System and method for determining weathering degree of igneous rock in tunnel

Abstract

The present disclosure provides a system and method for determining the weathering degree of igneous rock in a tunnel. By constructing a deep learning model, training is performed based on stored image scanning results and rock composition information to obtain the corresponding relationship between each image scanning result and rock composition information and the corresponding weathering degree; obtaining a rock sample in front of the tunnel face, performing image scanning and component detection on the rock sample, and using the trained model to analyze the detection results to obtain the weathering degree of the igneous rock in front of the tunnel. The present disclosure can determine the weathering degree of the igneous rock in front in real time and quickly, improve the excavation efficiency, and the result is relatively accurate.

Description

Technical Field

[0001] The present invention belongs to the technical field of tunnel rock weathering detection, and relates to a system and method for determining the weathering degree of igneous rock in a tunnel. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art.

[0003] TBM (Tunnel Boring Machine) has been widely used in tunnel construction, with the advantages of reducing labor intensity, fast construction speed, small environmental disturbance, high safety, etc. During the TBM excavation process, the weathering degree of the rock in front has a great influence on the hardness and integrity of the rock mass, which plays a key role in whether the excavation can be successful.

[0004] In igneous rocks, minerals crystallize at different temperatures, and the crystallization order of different feldspars has an obvious order. Feldspar not only has a wide distribution range, but also has a variety of species due to its isomorphism problem. Feldspar is a common type of aluminum silicate rock-forming mineral containing calcium, sodium and potassium, which can be divided into two series: alkaline feldspar series (i.e., Or-Ab series) and plagioclase series (i.e., Ab-An series) (Or, Ab and An represent KAlSi3O8, NaAlSi3O8 and CaAl2Si2O8, respectively). The hardness of feldspars in different series is different, and their weathering resistance is also completely different, which has a great impact on the weathering resistance of igneous rocks.

[0005] According to the inventors' understanding, the current judgment of the weathering degree of the front rock is mainly based on human observation, the result of which depends on the professionalism of the operator, has great limitations, and the observation results are inaccurate. Summary of the invention

[0006] In order to solve the above problems, the present invention proposes a system and method for determining the weathering degree of igneous rock in a tunnel. The present invention can determine the weathering degree of the igneous rock ahead in real time and quickly, improve the excavation efficiency, and the result is relatively accurate.

[0007] According to some embodiments, the present disclosure adopts the following technical solutions:

[0008] A system for determining the weathering degree of igneous rock in a tunnel, comprising:

[0009] A data acquisition system is configured to obtain rock samples in front of the tunnel face, perform image scanning and component detection on the rock samples, and make glass slides of some of the samples;

[0010] A data transmission module is configured to transmit the glass slide specimen acquired by the data acquisition system to the data analysis system via the crawler, and to transmit the acquired image scanning results and rock composition information to the data analysis system via wireless transmission;

[0011] The data analysis system is configured to build a deep learning model, which is trained based on the stored image scanning results and rock composition information to obtain the correspondence between each image scanning result and rock composition information and the corresponding weathering degree, and receives the image scanning results and rock composition information of the rock in front to be determined, and uses the trained model to analyze to obtain the weathering degree in front of the tunnel.

[0012] As a further limitation, the data acquisition system includes a rock sampling module, an image scanning module and a rock testing module, wherein the rock sampling module samples and slices the rocks in the process of tunnel excavation, the sampling mechanism is mounted on a robotic arm, and includes a picking device and a drilling rig, the picking device directly picks up the rock slag on the conveyor belt, the drilling rig drills the rock core from the rock wall near the face, the slice making mechanism uses the existing method, the device includes a ball mill, a polishing machine and a tablet press, randomly selects the obtained rock slag or rock core to make a glass slide, and the sampling robotic arm can move in multiple dimensions; the image scanning module is configured as a camera, which takes pictures and scans the surface morphology of the taken rock sample to obtain the surface morphology characteristics of the rock; the rock testing module includes an XRF testing device and an XRD testing device, the XRF testing device uses a handheld device, and the existing test box can directly obtain the element type and abundance information in the sample igneous rock, the XRD device is equipped with a sample collection bowl, which can directly test the rock slag and rock powder, and identify and distinguish different feldspar end members based on the different chemical compositions of feldspar.

