CN117710747A

CN117710747A - Tunnel surrounding rock rapid grading method and device, electronic equipment and storage medium

Info

Publication number: CN117710747A
Application number: CN202311791790.5A
Authority: CN
Inventors: 张东海; 王凤玲; 李雪岩; 苏二威; 李岩; 王春霞; 郭志婷; 李梅; 王浩雷
Original assignee: China Road & Bridge Technology Co ltd
Current assignee: China Road & Bridge Technology Co ltd
Priority date: 2023-12-25
Filing date: 2023-12-25
Publication date: 2024-03-15
Anticipated expiration: 2043-12-25
Also published as: CN117710747B

Abstract

The application is applicable to the technical field of tunnel engineering, and provides a method and a device for rapidly grading tunnel surrounding rocks, electronic equipment and a storage medium, wherein the method for rapidly grading the tunnel surrounding rocks comprises the following steps: receiving a feature ID of the rock to be classified; screening out an image of the rock type corresponding to the feature ID and displaying the image; determining the rock type of the rock to be classified according to the rock type corresponding to the selected first target image; screening out a plurality of images of preset weathering characteristics corresponding to the rock types and displaying the images; determining the weathering characteristics corresponding to the selected second target image as the weathering characteristics of the rock to be classified; screening out a plurality of images of preset rock mass states corresponding to the rock types and displaying the images; determining the rock mass state corresponding to the selected third target image as the rock mass state of the rock to be classified; and determining the surrounding rock grade based on the rock type, the weathering characteristics and the rock mass state of the preset relation network. According to the rock grading method and device, the rock to be graded can be rapidly graded at any time, and grading efficiency is improved.

Description

Tunnel surrounding rock rapid grading method and device, electronic equipment and storage medium

Technical Field

The application belongs to the technical field of tunnel engineering, and particularly relates to a method and a device for rapidly grading tunnel surrounding rocks, electronic equipment and a storage medium.

Background

In the tunnel construction process, the conditions of severe geological environment and poor construction conditions are frequently met, and the surrounding rock grade is dynamically changed. The quality of surrounding rock is evaluated correctly and timely, and the method is a basic condition for economically and reasonably carrying out tunnel excavation and support design and rapid and safe construction. Surrounding rock classification is a parameter to be considered at any time in the process of tunnel design and construction.

At present, an indoor test method and an advanced forecasting method are generally adopted to obtain the mechanical parameters and the structural characteristics of the surrounding rock, but the defects of long time consumption, complex operation process and the like exist because the core is required to be sent to a laboratory for testing by the indoor test method; the advanced forecasting method has the problems of insufficient forecasting accuracy, long interval period, high price and the like. Both are difficult to meet the requirement of rapid classification of tunnel surrounding rock.

Disclosure of Invention

In order to solve the problem that the tunnel surrounding rock cannot be classified rapidly, the embodiment of the application provides a method, a device, electronic equipment and a storage medium for classifying the tunnel surrounding rock rapidly.

The application is realized by the following technical scheme:

in a first aspect, an embodiment of the present application provides a method for rapidly grading a tunnel surrounding rock, including:

receiving a feature ID of the rock to be classified, which is input by a user;

screening out images of all rock types corresponding to the feature IDs from a rock sample set, and displaying the images;

determining a first target image selected by a user from the images of all rock types, and determining the rock type corresponding to the first target image as the rock type of the rock to be classified;

screening out images of different weathering characteristics corresponding to rock types of the rock to be classified from the rock weathering collection, and displaying the images;

determining a second target image selected by a user from the images with different weathering characteristics, and determining the weathering characteristics corresponding to the second target image as the weathering characteristics of the rock to be classified;

screening out a plurality of images of different preset rock mass states corresponding to rock types of the rock to be classified from a rock mass state set, and displaying the images;

determining a third target image selected by a user from the images of the different preset rock mass states, and determining the rock mass state corresponding to the third target image as the rock mass state of the rock to be classified;

And determining the surrounding rock grade of the rock to be graded according to a preset relation network, the rock type, the weathering characteristic and the rock mass state of the rock to be graded, wherein the preset relation network is the corresponding relation between the rock type, the weathering characteristic and the rock mass state and the surrounding rock grade.

In a second aspect, an embodiment of the present application provides a rapid grading device for surrounding rock of a tunnel, including:

the rock characteristic receiving module is used for receiving the characteristic ID of the rock to be classified input by the user;

the rock type screening module is used for screening out images of all rock types corresponding to the feature IDs from a rock sample set and displaying the images;

the rock type determining module is used for determining a first target image selected by a user from the images of all rock types and determining the rock type corresponding to the first target image as the rock type of the rock to be classified;

the weathering degree screening module is used for screening out images of different weathering characteristics corresponding to rock types of the rock to be classified from the rock weathering collection and displaying the images;

the weathering degree determining module is used for determining a second target image selected by a user from the images with different weathering characteristics and determining the weathering characteristics corresponding to the second target image as the weathering characteristics of the rock to be classified;

The rock mass state screening module is used for screening out a plurality of images of different preset rock mass states corresponding to the rock types of the rock to be classified from a rock mass state set and displaying the images;

the rock mass state determining module is used for determining a third target image selected by a user from the images of the different preset rock mass states and determining the rock mass state corresponding to the third target image as the rock mass state of the rock to be classified;

and the surrounding rock grade determining module is used for determining the surrounding rock grade of the rock to be classified according to a preset relation network, the rock type, the weathering characteristics and the rock mass state of the rock to be classified.

In a third aspect, an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the tunnel surrounding rapid grading method according to any one of the first aspects when the processor executes the computer program.

In a fourth aspect, embodiments of the present application provide a computer readable storage medium storing a computer program which, when executed by a processor, implements a method for rapidly grading tunnel surrounding rock according to any one of the first aspects.

