Multi-source data fusion region settlement detection method and device

Abstract

The invention provides a multi-source data fusion-based region settlement detection method and device. The method comprises the following steps: carrying out deviation analysis based on GNSS data and level data of area settlement monitoring; under the condition that the deviation between the GNSS data and the leveling data is greater than a first threshold value, correcting the leveling data based on the GNSS data by utilizing a dynamic adjustment method; and based on the corrected level data, carrying out data fusion on the corrected level data and the InSAR data by utilizing a trend surface method to obtain target data, and generating a monitoring report of the area settlement based on the target data. According to the invention, the level data is corrected through the GNSS data, and the data fusion is carried out based on the corrected level data and the InSAR data to obtain the final area settlement monitoring data, so that multi-source data fusion monitoring is realized, and the problem of large deviation of area settlement monitoring through a single data element in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of data fusion, and in particular to a multi-source data fusion surface area settlement detection method, a multi-source data fusion surface area settlement detection device, a computer-readable storage medium and a surface area settlement monitoring system. Background Art

[0002] In areas rich in coal resources, monitoring of ground subsidence after coal mining has always been regarded as a key link. Ground subsidence not only affects the sustainable development of mining areas, but may also cause geological disasters, which has a serious impact on the surrounding environment and residents' lives. Therefore, the region has always been committed to developing and applying efficient and accurate ground subsidence monitoring technology. Traditional leveling is one of the widely used monitoring methods. With the continuous advancement of science and technology, advanced monitoring methods such as the Global Navigation Satellite System (GNSS), Interferometric Synthetic Aperture Radar (InSAR) and layered markers have also been gradually introduced and have shown their respective advantages in different application scenarios. Leveling has the advantages of high precision and stable and reliable data, but it requires a large number of monitoring points to be deployed, which is labor-intensive and difficult to cover large areas. GNSS measurement can operate all-weather and provide high-precision point data, but its point distribution has a great impact on the monitoring results and poor integrity. InSAR measurement can monitor a large range of ground subsidence with good integrity, but the accuracy is relatively low and is affected by factors such as surface vegetation and buildings. Layered markers can monitor the settlement of strata at different depths and provide more comprehensive settlement information, but they are expensive, complex to install, and have a limited monitoring range.

[0003] Although the above monitoring technologies have performed well in their respective application scenarios, there are still some technical challenges. Leveling and GNSS measurements can only provide point-type data, while InSAR measurements provide surface-type data. The inconsistency of data types leads to differences in monitoring results in time and space dimensions. It is difficult to obtain both high-precision point data and large-area coverage. The application of a single technology cannot fully reflect the ground subsidence situation. In order to obtain ground subsidence information more accurately and comprehensively, it is necessary to perform data fusion processing on different types of data to provide more reasonable and effective data support. Existing technologies mainly focus on a single monitoring technology or the fusion of simple data types. Ground subsidence monitoring technology with a single data source often cannot take into account the needs of high precision and large-area coverage in surface monitoring, resulting in large deviations in monitoring results. Summary of the invention

[0004] The main purpose of the present application is to provide a multi-source data fusion surface settlement detection method, a multi-source data fusion surface settlement detection device, a computer-readable storage medium and a surface settlement monitoring system, so as to at least solve the problem of large deviation in surface settlement monitoring through a single data element.

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a multi-source data fusion surface subsidence detection method is provided, including: performing deviation analysis based on GNSS data and leveling data for surface subsidence monitoring; when the deviation between the GNSS data and the leveling data is greater than a first threshold, correcting the leveling data based on the GNSS data using a dynamic adjustment method; based on the corrected leveling data, fusing the corrected leveling data with InSAR data using a trend surface method to obtain target data, and generating a surface subsidence monitoring report based on the target data.

[0006] Optionally, performing deviation analysis based on GNSS data and leveling data for surface settlement monitoring includes: substituting the GNSS data and the leveling data into a first preset formula to calculate an average error: Where θ is the average error, N is the total number of monitoring points, and d G is the GNSS data, d L is the leveling data, i is the monitoring point; the GNSS data and the leveling data are substituted into the second preset formula to calculate the standard deviation: Wherein, m is the standard deviation.

[0007] Optionally, before the leveling data is corrected based on the GNSS data using the dynamic adjustment method, the method further comprises: constructing a surface settlement observation equation based on the GNSS data to obtain a first observation equation: L Gi +V Gi =a 0 +b 0 T i +A 1 cos(2πT i )+B 1 sin(2πT i ), where L Gi is the elevation component observation value of the i-th GNSS monitoring point during the observation time, V Gi For L Gi The corresponding residual, a 0 is the mean value of the elevation component within the observation time, b 0 is the annual rate of change of the elevation component during the observation time, T i is the observation time, A 1 is the amplitude of the cosine component of the annual cycle, B1 is the amplitude of the sinusoidal component of the annual periodic variation.

[0008] Optionally, before correcting the leveling data based on GNSS data using a dynamic adjustment method, the method further includes: constructing a surface settlement observation equation according to the leveling data to obtain a second observation equation: Where V is the residual, v i and v j is the settlement rate of level monitoring point i and level monitoring point j, is the annual settlement of level monitoring point i, is the annual settlement of level monitoring point j, where satisfy: is the elevation value of level monitoring point i at the starting time, t 0 is the starting time, and t is the current time.

[0009] Optionally, the leveling data is corrected based on GNSS data using a dynamic adjustment method, including: constructing a settlement rate equation based on the second observation equation to obtain a first constraint equation: V=B*XL; wherein B is a fitting coefficient, X is a first settlement rate determined based on the leveling data, and L is a constant term; constructing a second constraint equation based on the first observation equation: G*X=0; wherein G is a second settlement rate determined based on GNSS data; jointly solving the first constraint equation and the second constraint equation: X=v i -v j =(B T PB+G T G) -1 B T PL; where P is a symmetric positive definite matrix.

