CN117168419A - Automatic rock mass structure and quality identification method based on machine vision system - Google Patents
Automatic rock mass structure and quality identification method based on machine vision system Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 22
- 238000012937 correction Methods 0.000 claims abstract description 8
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 8
- 239000000945 filler Substances 0.000 claims description 7
- 239000003673 groundwater Substances 0.000 claims description 5
- 238000010801 machine learning Methods 0.000 claims description 4
- 229920006395 saturated elastomer Polymers 0.000 claims description 4
- 238000010276 construction Methods 0.000 description 19
- 238000013461 design Methods 0.000 description 6
- 238000005422 blasting Methods 0.000 description 3
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- 238000005553 drilling Methods 0.000 description 2
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- 239000002360 explosive Substances 0.000 description 1
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Abstract
The invention provides a rock mass structure and quality automatic identification method based on a machine vision system, which comprises the following steps: obtaining a geological record diagram of a tunnel to be tested; dividing the geological record graph according to a preset footage to obtain rectangular units; evaluating the rectangular units according to the geological catalog to obtain rock mass basic factor scores of the rectangular units; performing state correction on the rock mass quality basic factor score to obtain a final rock mass quality score of the rectangular unit; classifying the rectangular units according to the final rock mass quality scores to obtain surrounding rock categories of the rectangular units; and determining surrounding rock types of different sections of the tunnel to be tested according to the surrounding rock types of each rectangular unit. The invention solves the problems of low manual recording efficiency and potential safety hazard caused by the fact that the structure and quality of the rock mass cannot be accurately reflected in the prior art.
Description
Technical Field
The invention relates to the field of underground engineering geological investigation, in particular to a rock mass structure and quality automatic identification method based on a machine vision system.
Background
The water and electricity and pumped storage engineering becomes an important guarantee for realizing energy structure adjustment in China, and great demands are put forward for energy development and construction of the water and electricity engineering, the pumped storage engineering and the like. In the early stage of investigation and design of hydropower and pumping and storage engineering, a large number of exploration caverns are required to be arranged to find out geological conditions of underground caverns, and in the construction stage, a large number of underground caverns and auxiliary caverns are required to be excavated. The conventional underground cavern is constructed by adopting a drilling and blasting method, the construction efficiency is low, the operation environment is bad, the control of the initiating explosive material is strict, and the exploration and construction progress of the underground cavern are seriously influenced. Along with the schedule of a large number of extraction and storage, the construction period of the underground cavern during construction becomes a key project period for project propulsion, and TBM construction method construction is introduced for accelerating the construction speed and exploring and constructing a large number of underground caverns.
After the conventional drilling and blasting method is completed for one blasting construction, a geological engineer carries out on-site geological recording, and after the recording is completed, a recording drawing is drawn indoors, and rock mass structure division and surrounding rock quality classification are carried out according to the recording result. After the TBM finishes the stepping construction, supporting construction is needed to be carried out according to the category of surrounding rock in time. The efficiency of manual geological logging is low, if manual logging is adopted after TBM finishes stepping, the construction progress is seriously affected, if no immediate surrounding rock quality data exists, the pertinence of construction support is insufficient, and potential safety hazards exist. The manual recording has great subjective factor influence at the same time, and is difficult to accurately and comprehensively reflect the rock mass quality. Therefore, how to quickly acquire surrounding rock images, objectively identify the development characteristics of structural planes in the rock mass based on artificial intelligence technology, and finish rock mass structural division and surrounding rock quality classification, thereby solving the technical problem of geological cataloging and being a problem to be solved by the technicians in the field.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide an automatic identification method for the structure and quality of a rock mass based on a machine vision system, and solves the problems that the efficiency of manual cataloging is low and potential safety hazards exist due to the fact that the structure and quality of the rock mass cannot be accurately reflected in the prior art.
In order to achieve the above object, the present invention provides the following solutions:
a rock mass structure and quality automatic identification method based on a machine vision system comprises the following steps:
obtaining a geological record diagram of a tunnel to be tested;
dividing the geological record graph according to a preset footage to obtain rectangular units;
evaluating the rectangular units according to the geological catalog to obtain rock mass basic factor scores of the rectangular units;
performing state correction on the rock mass quality basic factor score to obtain a final rock mass quality score of the rectangular unit;
classifying the rectangular units according to the final rock mass quality scores to obtain surrounding rock categories of the rectangular units;
and determining surrounding rock types of different sections of the tunnel to be tested according to the surrounding rock types of each rectangular unit.
