Engineering interference suppression method, system, device and medium based on double-scale factor

Abstract

The application discloses an engineering interference suppression method, system, device and medium based on double scale factors, and belongs to the technical field of engineering monitoring. The method comprises the following steps: performing feature recognition and confidence screening according to phase sequence data and amplitude sequence data of each monitoring point of an engineering, to obtain an engineering interference point set; inputting the phase sequence data into a robust regression model, so that the robust regression model performs interference suppression on the phase sequence data, weighting the phase sequence data based on a first weight function corresponding to a first scale factor, to obtain first interference suppression data; comparing a target phase difference value of the first interference suppression data with 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, if the target phase difference value is greater than the first threshold value, weighting the first interference suppression data based on a second weight function corresponding to a second scale factor, and outputting second interference suppression data. Therefore, by implementing the application, effective suppression of engineering interference can be realized.

Description

Engineering interference suppression methods, systems, devices, and media based on dual-scale factors Technical Field

[0001] This invention relates to the field of engineering monitoring technology, and in particular to an engineering interference suppression method, system, device and medium based on dual-scale factor. Background Technology

[0002] In practical engineering applications, such as large-scale infrastructure construction and mining, complex construction environments can interfere with the deformation monitoring of Ground-Based Synthetic Aperture Radar (GB-SAR), and even cause problems such as missed alarms or false alarms. Specifically, engineering interference manifests as: radar line-of-sight obstruction caused by construction machinery, sudden changes in scattering characteristics caused by personnel or vehicle activities, and unnatural surface deformation caused by construction activities.

[0003] Existing technologies typically suppress engineering interference points using a single weighting function or a fixed scaling factor. However, this method suffers from insufficient suppression or overly smoothed signals, resulting in poor suppression performance. Summary of the Invention

[0004] This 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] This 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 during the monitoring period, feature identification and confidence screening are performed to obtain the set of interference points in the project;

[0007] The phase sequence data of each interference point in the set of engineering interference points are input into a robust regression model so that the robust regression model can suppress interference in 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-suppressed data. The target phase difference of the first interference-suppressed data is compared with a first threshold. If it is less than or equal to the first threshold, the first interference-suppressed data is output. If it is greater than the first threshold, the first interference-suppressed data is weighted based on a second weight function corresponding to a second scale factor to output second interference-suppressed 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-suppressed data and the second interference-suppressed data are phase sequence data after interference suppression.

[0008] This invention accurately identifies interference points through feature recognition and confidence screening, and employs a two-step suppression strategy by introducing a dual-scale factor adjustment mechanism. First, a larger first-scale factor is selected, and a first weighting function is used to filter out significant outliers. Then, based on the cumulative phase, points with insufficient suppression are filtered, and the first-scale factor is optimized to a smaller second-scale factor for secondary suppression to eliminate residual interference. This method, through a multi-stage weighted adjustment mechanism, avoids insufficient suppression caused by large-scale factors and alleviates the signal over-smoothing problem caused by small-scale factors, thus achieving effective suppression of engineering interference.

[0009] Furthermore, the step of performing feature identification and confidence screening based on the phase sequence data and amplitude sequence data of each monitoring point in the project specifically involves:

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

[0011] For each monitoring point, the amplitude deviation index is calculated based on the amplitude sequence data. It is then determined whether the amplitude deviation index meets the second preset threshold condition. If it does, the monitoring point is the second interference identification point, and the set of second interference points corresponding to each second interference identification point is obtained.

[0012] Calculate the union of the first set of interference points and the second set of interference points to obtain the initial set of engineering interference points.

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

[0014] Further, the step of calculating the phase difference value based on the phase sequence data, determining whether the phase difference value meets the first preset threshold condition, and if it does, then the monitoring point is the first interference identification point, and a first interference point set corresponding to each first interference identification point is obtained. The first preset threshold condition includes a third preset threshold condition and a fourth preset threshold condition, specifically:

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

[0016] The third set of interference points is obtained by combining all monitoring points whose cumulative phase meets the third preset threshold condition;

[0017] Differential interference is performed based on the phase sequence data to obtain the temporal interference phase, and the temporal cumulative phase is obtained by gradually accumulating the temporal interference phase.

[0018] The difference between the maximum and minimum values ​​in the time-accumulated phase is calculated to obtain the second phase difference.

[0019] By combining all monitoring points whose second phase difference values ​​meet the fourth preset threshold condition, a fourth set of interference points is obtained;

[0020] The first set of interference points is obtained by calculating the union of the third set of interference points and the fourth set of interference points.

[0021] By using two phase features—cumulative phase difference and temporal cumulative phase range—the system can more precisely capture different types of engineering interference. Cumulative phase difference is suitable for identifying engineering interference that has accumulated over a long period, while temporal cumulative phase range can effectively identify short-term phase jump interference. This improves the system's ability to identify complex engineering interference and enhances the accuracy of interference identification.

