Landslide early warning method, device and equipment based on combination of multiple monitoring devices and medium

Abstract

The application discloses a landslide early warning method and device based on a combination of multiple monitoring devices, equipment and medium, and relates to the technical field of landslide monitoring and early warning. The method comprises the following steps: after obtaining the layout space position information, device attribute information and device reliability information of multiple landslide monitoring devices, firstly, for each landslide monitoring device, the corresponding initial weight coefficient and the normalized weight coefficient are determined according to the initial weight coefficient preset for different layout space position information, device attribute information and device reliability information and the corresponding layout space position information, device attribute information and device reliability information; then, after obtaining the single monitoring device early warning coefficient value of each landslide monitoring device, the multiple monitoring device combination early warning coefficient value is calculated through a weighted manner; finally, according to the multiple monitoring device combination early warning coefficient value and the preset multiple monitoring device combination early warning level division rule, the current multiple monitoring device combination early warning level is determined and outputted.

Description

Technical Field

[0001] This invention belongs to the field of landslide monitoring and early warning technology, specifically relating to a landslide early warning method, device, equipment, and medium based on a combination of multiple monitoring devices. Background Technology

[0002] In actual landslide monitoring and early warning work, the main focus is on landslide deformation early warning. However, deformation monitoring equipment used for landslide deformation early warning often collects obviously erroneous data due to external influences or defects in the equipment itself. This leads to the early warning system issuing incorrect warning information, resulting in false alarms or missed alarms (e.g., the steel rope of the crack gauge is touched by cattle, sheep, or passersby, causing a sudden increase in data; or the equipment itself is not installed properly, resulting in slow deformation data; or the sensor connector of the equipment is loose, resulting in excessively large data; etc.). In this case, the monitored object itself has not deformed, but the early warning system will issue a false alarm due to erroneous data.

[0003] To mitigate the impact of single-set monitoring equipment reliability issues on early warning conclusions, while multi-device cross-verification can be implemented using combined monitoring equipment to reduce false alarm rates to some extent, different types of landslides exhibit varying deformation patterns and failure mechanisms. Therefore, scientifically and accurately identifying the key control deformation areas is crucial for guiding equipment grouping and refining early warning systems. Furthermore, currently used landslide early warning models are primarily based on landslide surface deformation monitoring data, with monitoring equipment mainly consisting of GNSS (Global Navigation Satellite System) devices and crack gauges. These two types of equipment are susceptible to different interference factors. To reduce the impact of single-set monitoring equipment (i.e., GNSS devices or crack gauges) reliability on early warning accuracy, a cross-verification mechanism between multiple devices needs to be considered.

[0004] In summary, when multiple monitoring devices are used on the same target landslide, how to comprehensively consider the monitoring data and early warning results of multiple monitoring devices in the actual early warning process, so as to reduce the impact of the low reliability of a single (or a few) monitoring devices on the early warning results, is a topic that urgently needs to be studied by those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a landslide early warning method, device, computer equipment, and computer-readable storage medium based on a combination of multiple monitoring devices, in order to solve the problem that existing single or few monitoring devices may have an adverse impact on the early warning conclusion due to their low reliability, thus leading to false alarms or missed alarms.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] Firstly, a landslide early warning method based on a combination of multiple monitoring devices is provided, including:

[0008] Acquire spatial location information, equipment attribute information, and equipment reliability information of multiple landslide monitoring devices, wherein the multiple landslide monitoring devices are respectively deployed at different spatial locations within the potential instability range of the target landslide body;

[0009] For each landslide monitoring device among the multiple landslide monitoring devices, the corresponding initial weight coefficient is determined based on the first initial weight coefficient preset for different deployment spatial location information, the second initial weight coefficient preset for different device attribute information, the third initial weight coefficient preset for different device reliability information, and the corresponding deployment spatial location information, device attribute information, and device reliability information.

[0010] Based on the initial weighting coefficients of each landslide monitoring device, the normalized weighting coefficients of each landslide monitoring device are calculated according to the following formula:

[0011]

[0012] In the formula, N represents the total number of landslide monitoring devices, and n and Let w represent positive integers less than or equal to N. n Value represents the normalized weighting coefficient of the nth landslide monitoring device among the plurality of landslide monitoring devices. n This represents the initial weighting coefficient of the nth landslide monitoring device. Indicating the first of the plurality of landslide monitoring devices Initial weighting coefficients for each landslide monitoring device;

[0013] For each landslide monitoring device, the corresponding single monitoring device early warning coefficient value is determined based on the preset weight coefficient of the multidimensional early warning index of the corresponding device and the measured value of the multidimensional early warning index collected in real time by the corresponding device and corresponding one-to-one with the multidimensional early warning index.

[0014] Based on the normalized weighting coefficients of each landslide monitoring device and the early warning coefficient value of a single monitoring device, the combined early warning coefficient value CIOW of the multi-monitoring devices is calculated according to the following formula:

[0015]

[0016] In the formula, Indicates the first Normalized weighting coefficients for each landslide monitoring device Indicates the first The early warning coefficient value of a single landslide monitoring device;

[0017] Based on the multi-monitoring device combination early warning coefficient value CIOW and the preset multi-monitoring device combination early warning level classification rules, the current multi-monitoring device combination early warning level is determined and displayed.

[0018] Based on the above-mentioned invention, a novel scheme for landslide early warning based on landslide evolution mechanisms and monitoring equipment characteristic analysis results is provided. Specifically, after acquiring the spatial location information, equipment attribute information, and equipment reliability information of multiple landslide monitoring devices, for each landslide monitoring device, initial weight coefficients and normalized weight coefficients are determined according to preset initial weight coefficients for different spatial location information, equipment attribute information, and equipment reliability information, as well as the corresponding spatial location information, equipment attribute information, and equipment reliability information. Then, after obtaining the early warning coefficient values ​​of each individual landslide monitoring device, a multi-monitoring device combined early warning system is calculated through a weighted method. The numerical values ​​are then used to determine the current multi-monitoring equipment combination early warning level based on the combined early warning coefficient value and the preset multi-monitoring equipment combination early warning level classification rules, and the result is displayed. In this way, based on the landslide evolution mechanism and the analysis results of monitoring equipment characteristics, by allocating weights according to the spatial location and importance of the equipment and combining the weighted calculation of multiple early warning indicators, comprehensive monitoring and accurate early warning of landslides can be achieved. This avoids the adverse impact of low reliability of a single or a few monitoring devices on the early warning conclusion, thereby avoiding false alarms and missed alarms. The final early warning level can help relevant personnel take corresponding prevention and rescue measures, reduce the risk and impact of landslide disasters, and facilitate practical application and promotion.

[0019] In one possible design, the spatial location information includes the layout profile location and / or the layout relative location. The layout profile location is divided into a main profile location located in the middle of the target landslide body and secondary profile locations located on both sides of the main profile of the target landslide body. The layout relative location is divided into a leading edge location, a middle location, and a trailing edge location relative to the target landslide body under the landslide movement mode. The landslide movement mode refers to a shoving landslide movement mode, a progressive retreating landslide movement mode, or a composite landslide movement mode.

[0020] In one possible design, when the layout spatial location information includes the layout profile location, the first initial weight coefficient preset for different layout spatial location information includes a first initial weight coefficient preset for the main profile location and a first initial weight coefficient preset for the secondary profile location, wherein the first initial weight coefficient preset for the main profile location is greater than the first initial weight coefficient preset for the secondary profile location.

[0021] And / or, when the deployment spatial location information includes the deployment relative position, the first initial weight coefficient preset for different deployment spatial location information includes the first initial weight coefficient preset for the leading edge position, the first initial weight coefficient preset for the middle position and the first initial weight coefficient preset for the trailing edge position.

[0022] If the landslide movement mode refers to the push-type landslide movement mode, then the first initial weight coefficient preset for the leading edge position is greater than the first initial weight coefficient preset for the middle position, and the first initial weight coefficient preset for the middle position is greater than the first initial weight coefficient preset for the trailing edge position.

