Earth surface deformation prediction method, equipment, medium and product

Abstract

The invention discloses an earth surface deformation prediction method and device, a medium and a product, and relates to the technical field of geological disaster monitoring and surveying and mapping, and the method comprises the steps: according to a target factor in a research area, outputting an accumulated deformation quantity in a future period through employing a trained earth surface deformation prediction model, the target factors are data of which the influence degree on the accumulated deformation quantity in the future period is greater than an influence degree threshold value, and the target factors comprise time sequence deformation data in a historical period, topographic data in the future period and environmental data in the future period; the surface deformation prediction model is a model for optimizing the weight and bias parameters of a traditional BP neural network by using a particle swarm algorithm and a genetic algorithm, factors such as terrain and environment influencing the surface deformation are considered, the defect that the traditional neural network model is easy to fall into local optimum is overcome, the prediction precision of the neural network is improved, and the prediction efficiency is improved. And high-precision surface deformation prediction can be realized.

Description

Technical Field

[0001] The present application relates to the field of geological disaster monitoring and mapping technology, and in particular to a method, equipment, medium and product for predicting surface deformation. Background Art

[0002] High resolution and all-weather monitoring capabilities make InSAR technology an important means of surface monitoring. Although InSAR technology can effectively identify the distribution information of surface disasters, it cannot predict the risk of geological disasters that may occur in the future. As one of the important technical branches of artificial intelligence, neural network technology is widely used in surface deformation prediction. If time-series InSAR technology and neural network prediction technology can be combined, early warning and prevention of deformation disasters in mining areas can be carried out.

[0003] Currently, there are many methods for surface prediction models using InSAR technology combined with neural networks, but there is still a problem of insufficient accuracy in predicting surface deformation. Summary of the invention

[0004] The purpose of this application is to provide a method, device, medium and product for predicting surface deformation, which can improve the accuracy of surface deformation prediction.

[0005] To achieve the above objectives, this application provides the following solutions: In a first aspect, the present application provides a method for predicting surface deformation, comprising: Obtain the target factor under the study area, wherein the target factor is data whose influence on the cumulative deformation in the future period is greater than the influence degree threshold, and the target factor includes the time series deformation data of the historical period, the terrain data of the future period and the environmental data of the future period, the terrain data includes slope, roughness, and undulation, and the environmental data includes vegetation coverage and average annual precipitation; According to the target factor, the accumulated deformation amount in the future period is outputted using a trained surface deformation prediction model, wherein the surface deformation prediction model is an improved BP neural network, and the improved BP neural network refers to optimizing the weights and bias parameters of the traditional neural network using a particle swarm algorithm and a genetic algorithm on the basis of the traditional BP neural network.

[0006] In a second aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the surface deformation prediction method described in the first aspect.

[0007] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the surface deformation prediction method described in the first aspect.

[0008] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the surface deformation prediction method described in the first aspect.

[0009] According to the specific embodiments provided in this application, this application has the following technical effects: The present application provides a method, device, medium and product for predicting surface deformation. The method includes outputting the cumulative deformation amount of the future period according to the target factor in the study area by using a trained surface deformation prediction model, wherein the target factor is data whose influence on the cumulative deformation amount of the future period is greater than the influence degree threshold, and the target factor includes the time series deformation data of the historical period, the terrain data of the future period and the environmental data of the future period. The surface deformation prediction model is a model that optimizes the weights and bias parameters of the traditional BP neural network by using the particle swarm algorithm and the genetic algorithm. Since the factors such as the terrain and environment that affect the surface deformation are taken into consideration and the disadvantage that the traditional neural network model is prone to fall into the local optimum is overcome, the prediction accuracy of the neural network is improved, thereby realizing high-precision surface deformation prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0011] Figure 1 This is an application environment diagram of a surface deformation prediction method in Example 1 of the present application.

[0012] Figure 2 This is a schematic flow chart of a method for predicting ground deformation in Example 1 of the present application.