[0022] Furthermore, 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 it does, the monitoring point is a second interference identification point. Specifically:

[0023] For each monitoring point, the mean amplitude and standard deviation amplitude are calculated based on the amplitude sequence data, and the amplitude deviation index corresponding to each monitoring point is determined based on the mean amplitude and standard deviation amplitude.

[0024] The second threshold is determined based on the mean and standard deviation of each of the amplitude deviation indices.

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

[0026] By introducing the amplitude deviation index as a basis for interference identification, this approach fully utilizes radar scattering characteristics to effectively identify interference points with large amplitude fluctuations caused by engineering activities, thus overcoming the limitations of relying solely on phase information. Furthermore, by determining the threshold based on the mean and standard deviation of the amplitude deviation index, dynamic threshold setting is achieved, allowing for adaptive adjustment according to different monitoring scenarios and improving the applicability and accuracy of the method.

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

[0028] In the confidence screening, wavelet transform is performed on the time-series cumulative phase of each initial interference point in the initial engineering interference point set, and the first wavelet coefficients corresponding to each initial interference point are obtained by reconstructing the first and second layer coefficients after wavelet transform decomposition.

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

[0030] By analyzing the abrupt changes in phase using wavelet transform, we can effectively distinguish between natural deformation and 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 set of normal monitoring points, a preset number of normal monitoring points are obtained, and wavelet transform is performed on each of the normal monitoring points to obtain the second wavelet coefficients corresponding to each of the normal monitoring points.

[0033] Determine the maximum value of the second modulus corresponding to each of the second wavelet coefficients, and calculate the wavelet mean and wavelet standard deviation based on each of the second wavelet coefficients;

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

[0035] This approach employs the Monte Carlo method for adaptive threshold optimization. Through multiple random samplings and statistical analyses, it can adaptively determine reasonable thresholds based on different monitoring scenarios, improving the method's adaptability and robustness. Determining the threshold based on the statistical characteristics of normal monitoring points allows for a more accurate identification of which points represent normal deformation and which represent engineering interference, thereby reducing the probability of misclassifying natural deformation as engineering interference.

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

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

[0038] The training data is input into the linear regression model. For each monitoring point, the phase prediction value of the linear regression model for each data point is obtained. The model residual corresponding to each data point is calculated based on the phase prediction value. The weight of each data point is calculated based on the model residual and the weight function to obtain the weight matrix corresponding to the monitoring point. 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 a first weight function and a second weight function.

[0039] Based on the weight matrix and the weighted least squares estimation method, the initial parameters are iteratively updated until the maximum modulus between the current iteration model parameters and the previous iteration model parameters is less than the third threshold, at which point the iteration stops, and the robust regression model is obtained.

[0040] By weighting the phase data of different data points using a weighting function, the degree of suppression of abnormal phase disturbances can be dynamically adjusted, avoiding the problems of insufficient suppression or excessive signal smoothing that may occur with fixed weights in traditional methods. Iterative updates using weighted least squares estimation can quickly converge to stable model parameters, improving suppression efficiency while ensuring the stability of the suppression effect. Combined with a dual-scale factor adjustment mechanism, it can effectively address the complex situation of nonlinear jumps and persistent disturbances in engineering interference phases, improving the stability and reliability of monitoring data in complex construction environments.

[0041] Another embodiment of the present invention provides an engineering interference suppression system based on dual scale factors, comprising: an interference point determination module and an interference suppression module;

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

[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 can suppress the interference of 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 of the first interference suppression data is compared with a first threshold. If it is less than or equal to the first threshold, the first interference suppression data is output. If it is greater than the first threshold, 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 the phase sequence data after interference suppression.

[0044] This invention accurately identifies interference points through feature recognition and confidence screening, and employs a two-step suppression strategy by introducing a dual-scale factor adjustment mechanism. First, a larger first-scale factor is selected, and a first weighting function is used to filter out significant outliers. Then, based on the cumulative phase, points with insufficient suppression are filtered, and the first-scale factor is optimized to a smaller second-scale factor for secondary suppression to eliminate residual interference. This method, through a multi-stage weighted adjustment mechanism, avoids insufficient suppression caused by large-scale factors and alleviates the signal over-smoothing problem caused by small-scale factors, thus achieving effective suppression of engineering interference.

[0045] It effectively solves the problems of insufficient suppression or excessive signal smoothing in traditional methods, thus achieving efficient suppression of engineering interference.

[0046] Another embodiment of the present invention 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, wherein when the processor executes the computer program, it implements the steps of the engineering interference suppression method based on dual scale factors of the present invention.

[0047] Another embodiment of the present invention provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform the steps of the engineering interference suppression method based on dual scale factors of the present invention. Attached Figure Description

[0048] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0049] Figure 1 is a flowchart illustrating an engineering interference suppression method based on dual-scale factor according to an embodiment of the present invention.