[0023] If the landslide movement mode refers to the progressive retreat landslide movement mode, then the first initial weight coefficient preset for the rear edge position is greater than the first initial weight coefficient preset for the front edge position, and the first initial weight coefficient preset for the front edge position is greater than the first initial weight coefficient preset for the middle position.

[0024] If the landslide movement mode refers to the composite landslide movement mode, then the first initial weight coefficient preset for the middle position is greater than the first initial weight coefficient preset for the leading edge position, and the first initial weight coefficient preset for the leading edge position is equal to the first initial weight coefficient preset for the trailing edge position.

[0025] In one possible design, the device attribute information includes device type, device acquisition frequency and / or device acquisition deformation accuracy, wherein the device type is divided into GNSS device type and crack gauge device type, the device acquisition frequency is divided into second-level acquisition frequency, minute-level acquisition frequency, hour-level acquisition frequency and day-level acquisition frequency, and the device acquisition deformation accuracy is divided into millimeter-level deformation accuracy, centimeter-level deformation accuracy and decimeter-level deformation accuracy;

[0026] When the device attribute information includes a device type, the second initial weight coefficient preset for different device attribute information includes a second initial weight coefficient preset for the GNSS device type and a second initial weight coefficient preset for the crack meter device type, wherein the second initial weight coefficient preset for the crack meter device type is greater than the second initial weight coefficient preset for the GNSS device type.

[0027] When the device attribute information includes the device acquisition frequency, the preset second initial weight coefficient for different device attribute information includes a preset second initial weight coefficient for the second-level acquisition frequency, a preset second initial weight coefficient for the minute-level acquisition frequency, a preset second initial weight coefficient for the hour-level acquisition frequency, and a preset second initial weight coefficient for the day-level acquisition frequency. Among them, the preset second initial weight coefficient for the second-level acquisition frequency is greater than the preset second initial weight coefficient for the minute-level acquisition frequency, the preset second initial weight coefficient for the minute-level acquisition frequency is greater than the preset second initial weight coefficient for the hour-level acquisition frequency, and the preset second initial weight coefficient for the hour-level acquisition frequency is greater than the preset second initial weight coefficient for the day-level acquisition frequency.

[0028] When the device attribute information includes the device's deformation accuracy, the preset second initial weighting coefficients for different device attribute information include a preset second initial weighting coefficient for the millimeter-level deformation accuracy, a preset second initial weighting coefficient for the centimeter-level deformation accuracy, and a preset second initial weighting coefficient for the decimeter-level deformation accuracy. The preset second initial weighting coefficient for the millimeter-level deformation accuracy is greater than the preset second initial weighting coefficient for the centimeter-level deformation accuracy, and the preset second initial weighting coefficient for the centimeter-level deformation accuracy is greater than the preset second initial weighting coefficient for the decimeter-level deformation accuracy.

[0029] In one possible design, the device reliability information includes device data acquisition latency and / or device data acquisition volatility, wherein the device data acquisition latency is classified into one-minute latency, five-minute latency, half-hour latency and hour latency, and the device data acquisition volatility is classified into millimeter volatility, centimeter volatility and decimeter volatility.

[0030] When the device reliability information includes device data acquisition latency, the preset third initial weighting coefficients for different device reliability information include a preset third initial weighting coefficient for the one-minute latency, a preset third initial weighting coefficient for the five-minute latency, a preset third initial weighting coefficient for the half-hour latency, and a preset third initial weighting coefficient for the hour latency. Among these, the preset third initial weighting coefficient for the one-minute latency is greater than the preset third initial weighting coefficient for the five-minute latency, the preset third initial weighting coefficient for the five-minute latency is greater than the preset third initial weighting coefficient for the half-hour latency, and the preset third initial weighting coefficient for the half-hour latency is greater than the preset third initial weighting coefficient for the hour latency.

[0031] When the device reliability information includes fluctuations in the device's collected data, the preset third initial weighting coefficients for different device reliability information include a preset third initial weighting coefficient for millimeter-level fluctuations, a preset third initial weighting coefficient for centimeter-level fluctuations, and a preset third initial weighting coefficient for decimeter-level fluctuations. The preset third initial weighting coefficient for millimeter-level fluctuations is greater than the preset third initial weighting coefficient for centimeter-level fluctuations, and the preset third initial weighting coefficient for centimeter-level fluctuations is greater than the preset third initial weighting coefficient for decimeter-level fluctuations.

[0032] In one possible design, when the deployment spatial location information includes the deployment profile location and the relative deployment location, the equipment attribute information includes the equipment type, equipment acquisition frequency, and equipment acquisition deformation accuracy, and the equipment reliability information includes the equipment acquisition data latency and equipment acquisition data volatility, for each landslide monitoring device among the multiple landslide monitoring devices, based on a first initial weighting coefficient preset for different deployment spatial location information, a second initial weighting coefficient preset for different equipment attribute information, a third initial weighting coefficient preset for different equipment reliability information, and the corresponding deployment spatial location information, equipment attribute information, and equipment reliability information, a corresponding initial weighting coefficient is determined, including:

[0033] For the nth landslide monitoring device among the plurality of landslide monitoring devices, the corresponding initial weight coefficient Value is calculated according to the following formula, based on the first initial weight coefficient preset for different deployment spatial location information, the second initial weight coefficient preset for different device attribute information, the third initial weight coefficient preset for different device reliability information, and the corresponding deployment spatial location information, device attribute information, and device reliability information. n :

[0034] Value n =(a n1 +a n2 )×b n1 ×(c n1 +c n2 )×(d n1 +d n2 )

[0035] In the formula, n represents a positive integer less than or equal to N, N represents the total number of landslide monitoring devices, and a n1 a represents the first initial weighting coefficient preset for the deployment profile location of the nth landslide monitoring device. n2 b represents the first initial weighting coefficient preset for the relative position of the nth landslide monitoring device. n1c represents the second initial weighting coefficient preset for the equipment type of the nth landslide monitoring device. n1 c represents the second initial weighting coefficient preset for the device acquisition frequency of the nth landslide monitoring device. n2 This represents the second initial weighting coefficient preset for the deformation accuracy of the nth landslide monitoring device. n1 d represents the third initial weighting coefficient preset for the data acquisition delay of the nth landslide monitoring device. n2 This represents the third initial weighting coefficient preset for the fluctuation of the data collected by the nth landslide monitoring device.

[0036] In one possible design, for each landslide monitoring device, based on the preset weighting coefficients of the multidimensional early warning indicators of the corresponding device and the measured values ​​of the multidimensional early warning indicators collected in real time by the corresponding device and corresponding one-to-one with the multidimensional early warning indicators, the corresponding single monitoring device early warning coefficient value is determined, including:

[0037] For the nth landslide monitoring device among the plurality of landslide monitoring devices, the corresponding single monitoring device early warning coefficient value SIOW is calculated according to the preset weight coefficient of the multidimensional early warning index of the corresponding device and the measured value of the multidimensional early warning index collected in real time by the corresponding device and corresponding one-to-one with the multidimensional early warning index, according to the following formula. n :

[0038] SIOW n =S n1 ×k n1 +S n2 ×k n2 +…+S nm ×k nm +…+S nM ×k nM

[0039] In the formula, n represents a positive integer less than or equal to N, N represents the total number of landslide monitoring devices, M represents the total dimension of the multidimensional early warning indicators of the nth landslide monitoring device, m represents a positive integer less than or equal to M, and S nm k represents the measured value of the m-th dimension early warning indicator, which is collected in real time by the nth landslide monitoring device and corresponds to the m-th dimension early warning indicator in the multi-dimensional early warning indicators. nm This represents the preset weighting coefficient of the m-th dimension early warning indicator.