[0010] Optionally, before obtaining the target data by fusing the leveling data and the InSAR data using the trend surface method based on the corrected leveling data, the method further comprises: constructing a surface settlement value equation to obtain a first target equation: y n =k n x n +ε n ; Among them, y n is the ground subsidence monitoring value corresponding to monitoring mode n, k n is the coefficient of monitoring mode n, ε n is the monitoring error of monitoring mode n, x n is the monitoring data of monitoring mode n; based on the first objective equations, the first objective matrix is ​​obtained: Y = KX + ε; where ε is the pair ε nFusion is performed to obtain the first target matrix, construct the error equation, and obtain the second target equation: Among them, V * is the error, is the estimated value of X, satisfy:

[0011] Based on the second objective equation, an estimation error is determined.

[0012] Optionally, based on the corrected leveling data, the leveling data and InSAR data are fused using a trend surface method to obtain target data, including: determining a first ground subsidence amount based on an interpolation method, and determining a second ground subsidence amount based on an estimated value after data fusion, and constructing an equation for the relationship between the first ground subsidence amount, the second ground subsidence amount and the corrected value to obtain a third target equation: Among them, Z i is the correction value of monitoring point i, is the second ground subsidence at monitoring point i, is the first ground settlement amount of monitoring point i; based on the third objective equation and the polyhedral function, a correction value expression of each monitoring point is constructed to obtain the first objective function: Among them, a i is the target coefficient, x and y are the coordinates of the monitoring point, F(x, y, x i ,y i ) satisfies: F(x, y, x i ,y i )=[(xx i )+(yy i )+δ 2 ] k ; where k is the smoothing factor, δ 2 is the square of the error; based on the first objective function, the error equation is constructed to obtain the fourth objective equation: v i =Q i a i -t; Solve the fourth objective equation based on the least squares principle to obtain the target coefficient: a i =(Q i T Q i )Q i T f; where Q i =F(x,y,x i ,y i ); solving the correction value of each of the monitoring points based on the target coefficient, and correcting the first ground subsidence based on the correction value to obtain the target data.

[0013] According to another aspect of the present application, a multi-source data fusion surface settlement detection device is provided, and the device includes: a first calculation unit, used to perform deviation analysis based on GNSS data and leveling data for surface settlement monitoring; a second calculation unit, used to correct the leveling data based on GNSS data by using a dynamic adjustment method when the deviation between the GNSS data and the leveling data is greater than a first threshold; a third calculation unit, used to perform data fusion on the corrected leveling data and InSAR data by using a trend surface method based on the corrected leveling data to obtain target data, and generate a surface settlement monitoring report based on the target data.

[0014] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute any one of the methods described.

[0015] According to another aspect of the present application, a surface settlement monitoring system is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include methods for executing any one of the described methods.

[0016] Applying the technical solution of the present application, firstly, a deviation analysis is performed based on the GNSS data and leveling data of the surface settlement monitoring; then, when the deviation between the GNSS data and the leveling data is greater than a first threshold, the leveling data is corrected based on the GNSS data using a dynamic adjustment method; finally, based on the corrected leveling data, the corrected leveling data and InSAR data are fused using a trend surface method to obtain target data, and a surface settlement monitoring report is generated based on the target data. The present application corrects the leveling data using GNSS data, and fuses the corrected leveling data with InSAR data to obtain the final surface settlement monitoring data, thus realizing multi-source data fusion monitoring and solving the problem of large deviation in surface settlement monitoring using a single data element in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A hardware structure block diagram of a mobile terminal for surface settlement detection based on multi-source data fusion provided in an embodiment of the present application is shown;

[0018] Figure 2 A schematic flow chart of a method for detecting surface subsidence by multi-source data fusion according to an embodiment of the present application is shown;

[0019] Figure 3 A schematic diagram of a process of fusion processing of leveling data and InSAR data provided according to an embodiment of the present application is shown;

[0020] Figure 4 A structural block diagram of a surface settlement detection device with multi-source data fusion provided according to an embodiment of the present application is shown.

[0021] The above drawings include the following reference numerals:

[0022] 102, processor; 104, memory; 106, transmission device; 108, input and output devices. DETAILED DESCRIPTION

[0023] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0024] 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. 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 ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] For the convenience of description, some nouns or terms involved in the embodiments of the present application are explained below:

[0027] Global Navigation Satellite System (GNSS): The Global Navigation Satellite System (GNSS) is a technology that uses multiple satellites for navigation and positioning. It calculates the position of the receiving device by receiving signals from multiple satellites. Common GNSS systems include the US GPS, Russia's GLONASS, the EU's Galileo, and China's BeiDou system. GNSS is widely used in civil and military fields, providing global coverage, all-weather, high-precision positioning, navigation, and timing services.

[0028] Interferometric Synthetic Aperture Radar (InSAR): Interferometric Synthetic Aperture Radar (InSAR) is a method of ground monitoring using radar technology. It obtains radar images of the ground surface through synthetic aperture radar (SAR) carried by satellites or aircraft, and performs interference processing on these images to extract ground deformation information. InSAR technology can monitor a wide range of geological disasters such as ground subsidence, deformation and landslides, and has high temporal and spatial resolution and large area coverage capabilities. It is widely used in geological disaster monitoring, urban infrastructure monitoring, mining area subsidence monitoring and other fields. InSAR technology includes methods such as pair interferometry (PSI) and permanent scatterer interferometry (PSI).

[0029] The full name is Continuously Operating Reference Stationsystem (CORS): a technical infrastructure for real-time high-precision positioning and measurement. The CORS station network consists of multiple reference stations distributed in different locations, each of which is equipped with high-precision GNSS (Global Navigation Satellite System) receiving equipment, such as GPS, GLONASS, Galileo, etc. These reference stations continuously receive signals from satellites and transmit data in real time to the data processing center through the communication network.

[0030] Leveling Data: refers to the ground elevation change information obtained through leveling. Leveling is a traditional surveying technique that determines the ground elevation change by measuring the elevation difference between different points. Leveling data is usually more accurate than other surveying methods (such as GPS), so it has important applications in surface settlement monitoring.

