A landslide monitoring data processing method based on edge computing

Abstract

The present invention relates to the field of data processing, and in particular to a landslide monitoring data processing method based on edge computing. The method comprises: collecting original monitoring data, transmitting the original monitoring data to an edge computing node for preprocessing to obtain monitoring data; fusing the monitoring data using a dynamic weighted collaborative fusion mechanism to obtain fused data; extracting features from the fused data using an adaptive weighted wavelet transform algorithm to obtain wavelet transform coefficients, and using them as multi-dimensional features; calculating the amplitude of the wavelet transform based on the wavelet transform coefficients; weightedly reconstructing the multi-dimensional features to obtain weighted reconstructed comprehensive features, and uploading the weighted reconstructed comprehensive features to a data center. The method solves the technical problem that landslide monitoring relies on a centralized data processing method, resulting in delays in the data transmission process and excessive network load, and is difficult to ensure the real-time and accuracy of data processing when facing large-scale real-time data.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a landslide monitoring data processing method based on edge computing. Background Art

[0002] With the continuous development of landslide monitoring technology, more and more monitoring systems are incorporating various types of sensors, such as displacement sensors, meteorological sensors, and soil moisture sensors. These sensors can collect real-time environmental data from landslide areas and transmit this information via data transmission networks to a central platform for analysis and processing. In recent years, advances in sensor technology, the Internet of Things (IoT), and data processing algorithms have significantly improved the accuracy and efficiency of landslide monitoring. In particular, advanced data analysis methods such as wavelet transforms and machine learning have been widely used in landslide monitoring to extract meaningful features, analyze potential landslide risks, and support timely warnings and emergency responses.

[0003] However, existing landslide monitoring data processing methods have the following technical problems: landslide monitoring generally relies on centralized data processing methods, which leads to delays in data transmission and excessive network load. Especially when facing large-scale real-time data, it is difficult to ensure the real-time and accuracy of data processing; traditional sensor data fusion methods use fixed weights for data fusion, and lack dynamic adjustment of the real-time status, reliability and stability of sensors, resulting in inaccurate fusion results and difficulty in accurately reflecting the actual changes in the landslide area, reducing the effectiveness and accuracy of landslide monitoring; existing wavelet transform methods fail to fully consider the dynamic change characteristics of data when processing landslide monitoring data, and lack adaptive adjustment mechanisms for time and space dimensions. As a result, it is impossible to effectively capture subtle changes in data during the landslide monitoring process, especially the slow response to instantaneous changes, which affects the accuracy and sensitivity in practical applications. Summary of the Invention

[0004] The present invention provides a landslide monitoring data processing method based on edge computing to solve the problem that landslide monitoring relies on a centralized data processing method, which leads to delays in data transmission and excessive network load. When faced with large-scale real-time data, it is difficult to ensure the real-time and accuracy of data processing; the traditional sensor data fusion method uses fixed weights for data fusion, lacks dynamic adjustment of the real-time status, reliability and stability of the sensor, resulting in inaccurate fusion results, making it difficult to accurately reflect the actual changes in the landslide area, thereby reducing the effectiveness and accuracy of landslide monitoring; the existing wavelet transform method fails to fully consider the dynamic change characteristics of the data when processing landslide monitoring data, lacks an adaptive adjustment mechanism for time and space dimensions, making it impossible to effectively capture subtle changes in the data during the landslide monitoring process, especially the slow response to instantaneous changes, thereby affecting the accuracy and sensitivity in practical applications.