[0040] Secondly, a landslide early warning device based on a combination of multiple monitoring devices is provided, including a device information acquisition module, an initial weight determination module, a normalized weight calculation module, an early warning coefficient determination module, a combined weighted calculation module, and an early warning level determination module;

[0041] The equipment information acquisition module is used to acquire the spatial location information, equipment attribute information and equipment reliability information of multiple landslide monitoring devices, wherein the multiple landslide monitoring devices are respectively deployed at different spatial locations within the potential instability range of the target landslide body;

[0042] The initial weight determination module is communicatively connected to the device information acquisition module. It is used to determine the corresponding initial weight coefficient for each landslide monitoring device among the plurality of landslide monitoring devices, based on a first initial weight coefficient preset for different deployment spatial location information, a second initial weight coefficient preset for different device attribute information, a third initial weight coefficient preset for different device reliability information, and the corresponding deployment spatial location information, device attribute information, and device reliability information.

[0043] The normalized weight calculation module, communicatively connected to the initial weight determination module, is used to calculate the normalized weight coefficient of each landslide monitoring device according to the initial weight coefficient of each landslide monitoring device, using the following formula:

[0044]

[0045] In the formula, N represents the total number of landslide monitoring devices, and n and Let w represent positive integers less than or equal to N. n Value represents the normalized weighting coefficient of the nth landslide monitoring device among the plurality of landslide monitoring devices. n This represents the initial weighting coefficient of the nth landslide monitoring device. Indicating the first of the plurality of landslide monitoring devices Initial weighting coefficients for each landslide monitoring device;

[0046] The warning coefficient determination module is used to determine the warning coefficient value of each landslide monitoring device based on the preset weight coefficient of the multidimensional warning index of the corresponding device and the measured value of the multidimensional warning index collected in real time by the corresponding device and corresponding one-to-one with the multidimensional warning index.

[0047] The combined weighted calculation module is communicatively connected to the normalized weight calculation module and the early warning coefficient determination module, respectively. It is used to calculate the combined early warning coefficient value CIOW of multiple monitoring devices based on the normalized weight coefficients of each landslide monitoring device and the early warning coefficient value of a single monitoring device, according to the following formula:

[0048]

[0049] In the formula, Indicates the first Normalized weighting coefficients for each landslide monitoring device Indicates the first The early warning coefficient value of a single landslide monitoring device;

[0050] The warning level determination module is communicatively connected to the combined weighted calculation module. It is used to determine the current combined warning level of the multi-monitoring devices based on the combined warning coefficient value CIOW of the multi-monitoring devices and the preset multi-monitoring device combined warning level division rules, and then output and display it.

[0051] Thirdly, the present invention provides a computer device comprising a memory, a processor, and a transceiver connected in sequence for communication, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the landslide early warning method as described in the first aspect or any possible design in the first aspect.

[0052] Fourthly, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, perform the landslide early warning method as described in the first aspect or any possible design of the first aspect.

[0053] Fifthly, the present invention provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform the landslide early warning method as described in the first aspect or any possible design in the first aspect.

[0054] The beneficial effects of the above scheme are:

[0055] (1) This invention creatively provides a novel scheme for landslide early warning based on the landslide evolution mechanism and the analysis results of monitoring equipment characteristics. Specifically, after obtaining the spatial location information, equipment attribute information, and equipment reliability information of multiple landslide monitoring equipment, the initial weight coefficient and normalized weight coefficient are determined for each landslide monitoring equipment according to the preset initial weight coefficients for different spatial location information, equipment attribute information, and equipment reliability information, as well as the corresponding spatial location information, equipment attribute information, and equipment reliability information. Then, after obtaining the early warning coefficient value of each individual monitoring equipment, the combined early warning system of multiple monitoring equipment is calculated by weighting. The numerical values ​​are then used to determine the current multi-monitoring equipment combination early warning level based on the combined early warning coefficient value and the preset multi-monitoring equipment combination early warning level classification rules, and the result is displayed. In this way, based on the landslide evolution mechanism and the analysis results of monitoring equipment characteristics, by allocating weights according to the spatial location and importance of the equipment and combining the weighted calculation of multiple early warning indicators, comprehensive monitoring and accurate early warning of landslides can be achieved. This avoids the adverse impact of low reliability of a single or a few monitoring devices on the early warning conclusion, thereby avoiding false alarms and missed alarms. The final early warning level can help relevant personnel take corresponding prevention and rescue measures, reduce the risk and impact of landslide disasters, and facilitate practical application and promotion. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is a flowchart illustrating a landslide early warning method based on a combination of multiple monitoring devices, provided in an embodiment of this application.

[0058] Figure 2 This is an example diagram of landslide profile zoning provided in an embodiment of this application.

[0059] Figure 3 Examples of landslides with different landslide movement modes provided in embodiments of this application, wherein, Figure 3 Figure (a) shows an example landslide with a shoal-type landslide movement pattern. Figure 3 Figure (b) shows an example landslide with a progressive retreat landslide movement pattern. Figure 3 Figure (c) shows an example of a landslide with a complex landslide motion pattern.

[0060] Figure 4This is a schematic diagram of the landslide early warning device based on a combination of multiple monitoring devices provided in an embodiment of this application.

[0061] Figure 5 A schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0062] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in conjunction with the accompanying drawings and descriptions of the embodiments or the prior art. Obviously, the following description of the structure of the accompanying drawings is only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.

[0063] It should be understood that although the terms "first" and "second", etc., may be used herein to describe various objects, these objects should not be limited by these terms. These terms are only used to distinguish one object from another. For example, the first object may be referred to as the second object, and similarly, the second object may be referred to as the first object, without departing from the scope of the exemplary embodiments of the invention.

[0064] It should be understood that the term "and / or" that may appear in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, or A and B exist simultaneously. Another example is A, B and / or C, which can mean that any one of A, B, and C or any combination thereof exists. The term " / and" that may appear in this document describes another relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone or A and B exist simultaneously. In addition, the character " / " that may appear in this document generally indicates that the related objects before and after it are in an "or" relationship.

[0065] Example:

[0066] like Figure 1As shown, the landslide early warning method based on a combination of multiple monitoring devices provided in the first aspect of this embodiment can be executed, but is not limited to, by a computer device with certain computing resources that is communicatively connected to multiple sets of monitoring devices located on the same target landslide body. For example, it can be executed by electronic devices such as a platform server, a personal computer (PC, referring to a multi-purpose computer of a size, price, and performance suitable for personal use; desktop computers, laptops, mini-laptops, tablets, and ultrabooks are all considered personal computers), a smartphone, a personal digital assistant (PDA), or a wearable device. Figure 1 As shown, the landslide early warning method may include, but is not limited to, the following steps S1 to S6.

[0067] S1. Obtain the spatial location information, equipment attribute information, and equipment reliability information of multiple landslide monitoring devices, wherein the multiple landslide monitoring devices are respectively deployed at different spatial locations within the potential instability range of the target landslide body.

[0068] In step S1, the target landslide body is the landslide monitoring object, which has the following landslide attributes: (1) landslide zoning, that is, due to the differences in topographic conditions and soil and rock conditions, the entire landslide area will undergo differential deformation. According to the development of cracks, deformation and landslide structure, the landslide body is divided into different deformation zones. According to the deformation characteristics of the deformed body in the field investigation, the landslide area can be divided into strong deformation zone, medium deformation zone and weak deformation zone; (2) potential instability range, that is, a single landslide may produce multiple Secondary landslides can be further divided into secondary deformation zones within each deformation zone. For the analysis of the potential instability range of landslides, two levels need to be analyzed: the overall potential instability range of the landslide and the potential instability range of the secondary landslides; (3) Movement patterns, namely, the movement patterns of landslides are mainly divided into three categories: shoving landslides, progressive retreating landslides and composite landslides. For the movement patterns of the overall potential instability landslides, there may be shoving, progressive retreating or composite patterns, while for the secondary deformation zones, it is often only one of shoving and progressive retreating patterns.