[0031] As introduced in the background technology, the existing technology mainly focuses on the fusion of single monitoring technology or simple data types. In order to solve the problem of large deviation in surface settlement monitoring using a single data element, the embodiments of the present application provide a surface settlement detection method using multi-source data fusion, a surface settlement detection device using multi-source data fusion, a computer-readable storage medium and a surface settlement monitoring system.

[0032] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0033] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 FIG. 1 is a hardware structure block diagram of a mobile terminal for a multi-source data fusion surface settlement detection method according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0034] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the display method of device information in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific examples of the network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0035] In this embodiment, a multi-source data fusion face settlement detection method running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0036] The multi-source data fusion surface settlement detection method of the embodiment of the present application makes comprehensive use of GNSS data, leveling and InSAR data. The GNSS data comes from the continuous CORS stations in the area, and the measurement data is collected for about one year; the leveling data is obtained through a re-survey cycle of about one year, and the leveling wall corner points rigidly connected to the observation piers of the continuous CORS stations are measured and incorporated into the leveling network; the InSAR data is obtained through the SAR image data of the area. According to this method, the advantages of different data sources are combined, the InSAR data is optimized, and the ground settlement monitoring results with high precision and high temporal and spatial resolution are obtained, which provides a more realistic and accurate method for ground settlement monitoring.

[0037] Figure 2 FIG. 1 is a flow chart of a method for detecting surface subsidence by fusion of multi-source data according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0038] Step S201, performing deviation analysis based on GNSS data and leveling data for surface settlement monitoring;

[0039] Specifically, the system receives and analyzes surface settlement data obtained through the Global Navigation Satellite System (GNSS) and traditional leveling. GNSS data provides high-precision point settlement information, while leveling data provides accurate settlement along a line or at a point. By comparing the two sets of data, the deviation between them can be identified. This deviation analysis helps to discover potential problems in the data, such as GNSS data may have drift or errors in certain areas, and leveling data may be affected by environmental factors at certain monitoring points.

[0040] Step S202, when the deviation between the GNSS data and the leveling data is greater than a first threshold, correcting the leveling data based on the GNSS data using a dynamic adjustment method;

[0041] Specifically, if the deviation between the GNSS data and the leveling data exceeds the preset first threshold, it means that there is a significant inconsistency between the data. In this case, the system uses dynamic adjustment to correct the leveling data. Dynamic adjustment is a data processing technology that adjusts the settlement value of the leveling data based on the high-precision point information of the GNSS data to make it more consistent with the GNSS data. This method can effectively reduce the deviation between the data and improve the accuracy and reliability of the monitoring results.

[0042] Step S203, based on the corrected leveling data, the trend surface method is used to fuse the corrected leveling data and the InSAR data to obtain target data, and a monitoring report on the surface subsidence is generated based on the target data.

[0043] Specifically, the system first performs data fusion based on the corrected leveling data and InSAR data. InSAR data can provide a wide range of ground subsidence information, but the accuracy is relatively low. The trend surface method is a statistical method that generates a mathematical model that can reflect the trend of surface subsidence by fitting the corrected leveling data and InSAR data. Through this data fusion method, the system can combine the high precision of leveling data and the wide coverage of InSAR data to obtain more comprehensive and accurate target data. Finally, the system generates a monitoring report on surface subsidence based on these target data. The report can clearly show the subsidence situation in the target area and provide a scientific basis for decision-making.

[0044] It can be seen that the embodiment of the present application provides a multi-source data fusion face settlement detection method, firstly, based on the GNSS data and leveling data of face settlement monitoring, the deviation analysis is performed; then, when the deviation between the GNSS data and the leveling data is greater than the first threshold, the leveling data is corrected based on the GNSS data using the dynamic adjustment method; finally, based on the corrected leveling data, the trend surface method is used to fuse the corrected leveling data and InSAR data to obtain the target data, and a face settlement monitoring report is generated based on the target data. The present application corrects the leveling data through GNSS data, and fuses the corrected leveling data with InSAR data to obtain the final face settlement monitoring data. The face settlement detection method of the multi-source data fusion of the embodiment of the present application can effectively integrate monitoring data from different sources and different characteristics, and improve the accuracy and coverage of face settlement monitoring. At the same time, the application of the dynamic adjustment method and the trend surface method makes the data processing process more scientific and reasonable, realizes multi-source data fusion monitoring, and solves the problem of large deviation in face settlement monitoring through a single data element in the prior art.

[0045] As a possible implementation method, deviation analysis is performed based on GNSS data and leveling data for surface settlement monitoring, including the following steps:

[0046] Step S301, substituting the GNSS data and the leveling data into the first preset formula to calculate the average error: Among them, θ is the average error, N is the total number of monitoring points, and d G is the GNSS data, d L is the level data, i is the monitoring point;

[0047] Specifically, the difference (i.e., error) between the GNSS data and the leveling data at each monitoring point is calculated, and then the errors of all points are summed and divided by the total number of monitoring points to obtain the average error. This average error is an important indicator for measuring the overall consistency of GNSS data and leveling data, and helps to identify systematic deviations in the data set.

[0048] Step S302, substituting the GNSS data and the leveling data into the second preset formula to calculate the standard deviation: Where m is the standard deviation.

[0049] Specifically, the square of the error of each monitoring point minus the average error is calculated, and then the square differences of all points are summed and divided by the total number of monitoring points, and finally the square root is taken to obtain the standard deviation. The standard deviation measures the degree of dispersion of the data, that is, the fluctuation between the GNSS data and the leveling data.

[0050] It can be seen that when performing deviation analysis on GNSS data and leveling data based on surface settlement monitoring, a trend line can be drawn on the same graph for the data measured by GNSS and leveling to observe the difference between the two. Then, the average error θ and standard deviation m of GNSS data and leveling data are calculated by the first preset formula and the second preset formula to more intuitively evaluate the trend of the two data.

