CN113674264A - Support parameter determination method and device, electronic equipment and readable storage medium - Google Patents
Support parameter determination method and device, electronic equipment and readable storage medium Download PDFInfo
- Publication number
- CN113674264A CN113674264A CN202110994470.4A CN202110994470A CN113674264A CN 113674264 A CN113674264 A CN 113674264A CN 202110994470 A CN202110994470 A CN 202110994470A CN 113674264 A CN113674264 A CN 113674264A
- Authority
- CN
- China
- Prior art keywords
- surrounding rock
- quality index
- index value
- tunnel
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 60
- 239000011435 rock Substances 0.000 claims abstract description 438
- 238000004364 calculation method Methods 0.000 claims description 19
- 238000004422 calculation algorithm Methods 0.000 claims description 15
- 229920006395 saturated elastomer Polymers 0.000 claims description 13
- 238000004590 computer program Methods 0.000 claims description 8
- 238000013135 deep learning Methods 0.000 claims description 6
- 238000012937 correction Methods 0.000 description 13
- 239000003673 groundwater Substances 0.000 description 10
- 238000010586 diagram Methods 0.000 description 8
- 238000010276 construction Methods 0.000 description 7
- 230000008569 process Effects 0.000 description 7
- 238000013461 design Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 4
- 230000002093 peripheral effect Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 206010063385 Intellectualisation Diseases 0.000 description 3
- 230000009471 action Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 239000010438 granite Substances 0.000 description 3
- 230000003993 interaction Effects 0.000 description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 238000011835 investigation Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 241000350052 Daniellia ogea Species 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000013527 convolutional neural network Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 238000002372 labelling Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 239000004579 marble Substances 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000011514 reflex Effects 0.000 description 1
- 230000002787 reinforcement Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/13—Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformations in the plane of the image
- G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
- G06T3/4038—Image mosaicing, e.g. composing plane images from plane sub-images
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
- G06T2207/10012—Stereo images
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30132—Masonry; Concrete
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30236—Traffic on road, railway or crossing
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Geometry (AREA)
- Evolutionary Computation (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computer Hardware Design (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Computational Linguistics (AREA)
- Artificial Intelligence (AREA)
- Mathematical Physics (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Health & Medical Sciences (AREA)
- Data Mining & Analysis (AREA)
- Civil Engineering (AREA)
- Architecture (AREA)
- Structural Engineering (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Quality & Reliability (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Computer Graphics (AREA)
- Image Analysis (AREA)
Abstract
The application provides a method and a device for determining support parameters, electronic equipment and a readable storage medium, and the method comprises the following steps: determining a first tunnel surrounding rock quality index value according to the image information of the surrounding rock; receiving a second tunnel surrounding rock quality index value, wherein the second tunnel surrounding rock quality index value comprises one or more tunnel surrounding rock quality index values; and determining support parameters of the surrounding rock according to the first tunnel surrounding rock quality index value or the second tunnel surrounding rock quality index value. According to the method and the device, the supporting parameters are determined by selecting the more accurate value of the first tunnel surrounding rock quality index value and the second tunnel surrounding rock quality index value, so that the accuracy of the supporting parameters is guaranteed.
Description
Technical Field
The application relates to the technical field of tunnel construction, in particular to a method and a device for determining support parameters, electronic equipment and a readable storage medium.
Background
China is a mountainous country, mountain tunnels are key nodes for highway construction, but the information degree of the existing mountain construction is relatively low. The tunnel construction process depends on geological conditions, a 'multi-person' evaluation group (personnel of each party such as owners, construction, design parties, supervision, geological engineers and the like) is often required to be formed for field surrounding rock characteristic evaluation, and even more people exist, and the problems of more participators, uneven experience, poor decision timeliness and the like exist. Meanwhile, in the aspect of digitization of tunnel surrounding rock Information, a BIM (Building Information Modeling) technology is mainly adopted at present, but only in a design stage, and how to form complete Information digitization in a construction stage is a great technical problem faced at present.
Disclosure of Invention
In view of this, embodiments of the present application provide a method and an apparatus for determining support parameters, an electronic device, and a readable storage medium, so as to solve the problem that it is difficult to implement information digitization in the current construction stage.
In a first aspect, an embodiment of the present application provides a method for determining support parameters, including: determining a first tunnel surrounding rock quality index value according to the image information of the surrounding rock; receiving a second tunnel surrounding rock quality index value, wherein the second tunnel surrounding rock quality index value comprises one or more tunnel surrounding rock quality index values; and determining support parameters of the surrounding rock according to the first tunnel surrounding rock quality index value or the second tunnel surrounding rock quality index value.
According to the embodiment of the application, the quality index values of the surrounding rocks of the tunnel are obtained in two different modes respectively, the proper quality index values of the surrounding rocks of the tunnel are selected from the quality indexes of the surrounding rocks of the tunnel, the support parameters are determined according to the proper quality index values of the surrounding rocks of the tunnel, the accuracy of the quality index values of the surrounding rocks of the tunnel is improved through the selection of the quality index values of the surrounding rocks of the tunnel, and the determined support parameters are real-time and accurate.
With reference to the first aspect, an embodiment of the present application provides a first possible implementation manner of the first aspect, where: the determining the support parameters of the surrounding rock according to the first tunnel surrounding rock quality index value or the second tunnel surrounding rock quality index value comprises the following steps: acquiring a first recommendation accuracy corresponding to the first tunnel surrounding rock quality index value and a second recommendation accuracy corresponding to the second tunnel surrounding rock quality index value, wherein the first recommendation accuracy is a ratio of the adoption times of the support parameters corresponding to the first identity tag corresponding to the first tunnel surrounding rock quality index value to the total recommendation times of the support parameters corresponding to the first identity tag, and the second recommendation accuracy is a ratio of the adoption times of the support parameters corresponding to the second identity tag corresponding to the second tunnel surrounding rock quality index value to the total recommendation times of the support parameters corresponding to the second identity tag; determining a target recommendation accuracy rate according to the first recommendation accuracy rate and the second recommendation accuracy rate; and determining support parameters of the surrounding rock according to the first tunnel surrounding rock quality index value or the second tunnel surrounding rock quality index value corresponding to the target recommendation accuracy.
According to the method and the device, the recommendation accuracy is obtained through the ratio of the number of times of support parameter adoption to the total number of times of support parameter recommendation, and the recommendation accuracy of each mode in historical data is further obtained. And determining a target recommendation accuracy according to the first recommendation accuracy and the second recommendation accuracy obtained in each mode, and determining support parameters according to the target recommendation rate to obtain more accurate support parameters.
