Mine ground surface settlement deformation prediction method, device, equipment and medium

Abstract

The invention discloses a mine ground surface settlement deformation prediction method, device and equipment and a medium, and relates to the field of mine ground surface settlement deformation prediction.The method comprises the steps that monitoring data of a mining area in a historical time period are obtained; calculating relative displacement data of the historical time period according to the monitoring data of the historical time period; processing the relative displacement data of the historical time period by using an optimal noise reduction algorithm to obtain noise reduction data of the historical time period; the optimal noise reduction algorithm is an L2 regularization method; inputting the noise reduction data of the historical time period into the optimal prediction model to obtain ground surface settlement deformation prediction data of the future time period; the optimal prediction model is a BP neural network prediction model based on particle swarm optimization. The method improves the efficiency and precision of ground surface settlement deformation prediction.

Description

Technical Field

[0001] The present application relates to the field of mine surface settlement deformation prediction, and in particular to a mine surface settlement deformation prediction method, device, equipment and medium. Background Art

[0002] The rational development and application of mineral resources is the key to promoting social, economic and scientific and technological progress. With the continuous advancement of science and technology, the types of exploitable mineral resources are increasing. However, with the deepening of resource exploitation, the area of ​​goaf in the mining area has gradually expanded, which has led to the increasingly serious problem of surface settlement and deformation. These settlement deformations are mainly caused by the change in the force structure of the rock and soil in the goaf roof, which causes the static pressure of the overlying rock mass to concentrate around, thereby causing the bending, rupture and even collapse of the rock mass, and finally forming different degrees of settlement on the surface. In-depth research on the evolution law of mine surface settlement deformation is of great significance for preventing collapse accidents and reducing resource waste. Therefore, how to efficiently and accurately monitor the development characteristics and evolution laws of mine surface settlement deformation has become an important issue that needs to be solved urgently. Summary of the invention

[0003] The purpose of this application is to provide a method, device, equipment and medium for predicting mine surface subsidence deformation, which can improve the accuracy and efficiency of mine surface subsidence deformation prediction.

[0004] To achieve the above objectives, this application provides the following solutions: In a first aspect, the present application provides a method for predicting surface subsidence and deformation of a mine, comprising: Obtain monitoring data for historical time periods in mining areas; Calculating relative displacement data of the historical time period according to the monitoring data of the historical time period; The relative displacement data of the historical time period is processed using the best denoising algorithm to obtain the denoised data of the historical time period; the best denoising algorithm is the L2 regularization method; The noise reduction data of the historical time period is input into the optimal prediction model to obtain the surface settlement deformation prediction data of the future time period; the optimal prediction model is a BP neural network prediction model based on particle swarm optimization.

[0005] In a second aspect, the present application provides a device for predicting surface subsidence and deformation of a mine, comprising: The monitoring data acquisition module is used to: obtain monitoring data of the mining area in the historical period; A relative displacement data calculation module is used to calculate the relative displacement data of the historical time period according to the monitoring data of the historical time period; The noise reduction processing module is used to: process the relative displacement data of the historical time period using the best noise reduction algorithm to obtain the noise reduction data of the historical time period; the best noise reduction algorithm is the L2 regularization method; The surface settlement deformation prediction module is used to: input the noise reduction data of the historical time period into the optimal prediction model to obtain the surface settlement deformation prediction data of the future time period; the optimal prediction model is a BP neural network prediction model based on particle swarm optimization.

[0006] In a third 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 above-mentioned method for predicting surface subsidence and deformation of a mine.

[0007] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for predicting surface subsidence and deformation of a mine.

[0008] According to the specific embodiments provided in this application, this application discloses the following technical effects: The present application provides a method, device, equipment and medium for predicting surface subsidence and deformation of a mine, which obtain monitoring data of a historical time period of a mining area; calculate relative displacement data of a historical time period based on the monitoring data of the historical time period; use an optimal noise reduction algorithm to process the relative displacement data of the historical time period to obtain noise reduction data of the historical time period; the optimal noise reduction algorithm is an L2 regularization method; the noise reduction data of the historical time period is input into an optimal prediction model to obtain surface subsidence and deformation prediction data of a future time period; the optimal prediction model is a BP neural network prediction model based on particle swarm optimization. The present application uses an L2 regularization method to process and reduce the noise of the relative displacement data, and uses a BP neural network prediction model based on particle swarm optimization to predict surface subsidence and deformation, thereby improving the efficiency and accuracy of surface subsidence and deformation prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] 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.