[0005] The present invention provides a landslide monitoring data processing method based on edge computing, which specifically includes the following technical solutions:

[0006] A landslide monitoring data processing method based on edge computing includes the following steps:

[0007] S1: Collect the original monitoring data and transmit it to the edge computing node for preprocessing to obtain monitoring data; use the dynamic weighted collaborative fusion mechanism to fuse the monitoring data to obtain fused data. The data fusion formula is as follows:

[0008] ,

[0009] in, It's at the time fusion data; is a time variable; is the number of sensors; is the sensor index variable; It is Dynamic weights of sensors; It is The variance of each sensor; It is The average value of the sensors; It is The square of the mean of the sensors; Is the sensor index variable, indicating the sensor, which is expressed in the data fusion formula as Other sensors for correlation analysis of sensors; It is The sensor and The covariance between sensors is used to measure the The sensor and The linear relationship between the monitoring data series of the sensors reflects the The sensor and The mutual influence of sensors in landslide monitoring; It is The standard deviation of each sensor; It is The standard deviation of each sensor; and They are The sensor and The monitoring data sequence of each sensor; It is Sensors at time Reliability factor; After preprocessing, the Monitoring data of sensors;

[0010] S2: Use the adaptive weighted wavelet transform algorithm to extract features from the fused data and obtain wavelet transform coefficients. The adaptive weighted wavelet transform algorithm is a dynamic data processing method based on wavelet transform. By introducing an adaptive scale factor, the scale of the wavelet transform is dynamically adjusted according to the changes in the fused data. The adaptive scale factor calculation formula is as follows:

[0011] ,

[0012] in, is the adaptive scale factor; It's at the time fusion data; is the time interval; is the standard deviation of the fused data;

[0013] In the implementation process of the adaptive weighted wavelet transform algorithm, based on the adaptive scale factor, combined with the specific scale and scale translation parameters, the wavelet transform coefficients are obtained to form multi-dimensional features. The calculation formula of the wavelet transform coefficients is:

[0014] ,

[0015] in, It's at the time Wavelet transform coefficients; It is a specific scale; It is scale translation parameters; is a time variable; is the time window of the wavelet transform; is the time-integrated variable; It's at the time fusion data; It's at the time fusion data; is the mother wavelet function; is the standard deviation of the fused data; is the weighted term of the fusion data change;

[0016] And the wavelet transform coefficients are used as multi-dimensional features;

[0017] S3: Based on the wavelet transform coefficients, the amplitude of the wavelet transform is calculated; based on the amplitude of the wavelet transform, the multi-dimensional features are weighted reconstructed to obtain the weighted reconstructed comprehensive features, and the weighted reconstructed comprehensive features are uploaded to the data center for visualization.

[0018] Preferably, the S1 specifically includes:

[0019] A dynamic weighted collaborative fusion mechanism is introduced to fuse the monitoring data from different sensors through the weighted average method to form landslide monitoring data, namely fused data.

[0020] Preferably, the S1 specifically includes:

[0021] The dynamic weighted collaborative fusion mechanism performs collaborative fusion of different sensor data by dynamically adjusting the weight of each sensor. The core of the dynamic weighted collaborative fusion mechanism is to determine the dynamic weight of each sensor. The dynamic weight reflects the reliability and stability of each sensor in landslide monitoring and adjusts the contribution of each sensor in data fusion in real time.

[0022] Preferably, the S3 specifically includes:

[0023] By introducing the time variation of the wavelet transform coefficients and combining it with the exponential decay weighting function, the amplitude of the wavelet transform is calculated to measure the change of the wavelet coefficients over time. The specific calculation formula is as follows:

[0024] ,

[0025] in, It's time , at a specific scale , scale translation parameters and the amplitude of the wavelet transform under the adaptive scaling factor; It's at the time Wavelet transform coefficients of Indicates that within a specific time range, based on a specific scale and scale shift parameters The standard deviation of the wavelet transform coefficients.

[0026] Preferably, the S3 specifically includes:

[0027] The multi-dimensional features are weighted and reconstructed based on the amplitude of the wavelet transform to obtain the comprehensive features after weighted reconstruction. The specific calculation formula is as follows:

[0028] ,

[0029] in, It is a comprehensive feature after weighted reconstruction, which is used to monitor the landslide area at a certain time point. the overall trend of change; is a time variable; is a quantity of a specific scale; is an index variable of a specific scale; is the number of scale translation parameters; is the index variable of the scale translation parameter; Represents the relative degree of change and is used to weight the wavelet transform coefficients.

