides to form a comprehensive evaluation index. This progress enables geological disaster monitoring and early warning to more comprehensively evaluate landslide risks and improve the accuracy and comprehensiveness of early warnings. At the same time, the calculation of LPL also takes into account external factors such as rainfall intensity, so that the early warning system can adapt to different climatic conditions more flexibly. By calculating the landslide warning index WBG, compared with the traditional landslide warning method, WBG further refines the landslide risk into different warning levels and considers the comprehensive impact of multiple factors. This progress enables geological disaster monitoring and early warning to more timely discover the increasing trend of landslide risk and take corresponding emergency response measures. At the same time, the calculation of WBG also considers factors such as the change rate of soil shear strength, so that the early warning system can more accurately evaluate the stability of the soil. These substantial functions and characteristics make SOP, LPL and WBG have higher application value in geological disaster monitoring and early warning.

[0023] See also Figures 1 to 3 , the processing process of the soil saturation submodule is as follows: ; in: SOP refers to soil effective saturation; SOPA refers to soil bulk density, SOPB refers to soil particle density, SOPC refers to water density, SOPD refers to soil water content, SOPS refers to soil porosity, A1 refers to adjustment factor, and FA refers to influencing parameter; The processing process of the soil saturation submodule is as follows: the soil water content SOPD and soil bulk density SOPA are input into the soil landslide assessment submodule, and the soil effective saturation SOP is output based on the soil particle density SOPB and water density SOPC.

[0024] In this embodiment: the calculation of soil effective saturation SOP is a crucial first step in geological disaster monitoring and early warning. It comprehensively considers multiple factors such as soil moisture content, soil bulk density, soil particle density and soil porosity, thereby more accurately reflecting the impact of soil moisture content on soil stability. The contribution of this submodule is that it provides a quantitative indicator to evaluate the moisture status of the soil, which is of great significance for predicting the potential sliding risk of soil under the influence of external factors such as rainfall. By continuously monitoring the changes in SOP, the abnormal increase in soil saturation can be discovered in time, thereby providing early signals for early warning of geological disasters such as landslides. The introduction of SOP makes the assessment of soil moisture status more accurate and comprehensive. It considers multiple factors such as soil physical properties and moisture content, thereby more truly reflecting the moisture status of the soil. This progress enables geological disaster monitoring and early warning to more accurately capture abnormal changes in soil saturation, providing a more reliable basis for early warning of geological disasters such as landslides. By continuously monitoring the changes in SOP, the abnormal increase in soil saturation can be discovered in time, thereby providing early signals for early warning of landslides, which helps relevant departments to take preventive measures in time to reduce the occurrence and losses of landslide disasters.

[0025] See also Figures 1 to 3 , the processing process of the soil landslide assessment submodule is as follows: ; in: LPL refers to the Landslide Assessment Index; B1 refers to the weight adjustable coefficient of LPLA, B2 refers to the weight adjustable coefficient of LPLB, LPLA refers to rainfall intensity, LPLB refers to soil permeability coefficient, and α refers to the empirical coefficient; The processing process of the soil landslide assessment submodule is as follows: the soil effective saturation SOP and rainfall intensity LPLA are input into the landslide early warning analysis submodule, and the landslide assessment index LPL is output based on the soil permeability coefficient LPLB.

[0026] In this embodiment: the calculation of the landslide assessment index LPL is based on multiple parameters such as soil effective saturation, rainfall intensity and soil permeability coefficient. The contribution of this submodule is that it combines multiple factors that affect the occurrence of landslides to form a comprehensive assessment index. By calculating LPL, the landslide risk of a certain area under specific rainfall conditions can be quantitatively assessed. When the LPL value is high, it means that the landslide risk in the area is high and corresponding preventive measures need to be taken. Therefore, LPL provides a more accurate and comprehensive scientific basis for geological disaster monitoring and early warning. LPL combines multiple factors that affect the occurrence of landslides to form a comprehensive assessment index, which enables geological disaster monitoring and early warning to more comprehensively assess landslide risks and improve the accuracy and reliability of early warnings. At the same time, the calculation of LPL also takes into account external factors such as rainfall intensity, so that the early warning system can adapt to different climatic conditions more flexibly. The introduction of LPL makes the assessment of landslide risks more comprehensive and accurate. By calculating the LPL value, the landslide risk of a certain area under specific rainfall conditions can be quantitatively assessed, providing more reliable early warning information for relevant departments, which helps relevant departments to formulate disaster prevention and mitigation measures more scientifically and improve the ability to respond to landslide disasters.