With reference to the first possible implementation manner of the first aspect, an embodiment of the present application provides a second possible implementation manner of the first aspect, where: the determining the support parameters of the surrounding rock according to the first tunnel surrounding rock quality index value or the second tunnel surrounding rock quality index value further comprises: judging whether the difference value of the first tunnel surrounding rock quality index value and the tunnel surrounding rock quality index value corresponding to the target recommendation accuracy is within a first threshold range; and if the difference value is not within the first threshold value range, updating the surrounding rock data corresponding to the first tunnel surrounding rock quality index value by using the surrounding rock data corresponding to the tunnel surrounding rock quality index value corresponding to the target recommendation accuracy.
According to the embodiment of the application, the first tunnel surrounding rock quality index value is compared with the tunnel surrounding rock quality index value corresponding to the target recommendation accuracy, surrounding rock data corresponding to the first tunnel surrounding rock quality index value, of which the obtained difference value is not within the first threshold range, are updated, so that the surrounding rock data corresponding to the first tunnel surrounding rock quality index value can be updated into the actually adopted surrounding rock data in real time, and the accuracy of the surrounding rock data corresponding to the first tunnel surrounding rock quality index value is gradually achieved.
With reference to the second possible implementation manner of the first aspect, an embodiment of the present application provides a third possible implementation manner of the first aspect, where the updating, by using the surrounding rock data corresponding to the tunnel surrounding rock quality index value corresponding to the target recommendation accuracy, of the surrounding rock data corresponding to the first tunnel surrounding rock quality index value includes: comparing first data in the surrounding rock data corresponding to the first tunnel surrounding rock quality index value with second data in the surrounding rock data corresponding to the tunnel surrounding rock quality index value corresponding to the target recommendation accuracy one by one to obtain first data exceeding a second threshold; determining corresponding second data according to the first data exceeding the second threshold; and updating the first data exceeding the second threshold value into the corresponding second data.
According to the embodiment of the application, the first data exceeding the second threshold value is obtained by sequentially comparing the first data with the second data, the first data exceeding the second threshold value is updated to the second data corresponding to the first data, the inaccurate first data is updated to the correct second data in time, the accuracy of the first data is guaranteed, and meanwhile the first data can be improved in time.
With reference to the third possible implementation manner of the first aspect, an embodiment of the present application provides a fourth possible implementation manner of the first aspect, where the determining a first tunnel surrounding rock quality index value according to image information of surrounding rocks includes: calculating the image number information of the surrounding rock by adopting a deep learning algorithm to determine surrounding rock data corresponding to the image information of the surrounding rock; and calculating a first tunnel surrounding rock quality index value according to the surrounding rock data.
According to the embodiment of the application, the image information of the surrounding rock is deeply learned, the surrounding rock data are determined, the automation of surrounding rock data acquisition is realized, the surrounding rock data can be directly obtained by identifying and calculating the image information of the surrounding rock through a computer, the workers are not required to carry out on-site investigation, and the working intensity of the workers is reduced. Meanwhile, the computer can calculate the first tunnel surrounding rock quality index value according to the surrounding rock data and the algorithm, so that the automation of calculation of the first tunnel surrounding rock quality index value is realized, the working efficiency is improved, and the intelligentization of surrounding rock support parameter design is realized.
With reference to the fourth possible implementation manner of the first aspect, an example of the present application provides a fifth possible implementation manner of the first aspect, where the surrounding rock data includes: the calculation formula for calculating the first tunnel surrounding rock quality index value according to the surrounding rock data is as follows: BQ1＝A1+A2Rc+A3Kv; wherein, BQ1The quality index value of the surrounding rock of the first tunnel, Rc is the saturated compressive strength of the surrounding rock, Kv is the integrity coefficient of the surrounding rock, A1Is a first coefficient, A2Is the second coefficient, A3Is the third coefficient.
With reference to the fifth possible implementation manner of the first aspect, an embodiment of the present application provides a sixth possible implementation manner of the first aspect, and the method further includes: acquiring a target surrounding rock image; receiving target surrounding rock data matched with the target surrounding rock image; and storing the target surrounding rock image and the target surrounding rock data.
According to the embodiment of the application, the target surrounding rock data and the target surrounding rock image are stored in a one-to-one correspondence mode, the surrounding rock image and the surrounding rock data in the database are increased, meanwhile, the surrounding rock image and the surrounding rock data are in one-to-one correspondence, the surrounding rock data corresponding to the surrounding rock image can be directly called when the surrounding rock image is called, the calculation process is simplified, and the working efficiency is improved.
In a second aspect, an embodiment of the present application further provides a support parameter determining device, including: a first determination module: the first tunnel surrounding rock quality index value is determined according to the image information of the surrounding rock; a receiving module: the second tunnel surrounding rock quality index value is used for receiving a second tunnel surrounding rock quality index value, wherein the second tunnel surrounding rock quality index value comprises one or more tunnel surrounding rock quality index values; a second determination module: and the support parameters of the surrounding rock are determined according to the first tunnel surrounding rock quality index value or the second tunnel surrounding rock quality index value.
In a third aspect, an embodiment of the present application further provides an electronic device, including: a processor, a memory storing machine-readable instructions executable by the processor, the machine-readable instructions, when executed by the processor, performing the steps of the method of the first aspect described above, or any possible implementation of the first aspect, when the electronic device is run.
In a fourth aspect, the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and the computer program is executed by a processor to perform the steps of the method for determining support parameters in the first aspect or any one of the possible implementations of the first aspect.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, embodiments accompanied with figures are described in detail below.
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 will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a schematic block diagram of an electronic device of a method for determining support parameters according to an embodiment of the present disclosure;
fig. 2 is a flowchart of a method for determining support parameters according to an embodiment of the present disclosure;
fig. 3 is a flowchart of a support parameter determining method step 203 according to an embodiment of the present application;
fig. 4 is a functional module schematic diagram of a support parameter determination device according to an embodiment of the present application.
Detailed Description
The technical solution in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
At present, in the design of a tunnel supporting scheme, workers need to investigate the geological condition of a site, obtain surrounding rock data according to the investigation condition and historical experience, calculate the surrounding rock quality index value of a tunnel according to the surrounding rock data, and determine the tunnel supporting scheme according to the range of the surrounding rock quality index value of the tunnel.
With the development of intellectualization, a three-dimensional model is constructed by collecting on-site surrounding rock images, and the determination of surrounding rock data by combining a deep learning algorithm on the basis of the three-dimensional model becomes a very important research. Due to the diversity of the types of the surrounding rocks and the influence of different angles on the image recognition of the computer during image acquisition, the surrounding rock data determined by the computer often has larger difference with the actual surrounding rock data.
