CN115877421A - Deformation detection method and device for geological sensitive area of power transmission channel - Google Patents

Abstract

The invention discloses a deformation detection method and device for a geological sensitive area of a power transmission channel. The method comprises the following steps: calculating a radar image set in the power transmission channel geological sensitive area by using a small baseline set interferometry technology, and determining first deformation data information of the power transmission channel geological sensitive area; calculating the difference value of the front time phase point cloud data and the rear time phase point cloud data to determine second shape variable data information of the geological sensitive area of the power transmission channel; determining absolute deformation data information of a pole tower in a geological sensitive area of a power transmission channel by using a Beidou differential positioning technology; and determining the ground settlement information of the geological sensitive area of the power transmission channel according to the first deformation data information, the second deformation data information and the tower absolute deformation data information.

Description

Deformation detection method and device for geological sensitive area of power transmission channel

Technical Field

The invention relates to the technical field of deformation detection of power transmission channels, in particular to a deformation detection method and device for a geological sensitive area of a power transmission channel.

Background

The detection of the deformation change of the earth surface is an important detection content in the power transmission channel inspection. The ground surface deformation usually ranges from continuous deformation to abrupt change by combining the characteristics of a power transmission channel. Continuous deformation is researched more at present, the traditional means is developed based on a leveling method, the method is time-consuming and labor-consuming, the single detection range is small, the detection precision is high, and the method is suitable for developing fixed-point detection of buildings, such as foundation pit detection and bridge detection; in addition, the surface deformation detection by the time-series INSAR technology is also a new detection technology. The sudden change is mainly caused by natural disasters such as earthquakes, ground subsidence, debris flows and the like or other irresistible external force factors, and currently, detection is mostly carried out based on manual measurement, an INSAR mode or a multi-temporal laser radar mode.

The transmission line is an important component of a power grid and provides guarantee for transmission and distribution of electric energy. With the construction and development of super and extra high voltage in China, a power transmission channel often passes through a region with multiple geological disasters, such as a hillside land, a riverbed zone and a coal mine goaf. Under the influence of geological disasters, accidents such as cracking of a foundation and a protective surface, foundation settlement, inclination of a pole tower and even tower collapse can happen to the pole tower of the power transmission line. From the angle analysis of deformation monitoring, the displacement condition of the tower foundation under geological disasters can be generally divided into three categories: settling, tilting and slipping, in some cases also can cause compound deformation and affect multiple foundations.

Deformation of the geological sensitive area, movement and deformation of the tower cause failure and even damage of the tower structure, the safe and stable operation of the power transmission line is seriously influenced, and the safety of a power grid is seriously threatened. Therefore, it is imperative to detect geologically sensitive areas of power transmission channels.

Disclosure of Invention

The embodiment of the disclosure provides a deformation detection method and device for a geological sensitive area of a power transmission channel.

According to an aspect of the disclosed embodiments, a deformation detection method for a geological sensitive area of a power transmission channel is provided, and includes: calculating a radar image set in a power transmission channel geological sensitive area by using a small baseline set interferometry technology, and determining first deformation data information of the power transmission channel geological sensitive area, wherein the first deformation data information is wide area planar deformation information in the power transmission channel geological sensitive area; performing difference value calculation on the front and rear time phase point cloud data to determine second deformation data information of the power transmission channel geological sensitive area, wherein the second deformation data information is deformation information of a power transmission channel strip shape in the power transmission channel geological sensitive area; determining absolute deformation data information of a tower in a geological sensitive area of a transmission channel by using a Beidou differential positioning technology, wherein the absolute deformation data information of the tower is punctiform deformation information of the transmission tower in the geological sensitive area of the transmission channel; and determining ground settlement information of the geological sensitive area of the power transmission channel according to the first deformation data information, the second deformation data information and the tower absolute deformation data information.

According to another aspect of the embodiments of the present disclosure, there is also provided a storage medium including a stored program, wherein the method of any one of the above is performed by a processor when the program is executed.

There is also provided, in accordance with another aspect of the disclosed embodiments, apparatus for deformation detection of a geological sensitive area of a power transmission channel, including: the first determining module is used for calculating a radar image set in the power transmission channel geological sensitive area by using a small baseline set interferometry technology, and determining first deformation data information of the power transmission channel geological sensitive area, wherein the first deformation data information is wide area planar deformation information in the power transmission channel geological sensitive area; the second determining module is used for calculating a difference value of the front time phase point cloud data and the rear time phase point cloud data and determining second deformation data information of the power transmission channel geological sensitive area, wherein the second deformation data information is deformation information of a power transmission channel strip in the power transmission channel geological sensitive area; the third determining module is used for determining tower absolute deformation data information in the geological sensitive area of the transmission channel by utilizing a Beidou differential positioning technology, wherein the tower absolute deformation data information is point deformation information of the transmission tower in the geological sensitive area of the transmission channel; and the fourth determining module is used for determining the ground settlement information of the power transmission channel geological sensitive area according to the first deformation data information, the second deformation data information and the tower absolute deformation data information.

In the embodiment of the disclosure, the deformation detection technology of the power transmission channel geological sensitive area fusing SAR, laser scanning data and Beidou positioning data utilizes high-precision punctiform monitoring data obtained by multi-temporal laser scanning measurement and GNSS (Beidou positioning) to verify the effectiveness of large-range planar monitoring information obtained by the InSAR technology; then carrying out data fusion calculation at the coincident point; and finally, correcting and calculating the monitoring values on all point positions by using the fused data to obtain high-precision planar settlement information in a large range. And the fused result not only ensures the precision and reliability of the leveling data, but also has the advantage of high resolution of InSAR measurement, and can well depict the detail information of the deformation of the geological sensitive area of the power transmission channel.

Drawings

The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and not to limit the disclosure. In the drawings:

fig. 1 is a hardware block diagram of a computing device for implementing the method according to embodiment 1 of the present disclosure;

fig. 2 is a schematic flow chart of a deformation detection method for a geological sensitive area of a power transmission channel according to embodiment 1 of the present disclosure;

fig. 3 is a schematic view of a ground deformation monitoring multi-source data fusion process according to embodiment 1 of the present disclosure;

FIG. 4a is an intensity of each scattering point and a 100-time pixel phase distribution of a distributed target pixel according to embodiment 1 of the present disclosure;

FIG. 4b is the intensity of each scattering point and the phase distribution of 100 sub-pixels of a coherent pixel according to embodiment 1 of the present disclosure;

fig. 5 is another schematic flow chart of a deformation detection method for a power transmission channel geological sensitive area based on point cloud data according to embodiment 1 of the present disclosure;

FIG. 6a is a schematic diagram of the principle that the distance between points of the ICP algorithm is closest according to embodiment 1 of the present disclosure;

fig. 6b is a schematic diagram of the principle that the point-plane distance of the I CP algorithm is closest according to embodiment 1 of the present disclosure;

FIG. 7 is a schematic diagram of a grade entropy based thinning algorithm according to embodiment 1 of the present disclosure;

FIG. 8 is a schematic diagram of a terrain keypoint and an entropy threshold according to embodiment 1 of the present disclosure;

fig. 9 is a schematic structural diagram of a Beidou tower monitoring device according to embodiment 1 of the present disclosure;

fig. 10 is a schematic diagram of a Beidou differential positioning technology according to embodiment 1 of the present disclosure;

fig. 11 is a schematic diagram of a deformation detection method and device for a geological sensitive area of a power transmission channel according to embodiment 2 of the disclosure.

Detailed Description

In order to make those skilled in the art better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure. It is to be understood that the described embodiments are merely exemplary of some, and not all, of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.

It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the disclosure described herein are capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example 1

There is also provided in accordance with the present embodiment a method of deformation detection of a power transmission channel geological sensitive area, wherein the steps illustrated in the flowchart of the figure may be performed in a computer system such as a set of computer executable instructions, and wherein although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than that illustrated herein.

The method embodiments provided by the present embodiment may be executed in a mobile terminal, a computer terminal, a server or a similar computing device. Fig. 1 shows a hardware block diagram of a computing device for implementing a deformation detection method for a geological sensitive area of a power transmission channel. As shown in fig. 1, the computing device may include one or more processors (which may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory for storing data, and a transmission device for communication functions. Besides, the method can also comprise the following steps: a display, an input/output interface (I/O interface), a Universal Serial Bus (USB) port (which may be included as one of the ports of the I/O interface), a network interface, a power source, and/or a camera. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration and is not intended to limit the structure of the electronic device. For example, the computing device may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.

It should be noted that the one or more processors and/or other data processing circuitry described above may be referred to generally herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part in software, hardware, firmware, or any combination thereof. Further, the data processing circuit may be a single, stand-alone processing module, or incorporated, in whole or in part, into any of the other elements in the computing device. As referred to in the disclosed embodiments, the data processing circuit acts as a processor control (e.g., selection of variable resistance termination paths connected to the interface).

The memory may be configured to store a software program and a module of application software, such as a program instruction/data storage device corresponding to the deformation detection method for the power transmission channel geological sensitive area in the embodiment of the present disclosure, and the processor executes various functional applications and data processing by operating the software program and the module stored in the memory, that is, implements the deformation detection method for the power transmission channel geological sensitive area of the application program. The memory may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located from the processor, which may be connected to the computing device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The transmission device is used for receiving or transmitting data via a network. Specific examples of such networks may include wireless networks provided by communication providers of the computing devices. In one example, the transmission device includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the transmission device may be a Radio Frequency (RF) module, which is used to communicate with the internet in a wireless manner.