[0027] It is worth noting that the influencing parameter FA in the soil saturation submodule is iterated based on the landslide assessment index LPL, so that the soil effective saturation SOP and the landslide assessment index LPL are continuously iterated and optimized, thereby improving the accuracy of the overall calculation and analysis. The processing process is as follows: First: FA new =FA old -SA×LPL; Second: Set the iteration termination condition: Termination condition 1: The number of iterations is 120; Termination Condition 2: |FA new -FA old |<0.005; in: FA new Refers to the updated impact parameters, FA old Reference parameters before update, SA reference influence factor, LPL-based FA old Further calculations are performed to obtain FA new , to FA new Replace FA in the soil saturation submodule to achieve the effect of cyclic iteration; In this embodiment: This iterative method can achieve real-time monitoring and dynamic adjustment. By continuously monitoring the changes in the geological environment and adjusting FA in real time based on the calculation results of LPL, it can ensure that the early warning model is always synchronized with the current geological conditions, thereby improving the accuracy and timeliness of the early warning. By iteratively adjusting FA, the calculation of the effective soil saturation SOP can be indirectly affected, thereby more accurately assessing the risk of landslides. This iterative mechanism can capture subtle changes in soil moisture conditions and incorporate them into the early warning model to improve the accuracy of early warnings. Different geological environments and climatic conditions may have different effects on the occurrence of landslides. By iteratively adjusting FA, the early warning model can be better adapted to different geological and climatic conditions to improve its wide applicability. This method provides decision makers with a more scientific and reliable basis. By real-time monitoring and dynamic adjustment of the early warning model, the probability of occurrence and degree of harm of landslides can be more accurately predicted, thereby helping decision makers to formulate more effective disaster prevention and mitigation measures. By early warning and taking necessary disaster prevention measures, landslides can be significantly reduced. This method not only protects people's lives and property, but also reduces the cost of post-disaster reconstruction and socio-economic recovery. This method combines advanced technologies such as real-time monitoring, data analysis, and iterative adjustment to achieve intelligent early warning of geological disasters such as landslides. Compared with traditional monitoring methods, this method has higher technical content and broader application prospects. This method is not only suitable for current geological and climatic conditions, but can also adapt to future changes through continuous optimization and iterative adjustment. Therefore, it has stronger sustainability and development potential. In landslide early warning, by real-time monitoring of the value of LPL and adjusting FA according to its changes to indirectly affect soil moisture content and SOP, it can provide a more accurate and dynamic assessment for landslide early warning. This method can capture small changes in soil moisture conditions and incorporate them into the early warning model, thereby improving the accuracy and timeliness of the early warning. In addition, by continuously adjusting and optimizing parameter FA, we can make the early warning model better adapt to different geological and climatic conditions and improve its wide applicability in landslide early warning.

