Mine modeling method and system based on digital twin

Abstract

The present invention provides a method and system based on digital twin mine modeling, which relates to the technical field of digital mines. The present invention divides the mine area into unit square areas, collects data for each unit square area, collects point cloud data of the mine at different time points for analysis, comprehensively evaluates the accuracy level of the original modeling of the digital twin mine from static and dynamic aspects, performs correlation processing on the point cloud data in time, generates a mine time-shift deviation index reflecting the change value of the mine height in the time dimension, processes the point cloud data in space, generates a mine static deviation index reflecting the height deviation index of the actual height of the mine under static measurement conditions and the original three-dimensional model, comprehensively analyzes the mine static deviation index and the mine time-shift deviation index, and obtains a twin mine modeling evaluation index, thereby judging the accuracy level of the twin mine modeling.

Description

Technical Field

[0001] The present invention relates to the technical field of digital mines, and in particular to a method and system for modeling a digital twin mine. Background Art

[0002] Digital twin mine is an emerging method that uses digital twin technology to achieve real-time monitoring, simulation and optimization in mine management and operation. This technology can effectively improve the safety, efficiency and sustainability of mines by creating a virtual model of the mine, combining real-time data and advanced analytical tools.

[0003] The service life of a mine after digital 3D modeling is relatively long, generally not less than three years. However, due to the influence of geology, environment, and rain, the actual shape of the mine will change, resulting in a certain deviation from the original 3D modeling. When the deviation occurs, the original 3D modeling becomes inaccurate. The staff needs to judge and analyze the degree of inaccuracy to determine whether the deviation is within the threshold range.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] The purpose of the present invention is to provide a digital twin mine modeling method and system to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] Based on the digital twin mine modeling method, the specific steps include:

[0008] S1. Divide the mine area into N square areas of 1mX1m and number each square area;

[0009] S2, collecting point cloud data of the mining area at different time points, wherein the point cloud data is used to reflect the height of the mountain at different time points;

[0010] S3, collecting the height of the original three-dimensional model, wherein the original three-dimensional model mountain height data is a three-dimensional model generated by modeling the mine mountain through three-dimensional software;

[0011] S4. defining a distance feature, wherein the distance feature is used to reflect the height difference between the point cloud data and the original three-dimensional modeled mountain;

[0012] S5. Perform correlation analysis on the distance features to generate a mine time shift deviation index KSP, where the mine time shift deviation index KSP is used to reflect the change value of the mine height in the time dimension;

[0013] S6. Perform a correlation analysis on the height of the original three-dimensional model and the point cloud data collected on the first day to generate a mine static deviation index KJP, where the mine static deviation index KJP is used to reflect the height deviation index between the actual height of the mine and the original three-dimensional model under static measurement conditions;

[0014] S7. Perform correlation analysis on the mine static deviation index KJP and the mine time-shift deviation index KSP to generate a twin mine modeling evaluation index SKP, where the twin mine modeling evaluation index SKP is used to reflect the accuracy of the original three-dimensional model of the mine;

[0015] S8. Compare the twin mine modeling evaluation index SKP with the accuracy threshold, and output the accuracy level of the twin mine modeling.

[0016] Furthermore, the point cloud data is obtained by measuring with a laser sensor.

[0017] Furthermore, in S1 and S2, the point cloud data P (i,j) The collection time interval is 1 day, and the collection time is 12 o'clock every day. i is the index of the number of collections. The value range of i is 1, 2, 3, ..., T, T is the total number of collections and is not less than 30. j is the number of the square area, j is a positive integer, and the point cloud data P (i,j) is the height of the j-th square area on the i-th day, and the height collection point is the center position of the square area.

[0018] Furthermore, for the point cloud data P on the i-th day (i,j) Perform correlation analysis to generate distance feature d i , the formula based on is:

[0019]

[0020] Among them, Q j is the height of the jth region in the original 3D modeling, the distance feature d i Used to reflect the degree of change in the mine mountain height on the i-th day.

[0021] Furthermore, for the distance feature d i Correlation analysis is performed to generate the mine time shift deviation index KSP, based on the formula:

[0022]

[0023] The mine time shift deviation index KSP is used to reflect the change value of the mine height in the time dimension.

[0024] Furthermore, the height Q of the original 3D model j And the point cloud data P collected on the first day(1,j) Correlation analysis is performed to generate the mine static deviation index KJP, based on the formula:

[0025]

[0026] The mine static deviation index KJP is used to reflect the height deviation index between the actual height of the mine and the original three-dimensional model under static measurement conditions.

