Method, system, medium and program product for predicting a mining subsidence basin

Abstract

The application discloses a calculation method and system of a mine area subsidence basin prediction, a medium and a program product, and relates to the technical field of mine area subsidence calculation. In the method, a first calculation result and a second calculation result of a mine area subsidence basin to be calculated are obtained, the first calculation result is obtained by calculating leveling data by using a probability integral method, and the second calculation result is obtained by calculating synthetic aperture radar image data by using an interferometric synthetic aperture radar time series analysis method; the first calculation result and the second calculation result are fused according to a prediction rule to obtain a final calculation result of the mine area subsidence basin prediction to be calculated; and the prediction accuracy of the mine area subsidence basin is improved, and the management of the mine area subsidence basin is facilitated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mining area depression calculation, and in particular to a mining area subsidence basin prediction calculation method, system, medium and program product. BACKGROUND

[0002] For a long time, coal mining has not only ensured energy security and supported the rapid development of economy and society, but also caused serious land subsidence, ecological degradation and development constraints. Therefore, through comprehensive management of coal mining subsidence areas to promote industrial transformation and upgrading is an inevitable choice for the sustainable development of resource-based cities.

[0003] However, the key factor of comprehensive management is to obtain the calculation result of mining area subsidence basin prediction. Currently, there are mainly two kinds of mining area subsidence basin calculation methods: one is to use the data obtained by leveling measurement to perform inversion calculation by using the probability integral model. This method measures the change of the elevation of each point over time by arranging leveling points in the mining area, and then establishes a subsidence prediction model based on the probability integral theory to calculate the subsidence basin of the entire mining area. The other method is to use synthetic aperture radar to obtain radar image of the mining area, generate deformation interferogram by differential interference processing, and then calculate the subsidence amount of the mining area at different periods by using time series analysis methods such as permanent scatterers.

[0004] However, there are still some problems to be solved. For example, although the leveling measurement has high precision, the observation point arrangement is limited by topography, personnel and other conditions, and the obtained data is sparse and insufficient in representativeness, which cannot finely depict the subsidence of the entire mining area. Although the radar interferometric measurement can obtain high-density deformation observation values, meteorological noise is easily mixed in the calculation result, which reduces the precision. The calculation precision of the commonly used mining area subsidence basin calculation methods is not high, and the reference of the management of the mining area subsidence basin is unreliable. SUMMARY

[0005] The present application provides a mining area subsidence basin prediction calculation method, system, medium and program product for improving the precision of mining area subsidence basin prediction and facilitating the management of the mining area subsidence basin.

[0006] In a first aspect, the present application provides a mining area subsidence basin prediction calculation method, comprising:

[0007] obtaining a first calculation result and a second calculation result of the mining area subsidence basin prediction to be calculated, the first calculation result being obtained by calculating leveling measurement data by using a probability integral method, and the second calculation result being obtained by calculating synthetic aperture radar image data by using an interferometric synthetic aperture radar time series analysis method;

[0008] fusing the first calculation result and the second calculation result according to a prediction rule to obtain a final calculation result of the mining area subsidence basin prediction to be calculated.

[0009] The prediction rule comprises:

[0010] extracting data not lower than the upper critical threshold from the first calculation result as part of the final calculation result;

[0011] extracting data not higher than the lower critical threshold from the second calculation result as part of the final calculation result;

[0012] extracting third data higher than the lower critical threshold from the first calculation result and fourth data lower than the upper critical threshold from the second calculation result; and taking a result of weighted fusion of the third data and the fourth data according to a set first weight and a second weight as part of the final calculation result, the sum of the first weight and the second weight being one.

[0013] In the above embodiment, the hierarchical fusion strategy is adopted according to the difference of subsidence amplitudes in different areas, which can more reasonably play the advantages of each data. The subsidence center area often deforms violently and exceeds the detection saturation range of radar interferometry, but is exactly the advantage of high-precision leveling. By setting the upper threshold, data of large subsidence in the first calculation result is preferentially extracted into the final result. In contrast, the surface subsidence of the mine area edge area is relatively moderate and falls within the best sensitivity range of radar measurement. By setting the lower threshold, data of small deformation from the second calculation result is extracted, which can expand the effective monitoring range to the greatest extent and completely depict the spatial form of the entire subsidence basin without increasing the difficulty and cost of manual point layout. For the moderate subsidence in the transition area, the reliability of single data is reduced as it does not simultaneously meet the high-credibility conditions of leveling and radar analysis results. At this time, by extracting data between the upper and lower thresholds from the two types of results and performing weighted averaging with certain weights, the results can be mutually verified to some extent, reduce the accidental errors of each other, and make the subsidence calculation results in the transition area more robust. Through the above hierarchical and progressive fusion calculation, the final mine subsidence prediction result can overcome the limitations of traditional methods and give high-precision and high-spatial-resolution subsidence distribution in the center area, the transition area and the edge area, thereby realizing detailed evaluation of the entire mine subsidence basin and facilitating the management of the mine subsidence basin.

