A high-dust environment ash yard appearance imaging system and method

Abstract

The application discloses a high-dust-environment ash storage appearance imaging system and method, and relates to the technical field of ash storage appearance imaging, and specifically discloses the system, which comprises a millimeter wave radar, the millimeter wave radar is rigidly connected with an intelligent holder, the intelligent holder is used for posture control of the millimeter wave radar, the intelligent holder is installed at the center of the top wall of the ash storage, the millimeter wave radar is electrically connected with a data conversion module, the data conversion module and the intelligent holder are electrically connected with a central processor module, the central processor module is connected with a cloud computing platform through a data transmission module, and the cloud computing platform is connected with a display interaction module. The application adopts an Archimedes spiral non-uniform scanning path to scan the dust appearance of the ash storage, the selection of sampling points follows a compressed sensing sparse promotion algorithm, the compressed sensing sparse promotion algorithm is used to reconstruct the ash storage appearance monitoring points in a sparse transform domain, regularized and encrypted virtual sample points are obtained, error compensation and noise reduction processing are then performed on the virtual sample points, and finally, oblique distance to elevation conversion three-dimensional imaging is performed, and the imaging quality is good.

Description

Technical Field

[0001] This invention relates to the field of gray library three-dimensional imaging technology, specifically to a gray library morphology imaging system and method in high-dust environments. Background Technology

[0002] Currently, pulse radar level gauges are generally used to measure ash silos in thermal power plants and cement plants. The pulse radar level gauge antenna emits microwave pulses, and when these pulses encounter the surface of the measured medium, some of the energy is reflected back. By measuring the time interval between the transmitted and received pulses, the distance from the antenna to the surface of the measured medium can be calculated. However, radar installation is limited to fixed locations, typically mounted at several points on the top of the ash silo. The measurement range is limited to the ash height at a specific point within the silo, which is too small to estimate the ash height at other points within the silo or to understand the current ash accumulation status. Therefore, it cannot effectively monitor the operation of the ash silo. Summary of the Invention

[0003] This invention provides a dust silo topography imaging system and method for high-dust environments, aiming to solve the problems of small radar measurement range and single measurement point in current radar systems.

[0004] To achieve the above objectives, the present invention is implemented through the following technical solution: a high-dust environment ash silo topography imaging system, comprising a millimeter-wave radar, the millimeter-wave radar being rigidly connected to an intelligent gimbal, the intelligent gimbal being used for attitude control of the millimeter-wave radar, the intelligent gimbal being installed at the center of the top wall of the ash silo, the millimeter-wave radar being electrically connected to a data conversion module, the data conversion module and the intelligent gimbal being electrically connected to a central processing unit module, the central processing unit module being connected to a cloud computing platform through a data transmission module, and the cloud computing platform being connected to a display and interaction module.

[0005] A further option is to include an air valve, which is installed on one side of the smart gimbal and is used to blow away dust adhering to the millimeter-wave radar and the smart gimbal.

[0006] A further option is that the central processing unit module can be any one of a programmable gate array (FPGA), a digital signal processor (DSP), an ARM processor, or a general-purpose processor (GPP).

[0007] A method for imaging the morphology of a gray library in a high-dust environment, specifically including the following steps:

[0008] Step S1: Using the center point of the gray silo as the origin, the millimeter-wave radar performs an Archimedes-style spiral scan in a clockwise direction, taking non-uniform sampling points on the scanning path to obtain incomplete gray silo morphology data.

[0009] Step S2: The cloud computing platform reconstructs the incomplete gray library morphology data in the sparse transform domain using the compressed sensing sparsity promotion algorithm to obtain regularized and encrypted original morphology point vectors.

[0010] Step S3: Establish a three-dimensional plane coordinate system with the millimeter-wave radar as the origin, and use spline interpolation algorithm to densify the original topographic point vector to obtain a more complete and densely distributed topographic vector point.