Preferably, obtaining a geological record of the tunnel to be tested includes:
marking the tunnel to be tested by a preset distance to obtain the tunnel to be tested with the mark;
continuously scanning the tunnel to be tested with the mark by using a TBM tunnel image machine acquisition system to obtain a tunnel wall image of the tunnel to be tested;
generating a three-dimensional image model of the tunnel wall according to the tunnel wall image;
extracting the structural surface shape of the three-dimensional image model of the cavity wall to obtain a plurality of groups of structural surfaces;
expanding the three-dimensional image model of the tunnel wall by taking the center line of the tunnel roof as an axis to form an orthographic image;
and drawing a structural surface trace on the orthographic image based on a plurality of groups of structural surfaces to form a geological record graph.
Preferably, generating a three-dimensional image model of the cave wall according to the cave wall image comprises:
generating a ring tangent three-dimensional image model according to the hole wall image;
and automatically eliminating the ring tangent line of the ring tangent line three-dimensional image model by using a machine learning mode to obtain the cavity wall three-dimensional image model.
Preferably, the dividing the geological record according to a preset footage to obtain rectangular units includes:
drawing three parallel first auxiliary lines at the center of the tunnel top and the middle parts of the left wall and the right wall of the geological record graph respectively, wherein the direction of the first auxiliary lines is consistent with the axial direction of the tunnel;
drawing a second auxiliary line perpendicular to the tunnel axis on the geological catalog at preset intervals;
dividing the geological record into rectangular units according to the first auxiliary line and the second auxiliary line.
Preferably, the evaluating the rectangular unit according to the geological catalog to obtain a rock mass quality basic factor score of the rectangular unit includes:
determining a rock mass structure of a rectangular unit, and obtaining a rock mass integrity degree score according to the rock mass structure;
determining the structural surface opening degree, the filler thickness and the fluctuation difference of the rectangular unit according to the orthographic image, and obtaining the rock structural surface state score according to the structural surface opening degree, the filler thickness and the fluctuation difference;
obtaining a rock strength score according to the rock saturated uniaxial compressive strength of the rock;
and obtaining the rock mass quality basic factor score of the rectangular unit according to the rock mass integrity score, the rock mass structural plane state score and the rock mass intensity score.
Preferably, the determining the rock mass structure of a rectangular unit comprises:
calculating the number of intersection points of the structural surface in the rectangular unit and the first auxiliary line on the geological catalog to obtain the number of the structural surfaces;
calculating and obtaining the space between the inner structural surfaces of the rectangular units;
and determining the rock mass structure of the rectangular unit according to the structure surface distance and the structure surface number.
Preferably, performing state correction on the rock mass quality basic factor score to obtain a final rock mass quality score of the rectangular unit, including:
counting underground water leakage conditions, and correcting the ground water state according to the rock mass quality basic factor score according to the underground water leakage conditions to obtain an initial rock mass quality basic factor score;
and correcting the structural face shape according to the structural face shape to obtain a final rock mass quality score of the rectangular unit.
Preferably, the determining the surrounding rock types of different sections of the tunnel to be tested according to the surrounding rock types of each rectangular unit includes:
combining adjacent rectangular units with the same surrounding rock type to obtain combined rectangular units;
and determining surrounding rock categories of different sections of the tunnel to be tested according to the merging rectangular unit.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides a rock mass structure and quality automatic identification method based on a machine vision system, which comprises the steps of obtaining a geological catalog of a tunnel to be tested; dividing the geological record graph according to a preset footage to obtain rectangular units; evaluating the rectangular units according to the geological catalog to obtain rock mass basic factor scores of the rectangular units; performing state correction on the rock mass quality basic factor score to obtain a final rock mass quality score of the rectangular unit; classifying the rectangular units according to the final rock mass quality scores to obtain surrounding rock categories of the rectangular units; and determining surrounding rock types of different sections of the tunnel to be tested according to the surrounding rock types of each rectangular unit. By utilizing geological logging and rock mass quality division of a machine vision system, the logging efficiency and the data arrangement analysis quality can be greatly improved, so that a rock mass structure and surrounding rock category are provided for design in real time, the stability of the surrounding rock is analyzed, the supporting mode is dynamically adjusted, dynamic design and dynamic construction are realized, the construction efficiency and quality are improved, and safety and reliability are ensured.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a rock mass structure and quality automatic identification method based on a machine vision system provided by an embodiment of the invention;
FIG. 2 is a schematic diagram of a TBM tunnel image machine acquisition system provided by an embodiment of the present invention;
fig. 3 is a detailed flowchart of a rock mass structure and quality automatic identification method based on a machine vision system according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a rock mass structure and quality automatic identification method based on a machine vision system, which solves the problems that in the prior art, the manual recording efficiency is low and potential safety hazards exist due to the fact that the structure and quality of a rock mass cannot be accurately reflected.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
As shown in fig. 1, the invention provides a rock mass structure and quality automatic identification method based on a machine vision system, which comprises the following steps:
step 100: obtaining a geological record diagram of a tunnel to be tested;
step 200: dividing the geological record graph according to a preset footage to obtain rectangular units;
step 300: evaluating the rectangular units according to the geological catalog to obtain rock mass basic factor scores of the rectangular units;
step 400: performing state correction on the rock mass quality basic factor score to obtain a final rock mass quality score of the rectangular unit;
step 500: classifying the rectangular units according to the final rock mass quality scores to obtain surrounding rock categories of the rectangular units;
step 600: and determining surrounding rock types of different sections of the tunnel to be tested according to the surrounding rock types of each rectangular unit.