[0013] Figure 3 This is a schematic diagram of the InSAR line-of-sight deformation field in Example 1 of the present application.

[0014] Figure 4 This is a comparison chart of four discretization methods of the example detection factor in Example 1 of the present application.

[0015] Figure 5 This is a schematic diagram of the results of detecting the influence of a single factor on surface deformation in Example 1 of the present application.

[0016] Figure 6 This is a schematic diagram of the results of the influence of the interaction between detection factors on surface deformation in Example 1 of the present application.

[0017] Figure 7 It is a schematic diagram of the comparison between the predicted and actual results and the fitting results in Example 1 of the present application.

[0018] Figure 8 A schematic diagram of the structure of a computer device provided in Example 2 of the present application. DETAILED DESCRIPTION

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

[0020] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0021] Example 1 Research has found that in the current surface prediction model method using InSAR technology combined with neural networks, traditional neural networks are prone to fall into local optimal solutions, are sensitive to initial weights and biases, and the prediction model only uses time-series InSAR monitoring data and does not consider the impact of environmental factors such as precipitation and terrain on surface deformation.

[0022] In this regard, this embodiment proposes a surface deformation prediction method that combines InSAR technology with GA-PSO-BP (particle swarm optimization algorithm, PSO; genetic algorithm, GA) neural network, directly optimizing the basic parameters of the network, thereby avoiding the overfitting problem caused by the redundancy of the convolution kernel of CNN, and more suitable for the high-precision prediction requirements of small sample coal mine data. This method uses the geographic detector to optimize the time series deformation and environmental factor data set, integrates the global search capability and fast convergence of GA and PSO, and constructs a surface deformation prediction model for the mining area to achieve high-precision surface deformation prediction.

[0023] The surface deformation prediction method provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, or it can be integrated on the server 104, or it can be placed on the cloud or other servers. The terminal 102 can send the target factor under the study area to the server 104. After the server 104 receives the target factor, the server 104 outputs the cumulative deformation amount in the future period based on the target factor using the trained surface deformation prediction model, wherein the surface deformation prediction model is an improved BP neural network, and the improved neural network refers to the use of particle swarm algorithm and genetic algorithm to optimize the weights and bias parameters of the traditional BP neural network on the basis of the traditional BP neural network. The server 104 can feedback the obtained cumulative deformation amount in the future period to the terminal 102. In addition, in some embodiments, the surface deformation prediction method can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly use the surface deformation prediction method to process the target factors in the study area, or the server 104 can obtain the target factors in the study area from the data storage system and process them using the surface deformation prediction method.

[0024] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0025] This embodiment provides a method for predicting surface deformation. The method is executed by a computer device, which can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In this embodiment of the application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate, including the following steps 201 to 202. Among them: Step 201, obtaining the target factor under the study area, wherein the target factor is data whose influence on the cumulative deformation amount in the future period is greater than the influence degree threshold, and the target factor includes the time series deformation data of the historical period, the terrain data of the future period and the environmental data of the future period, the terrain data includes slope, roughness, and undulation, and the environmental data includes vegetation coverage and average annual precipitation.

[0026] Step 202: output the accumulated deformation amount in the future period according to the target factor using the trained surface deformation prediction model, wherein the surface deformation prediction model is an improved BP neural network, and the improved neural network refers to optimizing the weights and bias parameters of the traditional BP neural network using a particle swarm algorithm and a genetic algorithm on the basis of the traditional BP neural network.