[0011] Step S4: Use the least squares method to fit the data of the more complete and densely distributed topographic vector points, fit the points into lines, and then fit the lines into surfaces to obtain a high-precision three-dimensional topographic structure map of the gray library.

[0012] As can be seen from the above technical solution, the millimeter-wave radar of the present invention uses an Archimedes spiral non-uniform scanning path to scan the morphology of the ash storage dust. Starting from the origin, it performs a spiral scan in a clockwise direction. The selection of sampling points follows the compressed sensing sparsity promotion algorithm, taking non-uniform sampling points on the scanning path. With fewer sampling points and spline interpolation algorithm assistance, the entire scanning path signal can be reconstructed. The present invention first reconstructs the ash storage morphology monitoring points in the sparse transform domain through the compressed sensing sparsity promotion algorithm to obtain regularized and encrypted virtual sample points. Then, it performs error compensation and noise reduction processing, and finally performs oblique distance to elevation conversion three-dimensional imaging, resulting in good imaging quality.

[0013] A further embodiment is that step S1 specifically includes:

[0014] The central processing unit module controls the intelligent gimbal and millimeter-wave radar to perform an Archimedean spiral scan in a clockwise direction, with the center point of the gray silo as the origin. Non-uniform sampling points are taken along the scanning path to obtain incomplete gray silo topography data. Where H is the distance between the millimeter-wave radar (1) and the scanning point detected by the millimeter-wave radar (1), and θ is the radar scanning elevation angle. φ The radar scanning azimuth angle is the distance H between the millimeter-wave radar (1) and the scanning point. The analog signal corresponding to the distance H between the millimeter-wave radar (1) and the scanning point is sent to the data conversion module (3). After converting the analog signal into a data signal, it is fed back to the central processing unit module (4). The central processing unit module (4) transmits the processed distance H between the radar and the scanning point, the radar scanning elevation angle θ, and the scanning azimuth angle to the scanning point through the data transmission module (5). φ Send to the cloud computing platform (6).

[0015] A further embodiment is that step S2 specifically includes:

[0016] Step S21: The cloud computing platform sets the original topographic distribution vector of the gray database to a high-resolution, finite, and regular shape point vector. Where N is the number of uniformly scanned points in the gray library. Let E represent the set of all N-dimensional column vectors. The original topographic point vector E is sparse, and we have:

[0017]

[0018]

[0019] in (M << N) is the measurement matrix. Let M represent the set of all M×N dimensional column vectors, which follows the finite isometry property, where M is the actual number of sampling points. express Norm, k represents the number of non-zero elements in the vector, and the measurement matrix is ​​based on the collected gray morphology data. structure;

[0020] Step S22: Add sparsity constraints to the reconstructed signal and solve it using a sparsity-enhancing algorithm, resulting in:

[0021]

[0022]

[0023] because The nonconvexity of the norm relaxes the above equation to Norm optimization problem, and transform the constrained problem into an unconstrained optimization problem, that is

[0024]

[0025] in is the regularization coefficient; thus, the original topographic point vector E is obtained.

[0026] A further embodiment is that step S3 specifically includes:

[0027] A three-dimensional planar coordinate system with the millimeter-wave radar as the origin is established, and the original topographic point vector E is encrypted using a spline interpolation algorithm.

[0028] have

[0029]

[0030]

[0031]

[0032] Where μ is the three-dimensional coordinate vector of the scan point, H is the distance from the radar to the scan point, and θ is the radar scanning elevation angle. y is the radar scanning azimuth angle, x is the X coordinate value in the three-dimensional plane coordinate system of the scanning point, y is the Y coordinate value in the three-dimensional plane coordinate system of the scanning point, and z is the Z coordinate value in the three-dimensional plane coordinate system of the scanning point.