[0030] The beneficial effects of the technical solution of the present invention are:

[0031] 1. The present invention provides a landslide monitoring data processing method based on edge computing. By preprocessing monitoring data at edge computing nodes, the real-time and accuracy of monitoring data processing are effectively improved. Edge computing nodes can perform rapid calculations at locations close to data sources, avoiding the delay of traditional centralized data processing and improving the response speed of landslide monitoring.

[0032] 2. The present invention adopts a dynamic weighted collaborative fusion mechanism to enable various types of sensor monitoring data to be dynamically weighted according to their respective reliability, stability and health status, effectively overcoming the problem of poor fusion effect of different sensor data in traditional technology. By adjusting the weight of each sensor in real time, it can more accurately reflect the actual changes in the landslide area, significantly improving the fusion accuracy and data processing capabilities of the monitoring data, not only ensuring the scientific nature of data fusion, but also enhancing the adaptability and stability of landslide monitoring in complex environments.

[0033] 3. The present invention combines an adaptive weighted wavelet transform algorithm to extract features from the fused data, obtain wavelet transform coefficients, and use them as multi-dimensional features; it performs weighted reconstruction on the multi-dimensional features, effectively improving the multi-dimensional expression ability of landslide monitoring data. The adaptive weighted wavelet transform algorithm can capture the slight changes in the data on the time scale. Combined with the weighted reconstruction method, the comprehensive features after weighted reconstruction are obtained, which can not only accurately reflect the overall change trend of the landslide area at a specific time point, but also provide a scientific basis for subsequent landslide warning, risk assessment and emergency response. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a flow chart of a landslide monitoring data processing method based on edge computing described in the present invention. DETAILED DESCRIPTION

[0035] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0036] Unless defined otherwise, 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 invention belongs.

[0037] The following describes in detail a specific solution of a landslide monitoring data processing method based on edge computing provided by the present invention with reference to the accompanying drawings.

[0038] Refer to the attached Figure 1 , which shows a flow chart of a landslide monitoring data processing method based on edge computing provided by an embodiment of the present invention, the method comprising the following steps:

[0039] S1: Collect original monitoring data and transmit it to the edge computing node for preprocessing to obtain monitoring data; use the dynamic weighted collaborative fusion mechanism to fuse the monitoring data to obtain fused data;

[0040] First, real-time data collection is performed on the landslide area by deploying various types of sensors, including displacement sensors, meteorological sensors, and humidity sensors. These sensors monitor and record displacement data, rainfall data, temperature data, and humidity data in the landslide area. These data are used as raw monitoring data, which not only reflects changes in environmental conditions in the landslide area but also provides early warning of landslides. The collected raw monitoring data is transmitted in real time to edge computing nodes for subsequent processing.

[0041] After real-time data collection, raw monitoring data is obtained and transmitted to edge computing nodes for preprocessing to improve data quality and accuracy. The main steps of preprocessing include denoising, outlier detection, and data normalization. Specifically, the denoising process uses a low-pass filter to filter out high-frequency noise in the sensor signal, making the signal smoother and more stable, and reducing the impact of environmental noise on data analysis. The outlier detection process, depending on the specific implementation scenario, sets a threshold or uses statistical methods based on the raw monitoring data, such as z-value normalization, to calculate the standard deviation of the raw monitoring data, identify outliers that deviate too far from the mean, and identify and eliminate abnormal data caused by sensor failures or external factors, ensuring the reliability of subsequent analysis data. The data normalization process standardizes multidimensional data from different sensors to the same dimensional range, for example, through linear transformation so that all data values ​​fall within the range of [0, 1] to facilitate subsequent fusion and analysis. The above preprocessing process is existing technology and will not be described in detail here.

[0042] The original monitoring data are preprocessed to obtain monitoring data. The dynamic weighted collaborative fusion mechanism is introduced to fuse the monitoring data from different sensors through the weighted average method to form a comprehensive landslide monitoring data, namely fused data, which is convenient for subsequent analysis and processing.