[0027] Furthermore, the correlation analysis of the mine static deviation index KJP and the mine time-shift deviation index KSP is carried out to generate the twin mine modeling evaluation index SKP, based on the formula:

[0028] SKP=α*KSP+β*KJP

[0029] Among them, α is the time-shift deviation weight factor of the mine, β is the static deviation weight factor of the mine, α+β=1, and α<β. The twin mine modeling evaluation index SKP is used to reflect the accuracy of the original three-dimensional model of the mine.

[0030] Furthermore, the twin mine modeling evaluation index SKP is compared with the reliability threshold θ. When SKP ≥ θ, the output twin mine modeling accuracy is level 3, and the confidence level of the twin mine modeling matching the actual mine is 30%; when When , the output twin mine modeling accuracy is level 2, and the confidence level of the twin mine modeling matching the actual mine is 70%; when When the output twin mine modeling accuracy is level three, the confidence level of the twin mine modeling being consistent with the actual mine is 95%.

[0031] The present invention also provides a digital twin mine modeling system for executing a digital twin mine modeling method, comprising:

[0032] A region division module, which is used to divide the mine area into N square areas of 1mX1m and number each square area;

[0033] A point cloud data acquisition module, which is used to collect point cloud data of the mine area at different time points;

[0034] A three-dimensional model acquisition module, which is used to acquire the height of the original three-dimensional model of the mine;

[0035] A distance feature analysis module, wherein the distance feature analysis module is used to define and perform correlation analysis on point cloud data to generate distance features;

[0036] A time-shift deviation analysis module, which is used to perform correlation analysis on distance features and generate a mine time-shift deviation index KSP;

[0037] A static deviation analysis module, which is used to perform a correlation analysis on the height of the original three-dimensional model and the point cloud data collected on the first day to generate a mine static deviation index KJP;

[0038] A comprehensive analysis module, which is used to perform correlation analysis on the mine static deviation index KJP and the mine time-shift deviation index KSP to generate a twin mine modeling evaluation index SKP;

[0039] A comparison module is used to compare the twin mine modeling evaluation index SKP with the accuracy threshold and output the accuracy level of the twin mine modeling.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] The present invention divides the mining area into unit square areas, collects data for each unit square area, collects point cloud data of the mine at different time points for analysis, comprehensively evaluates the accuracy level of the original modeling of the digital twin mine from both static and dynamic aspects, performs correlation processing on the point cloud data in time, generates a mine time-shift deviation index that reflects the change value of the mine height in the time dimension, processes the point cloud data in space, generates a mine static deviation index that reflects the height deviation index of the actual height of the mine under static measurement conditions and the original three-dimensional model, and comprehensively analyzes the mine static deviation index and the mine time-shift deviation index to obtain a twin mine modeling evaluation index, thereby judging the accuracy level of twin mine modeling. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0043] Figure 2 It is a schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION

[0044] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0045] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0046] Example:

[0047] See also Figure 1 , the present invention provides a technical solution:

[0048] Based on the digital twin mine modeling method, the specific steps include:

[0049] Step 1: Divide the mine area into N 1mX1m square areas and number each square area;

[0050] Step 2: Collect point cloud data of the mine area at different time points, where the point cloud data is used to reflect the height of the mountain at different time points; the point cloud data is obtained by measuring with a laser sensor;

[0051] Point cloud data (i,j) The collection time interval is 1 day, and the collection time is 12 o'clock every day. i is the index of the number of collections. The value range of i is 1, 2, 3, ..., T, T is the total number of collections and is not less than 30. j is the number of the square area, j is a positive integer, and the point cloud data P (i,j) is the height of the j-th square area on the i-th day, and the height collection point is the center position of the square area.

[0052] Step 3, collecting the height of the original three-dimensional model, wherein the original three-dimensional model mountain height data is a three-dimensional model generated by modeling the mine mountain through three-dimensional software;

[0053] Step 4: define a distance feature, where the distance feature is used to reflect the height difference between the point cloud data and the original 3D modeled mountain.

[0054] For the point cloud data P on the i-th day (i,j) Perform correlation analysis to generate distance feature d i , the formula based on is:

[0055]

[0056] Among them, Q j is the height of the jth region in the original 3D modeling, the distance feature d i It is used to reflect the degree of change in the height of the mine mountain on the i-th day. The greater the degree of change in the height of the mine mountain, the greater the change in the mine over time after the original 3D model is collected. The greater the degree of change, the lower the accuracy of the 3D modeling.