[0033] Construct a bivariate function i = 1, 2, ..., n It is the X-coordinate value of the i-th scan point in the three-dimensional plane coordinate system. It is the Y-coordinate value of the i-th scan point in the three-dimensional plane coordinate system. It is the Z-coordinate value of the i-th scan point in the three-dimensional plane coordinate system;

[0034] By using this binary function to interpolate and refine the topographic points, a more complete and densely distributed topographic vector point F can be obtained.

[0035] A further embodiment includes, after step S4:

[0036] The cloud computing platform renders the 3D topographic structure map and transmits it to the display and interaction module for display.

[0037] A further option is that the data transmission module is an Ethernet or 4G wireless network.

[0038] A further option is that the display interaction module is an application (APP) on a mobile phone, computer, or tablet.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows: In order to minimize scanning time and ensure imaging quality, the millimeter-wave radar of the present invention uses an Archimedes spiral non-uniform scanning path to scan the morphology of the gray silo dust. Starting from the origin, a spiral scan is performed in a clockwise direction. The selection of sampling points follows the compressed sensing sparsity promotion algorithm, and non-uniform sampling points are taken on the scanning path. With fewer sampling points and spline interpolation algorithm assistance, the entire scanning path signal to be known can be restored. The present invention first reconstructs the gray silo morphology monitoring points in the sparse transform domain through the compressed sensing sparsity promotion algorithm to obtain regularized and encrypted virtual sample points. Then, error compensation and noise reduction processing are performed on them. Finally, three-dimensional imaging is performed by diagonal distance to elevation conversion, resulting in good imaging quality. Attached Figure Description

[0040] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a block diagram of the gray library morphology imaging system of the present invention;

[0042] Figure 2 This is a flowchart of the gray library morphology imaging method of the present invention;

[0043] Figure 3 This is a radar scanning path diagram of the gray-area topography imaging system of the present invention;

[0044] Figure 4 This is a schematic diagram of the millimeter-wave radar scanning circuit of the present invention.

[0045] Attached reference numerals: 1. Millimeter-wave radar; 2. Intelligent gimbal; 3. Data conversion module; 4. Central processing unit module; 5. Data transmission module; 6. Cloud computing platform; 7. Display and interaction module; 8. Air valve. Detailed Implementation

[0046] To make the objectives, features, and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0047] It should be noted that when a component is said to be "fixed to" another component, it can be directly attached to the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component.

[0048] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0049] Please see Figure 1A high-dust environment ash silo morphology imaging system includes a millimeter-wave radar 1 and an intelligent gimbal 2. The intelligent gimbal 2 is installed at the center of the top wall of the ash silo. The millimeter-wave radar 1 is rigidly connected to the intelligent gimbal 2. The intelligent gimbal 2 is used for attitude control of the millimeter-wave radar 1, including adjusting the azimuth and elevation angles of the millimeter-wave radar 1. The millimeter-wave radar 1 is used to detect the distance between the millimeter-wave radar 1 and the scanned dust morphology points in the ash silo. The millimeter-wave radar 1 is electrically connected to a data conversion module 3, which is used to convert the analog signals fed back by the millimeter-wave radar 1 into digital signals that are easy to process. The data conversion module 3 and the intelligent gimbal 2 are connected to the intelligent gimbal 2. Each of the two devices is electrically connected to a central processing unit module 4. The central processing unit module 4 is any one of a programmable gate array (FPGA), a digital signal processor (DSP), an ARM processor, or a general-purpose processor (GPP). The central processing unit module 4 is connected to a cloud computing platform 2 via a data transmission module 5. The data transmission module 5 can be an Ethernet or 4G wireless network. The cloud computing platform 2 is connected to a display interaction module 7. The cloud computing platform 2 uses a shape reconstruction algorithm to reconstruct and render a three-dimensional shape image. The display interaction module 7 is an application (APP) on a mobile phone, computer, or tablet. The display interaction module 7 is used to display the rendered three-dimensional shape image.

[0050] An air valve 8 is also installed on the top wall of the ash silo. The air valve 8 is installed on one side of the intelligent gimbal 2 and is used to blow away the dust attached to the millimeter-wave radar 1 and the intelligent gimbal 2.

