A geological disaster deformation monitoring method based on the BeiDou satellite navigation system

Abstract

This application discloses a geological disaster deformation monitoring method based on the BeiDou satellite navigation system, belonging to the field of geological disaster deformation monitoring technology. The method includes: acquiring real-time observation data of the monitoring target at preset intervals, performing real-time dynamic relative positioning to obtain a displacement time series, and performing sliding window filtering to obtain the displacement identifier corresponding to the sliding window; determining the displacement motion state of the monitoring target and the process noise adjustment factor for the next sliding window based on the comparison result of the displacement identifier of the current sliding window and the previous sliding window; continuing real-time dynamic relative positioning within the next sliding window based on the process noise adjustment factor; and determining the deformation of the monitoring target based on the real-time dynamic relative positioning result. This application solves the deficiency of related technologies in efficiently identifying the creep displacement of the monitoring target by adaptively adjusting the sliding window width and process noise, thereby achieving rapid and accurate identification of the displacement state and trend of the monitoring target.

Description

Technical Field

[0001] This application relates to the field of geological disaster deformation monitoring technology, and in particular to a geological disaster deformation monitoring method based on the Beidou satellite navigation system. Background Technology

[0002] The BeiDou Navigation Satellite System (BDS) is a global satellite navigation system independently developed by my country. It can provide users with all-weather three-dimensional coordinates, velocity, and time information at any location on the Earth's surface or in near-Earth space. Based on GNSS, Real-Time Kinematic (RTK) technology can obtain high-precision three-dimensional coordinates of the monitoring station in real time through differential processing of observation data from the base station and the monitoring station. It has advantages such as fast convergence speed, short response time, and high positioning accuracy. It is often used to capture abrupt changes in landslide information in real time, and has therefore become the most widely used GNSS landslide deformation monitoring technology.

[0003] However, the movement state of the monitored target is complex and variable at different stages of landslide development. The traditional RTK monitoring method based on Kalman filtering is not sensitive to slow creep displacement of the monitored target (e.g., displacement with a rate of less than 0.5 mm / s and a duration of more than 300 s). The displacement state is difficult to identify quickly and accurately, which can easily lead to slow deformation response and missed deformation detection. It cannot provide timely early warning for landslide deformation, which can easily cause adverse effects.

[0004] Therefore, there is an urgent need for a method that can quickly and accurately identify and reflect the slow motion characteristics and landslide displacement trends of monitored targets. Summary of the Invention

[0005] This application provides a geological disaster deformation monitoring method based on the BeiDou satellite navigation system to address the shortcomings of the aforementioned related technologies in quickly and accurately identifying slow creep displacement. It employs adaptive sliding window width adjustment and adaptive process noise adjustment to achieve rapid and accurate identification of the displacement state and trend of the detection target, providing real-time and accurate dynamic displacement sequence information for geological disaster deformation monitoring. The technical solution is as follows:

[0006] In a first aspect, embodiments of this application provide a geological disaster deformation monitoring method based on the BeiDou satellite navigation system, including:

[0007] Real-time observation data of the monitored target is acquired at preset intervals;

[0008] The real-time observation data is used to perform real-time dynamic relative positioning to obtain the displacement time series of the monitored target;

[0009] Based on the displacement time series, a sliding window filter is performed to calculate the corresponding displacement indicator within the sliding window.

[0010] The displacement indicator of the current sliding window is compared with the displacement indicator of the previous sliding window. Based on the comparison result, the displacement motion state of the monitored target is determined. Based on the displacement motion state, the process noise adjustment factor of Kalman filtering in the next sliding window is determined.

[0011] The process noise of the Kalman filter in the next window is adjusted based on the process noise adjustment factor; the step of performing real-time dynamic relative positioning on the real-time observation data continues to be executed in the next sliding window;

[0012] The deformation of the monitored target is determined based on the real-time dynamic relative positioning result corresponding to the current sliding window.

[0013] In one alternative embodiment of the first aspect, before performing sliding window filtering based on the displacement time series and calculating the corresponding displacement identifier within the sliding window, the method further includes:

[0014] Each displacement in the displacement time series is sorted according to its numerical value, and a corresponding displacement numerical sequence is generated based on the sorting result.