The display may be, for example, a touch screen type Liquid Crystal Display (LCD) that may enable a user to interact with a user interface of the computing device.

It should be noted here that in some alternative embodiments, the computing device shown in fig. 1 described above may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that FIG. 1 is only one example of a particular specific example and is intended to illustrate the types of components that may be present in a computing device as described above.

Under the operating environment, according to a first aspect of the present embodiment, there is provided a deformation detection method for a geological sensitive area of a power transmission channel, where fig. 2 shows a flow chart of the method, and with reference to fig. 2, the method includes:

s202: calculating a radar image set in the power transmission channel geological sensitive area by using a small baseline set interferometry technology, and determining absolute deformation data information, namely first deformation data information of the power transmission channel geological sensitive area, wherein the absolute deformation data information, namely the first deformation data information is wide-area planar deformation information in the absolute deformation data information power transmission channel geological sensitive area;

s204: performing difference value calculation on the front and rear time phase point cloud data, and determining second deformation data information of the geological sensitive area of the absolute deformation data information power transmission channel, wherein the second deformation data information of the absolute deformation data information is deformation information of the strip shape of the power transmission channel in the geological sensitive area of the absolute deformation data information power transmission channel;

s206: determining tower absolute deformation data information in a geological sensitive area of an absolute deformation data information transmission channel by using a Beidou differential positioning technology, wherein the tower absolute deformation data information is point-like deformation information of the transmission tower in the geological sensitive area of the absolute deformation data information transmission channel;

s208: and determining the ground settlement information of the geological sensitive area of the power transmission channel of the absolute deformation data information according to the first deformation data information of the absolute deformation data information, the second deformation data information of the absolute deformation data information and the absolute deformation data information of the tower of the absolute deformation data information.

As described in the background art, deformation of the geological sensitive area, and movement and deformation of the tower cause failure and even damage of the tower structure, which seriously affects the safe and stable operation of the transmission line and seriously threatens the safety of the power grid. Therefore, it is imperative to detect geologically sensitive areas of power transmission channels.

In view of the above, referring to fig. 2 and fig. 3, the deformation detection technology for the power transmission channel geological sensitive area is integrated with Synthetic Aperture Radar (SAR), laser scanning data and Beidou positioning data. The method is characterized in that the achievement fusion of the fused Synthetic Aperture Radar (SAR), laser scanning data and Beidou positioning data in the wide area, planar area and point object deformation detection is embodied in 4 aspects of early identification, hidden danger investigation, monitoring and early warning and disaster emergency, and the working mode of hierarchical progression, from surface to point, from coarse to fine and satellite-ground cooperation is followed.

The power transmission channel geological sensitive area deformation detection technology integrating SAR, laser scanning data and Beidou positioning data utilizes high-precision point monitoring data obtained by multi-temporal laser scanning measurement and GNSS (Beidou positioning) to verify the effectiveness of large-range inner surface monitoring information obtained by the InSAR technology; then carrying out data fusion calculation at the coincident point; and finally, correcting and calculating the monitoring values on all point positions by using the fused data to obtain high-precision planar settlement information in a large range. And the fused result not only ensures the precision and reliability of the leveling data, but also has the advantage of high resolution of InSAR measurement, and can well depict the detail information of the deformation of the geological sensitive area of the power transmission channel.

In addition, specifically, the deformation monitoring points obtained by the time sequence InSAR processing (1) have three-dimensional position coordinates, deformation rate and deformation evolution historical information. Deformation refers to deformation of the radar line of sight (LOS), positive values represent movement towards the radar, negative values represent movement away from the radar, and for convenience of expression, the deformation is respectively expressed as lifting and sinking.

(2) The effectiveness of large-range inner surface monitoring information acquired by InSAR technology is verified by using high-precision point-like monitoring data acquired by long-term GNSS measurement by using Beidou terminal equipment arranged on an ultra/extra-high voltage transmission tower or under the tower.

(3) And (3) selecting 10 pairs of the same name points by using a multi-phase airborne laser radar point cloud deformation detection result to verify the effectiveness of large-range inner area monitoring information acquired by the InSAR technology.

(4) And carrying out fusion calculation on data on Beidou differential monitoring points, airborne radar point cloud point locations and InSAR monitoring coincident points in the region, carrying out correction calculation on monitoring values on all points by using the fused data to form a deformation rate rendering map of the region, and predicting and analyzing ground settlement according to the fused ground settlement rate.

(5) The fused result not only ensures the precision and reliability of the level data, but also has the advantage of high resolution of InSAR measurement, and can well depict the detail information of the deformation of the geological sensitive area of the power transmission channel.

Optionally, the operation of determining the ground settlement information of the geological sensitive area of the absolute deformation data information transmission channel according to the first deformation data information of the absolute deformation data information, the second deformation data information of the absolute deformation data information, and the absolute deformation data information of the tower is further performed, the operation comprising:

performing homonymy point extraction on the absolute deformation data information second deformation data information and the absolute deformation data information tower absolute deformation data information on the absolute deformation data information first deformation data information by adopting a nearest neighbor weighted average algorithm to determine coincident point data;

fusing the data of the superposition point of the absolute deformation data information by using a preset data fusion algorithm to determine a fusion data value of the superposition point;

correcting the absolute deformation data information fusion data value of the absolute deformation data information coincident point by using a preset monitoring value correction algorithm, and determining a corrected data value of the absolute deformation data information coincident point;

and determining the absolute deformation data information ground settlement information of the geological sensitive area of the absolute deformation data information transmission channel according to the first deformation data information of the absolute deformation data information and the absolute deformation data information correction data value.

Specifically, referring to fig. 3, (1) verification of InSAR measurement accuracy by using dotted monitoring data

Interpolation of adjacent points: and extracting a corresponding InSAR sedimentation rate value according to the laser point cloud and the GNSS point coordinate position. The settlement value of a ground related target is obtained by InSAR observation, while the settlement value of a certain specific point position on the ground is obtained by airborne laser point cloud and GNSS measurement, and the two settlement values are not at the same position. Therefore, the InSAR monitoring results of the corresponding positions of the airborne laser point cloud and the GNSS point need to be extracted according to a certain criterion.

And (5) extracting the homologous points by adopting a method of weighted average of adjacent points. And selecting adjacent points within a certain distance rho from the position of the leveling point, and regarding the weighted average value as InSAR results corresponding to the laser point cloud and the GNSS point. If InSAR results do not exist within the distance rho, the points do not participate in the analysis.

And (3) precision evaluation: and taking the laser point cloud and the GPS observation result as the most probable values of the ground settlement, and selecting the standard deviation as an index for measuring the precision.

(2) Coincidence point bit data fusion algorithm

If n monitoring methods are used for monitoring the ground settlement, the settlement value obtained by each monitoring method is y _i The sedimentation values may then be formed into a matrix equation,

Y＝KX+ε

in the formula (I), the compound is shown in the specification,

is an unknown parameter vector; />

A monitoring coefficient for monitoring data; ε is the monitor data noise.

Estimating X by using a least square method, wherein the error equation is

V＝KX′-Y

The valuation should satisfy

The first order partial derivative is calculated for the estimated value to be 0, and the equation is solved

X′＝(K ^T PK) ^-1 K ^T PY

Due to second derivative

Estimate X' = (K) ^T PK) ^-1 K ^T PY has a minimum value.

The error of the estimate is:

ΔX′＝X-X′＝(K ^T PK) ^-1 K ^T P

(3) Monitoring value correction algorithm

Setting position M (X) _i ，Y _i ) Fused ground settlement estimate is Z' _i The settlement amount obtained by observation is Z _i Then the sedimentation amount at that point is corrected by the number

Z＝Z′ _i -Z _i

Correction number at any point position is

F(x，y，x _i ，y _i )＝[(x-x _i ) ² +(y-y _i ) ² +δ ² ] ^k

When k =1/2, equation (9) is a positive hyperboloid function; a negative hyperboloid function when k = one 1/2. Make Q ^ij ＝F(x，y，x _i ，y _i ) Then, then

The error equation is v = Qa-t according to the least square principle, and a = Q can be obtained ^T Q ^-1 Q ^T f, obtaining a multi-face function equation, further calculating a point location correction number, and obtaining a point location settlement amount estimated value Z' _i 。

Optionally, the operation of calculating a radar image set in a power transmission channel geological sensitive area by using a small baseline set interferometry technique to determine first deformation data information of the power transmission channel geological sensitive area includes:

registering a predetermined number N of radar images in a geological sensitive area of a power transmission channel, then resampling the radar images to a main image of the same scene, and determining an image set, wherein the dimensionality of the image set is N;

calculating pixels of each radar image in the image set, and determining coherence between a distributed target point and a permanent scattering point in each radar image in the image set;

calculating each radar image in the image set by using a maximum likelihood estimation algorithm, determining a time coherence dimension image, adding the time coherence dimension image into the image set, and determining a time coherence image set, wherein the dimension of the time coherence image set is N +1;

according to the small baseline set interferometry, calculating the radar image of the time coherence image set, and determining first deformation data information of the geological sensitive area of the power transmission channel.

Specifically, firstly, a predetermined number N of radar images are registered and then resampled to the same scene main image, and an image set is determined, wherein the dimension of the image set is N. The power transmission channel geological sensitive area can be an area of a power transmission channel designated by a user, namely an image of an area to be researched.

