Landslide disaster precursor identification and early warning method, device and equipment
By acquiring and processing slope dip angle data, landslide disaster risks can be identified, overcoming the shortcomings of traditional monitoring methods, achieving real-time and accurate early warning of landslide disasters, and improving the initiative and precision of prevention and control.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CENT SOUTH UNIV
- Filing Date
- 2026-02-28
- Publication Date
- 2026-04-17
AI Technical Summary
Traditional landslide disaster monitoring and early warning methods are inefficient, have limited coverage, and poor real-time performance, making it difficult to capture early warning information of disasters in a timely manner.
By acquiring slope inclination monitoring data, denoising is performed, the rate of change of inclination and target parameters are calculated, and the degree of landslide disaster risk is identified by combining pre-constructed parameter thresholds, and an alarm is output.
It has enabled real-time monitoring and precise early warning of landslide disasters, reduced casualties and economic losses, and promoted the development of prevention and control towards precision and proactivity.
Smart Images
Figure CN121884530A_ABST
Abstract
Description
Technical Field
[0001] The embodiments of the present invention relate to the field of landslide disaster monitoring technology, and in particular to a method, device and equipment for identifying and warning of landslide disaster precursors. Background Technology
[0002] Landslides are among the most common natural disasters globally, characterized by their suddenness, destructive power, and wide-ranging impact, seriously threatening human life, health, and property. Therefore, timely and effective identification of landslide precursors and subsequent early warning systems are increasingly crucial. Traditional landslide monitoring and early warning methods primarily rely on manual inspections and geological exploration. These methods suffer from low efficiency, limited coverage, and poor real-time performance, making it difficult to capture early warning information in a timely manner. This necessitates innovation based on traditional methods, breaking down traditional barriers and proposing a new, effective approach. With the rapid development of the Internet of Things, sensor technology, big data analytics, and artificial intelligence, constructing intelligent and scientific landslide monitoring and early warning systems and new methods has become possible. Summary of the Invention
[0003] To address the aforementioned technical problems, embodiments of the present invention provide a method for identifying and issuing early warnings of landslide hazards, including: Obtain the inclination monitoring data of the slope to be measured, the inclination monitoring data including the inclination value of the slope to be measured at different times, the inclination value being the angle between the slope surface of the slope to be measured and the inclination detector; The tilt angle monitoring data is denoised to obtain the target tilt angle monitoring data; The rate of change of tilt angle is determined based on the tilt angle monitoring data; The target parameters are calculated based on the rate of change of the inclination angle and the corresponding time. The target parameters are related to the slope of the inclination angle change function of the slope to be measured. The target risk level of the slope under test is determined based on the target parameter and a number of pre-constructed different parameter thresholds, with different parameter thresholds corresponding to different risk levels. Based on the target risk level, identify the precursors of landslide disasters on the slope to be tested; In response to the identification results indicating that the slope under test has early signs of landslide disaster, an alarm is output.
[0004] In one embodiment, calculating the target parameters based on the tilt angle change rate and time includes: The target velocity value is obtained by taking the reciprocal of the rate of change of the tilt angle. The target parameters are calculated based on the target velocity value and time.
[0005] In one embodiment, calculating the target parameters based on the target velocity value and time includes: Based on the target velocity value and time, and in conjunction with the following formula, the target parameters are calculated: ; The target speed value is K The K j For the first j The target velocity value at time t, the K i For the first i The target velocity value at time t, the t j For the first j At that time, the stated t i For the first i time.
[0006] In one embodiment, multiple different parameter thresholds are constructed, including: The target parameter corresponding to the constant change rate of tilt angle and the constant target degree velocity value is defined as the first parameter; The target parameter corresponding to the uniform increase of the tilt angle change rate and the uniform decrease of the target velocity value is defined as the second parameter; The range of parameters whose values are greater than the first parameter is defined as the first parameter threshold, and the risk level corresponding to the first parameter threshold is low risk. The range of values between the first parameter and the second parameter is defined as the threshold of the second parameter, and the risk level corresponding to the second parameter threshold is medium risk. The range of parameters whose values are less than the second parameter is defined as the third parameter threshold, and the risk level corresponding to the third parameter threshold is high risk.