[0015] In one alternative embodiment of the first aspect, the displacement identifier includes the median displacement and the interquartile range of displacement calculated based on the displacement value sequence;

[0016] The step of determining the displacement motion state of the monitored target based on the comparison results, and determining the process noise adjustment factor of the Kalman filter in the next sliding window based on the displacement motion state, includes:

[0017] If the displacement motion state is a stationary state or a uniform state, then the noise adjustment factor of the Kalman filter process in the next sliding window remains the same as that in the current sliding window;

[0018] If the displacement motion state is an acceleration state or a deceleration state, the noise adjustment factor of the Kalman filter process in the next sliding window is adjusted based on the median displacement and interquartile range of the current sliding window.

[0019] In one alternative to the first aspect, if the displacement motion state is an acceleration state or a deceleration state, adjusting the process noise adjustment factor of the Kalman filter within the sliding window based on the median displacement and interquartile range of the current sliding window includes:

[0020] Obtain the displacement identifier corresponding to the time series of displacement of the monitored target in each direction under the station center coordinate system;

[0021] The displacement rate in each direction is calculated based on the displacement identifier.

[0022] The process noise adjustment factor in the corresponding direction is calculated based on the displacement rate.

[0023] In one alternative embodiment of the first aspect, after determining the displacement motion state of the monitored target based on the comparison result, the method further includes:

[0024] If the displacement motion state is the stationary state or the uniform state, then the width of the current sliding window is used as the width of the next sliding window.

[0025] If the displacement motion state is the acceleration state, then the width of the current sliding window is reduced and used as the width of the next sliding window.

[0026] If the displacement motion state is the deceleration state, then the width of the current sliding window is increased and used as the width of the next sliding window;

[0027] Perform the step of determining the noise adjustment factor of the Kalman filter process within the next sliding window based on the displacement motion state.

[0028] Secondly, this application also provides a geological disaster deformation monitoring device based on the BeiDou satellite navigation system, comprising:

[0029] The data acquisition module is used to acquire real-time observation data of the monitoring target at preset intervals, perform real-time dynamic relative positioning on the real-time observation data, and obtain the displacement time series of the monitoring target.

[0030] A sliding window filtering module is used to perform sliding window filtering on the displacement time series;

[0031] The calculation module is used to calculate the corresponding displacement identifier within the sliding window based on the displacement time series.

[0032] An adaptive adjustment module is used to compare the displacement indicator of the current sliding window with the displacement indicator of the previous sliding window, determine the displacement motion state of the monitored target based on the comparison result, determine the process noise adjustment factor of the Kalman filter in the next sliding window based on the displacement motion state, and adjust the process noise of the Kalman filter in the next sliding window based on the process noise adjustment factor.

[0033] The data acquisition module is also used to continue executing the step of performing real-time dynamic relative positioning of the real-time observation data in the next sliding window;

[0034] The deformation monitoring module is used to determine the deformation of the monitored target based on the real-time dynamic relative positioning result corresponding to the current sliding window.

[0035] Thirdly, embodiments of this application also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method provided by the first aspect or any implementation thereof of the embodiments of this application.

[0036] Fourthly, this application also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method provided by the first aspect of the embodiments of this application or any implementation thereof.

[0037] The beneficial effects of the technical solutions provided in some embodiments of this application include at least the following:

[0038] (1) Based on the slow deformation displacement state characteristics of the detected geological disaster monitoring body, the sliding window width and the position state process noise variance matrix in the filtering and positioning solution can be adaptively adjusted so that the relevant monitoring parameters can match the displacement rate of the monitoring target.

[0039] (2) Under the premise of effectively suppressing observation noise and ensuring the accuracy of dynamic displacement monitoring, the filtering state model can be kept consistent with the actual motion state of the monitoring target by reasonably adjusting the noise adjustment factor in the filtering process. Thus, it can accurately and quickly reflect the slow displacement of the monitoring target throughout the entire process of the monitoring target's acceleration, uniform speed, deceleration, etc., greatly improving the sensitivity of monitoring the slow creep displacement of the monitoring target. This can provide accurate, reliable, and high time resolution dynamic displacement and motion trend information for early warning of geological disaster deformation monitoring, and provide reliable protection for preventing geological disasters. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a flowchart illustrating a geological disaster deformation monitoring method based on the BeiDou satellite navigation system provided in this application embodiment;

[0042] Figure 2 This is a flowchart illustrating a geological disaster deformation monitoring method based on the BeiDou satellite navigation system provided in this application embodiment;

[0043] Figure 3 This is a schematic diagram of the structure of a geological disaster deformation monitoring device based on the BeiDou satellite navigation system provided in this application embodiment;

[0044] Figure 4 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0046] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or modules is not limited to the steps or modules listed, but may optionally include steps or modules not listed, or may optionally include other steps or modules inherent to such process, method, product, or apparatus.

