Ground surface settlement monitoring method and system fusing Beidou and InSAR data

Abstract

The invention provides a ground surface settlement monitoring method and system fusing Beidou and InSAR data. The method comprises the following steps: receiving positioning data from a Beidou satellite system and earth surface deformation data acquired by a synthetic aperture radar interference measurement system; based on the positioning data and the earth surface deformation data, correcting the earth surface deformation data according to the environmental factors through a space-time fusion model to obtain corrected earth surface deformation data; by means of the corrected surface deformation data, a multi-stage analysis technology is adopted, the development trend of surface subsidence is determined, a surface subsidence distribution diagram is generated, and the surface subsidence distribution diagram is used for displaying spatial distribution characteristics of surface subsidence; and according to the ground surface settlement distribution diagram, the future ground surface settlement condition is monitored by using the space-time fusion model, and a ground surface settlement monitoring report can provide a scientific basis for early warning and prevention of geological disasters. The ground surface settlement monitoring efficiency and monitoring effect are improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of surface subsidence monitoring, and in particular to a surface subsidence monitoring method and system integrating Beidou and InSAR data. Background Art

[0002] With the acceleration of urbanization, land subsidence has become one of the important factors affecting urban safety and sustainable development. Land subsidence not only causes direct economic losses such as building damage and underground pipeline rupture, but may also induce geological disasters and threaten the safety of life and property.

[0003] At present, surface subsidence monitoring mainly relies on traditional ground measurement technology and space observation technology. Although ground measurement technologies such as leveling and GPS measurement have high accuracy, they are expensive and difficult to achieve large-scale continuous monitoring. Space observation technology mainly includes the use of synthetic aperture radar interferometry (InSAR) technology, which can achieve large-scale, high-resolution surface deformation monitoring. However, InSAR technology is greatly affected by factors such as atmospheric delay and terrain undulation, resulting in certain errors in its monitoring results.

[0004] Although traditional ground measurement technology is highly accurate, it requires a lot of manpower and material resources, making it difficult to promote and apply on a large scale, and it is impossible to achieve long-term continuous monitoring. Although the existing InSAR technology can achieve large-scale surface deformation monitoring, it is easily affected by environmental factors such as atmospheric delay and terrain undulation in actual application, resulting in deviations in monitoring results. Summary of the invention

[0005] The embodiments of the present application provide a surface subsidence monitoring method and system that integrate Beidou and InSAR data to solve the problem of poor technical level of surface subsidence monitoring in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a surface subsidence monitoring method integrating Beidou and InSAR data, comprising: Receive positioning data from the BeiDou satellite system and surface deformation data obtained by the synthetic aperture radar interferometry system; Based on the positioning data and the surface deformation data, a spatiotemporal fusion model is constructed to correct the surface deformation data according to environmental factors through the spatiotemporal fusion model to obtain corrected surface deformation data, wherein the environmental factors at least include atmospheric delay and terrain undulation; Using the corrected surface deformation data and adopting multi-period analysis technology, the development trend of surface subsidence is determined, and a surface subsidence distribution map is generated, which is used to show the spatial distribution characteristics of surface subsidence; According to the surface subsidence distribution map, the space-time fusion model is used to monitor future surface subsidence and generate a surface subsidence monitoring report, which can provide a scientific basis for early warning and prevention of geological disasters.

[0007] In a second aspect, an embodiment of the present application provides a surface subsidence monitoring system integrating Beidou and InSAR data, including: A receiving module is used to receive positioning data from the Beidou satellite system and surface deformation data obtained by the synthetic aperture radar interferometry system; A construction module, configured to construct a spatiotemporal fusion model based on the positioning data and the surface deformation data, so as to correct the surface deformation data according to environmental factors through the spatiotemporal fusion model to obtain corrected surface deformation data, wherein the environmental factors at least include atmospheric delay and terrain undulation; A generation module is used to use the corrected surface deformation data and multi-period analysis technology to determine the development trend of surface subsidence and generate a surface subsidence distribution map, which is used to show the spatial distribution characteristics of surface subsidence; based on the surface subsidence distribution map, the time-space fusion model is used to monitor future surface subsidence and generate a surface subsidence monitoring report, which can provide a scientific basis for early warning and prevention of geological disasters.

[0008] In a third aspect, an embodiment of the present application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a surface subsidence monitoring method that integrates Beidou and InSAR data as described in the first aspect above.

[0009] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program. When the computer program is executed by a computer, it implements a surface subsidence monitoring method that integrates Beidou and InSAR data as described in the first aspect.

[0010] In the embodiment of the present application, by integrating the positioning data of the Beidou satellite system with the surface deformation data obtained by InSAR (synthetic aperture radar interferometry), a spatiotemporal fusion model is constructed, which can effectively reduce the error caused by a single data source and improve the accuracy of surface subsidence monitoring. The influence of environmental factors such as atmospheric delay and terrain undulation on surface deformation data is taken into account. These factors are corrected through the model to make the monitoring results closer to the actual situation, and enhance the environmental adaptability and reliability of the system. The generated surface subsidence distribution map can intuitively display the spatial distribution characteristics of surface subsidence, and the surface subsidence monitoring report provides a scientific basis, which is conducive to the development of geological disaster warning and prevention and control work, and enhances the decision-making support capabilities of the government and relevant departments.

[0011] Furthermore, the present application uses the Kalman filter algorithm to perform noise suppression processing on the surface deformation data set, thereby improving the data quality and ensuring the accuracy of subsequent analysis. Through GIS spatial analysis tools, key characteristic data of surface subsidence, such as the location of the subsidence center, the maximum subsidence, the subsidence rate and the subsidence range, can be accurately extracted, providing a solid data foundation for the analysis of subsidence trends. The use of the autoregressive integral moving average model combined with wavelet transform technology can not only analyze the long-term development trend of surface subsidence, but also capture the subsidence patterns at different frequencies, thus achieving a comprehensive analysis of the surface subsidence trend. The surface subsidence distribution map drawn using three-dimensional visualization technology can more intuitively show the subsidence area division, subsidence intensity distribution, subsidence rate distribution and time changes, which helps professionals quickly understand the surface subsidence conditions.

[0012] Furthermore, this application analyzes the long-term development trend and periodic changes of surface subsidence, and combines the settlement patterns at different time scales obtained by wavelet transform technology to more accurately predict the short-term development trend of surface subsidence, thereby improving the timeliness and accuracy of the prediction. Combining long-term and short-term development trends, the generated surface subsidence analysis conclusions are more comprehensive and in-depth, providing strong support for the formulation of effective geological disaster prevention and control strategies. The surface subsidence development trend report generated based on detailed analysis conclusions not only contains rich charts and data, but also provides clear trend judgments, which will help relevant institutions and personnel better understand and respond to surface subsidence issues.

[0013] The above beneficial effects together constitute an efficient, accurate and widely applicable surface subsidence monitoring system, which has important practical significance for preventing and mitigating geological disasters.

[0014] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 A flow chart of a surface subsidence monitoring method that integrates Beidou and InSAR data provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of a surface subsidence monitoring system that integrates Beidou and InSAR data provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0018] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0019] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0020] Figure 1 A flow chart of a method for monitoring surface subsidence by integrating Beidou and InSAR data is provided for an embodiment of the present application. Figure 1 As shown, the method includes: 101. Receive positioning data from the Beidou satellite system and surface deformation data obtained by the synthetic aperture radar interferometry system; In this step, the BeiDou satellite system is a global satellite navigation system independently developed by China, which can provide accurate location information and services. The system consists of three parts: the space segment, the ground segment and the user segment, and can provide global users with all-weather, all-day, high-precision positioning, navigation and timing services. Synthetic Aperture Radar Interferometry (InSAR) is a remote sensing technology that detects tiny changes in the earth's surface by using two or more synthetic aperture radar (SAR) images. These images are usually taken by satellites at different points in time, and by comparing the phase differences between these images, the deformation of the surface can be calculated.