In a fifth aspect, embodiments of the present application provide a computer program product, which when run on an electronic device, causes the electronic device to perform the tunnel surrounding rock rapid grading method of any one of the first aspects above.

It will be appreciated that the advantages of the second to fifth aspects may be found in the relevant description of the first aspect, and are not described here again.

Compared with the related art, the embodiment of the application has the beneficial effects that: according to the embodiment of the application, through receiving the feature ID of the rock to be classified, which is input by a user, all rock type images corresponding to the feature ID are screened out from a rock sample set and displayed, and after the user selects a first target image, the rock type of the rock to be classified can be determined; then, screening out different weathered images of the rock types in the rock weathering set, displaying the images, and determining the weathering characteristics of the rock to be classified after the user selects the second target image; then screening out and displaying images of different rock mass states of the rock type in a rock mass state set, and determining the rock mass state of the rock to be classified after a third target image is selected by a user; and finally, determining the surrounding rock grade of the rock to be graded according to the preset relation network, the rock type, the weathering characteristics and the rock mass state of the rock to be graded. According to the method and the device, according to the relevance among rock types, weathering characteristics, rock mass states and surrounding rock grades, the rapid grading at any time can be realized by combining a preset relation network built based on expert knowledge and practical experience, and grading efficiency is improved.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments or the description of the related art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort to a person of ordinary skill in the art.

Fig. 1 is a schematic flow chart of a method for rapidly grading surrounding rocks of a tunnel according to an embodiment of the present application;

FIG. 2A is a schematic diagram of a typical image of the origin IDs of the regions;

FIG. 2B is a schematic illustration of a typical image of each granularity ID;

FIG. 2C is a schematic illustration of a typical image of the color ID of igneous rock;

FIG. 2D is a schematic illustration of a typical image of the structure ID of metamorphic rocks;

FIG. 2E is a schematic illustration of a typical image of particle morphology ID of sedimentary rock;

FIG. 2F is a schematic diagram of a rock mass image database display effect;

FIG. 3 is a schematic flow chart of determining a feature ID of rock to be classified according to an embodiment of the present application;

FIG. 4 is a schematic diagram of a preset relationship network according to an embodiment of the present application;

FIG. 5 is a schematic diagram of a preset relationship network according to another embodiment of the present application;

fig. 6 is a schematic structural diagram of a rapid grading device for surrounding rocks of a tunnel according to an embodiment of the present application;

fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It should also be understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.

As used in this specification and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".

In addition, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance.

Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.

For classification of surrounding rock, an indoor test method and an advanced prediction method are generally adopted to obtain mechanical parameters and structural characteristics of the surrounding rock, but the defects of long time consumption, complicated operation process and the like exist because the core is required to be sent to a laboratory for test by the indoor test method; the advanced forecasting method has the problems of insufficient forecasting accuracy, long interval period, high price and the like. Both are difficult to meet the requirement of rapid classification of tunnel surrounding rock.

Based on the above problems, the embodiments of the present application provide a rapid grading method for surrounding rocks of a tunnel, where the surrounding rock grades are mainly determined by lithology and rock status, and different combinations of lithology and rock status determine different surrounding rock grades. The rock property, the rock mass state and the surrounding rock grade are determined in any place, so the surrounding rock property and the rock mass state are expressed by three nodes, namely the rock type S, the weathering characteristic W and the rock mass state B, the three nodes are connected in series to be used as input, the surrounding rock grade D is used as output, and objective connection is necessarily present between the input and the output. The objective relation is used for creating a relation network, so that the purpose of presuming the surrounding rock grade is achieved. According to the rock grading method and device, the rock to be graded can be rapidly graded at any time, and grading efficiency is improved.

The method for rapidly grading the surrounding rocks of the tunnel can be applied to tunnels such as highway tunnels, railway tunnels, water conservancy tunnels, petroleum tunnels, electric power tunnels, water supply and drainage tunnels, gas tunnels, communication tunnels and the like, so as to rapidly grade the surrounding rocks in the tunnel. Among them, highway tunnels are the most common, and can solve the obstacle of land traffic, such as crossing valleys, rivers, etc. Railway tunnels are intended to improve the operation of railways so that they can cross mountains, rivers, etc., thereby shortening travel time. The water conservancy tunnel is used for improving water resource utilization, such as water storage, water diversion, water pumping, river improvement and the like. The oil tunnel is used to improve oil exploration and recovery, and it can recover oil from deep rock formations. The power tunnel is to improve the operation of a power system, and it may be constructed in a valley, a river, or the like. The water supply and drainage tunnel is used for improving the operation of a water supply and drainage system, and can be used for constructing pipelines in valleys, rivers and the like. The gas tunnel is to improve the operation of the gas system, and it can be constructed in the valley, river, etc. The communication tunnel is to improve the operation of the communication system, and it may be constructed in a valley, river, or the like.

The method for rapidly grading the tunnel surrounding rock is described in detail below.

Fig. 1 is a flow chart of a method for rapidly grading surrounding rocks of a tunnel according to an embodiment of the present application, and referring to fig. 1, the method for rapidly grading surrounding rocks of a tunnel is described in detail as follows:

in S101, a feature ID of rock to be classified input by a user is received.

Wherein the rock to be graded is a field surrounding rock. The feature ID comprises a region cause ID, a granularity ID and an index ID; the index ID includes any one of a color ID, a structure ID, and a particle morphology ID.

It should be understood that the feature ID may be composed of a region origin ID, a granularity ID, and a color ID, may be composed of a region origin ID, a granularity ID, and a structure ID, and may be composed of a region origin ID, a granularity ID, and a particle morphology ID.

