Engineering interference suppression method, system and equipment based on double scale factors, and medium

Abstract

The invention discloses an engineering interference suppression method, system and device based on double scale factors and a medium, and belongs to the technical field of engineering monitoring, and the method comprises the steps: carrying out the feature recognition and confidence screening according to the phase sequence data and amplitude sequence data of each monitoring point of an engineering, and obtaining an engineering interference point set; inputting the phase sequence data into a robust regression model to enable the robust regression model to perform interference suppression on the phase sequence data, and weighting the phase sequence data based on a first weighting function corresponding to the first scale factor to obtain first interference suppression data, and comparing a size relationship between a target phase difference value of the first interference suppression data and a first threshold value, if the target phase difference value is less than or equal to the first threshold value, outputting the first interference suppression data, and if the target phase difference value is greater than the first threshold value, weighting the first interference suppression data based on a second weighting function corresponding to a second scale factor, and outputting second interference suppression data. And effective suppression of engineering interference can be realized.

Description

Technical Field

[0001] The present invention relates to the field of engineering monitoring technology, and in particular to a method, system, equipment and medium for suppressing engineering interference based on a dual-scale factor. Background Art

[0002] In practical engineering applications, such as large-scale infrastructure construction and mining, complex construction environments can interfere with deformation monitoring using Ground-Based Synthetic Aperture Radar (GB-SAR), potentially leading to missed or false alarms. Engineering interference manifests itself in the following ways: radar line of sight obstructed by construction machinery, sudden changes in scattering characteristics caused by human or vehicle activity, and unnatural surface deformation resulting from construction activities.

[0003] Existing technologies usually suppress engineering interference points through a single weight function or a fixed scale factor. However, this method has problems such as insufficient suppression or over-smoothing of the signal, resulting in poor suppression effect. Summary of the Invention

[0004] The present invention provides a method, system, device and medium for suppressing engineering interference based on a dual-scale factor, which can solve the problem of poor suppression effect and achieve effective suppression of engineering interference.

[0005] The present invention provides an engineering interference suppression method based on a dual-scale factor, comprising:

[0006] Based on the phase sequence data and amplitude sequence data of each monitoring point in the project within the monitoring period, feature recognition and confidence screening are performed to obtain the set of engineering interference points;

[0007] The phase sequence data of each interference point in the engineering interference point set is input into a robust regression model so that the robust regression model performs interference suppression on the phase sequence data. For each phase sequence data, the robust regression model weights the phase sequence data based on a first weight function corresponding to a first scale factor to obtain first interference suppression data. The target phase difference value of the first interference suppression data is compared with a first threshold value. If the target phase difference value is less than or equal to the first threshold value, the first interference suppression data is output. If the target phase difference value is greater than the first threshold value, the first interference suppression data is weighted based on a second weight function corresponding to a second scale factor to output second interference suppression data. The first weight function and the second weight function are both Cauchy functions, and the first scale factor is greater than the second scale factor. The first interference suppression data and the second interference suppression data are phase sequence data after interference suppression.

[0008] The embodiments of the present invention accurately identify interference points through feature recognition and confidence screening, and adopt a two-step suppression strategy, introducing a dual-scale factor adjustment mechanism. First, a larger first scale factor is selected, and a first weighting function is used to weightedly filter out significant outliers. Subsequently, based on the cumulative phase, points with insufficient suppression are filtered out, and the first scale factor is optimized to a smaller second scale factor. Secondary suppression is then implemented to eliminate residual interference. This method, through a multi-stage weighted adjustment mechanism, avoids the insufficient suppression caused by large scale factors while alleviating the signal oversmoothing caused by small scale factors, effectively suppressing engineering interference.

[0009] Furthermore, the feature recognition and confidence screening are performed based on the phase sequence data and amplitude sequence data of each monitoring point of the project, specifically:

[0010] In feature recognition, for each monitoring point, a phase difference value is calculated based on the phase sequence data, and it is determined whether the phase difference value meets a first preset threshold condition. If so, the monitoring point is a first interference identification point, and a first interference point set corresponding to each first interference identification point is obtained;

[0011] For each monitoring point, calculating an amplitude deviation index based on the amplitude sequence data, determining whether the amplitude deviation index meets a second preset threshold condition; if so, determining that the monitoring point is a second interference identification point, and obtaining a second interference point set corresponding to each second interference identification point;

[0012] A union of the first interference point set and the second interference point set is calculated to obtain an initial engineering interference point set.

[0013] This multi-dimensional interference identification analysis, combining phase and amplitude information, captures engineering interference characteristics more comprehensively than methods based solely on phase information, improving interference identification accuracy. By setting threshold conditions, potential interference points can be quickly screened, providing a foundation for subsequent confidence screening, reducing the amount of data to be processed, and improving overall processing efficiency.

[0014] Furthermore, the phase difference value is calculated based on the phase sequence data, and it is determined whether the phase difference value meets the first preset threshold condition. If so, the monitoring point is a first interference identification point, and a first interference point set corresponding to each first interference identification point is obtained, wherein the first preset threshold condition includes a third preset threshold condition and a fourth preset threshold condition, specifically:

[0015] For each monitoring point, calculating the difference between the phase value corresponding to the end time and the phase value corresponding to the start time in the phase sequence data to determine the cumulative phase;

[0016] Collecting the monitoring points whose accumulated phases meet a third preset threshold condition to obtain a third interference point set;

[0017] Performing differential interference according to the phase sequence data to obtain a time-series interference phase, and gradually accumulating according to the time-series interference phase to obtain a time-series cumulative phase;

[0018] Calculating the difference between the maximum value and the minimum value of the time series cumulative phase to obtain a second phase difference;

[0019] Collecting monitoring points whose second phase difference value meets a fourth preset threshold condition to obtain a fourth interference point set;

[0020] A union of the third interference point set and the fourth interference point set is calculated to obtain the first interference point set.

[0021] In this way, by identifying two phase features, cumulative phase difference and time-series cumulative phase range difference, different types of engineering interference can be captured more accurately. Among them, cumulative phase difference is suitable for identifying engineering interference corresponding to long-term accumulation, while time-series cumulative phase range difference can effectively identify short-term phase jump interference, thereby improving the solution's ability to identify complex engineering interference and the accuracy of interference identification.