[0014] In combination with some embodiments of the first aspect, before the step of fusing the first calculation result and the second calculation result according to the prediction rule to obtain the final calculation result of the predicted mine subsidence basin, the method further comprises:

[0015] determine the average result of the first probability integral result and the second probability integral result as the upper critical threshold, the first probability integral result is obtained by calculating the working face starting position by using the probability integral method, and the second probability integral result is obtained by calculating the stop line position by using the probability integral method;

[0016] determine the maximum deformation gradient of the interferometric synthetic aperture radar time series analysis method as the lower critical threshold;

[0017] respectively calculate the mean absolute error of the interferometric synthetic aperture radar time series analysis method and the probability integral method;

[0018] determine the inverse proportional calculation result of the mean absolute error of the interferometric synthetic aperture radar time series analysis method and the probability integral method as the first weight and the second weight.

[0019] In the above embodiment, the starting position and the stop line position are the starting point and the end point of the subsidence process, representing the formation process of the entire subsidence basin, and the average of the two is taken as the upper threshold, and the data of the large subsidence amount in the first calculation result is extracted into the final result, which ensures the accurate control of the key risk area. The maximum deformation gradient reflects the "extreme value" of the subsidence in the radar image coverage area, corresponding to the most serious subsidence area. Taking this as the lower threshold can ensure that the weak subsidence area drawn will not contain too severe deformation, and can expand the effective monitoring range to the greatest extent. The mean absolute error is an important indicator for measuring the deviation degree of the model prediction result from the actual observation value. The smaller the MAE, the closer the calculation result of the method to the true situation, and the higher the reliability. By calculating the MAE of the InSAR time series analysis and the probability integral method respectively, the accuracy of the two methods can be quantitatively compared, and an objective basis is provided for subsequent weighted fusion. This weight determination method based on error evaluation avoids the uncertainty of subjective experience judgment, making the fusion process more scientific and reliable.

[0020] In combination with some embodiments of the first aspect, in some embodiments, the step of determining the maximum deformation gradient of the interferometric synthetic aperture radar time series analysis method as the lower critical threshold specifically comprises:

[0021] obtaining the maximum deformation gradient of the interferometric synthetic aperture radar time series analysis method;

[0022] determining whether the maximum deformation gradient is greater than a preset observation threshold;

[0023] if not, the maximum deformation gradient is determined as the lower critical threshold;

[0024] if greater, the maximum deformation gradient that has been verified to be problem-free is selected from the historical data of the to-be-calculated mining area subsidence basin prediction, and is determined as the lower critical threshold.

[0025] In the above embodiments, by obtaining the maximum deformation gradient of the interferometric synthetic aperture radar (IASAR) time-series analysis method and comparing it with a preset observation threshold, it is possible to determine whether the current radar data is severely affected by factors such as season and coherence. If the observation threshold is not exceeded, the maximum gradient is directly used as the lower threshold, forming a transition range between the upper and lower thresholds. If the deformation gradient of the radar method is abnormally large, a more reliable maximum gradient value is selected from historical verification data to avoid distortion of the transition range. This adaptive adjustment strategy improves the rationality of edge region data selection.

[0026] In conjunction with some embodiments of the first aspect, in some embodiments, after the step of fusing the first calculation result and the second calculation result according to the prediction rule to obtain the final calculation result of the subsidence basin of the mining area to be calculated, the method further includes:

[0027] A prediction model for the ground subsidence of the mining area's subsidence basin is constructed; the ground subsidence prediction model is as follows:

[0028] S s =I s Δh=I' s ΔhH

[0029] S C =I C Δh=I' C ΔhH

[0030]

[0031]

[0032] In the formula, S s S represents the rebound amount of a soil layer of thickness H when the water level rises by Δh. C I represents the settlement of a soil layer of thickness H when the water level drops by Δh. s I represents the unit deformation during the water level rise period. C I' is the unit deformation during the water level drop period. s I' represents the unit deformation during the water level rise period. C The unit deformation during the water level drop period, Δh s Let Δh' be the magnitude of the water level rise within the time interval Δh'. c Let ΔS be the magnitude of the water level rise within the time interval Δh'. s For the corresponding Δh s The change in soil layer ΔS C For the corresponding Δh C Changes in the underlying soil layers.

[0033] In the above embodiment, the elastic rebound and plastic compression deformation of the soil layer during the water level fluctuation process can be comprehensively considered. The model introduces the specific unit deformation variable during the water level rising period and the water level falling period, and can distinguish the influence of the water level change direction on the soil layer deformation. By calculating the rebound amount and the settlement amount respectively, the vertical displacement of the ground caused by the water level fluctuation can be more accurately estimated, and the precision of the settlement prediction can be improved.

[0034] In a second aspect, the embodiments of the present application provide a computing system for predicting a mining subsidence basin, the system comprising: one or more processors and a memory;

[0035] The memory is coupled to the one or more processors, and the memory is configured to store computer program code comprising computer instructions, the one or more processors invoking the computer instructions to cause the computing system for predicting the mining subsidence basin to perform the method as described in the first aspect and any possible implementation manner of the first aspect.

