A method, device, equipment, medium and product for estimating three-dimensional deformation of a surface in a mining subsidence

Abstract

The application discloses a kind of exploitation subsidence surface three-dimensional deformation estimation method, device, equipment, medium and product, is related to surface three-dimensional deformation estimation field, which comprises determining observation type according to the source and feature of different observation in subsidence monitoring mining area;Different observation includes the ascending track and descending track calculated by InSAR, and the vertical direction, coal seam strike and coal seam tendency simulated by PIM;According to observation error and observation type, construct observation matrix;Based on observation matrix, combine design matrix, according to the three-dimensional deformation variable to be solved and design matrix product error, construct real model;Based on real model, determine the variance matrix of observation error;According to variance matrix, construct observation equation;Based on observation matrix, the optimal estimation criterion of total least square is used to update weight matrix of the observation matrix, to determine the three-dimensional deformation estimation result of the three-dimensional deformation variable to be solved, and the north-south direction displacement is accurately estimated.

Description

Technical Field

[0001] The present application relates to the field of three-dimensional surface deformation estimation, and in particular to a method, device, equipment, medium and product for estimating three-dimensional surface deformation caused by mining subsidence. Background Art

[0002] Unlike the one-dimensional deformation variables obtained by the Synthetic Aperture Radar (InSAR) method, the three-dimensional deformation variables can more intuitively reflect the actual deformation of the surface. Traditional methods require Synthetic Aperture Radar (SAR) data from at least three different angles to calculate the three-dimensional deformation. However, due to the limitations of the satellite platform (flying in a north-south direction and shooting in an east-west direction), the north-south projection component of InSAR data is relatively small, making it impossible to accurately estimate the north-south displacement. Summary of the Invention

[0003] The purpose of this application is to provide a method, device, equipment, medium and product for estimating three-dimensional deformation of mining subsidence surface to solve the problem of being unable to accurately estimate north-south displacement.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In a first aspect, the present application provides a method for estimating three-dimensional deformation of a mining subsidence surface, comprising:

[0006] The observation type is determined according to the sources and characteristics of different observation quantities in the subsidence monitoring mining area; the different observation quantities include the ascending and descending orbits calculated by InSAR, as well as the vertical, coal seam strike and coal seam dip simulated by PIM; the observation type includes InSAR observation parameters and PIM simulation observation parameters; the InSAR observation parameters include the ascending orbit deformation L as and the orbital deformation L de The PIM simulation observation parameters include the strike deformation L str , tendency deformation L ran and vertical deformation L up ;

[0007] Construct an observation matrix based on the observation error and observation type;

[0008] Based on the observation matrix, combined with the design matrix, a true model is constructed according to the three-dimensional deformation variables to be determined and the product error of the design matrix; the three-dimensional deformation variables to be determined include the north-south deformation variable, the east-west deformation variable, and the vertical deformation variable; the design matrix is ​​calculated using a mobile phone satellite and the angle of the mining subsidence working surface;

[0009] determine a variance matrix of the observation error based on the true model;

[0010] construct an observation equation according to the variance matrix;

[0011] update a weight matrix of the observation matrix based on the observation matrix using an optimal estimation criterion of total least squares, and determine a three-dimensional deformation estimation result of the to-be-solved three-dimensional deformation variable.

[0012] In a second aspect, the present application provides a three-dimensional deformation estimation device for mining subsidence surface, comprising:

[0013] an observation type determination module configured to determine observation types according to sources and characteristics of different observations in a subsidence monitoring mining area; the different observations include ascending and descending tracks calculated by InSAR, and vertical, coal seam strike and coal seam dip simulated by PIM; the observation types include InSAR observation parameters and PIM simulation observation parameters; the InSAR observation parameters include ascending track deformation L as and descending track deformation L de ; the PIM simulation observation parameters include strike deformation L str , dip deformation L ran and vertical deformation L up ;

[0014] an observation matrix construction module configured to construct an observation matrix according to observation errors and observation types;