[0022] Furthermore, for each monitoring point, the amplitude deviation index is calculated according to the amplitude sequence data, and it is determined whether the amplitude deviation index meets the second preset threshold condition. If so, the monitoring point is the second interference identification point, specifically:

[0023] For each of the monitoring points, calculating an amplitude mean and an amplitude standard deviation based on the amplitude sequence data, and determining an amplitude deviation index corresponding to each of the monitoring points based on the amplitude mean and the amplitude standard deviation;

[0024] determining a second threshold based on the mean and standard deviation corresponding to each of the amplitude deviation indices;

[0025] If the amplitude deviation index is greater than the second threshold, the monitoring point is the second interference identification point.

[0026] By introducing the amplitude deviation index as a basis for interference identification, the method fully utilizes radar scattering characteristics and can effectively identify interference points with large amplitude fluctuations caused by engineering activities, thus overcoming the shortcomings of relying solely on phase information. Furthermore, the threshold is determined based on the mean and standard deviation of the amplitude deviation index, enabling dynamic threshold setting that can be adaptively adjusted to different monitoring scenarios, improving the method's applicability and accuracy.

[0027] Furthermore, feature recognition and confidence screening are performed based on the phase sequence data and amplitude sequence data of each monitoring point of the project to obtain a set of engineering interference points, specifically:

[0028] In the confidence screening, wavelet transform is performed on the time series cumulative phase of each initial interference point in the initial interference point set, and the first layer coefficients and the second layer coefficients after wavelet transform decomposition are reconstructed to obtain the first wavelet coefficient corresponding to each initial interference point;

[0029] Determine a first modulus maximum value corresponding to each of the first wavelet coefficients; if the first modulus maximum value is less than a third threshold, delete the initial interference point to obtain the engineering interference point set.

[0030] In this way, by analyzing the sudden change characteristics of the phase through wavelet transform, it is possible to effectively distinguish natural deformation from engineering interference, eliminate misidentified interference points, and improve the accuracy and reliability of interference identification.

[0031] Furthermore, the third threshold is specifically:

[0032] In the normal monitoring point set, a preset number of normal monitoring points are obtained, and a wavelet transform is performed on each of the normal monitoring points to obtain a second wavelet coefficient corresponding to each of the normal monitoring points;

[0033] Determine the second modulus maximum value corresponding to each second wavelet coefficient, and perform calculations based on each second wavelet coefficient to obtain a wavelet mean and a wavelet standard deviation;

[0034] The third threshold is calculated based on the second modulus maximum, the wavelet mean, and the wavelet standard deviation.

[0035] This adaptive optimization of the threshold using Monte Carlo methods, through multiple random sampling and statistical analysis, can adaptively determine a reasonable threshold for different monitoring scenarios, improving the method's adaptability and robustness. Determining the threshold based on the statistical characteristics of normal monitoring points more accurately determines which points represent normal deformation and which represent engineering interference, thereby reducing the probability of misidentifying natural deformation as engineering interference.

[0036] Furthermore, the robust regression model is specifically:

[0037] Constructing a linear regression model and determining initial parameters of the linear regression model using the least squares method;

[0038] Inputting training data into the linear regression model, obtaining, for each monitoring point, a phase prediction value of the linear regression model for each data point, calculating a model residual corresponding to each data point based on each phase prediction value, and calculating a weight of each data point based on the model residual and a weight function to obtain a weight matrix corresponding to the monitoring point, wherein the training data includes phase sequence training data of multiple monitoring points, each phase sequence training data includes multiple data points, and the weight function includes the first weight function and the second weight function;

[0039] According to the weight matrix and the weighted least squares estimation method, the initial parameters are iteratively updated until the maximum value of the modulus between the model parameters of the current iteration and the model parameters of the previous iteration is less than a third threshold, and the iteration is stopped to obtain the robust regression model.

[0040] By weighting the phase data of different data points through a weighting function, the degree of suppression of abnormal phase disturbances can be dynamically adjusted, avoiding the problems of insufficient suppression or over-smoothing of signals that may result from fixed weights in traditional methods. Using the weighted least squares estimation method for iterative updates can quickly converge to stable model parameters, improving the suppression efficiency while ensuring the stability of the suppression effect. Combined with the dual-scale factor adjustment mechanism, it can effectively cope with the complex situation where nonlinear jumps and continuous disturbances coexist in the engineering interference phase, improving the stability and reliability of monitoring data in complex construction environments.

[0041] Another embodiment of the present invention further provides an engineering interference suppression system based on a dual-scaling factor, comprising: an interference point determination module and an interference suppression module;

[0042] The interference point determination module is used to perform feature recognition and confidence screening based on the phase sequence data and amplitude sequence data of each monitoring point in the project within the monitoring period to obtain a set of engineering interference points;

[0043] The interference suppression module is used to input the phase sequence data of each interference point in the engineering interference point set into a robust regression model, so that the robust regression model performs interference suppression on the phase sequence data. For each phase sequence data, the robust regression model weights the phase sequence data based on a first weight function corresponding to a first scale factor to obtain first interference suppression data, compares the target phase difference value of the first interference suppression data with a first threshold value, and outputs the first interference suppression data if the target phase difference value is less than or equal to the first threshold value; if the target phase difference value is greater than the first threshold value, weights the first interference suppression data based on a second weight function corresponding to a second scale factor to output second interference suppression data, wherein the first weight function and the second weight function are both Cauchy functions, and the first scale factor is greater than the second scale factor, and the first interference suppression data and the second interference suppression data are phase sequence data after interference suppression.

[0044] The embodiments of the present invention accurately identify interference points through feature recognition and confidence screening, and adopt a two-step suppression strategy, introducing a dual-scale factor adjustment mechanism. First, a larger first scale factor is selected, and a first weighting function is used to weightedly filter out significant outliers. Subsequently, based on the cumulative phase, points with insufficient suppression are filtered out, and the first scale factor is optimized to a smaller second scale factor. Secondary suppression is then implemented to eliminate residual interference. This method, through a multi-stage weighted adjustment mechanism, avoids the insufficient suppression caused by large scale factors while alleviating the signal oversmoothing caused by small scale factors, effectively suppressing engineering interference.

[0045] It effectively solves the problems of insufficient suppression or over-smoothing of signals in traditional methods, thereby achieving efficient suppression of engineering interference.

[0046] Another embodiment of the present invention also provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps of the engineering interference suppression method based on the dual-scale factor of the present invention.

