Ground surface subsidence data acquisition method and device based on GNSS continuous mobile observation station
By constructing a log-normal probability density function model through a GNSS continuous mobile observation station, the problems of high cost and low efficiency of GNSS monitoring equipment were solved, and real-time dynamic monitoring and high-precision prediction of surface subsidence in mining areas were achieved, reducing monitoring costs and improving data reliability.
Patent Information
- Application Number
- CN202510939568.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-08
- Publication Date
- 2025-10-17
AI Technical Summary
Existing GNSS monitoring methods have high equipment costs and low efficiency in surface subsidence monitoring in mining areas, making it difficult to achieve high-precision and real-time dynamic monitoring, especially in complex environments.
A method based on GNSS continuous mobile observation stations is adopted to obtain observation data. A surface subsidence prediction model based on the log-normal probability density function is constructed, and real-time prediction of surface subsidence is achieved by optimizing model parameters.
It has achieved low-cost and high-efficiency surface subsidence monitoring in mining areas, improved monitoring accuracy and data reliability, and supported safe production and disaster prevention in mining areas.
Smart Images
Figure CN120804465A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of surface deformation monitoring in coal mining areas, and in particular relates to a method and device for acquiring surface subsidence data based on a GNSS continuous mobile observation station. Background Art
[0002] Surface subsidence is a common and serious environmental disaster in mining activities. It not only damages surface buildings, deforms roads, and ruptures underground pipelines, but also poses a serious threat to production safety in mining areas and the lives of surrounding residents. Therefore, accurate monitoring and prediction are crucial. Traditional monitoring methods, such as leveling and total station surveying, each have their own advantages, but also have significant limitations. Leveling offers high accuracy but is inefficient and time-consuming, making real-time dynamic monitoring difficult. Total station surveying, however, is limited by its measurement range and visibility, making it difficult to meet the needs of large-scale, high-precision, and real-time monitoring in mining areas with complex terrain or numerous obstacles.
[0003] In recent years, the application of Global Navigation Satellite System (GNSS) technology has provided a new solution for surface subsidence monitoring, but its widespread promotion still faces challenges. Existing GNSS methods usually require the deployment of a large number of fixed observation stations in the monitoring area, resulting in high equipment procurement, installation and maintenance costs. Especially in remote or complex mining areas, the difficulty of deployment and implementation costs further limit its application. Existing methods lack in-depth mining and effective utilization of limited monitoring data. When the number of observation stations is small, it is difficult to achieve high-precision surface subsidence prediction. Therefore, developing low-cost, high-efficiency monitoring technologies that can adapt to complex environments and improving data analysis and prediction capabilities are key issues that need to be urgently addressed in the current field of surface subsidence monitoring. Summary of the Invention
[0004] The purpose of the embodiments of the present invention is to provide a method and device for acquiring surface subsidence data at a GNSS continuously moving observation station, so as to solve the problem that it is difficult to accurately predict the amount of surface subsidence in a surface subsidence area.
[0005] In a first aspect, an embodiment of the present invention provides a method for acquiring surface subsidence data based on a GNSS continuous mobile observation station, comprising:
[0006] Obtain observation data and actual measurement data at each monitoring point;
[0007] A surface subsidence prediction model was constructed based on the log-normal probability density function according to the observed data;
[0008] Optimize the surface subsidence prediction model based on the prediction data of the surface subsidence prediction model and the actual measurement data, and determine the surface subsidence prediction model;
[0009] The optimized surface subsidence amount prediction model is used to predict the surface subsidence amount of the monitoring station.
[0010] Optionally, the step of constructing the surface subsidence amount prediction model based on the lognormal probability density function according to the observation data comprises:
[0011] constructing a surface subsidence velocity prediction model based on the lognormal probability density function according to the observation data;
[0012] constructing the surface subsidence amount prediction model according to the surface subsidence velocity prediction model.