[0015] a true model construction module configured to construct a true model based on the observation matrix and a design matrix according to to-be-solved three-dimensional deformation variables and product error of the design matrix; the to-be-solved three-dimensional deformation variables include north-south deformation variable, east-west deformation variable and vertical deformation variable; the design matrix is calculated by a mobile satellite and an angle of a mining subsidence working face;

[0016] a variance matrix determination module configured to determine a variance matrix of the observation error based on the true model;

[0017] an observation equation construction module configured to construct an observation equation according to the variance matrix;

[0018] a three-dimensional deformation estimation result determination module configured to update a weight matrix of the observation matrix based on the observation matrix using an optimal estimation criterion of total least squares, and determine a three-dimensional deformation estimation result of the to-be-solved three-dimensional deformation variable.

[0019] 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 any of the above-described methods for estimating three-dimensional deformation of a mining subsidence surface.

[0020] 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 any of the above-mentioned methods for estimating three-dimensional deformation of mining subsidence surfaces.

[0021] In a fifth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the above-mentioned methods for estimating three-dimensional deformation of mining subsidence surface.

[0022] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0023] This application integrates the Probability Integration Method (PIM) to simulate observation parameters and InSAR observation parameters. Since the coal seam strike and coal seam dip are perpendicular to each other on the plane, they can be projected to any direction in the plane according to the projection relationship, thereby improving the problem of poor north-south observation capability of InSAR and improving the estimation accuracy and precision in the north-south direction. In addition, this application determines the true model (errors-in-variables, EIV) based on the observation matrix, design matrix, and product error, and performs variance component estimation. The true model and variance estimation are combined to improve the data fusion error caused by the design matrix error caused by different observations and angle measurement errors. Finally, the optimal estimation criterion of the total least squares is used to update the weight matrix of the observation matrix to determine the three-dimensional deformation estimation result of the three-dimensional deformation variable to be determined, which jointly improves the three-dimensional deformation estimation accuracy and accurately estimates the north-south displacement. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. 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 creative work.

[0025] Figure 1 This is a flow chart of the method for estimating three-dimensional deformation of mining subsidence surface provided in this application. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0027] In order to make the above-mentioned purposes, 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.

[0028] This application takes into account the different types of observations and introduces variance component estimation weights. Taking into account the errors in the coefficient matrix due to angles, EIV is introduced to improve the estimation accuracy in order to optimize the three-dimensional deformation estimation.

[0029] The embodiment of the present application provides a method for estimating three-dimensional deformation of mining subsidence surface, which is executed by a computer device, specifically a computer device such as a terminal or a server, or a terminal and a server. In the embodiment of the present application, Figure 1 As shown, the method includes the following steps.

[0030] S1: Determine the type of observation according to the source and characteristics of different observation quantities in the subsidence monitoring mining area; different observation quantities include ascending and descending orbits calculated by InSAR, as well as vertical, coal seam strike and coal seam dip simulated by PIM; the observation type includes InSAR observation parameters and PIM simulation observation parameters; the InSAR observation parameters include ascending deformation L as and the orbital deformation L de The PIM simulation observation parameters include the strike deformation L str , tendency deformation L ran and vertical deformation L up .

[0031] S2: Construct the observation matrix based on the observation error and observation type.

[0032] S3: Based on the observation matrix and the design matrix, a real model is constructed according to the three-dimensional deformation variables to be determined and the product error of the design matrix; the three-dimensional deformation variables to be determined include the north-south deformation variable, the east-west deformation variable and the vertical deformation variable; the design matrix is ​​obtained by calculating the angle of the mobile phone satellite and the mining subsidence working surface.

[0033] S4: Based on the true model, determine the variance matrix of the observation error.

[0034] S5: Constructing an observation equation according to the variance matrix.

[0035] S6: Based on the observation matrix, the weight matrix of the observation matrix is ​​updated using the optimal estimation criterion of the total least squares to determine the three-dimensional deformation estimation result of the three-dimensional deformation variable to be determined.

[0036] In an exemplary embodiment, S1 obtains two observation quantities, namely, ascending and descending observation quantities calculated by InSAR, and three observation quantities, namely, vertical, (coal seam) strike, and (coal seam) dip, simulated by PIM. According to the sources and characteristics of different observation quantities, they are divided into two independent types of observations, namely, InSAR observation parameters L1 and PIM simulation observation parameters L2.