[0036] In a third aspect, the embodiments of the present application provide a computer program product comprising instructions, which, when executed on a server, cause the server to perform the method as described in the first aspect and any possible implementation manner of the first aspect.

[0037] In a fourth aspect, the embodiments of the present application provide a computer readable storage medium comprising instructions, which, when executed on a computing system for predicting a mining subsidence basin, cause the computing system for predicting the mining subsidence basin to perform the method as described in the first aspect and any possible implementation manner of the first aspect.

[0038] It can be understood that the computing system for predicting the mining subsidence basin provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to perform the method for predicting the mining subsidence basin provided in the embodiments of the present application. Therefore, the beneficial effects that can be achieved are referable to the beneficial effects in the corresponding method, which will not be repeated here.

[0039] The one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0040] 1. The calculation method for predicting a mining subsidence basin provided in the application, in view of the difference in subsidence amplitude in different regions, adopts a hierarchical fusion strategy, which can more reasonably give play to the advantages of each data. The subsidence center region often deforms violently and has exceeded the detection saturation range of radar interferometric measurement, but is exactly the advantage of high-precision leveling measurement. By setting an upper threshold, the data of large subsidence amount in the first calculation result is preferentially extracted and included in the final result. In comparison, the surface subsidence of the mining area edge region is relatively moderate and falls within the best sensitivity range of radar measurement. By setting a lower threshold, the data of small deformation amount is extracted from the second calculation result, which can expand the effective monitoring range to the greatest extent and completely depict the spatial form of the entire subsidence basin without increasing the difficulty and cost of artificial point layout. For the moderate subsidence amount in the transition zone, since the high-credibility conditions of both leveling measurement and radar analysis result are not met at the same time, the reliability of single data is reduced. At this time, by extracting the data between the upper and lower thresholds from the two types of results respectively and performing weighted averaging on them with a certain weight, the respective accidental errors can be reduced to some extent, and the subsidence amount calculation result of the transition zone is more robust. Through the hierarchical progressive fusion calculation described above, the final mining subsidence result can overcome the limitations of traditional methods and give a subsidence amount distribution with higher precision and higher spatial resolution in the center region, the transition zone and the edge region, so as to realize detailed evaluation of the entire mining subsidence condition and facilitate the management of the mining subsidence basin.

[0041] 2. The calculation method for predicting a mining subsidence basin provided in the application, the starting position and the stop line position are the starting point and the ending point of the subsidence process, representing the formation process of the entire subsidence basin. The average value of the two is taken as the upper threshold, the data of large subsidence amount in the first calculation result is extracted and included in the final result, which ensures accurate control of the key risk area. The maximum deformation gradient reflects the subsidence "extreme value" within the radar image coverage range, corresponding to the most serious subsidence region. Taking this as the lower threshold can ensure that the weak subsidence area drawn does not contain too violent deformation and can expand the effective monitoring range to the greatest extent. The mean absolute error is an important indicator for measuring the deviation degree of the model prediction result from the actual observation value. The smaller the MAE is, the closer the calculation result of the method is to the true situation, and the higher the reliability is. By calculating the MAE of InSAR time series analysis and the probability integral method respectively, the accuracy of the two methods can be quantitatively compared to provide an objective basis for subsequent weighted fusion. This weight determination method based on error evaluation avoids the uncertainty of subjective experience judgment, making the fusion process more scientific and reliable.

[0042] 3. The calculation method for predicting subsidence basins in mining areas provided in this application obtains the maximum deformation gradient from the interferometric synthetic aperture radar (IAPR) time-series analysis method and compares it with a preset observation threshold. This allows the method to determine whether the current radar data is severely affected by factors such as season and coherence. If the maximum gradient does not exceed the observation threshold, it is directly used as the lower threshold, forming a transition interval between the upper and lower thresholds. If the deformation gradient from the radar method is abnormally large, a more reliable maximum gradient value is selected from historical verification data to avoid distortion of the transition zone range. This adaptive adjustment strategy improves the rationality of data selection in the edge zone. Attached Figure Description

[0043] Figure 1 A flowchart illustrating the calculation method for predicting mining subsidence basins provided in this application.

[0044] Figure 2 A schematic diagram illustrating the calculation method for predicting mining subsidence basins provided in this application.

[0045] Figure 3 Another flowchart illustrating the calculation method for predicting mining subsidence basins provided in this application.

[0046] Figure 4 A schematic diagram of the physical device of the calculation system for predicting mining subsidence basins provided in this application. Detailed Implementation

[0047] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0048] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0049] The calculation method for predicting the subsidence basin in the mining area in this embodiment is described below:

[0050] like Figure 1 As shown, Figure 1A flowchart of a calculation method for predicting a mining subsidence basin according to the present application is provided.

