Mining subsidence ground surface three-dimensional deformation estimation method, device, equipment, medium and product

Abstract

The invention discloses a mining subsidence ground surface three-dimensional deformation estimation method, device and equipment, a medium and a product, relates to the field of ground surface three-dimensional deformation estimation, and particularly relates to a method comprising the following steps: determining observation quantity types according to sources and characteristics of different observation quantities in a subsidence monitoring mining area; the different observed quantities comprise the ascending orbit and the descending orbit calculated by InSAR, and the vertical direction, the coal seam trend and the coal seam tendency simulated by PIM; constructing an observed quantity matrix according to the observed quantity measurement error and the observed quantity type; based on the observed quantity matrix, combining with the design matrix, and according to the three-dimensional deformation quantity to be solved and the product error of the design matrix, constructing a real model; based on the real model, determining a variance matrix of observation measurement errors; constructing an observation equation according to the variance matrix; and on the basis of the observation matrix, updating a weight matrix of the observation quantity matrix by adopting an optimal estimation criterion of total least squares, and determining a three-dimensional deformation estimation result of the three-dimensional deformation quantity to be solved.

Description

Technical Field

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

[0002] Different from the one-dimensional deformation variables obtained by the Interferometric Synthetic Aperture Radar (InSAR) method, the three-dimensional deformation variables can more intuitively reflect the actual deformation of the surface. The traditional method requires at least three Synthetic Aperture Radar (SAR) data from different angles to calculate the three-dimensional deformation. However, due to the limitations of the satellite platform (flying in the north-south direction and shooting in the east-west direction), the InSAR data has a small north-south projection component and cannot 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, so as to solve the problem of inability 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] Determine the type of observation according to the sources and characteristics of different observations in the subsidence monitoring mining area; the different observations 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 by mobile phone satellite and the angle of the mining subsidence working surface;

[0009] Based on the true model, determining a variance matrix of the observation error;

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

[0011] Based on the observation matrix, the optimal estimation criterion of 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.

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

[0013] 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; the different observation quantities include the ascending orbit and descending orbit calculated by InSAR, and 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 ;

[0014] An observation matrix construction module is used to construct an observation matrix according to observation errors and observation types;

[0015] A real model construction module is used to construct a real model based on the observation matrix, in combination 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 ​​calculated by the angle of the mobile phone satellite and the mining subsidence working surface;

[0016] A variance matrix determination module, used to determine the variance matrix of the observation error based on the true model;

[0017] An observation equation construction module, used for constructing an observation equation according to the variance matrix;

[0018] The three-dimensional deformation estimation result determination module is used to update the weight matrix of the observation matrix based on the observation matrix and adopt 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.

[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-described methods for estimating three-dimensional deformation of a mining subsidence surface.

[0021] In a fifth aspect, the present application provides a computer program product, including 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] The present application integrates the Probability Integration Method (PIM) simulation 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, the present application determines the true model (errors-in-variables, EIV) based on the observation matrix, the design matrix, and the product error, and performs variance component estimation, combines the true model with the variance estimation, improves the data fusion error caused by the design matrix error caused by different observations and angle measurement errors, and finally, uses the optimal estimation criterion of the total least squares to update the weight matrix of the observation matrix, determines the three-dimensional deformation estimation result of the three-dimensional deformation variable to be determined, and 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 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.

[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 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.

[0027] 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.

[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 present application embodiment provides a method for estimating three-dimensional deformation of mining subsidence surface, which 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 present application embodiment, Figure 1 As shown, the method includes the following steps.

[0030] S1: Determine the type of observation according to the sources and characteristics of different observations in the subsidence monitoring mining area; different observations 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 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 .

[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 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 north-south deformation variables, east-west deformation variables and vertical deformation variables; the design matrix is ​​calculated through mobile satellite and the angle of the mining subsidence working face.

[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 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 observations of ascending and descending orbits calculated by InSAR, and three observations of vertical, (coal seam) strike and (coal seam) dip simulated by PIM, and divides them into two independent types of observations according to different sources and characteristics, namely, InSAR observation parameters L 1 The PIM simulation observation parameter L 2 .

[0037] This application only uses two observation quantities, namely ascending and descending orbits, to improve the defect of the traditional InSAR observation that the estimation of the north-south deformation quantity is relatively small, and reduces the InSAR deformation estimation that originally needed to rely 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 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 L 1 The line-of-sight deformation is obtained by InSAR data processing, and the PIM simulates the observation parameter L 2 The InSAR data and mining subsidence parameters are obtained by probability integral model inversion; α str Can be obtained by collecting data; as ,θ de , α as , α deCan be obtained from satellite data parameters; V as V de V str V ran V up It is the error of the five observed quantities, an unknown quantity that does not need to be solved. The subsequent calculation is to calculate the unknown parameters based on the premise that a certain function of the error is minimized.