[0021] In the embodiments of the present application, in the field of urban planning, the precise location information provided by the Beidou satellite system is combined with the high-resolution surface deformation data obtained by InSAR technology to effectively monitor the subsidence of the urban surface and provide important data support for the safety assessment and maintenance of urban infrastructure.

[0022] 102. Based on the positioning data and the surface deformation data, a spatiotemporal fusion model is constructed to correct the surface deformation data according to environmental factors through the spatiotemporal fusion model to obtain corrected surface deformation data, wherein the environmental factors at least include atmospheric delay and terrain undulation; In this step, the spatiotemporal fusion model refers to a method of combining data in the two dimensions of time and space to analyze and process surface deformation data through mathematical modeling. This model can more accurately reflect the actual situation of surface deformation and reduce data errors caused by environmental factors such as atmospheric delay and terrain undulation.

[0023] In the embodiments of the present application, in seismological research, researchers can use the space-time fusion model to analyze the surface deformation data before and after the earthquake to help understand the mechanism of earthquake occurrence and its impact on the surface, thereby improving the accuracy of earthquake prediction.

[0024] Optionally, the step 102 of "correcting the surface deformation data according to environmental factors through the space-time fusion model to obtain corrected surface deformation data" includes: using the space-time fusion model, combined with preset meteorological data and atmospheric model, to correct the atmospheric delay effect in the surface deformation data, reduce the influence of atmospheric delay on the surface deformation data, and obtain preliminary corrected surface deformation data; using the space-time fusion model, combined with preset elevation data and terrain model, to correct the terrain undulation effect in the preliminary corrected surface deformation data, reduce the influence of terrain undulation on the surface deformation data, and obtain the final corrected surface deformation data.

[0025] In the field of geographic information science and remote sensing, surface deformation data refers to information on changes in the position of the earth's surface obtained through satellite or ground measurement technology. This information can reflect surface changes caused by crustal movement, geological disasters, human activities, etc. The spatiotemporal fusion model is a data processing method that combines time and space dimensions, aiming to improve the spatial resolution and temporal continuity of data. In the process of correcting surface deformation data, meteorological data and atmospheric models are used to evaluate and correct signal delays caused by changes in atmospheric conditions (such as temperature, humidity, and air pressure), that is, the atmospheric delay effect. Elevation data and terrain models are used to analyze and adjust observation errors caused by terrain differences, that is, the terrain undulation effect.

[0026] The process of correcting surface deformation data first involves using the space-time fusion model in combination with preset meteorological data and atmospheric models to correct the atmospheric delay effect caused by changes in atmospheric conditions in the original surface deformation data. This step can significantly reduce the impact of atmospheric conditions on the surface deformation measurement results, thereby obtaining preliminary corrected surface deformation data. Next, based on the preliminary corrected data, the space-time fusion model is used again, this time in combination with preset elevation data and terrain models to correct the terrain undulation effect. This process helps to further eliminate the interference of terrain factors on surface deformation data, and ultimately outputs fully corrected surface deformation data, providing more accurate basic data for subsequent analysis and research.

[0027] The following is a more detailed description of the above steps: Receive positioning data from the Beidou satellite system and surface deformation data obtained by the synthetic aperture radar interferometry system.

[0028] Then the above data is preprocessed, where the preprocessing includes: time synchronization, noise removal, missing value filling and format conversion.

[0029] Time synchronization: Ensure that all data points have consistent timestamps to facilitate subsequent analysis.

[0030] Noise Removal: Use the Kalman filter algorithm to reduce random errors or outliers in the observed data.

[0031] Missing value filling: For missing data records, use interpolation or other statistical methods to estimate reasonable values.

[0032] Format conversion: unify the formats of data from different sources so that they can be processed on the same platform.

[0033] Next, the specific impact of the atmospheric delay effect on the surface deformation data is calculated, and then corrections are made accordingly to obtain preliminary corrected surface deformation data.

[0034] Based on the preliminary corrected data, the spatiotemporal fusion model is applied again, with special attention paid to how terrain undulations affect the measurement results of surface deformation, and finally the fully corrected surface deformation data are output.

[0035] The autoregressive integrated moving average model (ARIMA) is then used to process time series data with trend and seasonality to help predict surface subsidence in a certain period of time in the future.

[0036] Multi-resolution analysis of non-stationary signals using wavelet transform techniques is used to reveal the sedimentation patterns at different frequencies.

[0037] Finally, the spatial analysis tools of Geographic Information System (GIS) are used to process and analyze the geospatial data and extract key features, such as the location of the settlement center, maximum settlement amount, settlement rate and settlement range.

[0038] Use historical data to train the selected model and evaluate its performance through methods such as cross-validation to ensure that the model accurately reflects the actual situation.

[0039] The model parameters or structure can also be continuously optimized based on the feedback until satisfactory accuracy is achieved.

[0040] The corrected surface deformation data are organized into multi-period data sets according to time series. The Kalman filter algorithm is used to perform noise suppression on the surface deformation data sets of each period to obtain the optimized surface deformation data sets.

[0041] Based on the optimized surface deformation dataset, GIS spatial analysis tools are used to extract the key characteristic data of each period of surface subsidence, including the location of the subsidence center, maximum subsidence, subsidence rate and subsidence range.

[0042] The autoregressive integrated moving average model (ARIMA) combined with wavelet transform technology is used to conduct a comprehensive analysis of multi-period surface deformation data sets, analyze the long-term development trend, periodic changes and settlement patterns at different frequencies, so as to fully understand the surface settlement situation, determine the development trend of surface settlement, and generate a surface settlement development trend report. The final output is the fully corrected surface deformation data, which is closer to the actual surface settlement situation and provides a solid foundation for subsequent trend analysis.

[0043] The following further explains the process of building a spatiotemporal fusion model, meteorological data, and an atmospheric model, and how to use these models to correct surface deformation data: First, it is necessary to obtain high-precision positioning data, which usually comes from global navigation satellite systems (GNSS), such as GPS, GLONASS, Galileo, or BeiDou systems. These data provide time series measurements of surface positions. Collect surface deformation data obtained through various remote sensing technologies (such as synthetic aperture radar interferometry InSAR). This type of data can reflect the changes in the surface at different time points. Clean the original data, including removing noise, filling missing values, and unifying time and space coordinate systems to ensure data quality and consistency.

[0044] Secondly, extract time series features (such as timestamp, rate of change) and spatial features (such as location coordinates, deformation) from the preprocessed data. These features will be used in the subsequent modeling process. At the same time, collect meteorological data (temperature, humidity, air pressure, etc.) and elevation data (DEM) to prepare for the correction of atmospheric delay and terrain undulation effects.

[0045] Furthermore, according to the characteristics and needs of the research area, a suitable spatiotemporal fusion model is selected, such as the spatiotemporal autoregressive moving average model (STARMA), spatiotemporal kriging interpolation method, or machine learning / deep learning model. The extracted time series features and spatial features are used as input, combined with some known surface deformation results as output labels, to train and build a spatiotemporal fusion model. In addition, the model performance is evaluated through methods such as cross-validation, and the parameters are adjusted to optimize the model.

[0046] Furthermore, a suitable atmospheric model (such as GPT2, VMF3) is selected, which should be able to simulate the delay caused by the ionosphere and troposphere. The atmospheric model is calibrated using meteorological data, and the atmospheric model is combined with the spatiotemporal fusion model to form an atmospheric delay correction model so that the influence of atmospheric delay can be automatically considered during the analysis process. A terrain model is constructed based on DEM data to quantify the impact of terrain undulation on the observed data. The terrain model is integrated into the spatiotemporal fusion model to form a terrain undulation correction model to correct the deviation caused by the terrain.