[0027] The training process of the surface deformation prediction model includes: (a) obtaining a sample data set, wherein the sample data set includes a historical sample radar image sequence, a historical sample terrain time series data, and a historical sample environment time series data; (b) using remote sensing technology to process the historical sample radar image sequence to obtain InSAR line-of-sight historical time series deformation data; (c) using a discretization method to process the InSAR line-of-sight historical time series deformation data, the historical sample terrain time series data, and the historical sample environment time series data to obtain a preprocessed data set; (d) taking the preprocessed data set as an independent variable and the historical accumulated deformation variable as a dependent variable, and calculating the contribution intensity, wherein the historical accumulated deformation variable is a variable value calculated based on the historical time series deformation data of the InSAR line of sight, and the contribution intensity includes the contribution intensity of each variable to the dependent variable and the contribution intensity of the interaction between the independent variables to the dependent variable; (e) Using the independent variable whose contribution intensity is greater than the contribution intensity threshold as feature data and the historical accumulated deformation variable as label data to train the surface deformation prediction model.

[0028] In order to make the above process of this embodiment more clear to those skilled in the art, it is explained in detail below.

[0029] In view of the defect that the existing models of small sample areas of coal mines are insufficient in quantifying the contribution of complex environmental factors, and the generalization ability is limited under small sample conditions, which makes it difficult to meet the demand for high-precision prediction in small sample areas, this embodiment provides a surface deformation prediction method combining InSAR observation data and neural network based on the data characteristics of small sample coal mine areas, including solving InSAR line of sight, optimizing surface deformation driving factors, GA-PSO-BP prediction and analysis and verification. Figure 2 As shown, specifically including: (1) Calculating the InSAR line-of-sight deformation field S1: Using the SBAS-InSAR (Small Baseline Subset InSAR) method to perform time series processing on InSAR data (the InSAR data in this embodiment is a Sentinel-1B radar image sequence) to obtain the InSAR line of sight time series deformation field (i.e., InSAR line of sight historical time series deformation data), specifically including: 1) Baseline screening of InSAR data is performed based on the preset spatiotemporal baseline coherence threshold to obtain several image pairs that meet the spatiotemporal baseline coherence threshold constraints. Specifically, the acquired images are screened for primary images through baseline estimation, and July 4, 2020 is selected as the public primary image. The temporal baseline coherence threshold is set to 240 days, and the length of the spatial baseline coherence threshold is set to 45% of the total length.

[0030] 2) Group several image pairs that meet the spatiotemporal baseline coherence threshold constraints to obtain several small baseline sets.

[0031] 3) Perform multi-view processing on the small baseline set to obtain a baseline set after multi-view processing. In this embodiment, the multi-view ratio is set to 4:1, and the role of multi-view is to reduce noise.

[0032] 4) The Goldstein adaptive filtering algorithm is used to perform interference phase filtering on the baseline set after multi-view processing to generate a filtered baseline set.

[0033] 5) Use the minimum cost flow (MCF) to perform phase unwrapping on the filtered baseline set to generate an unwrapped phase matrix. The unwrapping coherence threshold of this embodiment is set to 0.25 (only retaining high coherence areas).

[0034] 6) Map the unwrapped phase matrix to the common master image time reference and construct the small baseline subset phase observation equation.

[0035] 7) Perform singular value decomposition (SVD) and reconstruction on the overall observation equation of all small baseline subset phase observation equations, jointly invert the deformation phase components and time series constraints of each subset, and iteratively solve the reconstructed observation equation using the weighted least squares method to obtain the millimeter-level time series deformation field of the radar line of sight (LOS) (i.e., the historical time series deformation data of the InSAR line of sight), such as Figure 3 shown.

[0036] (2) Optimizing surface deformation driving factors S2: Downsample the InSAR line-of-sight historical time series deformation data described in S1, and calculate the terrain environmental factor value at the sampling point (i.e., the value corresponding to the historical sample terrain time series data and the historical sample environment time series data).

[0037] The InSAR line-of-sight historical time series deformation data were downsampled and screened to 5140. Considering the influence of terrain and environmental factors on the cumulative surface deformation y (i.e., the historical cumulative deformation), this example selected elevation (X1), slope (X2), aspect (X3), plane curvature (X4), roughness (X5), undulation (X6), vegetation coverage (X7), average annual precipitation (X8), average annual surface deformation rate (X9) and 10 time series deformations (X10-X19) as driving factors x, and based on this, the terrain environmental factor value at the sampling point was calculated.