[0037] This application uses only two observation quantities, ascending and descending orbits, to improve the defect of the traditional InSAR observation of small north-south deformation quantity estimation, reducing the InSAR deformation estimation that originally required relying on at least three different observation directions to two, thereby reducing the dependence on SAR data observations in different directions.

[0038] In an exemplary embodiment, S2 specifically includes:

[0039] use Construct the observation matrix; where α str is the azimuth of the working face; b = tanα str -cotα str ,θ as is the incident angle in the ascending orbit mode; θ de is the incident angle in descending orbit mode; α as is the azimuth in orbit raising mode; α de is the azimuth in descending orbit mode; N is the north-south three-dimensional deformation; E is the east-west three-dimensional deformation; U is the vertical three-dimensional deformation; V as is the InSAR orbit raising observation error; V de is the InSAR down-orbit observation error; V str is the simulated observation error of the PIM trend; V ran is the PIM tendency simulation observation error; V up is the PIM vertical simulated observation error.

[0040] In practical applications, the InSAR observation parameter L1 is obtained by processing the InSAR data to obtain the line-of-sight deformation, and the PIM simulation observation parameter L2 is obtained by inverting the InSAR data and the mining subsidence parameters through the probability integral model; α str Can be obtained by collecting data; as ,θ de , α as , α de Can be obtained from satellite data parameters; V as V de V str Vran V up is the error of five observation quantities, unknown quantity, no need to solve, subsequent calculation is to make some function of error minimum as the premise of calculating the parameters to be solved.

[0041] In an exemplary embodiment, the above model only considers the error V as V de V str V ran V up of the observation quantity, without considering the error of the related variables in the design matrix (such as the error of angle measurement). Therefore, the more realistic model is to consider the error of all variables. Therefore, the realistic model considering the error of variables is shown as S3, and S3 specifically includes:

[0042] L-Δ L = AX+Δ AX to construct a realistic model; wherein, L is an observation matrix; Δ L is an observation error matrix; A is a design matrix, X is a three-dimensional deformation variable to be solved; Δ AX is the error of the product of AX, considering that A is a matrix, in the actual operation process, Δ AX can be represented as the error matrix E A of the design matrix A, the sum of the column vectors obtained by straightening from left to right in sequence by row and the corresponding product of the to-be-solved parameters X; E A is the error matrix of A; i is the column vector sequence number; t is the number of column vectors; X i is the three-dimensional deformation estimation value corresponding to the i-th observation component; E Ai is the error array of the design matrix corresponding to the i-th observation component.

[0043] In an exemplary embodiment, L is obtained by InSAR data processing and PIM (probability integral model) inversion, Δ L is the observation error, which is an unknown quantity; A is obtained by collecting the angles of satellites and mining subsidence working surfaces and calculating, X and Δ AX are unknown quantities, E A is also an unknown quantity, and the subsequent calculation principle is to minimize the error function composed of E A and Δ L , and the error function is the optimal estimation criterion of total least squares.

[0044] Then, the variance of E A X and L can be represented as:

[0045]

[0046] Among them, D L is the variance matrix of the observation matrix; is the variance matrix of the observation error; σ L is the variance of the column vector corresponding to the observation matrix L, L is L1 or L2, L1 is the InSAR observation parameter matrix, and L2 is the PIM simulation observation parameter matrix; is the variance component of L1; is the variance component of L2; I is the unit diagonal matrix.

[0047] Because observations come from different sources and have varying statistical characteristics, equally weighted parameter estimation can lead to biased results. Therefore, the variances of observations from different sources (InSAR data processing and PIM inversion) are differentiated. Subsequently, the variance components are used to reweight the observations and estimate the 3D deformation.