Specifically, N views of the SAR image are registered and resampled to the same main image geometry, and if d is a complex vector, the image set can be expressed as:

d(P)＝[d ₁ (P)，d ₂ (P)，...，d _N (P)] ^T

where P represents the pel, T represents the transpose, d _i (P) the ith SAR image and a plurality of data corresponding to the pixel P.

Further, pixels of each radar image in the image set are calculated, and coherence between the distributed target points and the permanent scattering points in each radar image in the image set is determined.

Here, the Distributed targets (DS) refer to features having substantially the same backscattering coefficient of all Scatterers in the resolution unit, and are mostly bare land, sparse vegetation, and the like.

Referring to fig. 4a and 4b, points DS are ubiquitous in suburban areas, and although they only maintain certain coherence properties in partial interferograms, extraction of interferometric geometry information of these targets is important for extending the application range of time series SAR data. Fig. 4a and 4b refer to the simulation result of Hooper in 2006, and show the difference between distributed image elements and coherent image elements. If there is a scatterer (permanent scatterer PS) in a pel that has a much higher backscatter intensity than the other scatterers in the pel and the dominant scatterer is more stable, the pel is called coherent pel. The coherent pixel has stable backward scattering intensity and stable phase, as shown in fig. 4b, and the pixel phase is basically maintained around a constant value as a result of 100 times of simulation. The ground features in the distributed pixel are uniform, the backscattering intensity of each scatterer is approximately equal, and no prominent strong scatterer exists. The phase appears randomly distributed between the scatterers as shown in figure 4 a. And a distributed target interference SAR technology is adopted, and the backscattering coefficient statistical characteristics of the PS point and the DS point are considered in a combined mode.

Further, each radar image in the image set is calculated by utilizing a maximum likelihood estimation algorithm, a time coherence dimension image is determined, the time coherence dimension image is added into the image set, and the time coherence image set is determined, wherein the dimension of the time coherence image set is N +1. Therefore, through the time coherence, the deformation information of the geological sensitive area of the power transmission channel in the time dimension can be analyzed conveniently.

And finally, calculating the radar image of the time coherence image set according to a small baseline set interferometry technology, and determining first deformation data information of the power transmission channel geological sensitive area. And resolving and removing various residual errors through a small baseline set interferometry technology to obtain deformation information.

Therefore, according to the method, the deformation detection method of the power transmission channel geological sensitive area based on the synthetic aperture radar is relatively changeable in the power transmission channel geological environment, and a distributed target interference SAR technology is adopted on the basis of deformation monitoring and the consideration of fully mining image information. The method has the advantages of realizing large-range, long-time and high-precision monitoring of the geological disasters of the power transmission channel, overcoming the problem of rare extraction points in suburbs, being suitable for multi-scale change monitoring of various complex geological conditions and being beneficial to evaluation of disaster influence factors. The distributed target interferometric SAR technology jointly considers the backscattering coefficient statistical characteristics of the PS point and the DS point on the premise of keeping the basic flow of the PS-InSAR technology, and finally realizes the surface deformation monitoring of the PS point and the DS point. And the technical problem that the information of the SAR image is not fully mined due to the fact that a large number of interference pairs with poor coherence are abandoned by the traditional time sequence INSAR technology in the prior art is solved.

Optionally, the operation of calculating a pixel of each radar image in the image set and determining coherence between a distributed target point and a permanent scattering point in each radar image in the image set includes: sorting the amplitude values of the pixels of each radar image in the image set, and determining the cumulative distribution function of the probability density function of the pixels; according to the accumulative distribution function, whether a first pixel and a second pixel of each radar image in the image set are homogeneous pixels or not is detected through a nonparametric detection method, wherein the first pixel and the second pixel are any two pixels of the radar image; determining a homogeneous area of the first pixel under the condition that the first pixel and the second pixel are homogeneous pixels; determining a complex coherence matrix of the distributed target points and the permanent scattering points according to the homogeneous region; and determining an interference pattern of complex elements of the complex coherence matrix according to the complex coherence matrix, wherein the interference pattern is used for describing coherence of the distributed target points and the permanent scattering points.

Specifically, the amplitude values (x = | d |) of the pixels P in each image are sorted, the cumulative distribution function CDF of the probability density function PDF is obtained through conversion, and the unbiased estimator F is used for the cumulative distribution function CDF _N (X) can be expressed as:

x _i is the ith element in the amplitude sequence. After defining the cumulative distribution function CDF, in order to check whether the two pixels p, q are statistically homogeneous SHP (i.e. the amplitude data have the same PDF), non-parametric functions may be usedNumber testing methods including the Kullback-Leibler distance method, the Kolmogorov-Smirnov test (KS) and the Anderson-Darling (AD) test.

A complex coherence matrix C of DS (distributed target) points and PS (permanent scattering) points is defined on the basis of the homogeneous region Ω. The phase value of complex element on the matrix off-diagonal is the interferogram after the spatial filtering corresponding to the phase value, the absolute value is the coherence of the interferogram, and the formula is as follows: .

Wherein C (P) is a complex coherence matrix requiring solution, y represents a vector matrix formed by each pixel element, and y is a complex coherence matrix ^H The method is characterized by comprising the following steps of representing a conjugate transpose matrix of y, | omega | representing an absolute value of a homogeneous region, y (p) representing projection of a polarization scattering vector, small p being a pixel point of the homogeneous region, and E being another expression form of a complex coherence matrix.

Therefore, by adopting the mode, the distributed target interference SAR technology is adopted, and the backscattering coefficient statistical characteristics of the PS point and the DS point are jointly considered.

Optionally, the operation of checking whether the first pixel and the second pixel of each radar image in the image set are homogeneous pixels through a non-parametric checking method includes: and (4) according to the accumulative distribution function, checking whether two pixels of each radar image in the image set are homogeneous pixels or not by a parameter-free double-tail checking method.

KS test statistical parameter D _N Is defined as:

of these methods, the parameterless two-tailed AD test is the most reliable. Statistical parameters

Is defined as:

is the empirical cumulative distribution function CDF of two points p and q, i =1,2.

D _N Or

If a certain threshold is met, p, q belong to a homogeneous pixel. For pixel p, a connected homogenous region Ω within the estimation window can be obtained.

Optionally, the operation of determining the temporal coherence dimension image by calculating each radar image in the image set by using a maximum likelihood estimation algorithm includes: calculating each radar image in the image set by adopting a maximum likelihood estimation method, and determining the real phase of each radar image in the image set; replacing the target phase of the distributed target point in each radar image by the real phase, and determining the time coherence of the evaluation index; and determining a time coherence dimension image according to the evaluation index time coherence.

Specifically, the true phase corresponding to each SAR image is solved by adopting maximum likelihood estimation, and the true phase is used for replacing the DS target phase. Setting the phase value of the first secondary SAR image as 0, and according to the maximum likelihood estimation criterion, optimally estimating the other N-1 phase values by lambda = [0, theta ] = ₂ ,...θ _N ] ^T This can be obtained by the following formula:

wherein Λ = exp (i λ) is an N-dimensional vector,

is the complex coherence matrix estimate. />

Is a Hadamard product. Adopting an iterative algorithm BFGS (Broyden-Fletch)er-Goldfarb-Shanno) can solve the above equations. Evaluating the optimized phase, and evaluating the time coherence (gamma) _PTA The expression is as follows:

therefore, through the mode, the time coherence of each radar image in the image set is obtained, and therefore the first deformation data information of the sensitive area of the power transmission channel on the time dimension can be calculated conveniently.

Optionally, the operation of calculating the radar image of the time coherence image set according to a small baseline set interferometry technique to determine first deformation data information of the geological sensitive area of the power transmission channel includes: determining a plurality of interference pairs according to a space-time baseline threshold and a temporal coherence image set; calculating the phase change rate between two adjacent radar images in the time coherence image set; determining deformation speed according to the interference pair and the phase change rate; and performing integral calculation on the deformation speed of the power transmission channel geological sensitive area in each time interval in a time domain, and determining first deformation data information of the power transmission channel geological sensitive area.

In particular, the baseline set (SBAS) is used to account for distortion on a low resolution, large scale. The basic idea of the SBAS algorithm is to divide the obtained SAR image combination into a plurality of sets, the baseline distance in each set is small, the baseline distance between the sets is large, and then a Singular Value Decomposition (SVD) method is adopted to combine a plurality of small baseline set data to carry out surface deformation time sequence inversion. Compared with the PS-InSAR technology, the SBAS adopts a plurality of main images, selects small baseline interference pairs, can detect partially coherent targets which only keep coherence in certain time periods, and the number of the targets is far more than that of PS points, so that the method is more suitable for suburban areas with less stable scatterers.