[0051] As a possible implementation method, before correcting the leveling data based on the GNSS data using the dynamic adjustment method, the method further includes:

[0052] According to the GNSS data, the surface settlement observation equation is constructed to obtain the first observation equation: L Gi +V Gi =a 0 +b 0 T i +A 1 cos(2πT i )+B 1 sin(2πT i ), where L Gi is the elevation component observation value of the i-th GNSS monitoring point during the observation time, V Gi For L Gi The corresponding residual, a 0 is the mean value of the elevation component during the observation time, b 0 T is the annual change rate of the elevation component during the observation time, i is the observation time, A 1 is the amplitude of the cosine component of the annual cycle, B 1 is the amplitude of the sinusoidal component of the annual periodic variation.

[0053] Specifically, the system constructs a surface settlement observation equation based on the surface settlement data obtained through the Global Navigation Satellite System (GNSS). This equation is used to describe the elevation changes of each monitoring point during the observation time. By constructing an observation equation containing linear and periodic change components, it can accurately describe the change of elevation over time and reflect the actual settlement of the ground.

[0054] As a possible implementation method, before correcting the leveling data based on the GNSS data using the dynamic adjustment method, the method further includes:

[0055] The surface settlement observation equation is constructed according to the leveling data to obtain the second observation equation: Where V is the residual, v i and v j is the settlement rate of level monitoring point i and level monitoring point j, is the annual settlement of level monitoring point i, is the annual settlement of level monitoring point j, where satisfy: is the elevation value of level monitoring point i at the starting time, t 0 is the starting time, and t is the current time.

[0056] Specifically, in the linear rate model, the movement rate of the level point can be considered constant. Let the annual change rate of a point i be v i , select a certain time as the starting point t 0 , the elevation value of this point is Then the elevation of the point at any time t is: Then the annual height difference between any two leveling points i and j is: In the formula is the annual height difference change between monitoring points i and j at time t; the system calculates the settlement rate based on the surface settlement data obtained through traditional leveling, taking the settlement of the monitoring points in the leveling route for several consecutive years as the observation value, and constructs a surface settlement observation equation. This equation is used to describe the settlement changes of each monitoring point during the observation time. By constructing an observation equation containing the settlement rate and annual settlement, it can accurately describe the settlement changes of each monitoring point and reflect the actual settlement of the ground.

[0057] As a possible implementation method, the leveling data is corrected based on GNSS data using the dynamic adjustment method, which includes the following steps:

[0058] Step S401, constructing a sedimentation rate equation based on the second observation equation to obtain a first constraint equation: V=B*XL; wherein B is a fitting coefficient, X is a first sedimentation rate determined based on leveling data, and L is a constant term;

[0059] Specifically, the system constructs a sedimentation rate equation based on the data obtained through leveling measurement to obtain the first constraint equation. This equation is used to describe the sedimentation rate determined based on the leveling data. The fitting coefficient matrix B is the linear combination coefficient of the sedimentation rate of each monitoring point obtained by linear regression or least squares fitting of the leveling data. The first sedimentation rate X is the sedimentation rate of each monitoring point calculated based on the time series changes of the leveling data. The constant term L is the initial condition or background value of each monitoring point determined by the fixed constant term in the leveling data.

[0060] Step S402, constructing a second constraint equation based on the first observation equation: G*X=0; wherein G is a second sedimentation rate determined based on GNSS data;

[0061] Specifically, G is the second sedimentation rate matrix determined based on GNSS data. According to the time series analysis of GNSS data, the sedimentation rate information of each monitoring point is extracted. The system constructs a sedimentation rate equation based on the data obtained through GNSS data to obtain the second constraint equation, which is used to describe the sedimentation rate determined according to the GNSS data.

[0062] Step S403: Solve the first constraint equation and the second constraint equation simultaneously: X=v i -v j =(B T PB+G T G) -1 B T PL; where P is a symmetric positive definite matrix.

[0063] Specifically, by assigning weights to each observation value, a symmetric positive definite matrix P is constructed, and the above equations are solved by the least squares method or other optimization algorithms to obtain the corrected settlement rate vector. By solving the equations simultaneously, the information of GNSS data and leveling data can be comprehensively utilized to improve the accuracy and reliability of the settlement rate. Through the optimization algorithm, an optimal settlement rate solution is provided, so that the corrected leveling data is closer to the actual settlement situation, and the accuracy of the monitoring results is improved.

[0064] As a possible implementation method, before fusing the leveling data and the InSAR data using the trend surface method based on the corrected leveling data to obtain the target data, the method further includes the following steps:

[0065] Step S501, construct the surface settlement value equation to obtain the first target equation: n =k n x n +ε n ; Among them, y n is the ground subsidence monitoring value corresponding to monitoring mode n, k n is the coefficient of monitoring mode n, ε n is the monitoring error of monitoring mode n, x n is the monitoring data of monitoring mode n;

[0066] Specifically, according to the settlement data obtained by each monitoring method, the ground settlement monitoring value y is calculated. n , determine the weight coefficient k of each monitoring method through data analysis or experience n , through statistical methods or other error analysis techniques, determine the error term ε of each monitoring method n , the settlement data x obtained from each monitoring method n This equation can integrate data from different monitoring methods to reflect the actual subsidence of the ground.

[0067] Step S502, based on the first target equations, the first target matrix is ​​obtained: Y = KX + ε; ε is the pair ε n Fusion is performed to obtain;

[0068] Specifically, the system combines the first objective equations to obtain a first objective matrix in matrix form. This matrix is ​​used to represent the settlement value equations under all monitoring methods, simplifying the integration and processing of multi-source data.

[0069] Step S503: construct an error equation based on the first target matrix to obtain a second target equation: Where V* is the error, is the estimated value of X, satisfy:

[0070] Specifically, for According to the least squares estimation, the system constructs an error equation based on the first target matrix. This equation is used to describe the relationship between the monitoring error and the estimated value.

[0071] Step S504: determining an estimation error based on the second objective equation.