Alternatively, the feature ID is in the form of an ID-region cause-granularity-index. Then when the feature ID consists of a region cause ID, a granularity ID, and a color ID, the feature ID is in the form of ID-region cause-granularity-color; when the feature ID is composed of a region cause ID, a granularity ID, and a structure ID, the feature ID is in the form of ID-region cause-granularity-structure; when the feature ID is composed of a region origin ID, a particle size ID, and a particle morphology ID, the feature ID is in the form of ID-region origin-particle size-particle morphology.

In addition, as shown in fig. 2A, 2B, 2C, 2D, and 2E, the region cause ID includes igneous rock H, metamorphic rock B, and sedimentary rock C; particle size ID includes coarse particle 1, medium particle 2, and fine particle 3; the color ID comprises light color H1, medium color H2 and dark color H3, the structure ID comprises a lamellar structure B1, a lamellar fibrilia structure B2 and a crystallization structure B3, and the particle form ID comprises edges C1, circles C2 and crystals C3. Fig. 2F is a schematic view showing the effect of a rock mass image database according to an embodiment of the present application, where the rock mass image database includes a rock sample set, a rock weathering set and a rock mass state set.

Each rock category may correspond to a feature ID, as shown in table 1, in the form of a feature ID, the feature IDs corresponding to the following rock categories are given.

TABLE 1 rock types and corresponding characteristic IDs

In some embodiments of the present application, referring to fig. 3, receiving the feature ID of the rock to be classified input by the user may include:

in S1011, rock images corresponding to different region cause IDs are displayed.

In S1012, a fourth target image selected by the user from the rock images corresponding to the different area cause IDs is determined, and the area cause ID corresponding to the fourth target image is determined as the area cause ID of the rock to be classified.

In S1013, rock images corresponding to different granularity IDs among the region formation IDs of the rock to be classified are displayed.

In S1014, a fifth target image selected by the user from rock images corresponding to different granularity IDs is determined, and the granularity ID corresponding to the fifth target image is determined as the granularity ID of the rock to be classified.

In S1015, the category of the index ID is determined according to the region cause ID of the rock to be classified and the preset correspondence between the region cause ID and the category of the index ID, and an image corresponding to the category is displayed.

In S1016, a sixth target image selected by the user from the rock images corresponding to the category of the determined index ID is determined, and the index ID corresponding to the sixth target image is determined as the index ID of the rock to be classified.

In S1017, a feature ID of the rock to be classified is determined from the region cause ID, the granularity ID, and the index ID of the rock to be classified.

The fourth target image is any image in the rock images corresponding to all the region cause IDs, namely any image in the rock images corresponding to the 3 region cause IDs. The fifth target image is any image in the rock images corresponding to all the granularity IDs respectively, namely any image in the rock images corresponding to the 3 granularity IDs respectively. The sixth target image is any image of rock images corresponding to all color IDs/structure IDs/particle morphology IDs, i.e., any image of rock images corresponding to 3 color IDs/3 structure IDs/3 particle morphology IDs.

Optionally, the preset correspondence between the category of the area cause ID and the index ID is:

if the region cause ID is igneous rock, the index ID is classified as color ID.

If the region cause ID is metamorphic rock, the category of the index ID is the structure ID.

If the region cause ID is sedimentary rock, the index ID is classified as particle form ID.

In an embodiment of the present application, typical images of igneous rock H, metamorphic rock B, and sedimentary rock C are displayed for selection by the user, after which it is determined which image the user selects.

If the user selects igneous rock H, the area cause ID of the igneous rock H is H, at the moment, typical images of coarse grain 1, middle grain 2 and fine grain 3 of the igneous rock H are displayed, then, which image is selected by the user is determined, if the user selects coarse grain 1, the granularity ID of the igneous rock H is 1, the category of the characteristic ID is determined as color ID because the user selects the typical image of the igneous rock H when selecting the area cause ID, then, after the granularity ID of the igneous rock H is determined, the typical images of light color H1, medium color H2 and dark color H3 of the igneous rock H are displayed, which image is selected by the user is determined, if the user selects dark color H3, the color ID of the igneous rock is H3, and finally, the characteristic ID of the igneous rock is ID-H-1-H3 according to the area cause ID, the granularity ID and the color ID of the igneous rock.

If the user selects metamorphic rock B, the regional cause ID of the rock to be classified is B, at the moment, typical images of coarse grains 1, medium grains 2 and fine grains 3 of the metamorphic rock B are displayed, then, which image is selected by the user is determined, if the user selects coarse grains 1, the granularity ID of the rock to be classified is 1, and if the user selects metamorphic rock B when selecting regional cause ID, the category of the characteristic ID is determined to be structure ID, then, after the granularity ID of the rock to be classified is determined, typical images of a sheet structure B1, a sheet structure B2 and a crystal structure B3 of the metamorphic rock B are displayed, which image is selected by the user is determined, if the user selects sheet structure B2, the structure ID of the rock to be classified is B2, and finally, the characteristic ID of the rock to be classified is ID-B-1-B2 according to the regional cause ID, the granularity ID and the structure ID of the rock to be classified is determined.

If the user selects sedimentary rock C, the area cause ID of the rock to be classified is C, typical images of coarse grains 1, medium grains 2 and fine grains 3 of the sedimentary rock C are displayed at the moment, then, which image is selected by the user is determined, if the user selects fine grains 3, the granularity ID of the rock to be classified is 3, the type of the characteristic ID is determined to be the granularity ID because the user selects sedimentary rock C when selecting the area cause ID, then, after the granularity ID of the rock to be classified is determined, the typical images of the edges C1, the round C2 and the crystal C3 of the sedimentary rock C are displayed, and determining which image is selected by the user, if the user selects round C2, the granularity ID of the rock to be classified is C2, and finally, the characteristic ID of the rock to be classified is determined to be ID-C-3-C2 according to the area cause ID, the granularity ID and the granularity ID of the rock to be classified.