[0007] In one embodiment, the noise reduction processing of the tilt angle monitoring data includes: The tilt angle monitoring data is subjected to outlier removal and logarithmic transformation.
[0008] In one embodiment, outlier removal from the tilt angle monitoring data includes: Calculate the mean and standard deviation of the tilt angle monitoring data; Determine the boundary judgment values; Based on the mean, standard deviation, and boundary judgment value, outlier values are identified in the tilt angle monitoring data.
[0009] In one embodiment, the step of identifying outliers in the tilt angle monitoring data based on the average value, standard deviation, and boundary judgment value includes: Based on the mean, standard deviation, boundary judgment value, and the following calculation formula, outlier judgment is performed on the tilt angle values in the tilt angle monitoring data: ; The tilt angle value is X The μ For the average value, the σ 2 represents the standard deviation, and 2 represents the boundary judgment value.
[0010] In one embodiment, the tilt angle monitoring data undergoes a logarithmic transformation, including: Perform a logarithmic transformation on the tilt angle monitoring data; The logarithmically transformed tilt angle monitoring data is then subjected to outlier removal processing again. The tilt monitoring data after outlier removal in the logarithmic coordinate system is mapped to the linear coordinate system.
[0011] Another embodiment of the present invention also provides a landslide disaster precursor identification and early warning device, comprising: The module is used to obtain the inclination monitoring data of the slope to be measured. The inclination monitoring data includes the inclination value of the slope to be measured at different times. The inclination value is the angle between the slope surface of the slope to be measured and the inclination detector. The processing module is used to denoise the tilt angle monitoring data to obtain the target tilt angle monitoring data; The first determining module is used to determine the rate of change of tilt angle based on the tilt angle monitoring data; The calculation module is used to calculate target parameters based on the rate of change of the inclination angle and the corresponding time. The target parameters are related to the slope of the inclination angle change function of the slope to be measured. The second determining module is used to determine the target risk level of the landslide disaster currently occurring on the slope to be measured based on the target parameters and a plurality of pre-constructed different parameter thresholds, wherein different parameter thresholds correspond to different risk levels; The identification module is used to identify landslide precursors of the slope under test based on the target risk level. The early warning module is used to output an alarm in response to the identification results indicating that the slope under test has early signs of landslide disaster.
[0012] Another embodiment of the present invention also provides an electronic device, comprising: One or more processors; Memory, configured to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the landslide disaster precursor identification and early warning method as described in any one of the above descriptions.
[0013] Based on the above, the beneficial effects of the embodiments of the present invention include the ability to identify and warn of disasters by means of tilt angle data acquisition, target parameter calculation, threshold comparison, identification of disaster gigabit, and output of alarms. This significantly improves the initiative and accuracy of landslide disaster prevention and control, provides technical support for "smart disaster prevention" and "intelligent disaster prevention", and promotes the transformation of landslide disaster prevention and control from "post-event emergency response" to "pre-event prevention".
[0014] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.
[0015] The technical solution of this application will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description
[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0017] Figure 1 This is a flowchart illustrating the landslide disaster precursor identification and early warning method in an embodiment of the present invention.
[0018] Figure 2 This is a schematic diagram of the tilt angle monitoring data after noise reduction in an embodiment of the present invention.
[0019] Figure 3 This is a schematic diagram of risk area division in an embodiment of the present invention.
[0020] Figure 4 This is a schematic diagram of the tilt angle change types in an embodiment of the present invention.
[0021] Figure 5 This is a schematic diagram of the tilt sensor layout in an application embodiment of the present invention.
[0022] Figure 6 This is a graph showing the changes in tilt angle monitoring data and rainfall information in an application embodiment of the present invention.
[0023] Figure 7This is a flowchart illustrating the stage identification process in an application embodiment of the present invention.