[0048] At this time, the observation equation can be written as:

[0049] L=AX+Δ AX +Δ L =AX+Δ

[0050] Among them, Δ represents the overall error, that is, the sum of the observation error and the independent variable error, △ AX +△ L , its variance can be expressed as:

[0051]

[0052] When the variance of the error component of A is ignored, the above formula can be written as:

[0053]

[0054] In the EIV model, this application estimates the variance components of L1 and L2 based on the observation error V The variance of the error component of matrix A is ignored (or not estimated), so the two variance components to be estimated can be estimated.

[0055] Therefore, the observation equation L = AX + Δ AX +Δ L =AX+Δ can be written as

[0056]

[0057] Where L1 is the InSAR observation parameter matrix, L2 is the PIM simulation observation parameter matrix; A is the design matrix; X is the three-dimensional deformation variable to be calculated; Δ Lis the observation error matrix; A1 is the design matrix corresponding to the first group of observation components; A2 is the design matrix corresponding to the second group of observation components; E A1 is the error matrix of A1; E A2 is the error matrix of A2; V1 is the residual corresponding to the first group of observation components; V2 is the residual corresponding to the second group of observation components; is the predicted value of the observation matrix L; is the predicted value of the design matrix A; Δ is the overall error; E A is the error matrix of A; is the estimated value of the vector obtained by straightening the design matrix A; vec(A) is the vector obtained after straightening the design matrix A; vec(E A ) is the straightening error matrix E A The vector obtained after .

[0058] In an exemplary embodiment, the optimal estimation criterion of the total least squares method is:

[0059] min=Δ T PΔ+vec(E A ) T vec(E A )

[0060]

[0061] Among them, P is the weight matrix; T is the transpose.

[0062] In order to satisfy the optimal solution of the above conditions and update the weight matrix of the observations at the same time, an iterative calculation method is adopted. The specific steps are as follows:

[0063] (1) An iterative method is used to estimate the variance components.

[0064] make Among them, N aa is the normal equation matrix, N aai is the normal equation matrix of the i-th observation component, A i is the design matrix of the i-th observation component, W i is the weight matrix of the i-th observation component; the weight matrix P of the first iteration i is a unit diagonal matrix, then the variance component and the observed residual W σ The relationship between them is:

[0065]

[0066] Among them, W σ =[V1 T P1V1 V2 T P2V2]T ,

[0067] n i is the number of observations in each group, i=1 or 2; tr represents the trace operation. Then update the weight matrix, Repeat the above operation until the unit weight errors are approximately equal, that is:

[0068] At this time, update the weight matrix P:

[0069]

[0070] (2) Based on the total least squares criterion, the iterative method is used to estimate the parameter X

[0071]

[0072] Take the least squares solution of X as the initial value The first valuation is:

[0073]

[0074] The iterative calculation process is as follows:

[0075]

[0076] The iteration termination conditions are as follows:

[0077]

[0078] Among them, ε0 is a pre-given positive constant, which can be given according to the accuracy of the observed data.

[0079] Based on the same inventive concept, embodiments of the present application also provide a device for estimating the three-dimensional deformation of a mining subsidence surface, which is used to implement the above-mentioned method for estimating the three-dimensional deformation of a mining subsidence surface. The solution provided by this device is similar to the solution described in the above-mentioned method. Therefore, the specific limitations of one or more embodiments of the device for estimating the three-dimensional deformation of a mining subsidence surface provided below can be found in the above-mentioned limitations of the method for estimating the three-dimensional deformation of a mining subsidence surface, and will not be repeated here.

[0080] In an exemplary embodiment, a device for estimating three-dimensional deformation of a mining subsidence surface is provided, comprising:

[0081] The observation type determination module is used to determine the observation type according to the sources and characteristics of different observation quantities in the subsidence monitoring mining area; different observation quantities include the ascending and descending orbits calculated by InSAR, as well as the vertical, coal seam strike and coal seam dip simulated by PIM; the observation type includes InSAR observation parameters and PIM simulation observation parameters; the InSAR observation parameters include the ascending deformation L as and the orbital deformation L de The PIM simulation observation parameters include the strike deformation L str , tendency deformation L ran and vertical deformation L up .

[0082] The observation matrix construction module is used to construct the observation matrix according to the observation error and observation type.