In S102, images of all rock types corresponding to the feature IDs are screened out from the rock sample set and displayed.

The rock sample set comprises images of various kinds of rock under a plurality of different preset shooting conditions, wherein the preset shooting conditions comprise angles and light rays, namely the rock sample set comprises images of the same kind of rock under a plurality of angles and a plurality of light rays. Common rock images in the tunnel are collected from a geological database and actual work to form a rock sample set.

The rock sample set comprises corresponding feature IDs and description information on each image, wherein the description information comprises one or more of rock names, colors, whether knocking sounds are crisp, whether water is absorbed or not, whether scratches can be carved on nails, weathering features and structural surface combination degrees, so that the specificity of the rock is described. For example, the descriptive information on the image of marble is light, prone to nicking, foaming in hydrochloric acid, and the descriptive information on the image of shale is dark, hard. When the rock types cannot be uniquely determined according to the feature IDs, the descriptive information on the image can assist the user in selecting the closest rock type, and further uniquely determining the type of rock to be classified. The feature ID and the description information on the images are more beneficial to subsequent screening, matching or searching of the images, and the user can conveniently and rapidly search the images in the rock sample set through the description information or the feature ID of the rock to be classified.

In the embodiment of the application, if the characteristic ID of the rock to be classified is ID-H-1-H3, all rock type images with the characteristic ID of ID-H-1-H3 are screened out from the rock sample set, and the characteristic ID is uniquely pointed to the rock type to be pyroxene, namely, the images of pyroxene are displayed.

If the characteristic ID of the rock to be classified is ID-B-3-B3, the characteristic ID points to marble and shale, and then the images of the marble and the shale are displayed. Because each image in the rock sample set also has corresponding description information, the user can select the image most similar to the rock type of the rock to be classified according to the description information on the images of marble rock and shale. If the characteristic ID of the rock to be classified is ID-B-3-B3 and the rock to be classified is light in color, a user can directly judge that the rock to be classified is marble.

In S103, a first target image selected by the user from among the images of all rock categories is determined, and the rock category corresponding to the first target image is determined as the rock category of the rock to be classified.

The first target image is any image in all rock types screened out in the rock sample set according to the feature ID.

In the embodiment of the application, the rock type corresponding to the first target image selected by the user is determined as the rock type of the rock to be classified.

In S104, images of a plurality of different preset weathering characteristics corresponding to rock types of the rock to be classified are selected from the rock weathering collection and displayed.

The rock weathering concentration comprises images of a plurality of different preset weathering characteristics of each rock type in a rock sample set, wherein the preset weathering characteristics are pre-divided weathering characteristics, and each weathering characteristic comprises characteristics of rock structure, mineral composition, color, crack surface and the like. Different weathering degree images of various rocks in the tunnel are collected from a geological database and actual work to form a rock weathering set.

The rock weathering concentration images comprise corresponding feature IDs and description information, and the description information comprises one or more of rock names, colors, whether knocking sounds are crisp, whether water is absorbed or not, whether scratches can be carved on nails or not, weathering features and structural surface combination degrees so as to describe the specificity of the rock. The feature ID and the description information on the images are more beneficial to subsequent screening, matching or searching of the images, and a user can conveniently and rapidly search the images in the rock weathering concentration through the description information or the feature ID of the rock to be classified.

In the embodiment of the present application, if the rock type of the rock to be classified is pyroxene, images of a plurality of different preset weathering characteristics of pyroxene are screened out in the rock weathering set, and displayed for the user to select, and the user can determine the image closest to the weathering characteristics of the rock to be classified according to the displayed images of the plurality of different preset weathering characteristics of pyroxene and the description information on each image.

In S105, a second target image selected by the user from the images of different weathering characteristics is determined, and the weathering characteristics corresponding to the second target image are determined as the weathering characteristics of the rock to be classified.

The second target image is any one of a plurality of images of different preset weathering characteristics screened out according to rock types in rock weathering concentration.

In the embodiment of the application, the weathering characteristics corresponding to the second target image selected by the user are determined as the weathering characteristics of the rock to be classified.

In S106, images of a plurality of different preset rock mass states corresponding to rock types of the rock to be classified are screened from the rock mass state set, and displayed.

The rock mass state set comprises images of a plurality of different preset rock mass states for each rock class in the rock sample set. The preset rock mass state is a pre-divided rock mass state, and the rock mass state is a description of the tunnel face or the structure type of the rock mass. Rock mass state (tunnel face state) images are collected from a geological or tunnel database to form a rock mass state set.

The images comprise corresponding feature IDs and description information, wherein the description information comprises one or more of rock names, colors, whether knocking sounds are crisp, whether water is absorbed, whether scratches can be carved on nails, weathering features and structural surface combination degrees, so that the specificity of the rock is described. The feature ID and the description information on the images are more beneficial to subsequent screening, matching or searching of the images, and the user can conveniently and rapidly search the images in the rock mass state set through the description information or the feature ID of the rock to be classified.

In the embodiment of the present application, if the rock type of the rock to be classified is pyroxene, a plurality of images of different preset rock states of the pyroxene are intensively screened out in the rock state, and displayed for the user to select, and the user can determine the image closest to the rock state of the rock to be classified according to the displayed images of the plurality of different preset rock states of the pyroxene and the description information on each image.

In S107, a third target image selected by the user from a plurality of images of different preset rock mass states is determined, and the rock mass state corresponding to the third target image is determined as the rock mass state of the rock to be classified.

The third target image is any one image of a plurality of images of different preset rock states screened out according to rock types in the rock state set.

In the embodiment of the application, the rock mass state corresponding to the third target image selected by the user is determined as the rock mass state of the rock to be classified.

In S108, the surrounding rock grade of the rock to be graded is determined according to the preset relation network, the rock type, the weathering characteristics and the rock mass state of the rock to be graded.