[0038] The factors of slope (X2), aspect (X3), curvature (X4), roughness (X5), and relief (X6) are all calculated from elevation (X1). The vegetation coverage (X7) is calculated using Sentinel-2 images. The average annual precipitation data is from the China Meteorological Network. The time series deformation is the InSAR line of sight historical time series deformation data described in S1. The accumulated surface deformation is a variable calculated based on the InSAR line of sight historical time series deformation data described in S1. The specific calculation methods for each factor are as follows:

[0039]

[0040]

[0041]

[0042]

[0043]

[0044]

[0045] In the formula, Indicates the altitude (elevation value) of a point, u and v represent the plane coordinates of the point, which are the east-west direction and the north-south direction respectively. and are the gradients of elevation in the east-west and north-south directions, is the elevation value of each pixel in the local area, is the average elevation of the local area, is the total number of pixels in the local area, is the normalized difference vegetation index, is the minimum value of the normalized difference vegetation index, is the maximum value of the normalized difference vegetation index, The near-infrared band, It is the red band.

[0046] S3: Use the sampling points obtained in S2 as basic data for geographic exploration.

[0047] The temporal deformation and terrain environmental factor values ​​described in S2 are used as independent variables, and the accumulated surface deformation is used as the dependent variable to prepare the basic data for driving factor detection.

[0048] Since the traditional geographic detector uses manual classification of factor values, which is easy to cause data distortion, the present embodiment first uses a discretization method to process the above-mentioned independent variables, converts continuous geographic factors (such as slope and elevation) into discrete categories, reduces noise interference and enhances the model's ability to capture nonlinear relationships, which can 1) reduce the dimensionality disaster: continuous variables under small samples are prone to model overfitting, and discretization can compress the feature space; 2) enhance feature separability: coal mine deformation often shows a threshold effect (such as a sudden increase in landslide risk after the slope exceeds a certain value), and discretization can highlight this segmentation law; 3) reduce subjective bias: traditional artificial division (such as fixed thresholds) may ignore the local geological characteristics of coal mines, and automated discretization (such as the natural breakpoint method) can adaptively classify according to data distribution.

[0049] The discretization methods in this embodiment include equal interval method, natural breakpoint method, quantile method and geometric interval method. Figure 4 As shown, four discretization methods are used to process the independent variables X11-X13. The expression corresponding to the equal interval method is: ; in, Indicates the value of the i-th dividing point, i ranges from 1 to An integer representing the demarcation point number. is the original data set, is the preset number of groups, and are the maximum and minimum values ​​of the data respectively.

[0050] The expression corresponding to the natural breakpoint method is: ; in, is the within-group variance and Represents the serial number of the group, y belongs to Group A single data value of for The data set of the group, No. The mean of the group data.

[0051] The corresponding expression of the quantile method is: ; in, Indicates The value of the dividing point is from 1 to An integer representing the demarcation point number. is the original data set, is the preset number of groups, For data No. Quantile.

[0052] The corresponding expression of the geometric interval method is: ; in, Indicates the value of the bth dividing point, where b ranges from 1 to An integer representing the demarcation point number. is the original data set, is the preset number of groups, and are the maximum and minimum values ​​of the data respectively.

[0053] S4: Use the geographic detector to detect driving factors on the basic data obtained after discretization in S3, obtain the contribution intensity of each driving factor and the interaction between factors to the cumulative deformation of the surface, and determine the optimal set of surface deformation driving factors based on the contribution intensity.