[0028] See also Figures 1 to 3 ,The processing process of the landslide early warning analysis submodule is as follows: ; in: WBG refers to the landslide warning index; Refers to the change rate of effective soil saturation, C1 refers to adjustment parameter 1, C2 refers to adjustment parameter 2, C1 and C2 are used to adjust the sensitivity of the warning level, WBGA refers to soil shear strength; MBB refers to the landslide size factor, and MBB is determined based on geological surveys and historical landslide events; e refers to the base of natural logarithms; The processing process of the landslide early warning analysis submodule is as follows: the soil effective saturation SOP and the landslide assessment index LPL are input into the landslide early warning analysis submodule, and based on the soil effective saturation change rate Output landslide warning index WBG; Input the landslide warning index WBG into the alarm response module; When WBG is less than 0.3, it is at the green warning level, which is a low-level warning; At this time, the likelihood of a landslide is low, but vigilance is still required; When at green alert level; Alarm: Issue warning information via SMS, radio and television to alert the public to landslide risks; Measures: Strengthen daily monitoring, pay close attention to changes in rainfall, soil saturation, etc., inspect potential geological disaster sites, and promptly identify and report problems; When 0.3≤WBG<0.6, it is at the yellow warning level, which is a medium-level warning; At this time, there is a possibility of landslide, so preventive measures need to be taken; When at yellow alert level; Alarm: On the basis of green warning, increase the release channels such as TV weather forecast column and electronic terminal display screen to ensure the wide dissemination of information; Measures: On-duty personnel are on duty for all-day emergency duty, and strengthen the monitoring and inspection of geological disaster risk points, especially real-time monitoring during rainfall, and then notify residents and enterprises and institutions in geological disaster-prone areas to suspend outdoor work and make emergency preparations; When WBG ≥ 0.6, it is at the red warning level, which is a high-level warning; At this point, the possibility of a landslide is high and emergency measures need to be taken immediately; When at red alert level; Alarm: Emergency warning information is released through multiple channels, including radio, television, mobile phone text messages and electronic display screens to ensure that the information covers every corner; Measures: Immediately activate the geological disaster emergency plan, organize professional teams to conduct on-site verification and monitoring, urgently evacuate people in and around geological disaster-prone areas to ensure their safety, close relevant roads to prevent vehicles and pedestrians from entering dangerous areas, and organize rescue teams to assemble and be on standby to be ready to carry out emergency rescue work at any time.

[0029] In this embodiment: the calculation of the landslide warning index WBG combines multiple factors such as the landslide assessment index, the soil effective saturation change rate and the soil shear strength change rate. The contribution of this submodule is that it further refines the landslide risk into different warning levels, such as low, medium and high, so as to more intuitively reflect the severity of the landslide risk. By real-time monitoring and calculating the WBG value, the increasing trend of landslide risk can be discovered in time, and corresponding emergency response measures can be taken. This is of great significance for improving the accuracy and timeliness of geological disaster monitoring and early warning. At the same time, the calculation of WBG also takes into account factors such as the soil shear strength change rate, so that the early warning system can more accurately evaluate the stability of the soil.

[0030] In the specific implementation process, multiple submodules in this method are used to form a geological disaster monitoring and early warning system. The soil moisture content SOPD and soil bulk density SOPA are input into the soil landslide assessment submodule, and the soil effective saturation SOP is output based on the soil particle density SOPB and the water density SOPC. SOP comprehensively considers multiple factors such as soil moisture content, soil bulk density, soil particle density and soil porosity, thereby accurately reflecting the influence of soil moisture content on soil stability. This submodule provides a quantitative indicator to evaluate the soil moisture status. By continuously monitoring the changes in SOP, the abnormal increase in soil saturation can be discovered in time, thereby providing an early signal for the early warning of geological disasters such as landslides. The introduction of SOP makes the assessment of soil moisture status more accurate and comprehensive. It considers multiple factors such as soil physical properties and moisture content, thereby more truly reflecting the soil moisture status. This progress enables geological disaster monitoring and early warning to more accurately capture abnormal changes in soil saturation, providing a more reliable basis for the early warning of geological disasters such as landslides. The soil effective saturation SOP and rainfall intensity LPLA are input into the landslide early warning analysis submodule, and the landslide assessment index LPL is output based on the soil permeability coefficient LPLB. By calculating LPL, the landslide risk of a certain area under specific rainfall conditions can be quantitatively assessed. When the LPL value is high, it means that the landslide risk in the area is high and corresponding preventive measures need to be taken. LPL provides a more accurate and comprehensive scientific basis for geological disaster monitoring and early warning. LPL combines multiple factors that affect the occurrence of landslides to form a comprehensive assessment index, which enables geological disaster monitoring and early warning to more comprehensively assess landslide risks and improve the accuracy and reliability of early warnings. LPL calculation also takes into account external factors such as rainfall intensity, so that the early warning system can adapt to different climatic conditions more flexibly. The introduction of LPL makes the assessment of landslide risks more comprehensive and accurate. The soil effective saturation SOP and landslide assessment index LPL are input into the landslide early warning analysis submodule, and based on the soil effective saturation change rate Output the landslide warning index WBG. The calculation of the landslide warning index WBG further refines the landslide risk into different warning levels, such as low, medium, and high, which more intuitively reflects the severity of the landslide risk. Through real-time monitoring and calculation of the WBG value, the increasing trend of landslide risk can be discovered in time, and corresponding emergency response measures can be taken. This is of great significance for improving the accuracy and timeliness of geological disaster monitoring and early warning. At the same time, the calculation of WBG takes into account factors such as the change rate of soil shear strength, so that the early warning system can more accurately evaluate the stability of the soil.