[0024] Figure 8 This is a structural block diagram of the landslide disaster precursor identification and early warning device in an embodiment of the present invention. Detailed Implementation
[0025] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings, but these are not intended to limit the scope of the invention.
[0026] It should be understood that various modifications can be made to the embodiments disclosed herein. Therefore, the following description should not be considered as limiting, but merely as an example of embodiments. Other modifications within the scope of this disclosure will be apparent to those skilled in the art.
[0027] The accompanying drawings, which are included in and form part of this specification, illustrate embodiments of the present disclosure and, together with the general description of the disclosure given above and the detailed description of the embodiments given below, serve to explain the principles of the disclosure.
[0028] These and other features of the invention will become apparent from the following description of preferred forms of embodiments given as non-limiting examples, with reference to the accompanying drawings.
[0029] It should also be understood that although the invention has been described with reference to some specific examples, those skilled in the art can certainly implement many other equivalent forms of the invention, which have the features described in the claims and are therefore all within the scope of protection defined herein.
[0030] The above and other aspects, features and advantages of this disclosure will become more apparent when taken in conjunction with the accompanying drawings and in view of the following detailed description.
[0031] Specific embodiments of the present disclosure are described thereafter with reference to the accompanying drawings; however, it should be understood that the disclosed embodiments are merely examples of the present disclosure and can be implemented in various ways. Well-known and / or repeated functions and structures are not described in detail to avoid unnecessary or redundant details that could obscure the present disclosure. Therefore, the specific structural and functional details disclosed herein are not intended to be limiting, but merely to serve as the basis and representative basis for the claims to teach those skilled in the art to use the present disclosure in a variety of substantially any suitable detailed structures.
[0032] This specification may use the phrases “in one embodiment,” “in another embodiment,” “in yet another embodiment,” or “in still another embodiment,” all of which may refer to one or more of the same or different embodiments according to this disclosure.
[0033] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
[0034] like Figure 1 As shown, this embodiment of the invention provides a method for identifying and issuing early warnings of landslide hazards, including: S1: Obtain the inclination monitoring data of the slope to be measured, the inclination monitoring data includes the inclination value of the slope to be measured at different times, the inclination value is the angle between the slope surface of the slope to be measured and the inclination detector; S2: Denoise the tilt angle monitoring data to obtain the target tilt angle monitoring data; S3: Determine the rate of change of tilt angle based on the tilt angle monitoring data; S4: Calculate the target parameters based on the rate of change of the inclination angle and the corresponding time. The target parameters are related to the slope of the inclination angle change function of the slope to be measured. S5: Determine the target risk level of the slope under test for a landslide disaster based on the target parameter and multiple pre-constructed different parameter thresholds, where different parameter thresholds correspond to different risk levels; S6: Identify landslide precursors on the slope to be tested based on the target risk level; S7: In response to the identification result indicating that the slope under test has early signs of landslide disaster, an alarm is output.
[0035] The landslide hazard precursor identification and early warning method in this embodiment utilizes Internet of Things (IoT) technology, data analysis, and control mechanisms, such as intelligent monitoring and early warning systems, combined with slope inclination monitoring equipment and inclination monitoring techniques. This method effectively compensates for the shortcomings of traditional monitoring and early warning methods, achieving real-time monitoring, accurate early warning, and rapid response, thereby reducing casualties and economic losses caused by landslides and promoting the development of landslide prevention and control towards precision and proactive approaches.
[0036] In this embodiment, when measuring the slope inclination angle, the degree of inclination of the slope is measured by an inclination sensor. The sensor is vertically installed on the slope surface via a carrier. The inclination sensor is also connected to a data logger for continuous, uninterrupted data recording and uploading to the control mechanism so that the control mechanism can calculate and determine the rate of change of the inclination angle.
[0037] After obtaining the tilt angle measurement data, the control mechanism first performs noise reduction processing on it. In this embodiment, the noise reduction processing of the tilt angle monitoring data includes: S201: Perform outlier removal and logarithmic transformation on the tilt angle monitoring data.
