A GNSS coordinate displacement detection method and system combining long and short time window segmentation

Abstract

The application relates to the technical field of displacement detection, and particularly discloses a GNSS coordinate displacement detection method and system combined with long and short time window segmentation, which comprises the following steps: collecting original GNSS coordinate data, and sampling the original GNSS coordinate data based on a preset time interval to obtain continuous time series; dividing the time series based on a long time detection window length and a short time detection window length respectively to obtain a long time window and a short time window; segmenting the long time window and the short time window, performing multiple hypothesis tests based on segmentation points, and determining possible displacement points of the short time window and the long time window according to test results and a preset threshold value respectively; and performing displacement detection on the time window in which the possible displacement points exist based on Bayesian inference to obtain accurate displacement points. Compared with the prior art, the application can more comprehensively and accurately detect displacements of different characteristic types.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of displacement detection, in particular to a GNSS coordinate displacement detection method and system combining long and short time window segmentation. BACKGROUND

[0002] Displacement measurement is an important technology for monitoring the spatial position changes of objects or ground points, and its application range is wide, including building deformation, earthquake crust movement, landslide and ground subsidence, etc. Global Navigation Satellite System (GNSS) is a key technical means for displacement measurement, which accurately calculates the position and motion trajectory of ground receivers through satellite signals. The advantage of GNSS lies in its global coverage, all-weather and long-time continuous monitoring capability, which is particularly suitable for large-scale and long-term displacement monitoring. Through accurate GNSS positioning technology, millimeter-level or even sub-millimeter-level displacement precision can be obtained, which is widely used in earthquake monitoring, bridge and tunnel monitoring, dam safety monitoring and other fields, and has become an indispensable tool in displacement measurement.

[0003] At present, the existing displacement detection based on time series segmentation usually only targets short-time displacement with obvious and stable changes, which may ignore displacement with slow changes in short time or changes not obvious due to GNSS observation conditions, resulting in missed detection. Using short-time window detection may also be affected by short-time abnormal jumps of time series, resulting in false detection. In this case, the review according to the number of possible change points is difficult to achieve the effect of review and screening. SUMMARY

[0004] In order to overcome the defects of the prior art that only short-time detection leads to missed detection or false detection, the present application provides a GNSS coordinate displacement detection method and system combining long and short time window segmentation.

[0005] In order to achieve the above technical effects, the technical scheme of the present application is as follows:

[0006] A GNSS coordinate displacement detection method combining long and short time window segmentation, comprising the following steps:

[0007] Collecting original GNSS coordinate data, and sampling the original GNSS coordinate data based on a preset time interval to obtain continuous time series;

[0008] Setting a long time window and a short time window, dividing the time series based on the long time window and the short time window respectively, segmenting the time series data in any window, performing multiple hypothesis testing based on the segmentation points, and determining the possible displacement points in the window according to the test results and a preset threshold.

[0009] Based on Bayesian inference, the time window where the possible displacement points exist is detected for displacement, and accurate displacement points are obtained.

[0010] The displacement estimation value is calculated based on the possible displacement point and the accurate displacement point.

[0011] The application further provides a GNSS coordinate displacement detection system combining long and short time window segmentation, which comprises:

[0012] The data acquisition module is used for acquiring original GNSS coordinate data and sampling the original GNSS coordinate data based on a preset time interval to obtain continuous time series.

[0013] The displacement point speculation module is used for dividing the time series based on a long time window and a short time window respectively, segmenting the time series data in any window, performing multiple hypothesis testing based on the segmentation point, and determining the possible displacement point in the window according to the test result and a preset threshold.

[0014] The displacement point review module is used for performing displacement detection on the time window in which the possible displacement point exists based on Bayesian inference to obtain an accurate displacement point.

[0015] The displacement value calculation module is used for calculating a displacement estimation value based on the possible displacement point and the accurate displacement point.

[0016] The application further provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor implements the GNSS coordinate displacement detection method combining long and short time window segmentation as described in the application when executing the computer program.

[0017] The application further provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the GNSS coordinate displacement detection method combining long and short time window segmentation as described in the application.

[0018] Compared with the prior art, the application has the following beneficial effects:

[0019] The application introduces a long time detection window in displacement detection based on time series segmentation, and combines the long time detection window with short time window segmentation, so that different characteristic types of displacement can be detected more comprehensively and effectively, and accurate displacement detection is realized while ensuring short time sudden displacement detection. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 It is a flowchart of the GNSS coordinate displacement detection method combining long and short time window segmentation.