[0050] Figure 2 is a flowchart illustrating the feature recognition steps provided in one embodiment;

[0051] Figure 3 is a schematic diagram of the cumulative phase shift characteristics under engineering interference provided in one embodiment;

[0052] Figure 4 is a schematic diagram of the offset characteristics of the time-accumulated phase under engineering interference provided in one embodiment;

[0053] Figure 5 is a schematic diagram illustrating the relationship between signal abrupt change points and wavelet transform modulus extrema provided in one embodiment;

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

[0055] Figure 7 is a schematic diagram of the spatial distribution of pseudo-natural deformation points added in a monitoring scenario provided in an embodiment;

[0056] Figure 8 is a schematic diagram of the timing cumulative phase curve provided in one embodiment;

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

[0058] Figure 10 is a schematic diagram of the iterative suppression process and the suppression result of the time-accumulated phase provided in an embodiment;

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

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

[0061] Figure 13 is a schematic diagram of an engineering interference suppression system based on a dual-scale factor provided in one embodiment. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0063] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0064] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

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

[0066] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0067] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0068] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0069] Referring to Figure 1, to address the problem of insufficient suppression or overly smoothed signals in existing technologies, resulting in poor suppression performance, an embodiment of the present invention provides an engineering interference suppression method based on a dual-scale factor, comprising steps S101-S102:

[0070] Step S101: Based on the phase sequence data and amplitude sequence data of each monitoring point in the project during the monitoring period, perform feature identification and confidence screening to obtain the set of interference points in the project.

[0071] In this embodiment, during the deformation monitoring of the physical engineering project using Ground-Based Synthetic Aperture Radar (GB-SAR), phase sequence data and amplitude sequence data of each monitoring point of the physical engineering project are acquired within the monitoring period. The amplitude and phase features are fused to identify and analyze the engineering interference points, thus obtaining the initial engineering interference points. Furthermore, the initial engineering interference points identified by feature identification can be confidence-filtered using a wavelet analysis-based abrupt change detection method to eliminate misjudged points and obtain a more accurate set of engineering interference points.

[0072] As an example of an embodiment of the present invention, the feature identification and confidence screening based on the phase sequence data and amplitude sequence data of each monitoring point in the project specifically includes: In feature identification, 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 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, 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 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, engineering activities in GB-SAR monitoring can disrupt the phase stability of monitoring points. Therefore, for each monitoring point, the phase difference is calculated based on its phase sequence data. The relationship between the phase difference and a preset threshold is then used to determine if the phase difference meets a first preset threshold condition. If it does, the phase of that monitoring point is abnormal, and it becomes the first interference identification point. This feature analysis process is performed on the phase sequence data of all monitoring points to obtain a first set of interference points. In GB-SAR monitoring, engineering activities can also significantly alter the scattering characteristics of monitoring points. Compared to normal points, the temporal amplitude of interference points exhibits greater fluctuations and instability. Based on the difference in scattering mechanisms between engineering interference points and normal points, the Amplitude Deviation Index (ADI) is introduced as a criterion for identifying engineering interference features. Therefore, the corresponding ADI is calculated based on the amplitude sequence data of each monitoring point. The ADI is then used to determine if it meets a second preset threshold condition. If it does, the monitoring point becomes a second interference identification point, and a second set of interference points is obtained for each second interference identification point. The union of the first set of identified interference points and the second set of identified interference points is used to obtain the initial set of engineering interference points, i.e., the candidate set of interference points. .

[0074] This embodiment constructs a multi-dimensional interference identification system, introduces amplitude information, and achieves collaborative identification of mechanical occlusion, human activity and ground disturbance through multi-threshold detection in amplitude-phase dual domain.

[0075] As an example of an embodiment of the present invention, the step of calculating the phase difference value based on the phase sequence data, determining whether the phase difference value meets a first preset threshold condition, and if it does, then the monitoring point is a first interference identification point, and a first interference point set corresponding to each first interference identification point is obtained. 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 termination time and the phase value corresponding to the start time in the phase sequence data is calculated to determine the cumulative phase; monitoring points whose cumulative phase meets the third preset threshold condition are collected to obtain a third interference point set; differential interference is performed based on the phase sequence data to obtain a temporal interference phase, and the temporal interference phase is gradually accumulated to obtain a temporal cumulative phase; the difference between the maximum and minimum values ​​in the temporal cumulative phase is calculated to obtain a second phase difference value; monitoring points whose second phase difference meets the fourth preset threshold condition are collected to obtain a fourth interference point set; and 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, Figure 2 shows a flowchart of a feature recognition step provided by an embodiment. The process of identifying engineering interference points based on phase sequence data can be divided into two steps. First, engineering interference points are identified through accumulated phase and a third preset threshold condition. The accumulated phase refers to the total phase change of the monitoring point throughout the entire monitoring period, and the third preset threshold condition is a threshold condition determined based on the accumulated 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 accumulated phase. The formula for calculating the accumulated phase is as follows:

[0077] ;

[0078] in, and These represent the phases of the monitored target at the end and start times of monitoring, respectively.