[0031] Based on the landslide assessment index LPL, the influencing parameter FA in the soil saturation submodule is iterated, so that the effective soil saturation SOP and the landslide assessment index LPL are continuously iterated and optimized. This iterative method can realize real-time monitoring and dynamic adjustment. By continuously monitoring the changes in the geological environment and adjusting FA in real time based on LPL, it can ensure that the early warning model is always synchronized with the current geological conditions, thereby improving the accuracy and timeliness of the early warning. By iteratively adjusting FA, the calculation of the effective soil saturation SOP can be indirectly affected, thereby accurately assessing the risk of landslides. By iteratively adjusting FA, the early warning model can better adapt to different geological and climatic conditions, thereby improving its wide applicability. In landslide early warning, the soil moisture content and SOP can be indirectly affected by real-time monitoring of the LPL value and adjusting FA according to its changes, providing a more accurate and dynamic assessment for landslide early warning.

[0032] This allows the various sub-modules to cooperate with each other in calculations, and to perform overall cycles and iterations, so that the overall system has the effect of automatic optimization and updating, and thus better adaptability.

[0033] Example 2: Please refer to Figure 1 , Figure 2 and Figure 3 ,The collection module uses soil moisture sensor, densitometer, soil bulk density meter, rainfall intensity meter and landslide monitoring equipment.

[0034] In this embodiment: the soil moisture sensor is inserted into the soil of the target soil body to measure the soil moisture content in real time, and the soil moisture sensor can be placed at different depths in the landslide-prone area to obtain the vertical distribution of the soil moisture content. The densitometer, soil bulk density meter and buried soil bulk density ultrasonic detection device can cooperate to measure the soil bulk density and soil particle density of the target soil body. Then the rainfall intensity meter can be installed in an open area to accurately measure the rainfall intensity. The rainfall intensity meter can automatically record the rainfall data and transmit it to the data center. The landslide monitoring equipment can be a displacement sensor, an inclinometer, etc., which is installed at the key parts of the landslide body to monitor the deformation of the landslide body. The landslide monitoring equipment can record the data in real time and transmit it to the data center.

[0035] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A geological disaster monitoring and early warning method, characterized in that: The monitoring and early warning steps include the following: Step I: collect the soil moisture content, soil bulk density and rainfall per unit time of the target soil through the acquisition module, and input the collected data into the processing module; Step II: Remove outliers, fill in missing values ​​and sort out soil moisture, soil bulk density and rainfall per unit time through the processing module, and output soil moisture content SOPD, soil bulk density SOPA and rainfall intensity LPLA; Step III: Input soil water content SOPD, soil bulk density SOPA and rainfall intensity LPLA into the early warning analysis module, and the early warning analysis module outputs soil effective saturation SOP, landslide assessment index LPL and landslide early warning index WBG; Step IV: Input the effective soil saturation SOP, landslide assessment index LPL and landslide warning index WBG into the alarm response module. The alarm response module outputs green warning, yellow warning and red warning based on the landslide warning index WBG, and implements corresponding response measures.