[0010] Figure 1 This is an application environment diagram of a mine surface settlement deformation prediction method in one embodiment of the present application; Figure 2 A schematic diagram of a flow chart of a method for predicting surface subsidence and deformation of a mine provided in one embodiment of the present application; Figure 3 A schematic diagram of a method flow of a self-iterative settlement prediction model of Beidou monitoring data for different mining conditions in mining areas provided in an embodiment of the present application; Figure 4 A schematic diagram of a specific process of a method for self-iterative settlement prediction model of Beidou monitoring data for different mining conditions in mining areas provided in an embodiment of the present application; Figure 5 A schematic diagram of the functional modules of a mine surface settlement deformation prediction device provided in one embodiment of the present application; Figure 6 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0011] 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.

[0012] 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 with reference to the accompanying drawings and specific implementation methods.

[0013] The mine surface settlement 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 monitoring data of the historical time period of the mining area to the server 104. After the server 104 receives the monitoring data of the historical time period of the mining area, for the monitoring data of the historical time period of the mining area, the server 104 calculates the relative displacement data of the historical time period according to the monitoring data of the historical time period, and processes the relative displacement data of the historical time period using the L2 regularization method to obtain the noise reduction data of the historical time period, and inputs the noise reduction data of the historical time period into the optimal prediction model to obtain the surface settlement deformation prediction data of the future time period. The server 104 can feed back the obtained surface settlement deformation prediction data for the future time period of the mining area to the terminal 102. In addition, in some embodiments, the mine surface subsidence and deformation prediction method can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly perform surface subsidence and deformation prediction based on the monitoring data of the historical time period of the mining area, or the server 104 can obtain the monitoring data of the historical time period of the mining area from the data storage system, and perform surface subsidence and deformation prediction based on the monitoring data of the historical time period of the mining area.

[0014] The terminal 102 may be, but is not limited to, various desktop computers and laptop computers. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0015] In an exemplary embodiment, Figure 2 As shown, a method for predicting surface settlement and deformation of a mine is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate the method, which includes the following steps 201 to 204. Among them: Step 201: Obtain monitoring data for a historical period of a mining area.

[0016] Step 202: Calculate the relative displacement data of the historical time period according to the monitoring data of the historical time period.

[0017] Step 203: using the best denoising algorithm to process the relative displacement data of the historical time period to obtain the denoised data of the historical time period; the best denoising algorithm is the L2 regularization method.

[0018] Step 204: input the noise reduction data of the historical time period into the best prediction model to obtain the surface settlement deformation prediction data of the future time period; the best prediction model is a BP neural network prediction model based on particle swarm optimization (PSO).

[0019] By implementing the above-mentioned steps 201 to 204, the relative displacement data is processed and denoised by using the L2 regularization method, and the BP neural network prediction model based on particle swarm optimization is used to predict the surface settlement deformation of the denoised data, thereby improving the efficiency and accuracy of the surface settlement deformation prediction.

[0020] In another exemplary embodiment of the present application, the monitoring data of the historical time period is obtained by monitoring the Beidou Navigation Satellite System (Global Navigation Satellite System, GNSS). Beidou satellite technology has shown significant advantages in monitoring surface deformation in mines. First, the technology has real-time dynamic monitoring capabilities and can provide continuous monitoring data, thereby more accurately depicting the continuous development process of surface deformation and providing detailed data support for the prediction of future deformation trends. Secondly, the monitoring accuracy has been significantly improved, especially when the Beidou system is used in combination with GNSS positioning technology, the monitoring accuracy can reach the millimeter level, providing an accurate and reliable data basis for in-depth analysis of mine surface deformation. In addition, the technology reduces the impact of environmental factors on monitoring results, can adapt to all-weather monitoring needs, and ensure that the interference of external environmental changes on monitoring accuracy is minimized. At the same time, the degree of automation of the monitoring process has been enhanced, and efficient automation has been achieved from data collection, processing, storage to data analysis, visualization, and early warning alarms. Through these advantages, Beidou satellite technology not only improves the efficiency and accuracy of mine surface deformation monitoring, but also provides solid data support for timely preventive measures and reduces potential safety risks. This application makes full use of high-precision Beidou monitoring data to simulate the subsidence of the mining area and establish a prediction model.

