CN116664553B - Explosion drilling method, device, equipment and medium based on artificial intelligence - Google Patents
Explosion drilling method, device, equipment and medium based on artificial intelligence Download PDFInfo
- Publication number
- CN116664553B CN116664553B CN202310919396.9A CN202310919396A CN116664553B CN 116664553 B CN116664553 B CN 116664553B CN 202310919396 A CN202310919396 A CN 202310919396A CN 116664553 B CN116664553 B CN 116664553B
- Authority
- CN
- China
- Prior art keywords
- information
- ore
- mine car
- weight
- blasting
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 38
- 238000005553 drilling Methods 0.000 title claims abstract description 37
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 30
- 238000004880 explosion Methods 0.000 title description 12
- 238000005422 blasting Methods 0.000 claims abstract description 100
- 238000005096 rolling process Methods 0.000 claims abstract description 54
- 238000012545 processing Methods 0.000 claims abstract description 36
- 238000012549 training Methods 0.000 claims abstract description 30
- 238000002372 labelling Methods 0.000 claims description 15
- 238000012937 correction Methods 0.000 claims description 14
- 238000001228 spectrum Methods 0.000 claims description 13
- 238000007781 pre-processing Methods 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 9
- 230000003595 spectral effect Effects 0.000 claims description 8
- 238000004422 calculation algorithm Methods 0.000 claims description 6
- 230000006870 function Effects 0.000 claims description 6
- 238000005065 mining Methods 0.000 claims description 6
- 241000251468 Actinopterygii Species 0.000 claims description 5
- 230000005856 abnormality Effects 0.000 claims description 5
- 238000006243 chemical reaction Methods 0.000 claims description 5
- 238000007499 fusion processing Methods 0.000 claims description 5
- 239000000463 material Substances 0.000 claims description 5
- 230000004927 fusion Effects 0.000 claims description 4
- 230000008676 import Effects 0.000 claims description 4
- 238000004590 computer program Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 2
- 238000001514 detection method Methods 0.000 abstract 1
- 238000004519 manufacturing process Methods 0.000 abstract 1
- 230000008569 process Effects 0.000 description 8
- 230000003287 optical effect Effects 0.000 description 4
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000006563 Carroll rearrangement reaction Methods 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000002093 peripheral effect Effects 0.000 description 2
- 239000011435 rock Substances 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 238000010422 painting Methods 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 230000001131 transforming effect Effects 0.000 description 1
- 230000005641 tunneling Effects 0.000 description 1
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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/80—Geometric correction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
- G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/70—Determining position or orientation of objects or cameras
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
- G06V10/80—Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
- G06V20/588—Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road
-
- 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
- G06T2207/10032—Satellite or aerial image; Remote sensing
- G06T2207/10036—Multispectral image; Hyperspectral image
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Multimedia (AREA)
- Computing Systems (AREA)
- Artificial Intelligence (AREA)
- Health & Medical Sciences (AREA)
- Databases & Information Systems (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Quality & Reliability (AREA)
- Image Analysis (AREA)
- Excavating Of Shafts Or Tunnels (AREA)
Abstract
The application relates to an artificial intelligence based blasting drilling method, device, equipment and medium, relating to the technical field of production detection, comprising the following steps: the method comprises the steps of obtaining road image information and mine car running tracks, carrying out training and identifying processing on the road image information to obtain ore identification information, determining whether ore rolling contact exists when the mine car runs to a preset range or not based on the ore identification information and the mine car running tracks, determining rolling ore information based on the ore identification information if the mine car runs to the preset range and judging whether the rolling ore information accords with preset standard ore information or not, if not, generating an ore carrying instruction, and controlling a carrying mechanical arm to carry ore corresponding to the rolling ore information away from the mine car running tracks. The application has the effect of improving the transportation safety coefficient of the mine car.
Description
Technical Field
The application relates to the field of mine blasting engineering, in particular to an artificial intelligence-based blasting drilling method, device, equipment and medium.
Background
The mine blasting belongs to the application of engineering blasting technology in mine exploitation, and in mine engineering, various blasting technologies are applied in the process of tunneling a well, in the process of surface exploitation and underground stope exploitation, the mine blasting is to peel ores from rock bodies and peel off surrounding rocks of roof plates of the ore bodies, blast the ores into a certain blasting pile according to engineering requirements, break the blasting pile into a certain block, and create conditions for subsequent shoveling and transportation work.
Currently, before mining, the position of a blasthole is determined, and then drilling equipment enters the mine, so that a drilling process is performed. The weight of the drilling equipment is large, the mine car is required to be transported to a designated position, but broken stones on the mine are many, and the road is rugged, so that inconvenience exists in the process that the mine car goes to the designated position, and particularly when the mine car rolls to large broken stones, the possibility of rollover even exists, so that the transportation safety coefficient of the mine car is reduced.