[0047] Another embodiment of the present invention further provides a computer-readable storage medium item, comprising: a stored computer program, which controls the device where the computer-readable storage medium is located to execute the steps of the engineering interference suppression method based on dual-scale factors of the present invention when the computer program is running. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for use in the implementation. Obviously, the drawings described below are only some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0049] Figure 1 1 is a flow chart of an engineering interference suppression method based on a dual-scaling factor provided by an embodiment of the present invention;

[0050] Figure 2 This is a flow chart of a feature recognition step provided by an embodiment;

[0051] Figure 3 This is a schematic diagram of the offset characteristics of the cumulative phase under engineering interference provided by an embodiment;

[0052] Figure 4 This is a schematic diagram of the offset characteristics of the cumulative phase of the time series under engineering interference provided by an embodiment;

[0053] Figure 5 A schematic diagram of the relationship between a signal mutation point and a wavelet transform modulus extreme value provided by an embodiment;

[0054] Figure 6 This is a schematic diagram of engineering interference identification results provided by an embodiment;

[0055] Figure 7 This is a schematic diagram of the spatial distribution of pseudo natural deformation points added to a monitoring scene provided by an embodiment;

[0056] Figure 8 This is a schematic diagram of a time series cumulative phase curve provided by an embodiment;

[0057] Figure 9 This is a schematic diagram of a recognition result provided by an embodiment;

[0058] Figure 10 A schematic diagram of an iterative suppression process and suppression results of a time-series cumulative phase provided by an embodiment;

[0059] Figure 11 This is a schematic diagram of the accumulated phase before engineering interference suppression provided by an embodiment;

[0060] Figure 12 This is a schematic diagram of the accumulated phase after engineering interference suppression provided by an embodiment;

[0061] Figure 13 The present invention is a structural diagram of an engineering interference suppression system based on dual-scaling factors provided by an embodiment. DETAILED DESCRIPTION

[0062] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions in this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0063] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0064] In the description of the embodiments of this application, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly specify the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "plurality" is more than two, unless otherwise clearly and specifically defined.

[0065] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0066] In the description of the embodiments of this application, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0067] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0068] In the description of the embodiments of the present application, unless otherwise expressly specified or limited, technical terms such as "installed," "connected," "connected," and "fixed" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integration; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; internal connections between two components or interactions between two components. Those skilled in the art can understand the specific meanings of the above terms in the embodiments of the present application based on specific circumstances.

[0069] See also Figure 1 To solve the problem of insufficient suppression or over-smoothing of signals in the prior art, which leads to poor suppression effect, an embodiment of the present invention provides a method for suppressing engineering interference based on a dual-scaling factor, including steps S101 and S102:

[0070] Step S101: Perform feature recognition and confidence screening based on the phase sequence data and amplitude sequence data of each monitoring point in the project within a monitoring period to obtain a set of engineering interference points.

[0071] In this embodiment, in the process of using Ground-Based Synthetic Aperture Radar (GB-SAR) to monitor the deformation of physical engineering, phase sequence data and amplitude sequence data of each monitoring point of the physical engineering are obtained within the monitoring period, and feature identification and feature analysis of the engineering interference points are performed by fusing amplitude and phase features to obtain initial engineering interference points. In addition, the initial engineering interference points determined by feature identification can be confidence screened by using the mutation detection method of wavelet analysis to eliminate misjudgment points in the initial engineering interference points and obtain a more accurate set of engineering interference points.

[0072] As an example of an embodiment of the present invention, feature recognition and confidence screening are performed based on the phase sequence data and amplitude sequence data of each monitoring point of the project, specifically: in feature recognition, for each monitoring point, the phase difference value is calculated based on the phase sequence data, and it is judged whether the phase difference value meets the first preset threshold condition. If it does, the monitoring point is a first interference identification point, and a first interference point set corresponding to each first interference identification point is obtained; for each monitoring point, the amplitude deviation index is calculated based on the amplitude sequence data, and it is judged whether the amplitude deviation index meets the second preset threshold condition. If it does, the monitoring point is a second interference identification point, and a second interference point set corresponding to each second interference identification point is obtained; the union of the first interference point set and the second interference point set is calculated to obtain an initial engineering interference point set.

[0073] In this embodiment, in GB-SAR monitoring, engineering activities can destroy the phase stability of the monitoring point. Therefore, for each monitoring point, a phase difference value is calculated based on the phase sequence data of the monitoring point, and based on the relationship between the phase difference value and the preset threshold, it is determined whether it meets the first preset threshold condition. If it meets the condition, the phase of the monitoring point is abnormal, and the monitoring point is the first interference identification point. The phase sequence data of all monitoring points are subjected to the above-mentioned feature analysis process to obtain the first interference point set. In GB-SAR monitoring, engineering activities can also significantly change the scattering characteristics of the monitoring point. Compared with the scattering characteristics of normal points, the time series amplitude of the interference point shows greater volatility and instability. Based on the difference in scattering mechanism between engineering interference points and normal points, the amplitude deviation index (ADI) is introduced as a judgment basis for engineering interference feature identification. Therefore, the corresponding amplitude deviation index is calculated based on the amplitude sequence data of each monitoring point, and the amplitude deviation index is determined to meet the second preset threshold condition. If it meets the condition, the monitoring point is a second interference identification point, and a second interference point set corresponding to each second interference identification point is obtained. Take the union of the first interference point set and the second interference point set identified to obtain the initial engineering interference point set, that is, the candidate interference point set Ω candidate .

[0074] This embodiment constructs a multi-dimensional interference recognition system, introduces amplitude information, and realizes the coordinated identification of mechanical occlusion, human activities and surface disturbances through multi-threshold detection in the amplitude-phase dual domain.

[0075] As an example of an embodiment of the present invention, a phase difference value is calculated based on the phase sequence data, and it is determined whether the phase difference value meets a first preset threshold condition. If so, the monitoring point is a first interference identification point, and a first interference point set corresponding to each first interference identification point is obtained, wherein the first preset threshold condition includes a third preset threshold condition and a fourth preset threshold condition, specifically: for each monitoring point, the difference between the phase value corresponding to the end time and the phase value corresponding to the start time in the phase sequence data is calculated to determine the cumulative phase; the monitoring points whose cumulative phase meets the third preset threshold condition are aggregated to obtain a third interference point set; differential interference is performed according to the phase sequence data to obtain a time-series interference phase, and the time-series interference phase is gradually accumulated to obtain a time-series cumulative phase; the difference between the maximum value and the minimum value in the time-series cumulative phase is calculated to obtain a second phase difference value; the monitoring points whose second phase difference value meets the fourth preset threshold condition are aggregated to obtain a fourth interference point set; the union of the third interference point set and the fourth interference point set is calculated to obtain the first interference point set.