[0038] The outlier removal process for the tilt angle monitoring data includes: S202: Calculate the average value and standard deviation of the tilt angle monitoring data; S203: Determine the boundary judgment value; S204: Based on the mean, standard deviation, and boundary judgment value, determine the outlier values in the tilt angle monitoring data.
[0039] Specifically, the step of identifying outliers in the tilt angle monitoring data based on the average value, standard deviation, and boundary judgment value includes: S205: Based on the mean, standard deviation, boundary judgment value, and the following calculation formula, outlier judgment is performed on the tilt angle values in the tilt angle monitoring data: ; The tilt angle value is X The μ For the average value, the σ 2 represents the standard deviation, and 2 represents the boundary judgment value.
[0040] In this embodiment, the boundary judgment value 2 is determined by taking the value corresponding to the 95% confidence interval. Using the above formula, values greater than 2 can be filtered out from the calculated values involving each tilt angle value; these are considered outliers and can be removed.
[0041] Furthermore, the tilt angle monitoring data undergoes a logarithmic transformation, including: S206: Perform a logarithmic transformation on the tilt angle monitoring data; S207: Perform outlier removal processing on the logarithmically transformed tilt angle monitoring data again; S208: Map the tilt monitoring data after outlier removal in the logarithmic coordinate system to the linear coordinate system.
[0042] For example, the logarithmic transformation processing involves taking the tilt angle monitoring data from the current linear coordinate system to the logarithmic coordinate system. Specifically, in this step of denoising, the visualization analysis effect of the data is optimized by transforming the data visualization analysis scale. Here, a logarithmic transformation (usually the natural logarithm ln or the commonly used log10) can be performed to map the tilt angle monitoring data from the linear scale to the logarithmic scale, achieving a compression effect on the dataset. Based on this, the control mechanism in this embodiment will further remove outliers from the data, using the same outlier removal method as described above. This step further optimizes the data structure of the monitoring data, which is then adjusted back to the linear coordinate system. At this point, the data denoising is complete, resulting in a data image with optimized data structure, i.e., optimized tilt angle monitoring data. The effect presented can be referenced... Figure 2 As shown.
[0043] pass Figure 2 It can be seen that there is a deformation acceleration stage before the landslide occurs. This accelerated deformation stage, acting as a precursor to landslides, allows for qualitative prediction of slope failure after this stage. However, the duration of this stage is unknown in actual monitoring, making it impossible to determine the timing of the landslide. Therefore, to achieve timely identification of landslide precursors and provide timely warnings, the method proposed in this embodiment calculates the rate of change of the dip angle and, based on this, calculates target parameters. Finally, based on the target parameters and pre-constructed threshold values for different parameters, it achieves accurate identification and early warning of landslide precursors.
[0044] Specifically, the calculation of the target parameters based on the rate of change of the tilt angle and time includes: S301: Take the reciprocal of the rate of change of tilt angle to obtain the target velocity value; S302: Calculate the target parameters based on the target velocity value and time.
[0045] For example, for any moment in the process of tilt angle change, let the previous time point be... t The recorded dip angle is deg1, and the next time point... t The recorded dip angle is deg2, and the corresponding rate of change of dip angle during this time period is... v The corresponding time for the rate is given by the following formula: The time corresponding to the rate: ; The corresponding rate of change of tilt angle: .
[0046] In this embodiment, calculating the target parameters based on the target velocity value and time includes: S303: Based on the target velocity value and time, and in conjunction with the following formula, calculate the target parameters: ; The target speed value is K The K j For the first j The target velocity value at time t, the K i For the first i The target velocity value at time t, the t j For the first j At that time, the stated t i For the first i time.
[0047] In other words, this embodiment involves taking the reciprocal of the calculated rate of change of tilt angle, and then performing data analysis based on this reciprocal and the time information involved in the tilt angle change phase. For example, the reciprocal of the rate of change of tilt angle is... K The rate of change of tilt angle is v Then we have: ; at this time, K With time t The functional relationship is denoted as K’ ( t ), denote the reciprocal of velocity K With time t The slope of the function is i That is, the objective parameter is denoted as i Then we have: .