[0054] Geographic detectors are used to detect the driving effect of independent variables on dependent variables, including the detection of single factor influence and factor interaction, and finally the optimal driving factor. Factor detectors are used to analyze the influence of independent variables on the spatial distribution of dependent variables and identify the driving force of different driving factors on the spatial distribution of dependent variables. The expression is: ; Where h is the number of layers of the driving factor x, h=1,2,…,L; and are the number of units in the whole area and the variance of y values ​​respectively; q is the driving force measurement value, and its range is [0,1]. The larger the q value is, the stronger the driving force of the driving factor x on the dependent variable y is, and vice versa. The single factor detection results are as follows: Figure 5 shown.

[0055] Interaction detection is used to detect the degree of influence of two different influencing factors on the cumulative deformation of the surface under the joint action. The specific process includes: first, the q values ​​of the two influencing factors X1 and X2 are calculated respectively through factor detection, and then q (X1∩X2) of X1 and X2 under the interaction is calculated, and their interaction relationship is determined by comparing q (X1), q (X2) and q (X1∩X2) (Table 1). The results of interaction detection between factors are shown in Figure 1. Figure 6 shown.

[0056] Table 1 Methods and relationships for determining the interaction of driving factors

[0057] The preferred driving factors in this embodiment include 16 factors, namely, natural characteristic factors X2, X5, X6, X7, X8 and deformation factors X9-X19, for subsequent prediction.

[0058] (3) Surface deformation prediction (i.e. GA-PSO-BP prediction) S5: Take the driving factor set (16 factors) described in S4 as input and the accumulated surface deformation as the label, set a reasonable neural network threshold of GA-PSO-BP, and compare it with traditional GA-BP and PSO-BP to establish the optimal prediction model and predict the accumulated surface deformation.

[0059] The implementation of the GA-PSO-BP neural network involves the following key steps: First, the number of driving factor sets described in S4 is divided into training and test sets in a ratio of 7:3, and the data set is preprocessed and normalized to ensure the quality and consistency of the input data. Second, the weights and biases of the neural network are initialized to provide a basis for the optimization process. Next, the parameters of the GA and PSO algorithms, including population size, crossover probability, mutation probability, and inertia weight, are configured to establish the initial settings. Finally, parameters are defined for each layer of the GA and PSO algorithms to achieve seamless integration and optimization within the neural network framework. The GA and PSO algorithms are used to optimize the parameters of the neural network (Table 2).

[0060] Table 2 Parameter configuration of each layer of GA and PSO algorithms

[0061] The detailed steps of the optimization process are as follows: a. Use GA and PSO algorithms to optimize the weights and bias parameters of the neural network. The optimized weights and biases are expressed by the following formula: ; ; ; ; In the formula, Represent the weight matrices from the input layer to the hidden layer and from the hidden layer to the output layer, respectively. Represent the bias vectors of the hidden layer and the output layer respectively, Represents the set of all parameters, n, h, and o represent the number of nodes in the input layer, hidden layer, and output layer, respectively. The reshape function is used to reshape the parameter vector into a two-dimensional shape suitable for the weight matrix.

[0062] b. Calculate the output of the neural network and calculate the error, and use the error function (mean square error) to evaluate the performance of the neural network. The evaluation index can be expressed as: ; Where: is the fitness function; For the The network output value of the training sample; For the The expected output value of the training samples; is the number of training sets.

[0063] c. Update the population or particles of GA and PSO according to the value of the error function to gradually optimize the parameters of the neural network. The population with the best adaptability is directly copied into the next generation. For the population with moderate adaptability, two particles are randomly selected and crossover operation is performed with the crossover probability Pc to select the best individual. The population with poor adaptability is mutated with the mutation probability Pm. The speed of particle i and particle j is crossovered, that is: ; ; In the formula, and is a random value between [0,1], which is used to control the ratio of speed and position crossover. and represents the velocity and position of particle i at iteration step t, and is the corresponding value of another randomly selected particle j, and the new speed and position generated by particle i through the crossover operation are recorded as and The new velocity and position of particle j generated by the crossover operation are recorded as and The fitness value of the offspring particles after the crossover operation is compared with the fitness value of the parent particles, and the particles with larger fitness values ​​are retained for the next iteration.