Based on the above problems, the embodiments of the present application provide a method for determining support parameters. The quality index value of the surrounding rock of the tunnel input from the outside is compared with the quality index value of the surrounding rock of the tunnel obtained by image recognition of the computer, the quality index value of the surrounding rock of the tunnel to be adopted is determined, and the image information and the surrounding rock data corresponding to the quality index value of the surrounding rock of the tunnel which is not adopted are updated, so that the image information and the surrounding rock data in the surrounding rock database are perfected, the accuracy of image information recognition of the computer is improved, and the intellectualization of support parameter determination is gradually realized.
Example one
To facilitate understanding of the present embodiment, first, an electronic device for performing the support parameter determination method disclosed in the embodiments of the present application will be described in detail.
As shown in fig. 1, is a block schematic diagram of an electronic device. The electronic device 100 may include a memory 111, a memory controller 112, a processor 113, a peripheral interface 114, an input-output unit 115, and a display unit 116. It will be understood by those of ordinary skill in the art that the structure shown in fig. 1 is merely exemplary and is not intended to limit the structure of the electronic device 100. For example, electronic device 100 may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
The above-mentioned elements of the memory 111, the memory controller 112, the processor 113, the peripheral interface 114, the input/output unit 115 and the display unit 116 are electrically connected to each other directly or indirectly, so as to implement data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The processor 113 is used to execute the executable modules stored in the memory.
The Memory 111 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 111 is configured to store a program, and the processor 113 executes the program after receiving an execution instruction, and the method executed by the electronic device 100 defined by the process disclosed in any embodiment of the present application may be applied to the processor 113, or implemented by the processor 113.
The processor 113 may be an integrated circuit chip having signal processing capability. The Processor 113 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The peripheral interface 114 couples various input/output devices to the processor 113 and memory 111. In some embodiments, the peripheral interface 114, the processor 113, and the memory controller 112 may be implemented in a single chip. In other examples, they may be implemented separately from the individual chips.
The input/output unit 115 is used to provide input data to the user. The input/output unit 115 may be, but is not limited to, a mouse, a keyboard, and the like.
The display unit 116 provides an interactive interface (e.g., a user operation interface) between the electronic device 100 and the user or is used for displaying the image data of the surrounding rock to the user for reference. In this embodiment, the display unit may be a liquid crystal display or a touch display. In the case of a touch display, the display can be a capacitive touch screen or a resistive touch screen, which supports single-point and multi-point touch operations. The support of single-point and multi-point touch operations means that the touch display can sense touch operations simultaneously generated from one or more positions on the touch display, and the sensed touch operations are sent to the processor for calculation and processing.
The electronic device 100 in this embodiment may be configured to perform each step in each method provided in this embodiment. The implementation of the support parameter determination method is described in detail below by means of several embodiments.
Example two
Please refer to fig. 2, which is a flowchart illustrating a method for determining support parameters according to an embodiment of the present application. The specific process shown in fig. 2 will be described in detail below.
Optionally, before step 201, the method comprises: and collecting image information of the surrounding rock.
Alternatively, the apparatus for acquiring image information of the surrounding rock may be a single-lens reflex camera, a CCD (Charge-coupled Device) camera, a laika camera, a Faro laser scanner, a mobile phone, or the like.
Optionally, the image information of the surrounding rock acquired by the acquisition device may be sent to the support parameter determination system through communication technologies such as 4G and 5G, Wifi.
Illustratively, after the image information of the surrounding rock is collected, a surrounding rock three-dimensional real scene model is constructed based on the image information of the surrounding rock, and the three-dimensional real scene model can be generated through an image splicing technology.
Optionally, the three-dimensional live-action model may be displayed on an electronic device of the support parameter determination method in the embodiment of the present application, or on other display devices, where the display devices may be a computer, a tablet, a mobile phone, and the like; the computer, tablet and cell phone may include a human-machine interface. The human-computer interaction interface can display a three-dimensional real scene model. The display mode of the three-dimensional live-action model can be as follows: animation, picture, VR (Virtual Reality) mode, etc. If the display mode is the VR mode, the VR image can be operated to carry out information interaction.
Optionally, the three-dimensional live-action model may be modified or supplemented, for example, a surrounding rock joint label, a structural surface label, a rock surface label, and the like.
Illustratively, when the surrounding rock joint identified by the system according to the three-dimensional realistic model is incorrect, the user may modify the surrounding rock joint label by touching the display area of the surrounding rock joint label on the display interface of the three-dimensional realistic model.
Illustratively, when the system identifies that the surrounding rock joint is wrong according to the three-dimensional real-scene model, the user can modify the surrounding rock joint label by clicking a mouse on the display area of the surrounding rock joint label of the three-dimensional real-scene model display interface.
Illustratively, when the surrounding rock joint identified by the system according to the three-dimensional realistic model is incorrect, the user can modify the surrounding rock joint label in the display area of the surrounding rock joint label of the three-dimensional realistic model display interface by operating the keyboard.
Optionally, the device displaying the three-dimensional real scene model may receive labeling operations of multiple users on the three-dimensional real scene model at the same time.
Step 201 comprises: calculating the image information of the surrounding rock by adopting a deep learning algorithm to determine surrounding rock data corresponding to the image information of the surrounding rock; and calculating a first tunnel surrounding rock quality index value according to the surrounding rock data.
Optionally, the deep learning algorithm includes: convolutional neural networks, residual neural networks, target characteristic identification, reinforcement learning algorithms, and the like.
Optionally, the image information of the surrounding rock includes: and the type of the surrounding rock and the integrity degree data of the rock mass of the surrounding rock. The surrounding rock type and surrounding rock mass integrity data can be obtained based on a computer vision algorithm.
Optionally, the surrounding rock types include: mudstone, conglomerate, marble rock, granite, and the like.
Optionally, the system identifies the type of the surrounding rock according to the image information of the surrounding rock, and receives surrounding rock data corresponding to the type of the surrounding rock through the type of the surrounding rock. This country rock data includes: the saturated compressive strength of the surrounding rock, the integrity coefficient of the surrounding rock and the like.
Optionally, the calculation formula of the basic value of the first tunnel surrounding rock quality index is as follows: BQ1＝A1+A2Rc+A3Kv; wherein, BQ1Is the basic value of the first tunnel surrounding rock quality index, and Rc is the saturation compressive strength of the surrounding rockDegree, Kv is the integrity coefficient of the surrounding rock, A1Is a first coefficient, A2Is the second coefficient, A3Is the third coefficient.