[0021] Different mining areas are divided into surface mining areas, shallow mining areas, metal mines and non-metal mines.

[0022] like Figure 3 and Figure 4As shown in the figure, GNSS monitoring data of the mining area are collected, and simply processed to convert them into relative displacement data; for the relative displacement data, the BP neural network prediction model based on particle swarm optimization, the long short-term memory (LSTM) algorithm and the differential integrated moving average autoregressive model (ARIMA) are used to process them respectively, and a model is selected according to the calculation results to determine the training set and the prediction set; for the relative displacement data, the L2 regularization method, the wavelet algorithm and the singular spectrum analysis method are used to perform denoising processing to obtain denoised data; for the denoised data, according to the training set and the prediction set, the BP neural network prediction algorithm based on particle swarm optimization, the long short-term memory algorithm and the differential integrated moving average autoregressive model are used to process them respectively, and a combination is selected according to the calculation results as the final combined prediction model. The present application can improve the ability to predict settlement and deformation in mining areas; determine the number of training sets and prediction sets suitable for the model according to the BP neural network prediction algorithm based on particle swarm optimization; use the L2 regularization method, wavelet algorithm and singular spectrum analysis method to reduce the noise of the data respectively, and use the BP neural network prediction algorithm based on particle swarm optimization, long short-term memory algorithm and differential integrated moving average autoregressive model to predict the denoised data respectively, so as to realize multi-method optimization of data.

[0023] The Beidou navigation satellite system monitoring data of the mining area was obtained. The monitoring data obtained included data from ten stations, each of which had ten years of data. The first row of data from each station was used as the benchmark, and the first row was subtracted from the remaining rows to obtain the relative displacement data required for the experiment. Each row of data from each station represents one day's data, and each station has three columns of data, each column representing the north-south direction, the east-west direction, and the vertical direction. Therefore, the relative displacement data is the relative displacement in the north-south direction, the east-west direction, or the vertical direction.

[0024] Based on the Beidou monitoring solution, the high-precision solution is the millimeter-level high-precision relative displacement in the north-south, east-west and vertical directions, and based on the particle swarm optimized BP neural network prediction algorithm, the long short-term memory algorithm and the differential integrated moving average autoregressive model, a prediction model is selected to determine the training set and the prediction set. In another exemplary embodiment of the present application, the determination process of the above training set and the prediction set may include the following steps 301 to 304: Step 301: inputting the relative displacement data into the BP neural network prediction model based on particle swarm optimization, the long short-term memory algorithm and the differential integrated moving average autoregressive model respectively to obtain the first prediction data, the second prediction data and the third prediction data; Step 302: Calculate the root mean square error and mean absolute error of the label data of the relative displacement data with the first prediction data, the second prediction data and the third prediction data to obtain a first error calculation result, a second error calculation result and a third error calculation result; Step 303: determining the best prediction model according to the first error calculation result, the second error calculation result and the third error calculation result, specifically comprising: taking the prediction model corresponding to the error calculation result with the smallest value as the best prediction model.

[0025] Step 304: Determine the best training set and the best prediction set according to the best prediction model; the time of the historical time period is the same as the time of the relative displacement data in the best training set; the time of the future time period is the same as the time of the surface settlement deformation prediction data in the best prediction set.

[0026] The process of determining the best training set and the best prediction set is as follows: Take the 10-year data of three directions of N stations in the Beidou navigation satellite system monitoring data, take the one-year relative displacement data of any station in any direction as a time series as a sample input, and get the first training set; take the two-year relative displacement data of any station in any direction as a time series as a sample input, and get the second training set, and so on, take the 10-year relative displacement data of any station in any direction as a time series as a sample input, and get the tenth training set. The relative displacement data in these training sets correspond to different times.

[0027] Input each training set into the best prediction model to obtain the prediction set corresponding to each training set; determine the best training set and the best prediction set based on all training sets and the prediction set corresponding to each training set, that is, the time of relative displacement data in the best training set finally determined is four or five years, and the time of surface settlement deformation prediction data in the best prediction set is one year, then the input of each prediction model is four or five years of relative displacement data in any direction of any station, and the output is the surface settlement deformation prediction data in any direction of any station in the next year. That is, the historical time period in step 201 can be four or five years, and the future time period is one year.