[0072] Specifically, the valuation in step 503 Find the first-order partial derivative and set it to zero, and we get Solving the above formula, we can get Since the second-order derivative Greater than 0, so the valuation When , there is a minimum value, so the estimated error of the valuation is: By calculating the error between the estimated value and the observed value, the effect of data fusion can be evaluated, the distribution and size of the error can be determined, and the data fusion method can be improved, the data fusion process can be optimized, and the accuracy of the final monitoring results can be improved.

[0073] As a possible implementation method, the process of fusion processing of level data and InSAR data is as follows: Figure 3 As shown, based on the corrected leveling data, the leveling data and InSAR data are fused using the trend surface method to obtain the target data, including the following steps:

[0074] Step S601, determine the first ground subsidence based on the interpolation method, determine the second ground subsidence based on the estimated value after data fusion, construct an equation based on the relationship between the first ground subsidence, the second ground subsidence and the correction value, and obtain the third target equation: Among them, Z i is the correction value of monitoring point i, is the second ground subsidence at monitoring point i, is the first ground settlement at monitoring point i;

[0075] Specifically, the first ground subsidence amount of each monitoring point is determined by interpolation method (such as Kriging interpolation, inverse distance weighted interpolation, etc.) Based on the estimated value after data fusion, the second ground subsidence amount of each monitoring point is determined By calculating the difference between the second ground subsidence and the first ground subsidence, the correction value of each monitoring point is quantified to reflect the effect of data fusion.

[0076] Step S602: construct the correction value expression of each monitoring point based on the third objective equation and the polyhedral function. The first objective function is: Among them, a i is the target coefficient, x and y are the coordinates of the monitoring point, F(x, y, x i ,y i ) satisfies: F(x, y, x i ,y i )=[(xx i )+(yy i )+δ 2 ] k ; where k is the smoothing factor, δ 2 is the square of the error;

[0077] Specifically, by selecting appropriate polyhedral functions (such as polynomials, radial basis functions, etc.), a spatial distribution model of monitoring points is constructed, and the target coefficients of each polyhedral function are determined by the least squares method or other optimization algorithms. k is the smoothing factor. When is a positive hyperbolic function, is a negative hyperbolic function. Through the multi-faceted function, a spatial distribution model of monitoring points is constructed to reflect the spatial changes of ground subsidence.

[0078] Step S603, construct an error equation based on the first objective function to obtain a fourth objective equation: i =Q i a i -t;

[0079] Step S604, solving the fourth target equation based on the least squares principle to obtain the target coefficient: a i =(Q i T Q i )Q i T f; where Q i =F(x,y,x i ,y i );

[0080] Specifically, let Q i =F(x,y,x i ,y i ), then the multifaceted function becomes: The constructed error equation (i.e., the fourth objective equation) is solved by applying the least squares principle to obtain the objective coefficients of the polyhedral function. The least squares method is an optimization method that aims to minimize the square difference between the observed value and the theoretical value.

[0081] Step S605, solving the correction value of each monitoring point based on the target coefficient, and correcting the first ground subsidence based on the correction value to obtain target data.

[0082] Specifically, the obtained target coefficient is substituted into the polyhedral function equation to obtain the polyhedral function equation. Then, the correction number of the point is obtained by using the coordinates of the point, and the obtained correction value is applied to the original ground subsidence data to obtain the corrected estimate of each point, thus realizing the accurate correction and optimization of the original ground subsidence data and improving the accuracy and reliability of the monitoring data.

[0083] Through this embodiment, the data of the leveling points, GNSS points and InSAR monitoring coincident points in the area are fused and calculated, and the monitoring values ​​at all points are corrected and calculated using the fused data to form a settlement rate rendering map of the area. The ground settlement can be predicted and analyzed based on the fused ground settlement rate.

[0084] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0085] The embodiment of the present application also provides a multi-source data fusion surface area settlement detection device. It should be noted that the multi-source data fusion surface area settlement detection device of the embodiment of the present application can be used to execute the surface area settlement detection method for multi-source data fusion provided by the embodiment of the present application. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0086] The following is an introduction to the surface settlement detection device with multi-source data fusion provided in an embodiment of the present application.

[0087] Figure 4is a structural block diagram of a surface settlement detection device for multi-source data fusion according to an embodiment of the present application. Figure 4 As shown, the device includes: a first computing unit 10, a second computing unit 20 and a third computing unit 30.

[0088] A first calculation unit 10 is used to perform deviation analysis based on GNSS data and leveling data for surface settlement monitoring;

[0089] Specifically, surface settlement data obtained through the Global Navigation Satellite System (GNSS) and traditional leveling are received and analyzed. GNSS data provides high-precision point settlement information, while leveling data provides accurate settlement along a line or at a point. By comparing the two sets of data, the deviation between them can be identified. This deviation analysis helps to discover potential problems in the data, such as GNSS data may have drift or errors in certain areas, and leveling data may be affected by environmental factors at certain monitoring points.

[0090] A second calculation unit 20 is used to correct the leveling data based on the GNSS data by using a dynamic adjustment method when the deviation between the GNSS data and the leveling data is greater than a first threshold;

[0091] Specifically, if the deviation between the GNSS data and the leveling data exceeds a preset first threshold, it indicates that there is a significant inconsistency between the data. In this case, the leveling data is corrected using the dynamic adjustment method. The dynamic adjustment method is a data processing technique that adjusts the settlement value of the leveling data based on the high-precision point information of the GNSS data to make it more consistent with the GNSS data. This method can effectively reduce the deviation between the data and improve the accuracy and reliability of the monitoring results.

[0092] The third calculation unit 30 is used to fuse the corrected leveling data and InSAR data using the trend surface method based on the corrected leveling data to obtain target data, and generate a monitoring report on the surface settlement based on the target data.