Optionally, the formula for calculating the basic value of the first tunnel surrounding rock quality index should comply with the following conditions: when Rc > C1Kv+C2And then, the calculation formula of the first tunnel surrounding rock quality index basic value is as follows: BQ1＝A4+A5Kv; when Kv > C3Rc+C4And then, the calculation formula of the first tunnel surrounding rock quality index basic value is as follows: BQ1＝A6+A7Rc; wherein, BQ1Is the basic value of the first tunnel surrounding rock quality index, Rc is the saturated compressive strength of the surrounding rock, Kv is the integrity coefficient of the surrounding rock, A4Is a fourth coefficient, A5Is a fifth coefficient, A6Is the sixth coefficient, A7Is a seventh coefficient, C1Is an eighth coefficient, C2Is the ninth coefficient, C3Is a tenth coefficient, C4Is the eleventh coefficient.
Optionally, the first coefficient, the second coefficient, the third coefficient, the fourth coefficient, the fifth coefficient, the sixth coefficient, the seventh coefficient, the eighth coefficient, the ninth coefficient, the tenth coefficient, the eleventh coefficient, and the like are specifically obtained according to multiple historical experiments.
Optionally, in an actual situation, it is further required to modify the basic value of the first tunnel surrounding rock quality index in combination with factors such as ground stress, groundwater, and the like, and the calculation formula of the modified value of the first tunnel surrounding rock quality index may be represented as: BQ2＝BQ1-P (K1+ K2+ K3); wherein, BQ2Is a first tunnel surrounding rock quality index correction value, BQ1The standard value is a basic value of the first tunnel surrounding rock quality index, K1 is an underground water influence coefficient, K2 is a weak structural plane attitude influence correction coefficient, and K3 is an initial stress state influence correction coefficient.
Optionally, the groundwater influence coefficient, the weak structural plane attitude influence correction coefficient, and the initial stress state influence correction coefficient may be received input data, or may be obtained by the system based on a three-dimensional real-scene model.
Optionally, the first tunnel surrounding rock quality index value is a first tunnel surrounding rock quality index correction value.
And 202, receiving a second tunnel surrounding rock quality index value.
Optionally, the second tunnel surrounding rock quality index value may be a value of the second tunnel surrounding rock quality index value directly acquired by the system; the second tunnel surrounding rock quality index value can also be calculated by the system through a specific algorithm.
Exemplarily, if the second tunnel surrounding rock quality index value is the value of the second tunnel surrounding rock quality index value directly acquired by the system, the user determines surrounding rock data according to the type of surrounding rock obtained by site survey, determines the second tunnel surrounding rock quality index value according to the surrounding rock data, and finally inputs the second tunnel surrounding rock quality index value.
Exemplarily, if the second tunnel surrounding rock quality index value is calculated by the system through a specific algorithm, the user determines surrounding rock data according to the surrounding rock type obtained from the site survey data, then inputs the surrounding rock data, and the system calculates according to the surrounding rock data and the specific algorithm to obtain the second tunnel surrounding rock quality index value.
Exemplarily, if the second tunnel surrounding rock quality index value is calculated through a specific algorithm, the user inputs the surrounding rock type into the system according to the surrounding rock type obtained by the site survey data, the system determines surrounding rock data according to the surrounding rock type, and finally, calculation is performed according to the surrounding rock data and the specific algorithm to obtain the second tunnel surrounding rock quality index value.
Alternatively, the specific algorithm may be a formula for calculating the quality index value of the surrounding rock of the second tunnel.
Illustratively, when the type of the surrounding rock is granite, surrounding rock data of the granite is obtained, and the surrounding rock data is substituted into a second tunnel surrounding rock quality index value calculation formula to obtain a second tunnel surrounding rock quality index value.
Optionally, the second tunnel surrounding rock quality index value comprises one or more tunnel surrounding rock quality index values.
Illustratively, after a plurality of users carry out site survey, each user carries out tunnel surrounding rock quality index value calculation according to the self-surveying result, the calculation result is input into the system, and the system can simultaneously receive second tunnel surrounding rock quality index values, surrounding rock data and surrounding rock types which are respectively input by the users so as to obtain a plurality of second tunnel surrounding rock quality index values.
Optionally, the calculation formula of the basic value of the second tunnel surrounding rock quality index is as follows: BQ3＝A1+A2Rc+A3Kv; wherein, BQ3Is the quality index basic value of the surrounding rock of the second tunnel, Rc is the saturated compressive strength of the surrounding rock, Kv is the integrity coefficient of the surrounding rock, A1Is a first coefficient, A2Is the second coefficient, A3Is the third coefficient.
Optionally, the formula for calculating the basic value of the quality index of the second tunnel surrounding rock should comply with the following conditions: when Rc > C1Ku+C2And then, the calculation formula of the quality index basic value of the second tunnel surrounding rock is as follows: BQ3＝A4+A5Ku; when Ku > C3Rc+C4And then, the calculation formula of the quality index basic value of the second tunnel surrounding rock is as follows: BQ3＝A6+A7Rc; wherein, BQ3Is the quality index basic value of the surrounding rock of the second tunnel, Rc is the saturated compressive strength of the surrounding rock, Kv is the integrity coefficient of the surrounding rock, A4Is a fourth coefficient, A5Is a fifth coefficient, A6Is the sixth coefficient, A7Is a seventh coefficient, C1Is an eighth coefficient, C2Is the ninth coefficient, C3Is a tenth coefficient, C4Is the eleventh coefficient.
Alternatively, the first coefficient, the second coefficient, the third coefficient, the fourth coefficient, the fifth coefficient, the sixth coefficient, the seventh coefficient, the eighth coefficient, the ninth coefficient, the tenth coefficient, the eleventh coefficient, and the like may be specifically obtained according to multiple historical experiments.
Optionally, in an actual situation, the basic value of the second tunnel surrounding rock quality index needs to be corrected by combining factors such as ground stress, groundwater and the like, and a calculation formula of the corrected value of the second tunnel surrounding rock quality index is：BQ4＝BQ3-P (K1+ K2+ K3); wherein, BQ4Is a second tunnel surrounding rock quality index correction value, BQ3And the standard value is the quality index basic value of the surrounding rock of the second tunnel, K1 is an underground water influence coefficient, K2 is a weak structural plane attitude influence correction coefficient, and K3 is an initial stress state influence correction coefficient.
Optionally, the groundwater influence coefficient, the weak structural plane attitude influence correction coefficient, and the initial stress state influence correction coefficient may be obtained by field direct survey, or may be obtained directly based on a field geological report.
Illustratively, the field geological report is a report about geological environment based on the type of surrounding rock and surrounding environment of the field, and the report may include data of groundwater type, groundwater water quantity, groundwater influence coefficient, weak structural plane attitude influence correction coefficient, rock type, rock hardness, rock fracture degree and the like.