[0048] After the target parameter is calculated, it can be compared with multiple pre-built parameter thresholds.
[0049] Furthermore, before determining the damage type, the system accurately identifies the start time of the acceleration phase before slope collapse. In existing solutions, this determination process is highly subjective and user-dependent, hindering accurate identification of the phase timing. Therefore, this embodiment employs a systematic quantitative method to ensure the consistency and repeatability of the results. The flowchart for identifying the initial acceleration phase is as follows: Figure 7 As shown in the figure, the specific process for identifying the start of the acceleration phase is illustrated, and the steps are as follows: (1) Preliminary delineation of the transition zone: First, a preliminary time zone containing potential starting points is determined from the complete tilt deformation record. The selection of this zone is intended to cover the transition process from quasi-linear deformation in the secondary stage to exponential acceleration in the acceleration stage.
[0050] (2) Calculation of the reciprocal of the tilt deformation rate: Calculate the tilt deformation rate between consecutive data points within the defined transition region, and then calculate the reciprocal of the rate (1 / v) for each time step.
[0051] (3) Objective determination of the initial stage of acceleration: The starting point of the acceleration stage is objectively determined by measuring the time corresponding to the maximum inverse tilt rate in the transition region. The inverse peak of this rate marks the moment of stable minimum speed before the start of continuous acceleration, providing a reliable quantitative indicator for the transition of the stage.
[0052] Furthermore, when starting to determine the type of damage, it is necessary to first construct several different parameter thresholds, including: S8: Define the target parameter as the first parameter when both the tilt angle change rate and the target degree rate value remain unchanged; S9: When the tilt angle change rate is uniformly increased and the target velocity value is uniformly decreased, the target parameter corresponding to this is defined as the second parameter; S10: Define the range of parameters whose values are greater than the first parameter as the first parameter threshold, and the risk level corresponding to the first parameter threshold is low risk; S11: Define the range of parameters whose values are between the first parameter and the second parameter as the threshold of the second parameter, and the risk level corresponding to the second parameter threshold is medium risk; S12: Define the range of parameters whose values are less than the second parameter as the third parameter threshold, and the risk level corresponding to the third parameter threshold is high risk.
[0053] For example, combining Figure 3 As shown, through analysis of tilt angle accelerated deformation data, this embodiment categorizes various acceleration processes into three types: the first type is rapid accelerated failure within an extremely short time; the second type is relatively rapid accelerated failure within a shorter time; and the third type is slow failure over a longer period. Corresponding to these three types, this embodiment constructs three parameter thresholds. Specifically, combined with... Figure 4 As shown, when v When unchanged, K Keep it unchanged, and take the value at this moment. i for i 1; when v When it increases uniformly, K Decrease uniformly, and take the value at this point. i for i 2. Based on this, the measured change in tilt rate can be... i 0 can be categorized into the following types depending on the circumstances: ① When i 0> i At 1 o'clock, K Follow t Gradually increase, that is v Follow t The acceleration gradually decreases, marking the end of the acceleration zone, denoted as Zone III. ② When i 2< i 0< i At 1 o'clock, K As it decreases slowly, that is v As t increases slowly, this is a region of slow acceleration, denoted as Region II. ③ When i 0< i At 2 o'clock, K Follow tA rapid decrease, meaning v increases rapidly with t, constitutes a rapid acceleration zone, denoted as Zone I. Zone I is the rapid acceleration zone, a high-risk zone; Zone II is the slow acceleration zone, a medium-risk zone; and Zone III is the de-acceleration zone, a low-risk zone. For high-risk zones, an alarm must be issued; for medium-risk zones, enhanced monitoring is required; and for low-risk zones, normal monitoring is sufficient.