[0028] Based on the above Beidou monitoring solution results, i.e., relative displacement data, the data are substituted into the L2 regularization method, the wavelet algorithm, and the singular spectrum analysis method to obtain denoised data; based on the denoised data, the data are substituted into the BP neural network prediction algorithm based on particle swarm optimization, the long short-term memory algorithm, and the differential integrated moving average autoregressive model according to the number of training sets to form 9 combined prediction models. The results obtained according to the number of prediction sets are evaluated by indicators to obtain the final combined prediction model. The determination process of the final combined prediction model may include the following steps 401 to 404: Step 401: using L2 regularization method, wavelet algorithm and singular spectrum analysis method to process relative displacement data respectively to obtain first denoised data, second denoised data and third denoised data; Step 402: inputting the target denoised data into the BP neural network prediction model based on particle swarm optimization, the long short-term memory algorithm and the differential integrated moving average autoregressive model respectively to obtain the fourth prediction data, the fifth prediction data and the sixth prediction data corresponding to the target denoised data; the target denoised data is the first denoised data, the second denoised data or the third denoised data; Step 403: Calculate the root mean square error and mean absolute error of the label data of the target denoised data and the fourth prediction data, the fifth prediction data and the sixth prediction data corresponding to the target denoised data, respectively, to obtain the fourth error calculation result, the fifth error calculation result and the sixth error calculation result corresponding to the target denoised data; Step 404: Determine the final combined prediction model according to the fourth error calculation result, the fifth error calculation result and the sixth error calculation result corresponding to all the target denoised data; the final combined prediction model is obtained by combining the best denoising algorithm and the best prediction model; the denoising algorithm includes an L2 regularization method, a wavelet algorithm and a singular spectrum analysis method; the prediction model includes a BP neural network prediction model based on particle swarm optimization, a long short-term memory algorithm and a differential integrated moving average autoregressive model.

[0029] The input of the prediction model is the relative displacement data of the same number of times as the optimal training set, and the output of the prediction model is the data obtained of the same number of times as the optimal training set. Then the root mean square error and mean absolute error are calculated with the relative displacement data of the training set.

[0030] The BP neural network prediction algorithm based on particle swarm optimization determines the number of training sets and prediction sets, and then iterates continuously until the maximum number of iterations, i.e., 1000, is reached.

[0031] Based on the long short-term memory algorithm, the input data of the historical time period is provided to the LSTM model to obtain the initial hidden state and cell state. During the prediction process, the input data of the current time period and the hidden state of the previous time period are input to the model each time. The output result of the current time period is calculated by the model, and the output result of the current time period is used as the input of the next time period. This process is repeated until the predicted time range is reached or the stopping condition is met.

[0032] Based on the differential integrated moving average autoregressive model, the Akaike information criterion, the Bayesian information criterion and the maximum value of the model parameters are initialized. Through loop traversal, when the results of the Akaike information criterion and the Bayesian information criterion are both small, the traversal is terminated and the model parameters at this time are used as the optimal parameters of the entire model to determine the prediction set results.

[0033] For the above three prediction models, by comparing the root mean square error and mean absolute error, the BP neural network prediction model based on particle swarm optimization is selected to determine the best training set and prediction set, and the training set is four or five years, and the prediction set is one year.

[0034] In another exemplary embodiment of the present application, the monitoring data of three sites were taken for experiments, and the average value of the root mean square error (RMSE) value of the LSTM of the three sites was calculated to be 7.4611, the average value of the RMSE value of ARIMA was 2.1677, and the average value of the RMSE value of the BP neural network prediction model based on particle swarm optimization was 2.0239; the average value of the mean absolute error (MAE) value of the LSTM was calculated to be 4.5938, the average value of the MAE value of ARIMA was 1.7825, and the average value of the MAE value of the BP neural network prediction model based on particle swarm optimization was 1.5803. It can be seen that the BP neural network prediction model based on particle swarm optimization has a better effect, so the BP neural network prediction model based on particle swarm optimization is used to determine the training set and the prediction set.

[0035] It should be noted that since the trend in the north-south direction is not obvious, it is not suitable for prediction using the BP neural network prediction algorithm based on particle swarm optimization. Therefore, this application only focuses on the vertical and east-west directions, that is, the relative displacement data used in this application includes relative displacements in the vertical and east-west directions.