Firstly, suppose an SAR image of N +1 scenes in the same area, the corresponding time sequence is (t) ₀ ,...,t _N ) Obtaining M interference pairs according to a space-time baseline threshold, wherein M satisfies a conditional formula:

assuming the ith interference pattern, the acquisition time of the main and auxiliary images is t _B And t _B (t _B >t _B ) The differential interference phase at pixel x can be expressed as:

in the formula d (t) _B X) and d (t) _A X) is relative to a reference time t ₀ The radar line of sight (LOS) first deformation data information, the reference time first deformation data information d (t) ₀ And x) is equal to 0. The equation is a simplified model with phases such as atmospheric and terrain errors omitted,

is the differential phase after registration and correct unwrapping. Taking a certain high coherence point as an example, the following equation is established: />

Let us assume that the ith interference pair, i =1 _IEi The auxiliary image is t _ISi And t is _IEi ≥t _ISi The differential interference phase can be expressed as:

δφ _i ＝φ(t _IEi )-φ(t _ISi )，i＝1，…，M

converting the expression into a matrix form to obtain a formula:

δφ＝Aφ

a (mxn) is a coefficient matrix, with rows corresponding to the interferogram and columns corresponding to the SAR image. In equations 3-20, there are M equations, N unknowns. When M is larger than or equal to N and the Rank (A) = N of A, the unknown number can be solved by using a least square method, such as:

φ＝(A ^T A) ^-1 A ^T δφ

in the practical solving process, the condition that Rank (A) < N often occurs, which is related to the selection of the interference pair. If the interference pair has L different baseline sets, rank (a) = N-L +1. In this case, the solution can be performed by Singular Value Decomposition (SVD). However, some phenomena of discontinuous jump of accumulated deformation may occur in the process of solving, and a way to solve this problem is to introduce a phase change rate between two adjacent images:

available as a substitute:

the new matrix equation can be expressed as:

δφ＝Bv

where B is a matrix of the same dimension as A. The minimum norm solution of the vector v can be obtained by performing SVD on the matrix B. And according to the deformation speed of each time interval, integrating the speed of each time interval on a time domain to obtain first deformation data information of each time interval. SBAS techniques can support various non-linear deformation models: v = M · P, M is a coefficient matrix, and P is an unknown variable of each deformation.

In addition, a PS-InSAR technology can be used for calculating radar images of the time coherence image set and determining first deformation data information of a geological sensitive area of a power transmission channel.

Optionally, the operation of performing difference calculation on the time phase point cloud data before and after and determining second variable data information of the power transmission channel geological sensitive area includes:

setting front and rear time phase point cloud data to detection point cloud data and target point cloud data, wherein the front and rear time phase point cloud data comprise front time phase point cloud data and rear time phase point cloud data;

classifying the front and rear time phase point cloud data to generate ground point data, and performing thinning on the ground point data based on the gradient entropy to determine ground key point data;

carrying out network construction on the time phase point cloud data before and after the time phase point cloud data is based on the ground key point data after rarefaction, and determining a network construction model;

and performing difference calculation on the front and rear time phase point cloud data according to the network model to determine second variable data information of the geological sensitive area of the power transmission channel.

Specifically, referring to fig. 5, first, front-rear time phase point cloud data is set to the detection point cloud data and the target point cloud data. And according to a forward detection principle, sequentially setting front and rear time phase point clouds to the detection point cloud and the target point cloud, and taking front time phase point cloud data as a reference. And further classifying the front-time phase point cloud data and the rear-time phase point cloud data to generate ground point data, and performing thinning on the ground point data based on the gradient entropy to determine the ground key point data. And further, carrying out network construction on the front-rear time phase point cloud data based on the ground key point data after rarefaction, and determining a network construction model. And carrying out TIN data structure design on the front and rear time phase point cloud data, and carrying out data networking based on the ground points after rarefaction. And finally, calculating the difference value of the front and rear time phase point cloud data according to the network construction model, and determining second deformation data information of the power transmission channel geological sensitive area. Based on the improved ICP algorithm, after gradient soil moisture extraction and dilution operator processing, a final three-dimensional earth surface change detection strategy is formed, and detection efficiency is improved.

Therefore, the method for detecting, analyzing and researching the surface deformation change is based on the point set matching of the improved ICP algorithm and the gradient entropy surface rarefaction calculation to carry out three-dimensional surface detection, and can improve the detection efficiency. And then solve the more ground surface deformation detection based on manual measurement, INSAR mode or the multi-temporal laser radar mode of using at present among the prior art, detection efficiency is lower technical problem.

Optionally, the method further comprises: and determining front and rear time phase point cloud data by registering the preset improved ICP point set matching algorithm to different time phase point cloud data.

Specifically, the Iterative Closest Point algorithm is abbreviated as ICP (Iterative Closest Point) algorithm. The method is a precise and efficient two-dimensional and three-dimensional shape registration algorithm, can quickly and efficiently process the registration problem with six degrees of freedom, has a matching result unrelated to shape representation, and is high in algorithm robustness and good in reliability. Therefore, the ICP algorithm is currently applied to complex laser point cloud registration and remote sensing image (hyperspectral image and SAR image) registration in a large amount, and performs well.

At present, the algorithm is researched and improved by a plurality of scholars at home and abroad, and a great deal of application is developed in scientific research and production. For ease of explanation, we refer herein to the ICP algorithms proposed by p.j.besl and n.d.mckay as the basic ICP algorithms, and this registration method can be used to describe point sets, discounts, implicit curves, parametric curves, and various curved surfaces.

The basic ICP algorithm mainly comprises corresponding point search and pose solution, and usually adopts an inter-point distance nearest principle (see fig. 6a left) and a point-to-surface distance nearest principle (see fig. 6 b). The method aims to search the optimal spatial matching relation of different point sets and solve the spatial translation and rotation relation among the point sets. Fig. 6a and 6b are three-dimensional space illustrations using different phase point cloud registration using the ICP algorithm. Thereby improving the matching precision of the point cloud data.

Optionally, the operation of determining the front-time phase point cloud data and the back-time phase point cloud data by registering different time phase point cloud data through a preset improved ICP point set matching algorithm includes: determining a first point set and a second point set of point cloud data of different time phases, matching the first point set and the second point set through nearest neighbor search, and determining a corresponding point set; and performing space parameter transformation on the corresponding point set through an iterative algorithm until the corresponding point set is converged, and determining front-rear time phase point cloud data. Optionally, the method further comprises: and carrying out error data elimination on the corresponding point set based on a robust estimation mode.

Specifically, for a more intuitive mathematical description, the present application represents two different sets of mathematical points by M and P. Wherein, P is used to represent the point set to be registered, and M is used to represent the reference data point set. The basic ICP algorithm calculation flow can be expressed as:

(1) And searching the nearest neighbor point. Any point i in the point set P is randomly extracted and is represented by P (i). Traversing the point set M, and searching the point M (i) nearest to the point set M, so that (pi, mi) successfully constructs a first group of corresponding point sets;

(2) The space transformation parameters are solved based on an iterative algorithm, and here, the variation relationship is represented by (R, T). In order to calculate the transformation relationship as accurately as possible, an iterative calculation method is usually adopted for solving;

(3) According to the precision threshold value specified by the user, when each point in the point set P1 is converted to P2 through the iterated variation relationship, if the precision meets the threshold condition, the iteration is stopped, and the precision can be expressed by the following formula.

Wherein, E _d (R, T) is an objective function, R _r Is a spatial rotation parameter, and T is a spatial translation parameter.

(4) And repeating the iteration on the point set P until the point set P is finished.

In the laser radar point cloud matching process, the ICP matching algorithm is used for matching two time phase point sets, and the core of the ICP matching algorithm is the correct estimation of three-dimensional similarity change in a least square mode. Then, in the data processing process, although a part of errors are eliminated through data preprocessing, certain errors necessarily exist, and the calculation based on the least square does not have the characteristics of optimal estimation, and finally, the calculation result is not converged or is wrong. In order to have certain resistance to gross errors in the adjustment calculation process, a robust estimation mode based on elimination is adopted to inhibit the influence of matching point errors caused by the gross errors to a certain extent.

Since M estimates theoretically belong to the estimation of the maximum likelihood estimation class, we can express it as follows:

it is assumed here that:

after it is brought in, it can be simplified as:

∑w _i v _i a _i ＝0

thus, the M-estimation can be converted to a least squares estimation based on an iterative manner of weights selection. The Huber weight function may be modified as:

optionally, the operation of thinning the ground point data based on the gradient entropy and determining the ground key point data includes: calculating the gradient of the triangular slope surface of the ground point data, and calculating a plurality of gradient entropies of a plurality of local terrains in the ground point data according to the gradient; and sequencing the gradient entropies, and removing ground points corresponding to the gradient entropies according to a preset precision threshold value to determine ground key point data.

Specifically, the gradient entropy is used as a quantitative index for gradient characteristic expression of the urban ground surface and the building region, and is used for regional terrain complexity. Here, the gradient entropy is defined as follows:

in the formula, i represents the ith gradient surface, si represents the gradient value of the ith gradient surface, and n is the number of slopes used in actual calculation. H (S) is a mathematical expression of gradient entropy.

In the terrain expression process, the characteristics of the gradient entropy include:

(1) The slope entropy is mathematical expression of the real earth surface condition, the numerical value of the slope entropy is established on a real earth surface model, no assumption condition is needed, and the calculation result is related to a slope surface constructed by laser point cloud; when the density of the actual point cloud is large, the constructed slope surfaces are multiple, and the calculation complexity is high. In order to improve the calculation efficiency, point cloud rarefying processing is required to be carried out on the basis that the terrain expression precision meets the conditions.

(2) The calculation result of the surface gradient entropy is a non-negative numerical expression, and the magnitude of the numerical expression directly determines the surface complexity, namely the terrain complexity. When the gradient entropy value is larger, the earth surface texture characteristics are rich, but the earth surface change condition is small, and the terrain fluctuation is relatively smooth; when the gradient entropy value is smaller, the surface texture is simple, and the terrain change is severe.