[0047] Furthermore, the atmospheric delay correction model is used to calculate the atmospheric delay effect of each observation point, and the preliminary surface deformation data is adjusted accordingly to reduce the impact of atmospheric delay and obtain preliminary corrected surface deformation data. The terrain undulation correction model is further used to process the preliminary corrected surface deformation data to eliminate the error caused by terrain undulation and obtain the final corrected surface deformation data.

[0048] Finally, throughout the correction process, continue to verify the results to ensure that each step has achieved the expected results. If necessary, return to the previous step to adjust parameters or improve the model. Based on the verification feedback, continuously optimize the model structure and parameter settings until the model performance reaches the optimal state.

[0049] This process ensures that every link from feature extraction to the final correction result is systematically considered and processed, thereby improving the accuracy of surface deformation monitoring.

[0050] It should be noted that in the above process, the process of combining the atmospheric model with the spatiotemporal fusion model mainly includes the following processes: First, choose a suitable atmospheric model (such as GPT2, VMF3) that can calculate the delayed effects of the ionosphere and troposphere based on geographic location, time, and meteorological parameters (temperature, humidity, pressure, etc.). Calibrate the atmospheric model using historical meteorological data (which can be obtained from global or regional meteorological services) to ensure that it is applicable to the study area.

[0051] Secondly, add a new input dimension to the spatiotemporal fusion model to represent the atmospheric delay value at each observation point. This can be achieved by adding the delay value calculated by the atmospheric model as an additional feature to the spatiotemporal fusion model. Modify the structure or algorithm of the spatiotemporal fusion model so that it can consider the influence of atmospheric delay when predicting surface deformation. For example, new features can be added to the machine learning model, or a mathematical expression for atmospheric delay can be introduced in the physical model.

[0052] Finally, an atmospheric delay correction model is constructed, which accepts the output of the spatiotemporal fusion model (uncorrected surface deformation data) and the delay estimate provided by the atmospheric model as input, and outputs the corrected surface deformation data. This correction process can be achieved by directly subtracting the estimated atmospheric delay from the original measurement value, or by more complex mathematical operations, such as iterative least squares method, to improve accuracy.

[0053] In the above process, the process of integrating the terrain model into the spatiotemporal fusion model mainly includes the following steps: First, a terrain model is constructed based on DEM data, which can accurately represent the elevation changes of the surface. The relationship between the terrain model and the surface deformation is analyzed to find out the terrain factors that may affect the observation results, such as slope and curvature.

[0054] Second, similar to the atmospheric model, the output of the terrain model (such as slope, elevation difference, etc.) is added as a new feature to the spatiotemporal fusion model. The spatiotemporal fusion model is adjusted so that it can take into account the impact of terrain fluctuations on the observed data during the prediction process. This may involve modifying the model's parameter settings or adopting algorithms that are more suitable for handling complex spatial correlations.

[0055] Finally, a terrain relief correction model is created that adjusts the surface deformation data based on the output of the terrain model. This process can again be a simple numerical subtraction or a more complex statistical or physical method to remove systematic biases caused by the terrain.

[0056] In the above process, the process of using two correction models to correct data includes: First, for each observation point, the atmospheric delay correction model is used to calculate the atmospheric delay effect at that point. This usually involves the following steps:

[0057] The timestamp, geographic location and relevant meteorological parameters of the observation point are input into the atmospheric model to obtain the atmospheric delay estimate of the point.

[0058] Apply the atmospheric delay estimate to the uncorrected surface deformation data and reduce the effect of the atmospheric delay by appropriate methods (such as subtraction or more complex adjustment formulas, which can be set as needed).

[0059] Output preliminary corrected surface deformation data.

[0060] Next, the preliminary corrected data is further processed using the terrain relief correction model: Input the geographical location of the observation point into the terrain model to obtain the terrain information of the observation point (such as elevation, slope, etc.).

[0061] Adjust the initially corrected surface deformation data according to the terrain information to eliminate the error caused by terrain undulations. This involves adjusting the deformation offset based on the terrain model prediction, which can be set according to needs.

[0062] Output the final corrected surface deformation data.

[0063] In the embodiment of the present application, it is assumed that in an earthquake monitoring project, researchers need to accurately understand the changes in the surface of a certain area to predict possible geological disasters. First, they collected the surface deformation data of the area, and obtained the corresponding meteorological data (such as temperature, humidity, air pressure, etc.) and atmospheric model parameters, as well as detailed topographic maps and elevation data. Then, applying the spatiotemporal fusion model, the researchers first calculated the specific impact of the atmospheric delay effect on the surface deformation data based on the meteorological data and the atmospheric model, and made corrections accordingly to obtain a preliminary revised data set. Subsequently, using the same spatiotemporal fusion model, combined with topographic maps and elevation data, the preliminary revised data was further adjusted, with special attention to how terrain undulations affect the measurement results of surface deformation. After these two steps of correction, the researchers obtained more accurate surface deformation data, which is of great significance for accurately assessing earthquake risks and guiding disaster prevention and mitigation work.

[0064] 103. Using the corrected surface deformation data and adopting multi-period analysis technology, the development trend of surface subsidence is determined, and a surface subsidence distribution map is generated, wherein the surface subsidence distribution map is used to display the spatial distribution characteristics of surface subsidence; In this step, multi-period analysis technology refers to the technology of identifying the trend and development pattern of surface changes by comparing and analyzing the surface deformation data in different time periods. This method can more clearly show the changing process of surface subsidence and help to assess the speed and scope of subsidence.

[0065] In the embodiments of the present application, in the environmental protection of mining areas, multi-period analysis technology can be used to regularly monitor the surface subsidence around the mining area, timely discover potential geological disaster risks, take corresponding preventive measures, and protect the lives and property of residents in the mining area and surrounding areas.

[0066] Optionally, in step 103, "using the corrected surface deformation data and adopting a multi-period analysis technique to determine the development trend of surface settlement and generate a surface settlement distribution map" includes: using the corrected surface deformation data to organize into a multi-period surface deformation data set according to a time series, and using a Kalman filter algorithm to perform noise suppression processing on the surface deformation data set of each period to obtain an optimized surface deformation data set; based on the optimized surface deformation data set, applying the spatial analysis tool of the geographic information system to extract the key characteristic data of the surface settlement of each period; According to the data, the key feature data include the location of the settlement center, the maximum settlement amount, the settlement rate and the settlement range; using the key feature data, the autoregressive integral moving average model is used to comprehensively analyze the surface deformation data sets of multiple periods, and the wavelet transform technology is used to analyze the settlement patterns at different frequencies to determine the development trend of surface settlement, and generate a surface settlement development trend report; based on the key feature data and the development trend report, a surface settlement distribution map is drawn using three-dimensional visualization technology, and the surface settlement distribution map is used to show the spatial distribution characteristics of surface settlement.

[0067] Among them, the displayed spatial distribution characteristics include settlement area division, settlement intensity distribution, settlement rate distribution and time change. The settlement area division refers to dividing the surface settlement area into different sub-areas according to the settlement center position and settlement range; the settlement intensity distribution refers to displaying the maximum settlement amount and average settlement amount in each sub-area, and indicating the settlement intensity by color or height; the settlement rate distribution refers to displaying the settlement rate in each sub-area, and indicating the direction and magnitude of the settlement rate by color or arrow; the time change refers to displaying the surface settlement situation at different time points, and showing the dynamic changes of surface settlement through animation or time series diagram.