[0013] As a further limitation, the data analysis system includes a microscopic imaging module, a computer analysis module and a deep learning module, wherein the microscopic imaging module is a polarizing microscope device, which performs microscopic imaging on rock slide samples made by a data acquisition system transmitted via a crawler, obtains images of the rock under a polarizing microscope, and assists in selecting feldspar particles; the computer analysis module is configured to obtain diffraction spectrum data of feldspar types and contents through testing based on relevant data of XRF and XRD devices, and to count the feldspar types and contents, compare the feldspar element content types in books and literature with the diffraction spectrum data provided by X-ray device manufacturers, and process and correct the data obtained by the XRF and XRD devices; the deep learning module is configured to learn and train the data based on a neural network model.

[0014] As a further limitation, when forming a discrimination model for different weathering degrees of igneous rocks, the deep learning module integrates the trained feature quantities according to preset standard thresholds of different feldspar characteristic levels, and establishes an automatic model evaluator for the weathering degree of igneous rocks according to preset classification standards.

[0015] The data output system and the output model compare the feldspar characteristics contained in the igneous rock to be evaluated with various information in the data storage center, evaluate the weathering degree of the igneous rock to be evaluated through the igneous rock automatic model evaluator, and make specific provisions according to different thresholds.

[0016] As an optional implementation, the data acquisition system is mounted on a TBM robotic arm.

[0017] As an optional implementation, it also includes a data storage center configured to store existing tunnel rock sample image scanning results and rock composition information and corresponding weathering degree, and preset standard thresholds for different feldspar characteristics.

[0018] A method for determining the weathering degree of igneous rock in a tunnel comprises the following steps:

[0019] Construct a deep learning model and train it based on the stored image scanning results and rock composition information to obtain the corresponding relationship between each image scanning result and rock composition information and the corresponding weathering degree;

[0020] Obtain rock samples in front of the tunnel face, perform image scanning and component detection on the rock samples, and use the trained model to analyze the detection results to obtain the weathering degree of the igneous rock in front of the tunnel.

[0021] The weathering degree of igneous rocks is classified into the following categories:

[0022] When feldspar contains only K element, its weathering resistance is the strongest, followed by Na element, and its weathering resistance is the worst when it contains only Ca element. When it contains both Na and Ca elements, its weathering resistance increases with the increase of Ca element content, that is, orthoclase (potassium feldspar) > acidic plagioclase (albite) > neutral plagioclase (andesine) > basic plagioclase (calcium feldspar);

[0023] The better the crystal structure of the rock mass is preserved, the lower the degree of weathering;

[0024] The higher the percentage of feldspar, the lower the degree of weathering.

[0025] Based on the above classification standards, the following classification levels are proposed:

[0026] The feldspar elements and composition types in igneous rocks are the main considerations. If the feldspar type is only potassium feldspar - Grade A; potassium feldspar> plagioclase - Grade B; potassium feldspar < plagioclase - Grade C; only plagioclase - Grade D.

[0027] The crystal structure of feldspar is that the euhedral content accounts for the highest proportion - grade a; the semi-hedral content accounts for the highest proportion and autohedral content is greater than allomorphic content - grade b; the semi-hedral content accounts for the highest proportion and autohedral content is less than allomorphic content - grade c; the allomorphic content accounts for the highest proportion - grade d.

[0028] If the content of feldspar in igneous rock exceeds 60% - grade a; 20%-60% - grade b; 5%-20% - grade c; <5% - grade d.

[0029] According to the above standards, the final judgment level of the degree of weathering of the rock strata based on the characteristics of feldspar is determined: if there is at least one grade A or grade B+a or 2 grades a, it is weakly weathered; if there is only 1 grade a or 2 grades b, it is moderately weathered; if there is no grade A / a or grade B / b, it is strongly weathered.