The preset relation network is the corresponding relation of rock types, weathering characteristics and rock mass states and surrounding rock grades. The method is characterized in that a preset relation network is formed by combining expert knowledge and practical experience according to the correlation among rock types, weathering characteristics, rock mass states and surrounding rock grades.

Optionally, the surrounding rock grades are classified into 5 grades according to the quality of the surrounding rock, and D1, D2, D3, D4 and D5 are sequentially selected.

Optionally, the surrounding rock grade D1 represents a grade i surrounding rock, the surrounding rock grade D2 represents a grade ii surrounding rock, the surrounding rock grade D3 represents a grade iii surrounding rock, the surrounding rock grade D4 represents a grade iv surrounding rock, and the surrounding rock grade D5 represents a grade v surrounding rock.

In some embodiments of the present application, the rock sample set classifies each type of rock into 5 grades according to rock hardness, S1, S2, S3, S4 and S5 in order from the hardest to the softest.

Rock weathering collection the rock of each type is classified into 4 classes according to rock weathering characteristics, W1, W2, W3 and W4 being in sequence from the least to the most severe degree of weathering.

The rock mass state set classifies various kinds of rocks into 5 grades according to rock mass states, and B1, B2, B3, B4 and B5 are sequentially performed.

It should be appreciated that in the preset relationship network, the logical relationship from the input node to the output node is referred to as a path. This path is derived from expert experience and extensive practical summary (as shown in fig. 5), and the modified path can be optimized continuously in the subsequent application links. One sub-node from each of the three input nodes is selected to form an input combination (Si, wi, bi), and all possible input combinations are listed and connected to one output sub-node (Di) by a path, which forms a surrounding rock hierarchical relationship network (Si, wi, bi) → (Di). Any one of the input node's sub-nodes can be arbitrarily connected with the other input node's sub-nodes, and three sub-nodes can only point to a single output sub-node after being connected. The relation network contains all surrounding rock conditions and grades, and quick surrounding rock grading can be realized based on the relation network. And each child node in the relation network uses images for matching, so that the influence of subjective factors is small, and the defect of low accuracy caused by overdependence on subjective experiences of geological staff is overcome.

Optionally, S1 in the rock sample set may include: granite, syenite, amphibole, pyroxene, basalt, andesite, gneiss, silicalite, quartzite, siliceous cementitious conglomerate, quartzite, siliceous limestone, and the like.

S2 in the rock sample set may include: sintered tuff, marble, slate, dolomite, limestone, calcareous sandstone, and the like.

S3 in the rock sample set may include: tuff, phyllite, mudstone, marl, argillite, siltstone, and the like.

S4 in the rock sample set may include: mudstone, shale, chlorite schist, sericite schist, and the like.

S5 in the rock sample set may include: other semi-diagenetic.

It should be understood that the rock types included in each of the above-listed rock hardness levels are only a portion, and that actual rock types are numerous, and that not all of the rock types included in each of the rock hardness levels are listed herein. Rock images of the same name in the rock sample set constitute a subset, each subset corresponding to one child node Si (i=1, 2,3,4, 5), respectively.

Optionally, the weathering characteristics corresponding to W1 in the rock weathering concentration may include: the rock structure, mineral composition and color are basically unchanged, and part of fracture surfaces are rendered by hammer or slightly discolored.

The weathering characteristics corresponding to W2 in rock weathering concentration may include: the rock structure is partially destroyed, the mineral composition and the color are obviously changed, and the crack surface is more severely weathered.

The weathering characteristics corresponding to W3 in rock weathering concentration may include: the rock structure is largely destroyed, the mineral composition and color are obviously changed, and feldspar, mica and iron magnesium minerals are weathered and changed.

The weathering characteristics corresponding to W4 in rock weathering concentration may include: the rock structure is completely destroyed, disintegrated and decomposed into loose soil or sand, the minerals are all discolored, the luster disappears, and most of the minerals except quartz particles are weathered and changed into secondary minerals.

It should be appreciated that the images of each of the weathering characteristics in the rock weathering set correspond to one child node Wi (i=1, 2,3, 4).

Optionally, the rock mass states corresponding to B1 in the rock mass state set may include: bulk or giant thick layer structures.

The rock mass states corresponding to B2 in the rock mass state set may include: bulk or thick layer structures.

The rock mass states corresponding to B3 in the rock mass state set may include: slit block-shaped, medium-thick lamellar, embedded broken-shaped and lamellar structures.

The rock mass states corresponding to B4 in the rock mass state set may include: a fragmentation structure.

The rock mass states corresponding to B5 in the rock mass state set may include: a discrete structure.

It should be appreciated that the images of each rock mass state in the set of rock mass states each correspond to one sub-node Bi (i=1, 2,3,4, 5).

In some embodiments of the present application, when determining the surrounding rock grade of the rock to be graded according to the preset relationship network, the rock type, the weathering characteristics and the rock mass state of the rock to be graded, the rock hardness grade of the rock to be graded may be determined according to the rock type of the rock to be graded and the preset relationship between the rock type and the rock hardness grade; determining the weathering grade of the rock to be graded according to the weathering characteristics of the rock to be graded and the preset relation between the weathering characteristics and the weathering grade; determining the rock mass state grade of the rock to be graded according to the rock mass state of the rock to be graded and the preset relation between the rock mass state and the rock mass state grade; and determining the surrounding rock grade of the rock to be classified according to the preset relation network, the rock hardness grade, the weathering grade and the rock mass state grade of the rock to be classified.

The preset relation between rock types and rock hardness levels is preset according to the corresponding relation between the rock types included in the rock hardness levels, the preset relation between the weathering characteristics and the weathering degree levels is preset according to the weathering characteristics corresponding to the weathering degree levels, and the preset relation between rock states and rock state levels is preset according to the rock states corresponding to the rock state levels.