[0064] d. Repeat the above steps until the following termination conditions are met: when the number of iterations reaches the preset maximum value, or the algorithm error is lower than a specific threshold.

[0065] (4) Result analysis and verification S6: Evaluate the accuracy of the prediction results.

[0066] In order to evaluate the prediction performance of the model, the root mean square error (RMSE) and the mean absolute error (MAE) are used as indicators to evaluate the prediction performance of the model. The calculation method of the root mean square error and the mean absolute error is:

[0067] Where: is the total sample size, is the InSAR monitoring value, is the predicted value.

[0068] GA-BP, PSO-BP and GA-PSO-BP were used to predict the results, and the root mean square error (RMSE) and mean absolute error (MAE) of the three prediction results were statistically analyzed. As can be seen from Table 3, the absolute coefficient R2 of the training set and the test set of the three models reached 0.99, indicating that the three models all showed strong fitting ability. In terms of error indicators, the GA-PSO-BP model performed slightly better than the GA-BP and PSO-BP models in root mean square error (RMSE) and mean absolute error (MAE). The RMSE of the test set was 2.68 mm and the MAE was 2.01 mm, indicating that its prediction accuracy was high. Fitting the predicted value with the InSAR monitoring value shows that ( Figure 7 ), the absolute coefficient R2 of the two reached 0.99, with a high correlation. Therefore, the GA-PSO-BP prediction model based on time-series InSAR technology has good reliability in the study of mining area deformation.

[0069] Table 3 Comparison of the accuracy of three prediction models

[0070] This embodiment proposes a surface deformation prediction method that combines InSAR observation data with a neural network, which can not only obtain a high-precision surface deformation field, but also construct a GA-PSO-BP prediction model (i.e., a surface deformation prediction model) that integrates the advantages of the GA algorithm's global search capability and PSO's fast convergence after considering the driving factors of the surface deformation.

[0071] (1) In view of the problem that previous deformation prediction only used historical deformation data to predict future deformation without considering factors such as terrain and environment that affect surface deformation, this embodiment uses a geographic detector with optimal parameters after discretization to quantitatively analyze the driving factors of surface deformation in the mining area, optimizes the driving factors, and predicts surface deformation based on InSAR deformation data and driving factors, thereby improving the scientificity and reliability of the prediction model.

[0072] (2) In view of the problem that the traditional neural network prediction method has slow convergence speed and is prone to falling into local optimum, this embodiment integrates the GA algorithm's global search capability and PSO's fast convergence advantages into a GA-PSO-BP prediction model to improve the prediction accuracy and convergence speed of the BP neural network and overcome its disadvantage of being prone to falling into local optimum. A high-precision surface prediction model is constructed.

[0073] (3) By comparing and analyzing the prediction effects of GA-BP and PSO-BP algorithms, a surface deformation prediction model with higher accuracy and stronger robustness is constructed, providing key technical support for deformation monitoring and early warning in mining areas.

[0074] Considering the particularity of small sample coal mine area data, this embodiment: 1) Through the joint dimensionality reduction of geographic detectors + discretization methods, and the use of improved neural networks to improve sample efficiency, the problem of data scarcity is solved (data scarcity: the coal mining area has limited sample size due to high monitoring costs, insufficient historical records or sudden disasters, and traditional big data driven models are prone to overfitting). Specifically: Through geographic detectors, the explanatory power of various factors (such as slope and precipitation) on deformation is quantified, key influencing factors are automatically screened, redundant features are eliminated, and the dimensionality disaster under small samples is reduced.

[0075] Four discretization methods (equal interval / natural breakpoint / quantile / geometric interval) convert continuous variables into categorical variables, compress the feature space, and reduce the model's requirement for sample size.

[0076] Traditional BP networks are prone to overfitting under small samples, while PSO-GA collaborative optimization avoids the gradient descent method's reliance on a large amount of iterative data by intelligently searching weights / biases.