[0021] Figure 2 A schematic diagram of a GNSS coordinate displacement detection system combining long and short time window segmentation. DETAILED DESCRIPTION

[0022] The accompanying drawings are only intended to illustrate the present application, and should not be construed as limiting the present application;

[0023] It is understandable for those skilled in the art that some well-known descriptions in the drawings can be omitted.

[0024] The technical solutions of the present application will be further described below in combination with the drawings and examples.

[0025] Example 1

[0026] The present embodiment proposes a GNSS coordinate displacement detection method combining long and short time window segmentation, as shown in Figure 1 The flowchart of the GNSS coordinate displacement detection method combining long and short time window segmentation of the present embodiment is shown in

[0027] The GNSS coordinate displacement detection method combining long and short time window segmentation proposed in the present embodiment includes the following steps:

[0028] Collecting original GNSS coordinate data and obtaining continuous time series based on pre-set time interval sampling of the original GNSS coordinate data;

[0029] Setting long and short time windows, dividing the time series based on the long and short time windows respectively, segmenting the time series data within any window, performing multiple hypothesis testing based on the segmentation points, and determining the possible displacement points within the window according to the test results and pre-set threshold value;

[0030] Based on Bayesian inference, the time window where the possible displacement point exists is detected for displacement, and the accurate displacement point is obtained;

[0031] Based on the possible displacement point and the accurate displacement point, the displacement estimation value is calculated.

[0032] In this embodiment, by setting long and short time windows, detection is carried out in the long and short time windows respectively, and displacement points are independently found. By introducing the cooperative mechanism of long and short time detection windows, the limitation of traditional single time scale is broken, and comprehensive and accurate detection of displacement of different characteristic types is realized. Specifically, the long time window can capture the slow and continuous displacement change trend, for example, the long time window monitors the slow displacement every 24 hours to monitor the possible impact of the rainy season on the stability of the mountain; the short time window retains the sensitivity to sudden displacement, for example, the short time window monitors the sudden displacement every 5 minutes to deal with sudden displacement caused by heavy rain; the cooperative work of the two ensures the accuracy of the original short-time sudden displacement detection, and significantly expands the application range of displacement detection. Secondly, the displacement detection method based on Bayesian inference is used as a review means, which effectively improves the accuracy of displacement detection and greatly reduces the risk of misjudgment.

[0033] In an optional embodiment, the step of sampling the original GNSS coordinate data based on a preset sampling interval to obtain a continuous time sequence further includes: performing data rejection on the time sequence sampled based on the quartile method and the z-score method; and the expression is as follows:

[0034]

[0035] wherein z X is a z-score statistic, if z X is greater than a threshold value, it is an outlier and is rejected, otherwise it is retained; X represents a coordinate data value, μ represents a mean value of time series statistics, σ represents a standard deviation of time series statistics, k1 is a coefficient term of the z-score threshold value; Q1 and Q3 are lower quartiles and upper quartiles of the time sequence, IQR is the interquartile range, k2 is the threshold coefficient of the quartile method, if X is greater than Q3+k2·IQR or less than Q1-k2·IQR, it is an outlier and is rejected, otherwise it is retained.

[0036] In this embodiment, in the GNSS data, abnormal data may be generated due to environmental changes, ionosphere, troposphere influence and multipath effect, which may affect subsequent statistical analysis, thereby affecting the positioning accuracy and the accuracy of the analysis result. By using the quartile method and the z-score method to reject outliers, the interference of errors on the data is reduced, and the stability and usability of the data are improved.

[0037] In an optional embodiment, the step of dividing the time sequence based on the long time window and the short time window respectively further includes: decomposing and reconstructing the original coordinate time sequence based on Fourier transform on the time sequence divided by the long time window; and the expression of the Fourier transform is as follows:

[0038]

[0039] Wherein, x(j) is time series, j is the serial number of data in time series; FFT(k) is frequency spectrum, k is corresponding frequency spectrum serial number; L is time series length.

[0040] In this embodiment, long time window data can be affected by observation noise, and window sequence denoising is beneficial to obtain relatively smooth sequence. Fourier transform can effectively decompose periodic components in the signal, remove high frequency noise, and restore the true trend or periodic change, which is helpful to improve the smoothness and stability of long time window data.