[0054] In practical application, taking a landslide on a mountain as an example, a typhoon accompanied by heavy rainfall caused a surface slippage on the slope beside a tourist road. In terms of the landslide's development sequence, a shallow landslide occurred first at the top, gradually progressing to deeper layers. Furthermore, rainfall caused surface water infiltration and erosion of the slope soil, loosening the surface soil and reducing internal soil cohesion, leading to a subsequent secondary slippage. Six tilt sensors, numbered K1-K6, were installed above the landslide surface. These tilt sensors, developed independently, provide continuous monitoring with an accuracy of 0.003°. The on-site device locations and installation diagram are shown below. Figure 5 As shown.
[0055] Combination Figure 6 As shown in the figure, after the K-2 sensor was installed for 6 hours, starting from around 20:00 on July 21, the soil at the K-2 sensor location deformed continuously for 12 hours at a rate of approximately 0.083° / h. Subsequently, in the 4.5 hours from 8:00 on July 22 to 12:00 before the rapid collapse began, it slid rapidly at an average rate of 0.89° / h, thus capturing the accelerated creep process 30 minutes later.
[0056] The monitoring station also detected signs of collapse 16 hours before the accelerated failure phase. Around 8:00 AM on the 22nd, rainfall suddenly increased from 10mm to about 18mm. For a period afterward, the K-2 tilt sensor showed a brief period of slow movement until around 12:00 PM, when the collapse occurred, with a significant change in the tilt angle, indicating the accelerated movement phase. It can be determined that the main triggering factor for this landslide was rainfall, and this event had a certain time delay; that is, the slope only began to develop dynamically some time after the rainfall. The main reason is that the infiltration of rainwater and the erosion of the slope require a certain amount of time to accumulate. The internal infiltration of rainwater led to increased pore water pressure, which in turn reduced the shear strength of the slip zone.
[0057] Substituting the acquired field data into the aforementioned early warning method, and taking the reciprocal of the slope rate data to explore its relationship with time, it was determined that the slope rapidly accelerated within a short period, belonging to Zone I, a high-risk area. This aligns with the actual situation, necessitating timely and appropriate measures. As an emergency strategy, slope weight reduction and counter-pressure measures were implemented to reshape the slope. As long-term management measures, anti-slide walls and anchor bolts were installed to enhance slope stability.
[0058] like Figure 8 As shown, another embodiment of the present invention also provides a landslide disaster precursor identification and early warning device, including: The module is used to obtain the inclination monitoring data of the slope to be measured. The inclination monitoring data includes the inclination value of the slope to be measured at different times. The inclination value is the angle between the slope surface of the slope to be measured and the inclination detector. The processing module is used to denoise the tilt angle monitoring data to obtain the target tilt angle monitoring data; The first determining module is used to determine the rate of change of tilt angle based on the tilt angle monitoring data; The calculation module is used to calculate target parameters based on the rate of change of the inclination angle and the corresponding time. The target parameters are related to the slope of the inclination angle change function of the slope to be measured. The second determining module is used to determine the target risk level of the landslide disaster currently occurring on the slope to be measured based on the target parameters and a plurality of pre-constructed different parameter thresholds, wherein different parameter thresholds correspond to different risk levels; The identification module is used to identify landslide precursors of the slope under test based on the target risk level. The early warning module is used to output an alarm in response to the identification results indicating that the slope under test has early signs of landslide disaster.
[0059] In one embodiment, calculating the target parameters based on the tilt angle change rate and time includes: The target velocity value is obtained by taking the reciprocal of the rate of change of the tilt angle. The target parameters are calculated based on the target velocity value and time.
[0060] In one embodiment, calculating the target parameters based on the target velocity value and time includes: Based on the target velocity value and time, and in conjunction with the following formula, the target parameters are calculated: ; The target speed value is K The K j For the first j The target velocity value at time t, theK i For the first i The target velocity value at time t, the t j For the first j At that time, the stated t i For the first i time.