[0030] A computer-readable storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor of a terminal device and executing part or all of the steps of a method for determining the weathering degree of igneous rock in a tunnel.

[0031] A terminal device includes a processor and a computer-readable storage medium, wherein the processor is used to implement various instructions; the computer-readable storage medium is used to store multiple instructions, wherein the instructions are suitable for being loaded by the processor and executing part or all of the steps of a method for determining the weathering degree of igneous rocks in a tunnel.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] The present invention evaluates the weathering degree of igneous rocks encountered during tunnel excavation to provide guidance for the excavation method. The present invention determines the weathering degree of the surrounding rock of the tunnel by studying the content of feldspar in the surrounding rock of the tunnel, thus filling the gap in the research on the weathering degree of the surrounding rock of the tunnel.

[0034] The present invention uses the feldspar composition and content combined with the igneous rock weathering image to establish an evaluation model, which not only realizes the qualitative evaluation of the weathering degree of the igneous rock, but also realizes the quantitative evaluation, and the qualitative evaluation is more accurate. After the evaluation model is obtained, continuous testing can be realized, which can serve the engineering progress of rapid tunnel excavation.

[0035] The present disclosure makes more use of in-situ rock testing to provide a good testing method for TBM excavation. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings constituting a part of the present disclosure are used to provide a further understanding of the present disclosure. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure.

[0037] Figure 1 It is a schematic diagram of the main process of this embodiment;

[0038] Figure 2 is a schematic side view of the tunnel detection of this embodiment;

[0039] Figure 3 is a schematic front view of the tunnel detection of this embodiment;

[0040] Figure 4 Schematic diagram of the data analysis system of this embodiment.

[0041] Among them, 1. is the data acquisition system; 2. is the rock sampling module; 3. is the rock testing module; 4. is the image scanning module; 5. is the wireless transmission system; 6. is the computer analysis module; 7. is the microscopic imaging module; 8. is the data analysis system; 9. is the deep learning module; 10. is the artificial assisted neural network trainer; 11. is the automatic model evaluator; 12. is the data output system and storage center; 13. is the transmission track. DETAILED DESCRIPTION

[0042] The present disclosure is further described below in conjunction with the accompanying drawings and embodiments.

[0043] It should be noted that the following detailed descriptions are all illustrative and are intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present disclosure belongs.

[0044] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0045] like Figure 1 As shown, the igneous rock weathering degree evaluation system considering the feldspar characteristics in the TBM tunnel includes a data acquisition system, a wireless transmission system, a data analysis system, a data output system and a storage center, wherein:

[0046] The data acquisition system is mounted on the TBM mechanical arm, and tests the surrounding rock as the mechanical arm moves. It collects information every time the TBM excavation is paused, and is stored inside the TBM after the collection is completed.

[0047] The data acquisition system includes a rock sampling module, an image scanning module and a rock testing module.

[0048] The rock sampling module samples the rocks during the tunnel excavation process, using rock slag / rock blocks as the basic samples to serve the subsequent detection; the image scanning module scans the surface morphology of the rock samples to obtain the surface morphology characteristics of the rocks, which are used for the qualitative weathering of the rock surface and the determination of the major rock categories; the rock element analysis module is configured as an XRF test device and an XRD test device. The XRF test device obtains the element types and abundance information in the sample igneous rock, and the XRD device identifies and distinguishes different feldspar end members based on the different chemical compositions of feldspar. The main components of feldspar are SiO2, Al2O3, K2O, Na2O, CaO, etc., such as potassium feldspar, with a molecular formula of K2O·Al2O3·6SiO2; sodium feldspar, with a molecular formula of Na2O·Al2O3·6SiO2; calcium feldspar, with a molecular formula of CaO·Al2O3·2SiO2.