[0041] In an optional embodiment, the step of performing multiple hypothesis testing based on the segmentation point comprises: obtaining sub-sequences based on the segmentation point in the window, and sequentially performing variance test, Z test and standard normal homogeneity test on each segmentation point; and regarding the segmentation point satisfying the variance test, Z test and standard normal homogeneity test as a possible displacement point, which is expressed as follows:

[0042] VT i = std1 i 2 + std2 i 2 <VT0

[0043]

[0044] Wherein, VT i , ZT i , SNHT i respectively represent the statistics of variance test, Z test and standard normal homogeneity test at the segmentation point i; VT0, ZT0 and SNHT0 represent the preset threshold of the corresponding test statistics; i represents the data serial number in the time series window corresponding to the segmentation point, L represents the length of the time series in the window, and L min ≤ i ≤ L-L min ; ave1 i and ave2 i respectively represent the mean of the sub-sequence segmented at the i point; std1 i and std2 i respectively represent the standard deviation of the sub-sequence segmented at the i point; x j represents the jth time series data; if the above expression is satisfied, the current segmentation point i is regarded as a possible displacement point.

[0045] In this embodiment, by segmenting on long and short time windows and performing multiple hypothesis testing based on the segmentation points, the accuracy and reliability of displacement point detection are significantly improved. Specifically, by sequentially performing variance test, Z-test and standard normal homogeneity test on each segmentation point, it is ensured that only points that meet all statistical conditions are determined as possible displacement points, thereby effectively filtering out false displacements caused by noise or abnormal fluctuations. Variance test can identify significant volatility changes in the data, Z-test further confirms whether these fluctuations are beyond the normal statistical range, and standard normal homogeneity test ensures the consistency of data distribution, avoiding misjudgment caused by non-normal distribution. Through the multiple hypothesis testing method, possible displacement points can be accurately identified in complex GNSS data, thereby improving the robustness and accuracy of the displacement detection system.

[0046] In an optional embodiment, the step of detecting displacement based on the time window in which the possible displacement point exists based on Bayesian inference comprises:

[0047] assuming that there is a displacement point in the time window in which the possible displacement point is located, constructing a prior probability distribution;

[0048] based on the time series data, performing Bayesian update on the prior probability distribution to obtain a posterior probability distribution;

[0049] based on the posterior probability distribution, sampling based on conditional probability using the Markov Chain Monte Carlo method to obtain a hypothetical displacement point, and re-performing the multiple hypothesis test on the hypothetical displacement point, if the test is passed, the hypothetical displacement point is regarded as an accurate displacement point, otherwise it is considered that there is no displacement point.

[0050] In this embodiment, the possible displacement points are re-detected based on Bayesian inference. Specifically, the Bayesian update process combines the actual observation information of the time series, and the accuracy of displacement point identification is further improved through the posterior probability distribution. Secondly, the conditional probability sampling through the Markov Chain Monte Carlo method ensures that the hypothesis of the displacement point conforms to the statistical law. Finally, the multiple hypothesis test is performed again to ensure that the displacement point that meets the test condition is output as an accurate displacement point, reducing the risk of misjudgment.

[0051] In an optional embodiment, the step of constructing a prior probability distribution based on the assumption that there is a displacement point in the time window in which the possible displacement point exists comprises:

[0052] assuming that the time series data in the time window follows a normal distribution and there is only one displacement point, the displacement observation likelihood function is represented as:

[0053]

[0054] where x represents the time series data, xi represents the ith time series data in the time window; θ = (μ0, μ1, τ, σ) represents the assumed displacement point; τ represents the displacement time of the assumed displacement point; μ0 and μ1 represent the means of the two sub-sequences after the time window is divided at the time τ; and σ represents the standard deviation of the normal distribution of the time series data;

[0055] Assuming that each parameter in θ is independent of each other, the prior distribution of the assumed displacement point is represented as:

[0056]

[0057] wherein P(θ) is the prior probability of the assumed displacement point; P(μ0) and P(μ1) represent the prior probabilities of the means μ0 and μ1 in the assumed displacement point, respectively; P(τ) represents the prior probability of the displacement time τ of the assumed displacement point; P(σ) represents the prior probability of the standard deviation σ of the assumed displacement point; U(μ min , μ max ) represents that the mean is subject to a continuous uniform distribution, μ min and μ max represent the upper and lower limits of the uniform distribution; DiscreteU(t1, t L ) represents that the displacement time is subject to a discrete uniform distribution, t1 and t L represent the upper and lower limits of the displacement time; and N(μ σ , σ σ ) represents that the standard deviation is subject to a Gaussian normal distribution, μ σ and σ σ represent the mean and standard deviation of the standard deviation parameter.

[0058] In the embodiment, the variation characteristics of the data are captured by the probability modeling based on the normal distribution, so as to realize the description of the displacement point position and the distribution characteristics. Secondly, the calculation complexity is simplified by assuming the independence of the parameters, and the prediction efficiency and the adaptability of the model are improved. In addition, the robustness of the model to uncertainty and noise is enhanced by the integration of the prior information, and the rationality and interpretability of the prediction result are ensured.