[0051] S101, obtain a first calculation result and a second calculation result of a mining subsidence basin prediction to be calculated, the first calculation result is obtained by calculating the leveling data using the probability integral method, and the second calculation result is obtained by calculating the synthetic aperture radar image data using the InSAR time series analysis method.

[0052] The mining subsidence basin prediction to be calculated refers to the mining area that needs to be monitored and calculated, which is usually a concave area in the shape of a circle or an ellipse.

[0053] The probability integral method is a classical mining subsidence prediction model based on the random medium theory, which can calculate the subsidence at any position on the ground surface by performing probability integral on the mining strata. The leveling data is the change in ground elevation data obtained by using precise measuring equipment such as a level, which can directly reflect the vertical displacement of subsidence.

[0054] InSAR time series analysis (InSAR time series analysis method) can obtain the time series deformation information of the mining area by performing differential interference processing on multi-scene SAR images (synthetic aperture radar image data), and can inverse the time and space evolution process of subsidence. SAR image data is a microwave remote sensing image collected by a radar satellite or an airborne SAR sensor, which contains the reflection characteristics and deformation information of the ground surface.

[0055] Reference Figure 2 , Figure 2 An understanding diagram of the calculation method for predicting a mining subsidence basin according to the present application is provided.

[0056] In the mining subsidence monitoring and calculation, first, data of the same mining subsidence basin to be calculated is obtained by using two different technical means, leveling data and SAR image data are obtained respectively, then the probability integral method is used to process the leveling data to obtain the predicted subsidence basin subsidence (first calculation result), and the InSAR time series analysis method is used to process the SAR image data to obtain the InSAR detection mining subsidence result.

[0057] It should be noted that in some embodiments, the calculation method of the mining subsidence basin in the present application is different from the traditional method, the leveling data obtained in the present application is only part of the normal leveling data, in other words, the number of leveling points can be reduced when setting up leveling points in the mining area, especially at the edge of the subsidence basin. The adjustment of this point arrangement strategy is based on the characteristics of the method in the present application. In the subsequent calculation step, only the leveling data in the central region and the transition region is selected, so the workload can be greatly reduced and the measurement efficiency can be improved.

[0058] In some embodiments, in order to further optimize the layout scheme, the density of the leveling points in different areas can be reasonably adjusted according to the spatial distribution characteristics of the subsidence. Specifically, the density of the leveling points in the central area of the subsidence basin is greater than that in the transition area, because the deformation in the central area is severe, and a higher observation frequency is needed to characterize it, so as to improve the accuracy of the final calculation result of the subsidence basin to be calculated in the subsequent calculation. In the transition area, the subsidence deformation is relatively slow, and appropriately reducing the density of the leveling points can save manpower and material resources while ensuring the measurement accuracy.

[0059] Similarly, in the calculation method of the prediction of the subsidence basin in the mine area in the present application, when the InSAR time series analysis method is used for deformation monitoring, appropriate SAR data can also be selected, and the transition area and the edge area in the SAR data which are relatively accurate can be focused on, and the SAR images of these areas can be selectively processed to extract the relevant differential interferometric phase and perform time series inversion to obtain detailed spatio-temporal evolution information of the ground deformation and reduce the calculation resources.

[0060] S102, fusing the first calculation result and the second calculation result according to a prediction rule to obtain a final calculation result of the prediction of the subsidence basin to be calculated,

[0061] The prediction rule comprises:

[0062] extracting data not lower than an upper critical threshold from the first calculation result as part of the final calculation result;

[0063] extracting data not higher than a lower critical threshold from the second calculation result as part of the final calculation result;

[0064] extracting third data higher than the lower critical threshold from the first calculation result, and extracting fourth data lower than the upper critical threshold from the second calculation result; and weighting and fusing the third data and the fourth data according to a first weight and a second weight to obtain a result as part of the final calculation result, and the sum of the first weight and the second weight is one.

[0065] The upper critical threshold represents the upper limit of the settlement, and the settlement exceeding the value is considered to be severe, which usually occurs near the center of the subsidence. The lower critical threshold refers to the lower limit of the settlement, and the settlement lower than the value is considered to be weak, which usually occurs in the edge area of the subsidence basin.

[0066] Reference Figure 2 Therefore, by the upper critical threshold and the lower critical threshold, the subsidence basin in the mine area can be divided into a central area, a transition area and an edge area.

[0067] After obtaining the preliminary results of probability integral method and InSAR time series analysis, further fusion of the two results is needed to obtain more comprehensive and accurate subsidence monitoring results. Due to the differences in principle, accuracy, resolution, etc. between the two methods, direct superposition may introduce large deviations, so it is necessary to design a reasonable fusion rule. According to the spatial differentiation characteristics of subsidence, the subsidence can be classified by using upper and lower critical thresholds first, and the data of strong subsidence area (higher than the upper critical value), weak subsidence area (lower than the lower critical value) and moderate subsidence area (between the two thresholds) can be extracted. Considering that the overall reliability of the probability integral result is higher, while the InSAR result has more advantages in local deformation monitoring, the strong subsidence and weak subsidence data can be extracted from the two results respectively, and directly used to construct the final result; while for the moderate subsidence in the transition area, the weighted average method is used for fusion, which combines the advantages of the two methods. By reasonably setting the first and second weights, the relative contribution of the two methods in the fusion result can be adjusted, and the overall calculation accuracy can be improved. The final subsidence calculation result can reflect the overall shape of the subsidence basin in a macroscopic way, and depict the fine changes of local subsidence in a microscopic way, providing a reliable basis for the assessment and prevention of coal mining subsidence disasters.