[0049] The data analysis system includes a microscopic image module, a computer analysis module and a deep learning module. The microscopic image module is configured as a polarizing microscope device to perform microscopic imaging of sample rocks, obtain images of rocks under a polarizing microscope, and assist in selecting feldspar particles; the computer analysis module is a data processing software for configuring XRF and XRD devices on a computer, which obtains feldspar type and content diffraction spectrum data through testing, and counts the feldspar type and content displayed in the matching computer sample data processing software, and processes and corrects the data obtained by the equipment. The deep learning module includes an artificially assisted neural network trainer and an automatic model evaluator.

[0050] The artificially assisted neural network trainer receives a large amount of tunnel excavation rock data, various surface rock data, and domestic oil and coal drilling lithology data in advance, and these data are stored in the data storage center. The trainer can continuously obtain data from the data storage center. The trainer receives information from the element analysis module and the mineral quantification module, and through comparison and training with the existing data, it can finally be classified according to the following principles:

[0051] Principle 1: Types of feldspar in igneous rocks, this is the main consideration. The lower the crystallization temperature of a mineral, the stronger its weathering resistance. In igneous rocks, different feldspars have a clear order of crystallization. Albite and anorthite form a continuous solid solution series. Generally, based on the percentage of anorthite molecules (CaAl2Si2O8, An) (calculated by mol), they are divided into albite (A b, An = 0-10%), Olg, An = 10-30%), andesine (And, An = 30-50%), labradorite (Lab, An = 50-70%), Byt, An = 70-90%) and anorthite (An, An = 90-100%). When An < 30, it is called acidic plagioclase, when An = 30-50%, it is called neutral plagioclase, and when An > 50%, it is called basic plagioclase. Potassium feldspar is easily weathered and altered, and basic plagioclase is more susceptible to weathering than acidic plagioclase.

[0052] Therefore, the weathering resistance of potassium feldspar is higher than that of plagioclase (i.e., orthoclase is stronger than plagioclase in weathering resistance), or the weathering resistance of alkaline feldspar is greater than that of plagioclase, and among plagioclase, acidic plagioclase is greater than neutral plagioclase, which is greater than basic plagioclase. In summary, according to the discrimination criteria provided by different types of feldspar, orthoclase (potassium feldspar)> acidic plagioclase (albite, oligoclase)> neutral plagioclase (medium feldspar)> basic plagioclase (labradorite, calcite, calcium feldspar). By comparing their element contents, this embodiment provides a simpler division, that is, when only K element is contained in feldspar, its weathering resistance is the strongest, followed by Na element, and its weathering resistance is the worst when only Ca element is contained. When Na and Ca elements are contained at the same time, its weathering ability increases with the increase of Ca element content.

[0053] Principle 2: Crystal structure. If a mineral has sufficient crystallization time and growth space during crystallization, it will grow into a fixed shape according to its own crystal structure. Therefore, when a mineral retains its own crystal structure and shape after crystallization, it is called an euhedral crystal; if most of the crystal shape is retained, it is called a subhedral crystal; if it completely loses its own shape, it is called an allelic crystal. The better the crystal shape of a mineral is preserved, the more stable it is and the lower the degree of rock weathering.

[0054] Principle 3: The percentage of feldspar in igneous rocks is different from other rock types. The feldspar content can reach a very high percentage. For example, the feldspar content of sedimentary rocks generally does not exceed 50%, while the feldspar content in igneous rocks can reach 80%-90%. Feldspar is a mineral that is more resistant to weathering. Generally speaking, the higher its content in the rock, the stronger its weathering resistance and the lower the degree of weathering. However, due to different crystallization environments, there are also differences. For example, the feldspar content of syenite is higher than that of granite, but its weathering resistance is not necessarily better than that of granite. Therefore, this standard is only used as an auxiliary evaluation.

[0055] Based on the above principles, the following classification grades are proposed: the type of feldspar in igneous rocks is the main consideration standard. If the feldspar type is only potassium feldspar - grade A; potassium feldspar> plagioclase - grade B; potassium feldspar < plagioclase - grade C; only plagioclase - grade D.