[0076] In this embodiment, if Figure 2The figure shows a flow chart of the feature recognition steps provided by one embodiment. The process of identifying engineering interference points based on phase sequence data can be divided into two steps. First, the engineering interference points are identified by cumulative phase and a third preset threshold condition. The cumulative phase refers to the total phase change of the monitoring point throughout the entire monitoring cycle, and the third preset threshold condition is a threshold condition determined based on the cumulative phase threshold. Specifically, for each monitoring point, the difference between the phase value corresponding to the end time and the phase value corresponding to the start time in the phase sequence data is calculated to determine the cumulative phase. The calculation formula for the cumulative phase is expressed as follows:

[0077] φ sum =φ(T)-φ(0);

[0078] Among them, φ(T) and φ(0) are the phases of the monitoring target at the end and start time of monitoring, respectively.

[0079] Under the influence of engineering activities, factors such as blasting, excavation, and mechanical shielding may cause the phase of the disturbed monitoring point to change dramatically in a short period of time, making the total cumulative phase φ of the monitoring point sum Showing high variation characteristics, such as Figure 3 The figure shows a schematic diagram of the cumulative phase offset characteristics under engineering interference provided by an embodiment. Therefore, preliminary identification of engineering interference points can be performed by setting a cumulative phase threshold. The cumulative phase threshold needs to be set according to the specific monitoring scenario and can generally be determined using the 3σ criterion. The calculation formula for the cumulative phase threshold is expressed as follows:

[0080] |φ sum -μ Δσ |>3σ Δφ ;

[0081] Among them, μ Δφ and σ Δφ represent the phase mean and standard deviation of the monitoring area respectively.

[0082] When the cumulative phase of the monitoring point meets the third preset threshold condition determined by the cumulative phase threshold, the monitoring point is preliminarily determined to be an engineering interference point and added to the third interference point set.

[0083] Although the cumulative phase threshold can identify most of the interfered points, for short-term interference, such as instantaneous occlusion or phase jump, the phase of the monitoring point may return to normal level after the interference ends, resulting in the inability to identify the interfered point based on the cumulative phase and the cumulative phase threshold. Figure 4The figure shows a schematic diagram of the offset characteristics of the time series cumulative phase under engineering interference provided by an embodiment. Therefore, in order to effectively and accurately identify the engineering interference point, it is necessary to quantitatively analyze the fluctuation characteristics of the phase sequence, and then introduce the time series cumulative phase extreme difference as an auxiliary criterion. Among them, the time series interference phase is a phase time series generated by differential interference processing of SAR images at adjacent moments. Therefore, differential interference is first performed based on the acquired phase sequence data to obtain the time series interference phase, wherein the time series interference phase Δφ(t k ) is calculated as follows:

[0084] Δφ(t k )=φ(t k )-φ(t k-1 ),k=1,2,…,N;

[0085] Among them, φ(t k ) is the phase of the k-th SAR image.

[0086] Based on the time series interference phase, the time series cumulative phase is gradually accumulated, where the time series cumulative phase φ cum (t k ) is calculated as follows:

[0087]

[0088] On this basis, the cumulative phase range of the time series is defined as the difference between the maximum and minimum values ​​in the phase time series, where the cumulative phase range R φ The calculation formula is as follows:

[0089] R φ =max(φ cum )-min(φ cum );

[0090] For different monitoring scenarios, the cumulative phase range of each monitoring target is calculated and an appropriate threshold is set. When the phase range of a monitoring point exceeds the threshold, it is identified as an engineering interference point and added to the fourth interference point set. The union of the third and fourth interference point sets is calculated to obtain the first interference point set.

[0091] As an example of an embodiment of the present invention, for each monitoring point, the amplitude deviation index is calculated according to the amplitude sequence data, and it is determined whether the amplitude deviation index meets the second preset threshold condition. If it meets the condition, the monitoring point is the second interference identification point. Specifically, for each monitoring point, the amplitude mean and the amplitude standard deviation are calculated according to the amplitude sequence data, and the amplitude deviation index corresponding to each monitoring point is determined according to the amplitude mean and the amplitude standard deviation; based on the mean and standard deviation corresponding to each amplitude deviation index, a second threshold is determined; if the amplitude deviation index is greater than the second threshold, the monitoring point is the second interference identification point.

[0092] In this embodiment, for each monitoring point, the amplitude mean and the amplitude standard deviation are calculated based on the amplitude sequence data, and the amplitude deviation index corresponding to each monitoring point is determined based on the amplitude mean and the amplitude standard deviation. The calculation formula of the amplitude deviation index is expressed as follows:

[0093]

[0094] Among them, μ A and σ A represent the mean and standard deviation of the time series amplitude, respectively.

[0095] Under normal circumstances, the ADI values ​​of most normal points in the monitoring area should be concentrated within a certain range. However, due to the large amplitude fluctuations of the disturbed targets, their ADI values ​​are usually higher than normal points. Therefore, we use the regional 3σ criterion for interference detection. The ADI values ​​of all points in the monitoring area are calculated and their distribution characteristics are statistically analyzed to set the dynamic detection threshold. Specifically, this threshold is the second threshold, which is determined based on the mean and standard deviation of the amplitude deviation index corresponding to each monitoring point in the monitoring area. The calculation formula of the second threshold is expressed as follows:

[0096] T ADI =μ ADI +3σ ADI ;

[0097] Among them, μ ADI and σ ADI are the mean and standard deviation of ADI in the monitoring area, respectively.

[0098] When the ADI value of a monitoring point exceeds the second threshold, it can be considered that the point is disturbed by the engineering activity, and the monitoring is the second interference identification point.

[0099] As an example of an embodiment of the present invention, feature recognition and confidence screening are performed based on the phase sequence data and amplitude sequence data of each monitoring point of the project to obtain a set of engineering interference points. Specifically, in the confidence screening, wavelet transform is performed on the time series cumulative phase of each initial interference point in the initial interference point set, and the first layer coefficients and second layer coefficients after wavelet transform decomposition are reconstructed to obtain the first wavelet coefficient corresponding to each initial interference point; the first modulus maximum value corresponding to each first wavelet coefficient is determined. If the first modulus maximum value is less than a third threshold, the initial interference point is deleted to obtain the set of engineering interference points.