Optionally, the second tunnel surrounding rock quality index value is a second tunnel surrounding rock quality index correction value.
And step 203, determining support parameters of the surrounding rock according to the first tunnel surrounding rock quality index value or the second tunnel surrounding rock quality index value.
In one embodiment, as shown in fig. 3, step 203 may include the following steps 2031 to 2033.
Optionally, the first recommendation accuracy is a ratio of the number of times of using the support parameter corresponding to the first identity tag corresponding to the first tunnel surrounding rock quality index value to the total number of times of recommending the support parameter corresponding to the first identity tag, and the second recommendation accuracy is a ratio of the number of times of using the support parameter corresponding to the second identity tag corresponding to the second tunnel surrounding rock quality index value to the total number of times of recommending the support parameter corresponding to the second identity tag.
For example, if the number of times of using the support parameter corresponding to the first identity tag is 10, and the total recommended number of times of the support parameter corresponding to the first identity tag is 100, the first recommended accuracy is 10%; if the number of times of using the support parameter corresponding to the second identity tag is 20, and the total recommended number of times of the support parameter corresponding to the second identity tag is 100, the second recommended accuracy is 20%.
For example, if the number of times of using the support parameter corresponding to the first identity tag is 5 times, and the total recommended number of times of the support parameter corresponding to the first identity tag is 20 times, the first recommendation accuracy rate is 25%; if the number of times of using the support parameter corresponding to the second identity tag is 10 times, and the total recommended number of times of the support parameter corresponding to the second identity tag is 20 times, the second recommended accuracy is 50%.
Optionally, the first recommendation accuracy rate may be directly obtained from a database; the first recommendation accuracy rate can also be obtained by obtaining the adoption times of the support parameters corresponding to the first identity tag and the recommended total times of the support parameters corresponding to the first identity tag from the database and calculating the ratio of the adoption times of the support parameters corresponding to the first identity tag and the recommended total times of the support parameters corresponding to the first identity tag.
Optionally, the second recommendation accuracy rate may be directly obtained from a database; the number of times of adoption of the support parameters corresponding to the second identity tag and the total recommended number of times of the support parameters corresponding to the second identity tag can be obtained from the database, and the second recommended accuracy is obtained by calculating the ratio of the number of times of adoption of the support parameters of the second identity tag to the total recommended number of times of the support parameters corresponding to the second identity tag.
Optionally, the first identity tag may be an identity number, a hardware address, or the like of the computer; the second identity tag may be a user name, a user fingerprint, a user face, or the like.
Alternatively, the target recommendation rate may be determined from a first recommendation accuracy rate and a second recommendation accuracy rate within a month; the target recommendation rate may be determined from a first recommendation accuracy rate and a second recommendation accuracy rate within three months; the target recommendation rate may also be determined from a first recommendation accuracy rate and a second recommendation accuracy rate over a year.
Optionally, step 2032 comprises: and sorting the first recommendation accuracy rate and the second recommendation accuracy rate.
Optionally, the target recommendation accuracy rate may be a recommendation accuracy rate with a highest accuracy rate of the first recommendation accuracy rate and the second recommendation accuracy rate.
Optionally, as shown in table 1, determining support parameters and a corresponding support scheme by using the first tunnel surrounding rock quality index value or the second tunnel surrounding rock quality index value with reference to table 1 below.
Illustratively, if the first recommended accuracy is P1 and the second recommended accuracy is P2, P3, P4, P5, the accuracy rates are ranked from high to low as: p5, P4, P3, P2 and P1, the target recommendation accuracy at this time is P5. And if the second tunnel surrounding rock quality index value corresponding to the P5 is 330, the corresponding surrounding rock grade is IV, the surrounding rock supporting parameters adopted at this time are determined according to the table 1, and the corresponding supporting scheme is selected according to the supporting parameters.
Illustratively, if the first recommended accuracy is P1 and the second recommended accuracy is P2, P3, P4, P5, the accuracy rates are ranked from high to low as: p1, P4, P5, P2 and P3, the target recommendation accuracy at this time is P1. And if the first tunnel surrounding rock quality index value corresponding to the P1 is 400, the corresponding surrounding rock grade is III, the surrounding rock supporting parameters adopted at this time are determined according to the table 1, and the corresponding supporting scheme is selected according to the supporting parameters.
TABLE 1
After the support parameters of the surrounding rock are determined according to the first tunnel surrounding rock quality index value or the second tunnel surrounding rock quality index value, the method for determining the support parameters can further comprise the following steps: and judging whether the difference value of the first tunnel surrounding rock quality index value and the tunnel surrounding rock quality index value corresponding to the target recommendation accuracy is within a first threshold range.
Optionally, if the difference is not within the first threshold range, updating the surrounding rock data corresponding to the first tunnel surrounding rock quality index value by using the surrounding rock data corresponding to the tunnel surrounding rock quality index value corresponding to the target recommendation accuracy.
Exemplarily, comparing first data in the surrounding rock data corresponding to the first tunnel surrounding rock quality index value with second data in the surrounding rock data corresponding to the tunnel surrounding rock quality index value corresponding to the target recommendation accuracy one by one to obtain first data exceeding a second threshold; determining corresponding second data according to the first data exceeding the second threshold; and updating the first data exceeding the second threshold value into corresponding second data.
Optionally, the first data may be the saturated compressive strength of the surrounding rock, the integrity coefficient or groundwater coefficient of the surrounding rock, and the like in the first tunnel surrounding rock quality index value; the second data can be the type of the surrounding rock, the saturated compressive strength of the surrounding rock, the integrity coefficient or the groundwater coefficient of the surrounding rock and the like in the quality index value of the surrounding rock of the second tunnel.
Exemplarily, if the first tunnel surrounding rock quality index value is X, the tunnel surrounding rock quality index value corresponding to the target recommendation accuracy rate is Y, the set first threshold range is M-N, and the difference value between X and Y exceeds the range of M-N, it is determined that the first tunnel surrounding rock quality index value is inaccurate. Then, the surrounding rock data used for calculating the first tunnel surrounding rock quality index value is, for example: and comparing the saturated compressive strength, the integrity coefficient, the groundwater coefficient and the like of the surrounding rock with the surrounding rock data of the tunnel surrounding rock quality index value corresponding to the calculated target recommendation accuracy one by one, and updating the surrounding rock data corresponding to the first tunnel surrounding rock quality index value with the difference value larger than a first set threshold value into the surrounding rock data of the tunnel surrounding rock quality index value corresponding to the corresponding target recommendation accuracy.