[0061] In one embodiment, multiple different parameter thresholds are constructed, including: The target parameter corresponding to the constant change rate of tilt angle and the constant target degree velocity value is defined as the first parameter; The target parameter corresponding to the uniform increase of the tilt angle change rate and the uniform decrease of the target velocity value is defined as the second parameter; The range of parameters whose values are greater than the first parameter is defined as the first parameter threshold, and the risk level corresponding to the first parameter threshold is low risk. The range of values between the first parameter and the second parameter is defined as the threshold of the second parameter, and the risk level corresponding to the second parameter threshold is medium risk. The range of parameters whose values are less than the second parameter is defined as the third parameter threshold, and the risk level corresponding to the third parameter threshold is high risk.
[0062] In one embodiment, the noise reduction processing of the tilt angle monitoring data includes: The tilt angle monitoring data is subjected to outlier removal and logarithmic transformation.
[0063] In one embodiment, outlier removal from the tilt angle monitoring data includes: Calculate the mean and standard deviation of the tilt angle monitoring data; Determine the boundary judgment values; Based on the mean, standard deviation, and boundary judgment value, outlier values are identified in the tilt angle monitoring data.
[0064] In one embodiment, the step of identifying outliers in the tilt angle monitoring data based on the average value, standard deviation, and boundary judgment value includes: Based on the mean, standard deviation, boundary judgment value, and the following calculation formula, outlier judgment is performed on the tilt angle values in the tilt angle monitoring data: ; The tilt angle value is X The μ For the average value, the σ 2 represents the standard deviation, and 2 represents the boundary judgment value.
[0065] In one embodiment, the tilt angle monitoring data undergoes a logarithmic transformation, including: Perform a logarithmic transformation on the tilt angle monitoring data; The logarithmically transformed tilt angle monitoring data is then subjected to outlier removal processing again. The tilt monitoring data after outlier removal in the logarithmic coordinate system is mapped to the linear coordinate system.
[0066] Another embodiment of the present invention also provides an electronic device, comprising: One or more processors; Memory, configured to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the landslide disaster precursor identification and early warning method as described in any one of the above descriptions.
[0067] Furthermore, one embodiment of the present invention also provides a storage medium storing a computer program, which, when executed by a processor, implements the landslide disaster precursor identification and early warning method described above. It should be understood that the various solutions in this embodiment have the corresponding technical effects in the above-described method embodiments, and will not be repeated here.
[0068] Furthermore, embodiments of the present invention also provide a computer program product, which is tangibly stored on a computer-readable medium and includes computer-readable instructions, which, when executed, cause at least one processor to perform a landslide disaster precursor identification and early warning method as described in the embodiments above.
[0069] It should be noted that the computer storage medium of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, system, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access storage medium (RAM), a read-only storage medium (ROM), an erasable programmable read-only storage medium (EPROM or flash memory), an optical fiber, a portable compact disk read-only storage medium (CD-ROM), an optical storage medium, a magnetic storage medium, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program configured for use by or in connection with an instruction execution system, system, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, antenna, optical fiber, RF, etc., or any suitable combination thereof.
[0070] Furthermore, those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.
[0071] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.
[0072] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction set implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0073] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of protection of this application is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of one or more embodiments of this application as described above, which are not provided in detail for the sake of brevity.
Claims
1. A method for identifying and issuing early warnings of landslide hazards, characterized in that, include: Obtain the inclination monitoring data of the slope to be measured, the inclination monitoring data including the inclination value of the slope to be measured at different times, the inclination value being the angle between the slope surface of the slope to be measured and the inclination detector; The tilt angle monitoring data is denoised to obtain the target tilt angle monitoring data; The rate of change of tilt angle is determined based on the tilt angle monitoring data; The target parameters are calculated based on the rate of change of the inclination angle and the corresponding time. The target parameters are related to the slope of the inclination angle change function of the slope to be measured. The target risk level of the slope under test is determined based on the target parameter and a number of pre-constructed different parameter thresholds, with different parameter thresholds corresponding to different risk levels. Based on the target risk level, identify the precursors of landslide disasters on the slope to be tested; In response to the identification results indicating that the slope under test has early signs of landslide disaster, an alarm is output.