When the laser radar point cloud data is used for building a ground model, ground points which are finished by filtering and classification are generally used. In a thick vegetation area, the number of penetrated ground points is limited, the data volume of the ground points is less, and the gradient value difference of the TIN model surface is larger. However, in a flat ground or an open ground, laser points are almost ground points, the data volume of the ground points is huge, the constructed TIN model surface is very huge, but the gradient values of the TIN model surface tend to be consistent, and in this case, point clouds need to be thinned so as to improve the retrieval efficiency.

When carrying out the rarefaction to airborne LiDAR point cloud, in order to compromise topography expression and rarefaction scale, need consider two aspects' factor:

(1) Retaining topographic features, and extracting key areas and key points based on ground point classification results;

(2) The point cloud thinning scale is consistent with DEM expression precision, and the key point positions are reasonably (uniformly) distributed.

Referring to fig. 7, the gradient entropy-based rarefaction algorithm specifically includes the steps of:

(1) Classifying the loaded airborne LiDAR point cloud data according to a multi-level filtering classification method based on characteristic attributes to generate ground points;

(2) Constructing a TIN model under constraint conditions based on ground points according to a network construction principle;

reading a first vertex in the triangular net, and searching adjacent triangles based on the spatial attributes;

(3) Reading first triangular mesh data in a first mesh according to mesh division, and searching adjacent side triangles of the first triangle based on spatial attributes and topological relations;

(4) Calculating the slopes of all triangular surfaces adjacent to the center top in sequence;

(5) Counting the gradient entropy of local terrains in the blocks on the basis of the block files;

(6) Traversing all grid data blocks, and calculating gradient entropy in an experimental range;

(7) And setting an entropy threshold value, and effectively accepting or rejecting the data points according to the threshold value. The entropy threshold is the elevation difference between the triangulation model constructed by the key points and the triangulation model constructed by the topographical points. As shown in fig. 8, 0.15m is the entropy threshold set by the user, and data points exceeding the region are retained.

Optionally, the method further comprises: and preprocessing the front time phase point cloud data and the rear time phase point cloud data in sequence according to the front time phase point cloud data. Optionally, the operation of sequentially preprocessing the time phase point cloud data according to the time phase point cloud data includes: and taking the front time phase point cloud data as a reference, and sequentially carrying out the calculation of the fairway range adjustment based on the connecting line and the multi-level filtering processing based on the characteristic attribute on the front time phase point cloud data and the rear time phase point cloud data.

Specifically, referring to fig. 5, the quality of the point cloud data is improved by preprocessing the point cloud data.

Optionally, the operation of performing difference calculation on the front-time phase point cloud data and the rear-time phase point cloud data according to the network model to determine second type data information of the geological sensitive area of the power transmission channel further includes: calculating the difference value of the front time phase point cloud data and the rear time phase point cloud data according to the network construction model, and determining the difference result; and performing refined calculation on the difference result according to the detection of the change area of the time phase point cloud data before and after the time phase point cloud data, and determining second shape data information of the geological sensitive area of the power transmission channel.

Specifically, referring to fig. 5, by performing refinement on the difference result, the result of deformation detection is more accurate, and thus, the precision analysis and evaluation can be better performed. The technical effect of accurately calculating the second type data information of the geological sensitive area of the power transmission channel is achieved.

In addition, referring to fig. 5, the method for detecting deformation of the power transmission channel geological sensitive area based on the laser radar scanning data forms a final three-dimensional earth surface change detection strategy after being processed by the gradient moisture extraction operator based on the improved ICP algorithm, and includes the following specific steps:

(1) According to the forward detection principle, the front time phase point cloud and the rear time phase point cloud are sequentially arranged for the detection point cloud and the target point cloud, and the point cloud of the front time phase serves as a reference. And (4) preprocessing the point cloud data, and sequentially carrying out connection line-based air band adjustment calculation and characteristic attribute-based multi-level filtering processing on the front and rear time phase point cloud data.

(2) Carrying out point cloud classification on the front and rear time phase point cloud data, and carrying out slope entropy-based thinning;

(3) Carrying out TIN data structure design on the time phase point cloud data before and after and carrying out data networking on the basis of the sparse ground points;

(4) And performing difference operation according to the network construction model in the third step, and performing refinement processing on the calculation result.

(5) And analyzing and evaluating the change detection precision according to the calculation result.

Optionally, the operation of determining tower absolute deformation data information in the geological sensitive area of the transmission channel by using the Beidou differential positioning technology comprises:

receiving two preset Beidou satellite navigation time sequence data acquired by two Beidou tower monitoring devices at intervals of preset time in real time, wherein the Beidou tower monitoring devices are used for monitoring towers;

calculating the two Beidou satellite navigation time sequence data through a Kalman filtering model to respectively obtain first time sequence position information and second time sequence position information of the tower;

determining first deformation information of the tower according to the first time sequence position information of the tower, and determining second deformation information of the tower according to the second time sequence position information of the tower;

and determining the absolute deformation data information of the tower according to the first deformation information and the second deformation information.

Specifically, referring to fig. 9, a schematic structural diagram of a Beidou tower monitoring device is shown, wherein the Beidou tower monitoring device is called monitoring device for short, the monitoring device is composed of a main control module, a Beidou RTK module, a tilt measurement module, a wireless transmission module, a data storage module, a security encryption module, a power management module and the like, and the external stress sensor, the meteorological sensor and other devices can be extended through an external management module, so that the Beidou tower monitoring device has the functions of RTK positioning, attitude angle measurement, internal data storage, measurement data reporting and the like, and can report attitude change measurement information of an electric power tower to a supervision center through a mobile communication network or local storage.

The software system can automatically collect data, automatically analyze deformation, automatically forecast and warn, and automatically give dynamic curve graphs of single and accumulated measurement data and dynamic curve graphs of deformation rate change. The system is designed for a B/S architecture, monitoring conditions can be inquired through a webpage, and monitoring change data can be visually displayed in a curve mode; the system is provided with modules for displacement vector analysis, historical data query, hierarchical user management, hierarchical alarm and the like, can display a monitoring structure diagram, a sensor layout diagram and the like, and a storage module is a database.

And the Beidou reference station receiver and the Beidou tower inclination deformation monitoring equipment acquire data in real time according to a certain sampling interval, and perform calculation such as quality inspection, baseline processing, net adjustment and the like. Obtaining a real-time position result of a monitoring point through RTK real-time positioning; obtaining a relative deformation result of the time sequence through sliding processing among epochs; obtaining an absolute deformation result through 2 deformation monitoring devices; the inclination sensor and the stress sensor acquire monitoring data in real time and store the monitoring data locally. When the 4G mobile communication network is available, real-time monitoring results such as a real-time position result, a relative deformation result, an absolute deformation result and the like are reported to the supervision center through the 4G network. When the 4G network is not available, the data stored locally in the equipment is exported for use.

The main functions are as follows:

(1) Automatic monitoring function

The system can realize automatic acquisition, transmission, storage, processing and analysis of monitoring data, and has the capability of real-time monitoring under various climatic conditions.

(2) Alarm function

The alarm is divided into a first-level alarm, a second-level alarm and a third-level alarm; the alarm device has the function of concurrent alarm of multiple places and multiple events. The alarm device has the functions of automatically displaying the alarm and inquiring the detailed information of the alarm, including the alarm type, the alarm position, the alarm grade, the alarm monitoring value, the alarm occurrence time, the alarm confirmation time, the alarm recovery time, the alarm confirmer, the alarm state and the like.

(3) On-line analysis and early warning function

And according to the related monitoring data, synthesizing the historical monitoring data, predicting and analyzing the operation state and the change trend of the tower, and providing early warning information.

(4) Authority management function

The method has the advantages that user information is added, deleted, modified, inquired and the like; and setting and managing user authority levels of different levels.

(5) Query statistics functionality

The method has the function of carrying out multi-condition query and statistics on historical alarm information according to alarm time, alarm positions, alarm types, alarm levels and the like.

The system has the functions of inquiring the daily, monthly and annual statistical report forms of monitoring data and monitoring data curves.

Further, referring to fig. 10, the beidou satellite navigation system is a national important space infrastructure, and the beidou three system is officially opened in 7/month/31/2020, and provides high-quality PNT service. In 2020, the book 'white paper of the Beidou Standard System for electric Power' is issued, and guidance is provided for the application of the Beidou system in the electric power industry.

Referring to fig. 10, an RTK (Real-time kinematic) carrier-phase differential technique is a differential method for processing carrier-phase observations of two measurement stations in Real time, and sends carrier phases acquired by a reference station to a user receiver for difference calculation and coordinate calculation.

The preset time interval may be half an hour, etc., which is not limited herein and is defined by the user according to the requirement. Because big dipper shaft tower monitoring facilities probably have the error thereby carry out shaft tower absolute deformation data information through two big dipper shaft tower monitoring facilities and confirm. In addition, this application also can carry out the detection of shaft tower deformation information through a big dipper shaft tower monitoring facilities, also can be monitored by two or a plurality of big dipper shaft tower monitoring facilities simultaneously.

Specifically, for the deformation detection problem of solving transmission of electricity passageway geological sensitive area based on big dipper differential positioning technique, this application provides compatible big dipper No. three high accuracy real-time differential positioning techniques. Based on real-time data acquisition of a reference station and a monitoring station, high-precision real-time differential positioning compatible with Beidou III is realized through models such as a double-difference observation model, kalman filtering estimation, ambiguity fixing and the like. Therefore, the deformation information of the transmission channel tower can be monitored in real time, and the deformation information of the transmission channel can be monitored in real time through the deformation information of the tower.