[0079] Under the influence of engineering activities, factors such as blasting, excavation, and mechanical obstruction may cause drastic changes in the phase of the affected monitoring points within a short period of time, resulting in a decrease in the total cumulative phase of the monitoring points. The system exhibits significant variation characteristics. Figure 3 shows a schematic diagram of the cumulative phase shift characteristics under engineering interference provided in one 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 adopted. The criterion is established, and the formula for calculating the cumulative phase threshold is expressed as follows:

[0080] ;

[0081] in, and These represent the phase mean and standard deviation of the monitoring area, respectively.

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

[0083] Although the cumulative phase threshold can identify most interference points, for transient interference, such as momentary occlusion or phase jumps, the phase of the monitoring point may return to normal after the interference ends. This means that the interference point cannot be identified based on the cumulative phase and the cumulative phase threshold, as shown in Figure 4, which illustrates the offset characteristics of the time-series cumulative phase under engineering interference in one embodiment. Therefore, to effectively and accurately identify engineering interference points, it is necessary to quantitatively analyze the fluctuation characteristics of the phase sequence and introduce the time-series cumulative phase range as an auxiliary criterion. The time-series interferometric phase is a phase time series generated by differential interferometry processing of SAR images at adjacent times. Therefore, differential interferometry is first performed on the acquired phase sequence data to obtain the time-series interferometric phase. The calculation formula is expressed as follows:

[0084] ;

[0085] in, For the first Phase of the SAR image.

[0086] Based on the aforementioned time-series interference phase, a time-series accumulated phase is obtained by progressively accumulating the phases, wherein the time-series accumulated phase... The calculation formula is expressed as follows:

[0087] ;

[0088] Based on this, the cumulative phase range of a time series is defined as the difference between the maximum and minimum values ​​in the phase time series. The calculation formula is expressed as follows:

[0089] ;

[0090] For different monitoring scenarios, the time-series 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 can be identified as an engineering interference point and added to the 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.

[0091] As an example of an embodiment of the present invention, for each monitoring point, the 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 it does, the monitoring point is a second interference identification point. Specifically, for each monitoring point, the amplitude mean and 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 amplitude standard deviation; a second threshold is determined based on the mean and standard deviation corresponding to each amplitude deviation index; if the amplitude deviation index is greater than the second threshold, the monitoring point is a second interference identification point.

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

[0093] ;

[0094] in, and These represent the mean and standard deviation of the time series amplitude, respectively.

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

[0096] ;

[0097] in, and These represent the mean and standard deviation of ADI within the monitoring area, respectively.

[0098] When the ADI value of a monitoring point exceeds the second threshold, the point can be considered to be disturbed by engineering activities, and the monitoring point is designated as the second interference identification point.

[0099] As an example of an embodiment of the present invention, the step of performing feature identification and confidence screening based on the phase sequence data and amplitude sequence data of each monitoring point in the project to obtain a set of project interference points specifically involves: in the confidence screening, performing wavelet transform on the time-series cumulative phase of each initial interference point in the initial set of project interference points, and reconstructing it using the first-level coefficients and second-level coefficients after wavelet transform decomposition to obtain the first wavelet coefficients corresponding to each initial interference point; determining the first modulus maximum value corresponding to each first wavelet coefficient; if the first modulus maximum value is less than a third threshold, deleting the initial interference point to obtain the set of project interference points.

[0100] In this embodiment, after initial interference identification, since the results of the initial identification stage may contain 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 lead to a large phase accumulation value, causing some non-construction interference points, or even naturally deformed points, to be 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 Interferometric Synthetic Aperture Radar (InSAR) usually exhibits a gradual change in time and space, while the phase of engineering interference shows significant abrupt changes. Based on this characteristic difference, this technology uses a wavelet analysis-based abrupt change detection method to screen the temporal interferometric phase sequence of the initial engineering interference points to eliminate misidentified points. A low-pass smoothing function is used... first derivative As wavelet basis functions, for synthetic aperture radar interferometry signals Perform wavelet transform on the signal Corresponding to the time-series interferometric phase sequence, signal The wavelet transform is represented as follows:

[0101] ;

[0102] in, for In scale The expansion and contraction below is denoted as .