[0077] 2) Through the coordinated input of multi-source target factors and discretization to enhance nonlinear expression, the problems of nonlinearity and spatial heterogeneity (nonlinearity: coal mine deformation is affected by the coupling of multiple factors such as goaf settlement and groundwater extraction, and the data presents highly nonlinear characteristics; spatial heterogeneity: the geological structure of coal mines varies greatly, and the deformation laws of local areas may be significantly different) are solved. Specifically: Time series deformation data (historical settlement patterns), terrain data, and environmental data are input simultaneously, and the model’s ability to model complex mechanisms is enhanced through multimodal data complementarity.

[0078] Discretization transforms continuous nonlinear relationships into piecewise linear relationships, allowing simple neural networks to fit complex rules.

[0079] 3) Through the dynamic encoding of historical deformation data and PSO-GA optimization of anti-disturbance, the problem of temporal instability (temporal instability: the phased mining activities lead to sudden changes in deformation rate, and small samples are difficult to cover all working conditions) is solved. Specifically: The historical time series deformation data is used as input factors, and the time dependency is implicitly learned through neural networks, avoiding the strict requirements of traditional time series models on the length of small sample time series.

[0080] The swarm intelligence of PSO and the mutation mechanism of GA enable the model to cover more potential solution space in parameter search and remain robust even if there are outliers caused by mutations in mining activities in the samples.

[0081] In summary, this embodiment solves the problem of factor representation distortion under small samples through automated discretization, and uses hybrid intelligent optimization to break through the local optimal limitations of traditional neural networks, ultimately achieving the "little data, high precision" deformation prediction requirements in coal mine scenarios.

[0082] Compared with the prior art, this embodiment has the following advantages: Improved prediction accuracy: Through the joint optimization of GA and PSO, the prediction accuracy of the BP neural network was improved, the root mean square error RMSE of the test set was reduced to 2.68 mm, and the mean absolute error MAE was reduced to 2.01 mm.

[0083] Scientific screening of key factors: The geographic detector method with optimal parameters after discretization is used to quantify driving factors, reduce factor data distortion, and ensure the scientificity and reliability of model input.

[0084] Enhanced model robustness: Through comparative analysis, the GA-PSO-BP model shows better fitting ability and stability than the GA-BP and PSO-BP models.

[0085] High practical application value: The research results verify the effectiveness of the prediction model in surface deformation monitoring and early warning, and provide important technical support for the early prevention of surface disasters.

[0086] This embodiment aims at the special complexity of the multi-scale coupling of mining disturbance and terrain environmental factors (such as slope changes affecting stress distribution, precipitation infiltration aggravating crack development, and vegetation coverage reflecting surface stability) in the surface deformation of coal mining areas. The traditional BP neural network is more suitable for processing low-dimensional time series-environmental feature data due to its fully connected structure, but it is difficult to achieve reliable generalization performance due to the overfitting risk under small sample conditions and the local convergence characteristics of the traditional back propagation algorithm. The surface deformation prediction model uses GA to globally optimize the initial weights of the neural network and combines PSO to dynamically optimize the network bias parameters. It not only retains the architectural advantages of the BP neural network in processing low-dimensional time series-environmental feature data, but also significantly improves the model convergence and generalization ability under small sample conditions; at the same time, in order to quantify the factor contribution caused by the scarcity of mining area data, the geographic detector is used to integrate multiple discretization methods to realize the adaptive optimization of the classification threshold, which effectively overcomes the subjective influence of traditional human reclassification and reduces the distortion of factor data, so that the dynamic coupling relationship between key factors such as terrain environment and cumulative deformation is more accurately characterized, and high-precision surface deformation prediction is achieved.