[0043] The dynamic weighted collaborative fusion mechanism achieves collaborative fusion of data from different sensors by dynamically adjusting the weight of each sensor. The core of the dynamic weighted collaborative fusion mechanism lies in determining the dynamic weight of each sensor. This dynamic weight reflects the reliability and stability of each sensor in landslide monitoring. The calculation of the dynamic weight not only considers the sensor's mean and variance, but also incorporates the correlation and health status of each sensor. This allows for real-time adjustment of each sensor's contribution to data fusion, providing more accurate and reliable landslide monitoring results.

[0044] The data fusion formula is as follows:

[0045] ,

[0046] in, It's at the time The fused data represents the time point Comprehensive landslide monitoring results; is a time variable, indicating the specific time point of landslide monitoring; is the number of sensors, which is set according to the specific implementation scenario; Is the sensor index variable, indicating the sensors; It is Dynamic weights of sensors; It is The variance of the sensor, The fluctuation range of the monitoring data of each sensor is calculated based on the monitoring data sequence. The time range of the monitoring data sequence is set based on expert experience to capture the changes in the monitoring data; It is The average value of the sensors, indicating the The average value of the monitoring data output by the sensors is used to measure the The baseline output of each sensor is calculated based on the monitoring data sequence; Is the sensor index variable, indicating the sensor, which is expressed in the fusion data formula as Other sensors for correlation analysis of sensors; It is The sensor and The covariance between sensors is used to measure the The sensor and The linear relationship between the monitoring data series of the sensors reflects the The sensor and The mutual influence of sensors in landslide monitoring; It is The standard deviation of each sensor; It is The standard deviation of each sensor; and They are The sensor and The monitoring data sequence of each sensor; It is Sensors at time The reliability factor is calculated by using the expert experience method according to Adjust the health status of each sensor (such as failure rate, usage time, signal stability, etc.); After preprocessing, the Monitoring data of sensors;

[0047] Through the dynamic weighted collaborative fusion mechanism, the weights of each sensor are reasonably distributed, and the resulting fusion data can more accurately reflect the actual changes in the landslide area, ensuring the accuracy of the monitoring results;

[0048] S2: Use the adaptive weighted wavelet transform algorithm to extract features from the fused data, obtain wavelet transform coefficients, and use the wavelet transform coefficients as multi-dimensional features;

[0049] An adaptive weighted wavelet transform algorithm is used to extract features from the fused data. This dynamic data processing method, based on the wavelet transform, enhances the adaptability of the wavelet transform by introducing an adaptive scaling factor. This adaptive scaling factor dynamically adjusts the scale of the wavelet transform based on changes in the fused data, increasing its ability to capture transient changes in the landslide area. A weighted term is introduced into the wavelet transform formula to adjust the amplitude of changes in the fused data, enhancing the wavelet transform's responsiveness to data changes and further improving its adaptability to dynamic changes.

[0050] The adaptive scale factor calculation formula is as follows:

[0051] ,

[0052] in, It is an adaptive scale factor, which is a wavelet scale factor that is dynamically adjusted according to the changes in the fusion data, so that the wavelet transform can automatically adjust the scale of the wavelet transform according to the actual dynamic change process of the landslide; It's at the time fusion data; It's at the time fusion data; is the time interval, which is set according to the specific implementation scenario; It is the standard deviation of the fused data, which is used to standardize the fused data. The time range required to calculate the standard deviation of the fused data is set according to the needs of the specific implementation scenario;

[0053] The calculation formula of wavelet transform coefficients is:

[0054] ,

[0055] in, is the wavelet transform coefficient, reflecting the fusion data at the time point , based on the adaptive scale factor , specific scale and scale shift parameters The following features; It is A specific scale is used to control the degree of scaling of the wavelet transform. The value and number of the specific scale are set according to the specific implementation scenario; It is A scale shift parameter indicates the position of the wavelet transform on the time axis. The value and number of the scale shift parameter are set according to the specific implementation scenario. It is an adaptive scale factor, which is a wavelet scale factor that is dynamically adjusted according to the changes in the fusion data, so that the wavelet transform can automatically adjust the scale of the wavelet transform according to the actual dynamic change process of the landslide; is a time variable; is the time window of the wavelet transform, which is set according to the specific implementation scenario; is the time-integrated variable; It's at the time fusion data; It's at the time fusion data; It is the mother wavelet function, which represents the basis function used by wavelet transform and is used to analyze the changes of signals at different times and scales. It is set according to the specific implementation scenario, such as Gaussian wavelet and Morey wavelet. is the standard deviation of the fused data; It is a weighted term for fusion data changes, which adjusts the influence of time points in wavelet transform according to the amplitude of data changes;