[0051] Please see Figures 2-4 The present invention also provides a method for imaging the morphology of a gray library in a high-dust environment, which reconstructs a three-dimensional morphology image based on a morphology reconstruction algorithm, specifically including the following steps:

[0052] Step S1: With the center point of the gray silo as the origin, the millimeter-wave radar 1 performs an Archimedes-style spiral scan in a clockwise direction, taking non-uniform sampling points on the scanning path to obtain incomplete gray silo morphology data.

[0053] Specifically, the central processing unit module 4 controls the intelligent gimbal 2 and the millimeter-wave radar 1 to perform an Archimedean spiral scan in a clockwise direction, with the center point of the gray silo as the origin. Non-uniform sampling points are taken along the scanning path to obtain incomplete gray silo morphology data. Where H is the distance between millimeter-wave radar 1 and the scanned point on the dust morphology, as detected by millimeter-wave radar 1, and θ is the radar scanning elevation angle. φThis refers to the radar scanning azimuth angle. The millimeter-wave radar 1 sends the analog signal corresponding to the distance H between the millimeter-wave radar 1 and the scanning point to the data conversion module 3. After converting the analog signal into a data signal, the data is fed back to the central processing unit module 4. The central processing unit module 4 then transmits the processed distance H between the radar and the scanning point, the radar scanning elevation angle θ, and the scanning azimuth angle to the central processing unit module 4 via the data transmission module 5. φ Send to cloud computing platform 6.

[0054] Step S2: The cloud computing platform 6 reconstructs the incomplete gray library morphology data in the sparse transform domain using the compressed sensing sparsity promotion algorithm to obtain regularized and encrypted original morphology point vectors.

[0055] Specifically, step S2 includes the following steps:

[0056] Step S21, Cloud computing platform 6: Let the original topographic distribution topographic point vector of the high-resolution, finite, and regular gray database be... Where N is the number of uniformly scanned points in the gray library. Let E represent the set of all N-dimensional column vectors. The original topographic point vector E is sparse, and we have:

[0057]

[0058]

[0059] in (M << N) is the measurement matrix. Let M represent the set of all M×N dimensional column vectors, which follows the finite isometry property, where M is the actual number of sampling points. express Norm, k represents the number of non-zero elements in the vector, and the measurement matrix is ​​based on the collected gray morphology data. structure;

[0060] Step S22: Solving for the original high-dimensional E from the observations in the low-dimensional space, where the number of unknowns is greater than the number of knowns, is, from the perspective of linear algebra theory, solving an underdetermined equation with infinite solutions. Constraints must be added to obtain a definite solution. According to compressed sensing theory, adding sparsity constraints to the reconstructed signal and using a sparsity promotion algorithm can solve the problem, transforming it into an optimization problem:

[0061]

[0062]

[0063] because The nonconvexity of the norm relaxes the above equation to Norm optimization problem, and transform the constrained problem into an unconstrained optimization problem, that is

[0064]

[0065] Where λ is the regularization coefficient; thus, the original topographic point vector E is obtained.

[0066] Step S3: Establish a three-dimensional planar coordinate system with millimeter-wave radar 1 as the origin, and use spline interpolation algorithm to densify the original topographic point vector to obtain a more complete and densely distributed topographic vector point.

[0067] Specifically, a three-dimensional planar coordinate system with millimeter-wave radar 1 as the origin is established, and the original topographic point vector E is encrypted using a spline interpolation algorithm;

[0068] have

[0069]

[0070]

[0071]

[0072] Where μ is the three-dimensional coordinate vector of the scanning point, H is the distance from millimeter-wave radar 1 to the scanning point, and θ is the radar scanning elevation angle. y is the radar scanning azimuth angle, x is the X coordinate value in the three-dimensional plane coordinate system of the scanning point, y is the Y coordinate value in the three-dimensional plane coordinate system of the scanning point, and z is the Z coordinate value in the three-dimensional plane coordinate system of the scanning point.