[0100] In this embodiment, after the preliminary identification of interference, since the results of the preliminary identification stage may include misidentified interference points, it is necessary to further perform confidence screening on the interference points in the initial engineering interference point set. Specifically, natural deformation may cause a large phase accumulation value, so that some non-construction interference points, or even natural deformation points, are misidentified as interference points. Therefore, it is necessary to perform confidence screening on each interference point in the initial engineering interference point set to improve the reliability and accuracy of identification. The natural deformation signal of the synthetic aperture radar interferometry (InSAR) usually shows a gentle change in time and space, while the engineering interference phase shows a significant mutation characteristic. Based on this characteristic difference, this technology adopts a mutation detection method based on wavelet analysis to screen the time-series interference phase sequence of the initial engineering interference point to eliminate the misjudgment points. The first-order derivative ψ of the low-pass smoothing function θ(t) is used as the (1) (t) is used as the wavelet basis function to perform wavelet transform on the synthetic aperture radar interferometry signal f(t). The signal f(t) corresponds to the time-series interferometry phase sequence. The wavelet transform of the signal f(t) is expressed as follows:

[0101]

[0102] Among them, θ a (t) is the expansion and contraction of θ(t) at scale a, denoted as

[0103] like Figure 5 The figure shows the relationship between the signal mutation point and the wavelet transform modulus extreme value provided by an embodiment. At the same scale a, the signal f(t) and its wavelet transform coefficient W 1 The relationship between f(a, t) is that the mutation point of the signal corresponds to W after wavelet transform. 1The maximum value point in f(a,t). Therefore, through the mutation detection method, the mutation point in the signal f(t) is mapped to the modulus extreme point of the wavelet transform, so as to determine whether the signal f(t) has a mutation. In the specific screening process, the time series cumulative phase of each initial interference point in the initial interference point set is firstly subjected to wavelet analysis, and the first wavelet coefficient s is obtained by reconstructing the d1 and d2 layer coefficients after wavelet transform decomposition. Then, the third threshold s is set. th , if the maximum value of the modulus in the first wavelet coefficient satisfies max(|s|) th , then it is determined that the point does not belong to the engineering interference point, and it is deleted from the initial engineering interference point set to obtain the engineering interference point set.

[0104] As an example of an embodiment of the present invention, the third threshold is specifically: in a set of normal monitoring points, a preset number of normal monitoring points are obtained, and a wavelet transform is performed on each of the normal monitoring points to obtain a second wavelet coefficient corresponding to each of the normal monitoring points; a second modulus maximum value corresponding to each of the second wavelet coefficients is determined, and calculation is performed based on each of the second wavelet coefficients to obtain a wavelet mean and a wavelet standard deviation; and the third threshold is calculated based on the second modulus maximum value, the wavelet mean, and the wavelet standard deviation.

[0105] In this embodiment, to address the problem that a fixed threshold may be difficult to adapt to different monitoring scenarios, this embodiment uses the Monte Carlo method to adaptively optimize the third threshold. Through multiple random sampling and statistical analysis, the wavelet coefficient distribution characteristics of normal points are estimated from the overall perspective, thereby adaptively determining a reasonable third threshold to adapt to different monitoring environments. Specifically, from the unmarked point set Ω normal , that is, in the set of points judged to be normal, 50 independent random samplings are performed, and 100 points are randomly selected each time. The reason for choosing random sampling instead of wavelet analysis on all monitoring points is that the computational cost of directly performing wavelet analysis on all monitoring points is high, especially when the number of monitoring points reaches hundreds of thousands, the computational overhead will increase significantly. By performing multiple random samplings, while reducing the computational complexity, more representative overall distribution characteristics are obtained to ensure the robustness of the threshold calculation. Subsequently, wavelet analysis is performed on the 100 points sampled each time, and the modulus maximum value {m i}, and calculate its mean μ m and standard deviation σ m Based on these statistical parameters, the dynamic third threshold T is calculated. dyn , the calculation formula is as follows:

[0106]

[0107] ​Among them, μ+2σ is selected as the threshold, that is, at a confidence level of more than 95%, points less than the third threshold are considered normal points, and points exceeding the third threshold are considered interference points. Finally, for the initial engineering interference point set Ω candidate Perform wavelet analysis and eliminate the i <T dyn The pseudo interference points are obtained to obtain the set of engineering interference points.

[0108] This embodiment combines mutation detection based on wavelet analysis with Monte Carlo threshold adaptive optimization to effectively distinguish natural deformation from engineering interference, ensuring that engineering interference is suppressed without affecting normal deformation monitoring, reducing the probability of misjudgment of natural deformation and avoiding long calculation time.

[0109] S2. Input the phase sequence data of each interference point in the engineering interference point set into a robust regression model so that the robust regression model performs interference suppression on the phase sequence data. For each phase sequence data, the robust regression model weights the phase sequence data based on a first weight function corresponding to a first scale factor to obtain first interference suppression data. Compare the target phase difference value of the first interference suppression data with the first threshold value. If it is less than or equal to, output the first interference suppression data. If it is greater than, weight the first interference suppression data based on a second weight function corresponding to a second scale factor to output second interference suppression data. Wherein, the first weight function and the second weight function are both Cauchy functions, and the first scale factor is greater than the second scale factor. The first interference suppression data and the second interference suppression data are phase sequence data after interference suppression.

[0110] In this embodiment, after identifying the engineering interference points, a corresponding suppression strategy is required to restore the target phase characteristics of each interference point and ensure the continuity of monitoring data during the non-construction period. During the suppression phase, this embodiment adopts an engineering interference suppression method based on robust regression. Abnormal phase disturbances are suppressed through dynamic weight adjustment. Given the complex and diverse disturbance characteristics of engineering interference phases, a single weight function or fixed scaling factor cannot adequately address the suppression requirements of all engineering interference points, potentially leading to insufficient suppression or overly smoothed signals. Therefore, the robust regression model proposed in this embodiment is a two-stage optimization model based on robust estimation theory. This model introduces a dual scaling factor adjustment mechanism to achieve hierarchical suppression of engineering interference. Specifically, the phase sequence data of each interference point in the set of engineering interference points is input into the robust regression model, so that the robust regression model performs interference suppression on the phase sequence data. In the robust regression model, a larger first scaling factor is first determined. Based on the Cauchy function corresponding to the first scaling factor, the phase sequence data is weighted to obtain first interference suppression data to filter out significant abnormal points among the interference points. Subsequently, the cumulative phase of the first interference suppression data is defined as the target phase difference value. If the target phase difference values ​​of the first interference suppression data are all less than or equal to the first threshold value, it means that the suppression effect of the first interference suppression data of each interference point meets the preset conditions, and the robust regression model can directly output the first interference suppression data; if there is an interference point with a target phase difference value greater than the first threshold value of the first interference suppression data, the suppression effect of the interference point is insufficient, and the first scale factor needs to be optimized to the second scale factor, and the first interference suppression data is subjected to secondary suppression, that is, based on the Cauchy function corresponding to the second scale factor, the first interference suppression data is quadratically weighted, and the second interference suppression data is output to complete the interference suppression of the phase sequence data of all interference points in the GB-SAR monitoring process of the project to eliminate residual interference.