[0093] Specifically, data fusion is first performed based on the corrected leveling data and InSAR data. InSAR data can provide a wide range of ground subsidence information, but the accuracy is relatively low. The trend surface method is a statistical method that generates a mathematical model that can reflect the trend of surface subsidence by fitting the corrected leveling data and InSAR data. Through this data fusion method, the high precision of leveling data and the large-scale coverage of InSAR data can be combined to obtain more comprehensive and accurate target data. Finally, a monitoring report on surface subsidence is generated based on these target data. The report can clearly show the subsidence situation in the target area and provide a scientific basis for decision-making.

[0094] It can be seen that the embodiment of the present application provides a multi-source data fusion surface settlement detection device, which includes a first calculation unit, a second calculation unit and a third calculation unit. The first calculation unit is used to perform deviation analysis based on GNSS data and leveling data for surface settlement monitoring; the second calculation unit is used to correct the leveling data based on GNSS data using a dynamic adjustment method when the deviation between the GNSS data and the leveling data is greater than a first threshold; the third calculation unit is used to fuse the corrected leveling data and InSAR data based on the corrected leveling data using a trend surface method to obtain target data, and generate a surface settlement monitoring report based on the target data. The present application corrects the leveling data through GNSS data, and fuses the corrected leveling data with InSAR data to obtain the final surface settlement monitoring data. The surface settlement detection method of multi-source data fusion in the embodiment of the present application can effectively integrate monitoring data from different sources and with different characteristics, and improve the accuracy and coverage of surface settlement monitoring. At the same time, the application of dynamic adjustment method and trend surface method makes the data processing process more scientific and reasonable, realizes multi-source data fusion monitoring, and solves the problem of large deviation in surface settlement monitoring through a single data element in the existing technology.

[0095] As a possible implementation manner, the first computing unit includes: a first computing module and a second computing module.

[0096] The first calculation module is used to substitute the GNSS data and the leveling data into the first preset formula to calculate the average error: Among them, θ is the average error, N is the total number of monitoring points, and d G is the GNSS data, d L is the level data, i is the monitoring point;

[0097] The second calculation module is used to substitute the GNSS data and the leveling data into the second preset formula to calculate the standard deviation: Where m is the standard deviation.

[0098] It can be seen that when performing deviation analysis on GNSS data and leveling data based on surface settlement monitoring, a trend line can be drawn on the same graph for the data measured by GNSS and leveling to observe the difference between the two. Then, the average error θ and standard deviation m of GNSS data and leveling data are calculated by the first preset formula and the second preset formula to more intuitively evaluate the trend of the two data.

[0099] As a possible implementation manner, the device further includes: a first observation unit.

[0100] The first observation unit is used to construct the surface settlement observation equation according to the GNSS data to obtain the first observation equation: L Gi+V Gi =a 0 +b 0 T i +A 1 cos(2πT i )+B 1 sin(2πT i ), where L Gi is the elevation component observation value of the i-th GNSS monitoring point during the observation time, V Gi For L Gi The corresponding residual, a 0 is the mean value of the elevation component during the observation time, b 0 T is the annual change rate of the elevation component during the observation time, i is the observation time, A 1 is the amplitude of the cosine component of the annual cycle, B 1 is the amplitude of the sinusoidal component of the annual periodic variation.

[0101] Specifically, the system constructs a surface settlement observation equation based on the surface settlement data obtained through the Global Navigation Satellite System (GNSS). This equation is used to describe the elevation changes of each monitoring point during the observation time. By constructing an observation equation containing linear and periodic change components, it can accurately describe the change of elevation over time and reflect the actual settlement of the ground.

[0102] As a possible implementation manner, the device further includes: a second observation unit.

[0103] The second observation unit is used to construct a surface settlement observation equation according to the leveling data to obtain a second observation equation: Where V is the residual, v i and v j is the settlement rate of level monitoring point i and level monitoring point j, is the annual settlement of level monitoring point i, is the annual settlement of level monitoring point j, where satisfy: is the elevation value of level monitoring point i at the starting time, t 0 is the starting time, and t is the current time.

[0104] Specifically, in the linear rate model, the movement rate of the level point can be considered constant. Let the annual change rate of a point i be v i , select a certain time as the starting point t 0 , the elevation value of this point is Then the elevation of the point at any time t is: Then the annual height difference between any two leveling points i and j is: In the formula is the annual height difference change between monitoring points i and j at time t; the system calculates the settlement rate based on the surface settlement data obtained through traditional leveling, taking the settlement of the monitoring points in the leveling route for several consecutive years as the observation value, and constructs a surface settlement observation equation. This equation is used to describe the settlement changes of each monitoring point during the observation time. By constructing an observation equation containing the settlement rate and annual settlement, it can accurately describe the settlement changes of each monitoring point and reflect the actual settlement of the ground.

[0105] As a possible implementation manner, the second calculation unit includes: a first constraint module, a second constraint module and a constraint calculation module.

[0106] The first constraint module is used to construct a sedimentation rate equation based on the second observation equation to obtain a first constraint equation: V=B*XL; wherein B is a fitting coefficient, X is a first sedimentation rate determined based on leveling data, and L is a constant term;

[0107] A second constraint module is used to construct a second constraint equation based on the first observation equation: G*X=0; wherein G is a second sedimentation rate determined based on GNSS data;

[0108] Constraint calculation module, used to solve the first constraint equation and the second constraint equation simultaneously: X = v i -v j =(B T PB+G T G) -1 B T PL; where P is a symmetric positive definite matrix.

[0109] Specifically, by assigning weights to each observation value, a symmetric positive definite matrix P is constructed, and the above equations are solved by the least squares method or other optimization algorithms to obtain the corrected settlement rate vector. By solving the equations simultaneously, the information of GNSS data and leveling data can be comprehensively utilized to improve the accuracy and reliability of the settlement rate. Through the optimization algorithm, an optimal settlement rate solution is provided, so that the corrected leveling data is closer to the actual settlement situation, and the accuracy of the monitoring results is improved.

[0110] As a possible implementation manner, the device further includes: a first constructing unit, a combining unit, a second constructing unit and a determining unit.