Further, obtaining a geological record of the tunnel to be tested, including:
marking the tunnel to be tested by a preset distance to obtain the tunnel to be tested with the mark;
continuously scanning the tunnel to be tested with the mark by using a TBM tunnel image machine acquisition system to obtain a tunnel wall image of the tunnel to be tested;
generating a three-dimensional image model of the tunnel wall according to the tunnel wall image;
extracting the structural surface shape of the three-dimensional image model of the cavity wall to obtain a plurality of groups of structural surfaces;
expanding the three-dimensional image model of the tunnel wall by taking the center line of the tunnel roof as an axis to form an orthographic image;
and drawing a structural surface trace on the orthographic image based on a plurality of groups of structural surfaces to form a geological record graph.
As shown in fig. 2, the embodiment further specifically discloses a TBM tunnel image machine acquisition system (image acquisition system), including:
array camera, lighting equipment, power supply equipment, carrying trolley and the like; the array camera is used for continuously scanning the tunnel wall to obtain a high-resolution image of the tunnel wall; the TBM tunnel is circular, and the geological recording range is a sector range of 270 degrees above the two side walls and the top of the tunnel, namely the bottom of the tunnel; the array cameras are distributed annularly, cameras with the visual angles being more than or equal to 75 degrees are adopted, in order to avoid missing image acquisition and three-dimensional modeling, the visual angles between adjacent cameras need to be overlapped by 15 degrees, 5 cameras are required to form an annular array camera, and the acquisition range is 75 degrees+3×45 degrees+75 degrees=275 degrees (the scanning range of the No. 1 camera is the scanning range of the No. 5 camera); an illumination device is arranged between the annular array cameras, so that illumination support is provided for tunnel wall image acquisition; the power supply equipment is used for providing power for the camera and the lighting equipment and adopts a storage battery for power supply; the acquisition system carries a trolley and is used for carrying an array camera, lighting equipment and power supply equipment, so that continuous tunnel wall image acquisition can be realized.
Specifically, a geological catalog of a tunnel to be tested is catalogued, which comprises the following steps:
step 1, identifying pile numbers every 2m on two side walls of a tunnel for image positioning, wherein the pile numbers are identified by cross-shaped digital pile numbers;
step 2, placing the array camera, the lighting equipment and the power supply equipment on a carrying trolley, and pushing the carrying trolley to continuously scan the images of the tunnel wall;
step 3, generating a three-dimensional image model of the wall of the hole, removing ring tangent lines generated by the wall of the hole when TBM in the image steps, wherein the ring tangent lines are vertical to the wall of the hole, have fixed intervals, penetrate through three walls, have staggered platforms on two sides of the ring tangent lines, have no opening, are filled with substances and the like, and automatically removing the ring tangent lines after adopting a machine learning mode;
step 4, extracting the output of the main structural surface, and grouping the structural surfaces;
step 5, unfolding the three-dimensional model of the tunnel wall into an orthographic image by taking the center line of the tunnel roof as an axis;
and 6, drawing a structural surface trace on the orthographic image to form a geological record graph.
Further, generating a three-dimensional image model of the cave wall according to the cave wall image comprises the following steps:
generating a ring tangent three-dimensional image model according to the hole wall image;
and automatically eliminating the ring tangent line of the ring tangent line three-dimensional image model by using a machine learning mode to obtain the cavity wall three-dimensional image model.
Further, the dividing the geological record graph according to a preset footage to obtain rectangular units includes:
drawing three parallel first auxiliary lines at the center of the tunnel top and the middle parts of the left wall and the right wall of the geological record graph respectively, wherein the direction of the first auxiliary lines is consistent with the axial direction of the tunnel;
drawing a second auxiliary line perpendicular to the tunnel axis on the geological catalog at preset intervals;
dividing the geological record into rectangular units according to the first auxiliary line and the second auxiliary line.