Disclosure of Invention
In order to solve the technical problems, the application provides an artificial intelligence-based blasting drilling method, device, equipment and medium.
In a first aspect, the application provides an artificial intelligence based blasting drilling method, which adopts the following technical scheme:
an artificial intelligence based blast drilling method, comprising:
acquiring road image information and mine car running tracks, wherein the road image information is used for representing road image information positioned in a preset range in front of a mine car, and the mine car running tracks are used for representing actual running tracks of wheels of the mine car;
training and identifying the road image information to obtain ore identification information;
determining whether ore rolling contact exists when the mine car runs within the preset range or not based on the ore identification information and the mine car running track;
if ore rolling contact exists when the mine car runs to the preset range, determining rolling ore information based on the ore identification information, judging whether the rolling ore information accords with preset standard ore information, if not, generating an ore conveying instruction, and controlling a conveying mechanical arm to convey ore corresponding to the rolling ore information away from the mine car running track.
In another possible implementation manner, the training and identifying the road image information to obtain ore identification information includes:
preprocessing the road image information to obtain spectrum image information;
inputting the spectrum image information into a trained classification and identification model for training to obtain ore image information and labeling vector information corresponding to the ore image information, wherein the labeling vector information is used for representing coordinate information of each ore in the ore image information;
and correspondingly binding the ore image information with the labeling vector information to obtain ore identification information.
In another possible implementation manner, the preprocessing the road image information to obtain spectral image information includes:
performing geometric correction processing on the road image information to obtain corrected image information;
performing image fusion processing on the corrected image information and the multispectral image to obtain fusion image information;
and performing image mosaic processing on the fused image information to obtain spectrum image information.
In another possible implementation manner, the controlling and transporting mechanical arm carries the ore corresponding to the rolled ore information away from the mine car running track, and then further includes:
acquiring weight information and running gradient information, wherein the weight information comprises mine car weight information and ore weight information, the ore weight information is used for representing ore weight information corresponding to the rolling ore information, and the running gradient information is used for representing gradient information of a current running road of the mine car;
calculating the ore bearing weight of the mine car according to the running gradient information and the mine car weight information to obtain a standard bearing weight;
judging whether the ore weight information exceeds the standard bearing weight, if so, generating alarm information, and sending the alarm information to the mine car terminal.
In another possible implementation manner, the calculating the ore bearing weight of the mine car according to the running gradient information and the mine car weight information to obtain a standard bearing weight includes:
calculating gradient branch weights of the mine car under different gradients based on the trigonometric function and the weight information of the mine car;
and matching the gradient in the driving gradient information with the different gradients to obtain the standard bearing weight.
In another possible implementation, the method further includes:
acquiring historical blasting information and current blasting information, wherein the historical blasting information is blasting information of mining mine cars with different materials in different degrees in a preset historical time period;
creating a blasting network model, and iterating the blasting network model based on an artificial fish swarm algorithm and a parameter type sequence in the historical blasting information to obtain an iterated blasting network model;
inputting the historical blasting information into the blasting network model for training to obtain a trained blasting network model;
and traversing and inputting the current blasting information into the trained blasting network model according to the parameter type sequence for iterative recognition to obtain the current blasting parameters.
In another possible implementation manner, the training and identifying the road image information to obtain ore identification information further includes:
finely crushing and classifying the ore identification information to obtain optimized identification information;
based on the road image information, carrying out corresponding spatial data import on the optimized identification information to obtain coordinate identification information;
performing grid vector conversion processing on the coordinate identification information to obtain vector identification information;
judging whether the vector identification information has preset abnormality or not, if so, generating intervention information to inform staff to perform intervention correction on the vector identification information.
In a second aspect, the application provides an artificial intelligence based blasting drilling device, which adopts the following technical scheme:
an artificial intelligence based blast drilling apparatus comprising:
the information acquisition module is used for acquiring road image information and mine car running tracks, wherein the road image information is used for representing the road image information positioned in a preset range in front of a mine car, and the mine car running tracks are used for representing the actual running tracks of the mine car wheels;
the recognition processing module is used for carrying out training recognition processing on the road image information to obtain ore recognition information;
the rolling judgment module is used for determining whether ore rolling contact exists when the mine car runs within the preset range or not based on the ore identification information and the mine car running track;
and the ore carrying module is used for determining the rolling ore information based on the ore identification information when ore rolling contact exists when the mine car runs to the preset range, judging whether the rolling ore information accords with preset standard ore information, if not, generating an ore carrying instruction, and controlling the carrying mechanical arm to carry ore corresponding to the rolling ore information away from the mine car running track.