It should be understood that the preset relationship between the rock types and the rock hardness levels is the correspondence between each subset in the rock sample set and each sub-node Si, that is, the correspondence between the images included in each subset in the rock sample set and each sub-node Si, and the specific correspondence may be set by referring to each rock type included in each sub-node Si. Similarly, the preset relationship between the weathering characteristics and the weathering degree level is the corresponding relationship between the images of the weathering characteristics in the rock weathering concentration and each sub-node Wi, and the specific corresponding relationship can be set by referring to the weathering characteristics corresponding to each sub-node Wi. The preset relationship between the rock mass state and the rock mass state grade is the corresponding relationship between the image of each rock mass state in the rock mass state set and each sub-node Bi, and the specific corresponding relationship can be set by referring to the rock mass state corresponding to each sub-node Bi.

Fig. 4 is a schematic diagram of a preset relationship network according to an embodiment of the present application, and fig. 5 is a schematic diagram of a preset relationship network according to another embodiment of the present application.

Referring to fig. 4 and 5, in an embodiment of the present application, rock hardness levels of rock to be classified are determined based on rock types of the rock to be classified; determining the weathering degree grade of the rock to be classified based on the weathering characteristics of the rock to be classified; and determining the rock mass state grade of the rock to be classified based on the rock mass state of the rock to be classified, and then determining the surrounding rock grade of the rock to be classified by combining a preset relation network.

In the embodiment of the application, 3 typical images and description information of different area cause IDs are provided to assist a person with poor experience in judging, if the typical image of igneous rock H is selected by a user at this time, the area cause ID of the field rock is H, then the typical images corresponding to the different granularity IDs of igneous rock H are displayed, if the typical image of coarse grain 1 is selected by the user, then the typical images corresponding to the different color IDs of igneous rock H are displayed, if the typical image of dark color H3 is selected by the user, the color ID of the field rock is H3, at this time, the characteristic ID of the field rock is ID-H-1-H3, the characteristic ID corresponds to the unique rock type-pyroxene, the images of pyroxene are screened out from the rock sample set, and are displayed for the user to select, and after one of the images is selected by the user, the rock type of the field rock can be determined to be pyroxene. After the rock types are determined, images of different weathering characteristics of the pyroxene are screened out in the rock weathering concentration and displayed for a user to select an image consistent with the on-site rock weathering characteristics from the images. After the rock types are determined, different rock state images of the pyroxene are screened out in the rock state set, and an image consistent with the on-site rock state is selected from the images. The rock type, the weathering characteristic and the rock mass state of the field rock are determined, the rock type, the weathering characteristic and the rock mass state of the field rock correspond to three input sub-nodes in a preset relation network respectively, and then the surrounding rock grade of the field rock is determined by combining the preset relation network. If the user selects the pyroxene image corresponding sub-node S1 from the rock sample set, selecting an image corresponding sub-node W3 similar to the on-site rock weathering characteristics from the rock weathering set; a sub-node B3 corresponding to an image selected from the rock mass state set to be similar to the rock mass state of the rock in the field; and according to the input and output relation of the nodes in the preset relation network, inputting (S1, W3, B3) and outputting D4, and determining the level IV of the surrounding rock of the tunnel face.

The operation main body of the tunnel surrounding rock rapid grading method provided by the embodiment of the application can be a mobile terminal which is convenient to carry, such as a mobile phone, a tablet, a notebook computer and the like, and the mobile terminal can display images. When the user needs to judge the surrounding rock grade of the field rock mass, the user can hold the terminal by hand to select the rock image closest to the condition of the field rock mass according to the field rock mass, and the surrounding rock grade of the field rock mass can be rapidly and accurately determined through one-step operation of the user (namely, operation of selecting the rock image corresponding to the regional origin ID, the rock image corresponding to the granularity ID and the like), and the problem of lower accuracy caused by overreliance on subjective experience of geological staff is effectively solved.

It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic of each process, and should not limit the implementation process of the embodiment of the present application in any way.

Corresponding to the rapid grading method for tunnel surrounding rock described in the above embodiments, fig. 6 shows a block diagram of the rapid grading device for tunnel surrounding rock provided in the embodiment of the present application, and for convenience of explanation, only the portions relevant to the embodiments of the present application are shown.

Referring to fig. 6, the tunnel surrounding rock rapid grading device in the embodiment of the present application may include a rock feature receiving module 601, a rock type screening module 602, a rock type determining module 603, a weathering feature screening module 604, a weathering feature determining module 605, a rock mass status screening module 606, a rock mass status determining module 607, and a surrounding rock grade determining module 608.

The rock feature receiving module 601 is configured to receive a feature ID of a rock to be classified input by a user.

The rock type screening module 602 is configured to screen out and display images of all rock types corresponding to the feature IDs from the rock sample set.

The rock type determining module 603 is configured to determine a first target image selected by a user from images of all rock types, and determine a rock type corresponding to the first target image as a rock type of the rock to be classified.

And the weathering characteristic screening module 604 is used for screening out images of different weathering characteristics corresponding to rock types of the rock to be classified from the rock weathering set and displaying the images.

The weathering characteristic determining module 605 is configured to determine a second target image selected by the user from the images of different weathering characteristics, and determine a weathering characteristic corresponding to the second target image as a weathering characteristic of the rock to be classified.

The rock mass status screening module 606 is configured to screen and display a plurality of images of different preset rock mass statuses corresponding to rock types of the rock to be classified from the rock mass status set.

The rock mass state determining module 607 is configured to determine a third target image selected by the user from a plurality of images of different preset rock mass states, and determine a rock mass state corresponding to the third target image as a rock mass state of the rock to be classified.

The surrounding rock grade determining module 608 is configured to determine a surrounding rock grade of the rock to be graded according to a preset relationship network, a rock type, a weathering feature and a rock mass state of the rock to be graded, where the preset relationship network is a corresponding relationship between the rock type, the weathering feature and the rock mass state and the surrounding rock grade.