Exemplarily, if the saturated compressive strength of the surrounding rock corresponding to the surrounding rock data of the first tunnel surrounding rock quality index value is F1, the saturated compressive strength of the tunnel surrounding rock quality index value corresponding to the target recommendation accuracy rate is F2, and the difference between F1 and F2 exceeds a second threshold value F, it is determined that F1 is inaccurate, and the saturated compressive strength F1 of the surrounding rock corresponding to the surrounding rock data of the first tunnel surrounding rock quality index value is updated to F2.
Exemplarily, if the integrity coefficient of the surrounding rock corresponding to the surrounding rock data of the first tunnel surrounding rock quality index value is E1, the integrity coefficient of the surrounding rock of the tunnel surrounding rock quality index value corresponding to the target recommendation accuracy rate is E2, and the difference between E1 and E2 exceeds the second threshold value E, it is determined that E1 is inaccurate, and the saturated compressive strength E1 of the surrounding rock corresponding to the surrounding rock data of the first tunnel surrounding rock quality index value is updated to E2.
Optionally, if the difference is within a first threshold range, storing the surrounding rock data corresponding to the first tunnel surrounding rock quality index value.
Optionally, if the quality index value of the surrounding rock of the tunnel corresponding to the target recommendation accuracy is the first quality index value of the surrounding rock of the tunnel, it is indicated that the first quality index value of the surrounding rock of the tunnel is within the first threshold range, and the surrounding rock data corresponding to the first quality index value of the surrounding rock of the tunnel is stored.
Optionally, the method for determining support parameters further includes: and if the difference value is not within the first threshold value range, updating the type of the surrounding rock in the image information of the surrounding rock.
Specifically, if the difference value is not within the first threshold range, determining incorrect image information of the surrounding rock in the image information of the surrounding rock, and updating the type of the surrounding rock in the incorrect image information of the surrounding rock to the type of the surrounding rock corresponding to the target recommendation accuracy.
Optionally, if the difference is not within the first threshold range, after updating the type of the surrounding rock in the image information of the surrounding rock, the method for determining the supporting parameters further includes: and storing the surrounding rock type in the surrounding rock image and the updated image information of the surrounding rock, and performing model training to obtain an updated live-action three-dimensional model.
Optionally, the method for determining support parameters further includes: acquiring a target surrounding rock image; receiving target surrounding rock data matched with the target surrounding rock image; and storing the target surrounding rock image and the target surrounding rock data.
For example, after the system acquires the target surrounding rock image, the user may edit the target surrounding rock image, for example: the user can input target surrounding rock data corresponding to the target surrounding rock image according to the target surrounding rock image, the system receives the target surrounding rock data corresponding to the target surrounding rock image, and the target surrounding rock image and the target surrounding rock data corresponding to the target surrounding rock image are stored.
To sum up, this application embodiment compares the accuracy of first tunnel country rock quality index value with the accuracy of second tunnel country rock quality index value, selects the support parameter that the higher tunnel country rock quality index value of accuracy corresponds to and struts the design, makes the support parameter that obtains more accurate. Meanwhile, after the support parameters are selected, the surrounding rock data corresponding to the first tunnel surrounding rock quality index value obtained through system calculation are updated to be the surrounding rock data corresponding to the tunnel surrounding rock quality index value with high accuracy, the updated surrounding rock data are stored, the surrounding rock data corresponding to the surrounding rock image can be updated in real time, the surrounding rock data in the database are gradually enriched, subsequent direct calling is facilitated, meanwhile, the accuracy of computer identification is improved, the situation that workers need to carry out surveying, calculation and other work on the surrounding rock personally is gradually replaced, and the intellectualization of support parameter determination is achieved.
EXAMPLE III
Based on the same application concept, a support parameter determination device corresponding to the support parameter determination method is further provided in the embodiment of the present application, and since the principle of solving the problem of the device in the embodiment of the present application is similar to that in the embodiment of the support parameter determination method, the implementation of the device in the embodiment of the present application may refer to the description in the embodiment of the method, and repeated details are omitted.
Please refer to fig. 4, which is a functional block diagram of a support parameter determining apparatus according to an embodiment of the present disclosure. Each module in the support parameter determination device in this embodiment is configured to perform each step in the above-described method embodiment. The support parameter determination device comprises a first determination module 301, a receiving module 302 and a determination module 303; wherein the content of the first and second substances,
the first determining module 301 is configured to determine a first tunnel surrounding rock quality index value according to the image information of the surrounding rock.
A receiving module 302, configured to receive a second tunnel surrounding rock quality index value, where the second tunnel surrounding rock quality index value includes one or more tunnel surrounding rock quality index values.
And a second determining module 303, configured to determine support parameters of the surrounding rock according to the first tunnel surrounding rock quality index value or the second tunnel surrounding rock quality index value.
In a possible implementation, the second determining module 303 is further configured to: acquiring a first recommendation accuracy rate corresponding to a first tunnel surrounding rock quality index value and a second recommendation accuracy rate corresponding to a second tunnel surrounding rock quality index value; determining a target recommendation accuracy rate according to the first recommendation accuracy rate and the second recommendation accuracy rate; and determining support parameters of the surrounding rock according to the first tunnel surrounding rock quality index value or the second tunnel surrounding rock quality index value corresponding to the target recommendation accuracy.
In a possible implementation, the support parameter determining apparatus further includes an updating module 304, configured to: judging whether the difference value of the first tunnel surrounding rock quality index value and the tunnel surrounding rock quality index value corresponding to the target recommendation accuracy is within a first threshold range; and if the difference value is not within the first threshold range, updating the surrounding rock data corresponding to the first tunnel surrounding rock quality index value by using the surrounding rock data corresponding to the tunnel surrounding rock quality index value corresponding to the target recommendation accuracy.
In a possible implementation manner, the updating module 304 is further configured to compare first data in the surrounding rock data corresponding to the first tunnel surrounding rock quality index value with second data in the surrounding rock data corresponding to the tunnel surrounding rock quality index value corresponding to the target recommendation accuracy one by one, so as to obtain first data exceeding a second threshold; determining corresponding second data according to the first data exceeding the second threshold; and updating the first data exceeding the second threshold value into corresponding second data.
In a possible implementation manner, the first determining module 301 is further configured to calculate the image information of the surrounding rock by using a deep learning algorithm to determine surrounding rock data in the image information of the surrounding rock; and calculating a first tunnel surrounding rock quality index value according to the surrounding rock data.
In a possible implementation manner, the support parameter determining apparatus further includes an image processing module 305, configured to obtain an image of the target surrounding rock; receiving target surrounding rock data matched with the target surrounding rock image; and storing the target surrounding rock image and the target surrounding rock data.
In addition, an embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program performs the steps of the support parameter determination method in the foregoing method embodiment.