[0111] This embodiment uses a multi-stage weighted adjustment mechanism to avoid insufficient suppression caused by large-scale factors and alleviate the signal over-smoothing problem caused by small-scale factors.

[0112] As an example of an embodiment of the present invention, the robust regression model is specifically as follows: constructing a linear regression model, using the least squares method to determine the initial parameters of the linear regression model; inputting training data into the linear regression model, obtaining the phase prediction value of the linear regression model for each data point for each monitoring point, calculating the model residual corresponding to each data point based on each phase prediction value, and calculating the weight of each data point based on the model residual and the weight function to obtain the weight matrix corresponding to the monitoring point, wherein the training data includes phase sequence training data of multiple monitoring points, each phase sequence training data includes multiple data points, and the weight function includes the first weight function and the second weight function; according to the weight matrix and the weighted least squares estimation method, the initial parameters are iteratively updated until the maximum value of the modulus between the model parameters of the current iteration and the model parameters of the previous iteration is less than the third threshold, stopping the iteration, and obtaining the robust regression model.

[0113] In this embodiment, during the training process of the robust regression model, first, a linear regression model is constructed, a time-phase relationship is constructed based on the linear regression model, and the least squares method is used to estimate initialization parameters, where the initialization parameters are expressed as follows:

[0114]

[0115] Where X is the time-dependent design matrix, L is the temporal interferometric phase vector of a single data point, is the model parameter to be estimated, and T is the monitoring period.

[0116] Input the training data into the linear regression model, and obtain the phase prediction value L of each data point for each monitoring point. i , calculate the model residual r corresponding to each data point according to each phase prediction value i , and based on the model residual r i And the weight function ω(r) calculates the weight of each data point in the current iteration Then the weight matrix corresponding to each monitoring point is obtained. In view of the coexistence of nonlinear jumps and continuous disturbances in the engineering interference phase, it is necessary to select a weight function with strong anti-difference ability to suppress abnormal phase disturbances. The Cauchy function has a strong suppression ability for points with large deviations due to its fast descent speed and heavy tail, and has a scale factor c with an adjustable attenuation rate to adapt to scenarios with different interference intensities. Therefore, this embodiment uses the Cauchy function to suppress engineering interference, and the expression of the Cauchy function is expressed as follows:

[0117]

[0118] Among them, ω(r) is the weight function, r is the residual, and c is the scale factor.

[0119] The calculation formula of the residual is as follows:

[0120]

[0121] Among them, i is the data point of the monitoring point, k is the current iteration number, is the estimated value of the model parameters at the kth iteration.

[0122] Among them, the weight matrix is ​​a diagonal matrix, which is expressed as follows:

[0123]

[0124] Update the model parameters according to the weight matrix of each monitoring point and the weighted least squares estimation method to obtain the updated parameters The updated parameters are expressed as follows:

[0125]

[0126] Until the maximum value of the modulus between the model parameters of the current iteration and the model parameters of the previous iteration is less than the third threshold, the iteration is stopped to obtain the robust regression model, and the weighted estimated value {ω i L i}; This weighted estimate is equivalent to the filtered time-series interference phase. The convergence criterion is expressed as follows:

[0127]

[0128] Here, ε is the preset convergence capacity, that is, the third threshold.

[0129] This embodiment effectively improves the stability and reliability of GB-SAR monitoring data in complex construction environments by introducing a step-by-step suppression strategy with dual-scale factor adjustment.

[0130] The embodiments of the present invention accurately identify interference points through feature recognition and confidence screening, and adopt a two-step suppression strategy, introducing a dual-scale factor adjustment mechanism. First, a larger first scale factor is selected, and a first weighting function is used to weightedly filter out significant outliers. Subsequently, based on the cumulative phase, points with insufficient suppression are filtered out, and the first scale factor is optimized to a smaller second scale factor. Secondary suppression is then implemented to eliminate residual interference. This method, through a multi-stage weighted adjustment mechanism, avoids the insufficient suppression caused by large scale factors while alleviating the signal oversmoothing caused by small scale factors, effectively suppressing engineering interference.

[0131] In order to verify the implementation effect of the present invention, experimental verification was carried out using the measured data of the Dabaoshan mining area. The Dabaoshan mining area is located in Shaoguan City, Guangdong Province. The terrain is relatively steep as a whole. Affected by years of mining activities, the mountain has obvious stepped features, and a large mine pit has been formed at the bottom, accompanied by potential landslide risks. The data used in the experiment was collected by circular scanning SAR. The data observation period was from 15:00 on November 11, 2021 to 11:00 on November 12, 2021, with a time span of about 20 hours and a data time interval of about 6 minutes.

[0132] In the feature recognition stage, this embodiment adopts an amplitude-phase dual-domain multi-threshold method to achieve preliminary identification of engineering interference. This method combines the cumulative phase threshold, the time series phase extreme difference threshold, and the amplitude deviation index threshold to improve the recognition accuracy through multi-dimensional comprehensive judgment. Among them, the cumulative phase threshold method and the amplitude deviation index threshold method are both based on the 3σ criterion to screen outliers. Given that the phase of the engineering interference point may experience a 2π mutation phenomenon, the time series phase extreme difference threshold is set to 2π (radians). Finally, by combining the recognition results of the above three methods, a set of candidate interference points is obtained.

[0133] After preliminary identification, in order to effectively distinguish natural deformation points from construction interference points, this embodiment first uses Monte Carlo-based threshold adaptive optimization to determine the threshold of the wavelet coefficient. Subsequently, wavelet analysis is performed on the time series cumulative phase of the candidate interference point set to further filter out misidentified points. This experiment uses the DB2 wavelet for mutation detection, mainly because the DB2 wavelet has strong robustness and good localization characteristics, can accurately capture phase mutations, and at the same time reduce the impact of noise on the detection results. After confidence screening, the engineering interference identification results are obtained, such as Figure 6 The figure shows a schematic diagram of the engineering interference identification result provided by an embodiment. Figure 6 It can be seen that the actual construction operation area is basically consistent with the identified engineering interference area, indicating that the proposed method has high accuracy and reliability in construction interference identification.