[0068] The development trend analysis of surface subsidence refers to identifying the trend and development pattern of surface subsidence by analyzing the changes in surface deformation data at multiple time points. The corrected surface deformation data is a more accurate data set obtained by removing the atmospheric delay effect and terrain undulation effect. Multi-period analysis technology refers to arranging the surface deformation data collected at different time points in chronological order to form a series of data sets to track the changes of the surface over time. The Kalman filter algorithm is a method for estimating the state of a system from a series of incomplete and noisy measurements. It is used here to reduce the noise in the surface deformation data. The autoregressive integrated moving average model (ARIMA) is a statistical model that is often used in time series data analysis and can help predict future trends. Wavelet transform technology is a signal processing technology that is suitable for analyzing different frequency components of non-stationary signals. The spatial analysis tools of the geographic information system (GIS) can be used to process and analyze geospatial data and extract key characteristic data of surface subsidence, such as the location of the subsidence center, the maximum subsidence, the subsidence rate, and the subsidence range. Three-dimensional visualization technology is used to create intuitive graphical representations to help understand and display the spatial distribution characteristics of surface subsidence.

[0069] First, the corrected surface deformation data are organized into multi-period data sets according to the time series, and the Kalman filter algorithm is applied to reduce the noise and obtain the optimized surface deformation data set. Then, based on the optimized data set, the spatial analysis tools of GIS are used to extract the key characteristic data of each period of surface settlement, including the location of the settlement center, the maximum settlement, the settlement rate and the settlement range. Next, the autoregressive integrated moving average model (ARIMA) combined with the wavelet transform technology is used to conduct a comprehensive analysis of the multi-period surface deformation data set to determine the development trend of the surface settlement and compile a development trend report. Finally, based on the extracted key characteristic data and the compiled development trend report, the surface settlement distribution map is drawn using three-dimensional visualization technology to intuitively show the spatial distribution characteristics of surface settlement, including the division of settlement areas, the distribution of settlement intensity, the distribution of settlement rate and time changes.

[0070] In the embodiment of the present application, it is assumed that in an urban groundwater resource management project, in order to evaluate the impact of groundwater exploitation on surface subsidence, the project team collected and corrected surface deformation data for multiple periods. First, they used the Kalman filter algorithm to perform noise suppression on these data to ensure the accuracy of the data. Then, through GIS software, the team extracted the key characteristic data of surface subsidence in each period, such as the location of the center of subsidence, the maximum subsidence, etc. Then, the project team used the ARIMA model combined with wavelet transform technology to analyze the subsidence pattern at different frequencies, revealing the development trend of surface subsidence over time, and compiled a detailed development trend report. Finally, based on the above analysis results, the team used three-dimensional visualization technology to produce a surface subsidence distribution map, which not only showed the division of the subsidence area, but also vividly showed the distribution of subsidence intensity and rate in the form of color and height, as well as the dynamic changes of surface subsidence at different time points. This distribution map has become an important reference for decision makers to formulate groundwater management and urban planning strategies.

[0071] 104. Based on the surface subsidence distribution map, the space-time fusion model is used to monitor future surface subsidence conditions and generate a surface subsidence monitoring report, which can provide a scientific basis for early warning and prevention of geological disasters.

[0072] In this step, the surface subsidence distribution map is a map drawn based on the corrected surface deformation data, which is used to intuitively display the spatial distribution characteristics of surface subsidence. This kind of map can help decision makers quickly understand which areas have more serious surface subsidence and need to be paid special attention. The surface subsidence monitoring report is a document compiled based on the surface subsidence distribution map and the analysis results of the spatiotemporal fusion model, aiming to provide a scientific basis for the early warning and prevention of geological disasters. The report usually contains information such as specific data, development trends, and possible problems of surface subsidence.

[0073] In the embodiments of the present application, in flood control projects in coastal cities, the data analysis results in the surface subsidence monitoring report can be used to prepare in advance for changes in groundwater levels caused by rising sea levels, optimize drainage system design, strengthen coastline protection measures, and effectively reduce the risk of flood disasters.

[0074] Optionally, the step 104 of "monitoring future surface subsidence conditions according to the surface subsidence distribution map using the space-time fusion model to generate a surface subsidence monitoring report" includes: extracting spatial distribution characteristics and degree of surface subsidence from the surface subsidence distribution map; based on the extracted spatial distribution characteristics and degree of surface subsidence, continuously monitoring and predicting future surface subsidence conditions using the space-time fusion model in combination with historical surface deformation data and real-time surface deformation data to obtain the latest short-term development trend and the latest long-term development trend; generating a surface subsidence monitoring report based on the latest short-term development trend and long-term development trend of surface subsidence, wherein the surface subsidence monitoring report records in detail the current status, the latest short-term development trend and the latest long-term development trend of surface subsidence.

[0075] Among them, the process of using the space-time fusion model, combined with historical surface deformation data and real-time surface deformation data, to continuously monitor and predict future surface subsidence to obtain the latest short-term development trend and the latest long-term development trend includes: using historical surface deformation data and real-time surface deformation data to prepare data and form a comprehensive data set; based on the comprehensive data set, updating the space-time fusion model to ensure that the space-time fusion model can capture the latest surface subsidence time and spatial change characteristics; using the updated space-time fusion model to perform short-term prediction on the most recent surface deformation data set to obtain the latest short-term development trend of surface subsidence; using the updated space-time fusion model to perform long-term prediction on multiple periods of surface deformation data to obtain the latest long-term development trend of surface subsidence.

[0076] Surface subsidence monitoring refers to the process of continuously monitoring and predicting future surface subsidence by analyzing the spatial distribution characteristics and degree of surface subsidence, combining historical surface deformation data with real-time surface deformation data, and using a spatiotemporal fusion model. The spatial distribution characteristics of surface subsidence include the location, scope, and layout of the subsidence area on the map, while the degree of subsidence involves indicators such as the depth and speed of subsidence. The spatiotemporal fusion model is a prediction model that can simultaneously consider factors of time and space dimensions, aiming to improve the accuracy and reliability of predictions. Historical surface deformation data refers to surface deformation data collected over a period of time in the past, while real-time surface deformation data refers to data obtained recently. The combination of the two helps to more fully understand the development trend of surface subsidence. The surface subsidence monitoring report is a detailed document that records the current status of surface subsidence, the latest short-term development trends, and long-term development trends, providing a scientific basis for decision-making.

[0077] First, the spatial distribution characteristics and degree of surface subsidence are extracted from the surface subsidence distribution map. Then, based on this extracted information, combined with historical surface deformation data and real-time surface deformation data, the spatiotemporal fusion model is used to continuously monitor and predict future surface subsidence. Specifically, this includes the data preparation stage, which integrates historical and real-time data to form a comprehensive data set; then, the spatiotemporal fusion model is updated based on the comprehensive data set to ensure that the model can capture the latest temporal and spatial variation characteristics of surface subsidence. Next, the updated model is used to make short-term predictions on the most recent period of surface deformation data to obtain the latest short-term development trends; at the same time, long-term predictions are also made on multiple periods of surface deformation data to determine the latest long-term development trends. Finally, a surface subsidence monitoring report is generated based on these prediction results, which records the current situation and development trends in detail.

[0078] In the embodiment of the present application, it is assumed that in a surface subsidence monitoring project in a coastal city, the research team first extracted the spatial distribution characteristics and degree of surface subsidence from the surface subsidence distribution map previously produced, such as which areas have the most serious subsidence and how fast the subsidence is. Subsequently, the team integrated the historical surface deformation data of the past few years with the real-time surface deformation data of the last few months to form a comprehensive data set. Based on this comprehensive data set, the team updated their spatiotemporal fusion model to better reflect the temporal and spatial variation characteristics of the current surface subsidence. Using the updated model, the team made short-term predictions for the most recent period of surface deformation data and predicted the surface subsidence trend in the next few months; at the same time, it also made long-term predictions for the past multi-period data and predicted the surface subsidence development in the next few years. Based on these prediction results, the team compiled a detailed surface subsidence monitoring report, which not only summarizes the current surface subsidence status, but also provides the latest short-term and long-term development trends, providing important data support for urban planning and disaster prevention and mitigation.