[0013] Optionally, the calculation formula of the surface subsidence velocity prediction model is:
[0014]
[0015] wherein v(t) is the subsidence velocity at time t, w0 is the maximum subsidence amount of the surface point, A is a position parameter, and B is a shape parameter of the subsidence velocity curve.
[0016] Optionally, the calculation formula of the surface subsidence amount prediction model is:
[0017]
[0018] wherein erf is an error function, and w(t) is the surface subsidence amount at time t.
[0019] Optionally, the step of optimizing the surface subsidence amount prediction model according to the prediction data and the actual measurement data of the surface subsidence amount prediction model comprises:
[0020] comparing the prediction data of the surface subsidence amount prediction model with the actual measurement data, and calculating the error sum of squares;
[0021] adjusting the values of the parameters w0, A and B, recalculating the prediction data, comparing the recalculated prediction data with the actual measurement data, and calculating the error sum of squares;
[0022] iteratively optimizing, and obtaining the optimized values of the parameters w0, A and B when the error sum of squares reaches a preset value;
[0023] substituting the optimized values of the parameters w0, A and B into the surface subsidence amount prediction model to obtain the optimized surface subsidence amount prediction model.
[0024] Optionally, the step of constructing the surface subsidence amount prediction model based on the lognormal probability density function according to the observation data comprises:
[0025] obtaining the observation data of the reference station;
[0026] According to the observation data of the reference station, the observation data of each monitoring point is preprocessed by a differential positioning method;
[0027] According to the preprocessed observation data, a ground subsidence amount prediction model is constructed based on a lognormal probability density function.
[0028] Optionally, after the step of preprocessing the observation data of each monitoring point according to the observation data of the reference station by a differential positioning method, the method further comprises:
[0029] The preprocessed observation data is filtered by a sliding average filtering method;
[0030] According to the filtered observation data, a ground subsidence amount prediction model is constructed based on a lognormal probability density function.
[0031] In a second aspect, an embodiment of the present application provides a ground subsidence data acquisition device based on a GNSS continuous mobile observation station, comprising:
[0032] An acquisition module is configured to acquire observation data and actual measurement data of each monitoring point;
[0033] A construction module is configured to construct a ground subsidence amount prediction model based on a lognormal probability density function according to the observation data;
[0034] An optimization module is configured to optimize the ground subsidence amount prediction model according to prediction data and actual measurement data of the ground subsidence amount prediction model, and determine the ground subsidence amount prediction model;
[0035] A prediction module is configured to predict the ground subsidence amount of the monitoring station by using the optimized ground subsidence amount prediction model.
[0036] In a third aspect, an embodiment of the present application provides an electronic device, comprising a processor and a memory, wherein the memory stores programs or instructions executable on the processor, and the programs or instructions are executed by the processor to implement the steps of the acquisition method according to any one of the above embodiments.
[0037] In a fourth aspect, an embodiment of the present application provides a readable storage medium, wherein the readable storage medium stores programs or instructions, and the programs or instructions are executed by a processor to implement the steps of the acquisition method according to any one of the above embodiments.
[0038] In the method for acquiring ground subsidence data based on GNSS continuous mobile observation stations according to the embodiments of the present application, observation data and actual measurement data of each monitoring point are acquired; a ground subsidence amount prediction model is constructed based on a lognormal probability density function according to the observation data; the ground subsidence amount prediction model is optimized according to prediction data and actual measurement data of the ground subsidence amount prediction model, so as to determine the ground subsidence amount prediction model; and the ground subsidence amount of the monitoring station is predicted by using the optimized ground subsidence amount prediction model. By the method, real-time dynamic monitoring of the ground subsidence of a mining area can be realized by using a small number of GNSS observation stations, the ground subsidence amount of the monitoring station can be accurately monitored and predicted, the monitoring cost is reduced, the monitoring efficiency and the reliability of data are improved, and strong support is provided for safe production, ecological protection and disaster prevention of the mining area. BRIEF DESCRIPTION OF DRAWINGS
[0039] Figure 1 A profile map of a goaf monitoring point;
[0040] Figure 2 A connection diagram of an acquisition device in the embodiments of the present application.