[0083] A real model construction module is used to construct a real model based on the observation matrix, combined with the design matrix, according to the three-dimensional deformation variables to be determined and the product error of the design matrix; the three-dimensional deformation variables to be determined include the north-south deformation variable, the east-west deformation variable and the vertical deformation variable; the design matrix is ​​obtained by calculating the angle of the mobile phone satellite and the mining subsidence working surface.

[0084] A variance matrix determination module is used to determine the variance matrix of the observation error based on the true model.

[0085] The observation equation construction module is used to construct the observation equation according to the variance matrix.

[0086] The three-dimensional deformation estimation result determination module is used to update the weight matrix of the observation matrix based on the observation matrix using the optimal estimation criterion of the total least squares to determine the three-dimensional deformation estimation result of the three-dimensional deformation variable to be determined.

[0087] In an exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, a memory, an input / output (I / O) interface, and a communication interface. The processor, memory, and I / O interface are connected via a system bus, and the communication interface is connected to the system bus via the I / O interface. The processor of the computer device is configured 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 computer program in the non-volatile storage medium. The database of the computer device is configured to store three-dimensional deformation estimation data of a mining subsidence surface. The I / O interface of the computer device is configured to exchange information between the processor and an external device. The communication interface of the computer device is configured to communicate with an external terminal via a network connection. When executed by the processor, the computer program implements a method for estimating three-dimensional deformation of a mining subsidence surface.

[0088] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the above method when executing the computer program.

[0089] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the above method when executed by a processor.

[0090] In an exemplary embodiment, a computer program product is provided, including a computer program, which implements the above method when executed by a processor.

[0091] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented 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 memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may 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).

[0092] In this application, all actions to obtain signals, information or data are carried out in compliance with the relevant data protection laws and policies of the country where they are located and with the authorization given by the owner of the corresponding device.

[0093] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0094] The technical features of the above embodiments can 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.

[0095] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may 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 estimating three-dimensional deformation of mining subsidence surface, characterized in that: The method for estimating three-dimensional deformation of mining subsidence surface includes: The observation type is determined according to the sources and characteristics of different observation quantities in the subsidence monitoring mining area; the different observation quantities include the ascending and descending orbits calculated by InSAR, as well as the vertical, coal seam strike and coal seam dip simulated by PIM; the observation type includes InSAR observation parameters and PIM simulation observation parameters; the InSAR observation parameters include the ascending orbit deformation L as and the orbital deformation L de The PIM simulation observation parameters include the strike deformation L str , tendency deformation L ran and vertical deformation L up ; Construct an observation matrix based on the observation error and observation type; Based on the observation matrix, combined with the design matrix, a true model is constructed according to the three-dimensional deformation variables to be determined and the product error of the design matrix; the three-dimensional deformation variables to be determined include the north-south deformation variable, the east-west deformation variable, and the vertical deformation variable; the design matrix is ​​calculated using a mobile phone satellite and the angle of the mining subsidence working surface; Determining a variance matrix of the observation error based on the true model; constructing an observation equation based on the variance matrix; Based on the observation equation, the optimal estimation criterion of the total least squares is adopted to update the weight matrix of the observation matrix to determine the three-dimensional deformation estimation result of the three-dimensional deformation variable to be determined.

2. The method for estimating three-dimensional deformation of mining subsidence surface according to claim 1, characterized in that: Construct an observation matrix based on the observation error and observation type, including: use Construct the observation matrix; where α str is the strike azimuth of the working face; b = tanα str -cotα str ,θ as is the incident angle in the ascending orbit mode; θ de is the incident angle in descending orbit mode; α as is the azimuth in orbit raising mode; α de is the azimuth in descending orbit mode; N is the north-south three-dimensional deformation; E is the east-west three-dimensional deformation; U is the vertical three-dimensional deformation; V as is the InSAR orbit raising observation error; V de is the InSAR down-orbit observation error; V str is the PIM trend simulation observation error; V ran is the PIM tendency simulation observation error; V up is the PIM vertical simulated observation error.