[0069] In step S1, the landslide monitoring equipment is the landslide monitoring execution body. Considering that its location in different landslides will play a key role in landslide early warning, the following spatial positions of the landslide monitoring equipment need to be taken into account: (1) Planar position, that is, the planar position of the landslide area where the monitoring equipment is located refers to the situation outside the overall potential instability range of the landslide or the potential instability range of the secondary landslide where the monitoring equipment is located. For early warning of the overall landslide range: due to unprofessional monitoring design and installation or the development of landslide deformation, some monitoring equipment is outside the potential instability range of the landslide; while for early warning of secondary landslides: equipment outside the boundary of the secondary landslide cannot be used as early warning equipment; (2) Profile position, that is, the landslide deformation monitoring network is composed of monitoring A three-dimensional monitoring system consisting of lines (i.e. monitoring profiles, hereinafter referred to as survey lines) and monitoring points (hereinafter referred to as survey points) should be set up to monitor the deformation amount and deformation direction of the landslide. Usually, multiple longitudinal and transverse survey lines are nearly orthogonal to form a grid and form multiple profiles. Under normal circumstances, the displacement change in the middle of the landslide is more obvious and is the main profile, while the two sides are secondary profiles. The early warning results of the monitoring equipment located on the main profile are more critical. (3) Relative position: For landslides with different motion modes, the sequence of landslide movement during instability is quite different. It is necessary to determine the contribution of different equipment to the early warning based on the relative position (front edge, middle and rear edge) of the landslide profile where the monitoring equipment is installed. Therefore, after eliminating monitoring devices located outside the potential instability range based on their planar positions, the spatial location information includes, but is not limited to, the deployment profile position and / or the relative deployment position. The deployment profile position is divided into a main profile position located in the middle of the target landslide body and secondary profile positions located on both sides of the main profile (i.e., the main sliding direction profile, generally the middle) of the target landslide body. The relative deployment position is divided into the leading edge position, middle position, and trailing edge position relative to the target landslide body under the landslide movement mode. The landslide movement mode refers to the shoving landslide movement mode, the progressive retreating landslide movement mode, or the composite landslide movement mode.

[0070] In step S1, the landslide monitoring equipment will also have the following equipment attributes: (1) Equipment type, namely, mainly considering GNSS equipment and crack gauges for monitoring landslide surface deformation. Among them, the GNSS equipment uses the receiving of satellite signals to perform high-precision pseudorange or carrier phase differential positioning, realizing high-precision continuous monitoring of the three-dimensional deformation of the landslide surface. It requires a calculation time interval of tens of minutes or more to obtain high measurement accuracy. The plane positioning accuracy can reach 2.5mm±1ppm, and the elevation direction positioning accuracy can reach 5mm±1ppm, which is more suitable for continuous monitoring of the long-term deformation behavior of landslides; the crack gauge is a combination of sensor and tensile... The wire ends are fixed on both sides of the landslide crack to obtain the deformation value of the landslide crack. The data accuracy is at the millimeter level, which is suitable for daily 1-2 meter level deformation monitoring of landslides (range limited) and pre-landslide monitoring; (2) Acquisition frequency, that is, a higher sampling frequency can capture the complete deformation process and buy time for early warning. If the sampling time interval is too large, the landslide may become unstable and fail between two data acquisition intervals, which will lead to missed reports; (3) Acquisition accuracy, that is, the deformation acquisition accuracy of different types of monitoring equipment and the same type of monitoring equipment from different manufacturers will have large differences. Generally speaking, the higher the accuracy of the equipment, the higher the reliability of its early warning results. Therefore, the equipment attribute information includes, but is not limited to, equipment type, equipment acquisition frequency and / or equipment acquisition deformation accuracy, etc. Among them, the equipment type is divided into GNSS equipment type and crack meter equipment type, etc.; the equipment acquisition frequency is divided into second-level acquisition frequency, minute-level acquisition frequency, hour-level acquisition frequency and daily-level acquisition frequency, etc.; the equipment acquisition deformation accuracy is divided into millimeter-level deformation accuracy, centimeter-level deformation accuracy and decimeter-level deformation accuracy, etc.

[0071] In step S1, it is also considered that in actual landslide monitoring and early warning work, monitoring equipment often collects obviously erroneous data due to external influences or equipment defects, which leads to the early warning system issuing incorrect early warning information, resulting in false alarms or missed alarms, or due to the time delay in the process of monitoring data collection and storage, resulting in missed alarms. Therefore, the following equipment reliability of the landslide monitoring equipment also needs to be considered: (1) Data delay, that is, the timeliness of monitoring data is very important for landslide early warning. By comparing the "data collection time" and the "data storage time", the data delay time can be determined. If the time difference is too large, it will lead to the situation of using "historical data" for early warning, which violates the purpose of real-time monitoring and early warning. If the time difference is too large, it may lead to early warning failure, especially in the imminent landslide stage. The time difference from data collection to storage should be controlled within seconds; (2) Data fluctuation, that is, due to the influence of external factors and the characteristics of the equipment itself, the monitoring data will fluctuate to a certain extent, which will lead the early warning model to mistakenly believe that the data change is caused by real deformation, resulting in false alarms. The most intuitive manifestation is the sudden jump of the monitoring data curve. Therefore, the device reliability information includes, but is not limited to, device data acquisition latency and / or device data acquisition volatility, wherein the device data acquisition latency is classified into one-minute latency, five-minute latency, half-hour latency and hour latency, etc., and the device data acquisition volatility is classified into millimeter volatility, centimeter volatility and decimeter volatility, etc.

[0072] In step S1, the specific methods for obtaining the aforementioned deployment spatial location information, equipment attribute information, and equipment reliability information can be, but are not limited to, input by landslide monitoring personnel based on the actual situation.

[0073] S2. For each landslide monitoring device among the plurality of landslide monitoring devices, determine the corresponding initial weight coefficient based on the first initial weight coefficient preset for different deployment spatial location information, the second initial weight coefficient preset for different device attribute information, the third initial weight coefficient preset for different device reliability information, and the corresponding deployment spatial location information, device attribute information, and device reliability information.

[0074] In step S2, as Figure 2 As shown, considering that the early warning results of monitoring equipment located on the main profile are more critical, the monitoring equipment on the main profile has a higher weight than that on the secondary profile. That is, when the deployment spatial location information includes the deployment profile location, the first initial weight coefficient preset for different deployment spatial location information includes the first initial weight coefficient preset for the main profile location and the first initial weight coefficient preset for the secondary profile location. Among them, the first initial weight coefficient preset for the main profile location is greater than the first initial weight coefficient preset for the secondary profile location.

[0075] In step S2, as Figure 3 As shown in (a), considering the strong deformation at the rear of a shoveling landslide, the landslide instability process is characterized by deformation at the rear edge followed by gradual forward expansion. The displacement and deformation at the front edge are key to the overall instability warning of the landslide. Therefore, for combined early warning of shoveling landslides, the weight of the monitoring equipment should be: front edge > middle edge > rear edge. That is, when the spatial location information includes the relative location of the equipment, the first initial weight coefficient preset for different spatial location information includes the first initial weight coefficient preset for the front edge location, the first initial weight coefficient preset for the middle location, and the first initial weight coefficient preset for the rear edge location. If the landslide movement mode refers to the shoveling landslide movement mode, then the first initial weight coefficient preset for the front edge location is greater than the first initial weight coefficient preset for the middle location, and the first initial weight coefficient preset for the middle location is greater than the first initial weight coefficient preset for the rear edge location.

[0076] In step S2, as Figure 3 As shown in (b), the process of gradual retreat landslide instability involves the leading edge deforming and failing first, forming a new free surface, leading to the sliding of the next stage of landslide body, and generating a chain reaction with a gradual retreat trend, resulting in the overall failure of the slope. Deformation and failure at the leading edge of the landslide may cause damage and indicate a potential trend towards overall landslide failure, while severe deformation at the trailing edge indicates that the landslide may experience overall failure. Therefore, for early warning of gradual retreat landslide combinations, the key is to grasp the deformation and displacement of the leading and trailing edges. The weight of monitoring equipment should be: trailing edge > leading edge > middle, that is, when the spatial location information includes... When setting relative positions, the first initial weighting coefficients preset for different spatial position information include the first initial weighting coefficient preset for the leading edge position, the first initial weighting coefficient preset for the middle position, and the first initial weighting coefficient preset for the trailing edge position; and if the landslide movement mode refers to the progressive retreat landslide movement mode, then the first initial weighting coefficient preset for the trailing edge position is greater than the first initial weighting coefficient preset for the leading edge position, and the first initial weighting coefficient preset for the leading edge position is greater than the first initial weighting coefficient preset for the middle position.