[0079] Optionally, the development trend of the surface subsidence includes a long-term development trend and a short-term development trend; the use of the key feature data, the use of the autoregressive integral moving average model to comprehensively analyze the surface deformation data sets of multiple periods, and the wavelet transform technology is combined to analyze the settlement patterns at different frequencies to determine the development trend of the surface subsidence, and generate a surface subsidence development trend report, including: using the key feature data to construct an autoregressive integral moving average model, the autoregressive integral moving average model refers to a model that can reflect the time series characteristics of surface subsidence; based on the autoregressive integral moving average model, a comprehensive analysis of the surface deformation data sets of multiple periods is performed to obtain the long-term development trend and periodic changes of surface subsidence, wherein the long-term development trend of surface subsidence refers to the surface on a longer time scale. The changes in settlement; the periodic changes refer to the regular changes in surface settlement that occur repeatedly within a certain time period; using wavelet transform technology, multi-resolution analysis is performed on multiple periods of surface deformation data sets to obtain settlement patterns at different time scales. The settlement pattern refers to the manifestation and characteristics of surface settlement in time and space, including seasonal changes, short-term fluctuations and other periodic characteristics; the short-term development trend of surface settlement is determined by combining the long-term development trend, the periodic changes and the settlement pattern; wherein the short-term development trend of surface settlement refers to the changes in surface settlement on a shorter time scale; based on the long-term development trend and short-term development trend of surface settlement, an analysis conclusion of surface settlement is generated; based on the analysis conclusion, a development trend report of surface settlement is generated.

[0080] The development trend of surface subsidence includes long-term development trend and short-term development trend. The long-term development trend refers to the overall change direction of surface subsidence over a longer time span (such as several years or longer), reflecting the basic direction of surface subsidence. The short-term development trend refers to the specific changes in surface subsidence on a shorter time scale (such as a few months or a year), which is usually affected by factors such as seasonal changes and short-term fluctuations. The autoregressive integrated moving average model (ARIMA model) is a statistical method used to process time series data, especially suitable for data with trends and seasonality. The model captures the characteristics of the time series through the autoregressive part (AR), the difference part (I) and the moving average part (MA). Wavelet transform technology is a signal processing method that can perform multi-resolution analysis on data. It is suitable for analyzing non-stationary signals and can effectively reveal the settlement patterns at different time scales, including seasonal changes, short-term fluctuations and other periodic characteristics.

[0081] First, the key characteristic data of surface subsidence are used to construct an ARIMA model, which can reflect the characteristics of the surface subsidence time series. Based on the constructed ARIMA model, a comprehensive analysis of multi-period surface deformation data sets is performed to identify the long-term development trend and periodic changes of surface subsidence. The long-term development trend reveals the overall change direction of surface subsidence over time, while the periodic change shows the regular changes of surface subsidence within a specific time period. Next, the wavelet transform technology is used to perform multi-resolution analysis on the same data set to identify subsidence patterns at different time scales, including seasonal changes, short-term fluctuations, etc. Finally, the short-term development trend of surface subsidence is determined by combining the long-term development trend, periodic changes and subsidence patterns. Based on these analysis results, a surface subsidence development trend report is generated, which summarizes the current status of surface subsidence and its possible future direction.

[0082] In the embodiment of the present application, it is assumed that a city is experiencing a significant surface subsidence problem. In order to better understand and predict this phenomenon, the relevant departments decided to adopt the above scheme. First, they collected the key characteristic data of the city's surface subsidence in the past ten years, such as subsidence rate, subsidence area, etc., and used these data to build an ARIMA model. Through the analysis of the model, they found that there is an obvious long-term downward trend in surface subsidence, and the acceleration of subsidence caused by the increase in groundwater extraction every summer is also very obvious. Next, the research team used wavelet transform technology to perform multi-resolution analysis on the same data set, and found that in addition to the significant subsidence in summer, there is also a slight recovery trend in winter, which may be due to the natural rise of groundwater levels in winter. Combining these long-term trends, cyclical changes and subsidence patterns, the research team predicted the short-term development trend of surface subsidence in the next year, and it is expected that summer subsidence will further intensify. Based on the above analysis, the relevant departments generated a detailed surface subsidence development trend report, which not only provides an important reference for urban water resources management and land use planning, but also provides a scientific basis for public safety and disaster prevention.

[0083] Optionally, the short-term development trend of surface subsidence is determined by integrating the long-term development trend, the periodic changes and the settlement pattern, including: using the long-term development trend, the periodic changes and the settlement pattern to form a comprehensive data set; selecting a time series analysis model suitable for short-term prediction based on the comprehensive data set, and training the selected time series analysis model to ensure that the time series analysis model can capture the short-term change characteristics of surface subsidence; based on the trained time series analysis model, performing short-term prediction on the most recent surface deformation data set to obtain the short-term development trend of surface subsidence.

[0084] The short-term development trend of surface subsidence refers to the specific changes in surface subsidence in a short period of time (such as a few months or a year), which is usually affected by factors such as seasonal changes and short-term fluctuations. The long-term development trend refers to the overall change direction of surface subsidence over a longer time span (such as several years or longer), reflecting the basic trend of surface subsidence. Periodic changes refer to the regular changes in surface subsidence that occur repeatedly within a certain time period, such as seasonal changes. Subsidence patterns refer to the manifestations and characteristics of surface subsidence in time and space, including seasonal changes, short-term fluctuations and other periodic characteristics. A comprehensive data set is a data set composed of multiple data such as long-term development trends, periodic changes and subsidence patterns, which is used to comprehensively reflect the situation of surface subsidence. A time series analysis model is a statistical model used to predict the future values ​​of time series data. Common models include ARIMA and exponential smoothing.

[0085] First, a comprehensive data set is formed using long-term development trends, periodic changes, and settlement patterns. This data set contains information on changes in surface settlement on various time scales, providing a basis for subsequent analysis. Then, based on the characteristics of the comprehensive data set, a time series analysis model suitable for short-term prediction is selected and trained to ensure that the model can capture the short-term variation characteristics of surface settlement. Commonly used models include ARIMA and exponential smoothing. Finally, based on the trained time series analysis model, a short-term prediction is made for the most recent surface deformation data set to obtain the short-term development trend of surface settlement. This process can help decision makers keep abreast of the latest developments in surface settlement and provide a basis for taking appropriate management and response measures.

[0086] In the embodiment of the present application, it is assumed that a city is experiencing a significant surface subsidence problem. In order to predict the surface subsidence in the next few months, the relevant departments decided to adopt the above scheme. First, they integrated the surface subsidence data of the past ten years, including long-term development trends, seasonal changes and short-term fluctuations, to form a comprehensive data set. Then, according to the characteristics of the comprehensive data set, the ARIMA model was selected as the time series analysis model for short-term prediction, and the model was trained. During the training process, they ensured that the model could accurately capture the short-term variation characteristics of surface subsidence, especially seasonal changes and short-term fluctuations. Finally, based on the trained ARIMA model, the surface deformation data set of the most recent period was predicted in the short term, and the short-term development trend of surface subsidence in the next 6 months was obtained. The prediction results show that surface subsidence will intensify in summer, especially in areas with large groundwater extraction. Based on these prediction results, the relevant departments have formulated specific response measures, such as adjusting the groundwater extraction plan, strengthening monitoring and early warning, etc., to reduce the negative impact of surface subsidence. This short-term development trend report provides an important scientific basis for urban water resources management and land use planning.