[0103] Figure 5 shows a schematic diagram illustrating the relationship between signal abrupt change points and wavelet transform modulus extrema in one embodiment, at the same scale. down, signal and its wavelet transform coefficients The relationship is that the abrupt change point of the signal corresponds to the wavelet transform. The maximum point in the signal. Therefore, by using mutation detection methods, the signal... The abrupt change points in the signal are mapped to the modulus extrema of the wavelet transform, thereby determining the signal. Does a sudden change exist? In the specific screening process, wavelet analysis is first performed on the time-series cumulative phase of each initial interference point in the initial set of engineering interference points, and then the decomposed wavelet transform is reconstructed. and Layer coefficients, to obtain the first wavelet coefficients Subsequently, a third threshold was set. If the maximum modulus of the first wavelet coefficients satisfies If the point is not an engineering interference point, it is determined that the point is not an engineering interference point and is removed 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 specifically involves: obtaining a preset number of normal monitoring points from the set of normal monitoring points; performing wavelet transform on each normal monitoring point to obtain the second wavelet coefficients corresponding to each normal monitoring point; determining the second modulus maximum value corresponding to each second wavelet coefficient; and calculating the wavelet mean and wavelet standard deviation based on each second wavelet coefficient; and calculating the third threshold based on the second modulus maximum value, the wavelet mean, and the wavelet standard deviation.

[0105] In this embodiment, to address the issue that a fixed threshold may be difficult to adapt to different monitoring scenarios, this embodiment employs the Monte Carlo method to adaptively optimize the third threshold. Through multiple random samplings and statistical analyses, the wavelet coefficient distribution characteristics of normal points are estimated as a whole, thereby adaptively determining a reasonable third threshold to adapt to different monitoring environments. Specifically, for the unlabeled point set... From the set of points deemed normal, 50 independent random samples are performed, with 100 points randomly selected each time. Random sampling is chosen instead of wavelet analysis on all monitoring points because direct wavelet analysis on all points is computationally expensive, especially when the number of monitoring points reaches hundreds of thousands, where the computational cost increases significantly. Multiple random sampling reduces computational complexity while obtaining more representative overall distribution characteristics, ensuring the robustness of the threshold calculation. Subsequently, wavelet analysis is performed on each of the 100 sampled points, calculating the maximum modulus of the wavelet coefficients for each point. And calculate their mean. with standard deviation Based on these statistical parameters, a dynamic third threshold is calculated. The calculation formula is expressed as follows:

[0106] ;

[0107] Among them, the selection As a threshold, points below the third threshold are considered normal points at a confidence level of 95% or higher, while points exceeding the third threshold are identified as interference points. Finally, the initial set of engineering interference points is... Perform wavelet analysis and remove those that satisfy the following conditions. By identifying the pseudo-interference points, we obtain the set of engineering interference points.

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

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

[0110] In this embodiment, after identifying engineering interference points, corresponding suppression strategies need to be adopted to restore the target phase characteristics of each interference point and ensure the continuity of monitoring data during non-construction periods. In the suppression phase, this embodiment employs a robust regression-based engineering interference suppression method. This method uses dynamic weight adjustment to suppress abnormal phase disturbances. Considering the complex and diverse characteristics of engineering interference phase disturbances, a single weight function or fixed scale factor is insufficient to meet the suppression needs of all engineering interference points, potentially leading to insufficient suppression or overly smoothed signals. Therefore, this embodiment proposes a robust regression model based on robust estimation theory, a two-stage optimization model. This model introduces a dual-scale factor adjustment mechanism to achieve hierarchical suppression of engineering interference. Specifically, by inputting the phase sequence data of each interference point in the engineering interference point set into the robust regression model, the robust regression model suppresses the interference in the phase sequence data. In the robust regression model, firstly, a larger first scale factor is determined. Based on the Cauchy function corresponding to the first scale factor, the phase sequence data is weighted to obtain the first interference suppression data, filtering out significant anomalies among the interference points. Subsequently, the cumulative phase of the first interference suppression data is defined as the target phase difference. If the target phase difference of the first interference suppression data is less than or equal to the first threshold, it indicates that the suppression effect of the first interference suppression data at 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 greater than the first threshold, the suppression effect of the interference point is insufficient, and the first scale factor needs to be optimized to the second scale factor to perform secondary suppression on the first interference suppression data. That is, based on the Cauchy function corresponding to the second scale factor, the first interference suppression data is weighted twice to output the second interference suppression data, so as to complete the interference suppression of the phase sequence data of all interference points in the GB-SAR monitoring process of the project and eliminate residual interference.

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

[0112] As an example of an embodiment of the present invention, the robust regression model specifically comprises: constructing a linear regression model and determining the initial parameters of the linear regression model using the least squares method; inputting training data into the linear regression model; for each monitoring point, obtaining the phase prediction value of the linear regression model for each data 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. 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 a first weight function and a second weight function. The initial parameters are iteratively updated according to the weight matrix and the weighted least squares estimation method until the maximum modulus between the current iteration model parameters and the previous iteration model parameters is less than a third threshold, at which point the iteration stops, thus obtaining the robust regression model.

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

[0114] ;

[0115] in, For time-related design matrices, For a single data point, the time-series interferometric phase vector. The parameters of the model to be estimated are... For the monitoring cycle.