[0073] Construct a bivariate function i = 1, 2, ..., n It is the X-coordinate value of the i-th scan point in the three-dimensional plane coordinate system. It is the Y-coordinate value of the i-th scan point in the three-dimensional plane coordinate system. It is the Z-coordinate value of the i-th scan point in the three-dimensional plane coordinate system;

[0074] By using this binary function to interpolate and refine the topographic points, a more complete and densely distributed topographic vector point F can be obtained.

[0075] Step S4: The least squares method is used to fit the data of the more complete and densely distributed shape vector points. The points are fitted into lines, and the lines are fitted into surfaces to obtain a high-precision three-dimensional shape structure map inside the gray library. The cloud computing platform 6 renders the three-dimensional shape structure map and transmits it to the display interaction module 7 for display.

[0076] It should be noted that when a user issues a measurement command via an app on a mobile terminal, the cloud computing platform 2 transmits the command to the central processing unit module 4 via Ethernet or 4G. The central processing unit module 4 controls the intelligent gimbal 2 and the millimeter-wave radar 1 to sample the dust morphology data in the ash silo according to an Archimedean spiral. The millimeter-wave radar 1 sends the two analog I and Q signals it collects to the data conversion module 3 for conversion. After processing by the central processing unit module 4, the distance H from the millimeter-wave radar 1 to the dust morphology scanning point, the radar scanning elevation angle θ, and the scanning azimuth angle are obtained. The coordinates of the corresponding point in three-dimensional space can be obtained. The data is further processed and reconstructed into a three-dimensional shape map using a shape reconstruction algorithm on the cloud computing platform 6. After rendering, the map is transmitted to the mobile terminal for display.

[0077] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention.

[0078] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example.

[0079] Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The reference to "embodiment" herein means that a specific feature, structure, or characteristic described in connection with an embodiment can be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily indicate the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0080] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A dust silo morphology imaging system for high-dust environments, characterized in that: The system includes a millimeter-wave radar (1), which is rigidly connected to a smart gimbal (2). The smart gimbal (2) is used to control the attitude of the millimeter-wave radar (1). The smart gimbal (2) is installed at the center of the top wall of the ash storage. The millimeter-wave radar (1) is electrically connected to a data conversion module (3). Both the data conversion module (3) and the smart gimbal (2) are electrically connected to a central processing unit module (4). The central processing unit module (4) is connected to a cloud computing platform (6) through a data transmission module (5). The cloud computing platform (6) is connected to a display interaction module (7). The method for imaging the morphology of a ash silo in a high-dust environment using the aforementioned high-dust environment ash silo morphology imaging system specifically includes the following steps: Step S1: With the center point of the gray silo as the origin, the millimeter-wave radar (1) performs an Archimedes-style spiral scan in a clockwise direction, and takes non-uniform sampling points on the scanning path to obtain incomplete gray silo morphology data. Step S2: The cloud computing platform reconstructs the incomplete gray library morphology data in the sparse transform domain using the compressed sensing sparsity promotion algorithm to obtain regularized and encrypted original morphology point vectors. Step S3: Establish a three-dimensional plane coordinate system with the millimeter-wave radar (1) as the origin, and use spline interpolation algorithm to densify the original topographic point vector to obtain a more complete and densely distributed topographic vector point; Step S4: Use the least squares method to fit the data of the more complete and densely distributed topographic vector points, fit the points into lines, and then fit the lines into surfaces to obtain a high-precision three-dimensional topographic structure map of the gray library.

2. The high-dust environment ash silo morphology imaging system according to claim 1, characterized in that: It also includes an air valve (8), which is installed on one side of the smart gimbal (2) and is used to blow away dust attached to the millimeter-wave radar (1) and the smart gimbal (2). The air valve (8) is electrically connected to the output of the central processing unit module (4).