Further, the evaluating the rectangular unit according to the geological catalog to obtain a rock mass quality basic factor score of the rectangular unit includes:
determining a rock mass structure of a rectangular unit, and obtaining a rock mass integrity degree score according to the rock mass structure;
determining the structural surface opening degree, the filler thickness and the fluctuation difference of the rectangular unit according to the orthographic image, and obtaining the rock structural surface state score according to the structural surface opening degree, the filler thickness and the fluctuation difference;
obtaining a rock strength score according to the rock saturated uniaxial compressive strength of the rock;
and obtaining the rock mass quality basic factor score of the rectangular unit according to the rock mass integrity score, the rock mass structural plane state score and the rock mass intensity score.
Further, the determining the rock mass structure of the rectangular unit includes:
calculating the number of intersection points of the structural surface in the rectangular unit and the first auxiliary line on the geological catalog to obtain the number of the structural surfaces;
calculating and obtaining the space between the inner structural surfaces of the rectangular units;
and determining the rock mass structure of the rectangular unit according to the structure surface distance and the structure surface number.
Further, performing state correction on the rock mass quality basic factor score to obtain a final rock mass quality score of the rectangular unit, including:
counting underground water leakage conditions, and correcting the ground water state according to the rock mass quality basic factor score according to the underground water leakage conditions to obtain an initial rock mass quality basic factor score;
and correcting the structural face shape according to the structural face shape to obtain a final rock mass quality score of the rectangular unit.
Further, the determining the surrounding rock types of different sections of the tunnel to be tested according to the surrounding rock types of each rectangular unit includes:
combining adjacent rectangular units with the same surrounding rock type to obtain combined rectangular units;
and determining surrounding rock categories of different sections of the tunnel to be tested according to the merging rectangular unit.
Specifically, as shown in fig. 3, the embodiment further specifically discloses specific steps for distinguishing the rock types of the tunnel to be tested:
step 1, respectively drawing three parallel auxiliary lines at the center of a tunnel top and the middle parts of left and right side walls of a geological record graph, wherein the directions of the three parallel auxiliary lines are consistent with the axial direction of a tunnel; drawing auxiliary lines perpendicular to the axis of the tunnel at intervals of every 2m on the catalog, and dividing the catalog of the tunnel top and the left and right side walls into rectangular units with footage of 2m respectively;
step 2, respectively calculating the intersection point number of the structural planes in the rectangular units of the top and the left and right side walls of the hole and the auxiliary line drawn in the step 1 in the claim 3, namely the structural plane development number, and simultaneously calculating the spacing between the structural planes in the rectangular units of 2m, and automatically determining the rock mass structure of the rectangular units of 2m according to the structural plane number and the spacing;
step 3, evaluating the rock integrity in the 2m rectangular unit according to the number of structural surfaces, the spacing and the rock structure, and grading the rock integrity according to the specification;
step 4, automatically measuring the opening degree of the structural surface in the 2m rectangular unit, the thickness and fluctuation difference of the filler on the generated orthographic image, and scoring the state of the structural surface according to the specification;
step 5, according to the saturated uniaxial compressive strength of the rock, grading the rock strength according to the specification;
step 6, obtaining rock mass quality basic factor scores of every 2m rectangular units by rock mass integrity scores, structural surface state scores and rock strength scores;
step 7, manually counting the groundwater exposure conditions of different hole sections, and correcting the groundwater state of the rock mass quality scores of every 2m rectangular units; further, according to the determined dominant structural face shape of the tunnel, correcting the structural face shape of the rock mass quality scores of every 2m rectangular units to obtain final rock mass quality scores of the 2m rectangular units;
and 8, according to the quality scores of the rock mass, giving the surrounding rock category of each 2m rectangular unit, merging rectangular units with the same category of adjacent surrounding rocks, and finally giving the surrounding rock category of the rock mass with different sections.