[0111] The first construction unit is used to construct the surface settlement value equation and obtain the first target equation: y n =k n x n +ε n ; Among them, y nis the ground subsidence monitoring value corresponding to monitoring mode n, k n is the coefficient of monitoring mode n, ε n is the monitoring error of monitoring mode n, x n is the monitoring data of monitoring mode n;

[0112] Specifically, according to the settlement data obtained by each monitoring method, the ground settlement monitoring value y is calculated. n , determine the weight coefficient k of each monitoring method through data analysis or experience n , through statistical methods or other error analysis techniques, determine the error term ε of each monitoring method n , the settlement data x obtained from each monitoring method n This equation can integrate data from different monitoring methods to reflect the actual subsidence of the ground.

[0113] The simultaneous unit is used to perform simultaneous operation based on the first objective equations to obtain the first objective matrix: Y=KX+ε; wherein ε is the pair ε n Fusion is performed to obtain;

[0114] Specifically, the system combines the first objective equations to obtain a first objective matrix in matrix form. This matrix is ​​used to represent the settlement value equations under all monitoring methods, simplifying the integration and processing of multi-source data.

[0115] The second construction unit is used to construct an error equation based on the first target matrix to obtain a second target equation: Among them, V * is the error, is the estimated value of X, satisfy:

[0116] Specifically, for According to the least squares estimation, the system constructs an error equation based on the first target matrix. This equation is used to describe the relationship between the monitoring error and the estimated value.

[0117] The determining unit is used to determine the estimation error based on the second objective equation.

[0118] Specifically, the valuation in step 503 Find the first-order partial derivative and set it to zero, and we get Solving the above formula, we can get Since the second-order derivative Greater than 0, so the valuation When , there is a minimum value, so the estimated error of the valuation is: By calculating the error between the estimated value and the observed value, the effect of data fusion can be evaluated, the distribution and size of the error can be determined, and the data fusion method can be improved, the data fusion process can be optimized, and the accuracy of the final monitoring results can be improved.

[0119] As a possible implementation manner, the third calculation unit includes: a settlement construction module, a correction construction module, an error construction module, a coefficient solving module and a correction module.

[0120] The settlement construction module is used to determine the first ground settlement based on the interpolation method, and determine the second ground settlement based on the estimated value after data fusion, and construct an equation for the relationship between the first ground settlement, the second ground settlement and the correction value to obtain the third target equation: Among them, Z i is the correction value of monitoring point i, is the second ground subsidence at monitoring point i, is the first ground settlement at monitoring point i;

[0121] The correction construction module is used to construct the correction value expression of each monitoring point based on the third objective equation and the polyhedral function. The first objective function is: Among them, a i is the target coefficient, x and y are the coordinates of the monitoring point, F(x, y, x i ,y i ) satisfies: F(x, y, x i ,y i )=[(xx i )+(yy i )+δ 2 ] k ; where k is the smoothing factor, δ 2 is the square of the error;

[0122] The error building module is used to build an error equation based on the first objective function to obtain the fourth objective equation: i =Q i a i -t;

[0123] The coefficient solving module is used to solve the fourth objective equation based on the least squares principle to obtain the target coefficient: a i =(Q i T Q i )Q i T f; where Q i =F(x,y,x i ,y i );

[0124] The correction module is used to solve the correction value of each monitoring point based on the target coefficient, and to correct the first ground settlement amount based on the correction value to obtain the target data.

[0125] Through this embodiment, the data of the leveling points, GNSS points and InSAR monitoring coincident points in the area are fused and calculated, and the monitoring values ​​at all points are corrected and calculated using the fused data to form a settlement rate rendering map of the area. The ground settlement can be predicted and analyzed based on the fused ground settlement rate.

[0126] The above-mentioned multi-source data fusion surface settlement detection device includes a processor and a memory. The above-mentioned first calculation unit, the second calculation unit and the third calculation unit are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize the corresponding functions. The above-mentioned modules are all located in the same processor; or, the above-mentioned modules are located in different processors in the form of any combination.

[0127] The processor includes a kernel, which calls the corresponding program unit from the memory. One or more kernels can be set, and the communication efficiency can be improved by adjusting the kernel parameters.

[0128] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0129] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the multi-source data fusion surface area settlement detection method.

[0130] An embodiment of the present invention provides a surface subsidence monitoring system, characterized in that it includes: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a surface subsidence detection method for multi-source data fusion.

[0131] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0132] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0133] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0134] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0135] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0136] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0137] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0138] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0139] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0140] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0141] 1) The multi-source data fusion face settlement detection method of the present application first performs deviation analysis based on the GNSS data and leveling data of face settlement monitoring; then, when the deviation between the GNSS data and the leveling data is greater than the first threshold, the leveling data is corrected based on the GNSS data using the dynamic adjustment method; finally, based on the corrected leveling data, the trend surface method is used to fuse the corrected leveling data and InSAR data to obtain target data, and a face settlement monitoring report is generated based on the target data. The present application corrects the leveling data through GNSS data, and fuses the corrected leveling data with the InSAR data to obtain the final face settlement monitoring data. The multi-source data fusion face settlement detection method of the present application embodiment can effectively integrate monitoring data from different sources and different characteristics, and improve the accuracy and coverage of face settlement monitoring. At the same time, the application of the dynamic adjustment method and the trend surface method makes the data processing process more scientific and reasonable, realizes multi-source data fusion monitoring, and solves the problem of large deviation in face settlement monitoring through a single data element in the prior art.

[0142] 2) The multi-source data fusion surface settlement detection device of the present application includes a first calculation unit, a second calculation unit and a third calculation unit. The first calculation unit is used to perform deviation analysis based on GNSS data and leveling data for surface settlement monitoring; the second calculation unit is used to correct the leveling data based on GNSS data using a dynamic adjustment method when the deviation between the GNSS data and the leveling data is greater than a first threshold; the third calculation unit is used to fuse the corrected leveling data and InSAR data based on the corrected leveling data using a trend surface method to obtain target data, and generate a surface settlement monitoring report based on the target data. The present application corrects the leveling data through GNSS data, and fuses the corrected leveling data with InSAR data to obtain the final surface settlement monitoring data. The multi-source data fusion surface settlement detection method of the embodiment of the present application can effectively integrate monitoring data from different sources and with different characteristics, and improve the accuracy and coverage of surface settlement monitoring. At the same time, the application of dynamic adjustment method and trend surface method makes the data processing process more scientific and reasonable, realizes multi-source data fusion monitoring, and solves the problem of large deviation in surface settlement monitoring through a single data element in the existing technology.