[0056] The crystal structure of feldspar is that the euhedral content accounts for the highest proportion - grade a; the semi-hedral content accounts for the highest proportion and autohedral content is greater than allomorphic content - grade b; the semi-hedral content accounts for the highest proportion and autohedral content is less than allomorphic content - grade c; the allomorphic content accounts for the highest proportion - grade d.

[0057] If the content of feldspar in igneous rock exceeds 60% - grade a; 20%-60% - grade b; 5%-20% - grade c; <5% - grade d.

[0058] According to the above standards, the final judgment level of the degree of weathering of the rock strata based on the characteristics of feldspar is determined: if there is at least one grade A or grade B+a or 2 grades a, it is weakly weathered; if there is only 1 grade a or 2 grades b, it is moderately weathered; if there is no grade A / a or grade B / b, it is strongly weathered.

[0059] When the trainer forms a model for distinguishing different weathering degrees of igneous rocks, it imports preset standard thresholds of different feldspar characteristics into the data storage center as the basis for the evaluator to evaluate the weathering degree of igneous rocks. The characteristic quantities trained by the trainer are integrated, and the weathering degree level is given according to the preset classification standard, and an automatic model evaluator for the weathering degree of igneous rocks controlled by feldspar characteristics is established. The evaluator can evaluate the weathering degree of igneous rocks encountered in front of the TBM excavation in the tunnel based on the optimal model calculation of the response information obtained by the trainer.

[0060] The data output system and the output model compare the feldspar characteristics contained in the igneous rock to be evaluated with various information in the data storage center, evaluate the weathering degree of the igneous rock to be evaluated through the igneous rock automatic model evaluator, and make specific provisions according to different thresholds. The measured evaluation data of igneous rocks at different locations during the preliminary survey process are used as evaluation references, and the evaluation data of the weathering degree of igneous rocks are continuously obtained during the excavation process to continuously improve the model accuracy.

[0061] The system also includes a wireless transmission system and a data storage center. The data storage center stores various feldspar composition and content data and preset igneous rock weathering evaluation thresholds. The acquired rock data and igneous rock weathering grade data obtained by the data output system evaluation are stored in the data storage center.

[0062] Based on the working method of the above system, the operation steps are as follows:

[0063] In the early stage, igneous rock samples are collected in the early stage of TBM tunnel excavation. The image scanning device scans the surface morphology of the rock samples to obtain the surface morphology characteristics of the rock; the XRF test device and the XRD test device obtain the element type and abundance information in the sample igneous rock, the different chemical compositions and contents of feldspar; the sample element and mineral data are analyzed by computer, and the corresponding supporting software analyzes the element data and mineral data to obtain the chemical classification characteristics and content characteristics of feldspar; the microscope is used to extract the cleavage characteristics, interference color characteristics, protrusion characteristics and crystal structure characteristics of the target igneous rock.

[0064] The above features are transmitted to the deep learning module. Through the artificially preset evaluation standard threshold, the intelligent algorithm is used to learn the image information and feldspar feature information corresponding to the rock, and the weathering characteristics of the igneous rock in the image are extracted. The corresponding set evaluation standard threshold is used as the main evaluation reference to assist the neural network trainer in learning and establishing an automatic model evaluator.

[0065] The rock samples obtained by TBM excavation in the tunnel, face and wall excavation are used to identify and preliminarily estimate the lithology and weathering degree of the samples through the image scanning module and rock testing module, and the feldspar elements and mineral characteristics in the samples are tested to obtain the feldspar characteristic information in different igneous rocks. The data obtained in the above process are transmitted to the data analysis system through the wireless transmission system;

[0066] The microscopic image module and computer analysis module in the data analysis system analyze the microscopic crystal structure and the content of chemical elements and mineral components of igneous rocks, and transmit the obtained feldspar characteristic information and image information to the deep learning module. The automatic model evaluator evaluates the weathering degree of igneous rocks according to the previously obtained evaluation model, and transmits the evaluation results to the data storage center for storage, adding database data, and continuing to serve the next evaluation.