[0059] In an optional embodiment, the method further comprises: calculating the displacement amount based on the possible displacement point and the accurate displacement point, and the expression is as follows:

[0060]

[0061] wherein the subscript τ1 represents the possible displacement point, the subscript τ2 represents the accurate displacement point, and Δx represents the displacement amount.

[0062] In this embodiment, the accurate displacement amount can be obtained through the possible displacement point and the accurate displacement point, the error can be corrected by combining the two displacement points, the result accuracy is optimized, the influence of noise on displacement amount calculation is reduced, and the robustness of the model is improved.

[0063] Embodiment 2

[0064] This embodiment proposes a GNSS coordinate displacement detection system combining long and short time window segmentation, and applies the GNSS coordinate displacement detection method combining long and short time window segmentation proposed in embodiment 1. As shown in the figure, it is an architecture diagram of the GNSS coordinate displacement detection system combining long and short time window segmentation of this embodiment. Figure 2

[0065] This embodiment proposes a GNSS coordinate displacement detection system combining long and short time window segmentation, which comprises:

[0066] The data acquisition module is used to acquire the original GNSS coordinate data, and sample the original GNSS coordinate data based on the preset time interval to obtain continuous time series;

[0067] The displacement point speculation module is used to divide the time series based on the long time window and the short time window respectively, divide the time series data in any window, perform multiple hypothesis testing based on the division point, and determine the possible displacement point in the window according to the test result and the preset threshold;

[0068] The displacement point review module is used to perform displacement detection on the time window in which the possible displacement point exists based on Bayesian inference to obtain the accurate displacement point;

[0069] The displacement value calculation module is used to calculate the displacement estimation value based on the possible displacement point and the accurate displacement point.

[0070] It can be understood that the system of this embodiment corresponds to the method of embodiment 1 described above, and the optional items in embodiment 1 described above are also applicable to this embodiment, so they will not be described here.

[0071] Embodiment 3

[0072] This embodiment proposes a computer device comprising a memory and a processor, the memory stores computer readable instructions, wherein the computer readable instructions are executed by the processor to make the processor execute the steps of the GNSS coordinate displacement detection method combining long and short time window segmentation proposed in embodiment 1.

[0073] Embodiment 4

[0074] ​The embodiment provides a storage medium, which stores computer readable instructions, wherein the computer readable instructions are executed by a processor to implement steps of the GNSS coordinate displacement detection method combining long and short time window segmentation provided in the embodiment 1.

[0075] Exemplarily, the storage medium includes but is not limited to a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk and various media capable of storing program codes.

[0076] Exemplarily, the instructions, programs, code sets or instruction sets can be implemented by using a conventional programming language.

[0077] Exemplarily, the processor includes but is not limited to a smart phone, a personal computer, a server, a network device and the like, and is used to execute all or part of steps of the GNSS coordinate displacement detection method combining long and short time window segmentation based on stage learning.

[0078] The terms in the drawings are only used for exemplary illustration, and should not be understood as a limitation to the patent;

[0079] Obviously, the above embodiments of the present application are only examples for clearly illustrating the present application, and are not intended to limit the implementation manners of the present application. Any modification, equivalent replacement and improvement made on the basis of the above description for those skilled in the art should be included in the protection scope of the present application.

Claims

1. A GNSS coordinate displacement detection method combining long and short time window segmentation, characterized in that, Includes the following steps: Collect raw GNSS coordinate data and sample the raw GNSS coordinate data at preset time intervals to obtain a continuous time series; Set up a long time window and a short time window, divide the time series based on the long time window and the short time window respectively, segment the time series data within any window, perform multiple hypothesis tests based on the segmentation points, and determine the possible displacement points within the window based on the test results and preset thresholds. Based on Bayesian inference, displacement detection is performed within the time window where possible displacement points exist to obtain accurate displacement points; The displacement estimate is calculated based on the possible displacement points and the accurate displacement points; The steps for performing multiple hypothesis testing based on split points include: obtaining subsequences based on split points within the window, and sequentially performing variance tests, Z-tests, and standard normality tests on each split point; split points that satisfy the variance test, Z-test, and standard normality tests are considered as possible shift points, and their expressions are as follows: in, , , These represent the points at the dividing points. variance test Z The statistical test for normality and the test for standard normality; , , This indicates the preset threshold for the corresponding test statistic; L Indicates the length of the time series within the window. This indicates the data sequence number of the time series corresponding to the dividing point. ; and They represent in The mean of the point-sequenced subsequence; and They represent in The standard deviation of the point-sequence segment; Indicates the first j A number of time series data points; if the above expression is satisfied, then the current split point is considered to be... i These are possible displacement points; The steps for displacement detection based on Bayesian inference within the time window of possible displacement points include: Assuming that a displacement point exists within the time window of the possible displacement point, construct a prior probability distribution; Based on time series data, the prior probability distribution is updated using Bayesian method to obtain the posterior probability distribution. Based on the posterior probability distribution, the Markov chain Monte Carlo method is used to sample based on conditional probability to obtain the parameters of the hypothetical displacement point. The hypothetical displacement point is then subjected to the multiple hypothesis test again. If the test passes, the hypothetical displacement point is taken as the accurate displacement point; otherwise, it is considered that there is no displacement point.