[0068] In some embodiments, optionally, according to the general law of mining subsidence in mining area, combined with previous experience and measured data analysis, the upper and lower critical thresholds are manually set, then the subsidence of different grades is extracted from the two calculation results respectively, the strong subsidence and weak subsidence data are directly spliced, and the moderate subsidence data is weighted averaged, the weight coefficient is manually adjusted to make the fusion result consistent with the actual situation; it can be understood that other ways can also be used, which are not limited here.

[0069] It can be seen that, for the difference of subsidence amplitude in different areas, the hierarchical fusion strategy can more reasonably play the advantages of each data. The subsidence center area often deforms violently and exceeds the detection saturation range of radar interferometry, but it is exactly the advantage of high-precision leveling. By setting an upper threshold, the data of large subsidence in the first calculation result is preferentially extracted into the final result. In contrast, the surface subsidence of the mine area edge area is relatively moderate and falls within the best sensitivity range of radar measurement. By setting a lower threshold, the data of small deformation from the second calculation result is extracted, which can expand the effective monitoring range to the greatest extent and completely depict the spatial form of the entire subsidence basin without increasing the difficulty and cost of manual point layout. For the moderate subsidence in the transition zone, the reliability of a single data is reduced since it does not simultaneously meet the high-credibility conditions of leveling and radar analysis results. At this time, by extracting the data between the upper and lower thresholds from the two types of results respectively and weighting them with a certain weight, the results can be mutually verified to some extent and the accidental errors of each other can be reduced, so that the subsidence calculation results in the transition zone are more robust. Through the above hierarchical and progressive fusion calculation, the final mine subsidence results can overcome the limitations of traditional methods and give high-precision and high-spatial-resolution subsidence distribution in the center area, the transition area and the edge area, thereby realizing detailed evaluation of the entire mine subsidence basin and facilitating the management of the mine subsidence basin.

[0070] In the above embodiments, the calculation method of the mine subsidence basin prediction is mainly introduced. However, in actual mine management and disaster prevention, it is far from enough to only calculate the subsidence basin that has occurred. In order to better guide the mining work and land reclamation planning, it is also necessary to further predict the evolution trend of the mine subsidence basin in a future period of time, that is, to predict the subsequent ground subsidence. This is of great significance for evaluating the environmental impact of mining activities, optimizing the mining scheme and formulating disaster prevention measures. Therefore, after obtaining the calculation results of the mine subsidence basin prediction, the application also provides three specific ground subsidence prediction methods to realize accurate prediction of the future subsidence of the mine.

[0071] In some embodiments, after step S102, the method further comprises: constructing a ground subsidence prediction model of the mine subsidence basin to be calculated; and the ground subsidence prediction model is:

[0072] S s = I s Δh = I' s ΔhH

[0073] S C = I C Δh = I' C ΔhH

[0074]

[0075]

[0076] In the formula, S s is the rebound of the soil layer with thickness H when the water level rises by Ah, S C is the settlement of the soil layer with thickness H when the water level falls by Ah, I s is the unit deformation during the water level rising period, I C is the unit deformation during the water level falling period, I' s is the specific unit deformation during the water level rising period, I' C is the specific unit deformation during the water level falling period, Ah s is the water level rising amplitude during Ah' time, Ah C is the water level falling amplitude during Ah' time, AS s is the soil layer change corresponding to Ah s , AS C is the soil layer change corresponding to Ah C .

[0077] It can be seen that the elastic rebound and plastic compression deformation of the soil layer during the water level rising and falling process are comprehensively considered. The model introduces the specific unit deformation during the water level rising and falling period, and can distinguish the influence of the water level change direction on the soil layer deformation. By calculating the rebound and settlement respectively, the vertical displacement of the ground caused by the water level fluctuation can be more accurately estimated, and the precision of the settlement prediction is improved.

[0078] In one specific embodiment, as shown in Figure 3 , Fig. 4 is another flowchart of the calculation method for predicting the mining subsidence basin provided in the present application. Figure 3

[0079] S301, obtain a first calculation result and a second calculation result of a mining subsidence basin prediction to be calculated, the first calculation result is obtained by calculating leveling data using a probability integral method, and the second calculation result is obtained by calculating synthetic aperture radar image data using an interferometric synthetic aperture radar time series analysis method.

[0080] The principles and processes of related steps have been described in detail in step S101, and will not be repeated here.