[0041] In an exemplary embodiment, the above model only considers the observation error V as V de V str V ran V up , the errors of related variables in the design matrix (such as angle measurement errors) are not considered. Therefore, a more realistic model is one that considers the case where all variables have errors. Therefore, the real model constructed by considering the case where variables have errors is shown in S3, which specifically includes:

[0042] 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 error of the product of AX. Considering that A is a matrix, in the actual operation process, Δ AX It can be expressed as the error matrix E of the design matrix A A The sum of the products of the column vector obtained by straightening the rows from left to right and the corresponding parameter X to be determined; E A is the error matrix of A; i is the column vector number; 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; E Ai is the design matrix error 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 satellite and mining subsidence working surface angles and calculating, X and Δ AX Belong to the unknown quantity, E A It is also an unknown quantity. The subsequent calculation principle is to make E A With Δ L The error function constructed is minimized, which is the optimal estimation criterion of the overall least squares.

[0044] Then, E AThe variance of X and L can be expressed 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 L 1 or L 2 , L 1 is the InSAR observation parameter matrix, L 2 Simulate observation parameter matrix for PIM; For L 1 The variance components of ; For L 2 The variance components of ; I is the unit diagonal matrix.

[0047] Since the observations come from different sources, their statistical characteristics are somewhat different, and the equally weighted parameter estimation may lead to biased results. Therefore, the variances of the observations obtained from different observation sources (InSAR data processing, PIM inversion) are distinguished, and then the observations are re-weighted through variance component estimation to estimate the three-dimensional 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 observed 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] Among them, L 1 is the InSAR observation parameter matrix, L 2 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; A 1 is the design matrix corresponding to the first group of observation components; A 2 is the design matrix corresponding to the second group of observation components; E A1 A 1 The error matrix E A2 A 2 The error matrix of V 1 is the residual corresponding to the first group of observation components; V 2 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 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, an iterative calculation method is adopted to update the weight matrix of the observation quantity at the same time. The specific steps are as follows:

[0063] (1) The 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 iterationi 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 =[V 1 T P 1 V 1 V 2 T P 2 V 2 ] 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 overall least squares criterion, an 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, ε 0 is a normal number given in advance, which can be given according to the accuracy of the observed data.

[0079] Based on the same inventive concept, the embodiment of the present application also provides a mining subsidence surface three-dimensional deformation estimation device for implementing the mining subsidence surface three-dimensional deformation estimation 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 mining subsidence surface three-dimensional deformation estimation device provided below can refer to the limitations of the mining subsidence surface three-dimensional deformation estimation method above, 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; the different observation quantities include the ascending orbit and descending orbit calculated by InSAR, and 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 .

[0082] The observation matrix construction module is used to construct the observation matrix according to the observation error and the 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 ​​calculated through mobile phone satellites and the angle of the mining subsidence working face.

[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 and adopt 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 interface (I / O for short) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via 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 three-dimensional deformation estimation data of mining subsidence surface. 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 via a network connection. When the computer program is executed by the processor, a three-dimensional deformation estimation method of mining subsidence surface is implemented.

[0088] 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 above method is implemented when the processor executes 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 of ordinary skill 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 the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ReadOnlyMemory, ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (Magnetoresistive RandomAccess Memory, MRAM), ferroelectric random access memory (Ferroelectric RandomAccess Memory, FRAM), phase change memory (Phase Change Memory, PCM), graphene memory, etc. Volatile memory can include random access memory (RandomAccess 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 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.

[0094] 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.

[0095] 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 estimating three-dimensional deformation of mining subsidence surface, characterized in that: The mining subsidence surface three-dimensional deformation estimation method comprises: Determine the type of observation according to the sources and characteristics of different observations in the subsidence monitoring mining area; the different observations 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 according to 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 by mobile phone satellite and the angle of the mining subsidence working surface; Based on the true model, determining a variance matrix of the observation error; constructing an observation equation according to the variance matrix; Based on the observation matrix, the optimal estimation criterion of 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: The observation matrix is ​​constructed according to the observation error and observation type, including: 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 PIM trend; 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 real model is constructed, which specifically includes: 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; E Ai 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: Based on the true model, determining the variance matrix of the observation error 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 ) Among them, 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; the different observation quantities include the ascending orbit and descending orbit calculated by InSAR, and 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 ; An observation matrix construction module is used to construct an observation matrix according to observation errors and observation types; A real model construction module is used to construct a real model based on the observation matrix, in combination 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 ​​calculated by the angle of the mobile phone satellite and the mining subsidence working surface; A variance matrix determination module, used to determine the variance matrix of the observation error based on the true model; An observation equation construction module, used for constructing 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 matrix and adopt 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 as described in any one of claims 1-6.

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 described in 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 described in any one of claims 1 to 6 is implemented.

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