[0036] Substitute the relative displacement data into L2 regularization, wavelet algorithm and singular spectrum analysis respectively to obtain the denoised data results; For the L2 regularization method, the purpose of reducing the complexity of the model can be achieved by optimizing the weight coefficients to be very small, reducing the role of a single feature in the model, and avoiding a certain feature dominating the entire prediction direction; For the wavelet algorithm, the root mean square error and smoothness are normalized, and then the coefficient of variation weighting method is used. The composite evaluation index T is obtained by the linear combination method. When T is the smallest, the decomposition level is the optimal decomposition level. The wavelet basis is selected as Daubechies wavelet, which is often used to decompose and reconstruct signals. Based on the optimal decomposition level, dbN is traversed to select the optimal wavelet basis, and the 'heursure' threshold criterion and soft threshold processing function are used.

[0037] For the singular spectrum analysis method, it is necessary to determine the size of the window length. The total length of the time series used in this embodiment is 3650. The window length N should be between (0, 1825) and preferably an integer multiple of the period, because sedimentation monitoring will have different changes in different seasons. After consulting relevant literature and comparing the root mean square error and mean absolute error, the window length N is selected to be 365.

[0038] Preferably, by comparing the root mean square error and the mean absolute error, the combination of L2 regularization and the BP neural network prediction algorithm based on particle swarm optimization, and the combination of singular spectrum analysis and differential integrated moving average autoregressive model have better effects. However, because the data after singular spectrum analysis is too smooth and the loss of small data features is relatively large, the combination of L2 regularization and the BP neural network prediction algorithm based on particle swarm optimization is finally selected as the final combined prediction model.

[0039] The present application also provides an application scenario, which applies the above-mentioned mine surface settlement deformation prediction method. Specifically: the mine surface settlement deformation prediction method provided in this embodiment can be applied in the mine surface settlement deformation prediction scenario. The mine surface settlement deformation prediction scenario includes a data collection link and a surface settlement deformation prediction link; the monitoring data of the mining area enters the surface settlement deformation prediction link from the data collection link, and the corresponding surface settlement deformation prediction data for the future time period is obtained through human-computer collaboration. The mine surface settlement deformation prediction method provided in this embodiment belongs to the surface settlement deformation prediction link. Specifically, in the process of the surface settlement deformation prediction link for the mining area, the relative displacement data of the historical time period can be calculated based on the monitoring data of the historical time period, and the relative displacement data of the historical time period can be processed using the L2 regularization method to obtain the denoised data of the historical time period, and the denoised data of the historical time period is input into the optimal prediction model to obtain the surface settlement deformation prediction data for the future time period.

[0040] Based on the same inventive concept, the embodiment of the present application also provides a mine surface settlement deformation prediction device for implementing the mine surface settlement deformation prediction method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the mine surface settlement deformation prediction device provided below can refer to the limitations of the mine surface settlement deformation prediction method above, and will not be repeated here.

[0041] In an exemplary embodiment, Figure 5 As shown, a mine surface subsidence deformation prediction device is provided, which includes the following modules.

[0042] The monitoring data acquisition module T1 is used to obtain monitoring data of the mining area in the historical time period.

[0043] The relative displacement data calculation module T2 is used to calculate the relative displacement data of the historical time period according to the monitoring data of the historical time period.

[0044] The noise reduction processing module T3 is used to: use the best noise reduction algorithm to process the relative displacement data of the historical time period to obtain the noise reduction data of the historical time period; the best noise reduction algorithm is the L2 regularization method.

[0045] The surface settlement deformation prediction module T4 is used to: input the noise reduction data of the historical time period into the optimal prediction model to obtain the surface settlement deformation prediction data of the future time period; the optimal prediction model is a BP neural network prediction model based on particle swarm optimization.

[0046] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 6 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 surface settlement deformation prediction data. 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 method for predicting surface settlement deformation in a mine is implemented.

[0047] Those skilled in the art will understand that Figure 6 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.

[0048] In an exemplary embodiment, a computer device is further 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.

[0049] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0050] 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.

[0051] 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).

[0052] 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.

[0053] 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.