[0077] In step S2, as Figure 3As shown in (c), considering that both the leading and trailing edges of a composite landslide are areas of strong deformation, there is often a locking section in the middle, which has weaker deformation strength but is the key location for landslide instability and failure. Therefore, for the early warning of composite landslide combinations, the weight of the monitoring equipment should be: middle > leading edge = trailing edge. That is, when the spatial location information includes the relative location of the equipment, the first initial weight coefficient preset for different spatial location information includes the first initial weight coefficient preset for the leading edge location, the first initial weight coefficient preset for the middle location, and the first initial weight coefficient preset for the trailing edge location. If the landslide movement mode refers to the composite landslide movement mode, then the first initial weight coefficient preset for the middle location is greater than the first initial weight coefficient preset for the leading edge location, and the first initial weight coefficient preset for the leading edge location is equal to the first initial weight coefficient preset for the trailing edge location.

[0078] In step S2, considering that the acquisition frequency and data accuracy of the crack meter are higher than those of GNSS equipment, and that the crack meter is more critical for landslide early warning, the weight of the crack meter needs to be higher than that of GNSS equipment. That is, when the equipment attribute information includes equipment type, the preset second initial weight coefficient for different equipment attribute information includes a preset second initial weight coefficient for the GNSS equipment type and a preset second initial weight coefficient for the crack meter equipment type. The preset second initial weight coefficient for the crack meter equipment type is greater than the preset second initial weight coefficient for the GNSS equipment type.

[0079] In step S2, considering that a higher sampling frequency can capture the complete deformation process and gain time for early warning, when the device attribute information includes the device acquisition frequency, the preset second initial weighting coefficients for different device attribute information include a preset second initial weighting coefficient for the second-level acquisition frequency, a preset second initial weighting coefficient for the minute-level acquisition frequency, a preset second initial weighting coefficient for the hour-level acquisition frequency, and a preset second initial weighting coefficient for the day-level acquisition frequency. Specifically, the preset second initial weighting coefficient for the second-level acquisition frequency is greater than the preset second initial weighting coefficient for the minute-level acquisition frequency, the preset second initial weighting coefficient for the minute-level acquisition frequency is greater than the preset second initial weighting coefficient for the hour-level acquisition frequency, and the preset second initial weighting coefficient for the hour-level acquisition frequency is greater than the preset second initial weighting coefficient for the day-level acquisition frequency.

[0080] In step S2, considering that the higher the accuracy of the device, the higher the reliability of its warning result, when the device attribute information includes the deformation accuracy of the device acquisition, the preset second initial weight coefficient for different device attribute information includes a preset second initial weight coefficient for the millimeter-level deformation accuracy, a preset second initial weight coefficient for the centimeter-level deformation accuracy, and a preset second initial weight coefficient for the decimeter-level deformation accuracy. Among them, the preset second initial weight coefficient for the millimeter-level deformation accuracy is greater than the preset second initial weight coefficient for the centimeter-level deformation accuracy, and the preset second initial weight coefficient for the centimeter-level deformation accuracy is greater than the preset second initial weight coefficient for the decimeter-level deformation accuracy.

[0081] In step S2, considering that the time difference between data collection and storage should ideally be controlled within seconds, when the device reliability information includes device data collection latency, the preset third initial weighting coefficients for different device reliability information include a preset third initial weighting coefficient for the one-minute latency, a preset third initial weighting coefficient for the five-minute latency, a preset third initial weighting coefficient for the half-hour latency, and a preset third initial weighting coefficient for the hour latency. Among these, the preset third initial weighting coefficient for the one-minute latency is greater than the preset third initial weighting coefficient for the five-minute latency, the preset third initial weighting coefficient for the five-minute latency is greater than the preset third initial weighting coefficient for the half-hour latency, and the preset third initial weighting coefficient for the half-hour latency is greater than the preset third initial weighting coefficient for the hour latency.

[0082] In step S2, considering that the greater the data fluctuation, the more likely it is to cause false alarms, when the device reliability information includes the fluctuation of the device's collected data, the preset third initial weighting coefficient for different device reliability information includes a preset third initial weighting coefficient for the millimeter-level fluctuation, a preset third initial weighting coefficient for the centimeter-level fluctuation, and a preset third initial weighting coefficient for the decimeter-level fluctuation. The preset third initial weighting coefficient for the millimeter-level fluctuation is greater than the preset third initial weighting coefficient for the centimeter-level fluctuation, and the preset third initial weighting coefficient for the centimeter-level fluctuation is greater than the preset third initial weighting coefficient for the decimeter-level fluctuation.

[0083] In step S2, example values ​​for the first initial weighting coefficient preset for different deployment spatial location information, the second initial weighting coefficient preset for different equipment attribute information, and the third initial weighting coefficient preset for different equipment reliability information are shown in Table 1 below:

[0084] Table 1. Examples of values ​​for each initial weight coefficient

[0085]

[0086]

[0087] In step S2, specifically, when the deployment spatial location information includes the deployment profile location and the deployment relative location, the equipment attribute information includes the equipment type, equipment acquisition frequency, and equipment acquisition deformation accuracy, and the equipment reliability information includes the equipment acquisition data latency and equipment acquisition data volatility, for each landslide monitoring device among the plurality of landslide monitoring devices, based on the first initial weight coefficient preset for different deployment spatial location information, the second initial weight coefficient preset for different equipment attribute information, the third initial weight coefficient preset for different equipment reliability information, and the corresponding deployment spatial location information, equipment attribute information, and equipment reliability information, the corresponding initial weight coefficient is determined, including but not limited to: for the nth landslide monitoring device among the plurality of landslide monitoring devices, based on the first initial weight coefficient preset for different deployment spatial location information, the second initial weight coefficient preset for different equipment attribute information, the third initial weight coefficient preset for different equipment reliability information, and the corresponding deployment spatial location information, equipment attribute information, and equipment reliability information, the corresponding initial weight coefficient Value is calculated according to the following formula. n :

[0088] Value n =(a n1 +a n2 )×b n1 ×(c n1 +c n2 )×(d n1 +d n2 )

[0089] In the formula, n represents a positive integer less than or equal to N, N represents the total number of landslide monitoring devices, and a n1 a represents the first initial weighting coefficient preset for the deployment profile location of the nth landslide monitoring device. n2 b represents the first initial weighting coefficient preset for the relative position of the nth landslide monitoring device. n1 c represents the second initial weighting coefficient preset for the equipment type of the nth landslide monitoring device. n1 c represents the second initial weighting coefficient preset for the device acquisition frequency of the nth landslide monitoring device. n2 This represents the second initial weighting coefficient preset for the deformation accuracy of the nth landslide monitoring device. n1d represents the third initial weighting coefficient preset for the data acquisition delay of the nth landslide monitoring device. n2 This represents the third initial weighting coefficient preset for the fluctuation of the data collected by the nth landslide monitoring device.

[0090] S3. Based on the initial weighting coefficients of each landslide monitoring device, the normalized weighting coefficients of each landslide monitoring device are calculated according to the following formula:

[0091]

[0092] In the formula, N represents the total number of landslide monitoring devices, and n and Let w represent positive integers less than or equal to N. n Value represents the normalized weighting coefficient of the nth landslide monitoring device among the plurality of landslide monitoring devices. n This represents the initial weighting coefficient of the nth landslide monitoring device. Indicating the first of the plurality of landslide monitoring devices The initial weighting coefficients for each landslide monitoring device.