[0116] Input the training data into the linear regression model, and for each monitoring point, obtain the phase prediction value of the linear regression model for each data point. Calculate the model residual corresponding to each data point based on the predicted phase values. And based on model residuals and weight function Calculate the weights of each data point in the current iteration. This leads to the weight matrix corresponding to each monitoring point. Considering the coexistence of nonlinear jumps and persistent disturbances in the phase of engineering interference, a weighting function with strong robustness is needed to suppress anomalous phase disturbances. The Cauchy function, due to its fast descent rate and heavy tail, has a strong suppression capability for points with large deviations and also has a scale factor with adjustable decay rate. To adapt to scenarios with varying interference intensities, this embodiment employs the Cauchy function for engineering interference suppression. The expression for the Cauchy function is as follows:

[0117] ;

[0118] in, For the weight function, For residuals, is the scale factor.

[0119] The formula for calculating the residual is as follows:

[0120] ;

[0121] in, For each data point of the monitoring point, This represents the current iteration number. These are the estimated values ​​of the model parameters for the k-th iteration.

[0122] The weight matrix is ​​a diagonal matrix, represented as follows:

[0123] ;

[0124] The model parameters are updated based on the weight matrix of each monitoring point and the weighted least squares estimation method, resulting in the updated parameters. The updated parameters are represented as follows:

[0125] ;

[0126] The iteration stops when the maximum modulus between the model parameters of the current iteration and the model parameters of the previous iteration is less than the third threshold, thus obtaining the robust regression model. The model outputs the suppressed weighted estimate. This weighted estimate is equivalent to the filtered temporal interference phase. The convergence criterion is expressed as follows:

[0127] ;

[0128] in, This is the preset convergence capacity, i.e., the third threshold.

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

[0130] This invention accurately identifies interference points through feature recognition and confidence screening, and employs a two-step suppression strategy by introducing a dual-scale factor adjustment mechanism. First, a larger first-scale factor is selected, and a first weighting function is used to filter out significant outliers. Then, based on the cumulative phase, points with insufficient suppression are filtered, and the first-scale factor is optimized to a smaller second-scale factor for secondary suppression to eliminate residual interference. This method, through a multi-stage weighted adjustment mechanism, avoids insufficient suppression caused by large-scale factors and alleviates the signal over-smoothing problem caused by small-scale factors, thus achieving effective suppression of engineering interference.

[0131] To verify the effectiveness of this invention, experimental verification was conducted using measured data from the Dabaoshan mining area. Located in Shaoguan City, Guangdong Province, the Dabaoshan mining area has a generally steep terrain. Due to years of mining activities, the mountain exhibits a distinct stepped characteristic, with a large pit at the bottom and potential landslide risks. The data used in the experiment was acquired using circular arc scanning SAR. The data observation period was from 15:00 on November 11, 2021 to 11:00 on November 12, 2021, a time span of approximately 20 hours, with data intervals of approximately 6 minutes.

[0132] In the feature recognition stage, this embodiment employs an amplitude-phase dual-domain multi-threshold method to achieve preliminary identification of engineering interference. This method combines cumulative phase threshold, temporal phase range threshold, and amplitude deviation index threshold, improving recognition accuracy through multi-dimensional comprehensive judgment. Both the cumulative phase threshold method and the amplitude deviation index threshold method are based on the 3σ criterion to screen outliers. Given that the phase of engineering interference points may exhibit a 2π abrupt change, the temporal phase range threshold is set to 2π (radians). Finally, a candidate interference point set is obtained by performing a union operation on the identification results of the above three methods.

[0133] After initial identification, to effectively distinguish between natural deformation points and construction interference points, this embodiment first employs Monte Carlo-based threshold adaptive optimization to determine the threshold for wavelet coefficients. Subsequently, wavelet analysis is performed on the temporal cumulative phase of the candidate interference point set to further filter out misidentified points. This experiment uses the db2 wavelet for abrupt change detection, primarily because the db2 wavelet possesses strong robustness and good localization characteristics, accurately capturing phase abrupt changes while reducing the impact of noise on the detection results. After confidence filtering, the engineering interference identification results are obtained. Figure 6 shows a schematic diagram of the engineering interference identification results provided by one embodiment. As can be seen from Figure 6, the actual construction area basically matches 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; therefore, no actual natural deformation points existed in the candidate point set. To evaluate the method's ability to distinguish between natural deformation points, artificially added pseudo-natural deformation phases were tested. Figure 7 shows a spatial distribution diagram of the added pseudo-natural deformation points in the monitoring scene provided by one embodiment; Figure 8 shows a temporal cumulative phase curve diagram provided by one embodiment; and Figure 9 shows a diagram of the recognition results provided by one embodiment. Experimental results show that after confidence filtering, pseudo-natural deformation points in the interference point set were eliminated, further verifying the effectiveness of the method in distinguishing between natural deformation and engineering interference.