[0054] 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 surface subsidence and deformation of a mine, characterized in that: The mine surface subsidence deformation prediction method comprises: Obtain monitoring data for historical time periods in mining areas; Calculate the relative displacement data of the historical time period according to the monitoring data of the historical time period; the relative displacement data is the relative displacement in the north-south direction, the east-west direction or the vertical direction; The relative displacement data of the historical time period is processed using the best denoising algorithm to obtain the denoised data of the historical time period; the best denoising algorithm is the L2 regularization method; The noise reduction data of the historical time period is input into the optimal prediction model to obtain the surface settlement deformation prediction data of the future time period; the optimal prediction model is a BP neural network prediction model based on particle swarm optimization.

2. The method for predicting surface subsidence and deformation of a mine according to claim 1, characterized in that: The monitoring data for the historical time period is obtained by monitoring the Beidou navigation satellite system.

3. The method for predicting surface subsidence and deformation of a mine according to claim 1, characterized in that: The mine surface subsidence deformation prediction method also includes: The relative displacement data are respectively input into the BP neural network prediction model based on particle swarm optimization, the long short-term memory algorithm and the differential integrated moving average autoregressive model to obtain the first prediction data, the second prediction data and the third prediction data; Calculate the root mean square error and the mean absolute error of the label data of the relative displacement data with the first prediction data, the second prediction data and the third prediction data to obtain a first error calculation result, a second error calculation result and a third error calculation result; Determine the best prediction model according to the first error calculation result, the second error calculation result and the third error calculation result; The best training set and the best prediction set are determined according to the best prediction model; the time of the historical time period is the same as the time of the relative displacement data in the best training set; the time of the future time period is the same as the time of the surface settlement deformation prediction data in the best prediction set.

4. The method for predicting surface subsidence and deformation of a mine according to claim 3, characterized in that: Determining the best prediction model according to the first error calculation result, the second error calculation result and the third error calculation result specifically includes: The prediction model corresponding to the error calculation result with the smallest value is taken as the best prediction model.

5. The method for predicting surface subsidence and deformation of a mine according to claim 3, characterized in that: The mine surface subsidence deformation prediction method also includes: The relative displacement data are processed by using an L2 regularization method, a wavelet algorithm and a singular spectrum analysis method respectively to obtain first denoised data, second denoised data and third denoised data; The target denoised data is respectively input into a BP neural network prediction model based on particle swarm optimization, a long short-term memory algorithm and a differential integrated moving average autoregressive model to obtain fourth prediction data, fifth prediction data and sixth prediction data corresponding to the target denoised data; the target denoised data is the first denoised data, the second denoised data or the third denoised data; Calculate the root mean square error and the mean absolute error of the label data of the target denoised data and the fourth prediction data, the fifth prediction data and the sixth prediction data corresponding to the target denoised data, respectively, to obtain the fourth error calculation result, the fifth error calculation result and the sixth error calculation result corresponding to the target denoised data; The final combined prediction model is determined according to the fourth error calculation result, the fifth error calculation result and the sixth error calculation result corresponding to all the target denoising data; the final combined prediction model is obtained by combining the best denoising algorithm and the best prediction model; the denoising algorithm includes the L2 regularization method, the wavelet algorithm and the singular spectrum analysis method; the prediction model includes the BP neural network prediction model based on particle swarm optimization, the long short-term memory algorithm and the differential integrated moving average autoregressive model.

6. The method for predicting surface subsidence and deformation of a mine according to claim 1, characterized in that: Mining areas are divided into surface mining areas, shallow mining areas, metal mines and non-metal mines.

7. A mine surface settlement deformation prediction device, characterized in that: The mine surface subsidence deformation prediction device comprises: The monitoring data acquisition module is used to: obtain monitoring data of the mining area in the historical period; A relative displacement data calculation module is used to calculate the relative displacement data of the historical time period according to the monitoring data of the historical time period; the relative displacement data is the relative displacement in the north-south direction, the east-west direction or the vertical direction; The noise reduction processing module is used to: process the relative displacement data of the historical time period using the best noise reduction algorithm to obtain the noise reduction data of the historical time period; the best noise reduction algorithm is the L2 regularization method; The surface settlement deformation prediction module is used to: input the noise reduction data of the historical time period into the optimal prediction model to obtain the surface settlement deformation prediction data of the future time period; the optimal prediction model is a BP neural network prediction model based on particle swarm optimization.

8. 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 mine surface subsidence deformation prediction method according to any one of claims 1 to 6.

9. 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 surface subsidence and deformation of a mine according to any one of claims 1 to 6 is implemented.

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