[0056] Through the adaptive weighted wavelet transform algorithm, wavelet transform coefficients containing multiple specific scales and scale translation parameters are obtained to form multi-dimensional features;

[0057] S3: Based on the wavelet transform coefficients, the amplitude of the wavelet transform is calculated; based on the amplitude of the wavelet transform, the multi-dimensional features are weighted reconstructed to obtain the weighted reconstructed comprehensive features, and the weighted reconstructed comprehensive features are uploaded to the data center for visualization.

[0058] The wavelet transform amplitude is calculated for each wavelet transform coefficient. By introducing the temporal variation of the wavelet transform coefficient and combining it with an exponential decay weighting function, the wavelet transform amplitude is calculated to reflect the variation of the wavelet transform coefficient at a specific scale and scale shift parameter. This measures the temporal variation of the wavelet transform coefficient and thus assesses the relative importance of the wavelet transform coefficient at a specific scale and scale shift parameter.

[0059] The specific calculation formula of the wavelet transform amplitude is as follows:

[0060] ,

[0061] in, It's time , at a specific scale , scale translation parameters and the amplitude of the wavelet transform under the adaptive scaling factor; is the wavelet transform coefficient, reflecting the fusion data at the time point , based on the adaptive scale factor , specific scale and scale shift parameters The following features; is the wavelet transform coefficient, reflecting the fusion data at the time point , based on the adaptive scale factor , specific scale and scale shift parameters The following features; is the time interval; Indicates that within a specific time range, based on a specific scale and scale shift parameters The standard deviation of the wavelet transform coefficients reflects the specific scale and scale shift parameters The degree of fluctuation of the wavelet transform coefficients, the time range of which is set according to the specific implementation scenario; is the time variation of the wavelet transform coefficients; is an exponentially decaying weighting function.

[0062] The multi-dimensional features are weighted and reconstructed based on the amplitude of the wavelet transform to obtain the comprehensive features after weighted reconstruction. The specific calculation formula is as follows:

[0063] ,

[0064] in, It is a comprehensive feature after weighted reconstruction, which is used to monitor the landslide area at a certain time point. the overall trend of change; is a time variable; is a quantity of a specific scale; is an index variable of a specific scale; is the number of scale translation parameters; is the index variable of the scale translation parameter; is the wavelet transform coefficient, reflecting the fusion data at the time point , based on the adaptive scale factor , specific scale and scale shift parameters The following features; It's time , at a specific scale , scale translation parameters and the amplitude of the wavelet transform under the adaptive scaling factor; Represents the relative degree of change and is used to weight the wavelet transform coefficients.

[0065] The weighted reconstructed comprehensive features reflect the overall trend of changes in the landslide monitoring area at a specific point in time, demonstrating the dynamics of the landslide area and enabling various processing options based on specific scenarios. For example, by analyzing the long-term trends in the landslide monitoring area, the stability or potential risk of the landslide area can be assessed, providing a basis for risk assessment and early warning. During landslide monitoring, if the magnitude of the change in the weighted reconstructed comprehensive features exceeds a preset threshold, a real-time alarm can be triggered, prompting personnel to take timely preventative measures.

[0066] Finally, the weighted reconstructed comprehensive features are uploaded to the cloud or data center using edge computing nodes for visualization, allowing monitoring personnel to view the dynamic changes of landslide activities in real time, thereby providing support for emergency response and disaster prevention and mitigation.

[0067] In summary, a landslide monitoring data processing method based on edge computing was completed.