[0087] Optionally, generating an analysis conclusion of the surface subsidence based on the long-term development trend and the short-term development trend of the surface subsidence includes: generating a short-term analysis conclusion of the surface subsidence based on the short-term development trend of the surface subsidence, wherein the short-term development trend includes the rate of change of recent data, the fluctuation of recent data and the periodic change of recent data; generating a long-term analysis conclusion of the surface subsidence based on the long-term development trend of the surface subsidence, wherein the long-term development trend includes the rate of change of historical data, the fluctuation of historical data and the periodic change of historical data; generating an analysis conclusion of the surface subsidence based on the long-term analysis conclusion of the surface subsidence and the short-term analysis conclusion of the surface subsidence.

[0088] Among them, generating a short-term analysis conclusion of the surface subsidence based on the short-term development trend of the surface subsidence includes: using the change rate of recent data to analyze the surface subsidence, judging whether the surface subsidence is accelerating or decelerating, and generating a first conclusion based on the judgment result; using the fluctuation of recent data to analyze the surface subsidence, judging whether the surface subsidence is in a stable state or unstable, and generating a second conclusion based on the judgment result; using the periodic changes of recent data to analyze the surface subsidence, judging whether the surface subsidence has periodic characteristics, and generating a third conclusion based on the judgment result; generating a short-term analysis conclusion of the surface subsidence based on the first conclusion, the second conclusion and the third conclusion.

[0089] Among them, generating a long-term analysis conclusion of surface subsidence based on the long-term development trend of the surface subsidence includes: using the change rate of historical data to analyze the surface subsidence, judging the long-term change direction of the surface subsidence, and generating a fourth conclusion based on the judgment result; using the fluctuation of historical data to analyze the surface subsidence, judging the long-term stability of the surface subsidence, and generating a fifth conclusion based on the judgment result; using the periodic changes of historical data to analyze the surface subsidence, judging the long-term periodic characteristics of the surface subsidence, and generating a sixth conclusion based on the judgment result; generating a long-term analysis conclusion of surface subsidence based on the fourth conclusion, the fifth conclusion and the sixth conclusion.

[0090] The analysis conclusion of surface subsidence refers to the conclusion drawn after a comprehensive analysis of the short-term and long-term development trends of surface subsidence, which is used to describe the current state of surface subsidence and its future change trend. Short-term development trends include the change rate, fluctuations and periodic changes of recent data, which are used to evaluate the specific changes of surface subsidence in the short term. Long-term development trends include the change rate, fluctuations and periodic changes of historical data, which are used to evaluate the overall change direction of surface subsidence over a longer time span. The change rate refers to the speed of change of surface subsidence per unit time, the fluctuation refers to the degree of fluctuation of surface subsidence in the time series, and the periodic change refers to the repeated regular changes of surface subsidence in a specific time period.

[0091] First, according to the short-term development trend of surface subsidence, a short-term analysis conclusion is generated. The specific steps include: using the change rate of recent data to determine whether the surface subsidence is accelerating or decelerating, and generating the first conclusion; using the fluctuation of recent data to determine whether the surface subsidence is in a stable state or unstable, and generating the second conclusion; using the periodic changes of recent data to determine whether the surface subsidence has periodic characteristics, and generating the third conclusion. Then, the first, second, and third conclusions are combined to generate a short-term analysis conclusion of surface subsidence. Secondly, according to the long-term development trend of surface subsidence, a long-term analysis conclusion is generated. The specific steps include: using the change rate of historical data to determine the long-term change direction of surface subsidence, and generating the fourth conclusion; using the fluctuation of historical data to determine the long-term stability of surface subsidence, and generating the fifth conclusion; using the periodic changes of historical data to determine the long-term periodic characteristics of surface subsidence, and generating the sixth conclusion. Then, the fourth, fifth, and sixth conclusions are combined to generate a long-term analysis conclusion of surface subsidence. Finally, based on the short-term analysis conclusions and the long-term analysis conclusions, the final surface subsidence analysis conclusion is generated, which comprehensively describes the current state of surface subsidence and its future change trend.

[0092] In the example of this application, it is assumed that a city has experienced significant surface subsidence in the past few years. In order to better understand this phenomenon and predict its future trends, the relevant departments decided to use the above scheme for analysis. First, they collected surface subsidence data for the past year, including monthly subsidence rates, fluctuations, and periodic changes. By analyzing these data, they found that:

[0093] Short-term analysis: First conclusion: The rate of change of recent data shows that surface subsidence accelerates in summer and slows down in winter. Second conclusion: The fluctuation of recent data shows that surface subsidence has large fluctuations in some months, especially during the rainy season. Third conclusion: The cyclical changes of recent data show that there is a clear seasonal variation in surface subsidence, with acceleration in summer and easing in winter.

[0094] Combining the above three conclusions, the short-term analysis conclusion of surface subsidence is generated: surface subsidence shows obvious seasonal changes in the short term, accelerating in summer and slowing down in winter, and there are large fluctuations during the rainy season.

[0095] The fourth conclusion of the long-term analysis: The rate of change of historical data shows that the surface subsidence has generally been declining in the past decade, but the rate of decline has slowed down in recent years. The fifth conclusion: The fluctuation of historical data shows that the surface subsidence has large fluctuations in some years, especially in years with large groundwater extraction. The sixth conclusion: The cyclical changes in historical data show that surface subsidence has certain cyclical characteristics, especially the cyclical changes related to groundwater extraction and seasonal rainfall.

[0096] Combining the above three conclusions, the long-term analysis conclusion of surface subsidence is generated: surface subsidence shows a downward trend in the long term, but the rate of decline is gradually slowing down, and there are large fluctuations in years with large groundwater extraction, showing periodic characteristics related to groundwater extraction and seasonal rainfall.

[0097] Combining the short-term and long-term analysis conclusions, the final surface subsidence analysis conclusions were generated: surface subsidence showed obvious seasonal changes in the short term, accelerating in summer and slowing down in winter, and there were large fluctuations during the rainy season. In the long term, surface subsidence showed a downward trend, but the rate of decline gradually slowed down, and there were large fluctuations in years with large groundwater extraction, showing periodic characteristics related to groundwater extraction and seasonal rainfall.

[0098] This application takes into account that with the acceleration of urbanization, the development and utilization of underground space are becoming more and more frequent, and surface subsidence has become an issue that cannot be ignored. Surface subsidence not only affects the safety of buildings, but may also cause a series of environmental problems. Therefore, accurate prediction of surface subsidence is of great significance for urban planning and environmental protection. As a time series analysis method, the autoregressive integrated moving average model (ARIMA) has good performance in processing non-stationary time series data and is widely used in many fields such as economy, meteorology, and engineering. This case applies the ARIMA model to the prediction of surface subsidence, aiming to improve the prediction accuracy.

[0099] Optionally, using the key feature data to construct an autoregressive integrated moving average model includes: Using the key feature data, an autoregressive integrated moving average model is constructed, wherein the key feature data includes the center position of the settlement , Maximum settlement , sedimentation rate And settlement range The autoregressive integrated moving average model refers to a model that can reflect the time series characteristics of surface settlement. The model formula of the autoregressive integrated moving average model is defined as: in, Surface subsidence over time Observed value of The constant term of the model represents the average level of surface subsidence The order of the autoregressive term, which indicates the lag order of the autoregressive part of the model The autoregressive coefficient indicates the surface subsidence over time. and the relationship between; The order of the moving average term, which indicates the lag order of the moving average part of the model Moving average coefficient, which indicates the surface settlement over time and The error relationship between time The white noise error term represents the random disturbance Center of settlement The coefficient of Maximum settlement The coefficient of Sedimentation rate The coefficient of Settlement range The coefficient of .

[0100] The above formula is based on the ARIMA model and is especially extended for the problem of surface subsidence. The traditional ARIMA model is mainly used to process one-dimensional time series data, while surface subsidence is affected by many factors, such as the location of the subsidence center ( )、Maximum Settlement( ), sedimentation rate ( ) and the settlement range ( ) etc. These key characteristic data directly reflect the physical characteristics of surface subsidence. Incorporating them into the model can more accurately capture the time series characteristics of surface subsidence, thereby improving the accuracy of prediction.