[0067] Those skilled in the art will appreciate that the embodiments of the present disclosure may be provided as methods, systems, or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0068] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0069] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0070] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0071] The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. For those skilled in the art, the present disclosure may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

[0072] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Technical personnel in the relevant field should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. A system for determining the weathering degree of igneous rock in a tunnel, characterized by: include: A data acquisition system is configured to obtain rock samples in front of the tunnel face, perform image scanning and component detection on the rock samples, obtain rock surface morphology characteristics and element types and abundance information in the sample igneous rock, and make some samples into glass slides; The data transmission module is configured to transmit the glass slide specimen acquired by the data acquisition system to the data analysis system through the crawler, and to transmit the acquired rock surface morphology characteristics and the element types and abundance information in the sample igneous rock to the data analysis system through wireless transmission; The data analysis system is configured to: comprehensively analyze the rock surface morphology, the microscopic scanning characteristics of the target igneous rock, and the element types and abundance information in the sample igneous rock, the different chemical compositions and contents of feldspar, use a computer to analyze the sample element and mineral data, use the corresponding supporting software to analyze the element data and mineral data, obtain the chemical classification characteristics and content characteristics of the feldspar, and construct a deep learning model, wherein the microscopic scanning characteristics include cleavage characteristics, interference color characteristics, protrusion characteristics and crystal structure characteristics; The deep learning model includes an artificially assisted neural network trainer and an automatic model evaluator. When the trainer forms a model for distinguishing different weathering degrees of igneous rocks, it imports preset standard thresholds of different feldspar characteristics into a data storage center as an evaluation basis for the evaluator to evaluate the weathering degree of igneous rocks. The artificially assisted neural network trainer first receives the tunnel excavation rock data, various surface rock data, and drilling lithology data of oil fields and coal fields stored in the data storage center, and combines the surface morphology characteristics, microscopic scanning characteristics, and feldspar chemical classification characteristics and content characteristics of the target igneous rock to give the level of weathering degree according to the preset classification standards, and establishes an automatic model evaluator for the weathering degree of igneous rocks with feldspar characteristics as the classification standard to evaluate the weathering degree of the igneous rock to be evaluated.

2. A system for determining the weathering degree of igneous rock in a tunnel as claimed in claim 1, characterized in that: The data acquisition system includes a rock sampling module, an image scanning module and a rock testing module, wherein the rock sampling module samples and slices the rocks in the process of tunnel excavation, the sampling mechanism is mounted on a mechanical arm, and includes a picking device and a drilling rig, the picking device directly picks up the rock slag on the conveyor belt, the drilling rig drills the rock core from the rock wall near the face, the slice making device includes a ball mill, a polishing machine and a tablet press, the obtained rock slag or rock core is randomly selected for glass slide production, and the sampling mechanical arm can move in multiple dimensions; the image scanning module is configured as a camera to take pictures and scan the surface morphology of the taken rock sample to obtain the surface morphology characteristics of the rock; the rock testing module includes an XRF testing device and an XRD testing device, the XRF testing device uses a handheld device and a test box to obtain the type and abundance information of elements in the sample igneous rock, the XRD device is configured with a sample collection bowl, the rock slag and rock powder are tested, and different feldspar end members are identified and distinguished based on the different chemical compositions of feldspar.

3. A system for determining the weathering degree of igneous rock in a tunnel as claimed in claim 2, characterized in that: The data analysis system includes a microscopic imaging module, a computer analysis module and a deep learning module, wherein the microscopic imaging module is a polarizing microscope device, which performs microscopic imaging on rock slide samples made by a data acquisition system transmitted via a crawler, obtains images of rocks under a polarizing microscope, and assists in selecting feldspar particles; the computer analysis module is configured to obtain feldspar type and content diffraction spectrum data through testing based on relevant data of XRF and XRD devices, and to count the feldspar type and content, compare the feldspar element content type and diffraction spectrum data, and process and correct the data obtained by the XRF and XRD devices; the deep learning module is configured to learn and train the data based on a neural network model.