[0068] The order in which the embodiments of the invention are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0069] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0070] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A landslide monitoring data processing method based on edge computing, characterized in that: The following steps are involved: S1: Collect the original monitoring data and transmit it to the edge computing node for preprocessing to obtain monitoring data; use the dynamic weighted collaborative fusion mechanism to fuse the monitoring data to obtain fused data. The data fusion formula is as follows: ， in, It's at the time fusion data; is a time variable; is the number of sensors; is the sensor index variable; It is Dynamic weights of sensors; It is The variance of each sensor; It is The average value of the sensors; It is The square of the mean of the sensors; Is the sensor index variable, indicating the sensor, which is expressed in the data fusion formula as Other sensors for correlation analysis of sensors; It is The sensor and The covariance between sensors is used to measure the The sensor and The linear relationship between the monitoring data series of the sensors reflects the The sensor and The mutual influence of sensors in landslide monitoring; It is The standard deviation of each sensor; It is The standard deviation of each sensor; and They are The sensor and The monitoring data sequence of each sensor; It is Sensors at time Reliability factor; After preprocessing, the Monitoring data of sensors; S2: Use the adaptive weighted wavelet transform algorithm to extract features from the fused data and obtain wavelet transform coefficients. The adaptive weighted wavelet transform algorithm is a dynamic data processing method based on wavelet transform. By introducing an adaptive scale factor, the scale of the wavelet transform is dynamically adjusted according to the changes in the fused data. The adaptive scale factor calculation formula is as follows: ， in, is the adaptive scale factor; It's at the time fusion data; is the time interval; is the standard deviation of the fused data; In the implementation process of the adaptive weighted wavelet transform algorithm, based on the adaptive scale factor, combined with the specific scale and scale translation parameters, the wavelet transform coefficients are obtained to form multi-dimensional features. The calculation formula of the wavelet transform coefficients is: ， in, It's at the time Wavelet transform coefficients; It is a specific scale; It is scale translation parameters; is a time variable; is the time window of the wavelet transform; is the time-integrated variable; It's at the time fusion data; It's at the time fusion data; is the mother wavelet function; is the standard deviation of the fused data; is the weighted term of the fusion data change; And the wavelet transform coefficients are used as multi-dimensional features; S3: Based on the wavelet transform coefficients, the amplitude of the wavelet transform is calculated; based on the amplitude of the wavelet transform, the multi-dimensional features are weighted reconstructed to obtain the weighted reconstructed comprehensive features, and the weighted reconstructed comprehensive features are uploaded to the data center for visualization; the comprehensive features are used to monitor the landslide area at the time point overall trend of change.

2. The landslide monitoring data processing method based on edge computing according to claim 1 is characterized in that: Said S1 specifically includes: The dynamic weighted collaborative fusion mechanism performs collaborative fusion of different sensor data by dynamically adjusting the weight of each sensor. The core of the dynamic weighted collaborative fusion mechanism is to determine the dynamic weight of each sensor. The dynamic weight reflects the reliability and stability of each sensor in landslide monitoring and adjusts the contribution of each sensor in data fusion in real time.

3. The landslide monitoring data processing method based on edge computing according to claim 1 is characterized in that: Said S3 specifically includes: By introducing the time variation of the wavelet transform coefficients and combining it with the exponential decay weighting function, the amplitude of the wavelet transform is calculated to measure the change of the wavelet coefficients over time. The specific calculation formula is as follows: ， in, It's time , at a specific scale , scale translation parameters and the amplitude of the wavelet transform under the adaptive scaling factor; It's at the time Wavelet transform coefficients of Indicates that within a specific time range, based on a specific scale and scale shift parameters The standard deviation of the wavelet transform coefficients.

4. The landslide monitoring data processing method based on edge computing according to claim 3 is characterized in that: Said S3 specifically includes: The multi-dimensional features are weighted and reconstructed based on the amplitude of the wavelet transform to obtain the comprehensive features after weighted reconstruction. The specific calculation formula is as follows: ， in, It is a comprehensive feature after weighted reconstruction, which is used to monitor the landslide area at a certain time point. the overall trend of change; is a time variable; is a quantity of a specific scale; is an index variable of a specific scale; is the number of scale translation parameters; is the index variable of the scale translation parameter; Represents the relative degree of change and is used to weight the wavelet transform coefficients.

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