[0041] REFERENCE NUMERALS
[0042] An acquisition module 10; a construction module 20;
[0043] An optimization module 30; a prediction module 40. DETAILED DESCRIPTION
[0044] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.
[0045] The terms “first”, “second”, and the like in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein. In addition, “and / or” in the specification and claims indicates at least one of the connected objects, and the character “ / ” generally indicates that the front and rear associated objects are in an “or” relationship.
[0046] The method for acquiring ground subsidence data based on GNSS continuous mobile observation stations according to the embodiments of the present application comprises:
[0047] Acquiring observation data and actual measurement data of each monitoring point;
[0048] According to the observation data, a ground surface subsidence amount prediction model is constructed based on a lognormal probability density function;
[0049] According to the prediction data of the ground surface subsidence amount prediction model and the actual measurement data, the ground surface subsidence amount prediction model is optimized, and the ground surface subsidence amount prediction model is determined;
[0050] The ground surface subsidence amount of the monitoring station is predicted by using the optimized ground surface subsidence amount prediction model.
[0051] In the embodiments of the present application, real-time dynamic monitoring of the ground surface subsidence in the mining area can be realized by using a small number of GNSS observation stations, the ground surface subsidence amount of the monitoring station can be accurately monitored and predicted, the monitoring cost is reduced, the monitoring efficiency and the reliability of the data are improved, and strong support is provided for the safety production, ecological protection and disaster prevention of the mining area. It is found through a large amount of engineering practice research that the ground surface movement often presents a skewed distribution characteristic in time and space, the prediction accuracy of the lognormal density function selected in the present application is higher, a large area and high precision ground surface subsidence monitoring can be realized by using a small number of GNSS continuous movement observation stations, the equipment cost and maintenance cost are significantly reduced, the GNSS continuous observation station can obtain the three-dimensional coordinates and deformation amount of the ground surface point in real time, realize real-time dynamic monitoring of the ground surface subsidence, and is suitable for various scenes such as coal mining area, mining area and urban land subsidence, and has a wide application prospect.
[0052] It is found through a large amount of engineering practice research that the ground surface movement often presents a skewed distribution characteristic in time and space, the prediction accuracy of the lognormal density function selected in the present application is higher.
[0053] In some embodiments, the step of constructing a ground surface subsidence amount prediction model based on a lognormal probability density function according to the observation data can include:
[0054] According to the observation data, a ground surface subsidence velocity prediction model is constructed based on a lognormal probability density function;
[0055] The ground surface subsidence amount prediction model is constructed according to the ground surface subsidence velocity prediction model.
[0056] Further, based on the lognormal probability density function, the subsidence velocity prediction model is constructed in combination with the right skewed distribution characteristic of the subsidence velocity curve of the ground surface point, and the calculation formula of the ground surface subsidence velocity prediction model can be:
[0057]
[0058] Wherein, v(t) is the subsidence velocity at time t, w0 is the maximum subsidence amount of the ground surface point, A is a position parameter, A is related to the time when the subsidence velocity reaches the maximum value; B is a shape parameter of the subsidence velocity curve, B reflects the width of the subsidence velocity curve.
[0059] In some embodiments, the calculation formula of the ground subsidence amount prediction model can be:
[0060]
[0061] wherein erf is an error function, and w(t) is the ground subsidence amount at time t.
[0062] In the embodiments of the present application, the step of optimizing the ground subsidence amount prediction model according to the prediction data and the actual measurement data of the ground subsidence amount prediction model can include:
[0063] comparing the prediction data of the ground subsidence amount prediction model with the actual measurement data, and calculating the error sum of squares;
[0064] adjusting the values of the parameters w0, A and B, recalculating the prediction data, and comparing the recalculated prediction data with the actual measurement data, and calculating the error sum of squares;
[0065] iterative optimization, when the error sum of squares reaches a preset value, the optimized values of the parameters w0, A and B are obtained;
[0066] substituting the optimized values of the parameters w0, A and B into the ground subsidence amount prediction model to obtain the optimized ground subsidence amount prediction model.