The beneficial effects of the invention are as follows:
the problem that the construction progress of TBM is affected due to low efficiency of traditional geological logging is solved. The catalogue result and the rock mass quality classification result are greatly influenced by subjective factors of the catalogue personnel, and the pertinence and the reliability are relatively low. The instantaneity of the cataloging result is low, dynamic design and dynamic support cannot be achieved, and the reliability and safety of the support are difficult to guarantee. Through machine vision system geology record and rock mass quality division, can be very big improvement record efficiency and data arrangement analysis quality to provide rock mass structure and country rock class for the design promptly, analysis country rock stability, dynamic adjustment support mode realizes dynamic design and dynamic construction, improves efficiency of construction and quality, ensures safe and reliable.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (8)
1. The automatic rock mass structure and quality identification method based on the machine vision system is characterized by comprising the following steps of:
obtaining a geological record diagram of a tunnel to be tested;
dividing the geological record graph according to a preset footage to obtain rectangular units;
evaluating the rectangular units according to the geological catalog to obtain rock mass basic factor scores of the rectangular units;
performing state correction on the rock mass quality basic factor score to obtain a final rock mass quality score of the rectangular unit;
classifying the rectangular units according to the final rock mass quality scores to obtain surrounding rock categories of the rectangular units;
and determining surrounding rock types of different sections of the tunnel to be tested according to the surrounding rock types of each rectangular unit.
2. The method for automatically identifying the structure and the quality of a rock mass based on a machine vision system according to claim 1, wherein the step of obtaining a geological catalog of a tunnel to be tested comprises the following steps:
marking the tunnel to be tested by a preset distance to obtain the tunnel to be tested with the mark;
continuously scanning the tunnel to be tested with the mark by using a TBM tunnel image machine acquisition system to obtain a tunnel wall image of the tunnel to be tested;
generating a three-dimensional image model of the tunnel wall according to the tunnel wall image;
extracting the structural surface shape of the three-dimensional image model of the cavity wall to obtain a plurality of groups of structural surfaces;
expanding the three-dimensional image model of the tunnel wall by taking the center line of the tunnel roof as an axis to form an orthographic image;
and drawing a structural surface trace on the orthographic image based on a plurality of groups of structural surfaces to form a geological record graph.
3. The automated machine vision system-based rock mass structure and quality identification method of claim 2, wherein generating a three-dimensional image model of the cavity wall from the cavity wall image comprises:
generating a ring tangent three-dimensional image model according to the hole wall image;
and automatically eliminating the ring tangent line of the ring tangent line three-dimensional image model by using a machine learning mode to obtain the cavity wall three-dimensional image model.
4. The automatic identification method for rock mass structure and quality based on machine vision system according to claim 1, wherein the dividing the geological record map according to a preset footage to obtain rectangular units comprises:
drawing three parallel first auxiliary lines at the center of the tunnel top and the middle parts of the left wall and the right wall of the geological record graph respectively, wherein the direction of the first auxiliary lines is consistent with the axial direction of the tunnel;
drawing a second auxiliary line perpendicular to the tunnel axis on the geological catalog at preset intervals;
dividing the geological record into rectangular units according to the first auxiliary line and the second auxiliary line.
5. The automatic identification method for rock mass structure and quality based on machine vision system according to claim 2, wherein the evaluating the rectangular unit according to the geological catalog to obtain the rock mass quality basic factor score of the rectangular unit comprises:
determining a rock mass structure of a rectangular unit, and obtaining a rock mass integrity degree score according to the rock mass structure;
determining the structural surface opening degree, the filler thickness and the fluctuation difference of the rectangular unit according to the orthographic image, and obtaining the rock structural surface state score according to the structural surface opening degree, the filler thickness and the fluctuation difference;
obtaining a rock strength score according to the rock saturated uniaxial compressive strength of the rock;
and obtaining the rock mass quality basic factor score of the rectangular unit according to the rock mass integrity score, the rock mass structural plane state score and the rock mass intensity score.
6. The automated machine vision system-based rock mass structure and quality identification method of claim 5, wherein said determining a rock mass structure of a rectangular unit comprises:
calculating the number of intersection points of the structural surface in the rectangular unit and the first auxiliary line on the geological catalog to obtain the number of the structural surfaces;
calculating and obtaining the space between the inner structural surfaces of the rectangular units;
and determining the rock mass structure of the rectangular unit according to the structure surface distance and the structure surface number.
7. The automated machine vision system-based rock mass structure and quality identification method of claim 2, wherein performing a state correction on the rock mass quality basic factor score to obtain a final rock mass quality score for a rectangular unit comprises:
counting underground water leakage conditions, and correcting the ground water state according to the rock mass quality basic factor score according to the underground water leakage conditions to obtain an initial rock mass quality basic factor score;
and correcting the structural face shape according to the structural face shape to obtain a final rock mass quality score of the rectangular unit.
8. The automatic identifying method for rock mass structure and quality based on machine vision system according to claim 1, wherein the determining the surrounding rock types of different sections of the tunnel to be tested according to the surrounding rock types of each rectangular unit comprises:
combining adjacent rectangular units with the same surrounding rock type to obtain combined rectangular units;
and determining surrounding rock categories of different sections of the tunnel to be tested according to the merging rectangular unit.
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