[0093] S4. For each landslide monitoring device, determine the corresponding single monitoring device warning coefficient value based on the preset weight coefficient of the multidimensional warning index of the corresponding device and the measured value of the multidimensional warning index collected in real time by the corresponding device and corresponding one-to-one with the multidimensional warning index.

[0094] In step S4, different multidimensional early warning indicators will be used for different types of landslide monitoring equipment. For example, GNSS equipment will have 5-dimensional early warning indicators as shown in Table 2: horizontal displacement value, horizontal displacement deformation rate / horizontal displacement value, vertical displacement value, vertical displacement deformation rate / vertical displacement value, and horizontal displacement deformation rate / vertical deformation rate, etc. Example values ​​of their corresponding preset weighting coefficients can also be seen in Table 2.

[0095] Table 2. Five-dimensional early warning indicators for GNSS equipment and corresponding text and preset weight coefficients

[0096]

[0097] The crack gauge provides two-dimensional early warning indicators as shown in Table 3: the current monitored value and the rate of change of the monitored value / current monitored value. Example values ​​of their corresponding preset weighting coefficients are also shown in Table 3.

[0098] Table 3. Two-dimensional early warning indicators of crack gauge and corresponding text and preset weight coefficients

[0099]

[0100] The measured values ​​of the multidimensional early warning indicators that correspond one-to-one with the aforementioned multidimensional early warning indicators can be obtained routinely from the corresponding monitoring equipment.

[0101] In step S4, specifically, for each landslide monitoring device, the corresponding single monitoring device early warning coefficient value is determined based on the preset weight coefficients of the multidimensional early warning indicators of the corresponding device and the measured values ​​of the multidimensional early warning indicators collected in real time by the corresponding device and corresponding one-to-one with the multidimensional early warning indicators. This includes, but is not limited to, the following: for the nth landslide monitoring device among the multiple landslide monitoring devices, the corresponding single monitoring device early warning coefficient value SIOW is calculated according to the following formula based on the preset weight coefficients of the multidimensional early warning indicators of the corresponding device and the measured values ​​of the multidimensional early warning indicators collected in real time by the corresponding device and corresponding one-to-one with the multidimensional early warning indicators. n :

[0102] SIOW n =S n1 ×k n1 +S n2 ×k n2 +…+S nm ×k nm +…+S nM ×k nM

[0103] In the formula, n represents a positive integer less than or equal to N, N represents the total number of landslide monitoring devices, M represents the total dimension of the multidimensional early warning indicators of the nth landslide monitoring device, m represents a positive integer less than or equal to M, and S nm k represents the measured value of the m-th dimension early warning indicator, which is collected in real time by the nth landslide monitoring device and corresponds to the m-th dimension early warning indicator in the multi-dimensional early warning indicators. nm This represents the preset weighting coefficient of the m-th dimension early warning indicator.

[0104] S5. Based on the normalized weighting coefficients of each landslide monitoring device and the early warning coefficient value of a single monitoring device, the combined early warning coefficient value CIOW of the multi-monitoring devices is calculated according to the following formula:

[0105]

[0106] In the formula, Indicates the first Normalized weighting coefficients for each landslide monitoring device Indicates the first The early warning coefficient value of a single landslide monitoring device.

[0107] S6. Based on the multi-monitoring device combination early warning coefficient value CIOW and the preset multi-monitoring device combination early warning level classification rules, determine the current multi-monitoring device combination early warning level and output and display it.

[0108] In step S6, the specific rules for classifying the combined early warning levels of the multi-monitoring devices are shown in Table 4:

[0109] Table 4. Classification of Early Warning Levels for Multi-Monitoring Equipment Combinations

[0110] 0≤CIOW<0.2 No release level required 0.2≤CIOW<0.4 Attention level 0.4≤CIOW<0.6 Warning level 0.6≤CIOW<0.8 Alert level 0.8≤CIOW≤1 Alert Level

[0111] Based on Table 4 above, the current warning level of the multi-monitoring device combination can be accurately determined and displayed through conventional methods.

[0112] Therefore, based on the landslide early warning method described in steps S1 to S6 above and based on the combination of multiple monitoring devices, a new scheme for landslide early warning based on the landslide evolution mechanism and the analysis results of monitoring device characteristics is provided. Specifically, after obtaining the spatial location information, equipment attribute information, and equipment reliability information of multiple landslide monitoring devices, for each landslide monitoring device, according to the preset initial weight coefficients for different spatial location information, equipment attribute information, and equipment reliability information, and the corresponding spatial location information, equipment attribute information, and equipment reliability information, the corresponding initial weight coefficients and normalized weight coefficients are determined. Then, after obtaining the early warning coefficient value of each individual monitoring device, a weighted average is calculated... The multi-monitoring equipment combination early warning coefficient value is calculated. Finally, based on the multi-monitoring equipment combination early warning coefficient value and the preset multi-monitoring equipment combination early warning level classification rules, the current multi-monitoring equipment combination early warning level is determined and displayed. In this way, based on the landslide evolution mechanism and the analysis results of monitoring equipment characteristics, by allocating weights according to the spatial location and importance of the equipment and combining the weighted calculation of multiple early warning indicators, comprehensive monitoring and accurate early warning of landslides can be achieved. This avoids the adverse impact of low reliability of a single or a few monitoring devices on the early warning conclusion, thereby avoiding false alarms and missed alarms. The final early warning level can help relevant personnel take corresponding prevention and rescue measures, reduce the risk and impact of landslide disasters, and facilitate practical application and promotion.

[0113] like Figure 4 As shown, the second aspect of this embodiment provides a virtual device for implementing the landslide early warning method described in the first aspect, including an equipment information acquisition module, an initial weight determination module, a normalized weight calculation module, an early warning coefficient determination module, a combined weighted calculation module, and an early warning level determination module;

[0114] The equipment information acquisition module is used to acquire the spatial location information, equipment attribute information and equipment reliability information of multiple landslide monitoring devices, wherein the multiple landslide monitoring devices are respectively deployed at different spatial locations within the potential instability range of the target landslide body;

[0115] The initial weight determination module is communicatively connected to the device information acquisition module. It is used to determine the corresponding initial weight coefficient for each landslide monitoring device among the plurality of landslide monitoring devices, based on a first initial weight coefficient preset for different deployment spatial location information, a second initial weight coefficient preset for different device attribute information, a third initial weight coefficient preset for different device reliability information, and the corresponding deployment spatial location information, device attribute information, and device reliability information.

[0116] The normalized weight calculation module, communicatively connected to the initial weight determination module, is used to calculate the normalized weight coefficient of each landslide monitoring device according to the initial weight coefficient of each landslide monitoring device, using the following formula:

[0117]

[0118] In the formula, N represents the total number of landslide monitoring devices, and n and Let w represent positive integers less than or equal to N. n Value represents the normalized weighting coefficient of the nth landslide monitoring device among the plurality of landslide monitoring devices. n This represents the initial weighting coefficient of the nth landslide monitoring device. Indicating the first of the plurality of landslide monitoring devices Initial weighting coefficients for each landslide monitoring device;

[0119] The warning coefficient determination module is used to determine the warning coefficient value of each landslide monitoring device based on the preset weight coefficient of the multidimensional warning index of the corresponding device and the measured value of the multidimensional warning index collected in real time by the corresponding device and corresponding one-to-one with the multidimensional warning index.

[0120] The combined weighted calculation module is communicatively connected to the normalized weight calculation module and the early warning coefficient determination module, respectively. It is used to calculate the combined early warning coefficient value CIOW of multiple monitoring devices based on the normalized weight coefficients of each landslide monitoring device and the early warning coefficient value of a single monitoring device, according to the following formula:

[0121]

[0122] In the formula, Indicates the first Normalized weighting coefficients for each landslide monitoring device Indicates the first The early warning coefficient value of a single landslide monitoring device;

[0123] The warning level determination module is communicatively connected to the combined weighted calculation module. It is used to determine the current combined warning level of the multi-monitoring devices based on the combined warning coefficient value CIOW of the multi-monitoring devices and the preset multi-monitoring device combined warning level division rules, and then output and display it.