[0057] Step 5: Perform correlation analysis on the distance features to generate a mine time shift deviation index KSP, where the mine time shift deviation index KSP is used to reflect the change value of the mine height in the time dimension;

[0058] For distance feature d i Correlation analysis is performed to generate the mine time shift deviation index KSP, based on the formula:

[0059]

[0060] The mine time-shift deviation index KSP is used to reflect the change value of the mine height in the time dimension. The larger the value of the mine time-shift deviation index KSP is, the greater the degree of change of the mine in unit time is, which leads to a decrease in the accuracy of the original three-dimensional modeling.

[0061] Step 6: Perform a correlation analysis on the height of the original three-dimensional model and the point cloud data collected on the first day to generate a mine static deviation index KJP, which is used to reflect the height deviation index between the actual height of the mine and the original three-dimensional model under static measurement conditions;

[0062] The height Q of the original 3D model j And the point cloud data P collected on the first day (1,j) Correlation analysis is performed to generate the mine static deviation index KJP, based on the formula:

[0063]

[0064] The mine static deviation index KJP is used to reflect the height deviation index between the actual height of the mine under static measurement conditions and the original three-dimensional model. The mine static deviation index KJP is used to perform difference analysis between the overall actual height of the mine and the height of the original three-dimensional model, reflecting the deviation index between the current mine modeling and the point cloud data collected on the first day. The larger the deviation, the lower the accuracy of the original three-dimensional model.

[0065] Step 7: Perform correlation analysis on the mine static deviation index KJP and the mine time-shift deviation index KSP to generate a twin mine modeling evaluation index SKP, where the twin mine modeling evaluation index SKP is used to reflect the accuracy of the original three-dimensional model of the mine;

[0066] The correlation analysis of the mine static deviation index KJP and the mine time-shift deviation index KSP is carried out to generate the twin mine modeling evaluation index SKP, based on the formula:

[0067] SKP=α*KSP+β*KJP

[0068] Among them, α is the time-shift deviation weight factor of the mine, β is the static deviation weight factor of the mine, α+β=1, and α<β. The twin mine modeling evaluation index SKP is used to reflect the accuracy of the original three-dimensional model of the mine.

[0069] Step 8: Compare the twin mine modeling evaluation index SKP with the accuracy threshold and output the accuracy level of the twin mine modeling.

[0070] The twin mine modeling evaluation index SKP is compared with the reliability threshold θ. When SKP ≥ θ, the output twin mine modeling accuracy is level 3, and the confidence level of the twin mine modeling matching the actual mine is 30%; when When , the output twin mine modeling accuracy is level 2, and the confidence level of the twin mine modeling matching the actual mine is 70%; when When the twin mine modeling accuracy is output as level 3, the confidence level of the twin mine modeling and the actual mine is 95%. The higher the confidence level, the higher the accuracy of the twin mine modeling, and the larger the value of the twin mine modeling evaluation index SKP, the lower the confidence level. The confidence level is used to reflect the degree of consistency between the twin mine modeling and the actual mine.

[0071] Reference Figure 2 The present invention also provides a digital twin mine modeling system for executing a digital twin mine modeling method, including:

[0072] A region division module, which is used to divide the mine area into N square areas of 1mX1m and number each square area;

[0073] A point cloud data acquisition module, which is used to collect point cloud data of the mine area at different time points;

[0074] A three-dimensional model acquisition module, which is used to acquire the height of the original three-dimensional model of the mine;

[0075] A distance feature analysis module, wherein the distance feature analysis module is used to define and perform correlation analysis on point cloud data to generate distance features;

[0076] A time-shift deviation analysis module, which is used to perform correlation analysis on distance features and generate a mine time-shift deviation index KSP;

[0077] A static deviation analysis module, which is used to perform a correlation analysis on the height of the original three-dimensional model and the point cloud data collected on the first day to generate a mine static deviation index KJP;

[0078] A comprehensive analysis module, which is used to perform correlation analysis on the mine static deviation index KJP and the mine time-shift deviation index KSP to generate a twin mine modeling evaluation index SKP;

[0079] A comparison module is used to compare the twin mine modeling evaluation index SKP with the accuracy threshold and output the accuracy level of the twin mine modeling.

[0080] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0081] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0082] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0083] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be covered by the protection scope of the present application.