Optionally, the feature ID includes a region cause ID, a granularity ID, and an index ID; the index ID includes any one of a color ID, a structure ID, and a particle morphology ID.

The rock characteristic receiving module 601 is specifically configured to: displaying rock images corresponding to the different zone cause IDs; determining a fourth target image selected from rock images corresponding to different region cause IDs by a user, and determining the region cause ID corresponding to the fourth target image as the region cause ID of the rock to be classified; displaying rock images corresponding to different granularity IDs in the region formation IDs of the rock to be classified; determining a fifth target image selected by a user from rock images corresponding to different granularity IDs, and determining the granularity ID corresponding to the fifth target image as the granularity ID of the rock to be classified; determining the category of the index ID according to the regional cause ID of the rock to be classified and the preset corresponding relation between the regional cause ID and the category of the index ID, and displaying an image corresponding to the category; determining a sixth target image selected by a user from rock images corresponding to the category … of the determined index ID …, and determining the index ID corresponding to the sixth target image as the index ID of the rock to be classified; and determining the characteristic ID of the rock to be classified according to the region cause ID, the granularity ID and the index ID of the rock to be classified.

Optionally, the zone cause ID includes igneous, metamorphic, and sedimentary rocks; particle size ID includes coarse, medium and fine; the color IDs include light, medium and dark colors, the structure IDs include lamellar structures, fibrilia structures and crystalline structures, and the particle morphology IDs include angular, circular and crystalline.

Optionally, the preset correspondence between the category of the area cause ID and the index ID is: if the region cause ID is igneous rock, the category of the index ID is color ID; if the region cause ID is metamorphic rock, the category of the index ID is structural ID; if the region cause ID is sedimentary rock, the index ID is classified as particle form ID.

Optionally, the rock sample set includes images of various kinds of rocks under a plurality of different preset shooting conditions; the preset shooting conditions comprise angles and rays; the rock weathering concentration includes images of a plurality of different preset weathering characteristics for each rock category in the rock sample set; the rock mass state set comprises images of a plurality of different preset rock mass states for each rock class in the rock sample set; the images comprise corresponding feature IDs and description information, and the description information comprises one or more of rock names, colors, whether knocking sounds are crisp, whether water is absorbed, whether scratches can be carved on nails, weathering features and structural surface combination degrees.

Optionally, the rock sample set classifies the rocks of each type into 5 grades according to the hardness of the rock, and S1, S2, S3, S4 and S5 are sequentially arranged from the hardest to the softest; the rock weathering collection divides various rocks into 4 grades according to rock weathering characteristics, and the rock weathering collection comprises W1, W2, W3 and W4 from the least weathering degree to the most weathering degree in sequence; the rock mass state set classifies various kinds of rocks into 5 grades according to rock mass states, and B1, B2, B3, B4 and B5 are sequentially performed.

Optionally, the surrounding rock grade determining module 608 is specifically configured to determine the rock hardness grade of the rock to be graded according to the rock type of the rock to be graded and the preset relationship between the rock type and the rock hardness grade; determining the weathering grade of the rock to be graded according to the weathering characteristics of the rock to be graded and the preset relation between the weathering characteristics and the weathering grade; determining the rock mass state grade of the rock to be graded according to the rock mass state of the rock to be graded and the preset relation between the rock mass state and the rock mass state grade; and determining the surrounding rock grade of the rock to be classified according to the preset relation network, the rock hardness grade, the weathering grade and the rock mass state grade of the rock to be classified.

It should be noted that, because the content of information interaction and execution process between the above devices/units is based on the same concept as the method embodiment of the present application, specific functions and technical effects thereof may be referred to in the method embodiment section, and will not be described herein again.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.

The embodiment of the application also provides an electronic device, referring to fig. 7, the electronic device 700 may include: at least one processor 710, a memory 720 and a computer program stored in the memory 720 and executable on the at least one processor 710, the processor 710 implementing the steps of any of the various method embodiments described above, such as S101 to S108 in the embodiment shown in fig. 1, when the computer program is executed. Alternatively, the processor 710 may perform the functions of the modules/units of the apparatus embodiments described above, such as the functions of the modules 601 to 608 shown in fig. 6, when executing the computer program.

By way of example, a computer program may be partitioned into one or more modules/units that are stored in memory 720 and executed by processor 710 to complete the present application. The one or more modules/units may be a series of computer program segments capable of performing the specified functions, which are used to describe the execution of the computer program in the electronic device 700.

It will be appreciated by those skilled in the art that fig. 7 is merely an example of an electronic device and is not limiting of an electronic device and may include more or fewer components than shown, or may combine certain components, or different components, such as input-output devices, network access devices, buses, etc.

The processor 710 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory 720 may be an internal storage unit of the electronic device, or may be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card), or the like. The memory 720 is used to store the computer program as well as other programs and data required by the electronic device. The memory 720 may also be used to temporarily store data that has been output or is to be output.

The bus may be an industry standard architecture (Industry Standard Architecture, ISA) bus, an external device interconnect (Peripheral Component, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, the buses in the drawings of the present application are not limited to only one bus or one type of bus.

The tunnel surrounding rock rapid grading method provided by the embodiment of the application can be applied to electronic equipment such as a computer, a wearable device, a vehicle-mounted device, a tablet personal computer, a notebook computer, a netbook, a personal digital assistant (personal digital assistant, PDA), an augmented reality (augmented reality, AR)/Virtual Reality (VR) device, a mobile phone and the like, and the embodiment of the application does not limit the specific type of the electronic equipment.

The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the steps in each embodiment of the tunnel surrounding rock rapid grading method when being executed by a processor.