[0047] It should be noted that the terms "first" and "second" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects. It is understood that "first" and "second" can be interchanged in a specific order or sequence where permitted. It should be understood that the objects distinguished by "first" and "second" can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in an order other than those described or illustrated herein.

[0048] It should be noted that the geological disaster deformation monitoring method based on the Beidou satellite navigation system provided in this application embodiment can be applied to the field of geological disaster prediction, such as monitoring landslides and collapses. Monitoring stations and reference stations can be set up in areas with geological disaster risks. The reference stations provide a benchmark for positioning and measurement, and the monitoring stations measure the coordinates of the monitoring station's location. The coordinates of the monitoring station obtained through multiple measurements can determine the displacement at the location of the monitoring station, thereby reflecting the deformation at the corresponding location and providing early warning information to indicate that there is a potential geological disaster risk in the area where the monitoring station is located.

[0049] It should be noted that because slow displacement deformation is characterized by small instantaneous displacement, long duration, and large cumulative displacement, related technologies can often only detect sudden deformations and cannot identify slow cumulative displacements.

[0050] To address the aforementioned problems, this application provides a geological disaster deformation monitoring method based on the BeiDou satellite navigation system. The following detailed description of the application is provided in conjunction with specific embodiments.

[0051] Next, combine Figure 1 This application introduces a geological disaster deformation monitoring method based on the BeiDou satellite navigation system, as provided in its embodiments. Figure 1 This paper presents a flowchart illustrating a geological disaster deformation monitoring method based on the BeiDou satellite navigation system, according to an embodiment of this application. The method includes the following steps:

[0052] S101: Acquire real-time observation data of the monitored target at preset intervals.

[0053] S102, Kalman filtering is performed on the real-time observation data to achieve real-time dynamic relative positioning, and the displacement time series of the monitored target is obtained within the current sliding window.

[0054] S103, Perform sliding window filtering on the displacement time series to calculate the displacement identifier corresponding to the sliding window.

[0055] S104, compare the displacement indicator of the current sliding window with the displacement indicator of the previous sliding window, determine the displacement motion state of the monitored target based on the comparison result, and determine the process noise adjustment factor of Kalman filtering in the next sliding window based on the displacement motion state.

[0056] S105, based on the process noise adjustment factor, perform the step of real-time dynamic relative positioning of the real-time observation data in the next sliding window;

[0057] S106, Determine the deformation of the monitored target based on the result of the real-time dynamic relative positioning corresponding to the current sliding window.

[0058] It should be noted that in S101, the original observation data, including pseudorange observations and carrier phase observations, are first obtained through monitoring stations and reference stations deployed at the monitoring target. Based on the pseudorange observations and carrier phase observations, the displacement data that can be processed by sliding window filtering can be obtained through real-time dynamic relative positioning calculation, that is, the real-time observation data is processed into a real-time displacement time series.

[0059] Specifically, in S102, sliding window filtering is performed based on the displacement time series. The width of the first sliding window can be set to τ0, where the width of the sliding window is the time span, that is, the displacement data within the window from time t to time t+τ0 is processed.

[0060] Specifically, within the corresponding window, the displacement time series of the monitored target can be obtained based on high-precision real-time dynamic relative positioning calculation, denoted as {x1, x2, ..., x...}. n}, where x1 to x n Arranged chronologically from earliest to latest, x1 represents the earliest displacement, x n It represents the displacement at the latest time.

[0061] Specifically, before S103, each displacement in the displacement time series can be sorted according to its numerical value, and a corresponding displacement numerical sequence can be generated based on the sorting result.

[0062] Specifically, the values ​​can be arranged in descending order or in ascending order; this application does not limit this.

[0063] For example, if the values ​​are arranged in ascending order, we get {x} (1) ,x (2) ,…,x (n)}, where x (1) x represents the smallest displacement value in the corresponding displacement time series. (n) This represents the largest displacement value in the displacement time series.