[0081] S302, determine the average result of the first probability integral result and the second probability integral result as an upper critical threshold, the first probability integral result is obtained by calculating the working face starting position using the probability integral method, and the second probability integral result is obtained by calculating the stop line position using the probability integral method.

[0082] ​The starting position of the working face usually refers to the location where coal mining activities begin, and the subsidence at this point is relatively small. The stop-mining line refers to the location where coal mining activities end, and the subsidence at this point is usually close to its maximum value.

[0083] After obtaining the calculation results using the probability integral method, it is necessary to further determine the upper threshold for subsidence classification to provide a basis for subsequent result fusion. By calculating the subsidence at the starting position and the stop line of the working face, it is generally found that the subsidence at the starting position is relatively small, while the subsidence at the stop line is close to the maximum value. To reasonably divide the severely subsidence area, the subsidence at the two characteristic positions can be arithmetically averaged to obtain the upper critical threshold.

[0084] In some embodiments, the probability integral prediction result at the starting position of the working face is 400mm, and the probability integral prediction result at the stop line position is 300mm. The average of the two, 350mm, is taken as the upper critical threshold for fusion.

[0085] S303. The maximum deformation gradient of the interferometric synthetic aperture radar timing analysis method is determined as the lower critical threshold.

[0086] The maximum deformation gradient reflects the subsidence "extreme value" within the radar image coverage area, corresponding to the area with the most severe subsidence.

[0087] In the marginal areas of subsidence basins, due to their distance from mining areas, the surface subsidence rate is slower, and the deformation time series is relatively flat. Therefore, the maximum deformation gradient obtained from InSAR time series analysis can be used as the lower critical threshold, that is, subsidence below this rate is classified as marginal areas. Using this as the lower threshold can ensure that the delineated marginal areas do not contain excessively drastic deformation.

[0088] In other embodiments, some of the observed images were taken during the summer, resulting in low image coherence and poor quality. Consequently, the maximum deformation that InSAR could detect in actual observations was much smaller than the maximum deformation gradient.

[0089] Therefore, in some other embodiments, step S303 can be replaced by:

[0090] S3031. Obtain the maximum deformation gradient of the interferometric synthetic aperture radar timing analysis method;

[0091] S3032. Determine whether the maximum deformation gradient is greater than the preset observation threshold.

[0092] S3033. If it is not greater than, then the maximum deformation gradient is determined as the lower critical threshold.

[0093] S3034, if greater, then the maximum deformation gradient of the historical data of the mining area subsidence basin to be calculated is selected, which has been verified to be no problem, and is determined as the lower critical threshold.

[0094] It should be noted that the maximum deformation gradient of the historical data of the mining area subsidence basin to be calculated is selected, which has been verified to be no problem. The data referred to as "verified to be no problem" here usually refers to data that has been normally used in previous actual calculations and has reliable results. Generally, these data mainly come from SAR images in spring, autumn and winter, because the ground surface conditions in these periods are relatively stable, and the atmospheric effect is smaller, which is more conducive to obtaining high-quality differential interference results. It should be noted that the selected maximum deformation gradient should be less than or equal to the maximum deformation value actually monitored by InSAR technology in the mining area.

[0095] In some other embodiments, if there is no historical monitoring data available in the mining area to be calculated, or there is a lack of verified reliable maximum deformation gradient value in the historical data, the preset observation threshold is directly selected as the lower critical threshold.

[0096] It can be seen that by obtaining the maximum deformation gradient of the interferometric synthetic aperture radar time series analysis method and comparing it with the preset observation threshold, it can be judged whether the current radar data is seriously affected by factors such as season and coherence. If it does not exceed the observation threshold, the maximum gradient is directly used as the lower threshold, forming a transition interval between the upper threshold and the lower threshold. If the deformation gradient of the radar method is abnormally large, a more reliable maximum gradient value is selected from the historical verification data, avoiding distortion of the transition area. This adaptive adjustment strategy improves the rationality of data screening in the edge area.

[0097] S304, respectively calculate the average absolute error of the interferometric synthetic aperture radar time series analysis method and the probability integral method.

[0098] It should be noted that the average absolute error of the InSAR time series analysis method and the average absolute error of the probability integral method respectively represent the accuracy of the InSAR time series analysis method and the probability integral method;

[0099] In some embodiments, the error of the InSAR time series analysis method is calculated using a multi-baseline interferometric configuration (InSAR) height measurement error equation;

[0100] The multi-baseline interferometric configuration uses multiple radar images at different angles to increase the diversity of observation angles, thereby improving the accuracy and reliability of height measurement.

[0101] Error=f(σ φ ,B,λ,L,θ)

[0102] In the formula, σ φis the standard deviation of the phase of the interference, B is the baseline length; λ is the radar wavelength, L is the distance of the radar to the target, and θ is the angle of incidence.

[0103] In some embodiments, the precision of the probability integral method is calculated by using the variable step Simpson double integral; Simpson's rule is a numerical integration method used to approximate the definite integral; the variable step Simpson rule is a variant of it, which improves the precision of integration by adaptively adjusting the step size; in the time series analysis method, the cumulative effect of some variables over time may need to be calculated by integration; using the variable step Simpson double integral can effectively calculate these effects and reduce the error of numerical integration.