[0101] The following is a brief introduction to the design reasons of each sub-item of the formula: Autoregressive term :Surface subsidence is a time-dependent process, that is, the current subsidence is affected by the past subsidence. By introducing the autoregressive term, this temporal dependency can be captured, allowing the model to better predict future trends. It is usually determined by analyzing the autocorrelation function (ACF) and partial autocorrelation function (PACF) of the data to find the optimal number of lags.

[0102] Constant term : represents the long-term average level of surface subsidence, which reflects the overall trend of surface subsidence. The introduction of constant terms can ensure that the model can still reflect the basic level of surface subsidence when there is no other external influence. This is very important for the stability and interpretability of the model.

[0103] Settlement characteristic variables and its coefficient :Surface subsidence is not only a function of time, but also affected by a variety of geographical and physical factors. Location of the center of subsidence , Maximum settlement , sedimentation rate and settlement range It is the specific embodiment of these factors. By introducing these variables and their coefficients, the complexity of surface subsidence can be more comprehensively described and the prediction accuracy of the model can be improved. The influence of these characteristic variables on surface subsidence is determined through data analysis and fitting.

[0104] Moving Average : Moving average term is used to capture the random error term in the model There may be some random disturbances in the surface settlement process, and these disturbances may have a certain correlation in time. By introducing the moving average term, the impact of these random fluctuations on the prediction results can be reduced, making the model more robust. The order of the moving average term The same is determined by analyzing the ACF and PACF of the data.

[0105] White noise error term : represents random disturbances that cannot be explained by the model. In any practical application, there will always be a certain amount of uncertainty, which can be modeled by white noise error terms. Introducing white noise error terms can make the model more consistent with the actual situation and improve the reliability of the prediction.

[0106] The following is a brief introduction to how to obtain the parameters of the formula: Center of settlement , Maximum settlement , sedimentation rate And settlement range :These data can usually be obtained through geological surveys, remote sensing monitoring or ground measurements. For example, high-precision surface subsidence data can be obtained through satellite radar interferometry (InSAR).

[0107] Autoregressive coefficient , moving average coefficient , settlement characteristic variable coefficient and constant term :These parameters need to be fitted through historical surface settlement data. Commonly used methods include least squares method, maximum likelihood estimation, etc.

[0108] In the embodiment of the present application, it is assumed that there is a set of surface subsidence data, which contains monthly observations for the past 5 years. In order to simplify the calculation, a simplified example is set:

[0109] Model parameter settings: (i.e. first-order autoregression and first-order moving average) Assuming the parameters are known: Key feature data: Last period observations: Error term of the previous period: According to the above settings, the predicted value of surface settlement in period t can be calculated: as follows: The calculation results show that based on the given key characteristic data and model parameters, the expected surface subsidence in the next period (period t) is about 2.405 units. This result shows that considering the historical trend of surface subsidence and the influence of key characteristics, surface subsidence is expected to continue to develop at a certain rate. This prediction can help urban planners and decision makers take preventive measures to mitigate the negative impact of surface subsidence. However, it should be noted that in actual applications, more real-time data and environmental conditions need to be combined for adjustment to ensure the accuracy of the prediction.

[0110] Figure 2 A schematic diagram of a surface subsidence monitoring system integrating Beidou and InSAR data is provided for the present application embodiment. Figure 2 As shown, the device comprises: A receiving module 21 is used to receive positioning data from the Beidou satellite system and surface deformation data obtained by the synthetic aperture radar interferometry system; A construction module 22 is used to construct a spatiotemporal fusion model based on the positioning data and the surface deformation data, so as to correct the surface deformation data according to environmental factors through the spatiotemporal fusion model to obtain corrected surface deformation data, wherein the environmental factors at least include atmospheric delay and terrain undulation; The generation module 23 is used to use the corrected surface deformation data and multi-period analysis technology to determine the development trend of surface subsidence and generate a surface subsidence distribution map, which is used to show the spatial distribution characteristics of surface subsidence; based on the surface subsidence distribution map, the space-time fusion model is used to monitor future surface subsidence and generate a surface subsidence monitoring report, which can provide a scientific basis for early warning and prevention of geological disasters.

[0111] Figure 2 The surface subsidence monitoring system integrating Beidou and InSAR data can be executed Figure 1 The implementation principle and technical effect of the surface subsidence monitoring method integrating Beidou and InSAR data described in the illustrated embodiment are not repeated here. The specific manner in which each module and unit performs operations in the surface subsidence monitoring system integrating Beidou and InSAR data in the above embodiment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0112] In one possible design, Figure 2 A surface subsidence monitoring system integrating Beidou and InSAR data in the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32; The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0113] The processing component 32 is used to: receive positioning data from the Beidou satellite system and surface deformation data obtained by the synthetic aperture radar interferometry system; based on the positioning data and the surface deformation data, correct the surface deformation data according to environmental factors through a spatiotemporal fusion model to obtain corrected surface deformation data; use the corrected surface deformation data and a multi-period analysis technique to determine the development trend of surface subsidence and generate a surface subsidence distribution map, which is used to show the spatial distribution characteristics of surface subsidence; based on the surface subsidence distribution map, use the spatiotemporal fusion model to monitor future surface subsidence conditions, and the surface subsidence monitoring report can provide a scientific basis for early warning and prevention of geological disasters.

[0114] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0115] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0116] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0117] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc.

[0118] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0119] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0120] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The illustrated embodiment is a method for monitoring surface subsidence by integrating Beidou and InSAR data.

[0121] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0122] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0123] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0124] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, 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 application.

Claims

1. A surface subsidence monitoring method integrating Beidou and InSAR data, characterized in that: include: Receive positioning data from the BeiDou satellite system and surface deformation data obtained by the synthetic aperture radar interferometry system; Based on the positioning data and the surface deformation data, a spatiotemporal fusion model is constructed to correct the surface deformation data according to environmental factors through the spatiotemporal fusion model to obtain corrected surface deformation data, wherein the environmental factors at least include atmospheric delay and terrain undulation; Using the corrected surface deformation data and adopting multi-period analysis technology, the development trend of surface subsidence is determined, and a surface subsidence distribution map is generated, which is used to show the spatial distribution characteristics of surface subsidence; According to the surface subsidence distribution map, the space-time fusion model is used to monitor future surface subsidence and generate a surface subsidence monitoring report, which can provide a scientific basis for early warning and prevention of geological disasters.

2. The method according to claim 1, characterized in that The modified surface deformation data is used to determine the development trend of surface subsidence by adopting multi-period analysis technology, and generate a surface subsidence distribution map, including: The corrected surface deformation data are organized into multiple-period surface deformation data sets according to the time series, and the Kalman filter algorithm is used to perform noise suppression on the surface deformation data sets of each period to obtain the optimized surface deformation data set. Based on the optimized surface deformation data set, the spatial analysis tools of the geographic information system are used to extract the key characteristic data of each period of surface settlement, which include the settlement center position, maximum settlement amount, settlement rate and settlement range; Using the key characteristic data, the autoregressive integrated moving average model is used to comprehensively analyze the surface deformation data sets of multiple periods, and the settlement patterns under different frequencies are analyzed in combination with the wavelet transform technology to determine the development trend of surface settlement and generate a development trend report of surface settlement; Based on the key feature data and the development trend report, a surface subsidence distribution map is drawn using three-dimensional visualization technology, wherein the surface subsidence distribution map is used to display the spatial distribution characteristics of surface subsidence; Among them, the displayed spatial distribution characteristics include settlement area division, settlement intensity distribution, settlement rate distribution and time change; the settlement area division refers to dividing the surface settlement area into different sub-areas according to the settlement center position and settlement range; the settlement intensity distribution refers to displaying the maximum settlement amount and average settlement amount in each sub-area, and expressing the settlement intensity with color or height; the settlement rate distribution refers to displaying the settlement rate in each sub-area, and expressing the direction and size of the settlement rate with color or arrows; the time change refers to displaying the surface settlement conditions at different time points, and showing the dynamic changes of surface settlement through animation or time series graphs.