[0064] In some embodiments, S103, the displacement identifier includes the median displacement and the interquartile range of displacement. The corresponding displacement identifier is calculated based on the displacement time series, including:

[0065] Calculate the first quantile at 25%, the second quantile at 50%, and the third quantile at 75% of the displacement value sequence. For example, this can be based on the displacement value q1 at 25%, the displacement value q2 at 50%, and the displacement value q3 at 75% of the displacement value sequence.

[0066] Using the second quantile q2 as the median of the displacement, the interquartile range of the displacement is determined based on the first quantile q1 and the third quantile q3.

[0067] In some embodiments, the median displacement can be denoted as M, and the interquartile range of displacement as Q. If the displacement values ​​are arranged in ascending order, the interquartile range Q can be calculated based on the difference between the third quantile q3 and the first quantile q1, using the following formula:

[0068] M = q2;

[0069] Q = q3 - q1;

[0070] The first, second, and third quantiles can be calculated using the following formulas:

[0071]

[0072] Where n is the sort index in the numerical sequence of displacements. For example, if the median and quartile of the displacement are selected for the sequence 1-2-3-4-5-6-7-8-9-10-11, then the corresponding q1 is x. ((11+1)×0.25) q2 is x3; similarly, q2 is x6 and q3 is x9.

[0073] It should be noted that if (n+1)×0.25, (n+1)×0.5, and (n+1)×0.75 are not integers, the corresponding quartile values ​​are obtained by averaging the sequence values ​​at two adjacent integer positions.

[0074] In some embodiments, S104, comparing the displacement indicator of the current sliding window with the displacement indicator of the previous sliding window specifically includes:

[0075] The median and interquartile range of the previous window are M. k-1 and Q k-1 The median and interquartile range of the current window are M. k and Q k Compare the median displacement M of the previous window k-1 And the median M of the current window k Compare the displacement interquartile range Q of the previous window. k-1 The interquartile range Q of the current window k The comparison results are obtained separately, and the displacement motion state of the detected target can be determined based on the comparison results. The displacement motion state includes a stationary state, a uniform state, an accelerating state, or a decelerating state.

[0076] For example, as shown in the attached figure Figure 2 As shown, the process of determining the displacement motion state of the monitored target based on the comparison results may include the following steps:

[0077] First, compare the median displacement M of the previous window. k-1 And the median M of the current window kThe absolute value of the difference is compared with the median test threshold. If the absolute value of the difference is less than or equal to the median test threshold ε, then... k This indicates that the displacement motion state is at rest, and the judgment condition is:

[0078] |M k -M k-1 |>ε k .

[0079] Otherwise, continue comparing the interquartile range Q of the previous window. k-1 The interquartile range Q of the current window k The ratio, the corresponding ratio Q k-1 / Q k and the ratio Q k / Q k-1 The threshold η for the displacement interquartile range test k Comparison, if Q k-1 / Q k And Q k / Q k-1 If all values ​​are less than or equal to the interquartile range test threshold, it indicates that the displacement motion is in a uniform state. The judgment condition is:

[0080] Q k-1 / Q k >η k or Q k / Q k-1 >η k ;

[0081] Otherwise, as long as Q k-1 / Q k Or Q k / Q k-1 If the displacement interquartile range is greater than the interquartile range test threshold, then continue comparing the displacement interquartile range Q of the current window. k The interquartile distance Q from the previous window k-1 The difference, if the current window's displacement is Q... k The interquartile distance Q of the displacement from the previous window k-1 If the difference is greater than 0, it indicates that the displacement motion is in an accelerating state; otherwise, it is in a decelerating state. The judgment condition is:

[0082] Q k Q k-1 .

[0083] For example, the median test threshold ε k It can take the value ε k =1.5Q k The interquartile range test threshold η k It can take the value η k=1.5, but this application does not limit this in the embodiments.

[0084] In some embodiments, S104, determining the process noise adjustment factor of the Kalman filter within the sliding window based on the comparison result includes:

[0085] If the displacement motion state is a stationary state or a uniform state, the process noise adjustment factor of the next sliding window remains unchanged, that is, the process noise adjustment factor of the current sliding window is used.

[0086] If the displacement motion is in an accelerating or decelerating state, the noise adjustment factor of the next sliding window filter is adjusted based on the median displacement and interquartile range of the current sliding window.

[0087] In some embodiments, assuming the target's movement speed is constant within the window, the displacement rate v of the monitoring station in the E, N, and U directions, i.e., the target's displacement rate, can be calculated based on the interquartile range of the target's displacement sequence in multiple directions, including East (E), North (N), and Height (U), in the station-centered coordinate system.

[0088] v = s / τ

[0089] =[(q3+0.5Q)-(q1-0.5Q)] / τ;

[0090] =2Q / τ

[0091] Where τ is the width of the corresponding sliding window.

[0092] Furthermore, the process noise adjustment factor of the Kalman filter in the corresponding direction can be calculated based on the displacement rate of the monitored target.

[0093] Specifically, within the first sliding window, initial values ​​for process noise in each direction can be preset, denoted as follows: Where the subscript w k This represents the corresponding sliding window. The noise adjustment factor for the next sliding window filtering process is calculated using the formula:

[0094]

[0095] Where d represents each direction, such as east (E), north (N), and apex (U); β d This represents the process noise adjustment factor in the corresponding direction.

[0096] Therefore, the adjusted state-process noise variance-covariance matrix, i.e., the process noise, is:

[0097]

[0098] As the real-time calculation progresses, the window moves forward. After the next time interval τ, the window moves by a step size equal to the window width. At this point, the sequences within the window have no duplicate samples from the previous window and are independent of each other. By recalculating the displacement statistics M, Q, and displacement rate v of the monitored body within this window according to the above steps, the Kalman filter state prediction process can be readjusted to keep the noise parameters of the filter state process updated.

[0099] It should be noted that slow displacement deformation is characterized by long duration and large cumulative displacement. The numerical characteristics of the displacement sequence within the statistical window in this application embodiment can realize the monitoring of the slow displacement state of the monitoring target, thereby realizing the adaptive adjustment of the state prediction process of Kalman filter positioning solution, so that the setting of process noise during state transmission matches the actual motion state of the monitored body, thus overcoming the defect of not being able to sensitively monitor slow displacement in related technologies.

[0100] In some embodiments, in S104, after determining the displacement motion state of the monitored target based on the comparison result and before determining the process noise adjustment factor of the Kalman filter within the sliding window based on the displacement motion state, it may also be determined whether to adjust the sliding window width of the current window based on the displacement motion state.

[0101] Specifically, as shown in the attached diagram. Figure 2 The illustration demonstrates the process of determining the displacement state of the monitored target based on comparison results. Therefore, it is possible to determine whether to adjust the sliding window based on the displacement state, and further, the width of the sliding window can be adaptively adjusted.

[0102]

[0103] The above are just examples of adjusting the width of the sliding window. When the displacement state of the monitored target is decelerating, the change in displacement slows down, so the width of the sliding window can be appropriately increased. The specific increase in width can be determined according to the actual degree of deceleration. When the displacement state of the monitored target is accelerating, the change in displacement increases, and the monitoring sensitivity needs to be improved. Therefore, the width of the sliding window needs to be reduced. The specific reduction value can be determined according to the actual degree of acceleration.

[0104] It should be noted that when the monitored target is in the early to mid-stages of a landslide and undergoing slow displacement, its motion state is not static but rather involves a combination of acceleration, deceleration, and uniform motion, resulting in different characteristics in the changes of displacement rate and displacement magnitude. Related technologies, due to the use of a single-width sliding window, may suffer from inaccurate window length, making it impossible for the window displacement statistics to accurately reflect the displacement state within that window. This leads to the inability to accurately identify the details of deformation rate changes and thus fails to accurately reflect the true deformation state of the monitored object.

[0105] This application embodiment employs a multi-level adaptive sliding window displacement detection based on the deformation characteristics of the monitored body. During real-time filtering, the deformation characteristics of the monitored body, such as static, uniform, accelerated, and decelerated states, are identified according to the sliding window displacement detection results. The window width is then adaptively adjusted, thereby overcoming the problem in related technologies where a single fixed-width sliding window is difficult to accurately and effectively detect complex displacement states of the monitored body.

[0106] Understandably, through the above embodiments, during the monitoring of the target, the parameters of the sliding window filter can be adjusted in real time and adaptively based on the displacement data obtained from real-time monitoring. The specific motion state is determined by comparing the displacement indicator of the current sliding window with the displacement indicator of the previous sliding window. Then, the sliding window width and noise adjustment factor are adjusted according to the motion state. The sliding window filter of the next sliding window is then performed using the adjusted sliding window width and noise adjustment factor. The above steps S101-S104 are repeated continuously, thereby achieving continuous motion state detection and high-precision position monitoring of the target.

[0107] Therefore, in S106, the geological deformation of the monitoring area can be determined based on the real-time dynamic relative positioning result corresponding to the current window, and a geological disaster warning can be given based on the geological deformation.

[0108] The following are apparatus embodiments of this application, which can be used to execute the method embodiments of this application. For details not disclosed in the apparatus embodiments of this application, please refer to the method embodiments of this application.

[0109] Please see below. Figure 3 This is a schematic diagram of a geological disaster deformation monitoring device based on the BeiDou satellite navigation system, provided as an exemplary embodiment of this application. This device can be implemented as all or part of a terminal through software, hardware, or a combination of both, or it can be integrated as an independent module on a server. The geological disaster deformation monitoring device based on the BeiDou satellite navigation system in this embodiment can be applied to a terminal or the cloud. The device 30 includes a data acquisition module 310, a sliding window filtering module 320, a calculation module 330, an adaptive adjustment module 340, and a deformation monitoring module 350, wherein:

[0110] The data acquisition module 310 is used to acquire real-time observation data of the monitoring target at preset intervals, perform real-time dynamic relative positioning on the real-time observation data, and obtain the displacement time series of the monitoring target.

[0111] The sliding window filtering module 320 is used to perform sliding window filtering on the displacement time series.

[0112] The calculation module 330 is used to calculate the corresponding displacement identifier within the sliding window based on the displacement time series.

[0113] The adaptive adjustment module 340 is used to compare the displacement indicator of the current sliding window with the displacement indicator of the previous sliding window, determine the displacement motion state of the monitored target based on the comparison result, determine the process noise adjustment factor of the Kalman filter in the next sliding window based on the displacement motion state, and adjust the process noise of the Kalman filter in the next sliding window based on the process noise adjustment factor.

[0114] The data acquisition module 310 is also used to continue executing the step of performing real-time dynamic relative positioning of the real-time observation data within the next sliding window.

[0115] The deformation monitoring module 350 is used to determine the deformation of the monitored target based on the result of the real-time dynamic relative positioning corresponding to the current sliding window.

[0116] It should be noted that the above embodiments of the device 30 are only illustrated by the division of the above functional modules when performing the geological disaster deformation monitoring method based on the Beidou satellite navigation system. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0117] Furthermore, the device provided in the above embodiments and the embodiment of the geological disaster deformation monitoring method based on the Beidou satellite navigation system belong to the same concept, and the implementation process can be found in the method embodiment, which will not be repeated here.

[0118] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of any of the methods described above.

[0119] Please see Figure 4 This is a structural block diagram of an electronic device provided in an embodiment of this application.

[0120] like Figure 4 As shown, the electronic device 400 includes a processor 401 and a memory 402.

[0121] In this embodiment, the processor 401 is the control center of the computer system, and can be a processor of a physical machine or a processor of a virtual machine. The processor 401 may include one or more processing cores, such as a 4-core processor or an 8-core processor. The processor 401 can be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array).

[0122] Processor 401 may also include a main processor and a coprocessor. The main processor is a processor used to process data in the wake-up state, also known as a CPU (Central Processing Unit). The coprocessor is a low-power processor used to process data in the standby state.

[0123] Memory 402 may include one or more computer-readable storage media, which may be non-transitory. Memory 402 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments of this application, the non-transitory computer-readable storage media in memory 402 is used to store at least one instruction, which is executed by processor 401 to implement the method in the embodiments of this application.

[0124] In some embodiments, the electronic device 400 further includes a peripheral device interface 403 and at least one peripheral device 404. The processor 401, memory 402, and peripheral device interface 403 can be connected via a bus or signal line. Each peripheral device 404 can be connected to the peripheral device interface 403 via a bus, signal line, or circuit board. Specifically, the peripheral device 404 includes: a display screen, a camera, and audio circuitry. The peripheral device interface 403 can be used to connect at least one I / O (Input / Output) related peripheral device to the processor 401 and memory 402.

[0125] In some embodiments of this application, the processor 401, memory 402, and peripheral device interface 403 are integrated on the same chip or circuit board; in other embodiments of this application, any one or two of the processor 401, memory 402, and peripheral device interface 403 can be implemented on separate chips or circuit boards. This application does not specifically limit the implementation in this regard.

[0126] The block diagram of the electronic device shown in the embodiments of this application does not constitute a limitation on the electronic device 400. The electronic device 400 may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0127] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the methods in any of the foregoing embodiments. The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, as well as magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0128] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of software products. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0129] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for monitoring geological disaster deformation based on a Beidou satellite navigation system, characterized in that, The method comprises the following steps: acquiring real-time observation data of a monitoring target at preset time intervals; performing real-time dynamic relative positioning on the real-time observation data to obtain a displacement time series of the monitoring target; performing sliding window filtering based on the displacement time series to calculate a displacement identifier corresponding to a sliding window; comparing the displacement identifier of the current sliding window with the displacement identifier of the previous sliding window, determining a displacement motion state of the monitoring target based on the comparison result, and determining a process noise adjustment factor of Kalman filtering in the next sliding window based on the displacement motion state; adjusting the process noise of Kalman filtering in the next sliding window based on the process noise adjustment factor; continuing to perform the step of performing real-time dynamic relative positioning on the real-time observation data in the next sliding window; determining a deformation amount of the monitoring target based on the result of real-time dynamic relative positioning corresponding to the current sliding window; before the step of performing sliding window filtering based on the displacement time series to calculate a displacement identifier corresponding to a sliding window, the method further comprises the following steps: sorting each displacement in the displacement time series according to the numerical value, and generating a displacement numerical sequence based on the sorting result; the displacement identifier comprises a displacement median and a displacement interquartile range calculated based on the displacement numerical sequence; the step of determining the displacement motion state of the monitoring target based on the comparison result, and determining the process noise adjustment factor of Kalman filtering in the next sliding window based on the displacement motion state, comprises the following steps: if the displacement motion state is a static state or a uniform speed state, the process noise adjustment factor of Kalman filtering in the next sliding window is the same as that in the current sliding window; if the displacement motion state is an acceleration state or a deceleration state, the process noise adjustment factor of Kalman filtering in the next sliding window is adjusted based on the displacement median and the displacement interquartile range of the current sliding window.

2. The method according to claim 1, wherein, the step of adjusting the process noise adjustment factor of Kalman filtering in the next sliding window based on the displacement median and the displacement interquartile range of the current sliding window if the displacement motion state is an acceleration state or a deceleration state, comprises the following steps: respectively acquiring displacement identifiers corresponding to displacement time series of the monitoring target in each direction under the station coordinate system; calculating the displacement rate in each direction based on the displacement identifiers; calculating the process noise adjustment factor of the corresponding direction based on the displacement rate. 3.The method according to claim 1, wherein, after the step of determining the displacement motion state of the monitoring target based on the comparison result, the method further comprises the following steps: if the displacement motion state is the static state or the uniform speed state, the sliding window width of the current sliding window is taken as the sliding window width of the next sliding window; if the displacement motion state is the acceleration state, the sliding window width of the current sliding window is reduced and then taken as the sliding window width of the next sliding window; if the displacement motion state is the deceleration state, the sliding window width of the current sliding window is increased and then taken as the sliding window width of the next sliding window; The step of determining the process noise adjustment factor of Kalman filtering in the next sliding window based on the displacement motion state is performed.

4. A device for monitoring geological disasters based on the method of claim 1-3, characterized in that, The device comprises: a data acquisition module configured to acquire real-time observation data of a monitoring target at every interval preset time, and to perform real-time dynamic relative positioning on the real-time observation data to obtain a displacement amount time series of the monitoring target; a sliding window filtering module configured to perform sliding window filtering on the displacement amount time series; a calculation module configured to calculate a corresponding displacement identification amount in a sliding window based on the displacement amount time series; an adaptive adjustment module configured to compare the displacement identification amount of a current sliding window with a displacement identification amount of a previous sliding window, to determine a displacement motion state of the monitoring target based on a comparison result, to determine a process noise adjustment factor of Kalman filtering in a next sliding window based on the displacement motion state, and to adjust the process noise of Kalman filtering in the next sliding window based on the process noise adjustment factor; the data acquisition module is further configured to continue to perform the step of performing real-time dynamic relative positioning on the real-time observation data in the next sliding window; a deformation monitoring module configured to determine a deformation amount of the monitoring target according to a result of real-time dynamic relative positioning corresponding to the current sliding window.

5. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the steps of the method of any one of claims 1 to 3 when executing the program. 6.A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 3.

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