In one possible implementation manner, the recognition processing module is specifically configured to, when performing training recognition processing on the road image information to obtain ore recognition information:
preprocessing the road image information to obtain spectrum image information;
inputting the spectrum image information into a trained classification and identification model for training to obtain ore image information and labeling vector information corresponding to the ore image information, wherein the labeling vector information is used for representing coordinate information of each ore in the ore image information;
and correspondingly binding the ore image information with the labeling vector information to obtain ore identification information.
In another possible implementation manner, the identification processing module is specifically configured to, when preprocessing the road image information to obtain spectral image information:
performing geometric correction processing on the road image information to obtain corrected image information;
performing image fusion processing on the corrected image information and the multispectral image to obtain fusion image information;
and performing image mosaic processing on the fused image information to obtain spectrum image information.
In another possible implementation, the apparatus further includes: the weight acquisition module, the bearing calculation module and the bearing judgment module, wherein,
the weight acquisition module is used for acquiring weight information and running gradient information, the weight information comprises mine car weight information and ore weight information, the ore weight information is used for representing ore weight information corresponding to the rolled ore information, and the running gradient information is used for representing gradient information of a current running road of the mine car;
the bearing calculation module is used for calculating the ore bearing weight of the mine car according to the running gradient information and the mine car weight information to obtain a standard bearing weight;
the bearing judgment module is used for judging whether the ore weight information exceeds the standard bearing weight, if so, generating alarm information and sending the alarm information to the mine car terminal.
In another possible implementation manner, the load-bearing calculation module is specifically configured to, when calculating the ore load weight of the mine car according to the running gradient information and the mine car weight information to obtain a standard load weight:
calculating gradient branch weights of the mine car under different gradients based on the trigonometric function and the weight information of the mine car;
and matching the gradient in the driving gradient information with the different gradients to obtain the standard bearing weight.
In another possible implementation, the apparatus further includes: a blasting acquisition module, a model creation module, a model training module and an iteration identification module, wherein,
the explosion acquisition module is used for acquiring historical explosion information and current explosion information, wherein the historical explosion information is explosion information of mining the mine cars with different materials to different degrees in a preset historical time period;
the model creation module is used for creating a blasting network model, and iterating the blasting network model based on an artificial fish swarm algorithm and a parameter type sequence in the historical blasting information to obtain an iterated blasting network model;
the model training module is used for inputting the historical blasting information into the blasting network model for training to obtain a trained blasting network model;
the iteration identification module is used for carrying out iteration identification on the current blasting information according to the parameter type sequence, traversing and inputting the current blasting information into the trained blasting network model, and obtaining current blasting parameters.
In another possible implementation, the apparatus further includes: a classification and identification module, a space import module, a vector processing module and a vector judgment module, wherein,
the classifying and identifying module is used for carrying out fine crushing classification and taking out on the ore identifying information to obtain optimized identifying information;
the space importing module is used for importing corresponding space data of the optimized identification information based on the road image information to obtain coordinate identification information;
the vector processing module is used for carrying out grid-to-vector conversion on the coordinate identification information to obtain vector identification information;
the vector judgment module is used for judging whether the vector identification information has preset abnormality or not, and if so, generating intervention information so as to inform staff to perform intervention correction on the vector identification information.
In a third aspect, the present application provides an electronic device, which adopts the following technical scheme:
an electronic device, the electronic device comprising:
one or more processors;
a memory;
one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications configured to: an artificial intelligence based blast drilling method according to any one of the possible implementations of the first aspect is performed.
In a fourth aspect, the present application provides a computer readable storage medium, which adopts the following technical scheme:
a computer-readable storage medium, comprising: a computer program capable of being loaded and executed by a processor to carry out an artificial intelligence based blast drilling method as shown in any one of the possible implementations of the first aspect is stored.
In summary, the present application includes at least one of the following beneficial technical effects:
through adopting above-mentioned technical scheme, in the mine car loading this blasting drilling equipment go to the blasting position process of predetermineeing, to obtaining road image information and mine car travel track, then to the road image information carries out training discernment processing, obtains ore discernment information, based on ore discernment information and mine car travel track, confirm whether there is ore rolling contact when the mine car is gone to in the predetermineeing the within range, when there is ore rolling contact when going to in the predetermineeing the within range, confirm and roll ore information based on ore discernment information, and judge whether roll ore information accords with preset standard ore information, if not accord with, then generate the transport instruction, control transport robotic arm will with roll ore that ore information corresponds to carry away from the mine car travel track to avoid rolling the condition emergence of ore and taking place to turn on one's side because of the mine car rolls the ore, and then improved the transportation factor of safety.
Drawings
FIG. 1 is a schematic flow diagram of an artificial intelligence based blasting drilling method according to an embodiment of the present application;
FIG. 2 is a schematic structural view of an artificial intelligence based blasting drilling device according to an embodiment of the present application;
FIG. 3 is a schematic diagram of an electronic device according to an embodiment of the present application;
Detailed Description
The application is described in further detail below with reference to fig. 1-3.