2. The geological disaster monitoring and early warning method according to claim 1 is characterized in that: The early warning analysis module includes: a soil saturation submodule, a soil landslide assessment submodule and a landslide early warning analysis submodule.

3. The geological disaster monitoring and early warning method according to claim 2 is characterized in that: The calculation formula of the soil saturation submodule is as follows: ； in: SOP refers to soil effective saturation; SOPA refers to soil bulk density, SOPB refers to soil particle density, SOPC refers to water density, SOPD refers to soil water content, SOPS refers to soil porosity, A1 refers to adjustment factor, and FA refers to influencing parameter; The processing process of the soil saturation submodule is as follows: the soil water content SOPD and the soil bulk density SOPA are input into the soil landslide assessment submodule, and the soil effective saturation SOP is output based on the soil particle density SOPB and the water density SOPC.

4. The geological disaster monitoring and early warning method according to claim 3 is characterized in that: The calculation formula of the soil landslide assessment submodule is as follows: ； in: LPL refers to the Landslide Assessment Index; B1 refers to the weight adjustable coefficient of LPLA, B2 refers to the weight adjustable coefficient of LPLB, LPLA refers to rainfall intensity, LPLB refers to soil permeability coefficient, and α refers to the empirical coefficient; The processing process of the soil landslide assessment submodule is as follows: the soil effective saturation SOP and rainfall intensity LPLA are input into the landslide early warning analysis submodule, and the landslide assessment index LPL is output based on the soil permeability coefficient LPLB.

5. The geological disaster monitoring and early warning method according to claim 4 is characterized in that: The calculation formula of the landslide early warning analysis submodule is as follows: ； in: WBG refers to the landslide warning index; refers to the rate of change of effective soil saturation, C1 refers to adjustment parameter 1, C2 refers to adjustment parameter 2, C1 and C2 are used to adjust the sensitivity of the warning level, WBGA refers to soil shear strength; MBB refers to landslide scale factor, e refers to the base of natural logarithm; The processing process of the landslide early warning analysis submodule is as follows: the soil effective saturation SOP and the landslide assessment index LPL are input into the landslide early warning analysis submodule, and based on the soil effective saturation change rate Output landslide warning index WBG.

6. The geological disaster monitoring and early warning method according to claim 5 is characterized in that: The processing process of the alarm response module is as follows: Input the landslide warning index WBG into the alarm response module; When WBG is less than 0.3, it is at the green warning level, which is a low-level warning; At this time, the likelihood of a landslide is low, but vigilance is still required; When 0.3≤WBG<0.6, it is at the yellow warning level, which is a medium-level warning; At this time, there is a possibility of landslide, so preventive measures need to be taken; When WBG ≥ 0.6, it is at the red warning level, which is a high-level warning; At this time, the possibility of landslide is high and emergency measures need to be taken immediately.

7. The geological disaster monitoring and early warning method according to claim 6 is characterized in that: The alarm response module is specifically: When at green alert level; Disseminate warning information via text messages, radio and television; When at yellow alert level; On the basis of green warning, increase the TV weather forecast column and electronic terminal display screen release channel; When at red alert level; Warning information is urgently released through multiple channels, including radio, television, mobile phone text messages and electronic display screens.

8. The geological disaster monitoring and early warning method according to claim 1 is characterized in that: The acquisition module uses a soil moisture sensor, a densitometer, a soil bulk density meter, a rainfall intensity meter and a landslide monitoring device to collect the soil moisture content, soil bulk density and rainfall per unit time of the target soil body.

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