2. The GNSS coordinate displacement detection method combining long and short time window segmentation according to claim 1, characterized in that, The step of sampling the original GNSS coordinate data to obtain a continuous time series based on a preset time interval further includes: removing data from the sampled time series based on the quartile method and the z-fraction method; the expression is as follows: in, for z Fractional statistics, if Values ​​exceeding the threshold are considered outliers and are removed; otherwise, they are retained. Represents coordinate data values. This represents the mean of a time series statistical analysis. The standard deviation of time series statistics for z The coefficient term of the score threshold; and These are the lower and upper quartiles of the time series. Interquartile range, The threshold coefficient for the quartile method is... Greater than or less If the value is an outlier, it will be removed; otherwise, it will be retained.

3. The GNSS coordinate displacement detection method combining long and short time window segmentation according to claim 1, characterized in that, The step of dividing the time series based on the long time window and the short time window further includes: decomposing and reconstructing the original coordinate time series based on the Fourier transform of the time series obtained by the long time window division; the expression of the Fourier transform is as follows: in, It is a time series. The sequence number of the data in the time series; Spectrum The corresponding spectrum index; This represents the length of the time series.

4. The GNSS coordinate displacement detection method combining long and short time window segmentation according to claim 1, characterized in that, The step of calculating the displacement based on possible displacement points and accurate displacement points includes: calculating the mean of the subsequence based on the accurate displacement points and possible displacement points, and calculating the displacement estimate based on the mean, the expression of which is as follows: Among them, subscript Indicates a possible displacement point, subscript Indicates the exact displacement point. This represents the estimated displacement value.

5. The GNSS coordinate displacement detection method combining long and short time window segmentation according to claim 1, characterized in that, The step of constructing a prior probability distribution based on the assumption that a displacement point exists within a time window includes: Assuming the time series data within the time window follows a normal distribution and has only one displacement point, the displacement observation likelihood function is expressed as: in, Representing time series data, Indicates the first time window One time series data; Indicates the assumed displacement point; This indicates the displacement time of the assumed displacement point; and express The mean of the two subsequences after dividing the time window at any given time; The standard deviation represents the time series data that follows a normal distribution. Assumption If the parameters within the range are independent of each other, then the prior distribution of the displacement points is assumed to be expressed as: in, This assumes the prior probability of the displacement point; and They represent the mean values ​​at the assumed displacement points, respectively. and The prior probability; Indicates the time of displacement at the assumed displacement point. The prior probability; This represents the standard deviation of the assumed displacement point sequence. The prior probability; This indicates that the mean follows a continuous uniform distribution. and Indicates the upper and lower limits of a uniform distribution; This indicates that the displacement at any given time follows a discrete uniform distribution. and Indicates the upper and lower limits of the displacement at any given time; This indicates that the standard deviation follows a Gaussian normal distribution. and This represents the mean and standard deviation of the standard deviation parameter.

6. A GNSS coordinate displacement detection system combining long and short time window segmentation, applied to the GNSS coordinate displacement detection method combining long and short time window segmentation as described in any one of claims 1 to 5, characterized in that, The system includes: Data acquisition module: used to acquire raw GNSS coordinate data and sample the raw GNSS coordinate data based on a preset time interval to obtain a continuous time series; Displacement point prediction module: used to divide the time series based on long time windows and short time windows respectively, segment the time series data within any window, perform multiple hypothesis tests based on the segmentation points, and determine the possible displacement points within the window based on the test results and preset thresholds; Displacement point verification module: used to detect displacement within the time window where possible displacement points exist based on Bayesian inference, and obtain accurate displacement points; Displacement calculation module: used to calculate the estimated displacement value based on the possible displacement point and the accurate displacement point.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the GNSS coordinate displacement detection method combined with long and short time window segmentation as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the GNSS coordinate displacement detection method combined with long and short time window segmentation as described in any one of claims 1-5.

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