[0134] During the monitoring process, no significant natural deformation occurred in the monitoring scene, so there were no actual natural deformation points in the candidate point set. In order to evaluate the ability of the method to distinguish natural deformation points, artificially added pseudo natural deformation phases for experiments, such as Figure 7 FIG. 1 is a schematic diagram showing the spatial distribution of pseudo natural deformation points added to a monitoring scene provided by an embodiment. Figure 8 FIG. 1 is a schematic diagram of a timing cumulative phase curve provided by an embodiment. Figure 9 The figure shows a schematic diagram of the recognition results provided by an embodiment. Experimental results show that after confidence screening, pseudo natural deformation points in the interference point set are eliminated, further verifying the effectiveness of this method in distinguishing natural deformation from engineering interference.

[0135] After completing the identification of construction interference, this paper uses robust regression to suppress engineering interference. First, the initial scale factor c1 is set, and the Cauchy function is used for preliminary suppression. Then, the residual disturbance area is screened according to the cumulative phase and 3σ criterion, and the scale factor is adjusted to c2, and quadratic Cauchy weighted suppression is implemented on these areas. In this experiment, the scale factors are set to c1 = 1 and c2 = 1 / 8. Figure 10 The figure shows an iterative suppression process and a schematic diagram of the suppression results of the time series cumulative phase provided by an embodiment. Experimental results show that in most cases, stable convergence can be achieved within 15 iterations, indicating that the design of the weight function takes into account both efficiency and stability. During the suppression process, there is a negative correlation between the weight value ω(r) and the residual r, that is, when the residual is small, the weight function assigns a larger weight to retain the normal phase information; while for points with more severe interference, the weight is significantly reduced, thereby effectively suppressing the influence of abnormal phase. Therefore, during the iterative process, as the interference gradually weakens, the overall weight distribution tends to be stable, and ultimately the optimization adjustment of the time series phase is achieved. The time series cumulative phases of all monitoring points before and after suppression are statistically analyzed, and the results are shown in the table.

[0136] Table 1. Time series cumulative phase results before and after engineering interference suppression in Dabaoshan mining area

[0137] Time series cumulative phase standard deviation Maximum average value Before inhibition 12.7116 0.1386 After inhibition 1.7489 0.1073

[0138] The experimental results show that the suppression of the time series phase fluctuation is significantly reduced. The maximum value of the standard deviation of the time series cumulative phase is reduced from 12.70rad to 1.74rad, a decrease of 86.2%, and the mean value is reduced from 0.136rad to 0.105rad, a decrease of 22.9%. The standard deviation of the time series cumulative phase is significantly reduced. Figure 11 FIG. 1 is a schematic diagram of the cumulative phase before engineering interference suppression provided by an embodiment. Figure 12 Shown is a schematic diagram of the accumulated phase after engineering interference suppression provided by an embodiment.

[0139] After engineering interference suppression, the cumulative phase standard deviation of the Dabaoshan mining area dropped from 1.0874 to 0.2810, a decrease of 74.16%, indicating that the overall fluctuation of the phase data has been significantly reduced and the data stability has been improved. In addition, after interference suppression, the phase of most data points is limited to within ±3rad, and the corresponding deformation is about 2.98mm, indicating that the number of extreme phase points is greatly reduced and the phase distribution is more concentrated. Although there are still a few data points with a phase of ±5rad (corresponding to a deformation of about 4.97mm), these points with large deviations may be due to the phase accumulation effect caused by missed judgment or insufficient suppression of engineering interference, but the overall suppression effect is still relatively ideal.

[0140] Experimental results show that this method effectively reduces the impact of engineering interference on GB-SAR deformation monitoring through a robust regression framework and a dual-scale factor adjustment mechanism, makes the phase distribution more uniform, and improves the stability and reliability of phase measurement.

[0141] like Figure 13 As shown, based on the above method embodiment, a corresponding system embodiment is provided;

[0142] An embodiment of the present invention provides an engineering interference suppression system 1300 based on a dual-scaling factor, comprising: an interference point determination module 1301 and an interference suppression module 1302;

[0143] The interference point determination module 1301 is used to perform feature recognition and confidence screening based on the phase sequence data and amplitude sequence data of each monitoring point in the project within the monitoring period to obtain a set of engineering interference points;

[0144] The interference suppression module 1302 is configured to input the phase sequence data of each interference point in the engineering interference point set into a robust regression model so that the robust regression model performs interference suppression on the phase sequence data. For each phase sequence data, the robust regression model weights the phase sequence data based on a first weight function corresponding to a first scale factor to obtain first interference suppression data, compares the target phase difference value of the first interference suppression data with a first threshold, and outputs the first interference suppression data if the target phase difference value is less than or equal to the first threshold; and if the target phase difference value is greater than the first threshold, weights the first interference suppression data based on a second weight function corresponding to a second scale factor to output second interference suppression data, wherein both the first weight function and the second weight function are Cauchy functions, and the first scale factor is greater than the second scale factor, and the first interference suppression data and the second interference suppression data are phase sequence data after interference suppression.

[0145] It can be understood that the above-mentioned system embodiment corresponds to the method embodiment of the present invention, which can implement any of the above-mentioned method embodiments of the present invention to provide an engineering interference suppression method based on a dual-scale factor.

[0146] It should be noted that the system embodiments described above are merely illustrative, and some or all of the modules may be selected to achieve the objectives of the present embodiments as needed. Furthermore, in the drawings of the system embodiments provided herein, the connection relationships between modules indicate that they have communication connections, which may be implemented as one or more communication buses or signal lines. Persons of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0147] Based on the above-mentioned embodiment of the engineering interference suppression method based on the dual-scale factor, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the engineering interference suppression method based on the dual-scale factor of any embodiment of the present invention is implemented.

[0148] For example, in this embodiment, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more module elements may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device.

[0149] The terminal device may be a computing device such as a desktop computer, a notebook computer, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0150] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, connecting various parts of the entire terminal device using various interfaces and lines.

[0151] Based on the above-mentioned method embodiments, another embodiment of the present invention provides a computer-readable storage medium, including a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the engineering interference suppression method based on dual-scale factors described in any one of the above-mentioned method embodiments of the present invention.

[0152] Wherein, the module / unit integrated in the device / terminal equipment, if implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0153] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for suppressing engineering interference based on a dual-scaling factor, characterized in that: include: Based on the phase sequence data and amplitude sequence data of each monitoring point in the project within the monitoring period, feature recognition and confidence screening are performed to obtain the set of engineering interference points; The phase sequence data of each interference point in the engineering interference point set is input into a robust regression model so that the robust regression model performs interference suppression on the phase sequence data. For each phase sequence data, the robust regression model weights the phase sequence data based on a first weight function corresponding to a first scale factor to obtain first interference suppression data. The target phase difference value of the first interference suppression data is compared with a first threshold value. If the target phase difference value is less than or equal to the first threshold value, the first interference suppression data is output. If the target phase difference value is greater than the first threshold value, the first interference suppression data is weighted based on a second weight function corresponding to a second scale factor to output second interference suppression data. The first weight function and the second weight function are both Cauchy functions, and the first scale factor is greater than the second scale factor. The first interference suppression data and the second interference suppression data are phase sequence data after interference suppression.