[0087] The surface deformation prediction method combining InSAR observation data and neural network proposed in this embodiment is highly scientific and applicable. This method can not only monitor surface deformation, but also use surface deformation driving factors such as terrain environment to realize the construction of a prediction model, thereby realizing a more complete monitoring and prediction integrated model. This method is easy to implement and can be widely used in the fields of geological disasters, engineering deformation monitoring and prediction.

[0088] Example 2 This embodiment provides a computer device, which may be a server or a terminal. Its internal structure diagram may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data in the surface deformation prediction method. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a surface deformation prediction method in Example 1 is implemented.

[0089] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0090] Example 3 This embodiment provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, a method for predicting ground surface deformation in Embodiment 1 is implemented.

[0091] Example 4 This embodiment provides a computer program product, including a computer program. When the computer program is executed by a processor, a method for predicting surface deformation in embodiment 1 is implemented.

[0092] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0093] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0094] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0095] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0096] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for predicting ground surface deformation, characterized in that: The surface deformation prediction method comprises: Obtain the target factor under the study area, wherein the target factor is data whose influence on the cumulative deformation in the future period is greater than the influence degree threshold, and the target factor includes the time series deformation data of the historical period, the terrain data of the future period and the environmental data of the future period, the terrain data includes slope, roughness, and undulation, and the environmental data includes vegetation coverage and average annual precipitation; According to the target factor, the accumulated deformation amount in the future period is outputted using a trained surface deformation prediction model, wherein the surface deformation prediction model is an improved BP neural network, and the improved BP neural network refers to optimizing the weights and bias parameters of the traditional neural network using a particle swarm algorithm and a genetic algorithm on the basis of the traditional BP neural network.

2. The method for predicting ground deformation according to claim 1, characterized in that: The training process of the surface deformation prediction model includes: Acquire a sample data set, wherein the sample data set includes a historical sample radar image sequence, a historical sample terrain time series data, and a historical sample environment time series data; The historical sample radar image sequence is processed by remote sensing technology to obtain InSAR line of sight historical time series deformation data; The InSAR sightline historical time series deformation data, the historical sample terrain time series data and the historical sample environment time series data are processed by a discretization method to obtain a preprocessed data set; The preprocessed data set is used as an independent variable, and the historical accumulated deformation variable is used as a dependent variable to calculate the contribution intensity, wherein the historical accumulated deformation variable is a variable value calculated according to the historical time series deformation data of the InSAR line of sight, and the contribution intensity includes the contribution intensity of each variable to the dependent variable and the contribution intensity of the interaction between the independent variables to the dependent variable; The independent variable whose contribution intensity is greater than the contribution intensity threshold is used as feature data, and the historical accumulated deformation variable is used as label data to train the surface deformation prediction model.

3. The method for predicting ground deformation according to claim 2, characterized in that: The discretization methods include equal interval method, natural breakpoint method, quantile method and geometric interval method.

4. The method for predicting ground deformation according to claim 2, characterized in that: The contribution strength is characterized by a driving force value, and the calculation formula of the driving force value is: ； Where q is the driving force value; h is the number of layers of the independent variable, h=1,2,…,L; and are the number of units in the whole area and the variance of the dependent variable value, respectively.

5. The method for predicting ground deformation according to claim 1, characterized in that: The calculation formula of the weight is: ； ； in, Represent the weight matrices from the input layer to the hidden layer and from the hidden layer to the output layer respectively; represents the set of all parameters; n, h, and o represent the number of nodes in the input layer, hidden layer, and output layer, respectively; the reshape function represents the function used to organize the parameter vector into a two-dimensional shape of the weight matrix.

6. The method for predicting ground deformation according to claim 1, characterized in that: The calculation formula of the bias parameter is: ； ； in, Bias vectors for hidden layer and output layer respectively; represents the set of all parameters; h and o represent the number of nodes in the hidden layer and output layer respectively.

7. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for predicting ground deformation according to any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for predicting ground surface deformation described in any one of claims 1 to 6 is implemented.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for predicting ground surface deformation described in any one of claims 1 to 6 is implemented.

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