[0067] In the parameter solving process of the ground subsidence amount prediction model, the least squares method can be used to solve the parameters w0, A and B of the model through GNSS time series observation data, and the least squares method can minimize the error sum of squares between the observation data and the model prediction value. GNSS time series observation data collected during the working face advancing process can be used to update the model parameters in real time, predict the subsequent subsidence amount of the ground point, and verify the prediction accuracy through the measured data.
[0068] In some embodiments of the present application, the step of constructing the ground subsidence amount prediction model based on the lognormal probability density function according to the observation data can include:
[0069] obtaining observation data of a reference station;
[0070] preprocessing the observation data of each monitoring point obtained by a differential positioning method according to the observation data of the reference station;
[0071] constructing the ground subsidence amount prediction model based on the lognormal probability density function according to the preprocessed observation data.
[0072] In the monitoring area, one reference station can be set up. The reference station is located in a stable area and serves as a base station to eliminate common errors in GNSS observations (such as atmospheric delay, satellite clock error, etc.). Two observation stations can be set up at the monitoring point. They can be located in the fully mined area along the direction and inclination of the working face, such as Figure 1 As shown in Figure 1, the surface subsidence at monitoring points A1 and A2 can be monitored by GNSS observation stations.
[0073] The three-dimensional coordinates of each monitoring point are obtained in real time through the GNSS observation station. The observation frequency can be adjusted according to the mining progress and subsidence rate. The observation frequency is usually increased during the active surface movement phase (such as during the advancement of the working face). The reference station data can be used to eliminate common errors through differential positioning methods to improve the accuracy of the monitoring station data. For example, assuming that the observation coordinates of the reference station are (x0, y0, z0) and the original observation coordinates of the monitoring station are (x1, y1, z1), the coordinates after differential positioning are (x, y, z), then the calculation formula is:
[0074] d 10 : The observed distance difference between the monitoring station and the reference station, d0: The theoretical distance between the reference station and the satellite.
[0075] Using the reference station data through the differential positioning method can eliminate common errors and improve the accuracy of monitoring station data.
[0076] In some embodiments, after the step of pre-processing the observation data of each monitoring point obtained by the differential positioning method based on the observation data of the reference station, the following steps may also be included:
[0077] The pre-processed observation data is filtered by the sliding average filtering method;
[0078] A surface subsidence prediction model is constructed based on the log-normal probability density function according to the filtered observation data.
[0079] In GNSS-based surface movement and deformation monitoring, the collected time series observation data usually contains noise and random errors. In order to eliminate these noises and extract the true surface subsidence trend, it is usually necessary to smooth the data. During the filtering process,
[0080] Assume there is a set of time series data: z=[z1,z2,z3,…,z n ], the sliding average filtering method generates a smoothed data sequence by taking the average of k consecutive points in the data sequence. The smoothed data point y i It can be expressed as: Where k is the size of the sliding window (usually an odd number); is the radius of the window, i is the index of the data point (i = m + 1, m + 2, …, n - m).
[0081] In the process of acquiring GNSS observation point observation data, the device can record the three-dimensional coordinate data of the monitoring point in real time during the observation process, including longitude, latitude and elevation. Through the acquired three-dimensional coordinate data, according to the formula ΔZ = Z 初始 -Z 当前 Calculate the ground subsidence value, where ΔZ is the subsidence value, Z 初始 is the elevation of the monitoring point before mining, Z 当前 is the elevation of the monitoring point at a certain time during mining.