[0143] The above are only preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A multi-source data fusion surface settlement detection method, characterized in that: include: Deviation analysis based on GNSS data and leveling data for surface settlement monitoring; When the deviation between the GNSS data and the leveling data is greater than a first threshold, correcting the leveling data based on the GNSS data using a dynamic adjustment method; Based on the corrected leveling data, the corrected leveling data and InSAR data are fused using the trend surface method to obtain target data, and a monitoring report on surface subsidence is generated based on the target data.

2. The method according to claim 1, characterized in that Deviation analysis based on GNSS data and leveling data for surface settlement monitoring, including: Substitute the GNSS data and the leveling data into the first preset formula to calculate the average error: Where θ is the average error, N is the total number of monitoring points, and d G is the GNSS data, d L is the leveling data, i is the monitoring point; Substitute the GNSS data and the leveling data into the second preset formula to calculate the standard deviation: Wherein, m is the standard deviation.

3. The method according to claim 1, characterized in that Before correcting the leveling data based on GNSS data using a dynamic adjustment method, the method further includes: The surface settlement observation equation is constructed according to the GNSS data to obtain the first observation equation: L Gi +V Gi =a0+b0T i +A1cos(2πT i )+B1sin(2πT i ); Among them, L Gi is the elevation component observation value of the i-th GNSS monitoring point during the observation time, V Gi For L Gi The corresponding residual, a0 is the mean value of the elevation component during the observation time, b0 is the annual change rate of the elevation component during the observation time, T i is the observation time, A1 is the amplitude of the cosine component of the annual periodic variation, and B1 is the amplitude of the sine component of the annual periodic variation.

4. The method according to claim 3, characterized in that Before correcting the leveling data based on GNSS data using a dynamic adjustment method, the method further includes: The surface settlement observation equation is constructed according to the leveling data to obtain the second observation equation: Where V is the residual, v i and v j is the settlement rate of level monitoring point i and level monitoring point j, is the annual settlement of level monitoring point i, is the annual settlement of level monitoring point j, where satisfy: is the starting time, the elevation value of the level monitoring point i, t0 is the starting time, and t is the current time.

5. The method according to claim 4, characterized in that The leveling data is corrected based on GNSS data using a dynamic adjustment method, including: Based on the second observation equation, the sedimentation rate equation is constructed to obtain the first constraint equation: V = B*XL; Where B is the fitting coefficient, X is the first sedimentation rate determined based on the leveling data, and L is the constant term; Based on the first observation equation, the second constraint equation is constructed: G*X=0; Wherein, G is the second sedimentation rate determined based on GNSS data; The first constraint equation and the second constraint equation are solved simultaneously: X=v i -v j =(B T PB+G T G) -1 B T PL; Where P is a symmetric positive definite matrix.

6. The method according to claim 1, characterized in that Before obtaining target data by fusing the leveling data and InSAR data using a trend surface method based on the corrected leveling data, the method further includes: Construct the surface settlement value equation and obtain the first objective equation: y n =k n x n +e n ; Among them, y n is the ground subsidence monitoring value corresponding to monitoring mode n, k n is the coefficient of monitoring mode n, ε n is the monitoring error of monitoring mode n, x n is the monitoring data of monitoring mode n; Based on the combination of the first objective equations, the first objective matrix is ​​obtained: Y = KX + ε; in ε is the pair ε n Fusion is performed to obtain; Based on the first target matrix, the error equation is constructed to obtain the second target equation: Where V* is the error, is the estimated value of X, satisfy: Based on the second objective equation, an estimation error is determined.

7. The method according to claim 6, characterized in that Based on the corrected leveling data, the leveling data and InSAR data are fused using the trend surface method to obtain target data, including: The first ground subsidence is determined based on the interpolation method, and the second ground subsidence is determined based on the estimated value after data fusion. The relationship between the first ground subsidence, the second ground subsidence and the correction value is constructed into an equation to obtain the third target equation: Among them, Z i is the correction value of monitoring point i, is the second ground subsidence at monitoring point i, is the first ground subsidence at monitoring point i; Based on the third objective equation and the polyhedral function, the correction value expression of each monitoring point is constructed to obtain the first objective function: Among them, a i is the target coefficient, x and y are the coordinates of the monitoring point, and F(x,y,xi,yi) satisfies: F(x,y,x i ,and i )=[(xx i )+(yy i )+δ 2 ] k ; Among them, k is the smoothing factor, δ 2 is the square of the error; Based on the first objective function, an error equation is constructed to obtain the fourth objective equation: v i =Q i a i -t; The fourth objective equation is solved based on the least squares principle to obtain the objective coefficient: Among them, Q i =F(x,y,x i ,y i ); The correction value of each of the monitoring points is solved based on the target coefficient, and the first ground subsidence is corrected based on the correction value to obtain the target data.

8. A multi-source data fusion surface settlement detection device, characterized in that: The device comprises: The first calculation unit is used for performing deviation analysis based on GNSS data and leveling data for surface settlement monitoring; a second calculation unit, configured to correct the leveling data based on the GNSS data by using a dynamic adjustment method when the deviation between the GNSS data and the leveling data is greater than a first threshold; The third calculation unit is used to fuse the corrected leveling data and InSAR data using the trend surface method based on the corrected leveling data to obtain target data, and generate a monitoring report on surface subsidence based on the target data.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

10. A surface settlement monitoring system, characterized in that: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include methods for executing any one of claims 1 to 7.

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