Embodiments of the present application provide a computer program product that, when executed on a mobile terminal, causes the mobile terminal to perform the steps of the embodiments of the tunnel surrounding rock rapid grading method described above.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application implements all or part of the flow of the method of the above embodiments, and may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, where the computer program, when executed by a processor, may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a camera device/electronic apparatus, a recording medium, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Such as a U-disk, removable hard disk, magnetic or optical disk, etc.

In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus/network device and method may be implemented in other manners. For example, the apparatus/network device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical functional division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of 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.

The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.

Claims

1. A method for rapidly grading surrounding rocks of a tunnel, comprising the steps of:

receiving a feature ID of the rock to be classified, which is input by a user;

screening out a plurality of images with different preset weathering characteristics corresponding to rock types of the rock to be classified from the rock weathering collection, and displaying the images;

2. The method for rapidly grading tunnel surrounding rock according to claim 1, wherein the feature IDs include a region cause ID, a granularity ID, and an index ID; the index ID includes any one of a color ID, a structure ID, and a particle morphology ID;

the receiving the characteristic ID of the rock to be classified input by the user comprises the following steps:

displaying rock images corresponding to the different zone cause IDs;

determining a fourth target image selected by a user from rock images corresponding to different region cause IDs, and determining the region cause ID corresponding to the fourth target image as the region cause ID of the rock to be classified;

displaying rock images corresponding to different granularity IDs in the region formation IDs of the rock to be classified;

determining a fifth target image selected by a user from rock images corresponding to different granularity IDs, and determining the granularity ID corresponding to the fifth target image as the granularity ID of the rock to be classified;

determining the category of the index ID according to the regional cause ID of the rock to be classified and the preset corresponding relation between the regional cause ID and the category of the index ID, and displaying an image corresponding to the category;

determining a sixth target image selected by a user from rock images corresponding to the determined category of the index ID, and determining the index ID corresponding to the sixth target image as the index ID of the rock to be classified;

And determining the characteristic ID of the rock to be classified according to the region cause ID, the granularity ID and the index ID of the rock to be classified.

3. The method of rapid grading of tunnel surrounding rock according to claim 2, wherein the zone cause IDs include igneous, metamorphic and sedimentary rocks;

the particle size ID includes coarse, medium and fine particles;

the color IDs include light, medium and dark colors, the structure IDs include lamellar structures, fibrilia structures and crystalline structures, and the particle morphology IDs include angular, rounded and crystalline.

4. The rapid grading method of tunnel surrounding rock according to claim 3, wherein the preset correspondence between the region cause ID and the category of the index ID is:

if the region cause ID is igneous rock, the category of the index ID is the color ID;

if the region cause ID is metamorphic rock, the category of the index ID is the structure ID;

and if the region cause ID is sedimentary rock, the category of the index ID is the particle form ID.

5. The method for rapid grading of tunnel surrounding rock according to claim 1, wherein the rock sample set includes images of various kinds of rock under a plurality of different preset photographing conditions; the preset shooting conditions comprise angles and light rays;

The rock weathering concentration includes images of a plurality of different preset weathering characteristics for each rock category in the rock sample set;

the rock mass state set includes images of a plurality of different preset rock mass states for each rock type in the rock sample set;

the method comprises the steps of displaying a plurality of images, wherein each image comprises a corresponding characteristic ID and descriptive information, and the descriptive information comprises one or more of rock names, colors, whether knocking sounds are crisp, whether water absorption is achieved, whether scratches can be carved on nails, weathering characteristics and structural surface combination degrees.

6. The rapid grading method for tunnel surrounding rock according to claim 5, wherein the rock sample set classifies each type of rock into 5 grades according to rock hardness, and S1, S2, S3, S4 and S5 are sequentially from the hardest to the softest;

the rock weathering collection divides various rocks into 4 grades according to rock weathering characteristics, and the rock weathering collection sequentially comprises W1, W2, W3 and W4 from the slightest weathering degree to the most serious weathering degree;

the rock mass state set classifies various kinds of rocks into 5 grades according to rock mass states, and the grades are sequentially B1, B2, B3, B4 and B5.

7. The method for rapidly grading surrounding rocks in a tunnel according to claim 6, wherein the determining the surrounding rock grade of the rock to be graded according to a preset relation network, the rock type, the weathering characteristics and the rock mass state of the rock to be graded comprises:

Determining the rock hardness level of the rock to be classified according to the rock type of the rock to be classified and the preset relation between the rock type and the rock hardness level;

determining the weathering grade of the rock to be graded according to the weathering characteristics of the rock to be graded and the preset relation between the weathering characteristics and the weathering grade;

determining the rock mass state grade of the rock to be graded according to the rock mass state of the rock to be graded and the preset relation between the rock mass state and the rock mass state grade;

and determining the surrounding rock grade of the rock to be graded according to the preset relation network, the rock hardness grade, the weathering grade and the rock mass state grade of the rock to be graded.

8. A rapid grading device for tunnel surrounding rock, comprising:

The weathering characteristic screening module is used for screening out images of different weathering characteristics corresponding to rock types of the rock to be classified from the rock weathering set and displaying the images;

the weathering characteristic determining module is used for determining a second target image selected by a user from the images with different weathering characteristics and determining the weathering characteristics corresponding to the second target image as the weathering characteristics of the rock to be classified;

the surrounding rock grade determining module is used for determining the surrounding rock grade of the rock to be classified according to a preset relation network, the rock type, the weathering characteristic and the rock mass state of the rock to be classified, wherein the preset relation network is the corresponding relation between the rock type, the weathering characteristic and the rock mass state and the surrounding rock grade.

9. An electronic device comprising a memory and a processor, the memory having stored therein a computer program executable on the processor, wherein the processor implements the method of any of claims 1 to 7 when the computer program is executed.

10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the method according to any one of claims 1 to 7.