In addition, aiming at the characteristics of displacement deformation, tower pole inclination deformation and the like of the GNSS monitoring station, self-adaptive inspection quantities suitable for deformation of the tower pole ground table and the tower body are constructed. For example, the data collected mainly includes: beidou satellite navigation message (i.e. beidou satellite navigation timing data): the satellite navigation message is a message which is broadcasted to a user by a navigation satellite and used for describing the operation state parameters of the navigation satellite, and comprises system time, ephemeris, almanac, correction parameters of a satellite clock, health conditions of the navigation satellite, parameters of an ionospheric delay model and the like. The parameters of the navigation message provide time information for the user, and the position coordinates and the speed of the user can be calculated by using the parameters of the navigation message. Tower inclination data: and (4) obtaining by an inclination measuring module, and calculating an included angle between the central line of the tower and a plumb line (gravity line). Therefore, whether the tower is inclined or not is judged. Attitude angle information: the three-dimensional attitude of the transmission tower is described by adopting a three-axis (XYZ) inclination angle. Meteorological data: and the microclimate sensor acquires information such as temperature and wind speed. And carrying out self-adaptive inspection on the absolute deformation data information of the tower through the tower inclination data, the attitude angle information and the meteorological data. For example, the self-adaptive inspection can be used for fusing multi-source deformation monitoring information, performing threshold evaluation on the deformation state of the power transmission line tower by combining the physical and mechanical states of the whole and local structures of the power transmission line tower, the working state of an important part and the environmental conditions, and realizing baseline calculation, coordinate calculation, precision estimation, offset and deformation amount calculation.

Optionally, the method further comprises:

and carrying out error elimination on the Beidou satellite navigation time sequence data according to a preset double-difference observation equation.

Specifically, a double-difference observation equation is constructed:

assuming that the reference station u and the monitoring station r observe the satellites i and j simultaneously, the pseudorange and carrier phase double difference observation equation in units of distance can be expressed as:

in the two formulas, Δ ^ is a double difference operator, and ρ is the geometric distance between the satellite and the receiver; phi is a carrier phase observed value; p is a code pseudo range observed value; n is the integer ambiguity; λ is the carrier wavelength; f is the frequency; i is ionospheric delay; t is tropospheric delay; epsilon is pseudo-range observation noise and residual error after double difference, and xi is phase observation noise and residual error after double difference.

For short baselines (generally considered to be within 10 km), the double-difference ionosphere and troposphere terms can be approximated as being completely eliminated, at which point the double-difference observation equation can be further simplified as:

assuming that u and r simultaneously observe mB +1 bd satellites in the same epoch, the error equation at the s-th frequency is linearized as follows:

wherein the content of the first and second substances,

respectively a carrier and a pseudo-range double-difference observed value,

to design a matrix; />

Is an integer ambiguity parameter; x is a 3-dimensional baseline vector parameter; lambda _s Is a matrix of ambiguity coefficients.

The double-difference observation equation of a plurality of frequencies of one epoch is linearized to obtain:

wherein the content of the first and second substances,

is a double difference integer ambiguity parameter; />

Λ＝diag(Λ ₁ ,…Λ _f ). I is an identity matrix; />

Is the Dirichlet product; diag () stands for diagonal matrix.

Optionally, the method further comprises:

and preprocessing the Beidou satellite navigation time sequence data.

Optionally, the operation of preprocessing the Beidou satellite navigation time series data includes:

performing baseline processing on the Beidou satellite navigation time sequence data;

and carrying out net adjustment processing on the Beidou satellite navigation time sequence data.

Specifically, baseline processing: the observation data (position information data) collected by simultaneous observation in the field by a plurality of GPS receivers is used to determine the inter-receiver baseline vector and its variance-covariance matrix. The baseline solution results are used to verify and assess the quality of field observation results in addition to subsequent net adjustment. The baseline vector provides a relative position relationship between the points and belongs to the same frame of reference as the satellite ephemeris used in the solution. From these baseline vectors, the geometry and orientation of the GPS network can be determined. blog-CSDN blog baseline solution for GPS baseline solution _ die _ job.

Net adjustment difference: the method is the final stage of data processing, in which a baseline vector determined during baseline solution is taken as an observed value, a posterior variance-covariance matrix of the baseline vector is used for determining a weight matrix of the observed value, appropriate calculation data is introduced, and coordinates of each shop in the network are determined by a parameter estimation method. And the gross error in the observed value can be found through the net adjustment, and the processing is carried out by adopting a corresponding method. And the geometric contradiction caused by the baseline vector error can be eliminated, and the accuracy of the observation result can be evaluated.

Optionally, the operation of calculating the two Beidou satellite navigation time sequence data through a Kalman filtering model to respectively obtain first time sequence position information and second time sequence position information of the tower includes:

calculating the two Beidou satellite navigation time sequence data through a Kalman filtering model to obtain first time sequence position information and a floating point solution of second time sequence position information;

and fixing the ambiguity of the float solutions of the first time sequence position information and the second time sequence position information, and determining the first time sequence position information and the second time sequence position information.

Specifically, the kalman filter model:

and estimating by using a Kalman filtering formula of a linear system. The model of the filtering can be expressed as:

in the formula, x _k Is an n-dimensional state vector, z _k Representing m-dimensional observation vectors, random variables w _k-1 ,v _k Representing the process noise vector and the metrology noise vector, respectively. They are assumed to be white noise with a mean of 0 and obey a normal distribution:

p(w)～N(0,Q)

p(v)～N(0,R)

where Q and R represent the variance of process noise and metrology noise, respectively.

A _k Representing an n x n dimensional state transition matrix, H _k Representing an m x n dimensional measurement matrix.

The filtering process is divided into the following 5 steps:

and (3) state prediction:

the prediction variance is as follows:

and (3) filtering gain:

and (3) state filtering:

filtering variance:

/>

and on the basis of a linearized observation equation, using Kalman filtering to carry out real-time solution to obtain a floating solution of the monitoring station, and then carrying out ambiguity fixing to obtain real-time accurate coordinates of the monitoring station.

Further, referring to fig. 1, according to a third aspect of the present embodiment, there is provided a storage medium. The storage medium includes a stored program, wherein the method of any of the above is performed by a processor when the program is run.

Therefore, according to the embodiment, the transmission channel geological sensitive area deformation detection technology fusing SAR, laser scanning data and Beidou positioning data utilizes high-precision punctiform monitoring data obtained by multi-temporal laser scanning measurement and GNSS (Beidou positioning) to verify the effectiveness of large-range inner surface monitoring information obtained by the InSAR technology; then carrying out data fusion calculation at the coincident point; and finally, correcting and calculating the monitoring values on all point positions by using the fused data to obtain high-precision planar settlement information in a large range. And the fused result not only ensures the precision and reliability of the leveling data, but also has the advantage of high resolution of InSAR measurement, and can well depict the detail information of the deformation of the geological sensitive area of the power transmission channel.

It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the invention. Further, those skilled in the art will appreciate that the embodiments described in this specification are presently preferred and that no acts or modules are required by the invention.

Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention or portions thereof contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (which may be a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.

Example 2

Fig. 11 shows a deformation sensing device 1100 for a geological sensitive area of a power transmission channel according to the present embodiment, the device 1100 corresponding to the method according to the first aspect of embodiment 1. Referring to fig. 11, the apparatus 1100 includes: the first determining module 1110 is configured to calculate a radar image set in the power transmission channel geological sensitive area by using a small baseline set interferometry technique, and determine first deformation data information of the power transmission channel geological sensitive area, where the first deformation data information is wide-area planar deformation information in the power transmission channel geological sensitive area; the second determining module 1120 is configured to perform difference calculation on the point cloud data of the front time phase and the rear time phase, and determine second shape data information of the power transmission channel geological sensitive area, where the second shape data information is deformation information of a power transmission channel strip shape in the power transmission channel geological sensitive area; the third determining module 1130 is configured to determine, by using a Beidou differential positioning technology, tower absolute deformation data information in the geological sensitive area of the transmission channel, where the tower absolute deformation data information is point-like deformation information of the transmission tower in the geological sensitive area of the transmission channel; and a fourth determining module 1140, configured to determine ground settlement information of the geological sensitive area of the power transmission channel according to the first deformation data information, the second deformation data information, and the tower absolute deformation data information.

Optionally, the fourth determining module 1140 includes: the first determining submodule is used for performing homonymy point extraction on the tower absolute deformation data information second deformation data information and the tower absolute deformation data information on the tower absolute deformation data information first deformation data information by adopting a nearest neighbor weighted average algorithm to determine coincident point data; the second determining submodule is used for fusing the position data of the superposition point of the absolute deformation data information of the tower by using a preset data fusion algorithm and determining a fusion data value of the superposition point; the third determining submodule is used for correcting the tower absolute deformation data information fusion data value of the tower absolute deformation data information coincident point by using a preset monitoring value correction algorithm, and determining a corrected data value of the tower absolute deformation data information coincident point; and the fourth determining submodule is used for determining tower absolute deformation data information ground settlement information of the geological sensitive area of the tower absolute deformation data information transmission channel according to the first deformation data information of the tower absolute deformation data information and the tower absolute deformation data information correction data value.