The computer program product of the support parameter determining method provided in the embodiment of the present application includes a computer-readable storage medium storing a program code, where instructions included in the program code may be used to execute the steps of the support parameter determining method in the above method embodiment, which may be specifically referred to in the above method embodiment, and are not described herein again.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes. It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method for determining support parameters is characterized by comprising the following steps:
determining a first tunnel surrounding rock quality index value according to the image information of the surrounding rock;
receiving a second tunnel surrounding rock quality index value, wherein the second tunnel surrounding rock quality index value comprises one or more tunnel surrounding rock quality index values;
and determining support parameters of the surrounding rock according to the first tunnel surrounding rock quality index value or the second tunnel surrounding rock quality index value.
2. The method of claim 1, wherein determining support parameters for the surrounding rock based on the first or second tunnel surrounding rock quality indicator value comprises:
acquiring a first recommendation accuracy corresponding to the first tunnel surrounding rock quality index value and a second recommendation accuracy corresponding to the second tunnel surrounding rock quality index value, wherein the first recommendation accuracy is a ratio of the adoption times of the support parameters corresponding to the first identity tag corresponding to the first tunnel surrounding rock quality index value to the total recommendation times of the support parameters corresponding to the first identity tag, and the second recommendation accuracy is a ratio of the adoption times of the support parameters corresponding to the second identity tag corresponding to the second tunnel surrounding rock quality index value to the total recommendation times of the support parameters corresponding to the second identity tag;
determining a target recommendation accuracy rate according to the first recommendation accuracy rate and the second recommendation accuracy rate;
and determining support parameters of the surrounding rock according to the first tunnel surrounding rock quality index value or the second tunnel surrounding rock quality index value corresponding to the target recommendation accuracy.
3. The method of claim 2, further comprising:
judging whether the difference value of the first tunnel surrounding rock quality index value and the tunnel surrounding rock quality index value corresponding to the target recommendation accuracy is within a first threshold range;
and if the difference value is not within the first threshold value range, updating the surrounding rock data corresponding to the first tunnel surrounding rock quality index value by using the surrounding rock data corresponding to the tunnel surrounding rock quality index value corresponding to the target recommendation accuracy.
4. The method of claim 3, wherein the updating the surrounding rock data corresponding to the first tunnel surrounding rock quality index value by using the surrounding rock data corresponding to the tunnel surrounding rock quality index value corresponding to the target recommendation accuracy comprises:
comparing first data in the surrounding rock data corresponding to the first tunnel surrounding rock quality index value with second data in the surrounding rock data corresponding to the tunnel surrounding rock quality index value corresponding to the target recommendation accuracy one by one to obtain first data exceeding a second threshold;
determining corresponding second data according to the first data exceeding the second threshold;
and updating the first data exceeding the second threshold value into the corresponding second data.
5. The method of claim 1, wherein determining a first tunnel surrounding rock quality index value from image information of surrounding rocks comprises:
calculating the image information of the surrounding rock by adopting a deep learning algorithm to determine surrounding rock data corresponding to the image information of the surrounding rock;
and calculating a first tunnel surrounding rock quality index value according to the surrounding rock data.
6. The method of claim 5, wherein the surrounding rock data comprises: the calculation formula for calculating the first tunnel surrounding rock quality index value according to the surrounding rock data is as follows:
BQ1＝A1+A2Rc+A3Kv；
wherein, BQ1The quality index value of the surrounding rock of the first tunnel, Rc is the saturated compressive strength of the surrounding rock, Kv is the integrity coefficient of the surrounding rock, A1Is a first coefficient, A2Is a second coefficient of the first coefficient,3is the third coefficient.
7. The method of claim 1, further comprising:
acquiring a target surrounding rock image;
receiving target surrounding rock data matched with the target surrounding rock image;
and storing the target surrounding rock image and the target surrounding rock data.
8. A support parameter determination apparatus, comprising:
a first determination module: the first tunnel surrounding rock quality index value is determined according to the image information of the surrounding rock;
a receiving module: the second tunnel surrounding rock quality index value is used for receiving a second tunnel surrounding rock quality index value, wherein the second tunnel surrounding rock quality index value comprises one or more tunnel surrounding rock quality index values;
a second determination module: and the support parameters of the surrounding rock are determined according to the first tunnel surrounding rock quality index value or the second tunnel surrounding rock quality index value.
9. An electronic device, comprising: a processor, a memory storing machine-readable instructions executable by the processor, the machine-readable instructions when executed by the processor performing the steps of the method of any of claims 1 to 7 when the electronic device is run.
10. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, is adapted to carry out the steps of the method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110994470.4A CN113674264A (en) | 2021-08-27 | 2021-08-27 | Support parameter determination method and device, electronic equipment and readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110994470.4A CN113674264A (en) | 2021-08-27 | 2021-08-27 | Support parameter determination method and device, electronic equipment and readable storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113674264A true CN113674264A (en) | 2021-11-19 |
Family
ID=78546873
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110994470.4A Pending CN113674264A (en) | 2021-08-27 | 2021-08-27 | Support parameter determination method and device, electronic equipment and readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113674264A (en) |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105781543A (en) * | 2016-05-11 | 2016-07-20 | 青岛理工大学 | Fracture fractal dimension index-based mining overburden rock quality evaluation method |
| WO2016141630A1 (en) * | 2015-03-11 | 2016-09-15 | 山东大学 | Tunnel boring machine rock breaking seismic source and active source three-dimensional seismic combined advanced detection system |