3. The method for estimating three-dimensional deformation of mining subsidence surface according to claim 1, characterized in that: Based on the observation matrix, combined with the design matrix, and according to the three-dimensional deformation variable to be determined and the product error of the design matrix, a true model is constructed, specifically including: Using L-Δ L =AX+Δ AX Build a realistic model; L is the observation matrix; Δ L is the observation error matrix; A is the design matrix, X is the three-dimensional deformation variable to be determined; Δ AX is the product error of AX; E A is the error matrix of A; i is the number of observation components; t is the number of column vectors; X i is the estimated value of the three-dimensional deformation corresponding to the i-th observation component; is the design matrix A corresponding to the i-th observation component i The error matrix.

4. The method for estimating three-dimensional deformation of mining subsidence surface according to claim 1, characterized in that: Determining the variance matrix of the observation error based on the true model specifically includes: use Determine the variance matrix of the observation error; where D L is the variance matrix of the observation matrix; is the variance matrix of the observation error; σ L is the variance of the column vector corresponding to the observation matrix L, L is L1 or L2, L1 is the InSAR observation parameter matrix, and L2 is the PIM simulation observation parameter matrix; is the variance component of L1; is the variance component of L2; I is the unit diagonal matrix.

5. The method for estimating three-dimensional deformation of mining subsidence surface according to claim 1, characterized in that: The observation equation is: Where L1 is the InSAR observation parameter matrix, L2 is the PIM simulation observation parameter matrix; A is the design matrix; X is the three-dimensional deformation variable to be calculated; Δ L is the observation error matrix; A1 is the design matrix corresponding to the first group of observation components; A2 is the design matrix corresponding to the second group of observation components; is the error matrix of A1; is the error matrix of A2; V1 is the residual corresponding to the first group of observation components; V2 is the residual corresponding to the second group of observation components; is the predicted value of the observation matrix L; is the predicted value of the design matrix A; Δ is the overall error; E A is the error matrix of A; is the estimated value of the vector obtained by straightening the design matrix A; vec(A) is the vector obtained after straightening the design matrix A; vec(E A ) is the straightening error matrix E A The vector obtained after .

6. The method for estimating three-dimensional deformation of mining subsidence surface according to claim 5, characterized in that: The optimal estimation criterion of the total least squares is: min=Δ T PΔ+vec(E A ) T vec(E A ) Where P is the weight matrix; T is the transpose; Δ is the overall error.

7. A device for estimating three-dimensional deformation of mining subsidence surface, characterized in that: The mining subsidence surface three-dimensional deformation estimation device comprises: The observation type determination module is used to determine the observation type according to the sources and characteristics of different observation quantities in the subsidence monitoring mining area; different observation quantities include the ascending and descending orbits calculated by InSAR, as well as the vertical, coal seam strike and coal seam dip simulated by PIM; the observation type includes InSAR observation parameters and PIM simulation observation parameters; the InSAR observation parameters include the ascending deformation L as and the orbital deformation L de The PIM simulation observation parameters include the strike deformation L str , tendency deformation L ran and vertical deformation L up ; An observation matrix construction module is used to construct an observation matrix according to the observation error and the observation type; A real model construction module is configured to construct a real model based on the observation matrix, in combination with a design matrix, and according to the three-dimensional deformation variables to be determined and the product error of the design matrix; the three-dimensional deformation variables to be determined include the north-south deformation variable, the east-west deformation variable, and the vertical deformation variable; the design matrix is ​​calculated using a mobile phone satellite and the angle of the mining subsidence working face; A variance matrix determination module, configured to determine the variance matrix of the observation error based on the true model; An observation equation construction module, configured to construct an observation equation according to the variance matrix; The three-dimensional deformation estimation result determination module is used to update the weight matrix of the observation matrix based on the observation equation using the optimal estimation criterion of the total least squares to determine the three-dimensional deformation estimation result of the three-dimensional deformation variable to be determined.

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 method for estimating three-dimensional deformation of a mining subsidence surface 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 estimating three-dimensional deformation of a mining subsidence surface according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for estimating three-dimensional deformation of a mining subsidence surface according to any one of claims 1 to 6 is implemented.

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