Optionally, the second determining module 1120 includes: the fifth determining submodule is used for registering a predetermined number N of radar images in the power transmission channel geological sensitive area, then resampling the radar images to the same scene main image, and determining a photographic set, wherein the dimensionality of the photographic set is N;

a sixth determining submodule 420, configured to calculate pixels of each radar image in the image set, and determine coherence between a distributed target point and a permanent scattering point in each radar image in the image set;

the seventh determining sub-module 430 is configured to calculate each radar image in the image set by using a maximum likelihood estimation algorithm, determine a temporal coherence dimension image, add the temporal coherence dimension image to the image set, and determine a temporal coherence image set, where a dimension of the temporal coherence image set is N +1;

and the eighth determining submodule 440 is configured to calculate the radar image of the time coherence image set according to the small baseline set interferometry, and determine a deformation amount of the power transmission channel geological sensitive area.

Optionally, the sixth determining sub-module 420 includes:

the first determining unit is used for sequencing the amplitude values of the pixels of each radar image in the image set and determining the cumulative distribution function of the probability density function of the pixels;

the system comprises a detection unit, a calculation unit and a calculation unit, wherein the detection unit is used for detecting whether a first pixel and a second pixel of each radar image in an image set are homogeneous pixels or not by a nonparametric detection method according to an accumulated distribution function, and the first pixel and the second pixel are any two pixels of the radar images;

the second determining unit is used for determining the homogeneous area of the first pixel under the condition that the first pixel and the second pixel are homogeneous pixels;

the third determining unit is used for determining a complex coherence matrix of the distributed target points and the permanent scattering points according to the homogeneous region;

and the fourth determining unit is used for determining an interference pattern of complex elements of the complex coherence matrix according to the complex coherence matrix, wherein the interference pattern is used for describing the coherence of the distributed target points and the permanent scattering points.

Optionally, a test subunit comprising:

and the inspection subunit is used for inspecting whether the two pixels of each radar image in the image set are homogeneous pixels or not by a parameter-free double-tail inspection method according to the accumulative distribution function.

Optionally, the seventh determining sub-module 430 includes:

the fifth determining unit is used for calculating each radar image in the image set by adopting a maximum likelihood estimation method and determining the real phase of each radar image in the image set;

a sixth determining unit, configured to replace the target phase of the distributed target point in each radar image with the real phase, and determine temporal coherence of the evaluation index;

and determining a time coherence dimension image according to the evaluation index time coherence.

Optionally, the eighth determining submodule 440 includes:

a seventh determining unit, configured to determine a plurality of interference pairs according to a spatio-temporal baseline threshold and the temporal coherence image set;

the first calculating unit is used for calculating the phase change rate between two adjacent radar images in the time coherence image set;

the eighth determining unit is used for determining the deformation speed according to the interference pair and the phase change rate;

and the ninth determining unit is used for performing integral calculation on the deformation speed of the power transmission channel geological sensitive area in each time interval in a time domain to determine the deformation quantity of the power transmission channel geological sensitive area.

Optionally, the third determining module 1130 includes: a setting sub-module 710 configured to set front-rear time phase point cloud data to the detection point cloud data and the target point cloud data, where the front-rear time phase point cloud data includes front-rear time phase point cloud data and rear-time phase point cloud data; the generation submodule 720 is used for classifying the time phase point cloud data before and after, generating ground point data, performing thinning on the ground point data based on the gradient entropy, and determining ground key point data; a ninth determining submodule 730, configured to perform network construction on the front-rear time phase point cloud data based on the rarefied ground key point data, and determine a network construction model; and the tenth determining submodule 740 is configured to perform difference calculation on the front-and-back time phase point cloud data according to the network formation model, and determine a deformation amount of the power transmission channel geological sensitive area.

Optionally, the third determining module 1130 further comprises: and the eleventh determining submodule is used for determining front and rear time phase point cloud data by registering the preset improved ICP point set matching algorithm to the different time phase point cloud data.

Optionally, the eleventh determining submodule includes: a tenth determining unit, configured to determine a first point set and a second point set of point cloud data at different time phases, match the first point set and the second point set through nearest neighbor search, and determine a corresponding point set; and the eleventh determining unit is used for performing space parameter transformation on the corresponding point set through an iterative algorithm until the corresponding point set is converged and determining front and rear time phase point cloud data.

Optionally, the third determining module 1130 further comprises: and the error elimination submodule is used for eliminating the error data of the corresponding point set based on the robust estimation mode.

Optionally, the ninth determining sub-module includes: the second calculation unit is used for calculating the gradient of the triangular slope surface of the ground point data and calculating a plurality of gradient entropies of a plurality of local terrains in the ground point data according to the gradient; and the twelfth determining unit is used for sequencing the gradient entropies, removing the ground points corresponding to the gradient entropies according to a preset precision threshold value, and determining ground key point data.

Optionally, the third determining module 1130 further comprises: and the first preprocessing submodule is used for sequentially preprocessing the front time phase point cloud data and the rear time phase point cloud data according to the front time phase point cloud data.

Optionally, the first preprocessing submodule includes: and the preprocessing subunit is used for sequentially carrying out the calculation of the fairway band adjustment based on the connecting line and the multi-level filtering processing based on the characteristic attribute on the front time phase point cloud data and the rear time phase point cloud data by taking the front time phase point cloud data as a reference.

Optionally, the tenth determining sub-module further includes: a thirteenth determining unit, configured to perform difference calculation on the time phase point cloud data before and after according to the network formation model, and determine a difference result; and the fourteenth determining unit is used for carrying out refined calculation on the difference result according to the detection of the change regions of the time phase point cloud data before and after the time phase point cloud data, and determining the deformation quantity of the power transmission channel geological sensitive area.

Optionally, the fourth determining module 1140 includes: the receiving sub-module 510 is used for acquiring two preset Beidou satellite navigation time sequence data of two Beidou tower monitoring devices at intervals of preset time, wherein the Beidou tower monitoring devices are used for monitoring towers;

the calculation submodule 520 is used for calculating the two Beidou satellite navigation time sequence data through a Kalman filtering model to respectively obtain first time sequence position information and second time sequence position information of the tower;

the eleventh determining submodule 530 is configured to determine first deformation information of the tower according to the first time sequence position information of the tower, and determine second deformation information of the tower according to the second time sequence position information of the tower;

and the twelfth determining submodule 540 is configured to determine the absolute deformation information of the tower according to the first deformation information and the second deformation information.

Optionally, the fourth determining module 1140 further comprises:

and the error elimination submodule is used for eliminating errors of the Beidou satellite navigation time sequence data according to a preset double-difference observation equation.

Optionally, the fourth determining module 1140 further comprises:

and the second preprocessing submodule is used for preprocessing the Beidou satellite navigation time sequence data.

Optionally, the second pre-processing module comprises:

the base line processing unit is used for carrying out base line processing on the Beidou satellite navigation time sequence data;

and the net adjustment processing unit is used for carrying out net adjustment processing on the Beidou satellite navigation time sequence data.

Optionally, a computation submodule comprising:

the third calculation unit is used for calculating the two Beidou satellite navigation time sequence data through a Kalman filtering model to obtain the first time sequence position information and the floating point solution of the second time sequence position information;

a fifteenth determining unit, configured to perform ambiguity fixing on the floating solutions of the first timing position information and the second timing position information, and determine the first timing position information and the second timing position information.

Therefore, according to the embodiment, the transmission channel geological sensitive area deformation detection technology fusing SAR, laser scanning data and Beidou positioning data utilizes high-precision punctiform monitoring data obtained by multi-temporal laser scanning measurement and GNSS (Beidou positioning) to verify the effectiveness of large-range inner surface monitoring information obtained by the InSAR technology; then performing data fusion calculation on the coincident points; and finally, correcting and calculating the monitoring values on all point positions by using the fused data to obtain high-precision planar settlement information in a large range. And the fused result not only ensures the precision and reliability of the leveling data, but also has the advantage of high resolution of InSAR measurement, and can well depict the detail information of the deformation of the geological sensitive area of the power transmission channel.

The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.

In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

In the embodiments provided by the present invention, it should be understood that the disclosed technical contents can be implemented in other manners. The above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one type of logical functional division, and other divisions may be implemented in practice, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk, and various media capable of storing program codes.

The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.

Claims (22)

1. A deformation detection method for a geological sensitive area of a power transmission channel is characterized by comprising the following steps:

calculating a radar image set in a power transmission channel geological sensitive area by using a small baseline set interferometry technology, and determining first deformation data information of the power transmission channel geological sensitive area, wherein the first deformation data information is wide area planar deformation information in the power transmission channel geological sensitive area;

performing difference value calculation on the front-time phase point cloud data and the rear-time phase point cloud data to determine second deformation data information of the power transmission channel geological sensitive area, wherein the second deformation data information is deformation information of a power transmission channel strip shape in the power transmission channel geological sensitive area;

determining tower absolute deformation data information in the geological sensitive area of the transmission channel by utilizing a Beidou differential positioning technology, wherein the tower absolute deformation data information is punctiform deformation information of the transmission tower in the geological sensitive area of the transmission channel;

and determining the ground settlement information of the geological sensitive area of the power transmission channel according to the first deformation data information, the second deformation data information and the tower absolute deformation data information.