| CN108153947A (en) * | 2017-12-12 | 2018-06-12 | 河海大学 | A kind of description method of the disintegration feature based on soft rock presence states |
| CN109765097A (en) * | 2018-12-21 | 2019-05-17 | 中铁三局集团有限公司 | A kind of tunnel surrounding rapid classification method based on RPD drilling machine |
| CN109783913A (en) * | 2018-12-30 | 2019-05-21 | 中铁十四局集团有限公司 | The supporting construction selection method and device in tunnel |
| CN110646282A (en) * | 2019-09-16 | 2020-01-03 | 中国国家铁路集团有限公司 | Surrounding rock elastic reaction coefficient k determination method based on surrounding rock quality index BQ |
| CN111208276A (en) * | 2020-01-15 | 2020-05-29 | 山东大学 | TBM (Tunnel boring machine) carrying type rock compressive strength rapid prediction system and method based on rock components and structure |
| CN111291934A (en) * | 2020-02-18 | 2020-06-16 | 山东科技大学 | Surrounding rock real-time grading prediction and self-checking method in tunnel construction process |
| WO2021042668A1 (en) * | 2019-09-06 | 2021-03-11 | 山东大学 | Tunnel surrounding rock structure virtual reproduction system carried on tbm, and method thereof |
| CN112765874A (en) * | 2020-12-31 | 2021-05-07 | 盾构及掘进技术国家重点实验室 | Method and device for obtaining surrounding rock parameters suitable for open type TBM tunnel |
| CN112943327A (en) * | 2021-02-08 | 2021-06-11 | 浙江数智交院科技股份有限公司 | Underground chamber active controllable yielding support system and method for stratum support |
| CN112950771A (en) * | 2021-04-02 | 2021-06-11 | 中国铁建重工集团股份有限公司 | Detection method and system for surrounding rock structure and related components |
| CN113295850A (en) * | 2021-05-24 | 2021-08-24 | 中交一公局集团有限公司 | Tunnel surrounding rock quantitative rapid grading method and device based on multi-source data fusion |
-
2021
- 2021-08-27 CN CN202110994470.4A patent/CN113674264A/en active Pending
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016141630A1 (en) * | 2015-03-11 | 2016-09-15 | 山东大学 | Tunnel boring machine rock breaking seismic source and active source three-dimensional seismic combined advanced detection system |
| CN105781543A (en) * | 2016-05-11 | 2016-07-20 | 青岛理工大学 | Fracture fractal dimension index-based mining overburden rock quality evaluation method |
| CN108153947A (en) * | 2017-12-12 | 2018-06-12 | 河海大学 | A kind of description method of the disintegration feature based on soft rock presence states |
| CN109765097A (en) * | 2018-12-21 | 2019-05-17 | 中铁三局集团有限公司 | A kind of tunnel surrounding rapid classification method based on RPD drilling machine |
| CN109783913A (en) * | 2018-12-30 | 2019-05-21 | 中铁十四局集团有限公司 | The supporting construction selection method and device in tunnel |
| WO2021042668A1 (en) * | 2019-09-06 | 2021-03-11 | 山东大学 | Tunnel surrounding rock structure virtual reproduction system carried on tbm, and method thereof |
| CN110646282A (en) * | 2019-09-16 | 2020-01-03 | 中国国家铁路集团有限公司 | Surrounding rock elastic reaction coefficient k determination method based on surrounding rock quality index BQ |
| CN111208276A (en) * | 2020-01-15 | 2020-05-29 | 山东大学 | TBM (Tunnel boring machine) carrying type rock compressive strength rapid prediction system and method based on rock components and structure |
| CN111291934A (en) * | 2020-02-18 | 2020-06-16 | 山东科技大学 | Surrounding rock real-time grading prediction and self-checking method in tunnel construction process |
| CN112765874A (en) * | 2020-12-31 | 2021-05-07 | 盾构及掘进技术国家重点实验室 | Method and device for obtaining surrounding rock parameters suitable for open type TBM tunnel |
| CN112943327A (en) * | 2021-02-08 | 2021-06-11 | 浙江数智交院科技股份有限公司 | Underground chamber active controllable yielding support system and method for stratum support |
| CN112950771A (en) * | 2021-04-02 | 2021-06-11 | 中国铁建重工集团股份有限公司 | Detection method and system for surrounding rock structure and related components |
| CN113295850A (en) * | 2021-05-24 | 2021-08-24 | 中交一公局集团有限公司 | Tunnel surrounding rock quantitative rapid grading method and device based on multi-source data fusion |
Non-Patent Citations (7)
| Title |
|---|
| VACLAV PALECEK等: "Assessment of Accuracy in the Identification of Rock Formations from Aerial and Terrestrial Laser-Scanning Data", 《ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2018》, vol. 7, no. 4, 4 April 2018 (2018-04-04), pages 1 - 6 * |
| 孙金山: "双护盾TBM施工过程力学仿真研究", 《武汉大学机构知识库》, 31 December 2018 (2018-12-31) * |
| 张延欢: "隧道掌子面围岩结构数字化获取与信息表征方法及工程应用", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》, no. 9, 15 September 2017 (2017-09-15), pages 034 - 56 * |
| 李汪石 等: "公路隧道围岩云分级系统的开发与应用", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》, no. 1, 15 January 2019 (2019-01-15), pages 034 - 1809 * |
| 柳厚祥 等: "基于深度学习技术的公路隧道围岩分级方法", 《岩土工程学报》, vol. 40, no. 10, 31 October 2018 (2018-10-31), pages 1809 - 1817 * |
| 苗长龙 等: "面向围岩结构面提取的快速采集设备与分域聚类方法", 《计算机应用》, vol. 41, no. 1, 30 June 2021 (2021-06-30), pages 336 - 340 * |
| 郑军华 等: "杭金衢高速公路新岭隧道改扩建设计", 《山西建筑》, vol. 47, no. 15, 19 July 2021 (2021-07-19), pages 139 - 142 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Son et al. | Automated schedule updates using as-built data and a 4D building information model | |
| Rohatgi | WebPlotDigitizer user manual version 3.4 | |
| CN111695488A (en) | Interest plane identification method, device, equipment and storage medium | |
| US10115165B2 (en) | Management of tax information based on topographical information | |
| CN104216895A (en) | Method and device for generating POI data | |
| CN111563448B (en) | Method and device for detecting illegal building, electronic equipment and storage medium | |
| CN110288624A (en) | Detection method, device and the relevant device of straightway in a kind of image | |
| CN107766316B (en) | Evaluation data analysis method, device and system | |
| CN108776660B (en) | ArcGIS-based method for matching road attributes in batches | |
| CN110955737A (en) | Relation graph display strategy determination method and device, electronic equipment and storage medium | |
| CN106778587A (en) | State of flight detection method and device based on unmanned plane image | |
| CN110427574B (en) | Route similarity determination method, device, equipment and medium | |
| US20130231897A1 (en) | Systems and methods for efficient analysis of topographical models | |
| CN117290561B (en) | Service state information feedback method, device, equipment and computer readable medium | |
| CN113361240B (en) | Method, apparatus, device and readable storage medium for generating target article | |
| CN113344673A (en) | Product pushing method and device, electronic equipment and storage medium | |
| CN113674264A (en) | Support parameter determination method and device, electronic equipment and readable storage medium | |
| CN111444833A (en) | Fruit measurement production method and device, computer equipment and storage medium | |
| CN113012255A (en) | Road sideline extraction method, apparatus, device and medium | |
| CN113901092B (en) | File management method and system for racing field operation monitoring | |
| CN116304251A (en) | Label processing method, device, computer equipment and storage medium | |
| CN107403050A (en) | Assembled architecture inspection method, apparatus and system | |
| CN104376064A (en) | User age sample mining method and device | |
| CN107388966A (en) | Assemble quality determining method and device | |
| CN110838078B (en) | Early warning method and system for judgment result |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |