Geological disaster deformation monitoring method based on Beidou satellite navigation system

Abstract

The embodiment of the invention discloses a geological disaster deformation monitoring method based on a Beidou satellite navigation system, and relates to the technical field of geological disaster deformation monitoring, and the method comprises the steps: obtaining the real-time observation data of a monitoring target at preset time intervals, carrying out the real-time dynamic relative positioning, obtaining a displacement time sequence, carrying out the sliding window filtering, and obtaining a displacement time sequence; obtaining a displacement identification quantity corresponding to the sliding window, determining a displacement motion state of the monitoring target and a process noise regulation factor of the next sliding window according to a comparison result of the displacement identification quantity of the current sliding window and the displacement identification quantity of the previous sliding window, and continuing real-time dynamic relative positioning in the next sliding window based on the process noise regulation factor; and determining the deformation quantity of the monitoring target based on the real-time dynamic relative positioning result. According to the method and the device, the defect that the peristaltic displacement of the monitored target cannot be efficiently identified in the prior art is overcome, and the displacement state and trend of the monitored target are rapidly and accurately identified through adaptive sliding window width adjustment and process noise adjustment.

Description

Technical Field

[0001] The present application relates to the technical field of geological disaster deformation monitoring, and in particular to a geological disaster deformation monitoring method based on the Beidou satellite navigation system. Background Art

[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, speed and time information at any location on the earth's surface or in near-Earth space. The real-time dynamic positioning (RTK) technology based on GNSS can obtain the high-precision three-dimensional coordinates of the current epoch of the monitoring station in real time through differential processing of the observation data between the base station and the monitoring station. It has the advantages of fast convergence speed, short response time and high positioning accuracy. It is often used to capture the sudden change information of landslides in real time, and has become the most widely used GNSS landslide deformation monitoring technology.

[0003] However, the movement state of the monitored target presents complex and changeable characteristics at different stages of landslide development. The traditional RTK monitoring method based on Kalman filtering is not sensitive to the slow creep displacement of the monitored target (for example, displacement with a rate less than 0.5 mm / s and a duration greater than 300 s). The displacement state is difficult to be identified quickly and accurately, which can easily lead to slow deformation response and missed deformation. It is impossible to give timely warnings for geological disaster landslide deformations, 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 state characteristics and landslide displacement trends of the monitored target. Summary of the invention

[0005] The embodiment of the present application provides a method for monitoring geological disaster deformation based on the Beidou satellite navigation system, which is used to solve the defect that the above-mentioned related technologies cannot quickly and accurately identify slow creeping displacement. Adaptive sliding window width adjustment and adaptive process noise adjustment are adopted to achieve rapid and accurate identification of the displacement state and displacement trend of the detection target, and can provide real-time and accurate dynamic displacement sequence information for geological disaster deformation monitoring. The technical solution is as follows:

[0006] In a first aspect, an embodiment of the present application provides a method for monitoring geological disaster deformation based on the Beidou satellite navigation system, comprising:

[0007] Obtain real-time observation data of the monitoring target at every preset time interval;

[0008] Performing real-time dynamic relative positioning on the real-time observation data to obtain a displacement time series of the monitoring target;

[0009] Perform sliding window filtering based on the displacement time series to calculate the corresponding displacement identification amount in the sliding window;

[0010] Compare the displacement identification amount of the current sliding window with the displacement identification amount of the previous sliding window, determine the displacement motion state of the monitoring target based on the comparison result, and determine the process noise adjustment factor of the Kalman filter in the next sliding window based on the displacement motion state;

[0011] Adjust the process noise of the Kalman filter in the next window based on the process noise adjustment factor; continue to perform the step of performing real-time dynamic relative positioning on the real-time observation data in the next sliding window;

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

[0013] In an optional solution of the first aspect, before performing sliding window filtering based on the displacement time series and calculating the corresponding displacement identification amount in the sliding window, the method further includes:

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

[0015] In an optional solution of the first aspect, the displacement identification quantity includes a displacement median and a displacement interquartile range calculated based on the displacement quantity numerical sequence;

[0016] 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 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 speed state, the process noise adjustment factor of the Kalman filter in the next sliding window remains the same as that of the current sliding window;

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

[0019] In an optional solution of 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 in the sliding window based on the displacement median and displacement interquartile range of the current sliding window includes:

[0020] Respectively obtain the displacement identification corresponding to the displacement time series of the monitoring target in each direction in the station center coordinate system;

[0021] Calculate the displacement rate in each direction based on the displacement identification amount;

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

[0023] In an optional solution of the first aspect, after determining the displacement motion state of the monitoring target based on the comparison result, the method further includes:

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

[0025] If the displacement motion state is the acceleration state, reducing the sliding window width of the current sliding window and using it as the sliding window width of the next sliding window;

[0026] If the displacement motion state is the deceleration state, increasing the sliding window width of the current sliding window as the sliding window width of the next sliding window;

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

[0028] In a second aspect, the embodiment of the present application further provides a geological disaster deformation monitoring device based on the Beidou satellite navigation system, comprising:

[0029] A data acquisition module is used to acquire real-time observation data of the monitoring target at each preset time interval, perform real-time dynamic relative positioning on the real-time observation data, and obtain a displacement time series of the monitoring target;

[0030] A sliding window filtering module, used for performing sliding window filtering on the displacement time series;

[0031] A calculation module, used for calculating the corresponding displacement identification amount in the sliding window based on the displacement time series;

[0032] An adaptive adjustment module, used for comparing the displacement identification amount of the current sliding window with the displacement identification amount of the previous sliding window, determining the displacement motion state of the monitoring target based on the comparison result, determining the process noise adjustment factor of the Kalman filter in the next sliding window based on the displacement motion state, and adjusting 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 to perform the step of performing real-time dynamic relative positioning on the real-time observation data within the next sliding window;

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

[0035] In a third aspect, an embodiment of the present application further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method provided in the first aspect of the embodiment of the present application or any one of the implementation methods of the first aspect is implemented.

[0036] In a fourth aspect, the present 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 embodiment of the present application or any one of the implementations of the first aspect.

[0037] The beneficial effects brought about by the technical solutions provided by some embodiments of the present application include at least:

[0038] (1) According to 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 filter 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 noise adjustment factor in the filtering process can be reasonably adjusted to make the filtering state model consistent with the actual motion state of the monitored target, so that the slow displacement of the monitored target can be accurately and quickly reflected in the whole process of acceleration, uniform speed, deceleration and other movements of the monitored target, greatly improving the sensitivity of monitoring the slow creeping displacement of the monitored target, thereby providing accurate, reliable, high-time-resolution dynamic displacement and motion trend information for early warning of geological disaster deformation monitoring, and providing a reliable guarantee for preventing geological disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the present application or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 It is a flow chart of a method for monitoring deformation of geological disasters based on the Beidou satellite navigation system provided in an embodiment of the present application;

[0042] Figure 2 It is a flow chart of a method for monitoring deformation of geological disasters based on the Beidou satellite navigation system provided in an embodiment of the present application;

[0043] Figure 3 It is a structural schematic diagram of a geological disaster deformation monitoring device based on the Beidou satellite navigation system provided in an embodiment of the present application;

[0044] Figure 4 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0046] The terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including a series of steps or modules is not limited to the listed steps or modules, but may optionally include steps or modules that are not listed, or may optionally include other steps or modules that are inherent to these processes, methods, products or devices.

[0047] It should be noted that the terms "first\second" involved in the present application are only used to distinguish similar objects, and do not represent a specific order for the objects. It is understandable that "first\second" can be interchanged with a specific order or sequence where permitted. It should be understood that the objects distinguished by "first\second" can be interchanged where appropriate, so that the embodiments of the present 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 the embodiment of the present application can be applied in the field of geological disaster prediction, such as monitoring landslides, collapses, etc. Monitoring stations and base stations can be deployed in areas where there are geological disaster risks. The base station provides a positioning and measurement benchmark, and the monitoring station is used to measure the coordinates of the location of the monitoring station. The coordinates of the monitoring station obtained through multiple measurements can be used to determine the displacement of the location of the monitoring station, and then reflect the deformation at the corresponding position, thereby giving 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, since slow displacement deformation has the characteristics of small instantaneous displacement, long duration and large cumulative displacement, relevant technologies can often only detect sudden deformation but cannot identify slow cumulative displacement.

[0050] In order to address the above problems, an embodiment of the present application provides a geological disaster deformation monitoring method based on the Beidou satellite navigation system. The present application is described in detail below in conjunction with specific embodiments.

[0051] Next, combine Figure 1 , introduces the geological disaster deformation monitoring method based on the Beidou satellite navigation system provided in the embodiment of the present application, Figure 1 A schematic diagram of a method for monitoring geological disaster deformation based on the Beidou satellite navigation system provided in an embodiment of the present application is shown, and the method comprises the following steps:

[0052] S101, acquiring real-time observation data of a monitoring target at every preset time interval.

[0053] S102, performing Kalman filtering real-time dynamic relative positioning on the real-time observation data to obtain a displacement time series of the monitored target in a current sliding window.

[0054] S103, performing sliding window filtering on the displacement time series, and calculating the displacement identification corresponding to the sliding window.

[0055] S104, comparing the displacement identification value of the current sliding window with the displacement identification value of the previous sliding window, determining the displacement motion state of the monitored target based on the comparison result, and determining the process noise adjustment factor of the Kalman filter in the next sliding window based on the displacement motion state.

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

[0057] S106: Determine the deformation amount of the monitoring 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 observation values ​​and carrier phase observation values, are first obtained by the monitoring stations and reference stations deployed at the monitoring target. Based on the pseudorange observation values ​​and carrier phase observation values, the displacement data that can be processed by the sliding window filter can be obtained through real-time dynamic relative positioning solution, 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, and 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 in 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, which is recorded as {x1, x2, …, x n}, where x1 to x n Arrange them in chronological order from earliest to latest, x1 represents the earliest displacement, x n Indicates the latest displacement.

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

[0062] Specifically, they can be arranged in order from large to small numerical values, or they can be arranged in order from small to large numerical values, and this embodiment of the present application does not limit this.

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

[0064] In some embodiments, in S103, the displacement identification quantity includes a displacement median and a displacement interquartile range, and the corresponding displacement identification quantity is calculated based on the displacement time series, including:

[0065] Calculate the first quantile ranked 25%, the second quantile ranked 50%, and the third quantile ranked 75% in the displacement value sequence. For example, the displacement value q1 ranked 25% in the displacement value sequence, the displacement value q2 ranked 50% in the displacement value sequence, and the displacement value q3 ranked 75% in the displacement value sequence.

[0066] The second quantile q2 is used as the shifted median, and the shifted interquartile range is determined based on the first quantile q1 and the third quantile q3.

[0067] In some embodiments, the displacement median may be recorded as M, and the displacement interquartile range may be recorded as Q. If the displacement value sequence is arranged from small to large, the displacement interquartile range Q may be calculated based on the difference between the third quartile q3 and the first quartile q1, using the formula:

[0068] M = q2;

[0069] Q = q3 - q1;

[0070] The first quantile, second quantile and third quantile can be calculated using the formula:

[0071]

[0072] Where n is the sorting subscript in the displacement value sequence. For example, for the sequence 1-2-3-4-5-6-7-8-9-10-11, the displacement median and displacement quartile are selected, and the corresponding q1 is x ((11+1)×0.25) , that 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 taking the average of the sequence values ​​at two adjacent integer positions.

[0074] In some embodiments, in S104, comparing the displacement identification amount of the current sliding window with the displacement identification amount 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 displacement median 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 and the displacement interquartile range Q of the current window k , respectively obtain comparison results, and the displacement motion state of the detection target can be determined based on the comparison results. The displacement motion state includes a stationary state, a uniform speed state, an accelerated state, or a decelerated state.

[0076] For example, as shown in the accompanying drawings Figure 2 As shown, the process of determining the displacement motion state of the monitoring target based on the comparison result may include the following steps:

[0077] First, compare the displacement median 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 k , it indicates that the displacement motion state is static, and the judgment condition is:

[0078] |M k -M k-1 |>ε k .

[0079] Otherwise, continue to compare the displacement interquartile range Q of the previous window k-1 and the displacement interquartile range Q of the current window k The corresponding ratio Q k-1 / Q k And the ratio Q k / Q k-1 Mean and displacement interquartile range test threshold η k Compare, if Q k-1 / Q k and Q k / Q k-1 If both are less than or equal to the displacement interquartile range test threshold, it indicates that the displacement motion state is uniform, and 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 it is greater than the displacement interquartile range test threshold, continue to compare the displacement interquartile range Q of the current window k The interquartile range Q of the previous window k-1 If the displacement interquartile range of the current window is Q k The interquartile range Q of the previous window k-1 If the difference is greater than 0, it indicates that the displacement motion state is in the acceleration state, otherwise it is in the deceleration state. The judgment condition is:

[0082] Q k >Q k-1 .

[0083] For example, the median test threshold ε k It can be taken as ε k =1.5Q k , displacement interquartile range test threshold η k It can be taken as η k=1.5, which is not limited in the present embodiment.

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

[0085] If the displacement motion state is a stationary state or a uniform speed 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 state is an acceleration state or a deceleration state, the process noise adjustment factor of the next sliding window filtering is adjusted based on the displacement median and displacement interquartile range of the current sliding window.

[0087] In some embodiments, assuming that the movement speed of the monitoring target is constant within the window, the displacement speed v of the monitoring station in the E, N and U directions, i.e., the displacement speed of the monitoring target, can be calculated according to the interquartile range of the displacement sequence of the monitoring target in multiple directions including the east (E), north (N) and high (U) directions in the station center coordinate system:

[0088] v=s / τ

[0089] =[(q3+0.5Q)-(q1-0.5Q)] / τ;

[0090] =2Q / τ

[0091] Among them, 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, for the first sliding window, the initial values ​​of the process noise in each direction can be preset, which are expressed as The subscript w k Represents the corresponding sliding window, and the process noise adjustment factor of the next sliding window filtering is calculated by the formula:

[0094]

[0095] Where d represents each direction, such as east E, north N, top U; β d Represents the process noise adjustment factor in the corresponding direction.

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

[0097]

[0098] As the real-time solution proceeds, the window also moves forward. After the next τ time, the step length of the window movement is equal to the window width. At this time, the sequence in the window has no sample duplication with the previous window and is independent of each other. According to the above steps, the displacement statistical identification quantity M, Q and displacement rate v of the monitoring body in the window can be recalculated to adjust the Kalman filter state prediction process again so that the noise parameters of the filter state process are kept updated.

[0099] It should be noted that slow displacement deformation has the characteristics of long duration and large cumulative displacement. The numerical characteristics of the displacement sequence within the statistical window of the embodiment of the present application can realize the monitoring of the slow displacement state of the monitored target, and then realize the adaptive adjustment of the state prediction process of the Kalman filter positioning solution, so that the setting of the process noise during state transmission is matched with the actual motion state of the monitored object, thereby overcoming the defect that the related technology cannot sensitively monitor slow displacement.

[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 in the sliding window based on the displacement motion state, it can 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 figure Figure 2 As shown, an example is given of the process of determining the displacement motion state of the monitoring target based on the comparison result, thereby determining whether to adjust the sliding window based on the displacement motion state, and further adaptively adjusting the sliding window width:

[0102]

[0103] The above is only an example of adjusting the width of the sliding window. When the displacement state of the monitored target is deceleration, the change in displacement becomes slower, so the width of the sliding window can be appropriately increased, and the specific increase in width can be determined according to the actual degree of deceleration movement; when the displacement state of the monitored target is acceleration, the change in displacement increases, and the monitoring sensitivity needs to be improved, so the width of the sliding window needs to be reduced, and the specific reduction value can be determined according to the actual degree of acceleration movement.

[0104] It should be noted that when the monitoring target is in the early or middle stage of a landslide and is undergoing slow displacement, its motion state will not remain unchanged, but will be a combination of acceleration, deceleration, and uniform motion, and the changes in displacement rate and displacement amount will present different characteristics. The related technology uses a sliding window of a single width, and the window displacement statistics cannot accurately reflect the displacement state within the window due to the unreasonable window length, resulting in the inability to accurately identify the details of the deformation rate change and the inability to accurately reflect the true deformation state of the monitored object.

[0105] The embodiment of the present application adopts a multi-level adaptive sliding window displacement detection based on the deformation characteristics of the monitoring body. In the process of real-time filtering, the deformation characteristics of the monitoring body such as static, uniform speed, acceleration, deceleration, etc. are identified according to the sliding window displacement detection results, and the window width is adaptively adjusted, thereby overcoming the problem that a single fixed-width sliding window in the related art is difficult to accurately and effectively detect complex displacement states of the monitoring body.

[0106] It can be understood that through the above embodiments, in the process of monitoring the monitoring target, the parameters of the sliding window filter can be adjusted in real time and adaptively according to the displacement data obtained by real-time monitoring, and the specific motion state can be determined according to the comparison result of the displacement identification amount of the current sliding window and the displacement identification amount of the previous sliding window. Then, the sliding window width and the noise adjustment factor are adjusted according to the motion state, and the sliding window filtering of the next sliding window is performed using the adjusted sliding window width and noise adjustment factor. The above steps S101-S104 are continuously repeated, thereby realizing continuous motion state detection and high-precision position monitoring of the monitoring target.

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

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

[0109] See next Figure 3 , which is a schematic diagram of the structure of a geological disaster deformation monitoring device based on the Beidou satellite navigation system provided by an exemplary embodiment of the present application. The device can be implemented as all or part of a terminal through software, hardware, or a combination of both, and can also be integrated on a server as an independent module. The geological disaster deformation monitoring device based on the Beidou satellite navigation system in the embodiment of the present application can be applied to a terminal or a 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 the real-time observation data of the monitoring target at each preset time interval, 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 identification quantity in the sliding window based on the displacement time series.

[0113] The adaptive adjustment module 340 is used to compare the displacement identification quantity of the current sliding window with the displacement identification quantity 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 to execute the step of performing real-time dynamic relative positioning on the real-time observation data in the next sliding window.

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

[0116] It should be noted that when the device 30 provided in the above embodiment executes the geological disaster deformation monitoring method based on the Beidou satellite navigation system, it only uses the division of the above-mentioned functional modules as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the equipment is divided into different functional modules to complete all or part of the functions described above.

[0117] In addition, the device provided in the above embodiment and the embodiment of the geological disaster deformation monitoring method based on the Beidou satellite navigation system belong to the same concept. The implementation process is detailed in the method embodiment and will not be repeated here.

[0118] An embodiment of the present 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 implements the steps of any of the above-mentioned method embodiments when executing the program.

[0119] See also Figure 4 , which is a structural block diagram of an electronic device provided in an embodiment of the present application.

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

[0121] In the embodiment of the present application, the processor 401 is the control center of the computer system, which 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, an 8-core processor, etc. The processor 401 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array).

[0122] The processor 401 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also called a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state.

[0123] The memory 402 may include one or more computer-readable storage media, which may be non-transitory. The memory 402 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments of the present application, the non-transitory computer-readable storage medium in the memory 402 is used to store at least one instruction, which is used to be executed by the processor 401 to implement the method in the embodiment of the present 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, the memory 402 and the peripheral device interface 403 can be connected via a bus or a signal line. Each peripheral device 404 can be connected to the peripheral device interface 403 via a bus, a signal line or a circuit board. Specifically, the peripheral device 404 includes: a display screen, a camera and an audio circuit. The peripheral device interface 403 can be used to connect at least one peripheral device related to I / O (Input / Output) to the processor 401 and the memory 402.

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

[0126] The electronic device structure block diagram shown in the embodiment of the present application does not constitute a limitation on the electronic device 400. The electronic device 400 may include more or fewer components than shown in the figure, or combine certain components, or adopt a different component arrangement.

[0127] The embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the method of any of the above embodiments are implemented. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a micro drive, and a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic card or an optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0128] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, by hardware. Based on this understanding, the above technical solution can be essentially or in other words, the part that contributes to the relevant technology can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiment.

[0129] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for monitoring geological disaster deformation based on Beidou satellite navigation system, characterized in that: include: Obtain real-time observation data of the monitoring target at every preset time interval; Performing real-time dynamic relative positioning on the real-time observation data to obtain a displacement time series of the monitoring target; Perform sliding window filtering based on the displacement time series to calculate the corresponding displacement identification amount in the sliding window; Compare the displacement identification amount of the current sliding window with the displacement identification amount of the previous sliding window, determine the displacement motion state of the monitoring target based on the comparison result, and determine the process noise adjustment factor of the Kalman filter in the next sliding window based on the displacement motion state; adjusting the process noise of the Kalman filter in the next sliding window based on the process noise adjustment factor; Continue to perform the step of performing real-time dynamic relative positioning on the real-time observation data within the next sliding window; The deformation amount of the monitoring target is determined based on the result of the real-time dynamic relative positioning corresponding to the current sliding window.

2. The method for monitoring geological disaster deformation based on Beidou satellite navigation system according to claim 1 is characterized in that: Before performing sliding window filtering based on the displacement time series and calculating the corresponding displacement identification amount in the sliding window, the method further includes: Each displacement in the displacement time series is sorted according to the numerical value, and a corresponding displacement numerical sequence is generated based on the sorting result.

3. The method for monitoring geological disaster deformation based on Beidou satellite navigation system according to claim 2 is characterized in that: The displacement identification quantity includes the displacement median and displacement interquartile range calculated based on the displacement quantity 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 the Kalman filter in the next sliding window based on the displacement motion state, includes: If the displacement motion state is a stationary state or a uniform speed state, the process noise adjustment factor of the Kalman filter in the next sliding window remains the same as that of the current sliding window; If the displacement motion state is an acceleration state or a deceleration state, the process noise adjustment factor of the Kalman filter in the next sliding window is adjusted based on the displacement median and displacement interquartile range of the current sliding window.

4. The method for monitoring geological disaster deformation based on Beidou satellite navigation system according to claim 3 is characterized in that: If the displacement motion state is an acceleration state or a deceleration state, adjusting the process noise adjustment factor of the Kalman filter in the next sliding window based on the displacement median and displacement interquartile range of the current sliding window includes: Respectively obtain the displacement identification corresponding to the displacement time series of the monitoring target in each direction in the station center coordinate system; Calculate the displacement rate in each direction based on the displacement identification amount; A process noise adjustment factor in a corresponding direction is calculated based on the displacement rate.

5. The method for monitoring geological disaster deformation based on Beidou satellite navigation system according to claim 3 is characterized in that: After determining the displacement motion state of the monitoring target based on the comparison result, the method further includes: If the displacement motion state is the stationary state or the uniform speed state, the sliding window width of the current sliding window is used as the sliding window width of the next sliding window; If the displacement motion state is the acceleration state, reducing the sliding window width of the current sliding window and using it as the sliding window width of the next sliding window; If the displacement motion state is the deceleration state, increasing the sliding window width of the current sliding window as the sliding window width of the next sliding window; The step of determining a process noise adjustment factor of the Kalman filter within the next sliding window based on the displacement motion state is performed.

6. A geological disaster deformation monitoring device based on the Beidou satellite navigation system, characterized in that: include: A data acquisition module is used to acquire real-time observation data of the monitoring target at each preset time interval, perform real-time dynamic relative positioning on the real-time observation data, and obtain a displacement time series of the monitoring target; A sliding window filtering module, used for performing sliding window filtering on the displacement time series; A calculation module, used for calculating the corresponding displacement identification quantity in the sliding window based on the displacement time series; An adaptive adjustment module, used for comparing the displacement identification amount of the current sliding window with the displacement identification amount of the previous sliding window, determining the displacement motion state of the monitoring target based on the comparison result, determining the process noise adjustment factor of the Kalman filter in the next sliding window based on the displacement motion state, and adjusting the process noise of the Kalman filter in the next sliding window based on the process noise adjustment factor; The data acquisition module is also used to continue to perform the step of performing real-time dynamic relative positioning on the real-time observation data within the next sliding window; The deformation monitoring module is used to determine the deformation amount of the monitoring target according to the result of the real-time dynamic relative positioning corresponding to the current sliding window.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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