4. A system for determining the weathering degree of igneous rock in a tunnel as claimed in claim 3, characterized in that: When forming a discrimination model for different weathering degrees of igneous rocks, the deep learning module integrates the trained feature quantities according to preset standard thresholds of different feldspar feature levels, and establishes an automatic model evaluator for the weathering degree of igneous rocks according to preset classification standards.

5. A system for determining the weathering degree of igneous rock in a tunnel as claimed in claim 1, characterized in that: It also includes a data output system. The output model compares the feldspar characteristics contained in the igneous rock to be evaluated with various information in the data storage center, evaluates the weathering degree of the igneous rock to be evaluated through the igneous rock automatic model evaluator, and makes specific provisions according to different thresholds.

6. A system for determining the weathering degree of igneous rock in a tunnel as claimed in claim 1, characterized in that: It also includes a data storage center configured to store existing tunnel rock sample image scanning results and rock composition information and corresponding weathering degree, and preset standard thresholds for different feldspar characteristics.

7. A method for determining the weathering degree of igneous rock in a tunnel, characterized by: The following steps are involved: Obtain rock samples in front of the tunnel face, perform image scanning and component testing on the rock samples, and obtain the surface morphology of the rock as well as the type and abundance information of the elements in the sample igneous rock; Obtain the rock surface morphology and microscopic scanning characteristics of the target igneous rock, and analyze the sample element and mineral data by computer according to the element type and abundance information in the sample igneous rock, the different chemical components and contents of feldspar, and use the corresponding supporting software to analyze the element data and mineral data to obtain the chemical classification characteristics and content characteristics of feldspar. The microscopic scanning characteristics include cleavage characteristics, interference color characteristics, protrusion characteristics and crystal structure characteristics; Constructing a deep learning model, the deep learning model includes an artificially assisted neural network trainer and an automatic model evaluator. When the trainer forms a discrimination model for different weathering degrees of igneous rocks, the trainer imports preset standard thresholds of different feldspar characteristics into a data storage center as an evaluation basis for the evaluator to evaluate the weathering degree of the igneous rocks; The artificially assisted neural network trainer first receives the tunnel excavation rock data, various surface rock data, and drilling lithology data of oil fields and coal fields stored in the data storage center, and combines the surface morphology characteristics, microscopic scanning characteristics, and feldspar chemical classification characteristics and content characteristics of the target igneous rock to give the level of weathering degree according to the preset classification standards, and establishes an automatic model evaluator for the weathering degree of igneous rocks with feldspar characteristics as the classification standard to evaluate the weathering degree of the igneous rock to be evaluated.

8. The method according to claim 7, characterized in that: The weathering degree of igneous rocks is classified into the following categories: When feldspar contains only K element, its weathering resistance is the strongest, followed by Na element. When it contains only Ca element, its weathering resistance is the worst. When it contains both Na and Ca elements, its weathering resistance increases with the increase of Ca element content, that is, orthoclase>acidic plagioclase>neutral plagioclase>basic plagioclase; The better the crystal structure of the rock mass is preserved, the lower the degree of weathering; The higher the percentage of feldspar, the lower the degree of weathering.

9. A computer-readable storage medium, characterized in that: There are multiple instructions, which are suitable for being loaded and executed by a processor of a terminal device. A method for determining the weathering degree of igneous rock in a tunnel as described in claim 7 or 8.

10. A terminal device, characterized in that: It includes a processor and a computer-readable storage medium, the processor is used to implement various instructions; the computer-readable storage medium is used to store multiple instructions, and the instructions are suitable for being loaded by the processor and executed by a method for determining the weathering degree of igneous rock in a tunnel as described in claim 7 or 8.

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