[0124] The working process, working details and technical effects of the aforementioned device provided in the second aspect of this embodiment can be found in the landslide early warning method described in the first aspect, and will not be repeated here.

[0125] like Figure 5 As shown, the third aspect of this embodiment provides a computer device for executing the landslide early warning method as described in the first aspect, including a memory, a processor, and a transceiver connected in sequence. The memory stores a computer program, the transceiver sends and receives messages, and the processor reads the computer program and executes the landslide early warning method as described in the first aspect. Specifically, the memory may include, but is not limited to, random-access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out (FIFO) memory, and / or first-in-last-out (FILO) memory, etc.; the processor may include, but is not limited to, a microprocessor of the STM32F105 series. Furthermore, the computer device may also include, but is not limited to, a power module, a display screen, and other necessary components.

[0126] The working process, working details and technical effects of the aforementioned computer equipment provided in the third aspect of this embodiment can be found in the landslide early warning method described in the first aspect, and will not be repeated here.

[0127] This fourth aspect of the embodiment provides a computer-readable storage medium storing instructions comprising the landslide early warning method as described in the first aspect. Specifically, the computer-readable storage medium stores instructions that, when executed on a computer, perform the landslide early warning method as described in the first aspect. The computer-readable storage medium refers to a data storage medium, and may include, but is not limited to, floppy disks, optical disks, hard disks, flash memory, USB flash drives, and / or Memory Sticks. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0128] The working process, working details and technical effects of the aforementioned computer-readable storage medium provided in the fourth aspect of this embodiment can be found in the landslide early warning method as described in the first aspect, and will not be repeated here.

[0129] This fifth aspect of the embodiment provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform the landslide early warning method as described in the first aspect. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0130] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A landslide early warning method based on a combination of multiple monitoring devices, characterized in that, include: Acquire spatial location information, equipment attribute information, and equipment reliability information of multiple landslide monitoring devices, wherein the multiple landslide monitoring devices are respectively deployed at different spatial locations within the potential instability range of the target landslide body; For each landslide monitoring device among the multiple landslide monitoring devices, the corresponding initial weight coefficient is determined based on the first initial weight coefficient preset for different deployment spatial location information, the second initial weight coefficient preset for different device attribute information, the third initial weight coefficient preset for different device reliability information, and the corresponding deployment spatial location information, device attribute information, and device reliability information. Based on the initial weighting coefficients of each landslide monitoring device, the normalized weighting coefficients of each landslide monitoring device are calculated according to the following formula: In the formula, This represents the total number of the various landslide monitoring devices. and They represent less than or equal to positive integers, Indicating the first of the plurality of landslide monitoring devices Normalized weighting coefficients for each landslide monitoring device Indicates the first The initial weighting coefficients for each landslide monitoring device Indicating the first of the plurality of landslide monitoring devices Initial weighting coefficients for each landslide monitoring device; For each landslide monitoring device, based on the preset weighting coefficients of the multidimensional early warning indicators of the corresponding device and the measured values ​​of the multidimensional early warning indicators collected in real time by the corresponding device and corresponding one-to-one with the multidimensional early warning indicators, the corresponding single monitoring device early warning coefficient value is determined. Specifically, this includes: for the first of the multiple landslide monitoring devices... For each landslide monitoring device, based on the preset weight coefficients of the multidimensional early warning indicators for that device and the measured values ​​of the multidimensional early warning indicators collected in real time by the corresponding device and corresponding one-to-one with the multidimensional early warning indicators, the early warning coefficient value of the corresponding single monitoring device is calculated according to the following formula. : In the formula, Indicates less than or equal to positive integers, This represents the total number of the various landslide monitoring devices. Indicates the first The total dimension of the multidimensional early warning indicators of each landslide monitoring device. Indicates less than or equal to positive integers, Indicates that by the first The landslide monitoring equipment collects data in real time, and this data is consistent with the first of the multidimensional early warning indicators. The first dimensional early warning indicator corresponding to the Actual measured values ​​of early warning indicators Indicates the first Preset weighting coefficients for early warning indicators; Based on the normalized weighting coefficients of each landslide monitoring device and the early warning coefficient value of a single monitoring device, the combined early warning coefficient value of the multi-monitoring devices is calculated according to the following formula. : In the formula, Indicates the first Normalized weighting coefficients for each landslide monitoring device Indicates the first The early warning coefficient value of a single landslide monitoring device; Based on the combined early warning coefficient value of the multi-monitoring devices Based on the preset rules for classifying early warning levels for multiple monitoring devices, the current early warning level for multiple monitoring devices is determined and displayed.

2. The landslide early warning method according to claim 1, characterized in that, The spatial location information includes the layout profile location and / or the layout relative location. The layout profile location is divided into a main profile location located in the middle of the target landslide body and secondary profile locations located on both sides of the main profile of the target landslide body. The layout relative location is divided into a leading edge location, a middle location, and a trailing edge location relative to the target landslide body under the landslide movement mode. The landslide movement mode refers to a shoving landslide movement mode, a progressive retreating landslide movement mode, or a composite landslide movement mode.

3. The landslide early warning method according to claim 2, characterized in that, When the deployment spatial location information includes the deployment profile location, the first initial weight coefficient preset for different deployment spatial location information includes a first initial weight coefficient preset for the main profile location and a first initial weight coefficient preset for the secondary profile location, wherein the first initial weight coefficient preset for the main profile location is greater than the first initial weight coefficient preset for the secondary profile location. And / or, when the deployment spatial location information includes the deployment relative position, the first initial weight coefficient preset for different deployment spatial location information includes the first initial weight coefficient preset for the leading edge position, the first initial weight coefficient preset for the middle position and the first initial weight coefficient preset for the trailing edge position. If the landslide movement mode refers to the push-type landslide movement mode, then the first initial weight coefficient preset for the leading edge position is greater than the first initial weight coefficient preset for the middle position, and the first initial weight coefficient preset for the middle position is greater than the first initial weight coefficient preset for the trailing edge position. If the landslide movement mode refers to the progressive retreat landslide movement mode, then the first initial weight coefficient preset for the rear edge position is greater than the first initial weight coefficient preset for the front edge position, and the first initial weight coefficient preset for the front edge position is greater than the first initial weight coefficient preset for the middle position. If the landslide movement mode refers to the composite landslide movement mode, then the first initial weight coefficient preset for the middle position is greater than the first initial weight coefficient preset for the leading edge position, and the first initial weight coefficient preset for the leading edge position is equal to the first initial weight coefficient preset for the trailing edge position.

4. The landslide early warning method according to claim 1, characterized in that, The device attribute information includes device type, device acquisition frequency and / or device acquisition deformation accuracy. The device type is divided into GNSS device type and crack gauge device type. The device acquisition frequency is divided into second-level acquisition frequency, minute-level acquisition frequency, hour-level acquisition frequency and day-level acquisition frequency. The device acquisition deformation accuracy is divided into millimeter-level deformation accuracy, centimeter-level deformation accuracy and decimeter-level deformation accuracy. When the device attribute information includes a device type, the second initial weight coefficient preset for different device attribute information includes a second initial weight coefficient preset for the GNSS device type and a second initial weight coefficient preset for the crack meter device type, wherein the second initial weight coefficient preset for the crack meter device type is greater than the second initial weight coefficient preset for the GNSS device type. When the device attribute information includes the device acquisition frequency, the preset second initial weight coefficient for different device attribute information includes a preset second initial weight coefficient for the second-level acquisition frequency, a preset second initial weight coefficient for the minute-level acquisition frequency, a preset second initial weight coefficient for the hour-level acquisition frequency, and a preset second initial weight coefficient for the day-level acquisition frequency. Among them, the preset second initial weight coefficient for the second-level acquisition frequency is greater than the preset second initial weight coefficient for the minute-level acquisition frequency, the preset second initial weight coefficient for the minute-level acquisition frequency is greater than the preset second initial weight coefficient for the hour-level acquisition frequency, and the preset second initial weight coefficient for the hour-level acquisition frequency is greater than the preset second initial weight coefficient for the day-level acquisition frequency. When the device attribute information includes the device's deformation accuracy, the preset second initial weighting coefficients for different device attribute information include a preset second initial weighting coefficient for the millimeter-level deformation accuracy, a preset second initial weighting coefficient for the centimeter-level deformation accuracy, and a preset second initial weighting coefficient for the decimeter-level deformation accuracy. The preset second initial weighting coefficient for the millimeter-level deformation accuracy is greater than the preset second initial weighting coefficient for the centimeter-level deformation accuracy, and the preset second initial weighting coefficient for the centimeter-level deformation accuracy is greater than the preset second initial weighting coefficient for the decimeter-level deformation accuracy.