[0104] In other embodiments, the calculation results and the corresponding actual observation values of the InSAR time series analysis method and the probability integral method are obtained respectively. Then, for each method, the average absolute error is calculated respectively, and the average absolute error of the InSAR time series analysis method and the probability integral method can be calculated in advance. Once these error values are determined, they can be directly used in subsequent analysis and application.

[0105] Of course, other methods can be used to calculate the average absolute error of the InSAR time series analysis method and the probability integral method, which are not limited here.

[0106] S305, the reciprocal of the average absolute error of the InSAR time series analysis method and the probability integral method is determined as the first weight and the second weight.

[0107] By calculating the average absolute error of the InSAR time series analysis and the probability integral method respectively, the prediction accuracy of the two methods can be compared. Generally speaking, the smaller the average absolute error, the closer the prediction result to the actual situation, and the higher the credibility of the method.

[0108] The reciprocal of the two error values is used as the initial weight, which is normalized so that the sum of the weights is 1, thereby obtaining the final fusion weight.

[0109] S306, according to the prediction rule, the first calculation result and the second calculation result are fused to obtain the final calculation result of the predicted mining area subsidence basin, and the prediction rule includes:

[0110] Extracting data not lower than the upper critical threshold from the first calculation result as part of the final calculation result;

[0111] Extracting data not higher than the lower critical threshold from the second calculation result as part of the final calculation result;

[0112] Third data higher than the lower critical threshold is extracted from the first calculation result, and fourth data lower than the upper critical threshold is extracted from the second calculation result; a result of weighted fusion of the third data and the fourth data according to a set first weight and a second weight is taken as part of the final calculation result, and a sum of the first weight and the second weight is one.

[0113] The principles and processes of the related steps have been described in detail in step S102, and will not be described here.

[0114] It can be seen that the starting position and the stop line position are the starting point and the ending point of the subsidence process, representing the formation process of the entire subsidence basin. The average value of the two is taken as the upper threshold, and the data of large subsidence amount in the first calculation result is extracted into the final result, which ensures accurate control of the key risk area. The maximum deformation gradient reflects the "extreme value" of subsidence in the radar image coverage area, corresponding to the most serious subsidence area. Taking this as the lower threshold can ensure that the weak subsidence area delineated does not contain too severe deformation, and can expand the effective monitoring range to the greatest extent. The mean absolute error is an important indicator for measuring the deviation degree of the model prediction result from the actual observation value. The smaller the MAE, the closer the calculation result of the method to the true situation, and the higher the reliability. By calculating the MAE of the InSAR time series analysis and the probability integral method respectively, the accuracy of the two methods can be quantitatively compared to provide an objective basis for subsequent weighted fusion. This weight determination method based on error evaluation avoids the uncertainty of subjective experience judgment, making the fusion process more scientific and reliable.

[0115] The following is an apparatus embodiment of the present application, which can be used to execute the method embodiments of the present application. For details not disclosed in the apparatus embodiments of the present application, refer to the method embodiments of the present application.

[0116] The present application also discloses a computing system for predicting a subsidence basin in a mining area. Refer to Figure 4 , the entity device of the computing system for predicting a subsidence basin in a mining area provided by the present application. The computer 400 can include at least one processor 401, at least one network interface 404, a user interface 403, a memory 405, and at least one communication bus 402.

[0117] The communication bus 402 is used to realize the connection and communication between the components.

[0118] The user interface 403 can include a display screen (Display) and a camera (Camera). Optionally, the user interface 403 can also include a standard wired interface and a wireless interface.

[0119] Optionally, the network interface 404 can include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0120] The processor 401 can include one or more processing cores. The processor 401 connects various parts within the server through various interfaces and lines, performs various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 405, and calling data stored in the memory 405. Alternatively, the processor 401 can be implemented in at least one of a hardware form of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor 401 can integrate a combination of one or more of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU mainly processes operating systems, user interfaces, and application programs; the GPU is responsible for rendering and drawing the content required to be displayed on the display screen; and the modem is used for processing wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor 401, but can be realized by a separate chip.

[0121] The memory 405 can include a random access memory (RAM) and can also include a read-only memory (ROM). Optionally, the memory 405 includes a non-transitory computer-readable storage medium. The memory 405 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 405 can include a program storage area and a data storage area, wherein the program storage area can store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area can store data involved in the above-mentioned various method embodiments, etc. The memory 405 can also be at least one storage device located away from the aforementioned processor 401. Referring to Figure 4 The memory 405 as a computer storage medium can include an operating system, a network communication module, a user interface module, and an application program for computing a mining subsidence basin.