2. The engineering interference suppression method based on dual-scaling factors according to claim 1, characterized in that: The feature recognition and confidence screening are performed based on the phase sequence data and amplitude sequence data of each monitoring point of the project, specifically: In feature recognition, for each monitoring point, a phase difference value is calculated based on the phase sequence data, and it is determined whether the phase difference value meets a first preset threshold condition. If so, the monitoring point is a first interference identification point, and a first interference point set corresponding to each first interference identification point is obtained; For each monitoring point, calculating an amplitude deviation index based on the amplitude sequence data, determining whether the amplitude deviation index meets a second preset threshold condition; if so, determining that the monitoring point is a second interference identification point, and obtaining a second interference point set corresponding to each second interference identification point; A union of the first interference point set and the second interference point set is calculated to obtain an initial engineering interference point set.

3. The engineering interference suppression method based on dual-scaling factors according to claim 2, characterized in that: The phase difference value is calculated based on the phase sequence data, and it is determined whether the phase difference value meets a first preset threshold condition. If so, the monitoring point is a first interference identification point, and a first interference point set corresponding to each first interference identification point is obtained, wherein the first preset threshold condition includes a third preset threshold condition and a fourth preset threshold condition, which are specifically: For each monitoring point, calculating the difference between the phase value corresponding to the end time and the phase value corresponding to the start time in the phase sequence data to determine the cumulative phase; Collecting the monitoring points whose accumulated phases meet a third preset threshold condition to obtain a third interference point set; Performing differential interference according to the phase sequence data to obtain a time-series interference phase, and gradually accumulating according to the time-series interference phase to obtain a time-series cumulative phase; Calculating the difference between the maximum value and the minimum value of the time series cumulative phase to obtain a second phase difference; Collecting monitoring points whose second phase difference value meets a fourth preset threshold condition to obtain a fourth interference point set; A union of the third interference point set and the fourth interference point set is calculated to obtain the first interference point set.

4. The engineering interference suppression method based on dual-scaling factors according to claim 2, characterized in that: For each monitoring point, an amplitude deviation index is calculated based on the amplitude sequence data, and it is determined whether the amplitude deviation index meets a second preset threshold condition. If so, the monitoring point is a second interference identification point, specifically: For each of the monitoring points, calculating an amplitude mean and an amplitude standard deviation based on the amplitude sequence data, and determining an amplitude deviation index corresponding to each of the monitoring points based on the amplitude mean and the amplitude standard deviation; determining a second threshold based on the mean and standard deviation corresponding to each of the amplitude deviation indices; If the amplitude deviation index is greater than the second threshold, the monitoring point is the second interference identification point.

5. The engineering interference suppression method based on dual-scaling factors according to claim 2, characterized in that: The phase sequence data and amplitude sequence data of each monitoring point of the project are used to perform feature recognition and confidence screening to obtain a set of engineering interference points, specifically: In the confidence screening, wavelet transform is performed on the time series cumulative phase of each initial interference point in the initial interference point set, and the first layer coefficients and the second layer coefficients after wavelet transform decomposition are reconstructed to obtain the first wavelet coefficient corresponding to each initial interference point; Determine a first modulus maximum value corresponding to each of the first wavelet coefficients; if the first modulus maximum value is less than a third threshold, delete the initial interference point to obtain the engineering interference point set.

6. The engineering interference suppression method based on dual-scaling factors according to claim 5, characterized in that: The third threshold is specifically: In the normal monitoring point set, a preset number of normal monitoring points are obtained, and a wavelet transform is performed on each of the normal monitoring points to obtain a second wavelet coefficient corresponding to each of the normal monitoring points; Determine the second modulus maximum value corresponding to each second wavelet coefficient, and perform calculations based on each second wavelet coefficient to obtain a wavelet mean and a wavelet standard deviation; The third threshold is calculated based on the second modulus maximum, the wavelet mean, and the wavelet standard deviation.

7. The engineering interference suppression method based on dual-scaling factors according to claim 1, characterized in that: The robust regression model is specifically: Constructing a linear regression model and determining initial parameters of the linear regression model using the least squares method; Inputting training data into the linear regression model, obtaining, for each monitoring point, a phase prediction value of the linear regression model for each data point, calculating a model residual corresponding to each data point based on each phase prediction value, and calculating a weight of each data point based on the model residual and a weight function to obtain a weight matrix corresponding to the monitoring point, wherein the training data includes phase sequence training data of multiple monitoring points, each phase sequence training data includes multiple data points, and the weight function includes the first weight function and the second weight function; According to the weight matrix and the weighted least squares estimation method, the initial parameters are iteratively updated until the maximum value of the modulus between the model parameters of the current iteration and the model parameters of the previous iteration is less than a third threshold, and the iteration is stopped to obtain the robust regression model.

8. An engineering interference suppression system based on dual-scaling factors, characterized in that: include: Interference point determination module and interference suppression module; The interference point determination module is used to perform feature recognition and confidence screening based on the phase sequence data and amplitude sequence data of each monitoring point in the project within the monitoring period to obtain a set of engineering interference points; The interference suppression module is used to input the phase sequence data of each interference point in the engineering interference point set into a robust regression model, so that the robust regression model performs interference suppression on the phase sequence data. For each phase sequence data, the robust regression model weights the phase sequence data based on a first weight function corresponding to a first scale factor to obtain first interference suppression data, compares the target phase difference value of the first interference suppression data with a first threshold value, and outputs the first interference suppression data if the target phase difference value is less than or equal to the first threshold value; if the target phase difference value is greater than the first threshold value, weights the first interference suppression data based on a second weight function corresponding to a second scale factor to output second interference suppression data, wherein the first weight function and the second weight function are both Cauchy functions, and the first scale factor is greater than the second scale factor, and the first interference suppression data and the second interference suppression data are phase sequence data after interference suppression.

9. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for suppressing engineering interference based on a dual-scale factor according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that include: A stored computer program, wherein when the computer program is run, the device where the computer-readable storage medium is located is controlled to perform the engineering interference suppression method based on the dual-scale factor according to any one of claims 1 to 7.

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