[0135] After identifying construction disturbances, this paper employs robust regression to suppress engineering disturbances. First, an initial scale factor is set. Initial suppression is performed using the Cauchy function. Subsequently, based on the accumulated phase and... Criteria are used to screen residual perturbation regions and adjust the scale factor to [value]. Secondary Cauchy-weighted suppression was applied to these regions. In this experiment, the scale factor was set to... , Figure 10 shows a schematic diagram of the iterative suppression process and the suppression result of the time-series cumulative phase provided in one embodiment. Experimental results show that, in most cases, stable convergence can be achieved within 15 iterations, indicating that the design of the weight function balances efficiency and stability. During the suppression process, the weight values... With residual There is a negative correlation between the two; that is, when the residual is small, the weight function assigns a larger weight to retain normal phase information; while for points with severe interference, the weight is significantly reduced, thus effectively suppressing the influence of abnormal phase. Therefore, during the iteration process, as the interference gradually weakens, the overall weight distribution tends to stabilize, ultimately achieving optimized adjustment of the temporal phase. Statistical analysis of the cumulative temporal phase of all monitoring points before and after suppression is shown in Table 1.

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

[0137]

[0138] Experimental results show that the suppressed temporal phase fluctuations are significantly reduced. The maximum value of the cumulative phase standard deviation decreases from 12.70 rad to 1.74 rad, a reduction of 86.2%, and the mean decreases from 0.136 rad to 0.105 rad, a reduction of 22.9%. The standard deviation of the cumulative phase is significantly reduced. Figure 11 shows a schematic diagram of the cumulative phase before engineering interference suppression provided in one embodiment, and Figure 12 shows a schematic diagram of the cumulative phase after engineering interference suppression provided in one embodiment.

[0139] After engineering interference suppression, the cumulative phase standard deviation of the Dabaoshan mining area decreased from 1.0874 to 0.2810, a reduction of 74.16%, indicating a significant reduction in the overall fluctuation of the phase data and improved data stability. Furthermore, after interference suppression, the phase of most data points was limited to within ±3 rad, corresponding to a deformation of approximately 2.98 mm, indicating a substantial reduction in the number of extreme phase points and a more concentrated phase distribution. Although a few data points still reached a phase deviation of ±5 rad (corresponding to a deformation of approximately 4.97 mm), these larger deviations may be due to the phase accumulation effect caused by missed detection or insufficient suppression of engineering interference, but the overall suppression effect remains quite satisfactory.

[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, making the phase distribution more uniform and improving the stability and reliability of phase measurements.

[0141] As shown in Figure 13, based on the above method implementation examples, a corresponding system implementation example is provided;

[0142] An embodiment of the present invention provides an engineering interference suppression system 1300 based on a dual-scale 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 identification and confidence screening based on the phase sequence data and amplitude sequence data of each monitoring point in the project within the monitoring period, so as to obtain a set of interference points in the project.

[0144] The interference suppression module 1302 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 can suppress the interference of 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 of the first interference suppression data is compared with a first threshold. If it is less than or equal to the first threshold, the first interference suppression data is output. If it is greater than the first threshold, 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.

[0145] It is understood that the above system embodiments correspond to the method embodiments of the present invention, and can implement the engineering interference suppression method based on dual scale factors provided by any of the above method embodiments of the present invention.

[0146] It should be noted that the system embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the system embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0147] Based on the above embodiments of the engineering interference suppression method based on dual scale factors, 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, it implements the engineering interference suppression method based on dual scale factors of any embodiment of the present invention.

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

[0149] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0150] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0151] Based on the above-described 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 executed, it controls the device where the computer-readable storage medium is located to execute the engineering interference suppression method based on dual scale factors as described in any of the above-described method embodiments of the present invention.

[0152] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0153] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and 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-scale factor, characterized in that, include: In the process of deformation monitoring of a physical engineering project using ground-based synthetic aperture radar, phase sequence data and amplitude sequence data of each monitoring point within the monitoring period are acquired. Based on the phase sequence data and amplitude sequence data of each monitoring point within the monitoring period, feature identification and confidence screening are performed to obtain a set of interference points. The phase sequence data of each interference point in the set is input into a robust regression model to suppress interference in 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 of the first interference suppression data is compared with a first threshold. If it is less than or equal to a first threshold, the first interference suppression data is output. If it is greater than a first threshold, 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. Both the first and second weighting functions are 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. The step of performing feature identification and confidence screening based on the phase sequence data and amplitude sequence data of each monitoring point in the project is as follows: In feature identification, for each monitoring point, the phase difference is calculated based on the phase sequence data, and it is determined whether the phase difference meets the first preset threshold condition. If it does, the monitoring point is the 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 determined whether the amplitude deviation index meets the second preset threshold condition. If it does, the monitoring point is the 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 the initial project interference point set.