2. The method of claim 1, wherein the operation of determining ground subsidence information for the geological sensitive area of the transmission channel based on the first deformation data information, the second deformation data information, and the tower absolute deformation data information comprises:

performing homonymy point extraction on the second deformation data information and the tower absolute deformation data information on the first deformation data information by adopting a nearest neighbor weighted average algorithm to determine coincident point data;

fusing the coincident point data by utilizing a preset data fusion algorithm to determine a fusion data value of the coincident point;

correcting the fused data value of the coincident point by using a preset monitoring value correction algorithm, and determining a corrected data value of the coincident point;

and determining the ground settlement information of the power transmission channel geological sensitive area according to the first deformation data information and the correction data value.

3. The method of claim 1, wherein the operation of determining first deformation data information for a power transmission channel geological sensitive area by computing a set of radar images within the power transmission channel geological sensitive area using a small baseline set interferometry technique comprises:

registering a preset number N of radar images in the power transmission channel geological sensitive area, then resampling the radar images to the same scene main image, and determining a photographic image set, wherein the dimensionality of the photographic image set is N;

calculating pixels of each radar image in the image set, and determining coherence between a distributed target point and a permanent scattering point in each radar image in the image set;

calculating each radar image in the image set by using a maximum likelihood estimation algorithm, determining a time coherence dimension image, adding the time coherence dimension image into the image set, and determining a time coherence image set, wherein the dimension of the time coherence image set is N +1;

and according to a small baseline set interferometry technology, calculating the radar image of the time coherence image set, and determining first deformation data information of the power transmission channel geological sensitive area.

4. The method of claim 3, wherein the operation of computing the image elements of each radar image in the image set to determine the coherence between the distributed target point and the permanent scattering point in each radar image in the image set comprises:

sorting the amplitude values of the pixels of each radar image in the image set, and determining the cumulative distribution function of the probability density function of the pixels;

according to the accumulative distribution function, whether a first pixel and a second pixel of each radar image in the image set are homogeneous pixels or not is detected through a nonparametric detection method, wherein the first pixel and the second pixel are any two pixels of the radar image;

determining a homogeneous region of the first pixel under the condition that the first pixel and the second pixel are homogeneous pixels;

determining a complex coherence matrix of the distributed target points and the permanent scattering points according to the homogeneous region;

and determining an interference pattern of complex elements of the complex coherence matrix according to the complex coherence matrix, wherein the interference pattern is used for describing the coherence of the distributed target points and the permanent scattering points.

5. The method of claim 4, wherein the operation of verifying whether the first pel and the second pel of each radar image in the image set are homogeneous pels by a non-parametric verification method comprises:

and according to the accumulative distribution function, checking whether the two pixels of each radar image in the image set are homogeneous pixels or not by a parameter-free double-tail checking method.

6. The method of claim 3, wherein the act of determining a temporal coherence dimension image by computing each radar image in the image set using a maximum likelihood estimation algorithm comprises:

calculating each radar image in the image set by adopting a maximum likelihood estimation method, and determining the real phase of each radar image in the image set;

replacing the target phase of the distributed target point in each radar image by the real phase, and determining the time coherence of the evaluation index;

and determining the time coherence dimension image according to the evaluation index time coherence.

7. The method of claim 3, wherein the operation of computing the radar image of the set of temporally coherent images according to a small baseline set interferometry technique to determine first deformation data information for the power transmission channel geological sensitive area comprises:

determining a plurality of interference pairs according to a space-time baseline threshold value and the temporal coherence image set;

calculating the phase change rate between two adjacent radar images in the time coherence image set;

determining deformation speed according to the interference pair and the phase change rate;

and performing integral calculation on the deformation speed of the power transmission channel geological sensitive area in each time interval in a time domain, and determining the first deformation data information of the power transmission channel geological sensitive area.

8. The method of claim 1, wherein the operation of performing a difference calculation on the front and back phase point cloud data to determine second type data information of the geological sensitive area of the power transmission channel comprises:

setting front and rear time phase point cloud data to detection point cloud data and target point cloud data, wherein the front and rear time phase point cloud data comprise front time phase point cloud data and rear time phase point cloud data;

classifying the front-time phase point cloud data and the rear-time phase point cloud data to generate ground point data, and performing thinning on the ground point data based on gradient entropy to determine ground key point data;

performing network construction on the front-time phase point cloud data and the back-time phase point cloud data based on the ground key point data after rarefaction, and determining a network construction model;

and performing difference calculation on the front and rear time phase point cloud data according to the network construction model, and determining second shape variable data information of the power transmission channel geological sensitive area.

9. The method of claim 8, further comprising:

and determining the front-time phase point cloud data and the rear-time phase point cloud data through the registration of a preset improved ICP point set matching algorithm to different time phase point cloud data.

10. The method according to claim 9, wherein the operation of determining the front-back phase point cloud data through the registration of the preset improved ICP point set matching algorithm to different phase point cloud data comprises:

determining a first point set and a second point set of point cloud data of different time phases, matching the first point set and the second point set through nearest neighbor search, and determining a corresponding point set;

and carrying out space parameter transformation on the corresponding point set through an iterative algorithm until the corresponding point set is converged, and determining the front-time phase point cloud data and the rear-time phase point cloud data.

11. The method of claim 10, further comprising:

and carrying out error data elimination on the corresponding point set based on a robust estimation mode.

12. The method of claim 8, wherein the operation of thinning the ground point data based on slope entropy to determine ground key point data comprises:

calculating the gradient of the triangular slope surface according to the ground point data, and calculating a plurality of gradient entropies of a plurality of local terrains in the ground point data according to the gradient;

and sequencing the gradient entropies, removing ground points corresponding to the gradient entropies according to a preset precision threshold value, and determining the ground key point data.

13. The method of claim 8, further comprising: and preprocessing the front and rear time phase point cloud data in sequence according to the front time phase point cloud data.

14. The method of claim 13, wherein the operation of sequentially pre-processing the forward-backward time phase point cloud data from the forward-temporal time phase point cloud data comprises:

and taking the front time phase point cloud data as a reference, and sequentially carrying out navigation band adjustment calculation based on a connecting line and multi-level filtering processing based on characteristic attributes on the front time phase point cloud data and the rear time phase point cloud data.

15. The method of claim 8, wherein the operation of determining second deformation data information of the power transmission channel geological sensitive area by performing a difference calculation on the front and back phase point cloud data according to the grid model further comprises:

calculating the difference of the front and rear time phase point cloud data according to the network construction model to determine a difference result;

and performing refined calculation on the difference result according to detection of the change region of the front-time phase point cloud data and the back-time phase point cloud data, and determining the second shape data information of the power transmission channel geological sensitive area.

16. The method of claim 1, wherein the operation of determining tower absolute deformation data information within the power transmission channel geological sensitive area using Beidou differential positioning technology comprises:

receiving two preset Beidou satellite navigation time sequence data acquired by two Beidou tower monitoring devices at intervals of preset time in real time, wherein the Beidou tower monitoring devices are used for monitoring towers;

calculating the two Beidou satellite navigation time sequence data through a Kalman filtering model to respectively obtain first time sequence position information and second time sequence position information of the tower;

determining first deformation information of the tower according to the first time sequence position information of the tower, and determining second deformation information of the tower according to the second time sequence position information of the tower;

and determining the absolute deformation data information of the tower according to the first deformation information and the second deformation information.

17. The method of claim 16, further comprising:

and carrying out error elimination on the Beidou satellite navigation time sequence data according to a preset double-difference observation equation.

18. The method of claim 17, further comprising:

and preprocessing the Beidou satellite navigation time sequence data.

19. The method of claim 18, wherein the act of pre-processing the Beidou satellite navigation timing data comprises:

performing baseline processing on the Beidou satellite navigation time sequence data;

and carrying out net adjustment processing on the Beidou satellite navigation time sequence data.

20. The method of claim 16, wherein the operation of calculating the two beidou satellite navigation time series data through a kalman filter model to obtain first time series position information and second time series position information of the tower respectively comprises:

calculating the two Beidou satellite navigation time sequence data through the Kalman filtering model to obtain floating point solutions of the first time sequence position information and the second time sequence position information;

and fixing the ambiguity of the floating solutions of the first time sequence position information and the second time sequence position information, and determining the first time sequence position information and the second time sequence position information.

21. A computer-readable storage medium, characterized in that the storage medium comprises a stored program, wherein the method of any of claims 1 to 20 is performed by a processor when the program is run.

22. A deformation detection device for a power transmission channel geological sensitive area, comprising:

the first determining module is used for calculating a radar image set in a power transmission channel geological sensitive area by using a small baseline set interferometry technology, and determining first deformation data information of the power transmission channel geological sensitive area, wherein the first deformation data information is wide area planar deformation information in the power transmission channel geological sensitive area;

the second determination module is used for performing difference calculation on the front-time phase point cloud data and the rear-time phase point cloud data and determining second deformation data information of the power transmission channel geological sensitive area, wherein the second deformation data information is deformation information of a power transmission channel strip shape in the power transmission channel geological sensitive area;

the third determining module is used for determining tower absolute deformation data information in the power transmission channel geological sensitive area by utilizing a Beidou differential positioning technology, wherein the tower absolute deformation data information is point-like deformation information of a power transmission tower in the power transmission channel geological sensitive area;

and the fourth determining module is used for determining the ground settlement information of the geological sensitive area of the power transmission channel according to the first deformation data information, the second deformation data information and the tower absolute deformation data information.

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