[0082] Taking a certain coal mine area as an example, the specific implementation steps are as follows:
[0083] (1) Layout of GNSS observation station
[0084] GNSS monitoring stations are set up at two characteristic points of surface movement and deformation of the working face, and one reference station is set up in the stable area. The observation station is equipped with a solar power supply system and a lightning protection device to ensure long-term stable operation.
[0085] (2) Data acquisition and transmission
[0086] The GNSS observation station collects data regularly and uploads the data to the central data processing system through the wireless data transmission module. In the data transmission process, 4G communication technology is used, and data encryption is used to ensure data security. At the same time, a data receiving and storage module is set up in the central data processing system to receive and store the uploaded data in real time.
[0087] (3) Data acquisition and preprocessing
[0088] Continuous observation data of GNSS monitoring points and periodic observation data of traditional monitoring points are collected to record the ground subsidence value at different time points during mining.
[0089] Taking part of the observation data of GNSS1 observation point as an example:
[0090]
[0091] (4) Model construction and parameter solving
[0092] 1. Model construction
[0093] The ground subsidence velocity prediction model based on the lognormal probability density function is:
[0094]
[0095] Integrating the subsidence velocity prediction model in the monitoring period, a subsidence amount prediction model is obtained as follows:
[0096] 2. Parameter solving
[0097] Taking GNSS1 as an example, according to experience or preliminary analysis, the initial parameters are set as w0=2.0 m, A=3.21, B=0.5, the predicted value of the subsidence amount is predicted by using the subsidence amount prediction model, and the error sum of squares S is calculated by comparing the predicted value with the actual observation value.
[0098] Iterative optimization: the parameters w0, A and B are adjusted to recalculate the predicted value and the error sum of squares, and the optimization algorithm is used to find the parameter value that minimizes S;
[0099] Optimal parameters: when the change of the error sum of squares is less than a set threshold (10 -6 ), the iteration is stopped, and the optimal parameters are obtained as w0=2.48 m, A=3.39, B=0.72;
[0100] 3. Prediction and verification
[0101] Predicted cumulative subsidence amount: the optimal parameters are used to predict the subsidence amount of the GNSS1 monitoring station on the 60th day:
[0102] Model verification: the prediction accuracy is verified by the measured data, and the measured subsidence value of the GNSS1 monitoring station on the 60th day is w 实际 =2.56 m. The absolute error is AE=|2.56-2.54|=0.02 m. Similar prediction and verification operations are performed on multiple GNSS observation stations at multiple time points for subsequent dynamic prediction. It can be seen that the method can accurately predict the subsequent subsidence amount of the ground point, effectively monitor the subsidence of the ground monitoring point, and take preventive measures in advance.
[0103] As shown in Figure 2 , the ground subsidence data acquisition device based on the GNSS continuous mobile observation station of the embodiment of the application comprises:
[0104] The acquisition module 10 is configured to acquire observation data and actual measurement data of each monitoring point.
[0105] The construction module 20 is configured to construct a subsidence amount prediction model based on a lognormal probability density function according to the observation data.
[0106] The optimization module 30 is configured to optimize the subsidence amount prediction model according to the predicted data and the actual measurement data of the subsidence amount prediction model, and determine the subsidence amount prediction model.
[0107] The prediction module 40 is configured to predict the ground subsidence of the monitoring station by using the optimized ground subsidence prediction model.
[0108] The electronic device of the embodiment of the present application comprises a processor and a memory, the memory stores programs or instructions which can be run on the processor, and the programs or instructions are executed by the processor to realize the steps of the acquisition method described in the above embodiment.
[0109] The readable storage medium of the embodiment of the present application stores programs or instructions, and the programs or instructions are executed by the processor to realize the steps of the acquisition method described in the above embodiment.
[0110] In the embodiment of the present application, by using the device in the present application, real-time dynamic monitoring of the ground subsidence in the mining area can be realized by a small number of GNSS observation stations, the ground subsidence of the monitoring station can be accurately monitored and predicted, the monitoring cost is reduced, the monitoring efficiency and the reliability of the data are improved, strong support is provided for the safety production, ecological protection and disaster prevention of the mining area, and the device is suitable for various scenes such as coal mining areas, mining areas and urban ground subsidence.