3. The high-dust environment ash silo morphology imaging system according to claim 1, characterized in that: The central processing unit module (4) can be any one of a programmable gate array (FPGA), a digital signal processor (DSP), an ARM processor, or a general-purpose processor (GPP).

4. The high-dust environment ash silo morphology imaging system according to claim 1, characterized in that: Step S1 specifically includes: The central processing unit module (4) controls the intelligent gimbal (2) and millimeter-wave radar (1) to perform an Archimedean spiral scan in a clockwise direction with the center point of the gray silo as the origin, and to obtain incomplete gray silo morphology data by taking non-uniform sampling points on the scanning path. Where H is the distance between the millimeter-wave radar (1) and the scanning point detected by the millimeter-wave radar (1), and θ is the radar scanning elevation angle. φ The radar scanning azimuth angle is the distance H between the millimeter-wave radar (1) and the scanning point. The analog signal corresponding to the distance H between the millimeter-wave radar (1) and the scanning point is sent to the data conversion module (3). After converting the analog signal into a data signal, it is fed back to the central processing unit module (4). The central processing unit module (4) transmits the processed distance H between the radar and the scanning point, the radar scanning elevation angle θ, and the scanning azimuth angle to the scanning point through the data transmission module (5). φ Send to the cloud computing platform (6).

5. The high-dust environment ash silo morphology imaging system according to claim 1, characterized in that: Step S2 specifically includes: Step S21, Cloud computing platform (6) Let the original morphological distribution morphological point vector of the high-resolution, finite, and regular gray library be Where N is the number of uniformly scanned points in the gray library. Let E represent the set of all N-dimensional column vectors. The original topographic point vector E is sparse, and we have: ； ； in (M << N) is the measurement matrix. Let M represent the set of all M×N dimensional column vectors, which follows the finite isometry property, where M is the actual number of sampling points. express Norm, k represents the number of non-zero elements in the vector, and the measurement matrix is ​​based on the collected gray morphology data. structure; Step S22: Add sparsity constraints to the reconstructed signal and solve it using a sparsity-enhancing algorithm, resulting in: ； ； because The nonconvexity of the norm relaxes the above equation to Norm optimization problem, and transform the constrained problem into an unconstrained optimization problem, that is ； in is the regularization coefficient; thus, the original topographic point vector E is obtained.

6. The high-dust environment ash silo morphology imaging system according to claim 5, characterized in that: Step S3 specifically includes: A three-dimensional plane coordinate system with the millimeter-wave radar (1) as the origin is established, and the original topographic point vector E is encrypted using spline interpolation algorithm; have ; ； ； ； Where μ is the three-dimensional coordinate vector of the scan point, H is the distance from the radar to the scan point, and θ is the radar scanning elevation angle. y is the radar scanning azimuth angle, x is the X coordinate value in the three-dimensional plane coordinate system of the scanning point, y is the Y coordinate value in the three-dimensional plane coordinate system of the scanning point, and z is the Z coordinate value in the three-dimensional plane coordinate system of the scanning point. Construct a bivariate function i = 1, 2, ..., n It is the X-coordinate value of the i-th scan point in the three-dimensional plane coordinate system. It is the Y-coordinate value of the i-th scan point in the three-dimensional plane coordinate system. It is the Z-coordinate value of the i-th scan point in the three-dimensional plane coordinate system; By using this binary function to interpolate and refine the topographic points, a more complete and densely distributed topographic vector point F can be obtained.

7. The high-dust environment ash silo morphology imaging system according to claim 6, characterized in that: The process following step S4 also includes: The cloud computing platform (6) renders the three-dimensional topographic structure diagram and transmits it to the display interaction module (7) for display.

8. The high-dust environment ash silo morphology imaging system according to claim 4, characterized in that: The data transmission module (5) is an Ethernet or 4G wireless network.

9. The high-dust environment ash silo morphology imaging system according to claim 4, characterized in that: The display interaction module (7) is an application (APP) on a mobile phone, computer, or tablet.

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