5. The landslide early warning method according to claim 1, characterized in that, The device reliability information includes the device data acquisition latency and / or the device data acquisition volatility. The device data acquisition latency is divided into one-minute latency, five-minute latency, half-hour latency, and hour latency. The device data acquisition volatility is divided into millimeter volatility, centimeter volatility, and decimeter volatility. When the device reliability information includes device data acquisition latency, the preset third initial weighting coefficients for different device reliability information include a preset third initial weighting coefficient for the one-minute latency, a preset third initial weighting coefficient for the five-minute latency, a preset third initial weighting coefficient for the half-hour latency, and a preset third initial weighting coefficient for the hour latency. Among these, the preset third initial weighting coefficient for the one-minute latency is greater than the preset third initial weighting coefficient for the five-minute latency, the preset third initial weighting coefficient for the five-minute latency is greater than the preset third initial weighting coefficient for the half-hour latency, and the preset third initial weighting coefficient for the half-hour latency is greater than the preset third initial weighting coefficient for the hour latency. When the device reliability information includes fluctuations in the device's collected data, the preset third initial weighting coefficients for different device reliability information include a preset third initial weighting coefficient for millimeter-level fluctuations, a preset third initial weighting coefficient for centimeter-level fluctuations, and a preset third initial weighting coefficient for decimeter-level fluctuations. The preset third initial weighting coefficient for millimeter-level fluctuations is greater than the preset third initial weighting coefficient for centimeter-level fluctuations, and the preset third initial weighting coefficient for centimeter-level fluctuations is greater than the preset third initial weighting coefficient for decimeter-level fluctuations.

6. The landslide early warning method according to claim 1, characterized in that, When the deployment spatial location information includes the deployment profile location and the relative deployment location, the equipment attribute information includes the equipment type, equipment acquisition frequency, and equipment acquisition deformation accuracy, and the equipment reliability information includes the equipment acquisition data latency and equipment acquisition data volatility, for each landslide monitoring device among the multiple landslide monitoring devices, the corresponding initial weight coefficient is determined based on a first initial weight coefficient preset for different deployment spatial location information, a second initial weight coefficient preset for different equipment attribute information, a third initial weight coefficient preset for different equipment reliability information, and the corresponding deployment spatial location information, equipment attribute information, and equipment reliability information, including: For the first of the multiple landslide monitoring devices For each landslide monitoring device, the initial weight coefficient is calculated according to the following formula, based on the first initial weight coefficient preset for different deployment spatial location information, the second initial weight coefficient preset for different device attribute information, the third initial weight coefficient preset for different device reliability information, and the corresponding deployment spatial location information, device attribute information, and device reliability information. : In the formula, Indicates less than or equal to positive integers, This represents the total number of the various landslide monitoring devices. Indicates that for the first The initial weighting coefficients are preset for the layout profile locations of each landslide monitoring device. Indicates that for the first The relative positions of each landslide monitoring device are preset with a first initial weighting coefficient. Indicates that for the first The second initial weighting coefficient is preset for each landslide monitoring device type. Indicates that for the first The second initial weighting coefficient is preset for the data acquisition frequency of each landslide monitoring device. Indicates that for the first The second initial weighting coefficient is preset for the deformation accuracy of the landslide monitoring equipment. Indicates that for the first The third initial weighting coefficient is preset for the data acquisition delay of each landslide monitoring device. Indicates that for the first The third initial weighting coefficient is preset for the volatility of the data collected by each landslide monitoring device.

7. A landslide early warning device based on a combination of multiple monitoring devices, characterized in that, It includes a device information acquisition module, an initial weight determination module, a normalized weight calculation module, a warning coefficient determination module, a combined weighted calculation module, and a warning level determination module; The equipment information acquisition module is used to acquire the spatial location information, equipment attribute information and equipment reliability information of multiple landslide monitoring devices, wherein the multiple landslide monitoring devices are respectively deployed at different spatial locations within the potential instability range of the target landslide body; The initial weight determination module is communicatively connected to the device information acquisition module. It is used to determine the corresponding initial weight coefficient for each landslide monitoring device among the plurality of landslide monitoring devices, based on a first initial weight coefficient preset for different deployment spatial location information, a second initial weight coefficient preset for different device attribute information, a third initial weight coefficient preset for different device reliability information, and the corresponding deployment spatial location information, device attribute information, and device reliability information. The normalized weight calculation module, communicatively connected to the initial weight determination module, is used to calculate the normalized weight coefficient of each landslide monitoring device according to the initial weight coefficient of each landslide monitoring device, using the following formula: In the formula, This represents the total number of the various landslide monitoring devices. and They represent less than or equal to positive integers, Indicating the first of the plurality of landslide monitoring devices Normalized weighting coefficients for each landslide monitoring device Indicates the first The initial weighting coefficients for each landslide monitoring device Indicating the first of the plurality of landslide monitoring devices Initial weighting coefficients for each landslide monitoring device; The warning coefficient determination module is used to determine the warning coefficient value for each landslide monitoring device based on the preset weight coefficients of the multidimensional warning indicators of the corresponding device and the measured values ​​of the multidimensional warning indicators collected in real time by the corresponding device and corresponding one-to-one with the multidimensional warning indicators. Specifically, this includes: determining the warning coefficient value for the first landslide monitoring device among the multiple landslide monitoring devices. For each landslide monitoring device, based on the preset weight coefficients of the multidimensional early warning indicators for that device and the measured values ​​of the multidimensional early warning indicators collected in real time by the corresponding device and corresponding one-to-one with the multidimensional early warning indicators, the early warning coefficient value of the corresponding single monitoring device is calculated according to the following formula. : In the formula, Indicates less than or equal to positive integers, This represents the total number of the various landslide monitoring devices. Indicates the first The total dimension of the multidimensional early warning indicators of each landslide monitoring device. Indicates less than or equal to positive integers, Indicates that by the first The landslide monitoring equipment collects data in real time, and this data is consistent with the first of the multidimensional early warning indicators. The first dimensional early warning indicator corresponding to the Actual measured values ​​of early warning indicators Indicates the first Preset weighting coefficients for early warning indicators; The combined weighted calculation module is communicatively connected to the normalized weight calculation module and the early warning coefficient determination module, and is used to calculate the combined early warning coefficient value of multiple monitoring devices according to the normalized weight coefficient of each landslide monitoring device and the early warning coefficient value of a single monitoring device, according to the following formula. : In the formula, Indicates the first Normalized weighting coefficients for each landslide monitoring device Indicates the first The early warning coefficient value of a single landslide monitoring device; The warning level determination module is communicatively connected to the combined weighted calculation module, and is used to determine the warning level based on the combined warning coefficient value of the multiple monitoring devices. Based on the preset rules for classifying early warning levels for multiple monitoring devices, the current early warning level for multiple monitoring devices is determined and displayed.

8. A computer device, characterized in that, The device includes a memory, a processor, and a transceiver that are sequentially connected in communication. The memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the landslide early warning method based on a combination of multiple monitoring devices as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that... The computer-readable storage medium stores instructions that, when executed on a computer, perform the landslide early warning method based on a combination of multiple monitoring devices as described in any one of claims 1 to 6.

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