Claims

1. Based on the digital twin mine modeling method, it is characterized by: The specific steps include: S1. Divide the mine area into N square areas of 1mX1m and number each square area; S2, collecting point cloud data of the mining area at different time points, wherein the point cloud data is used to reflect the height of the mountain at different time points; S3, collecting the height of the original three-dimensional model, wherein the original three-dimensional model mountain height data is a three-dimensional model generated by modeling the mine mountain through three-dimensional software; S4. defining a distance feature, wherein the distance feature is used to reflect the height difference between the point cloud data and the original three-dimensional modeled mountain; S5. Perform correlation analysis on the distance features to generate a mine time shift deviation index KSP, where the mine time shift deviation index KSP is used to reflect the change value of the mine height in the time dimension; S6. Perform a correlation analysis on the height of the original three-dimensional model and the point cloud data collected on the first day to generate a mine static deviation index KJP, where the mine static deviation index KJP is used to reflect the height deviation index between the actual height of the mine and the original three-dimensional model under static measurement conditions; S7. Perform correlation analysis on the mine static deviation index KJP and the mine time-shift deviation index KSP to generate a twin mine modeling evaluation index SKP, where the twin mine modeling evaluation index SKP is used to reflect the accuracy of the original three-dimensional model of the mine; S8. Compare the twin mine modeling evaluation index SKP with the accuracy threshold, and output the accuracy level of the twin mine modeling.

2. The method for modeling a digital twin mine according to claim 1, characterized in that: The point cloud data is obtained by measuring with a laser sensor.

3. The method for modeling a mine based on a digital twin according to claim 1, characterized in that: In S1 and S2, the point cloud data P (i,j) The collection time interval is 1 day, and the collection time is 12 o'clock every day. i is the index of the number of collections. The value range of i is 1, 2, 3, ..., T, T is the total number of collections and is not less than 30. j is the number of the square area, j is a positive integer, and the point cloud data P (i,j) is the height of the j-th square area on the i-th day, and the height collection point is the center position of the square area.

4. The method for modeling a mine based on a digital twin according to claim 3, characterized in that: For the point cloud data P on the i-th day (i,j) Perform correlation analysis to generate distance feature d i , the formula based on is: Among them, Q j is the height of the jth region in the original 3D modeling, the distance feature d i Used to reflect the degree of change in the mine mountain height on the i-th day.

5. The method for modeling a digital twin mine according to claim 4 is characterized in that: For distance feature d i Correlation analysis is performed to generate the mine time shift deviation index KSP, based on the formula: The mine time shift deviation index KSP is used to reflect the change value of the mine height in the time dimension.

6. The method for modeling a mine based on a digital twin according to claim 5, characterized in that: The height Q of the original 3D model j And the point cloud data P collected on the first day (1,j) Correlation analysis is performed to generate the mine static deviation index KJP, based on the formula: The mine static deviation index KJP is used to reflect the height deviation index between the actual height of the mine and the original three-dimensional model under static measurement conditions.

7. The method for modeling a mine based on digital twin according to claim 6, characterized in that: The correlation analysis of the mine static deviation index KJP and the mine time-shift deviation index KSP is carried out to generate the twin mine modeling evaluation index SKP, based on the formula: SKP=α*KSP+β*KJP Among them, α is the time-shift deviation weight factor of the mine, β is the static deviation weight factor of the mine, α+β=1, and α<β. The twin mine modeling evaluation index SKP is used to reflect the accuracy of the original three-dimensional model of the mine.

8. The method for modeling a mine based on digital twin according to claim 7, characterized in that: The twin mine modeling evaluation index SKP is compared with the reliability threshold θ. When SKP ≥ θ, the output twin mine modeling accuracy is level 3, and the confidence level of the twin mine modeling matching the actual mine is 30%; when When , the output twin mine modeling accuracy is level 2, and the confidence level of the twin mine modeling matching the actual mine is 70%; when When the output twin mine modeling accuracy is level three, the confidence level of the twin mine modeling being consistent with the actual mine is 95%.

9. A digital twin mine modeling system for executing the digital twin mine modeling method according to claim 1, characterized in that: include: A region division module, which is used to divide the mine area into N square areas of 1mX1m and number each square area; A point cloud data acquisition module, which is used to collect point cloud data of the mine area at different time points; A three-dimensional model acquisition module, which is used to acquire the height of the original three-dimensional model of the mine; A distance feature analysis module, wherein the distance feature analysis module is used to define and perform correlation analysis on point cloud data to generate distance features; A time-shift deviation analysis module, which is used to perform correlation analysis on distance features and generate a mine time-shift deviation index KSP; A static deviation analysis module, which is used to perform a correlation analysis on the height of the original three-dimensional model and the point cloud data collected on the first day to generate a mine static deviation index KJP; A comprehensive analysis module, which is used to perform correlation analysis on the mine static deviation index KJP and the mine time-shift deviation index KSP to generate a twin mine modeling evaluation index SKP; A comparison module is used to compare the twin mine modeling evaluation index SKP with the accuracy threshold and output the accuracy level of the twin mine modeling.

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