2. The engineering interference suppression method based on dual-scale factor as described in claim 1, characterized in that, The process involves calculating the phase difference based on the phase sequence data, determining whether the phase difference meets a first preset threshold condition, and if so, identifying the monitoring point as a first interference identification point, thus obtaining a first interference point set corresponding to each first interference identification point. 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 termination time and the phase value corresponding to the start time in the phase sequence data is calculated to determine the cumulative phase. Monitoring points whose cumulative phase meets the third preset threshold condition are then grouped together to obtain a third interference point set. Differential interference is performed based on the phase sequence data to obtain a temporal interference phase, and the temporal interference phase is gradually accumulated to obtain a temporal cumulative phase. The difference between the maximum and minimum values ​​in the temporal cumulative phase is calculated to obtain a second phase difference. Monitoring points whose second phase difference meets the fourth preset threshold condition are then grouped together to obtain a fourth interference point set. The union of the third and fourth interference point sets is calculated to obtain the first interference point set.

3. The engineering interference suppression method based on dual-scale factor as described in claim 1, characterized in that, For each monitoring point, an amplitude deviation index is calculated based on the amplitude sequence data. It is then determined whether the amplitude deviation index meets a second preset threshold condition. If it does, the monitoring point is designated as a second interference identification point. Specifically, for each monitoring point, the amplitude mean and amplitude standard deviation are calculated based on the amplitude sequence data. The amplitude deviation index corresponding to each monitoring point is determined based on the amplitude mean and amplitude standard deviation. A second threshold is determined based on the mean and standard deviation corresponding to each amplitude deviation index. If the amplitude deviation index is greater than the second threshold, the monitoring point is designated as the second interference identification point.

4. The engineering interference suppression method based on dual-scale factor as described in claim 1, characterized in that, The process involves feature identification and confidence screening based on the phase sequence data and amplitude sequence data of each monitoring point in the project to obtain a set of project 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 set of project interference points, and the first wavelet coefficients corresponding to each initial interference point are obtained by reconstructing the first layer coefficients and second layer coefficients after wavelet transform decomposition. 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 project interference points.

5. The engineering interference suppression method based on dual-scale factor as described in claim 4, characterized in that, The third threshold is specifically defined as follows: in the set of normal monitoring points, a preset number of normal monitoring points are obtained; wavelet transform is performed on each normal monitoring point to obtain the second wavelet coefficients corresponding to each normal monitoring point; the second modulus maximum value corresponding to each second wavelet coefficient is determined, and the wavelet mean and wavelet standard deviation are calculated based on each second wavelet coefficient; the third threshold is calculated based on the second modulus maximum value, the wavelet mean, and the wavelet standard deviation.

6. The engineering interference suppression method based on dual-scale factor as described in claim 1, characterized in that, The robust regression model is specifically defined as follows: A linear regression model is constructed, and the initial parameters of the linear regression model are determined using the least squares method; training data is input into the linear regression model, and for each monitoring point, the phase prediction value of the linear regression model for each data point is obtained. The model residual corresponding to each data point is calculated based on the phase prediction value, and the weight of each data point is calculated based on the model residual and the weight function to obtain the weight matrix corresponding to the monitoring point. The training data includes phase sequence training data from multiple monitoring points, and each phase sequence training data includes multiple data points. The weight function includes a first weight function and a second weight function. The initial parameters are iteratively updated based on the weight matrix and the weighted least squares estimation method until the maximum modulus between the current iteration's model parameters and the previous iteration's model parameters is less than a third threshold, at which point the iteration stops, resulting in the robust regression model.

7. An engineering interference suppression system based on a dual-scale factor, characterized in that, include: Interference point identification module and interference suppression module; In the process of deformation monitoring of a physical engineering project using ground-based synthetic aperture radar, phase sequence data and amplitude sequence data of each monitoring point within the monitoring period are acquired. The interference point determination module is used to perform feature identification and confidence screening based on the phase sequence data and amplitude sequence data of each monitoring point within the monitoring period to obtain a set of interference points. The interference suppression module is used to input the phase sequence data of each interference point in the set of interference points into a robust regression model, so that the robust regression model suppresses interference 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 of the first interference suppression data is compared with a first threshold. If it is less than or equal to a first threshold, the first interference suppression data is output; if it is greater than a first threshold, 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 weighting function and the second weighting 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. The step of performing feature identification and confidence screening based on the phase sequence data and amplitude sequence data of each monitoring point in the project is as follows: In feature identification, for each monitoring point, the phase difference is calculated based on the phase sequence data, and it is determined whether the phase difference meets the first preset threshold condition. If it does, the monitoring point is the 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 determined whether the amplitude deviation index meets the second preset threshold condition. If it does, the monitoring point is the 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 the initial project interference point set.

8. A terminal device, characterized in that, The system includes 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, it implements the engineering interference suppression method based on any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, include: A stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the engineering interference suppression method based on a dual-scale factor as described in any one of claims 1-6.

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