[0111] The embodiments of the present application are described above in combination with the drawings, but the present application is not limited to the above specific embodiments, the above specific embodiments are only illustrative but not restrictive, and those skilled in the art can make many forms under the inspiration of the present application without departing from the purpose of the present application and the scope protected by the claims, and all the forms belong to the protection of the present application.
Claims
1. A method for acquiring surface subsidence data based on a GNSS continuous mobile observation station, characterized in that: include: Obtain observation data and actual measurement data at each monitoring point; A surface subsidence prediction model was constructed based on the log-normal probability density function according to the observed data; Optimize the surface subsidence prediction model based on the prediction data of the surface subsidence prediction model and the actual measurement data, and determine the surface subsidence prediction model; The optimized surface subsidence prediction model is used to predict the surface subsidence at the monitoring station.
2. The acquisition method according to claim 1, characterized in that The steps of constructing a surface subsidence prediction model based on the lognormal probability density function according to the observed data include: A surface subsidence velocity prediction model was constructed based on the log-normal probability density function according to the observed data; A surface subsidence prediction model is constructed based on the surface subsidence velocity prediction model.
3. The acquisition method according to claim 2, characterized in that The calculation formula of the surface subsidence velocity prediction model is: Among them, v(t) is the sinking velocity at time t, w0 is the maximum sinking amount of the surface point, A is the position parameter, and B is the shape parameter of the sinking velocity curve.
4. The acquisition method according to claim 3, characterized in that The calculation formula of the surface subsidence prediction model is: Where erf is the error function and w(t) is the surface subsidence at time t.
5. The acquisition method according to claim 3 or 4, characterized in that: The steps of optimizing the surface subsidence prediction model based on the prediction data of the surface subsidence prediction model and the actual measurement data include: Compare the predicted data of the surface subsidence prediction model with the actual measured data and calculate the sum of squared errors; Adjust the values of parameters w0, A, and B, recalculate the predicted data, compare the recalculated predicted data with the actual measured data, and calculate the sum of squared errors; Iterative optimization, when the sum of squared errors reaches the preset value, the optimized values of parameters w0, A, and B are obtained; Substitute the optimized values of parameters w0, A, and B into the surface subsidence prediction model to obtain the optimized surface subsidence prediction model.
6. The acquisition method according to claim 1, characterized in that The steps of constructing a surface subsidence prediction model based on the lognormal probability density function according to the observed data include: Obtain observation data from reference stations; The observation data of each monitoring point are preprocessed by using the differential positioning method based on the observation data of the reference station; A surface subsidence prediction model is constructed based on the log-normal probability density function according to the preprocessed observation data.
7. The acquisition method according to claim 6, characterized in that: After the step of pre-processing the observation data of each monitoring point obtained by the differential positioning method based on the observation data of the reference station, the method further includes: The pre-processed observation data is filtered by the sliding average filtering method; A surface subsidence prediction model is constructed based on the log-normal probability density function according to the filtered observation data.
8. A surface subsidence data acquisition device based on a GNSS continuous mobile observation station, characterized in that: include: Acquisition module, used to obtain observation data and actual measurement data of each monitoring point; A construction module is used to construct a surface subsidence prediction model based on the log-normal probability density function according to the observed data; an optimization module, configured to optimize the surface subsidence prediction model based on the prediction data of the surface subsidence prediction model and actual measurement data, and determine the surface subsidence prediction model; The prediction module is used to predict the surface subsidence of the monitoring station using the optimized surface subsidence prediction model.
9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the acquisition method according to any one of claims 1 to 7 are implemented.
10. A readable storage medium, characterized in that: The readable storage medium stores a program or instruction, and when the program or instruction is executed by the processor, the steps of the acquisition method according to any one of claims 1 to 7 are implemented.