3. The method according to claim 2, characterized in that The development trend of the surface subsidence includes a long-term development trend and a short-term development trend; The key characteristic data is used to comprehensively analyze the multi-period surface deformation data set using an autoregressive integrated moving average model, and the settlement patterns at different frequencies are analyzed in combination with wavelet transform technology to determine the development trend of surface settlement, and generate a surface settlement development trend report, including: Using the key characteristic data, an autoregressive integrated moving average model is constructed, wherein the autoregressive integrated moving average model refers to a model that can reflect the time series characteristics of surface settlement; Based on the autoregressive integrated moving average model, a comprehensive analysis of multiple periods of surface deformation data sets is performed to obtain the long-term development trend and periodic changes of surface subsidence, wherein the long-term development trend of surface subsidence refers to the changes in surface subsidence on a longer time scale; the periodic changes refer to the regular changes in surface subsidence that occur repeatedly within a certain time period; Using wavelet transform technology, multi-resolution analysis is performed on multi-period surface deformation data sets to obtain settlement patterns at different time scales. The settlement pattern refers to the manifestation and characteristics of surface settlement in time and space, including seasonal changes, short-term fluctuations and other periodic characteristics. Determine the short-term development trend of surface subsidence by comprehensively considering the long-term development trend, the periodic changes and the subsidence pattern; wherein the short-term development trend of surface subsidence refers to the change of surface subsidence on a shorter time scale; Generating an analysis conclusion of the surface subsidence according to the long-term development trend and short-term development trend of the surface subsidence; Based on the analysis conclusions, a development trend report of surface subsidence is generated.

4. The method according to claim 3, characterized in that: The method of determining the short-term development trend of surface subsidence by integrating the long-term development trend, the periodic change and the subsidence pattern includes: utilizing the long-term trends, the cyclical changes, and the sedimentation patterns to form a comprehensive data set; Selecting a time series analysis model suitable for short-term prediction based on the comprehensive data set, and training the selected time series analysis model to ensure that the time series analysis model can capture the short-term variation characteristics of surface subsidence; Based on the trained time series analysis model, the short-term prediction of the most recent surface deformation data set is carried out to obtain the short-term development trend of surface subsidence.

5. The method according to claim 3, characterized in that: The analysis conclusion of the surface subsidence is generated according to the long-term development trend and short-term development trend of the surface subsidence, including: Generating a short-term analysis conclusion of the surface subsidence according to the short-term development trend of the surface subsidence, wherein the short-term development trend includes the rate of change of recent data, the fluctuation of recent data and the periodic change of recent data; Generate a long-term analysis conclusion of the surface subsidence according to the long-term development trend of the surface subsidence, wherein the long-term development trend includes the change rate of historical data, the fluctuation of historical data and the periodic change of historical data; generating an analysis conclusion of the surface subsidence according to the long-term analysis conclusion of the surface subsidence and the short-term analysis conclusion of the surface subsidence; Wherein, generating a short-term analysis conclusion of the surface subsidence according to the short-term development trend of the surface subsidence includes: analyzing the surface subsidence by using the change rate of recent data to determine whether the surface subsidence is accelerating or decelerating, and generating a first conclusion according to the determination result; analyzing the surface subsidence by using the fluctuation of recent data to determine whether the surface subsidence is in a stable state or unstable, and generating a second conclusion according to the determination result; analyzing the surface subsidence by using the periodic changes of recent data to determine whether the surface subsidence has periodic characteristics, and generating a third conclusion according to the determination result; generating a short-term analysis conclusion of the surface subsidence according to the first conclusion, the second conclusion and the third conclusion; Among them, generating a long-term analysis conclusion of surface subsidence based on the long-term development trend of the surface subsidence includes: using the change rate of historical data to analyze the surface subsidence, judging the long-term change direction of the surface subsidence, and generating a fourth conclusion based on the judgment result; using the fluctuation of historical data to analyze the surface subsidence, judging the long-term stability of the surface subsidence, and generating a fifth conclusion based on the judgment result; using the periodic changes of historical data to analyze the surface subsidence, judging the long-term periodic characteristics of the surface subsidence, and generating a sixth conclusion based on the judgment result; generating a long-term analysis conclusion of surface subsidence based on the fourth conclusion, the fifth conclusion and the sixth conclusion.

6. The method according to claim 1, characterized in that The method of monitoring future surface subsidence conditions using the spatiotemporal fusion model according to the surface subsidence distribution map and generating a surface subsidence monitoring report includes: Extracting the spatial distribution characteristics and the degree of surface subsidence from the surface subsidence distribution map; Based on the extracted spatial distribution characteristics and degree of surface subsidence, the spatiotemporal fusion model is used to continuously monitor and predict future surface subsidence in combination with historical surface deformation data and real-time surface deformation data, so as to obtain the latest short-term development trend and the latest long-term development trend; The process of using the spatiotemporal fusion model, in combination with historical surface deformation data and real-time surface deformation data, to continuously monitor and predict future surface subsidence to obtain the latest short-term development trend and the latest long-term development trend includes: using historical surface deformation data and real-time surface deformation data to prepare data and form a comprehensive data set; based on the comprehensive data set, updating the spatiotemporal fusion model to ensure that the spatiotemporal fusion model can capture the latest temporal and spatial variation characteristics of surface subsidence; using the updated spatiotemporal fusion model to perform short-term prediction on the most recent period of surface deformation data set to obtain the latest short-term development trend of surface subsidence; using the updated spatiotemporal fusion model to perform long-term prediction on multiple periods of surface deformation data to obtain the latest long-term development trend of surface subsidence; Based on the latest short-term development trend and long-term development trend of surface subsidence, a surface subsidence monitoring report is generated, and the surface subsidence monitoring report records in detail the current status of surface subsidence, the latest short-term development trend and the latest long-term development trend.

7. The method according to claim 1, characterized in that The surface deformation data is corrected according to environmental factors by the spatiotemporal fusion model to obtain the corrected surface deformation data, including: Using the spatiotemporal fusion model, combined with preset meteorological data and atmospheric models, the atmospheric delay effect in the surface deformation data is corrected to reduce the influence of the atmospheric delay on the surface deformation data, and obtain preliminary corrected surface deformation data; The spatiotemporal fusion model is used in combination with preset elevation data and terrain models to correct the terrain undulation effect in the initially corrected surface deformation data, reduce the impact of terrain undulation on the surface deformation data, and obtain the final corrected surface deformation data.

8. A surface subsidence monitoring system integrating Beidou and InSAR data, characterized in that: include: A receiving module is used to receive positioning data from the Beidou satellite system and surface deformation data obtained by the synthetic aperture radar interferometry system; A construction module, configured to construct a spatiotemporal fusion model based on the positioning data and the surface deformation data, so as to correct the surface deformation data according to environmental factors through the spatiotemporal fusion model to obtain corrected surface deformation data, wherein the environmental factors at least include atmospheric delay and terrain undulation; A generation module is used to use the corrected surface deformation data and multi-period analysis technology to determine the development trend of surface subsidence and generate a surface subsidence distribution map, which is used to show the spatial distribution characteristics of surface subsidence; based on the surface subsidence distribution map, the time-space fusion model is used to monitor future surface subsidence and generate a surface subsidence monitoring report, which can provide a scientific basis for early warning and prevention of geological disasters.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a surface subsidence monitoring method that integrates Beidou and InSAR data as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, a surface subsidence monitoring method integrating Beidou and InSAR data as described in any one of claims 1 to 7 is implemented.

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