2. The method for identifying and issuing early warning of landslide hazards according to claim 1, characterized in that, The calculation of the target parameters based on the tilt angle change rate and time includes: The target velocity value is obtained by taking the reciprocal of the rate of change of the tilt angle. The target parameters are calculated based on the target velocity value and time.
3. The method for identifying and issuing early warning of landslide hazards according to claim 2, characterized in that, The calculation of the target parameters based on the target velocity value and time includes: Based on the target velocity value and time, and in conjunction with the following formula, the target parameters are calculated: ； The target speed value is K The K j For the first j The target velocity value at time t, the K i For the first i The target velocity value at time t, the t j For the first j At that time, the stated t i For the first i time.
4. The method for identifying and issuing early warning of landslide hazards according to claim 3, characterized in that, Construct multiple different parameter thresholds, including: The target parameter corresponding to the constant change rate of tilt angle and the constant target degree velocity value is defined as the first parameter; The target parameter corresponding to the uniform increase of the tilt angle change rate and the uniform decrease of the target velocity value is defined as the second parameter; The range of parameters whose values are greater than the first parameter is defined as the first parameter threshold, and the risk level corresponding to the first parameter threshold is low risk. The range of values between the first parameter and the second parameter is defined as the threshold of the second parameter, and the risk level corresponding to the second parameter threshold is medium risk. The range of parameters whose values are less than the second parameter is defined as the third parameter threshold, and the risk level corresponding to the third parameter threshold is high risk.
5. The method for identifying and issuing early warning of landslide hazards according to claim 1, characterized in that, The noise reduction process for the tilt angle monitoring data includes: The tilt angle monitoring data is subjected to outlier removal and logarithmic transformation.
6. The method for identifying and issuing early warning of landslide hazards according to claim 5, characterized in that, Outlier removal from the tilt angle monitoring data includes: Calculate the mean and standard deviation of the tilt angle monitoring data; Determine the boundary judgment values; Based on the mean, standard deviation, and boundary judgment value, outlier values are identified in the tilt angle monitoring data.
7. The method for identifying and issuing early warning of landslide hazards according to claim 6, characterized in that, The process of identifying outliers in the tilt angle monitoring data based on the average value, standard deviation, and boundary judgment value includes: Based on the mean, standard deviation, boundary judgment value, and the following calculation formula, outlier judgment is performed on the tilt angle values in the tilt angle monitoring data: ； The tilt angle value is X The μ For the average value, the σ 2 represents the standard deviation, and 2 represents the boundary judgment value.
8. The method for identifying and warning of landslide precursors according to claim 5, characterized in that, The tilt angle monitoring data undergoes a logarithmic transformation, including: Perform a logarithmic transformation on the tilt angle monitoring data; The logarithmically transformed tilt angle monitoring data is then subjected to outlier removal processing again. The tilt monitoring data after outlier removal in the logarithmic coordinate system is mapped to the linear coordinate system.
9. A landslide disaster precursor identification and early warning device, characterized in that, include: The module is used to obtain the inclination monitoring data of the slope to be measured. The inclination monitoring data includes the inclination value of the slope to be measured at different times. The inclination value is the angle between the slope surface of the slope to be measured and the inclination detector. The processing module is used to denoise the tilt angle monitoring data to obtain the target tilt angle monitoring data; The first determining module is used to determine the rate of change of tilt angle based on the tilt angle monitoring data; The calculation module is used to calculate target parameters based on the rate of change of the inclination angle and the corresponding time. The target parameters are related to the slope of the inclination angle change function of the slope to be measured. The second determining module is used to determine the target risk level of the landslide disaster currently occurring on the slope to be measured based on the target parameters and a plurality of pre-constructed different parameter thresholds, wherein different parameter thresholds correspond to different risk levels; The identification module is used to identify landslide precursors of the slope under test based on the target risk level. The early warning module is used to output an alarm in response to the identification results indicating that the slope under test has early signs of landslide disaster.
10. An electronic device, characterized in that, include: One or more processors; Memory, configured to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the landslide disaster precursor identification and early warning method as described in any one of claims 1-8.