[0122] In Figure 4In the computer 400 shown, the user interface 403 is mainly used to provide an interface for the user to input, and obtain the data input by the user; and the processor 401 can be used to invoke the application program for calculating the mining subsidence basin stored in the memory 405, and when executed by one or more processors 401, the computer 400 is caused to perform the method described in one or more of the above embodiments. It should be noted that, for the foregoing method embodiments, in order to simply describe, they are all described as a combination of a series of actions, but those skilled in the art should know that the application is not limited to the order of the actions described, because according to the application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily required by the application.

[0123] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0124] In several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic. The division of units is only a logical function division. There can be another division in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some service interface, device or unit, and can be electrical or other forms.

[0125] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0126] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically independently, or two or more units can be integrated into one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0127] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable memory. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a memory and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The aforementioned memory includes: a U disk, a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0128] The above-described are only exemplary embodiments of the present disclosure, and cannot limit the scope of the present disclosure. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Other embodiments of the present disclosure will be readily apparent to those skilled in the art upon considering the specification and practicing the true principles of the present disclosure.

[0129] The present application is intended to cover any variations, uses or adaptive changes of the present disclosure that follow the general principles of the present disclosure and include common knowledge or conventional technical means in the technical field not recorded in the present disclosure. The specification and examples are only considered as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A calculation method for predicting subsidence basins in mining areas, characterized in that, include: The first calculation result and the second calculation result of the subsidence basin of the mining area to be calculated are obtained. The first calculation result is obtained by calculating leveling data using the probability integral method. The second calculation result is obtained by calculating synthetic aperture radar image data using the interferometric synthetic aperture radar time series analysis method. The average of the first probability integral result and the second probability integral result is determined as the upper critical threshold. The first probability integral result is obtained by calculating the starting position of the working face using the probability integral method, and the second probability integral result is obtained by calculating the stop mining line position using the probability integral method. The maximum deformation gradient of the interferometric synthetic aperture radar timing analysis method is determined as the lower critical threshold. Calculate the mean absolute error of the interferometric synthetic aperture radar timing analysis method and the probability integral method, respectively. The inverse proportional calculation results of the mean absolute error of the interferometric synthetic aperture radar time series analysis method and the probability integral method are determined as the first weight and the second weight; The first calculation result and the second calculation result are fused according to the prediction rules to obtain the final calculation result of the subsidence basin prediction of the mining area to be calculated. The prediction rules include: Extract data that is not lower than the upper critical threshold from the first calculation result and use it as part of the final calculation result; Data not exceeding the lower critical threshold is extracted from the second calculation result and used as part of the final calculation result. Extract third data that is above the lower threshold from the first calculation result, and extract fourth data that is below the upper threshold from the second calculation result; use the weighted fusion result of the third data and the fourth data according to the set first weight and second weight as part of the final calculation result, where the sum of the first weight and the second weight is one.

2. The calculation method for predicting subsidence basins in mining areas according to claim 1, characterized in that, The step of determining the maximum deformation gradient as the lower critical threshold in the interferometric synthetic aperture radar time series analysis method specifically includes: Obtain the maximum deformation gradient of the interferometric synthetic aperture radar timing analysis method; Determine whether the maximum deformation gradient is greater than the preset observation threshold; If it is not greater than, then the maximum deformation gradient is determined as the lower critical threshold. If the value is greater than the threshold, the maximum deformation gradient that has been verified to be without problems will be selected from the historical data of the subsidence basin in the mining area to be calculated and determined as the lower critical threshold.

3. The calculation method for predicting subsidence basins in mining areas according to claim 1, characterized in that, After the step of fusing the first calculation result and the second calculation result according to the prediction rule to obtain the final calculation result of the subsidence basin prediction of the mining area to be calculated, the method further includes: Construct a prediction model for the ground subsidence of the subsidence basin in the mining area to be calculated; the ground subsidence prediction model is as follows: S s =I s Δh=I’ s ΔhH S C =I C Δh=I’ C ΔhH In the formula, S s S represents the rebound amount of a soil layer of thickness H when the water level rises by Δh. C I represents the settlement of a soil layer of thickness H when the water level drops by Δh. s I represents the unit deformation during the water level rise period. C I' is the unit deformation during the water level drop period. s I' represents the unit deformation during the water level rise period. C The unit deformation during the water level drop period, Δh s Let Δh' be the magnitude of the water level rise within the time interval Δh'. C Let ΔS be the magnitude of the water level rise within the time interval Δh'. s For the corresponding Δh s The change in soil layer ΔS C For the corresponding Δh C Changes in the underlying soil layers.

4. A calculation system for predicting subsidence basins in mining areas, characterized in that, include: One or more processors and memory; The memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, and the one or more processors invoking the computer instructions to cause the calculation system for predicting the subsidence basin in the mining area to perform the method as described in any one of claims 1-3.

5. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is run on the calculation system for predicting subsidence basins in mining areas, the calculation system for predicting subsidence basins in mining areas performs the method as described in any one of claims 1-3.

6. A computer program product, characterized in that, When the computer program product is run on the calculation system for predicting subsidence basins in mining areas, the calculation system for predicting subsidence basins in mining areas performs the method as described in any one of claims 1-3.

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