Modifications of the embodiments which do not creatively contribute to the application may be made by those skilled in the art after reading the present specification, but are protected by patent laws only within the scope of the claims of the present application.
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In addition, the term "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In this context, unless otherwise specified, the term "/" generally indicates that the associated object is an "or" relationship.
Embodiments of the application are described in further detail below with reference to the drawings.
The embodiment of the application provides an artificial intelligence based blasting drilling method which is executed by electronic equipment, wherein the electronic equipment can be a server or terminal equipment, the server can be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, and a cloud server for providing cloud computing service. The terminal device may be, but is not limited to, a smart phone, a tablet computer, a notebook computer, a desktop computer, etc., and the terminal device and the server may be directly or indirectly connected through wired or wireless communication, which is not limited herein, and as shown in fig. 1, the method includes:
step S10, road image information and mine car running tracks are acquired, wherein the road image information is used for representing road image information positioned in a preset range in front of a mine car, and the mine car running tracks are used for representing actual running tracks of mine car wheels.
In the embodiment of the application, intelligent blasting drilling equipment is arranged on a mine car and comprises a car body support, a binocular camera, a carrying manipulator, a sliding rail and a solar cell panel, wherein the binocular camera is used for identifying road images in a preset range in front of the mine car to obtain road image information and sending the road image information to electronic equipment, and then a positioning module in a mine car running system is used for acquiring running tracks of mine car wheels running on a mine car road and sending the mine car running tracks to the electronic equipment.
For the embodiment of the application, the electronic device sets and adjusts the preset range of the photographing road of the binocular camera according to the brightness degree of the current environment, for example: when the brightness in the environment is between 120cd/m2 and 150cd/m2, the preset range is 10 meters.
And S11, training and identifying the road image information to obtain ore identification information.
And step S12, determining whether ore rolling contact exists when the mine car runs within a preset range or not based on the ore identification information and the mine car running track.
And S13, if ore rolling contact exists when the mine car runs to a preset range, determining rolling ore information based on the ore identification information, judging whether the rolling ore information accords with preset standard ore information, if not, generating an ore conveying instruction, and controlling a conveying mechanical arm to convey ore corresponding to the rolling ore information from the running track of the mine car.
In the embodiment of the application, in the process that the blasting and drilling equipment is loaded by a mine car and goes to a preset blasting position, the acquired road image information and the mine car running track are subjected to training and identifying treatment to obtain ore identifying information, whether ore rolling contact exists when the mine car runs to a preset range or not is determined based on the ore identifying information and the mine car running track, when the mine car runs to the preset range, the ore rolling contact exists, the ore rolling information is determined based on the ore identifying information and whether the ore rolling information accords with preset standard ore information or not is judged, if the ore rolling information does not accord with the preset standard ore information, an ore carrying instruction is generated, and a carrying mechanical arm is controlled to carry ore corresponding to the ore rolling information away from the mine car running track, so that rollover caused by ore rolling is avoided, and the transportation safety coefficient of the mine car is improved.
In one possible implementation manner of the embodiment of the present application, step S11 specifically includes step S111 and step S112, where,
step S111, preprocessing the road image information to obtain spectral image information.
In an embodiment of the present application, the preprocessing includes: geometric correction, image fusion and image mosaic, because the position, shape, size, orientation and other features of the ground feature in the road image information are influenced by various imaging factors and deviate from the corresponding features of the real ground feature, the geometric correction of the image is required. And then fusing the corrected road image information by using the full-color and multispectral images so that the fused road image information has new spatial and spectral resolutions.
And step S112, inputting the spectral image information into the trained classification recognition model for training to obtain ore image information and labeling vector information corresponding to the ore image information.
The labeling vector information is used for representing coordinate information of each ore in the ore image information.
And step S113, carrying out corresponding binding on the ore image information and the labeling vector information to obtain ore identification information.
In one possible implementation manner of the embodiment of the present application, step S111 specifically includes step S101, step S102, and step S103, where,
step S101, performing geometric correction processing on the road image information to obtain corrected image information.
Specifically, a process of geometrically correcting the geometric distortion of the road image information. Geometric corrections are typically made using electronic computers and optical instruments. The principle is that the element of one distorted image is transformed from the original position to another correct image through a certain coordinate transformation. The geometric correction of the image also comprises the steps of painting a coordinate grid, registering the multispectral image and transforming the road image information obtained by certain projection into map projection.
And step S102, performing image fusion processing on the corrected image information and the multispectral image to obtain fused image information.
In particular, multispectral images refer to images that contain many bands, sometimes only 3 bands (color images are one example) but sometimes much more, even hundreds. Each band is a gray scale image representing scene brightness derived from the sensitivity of the sensor used to generate the band. In such an image, each pixel is associated with a string of values in different bands, i.e. a vector, by the pixel. This series is called a spectral signature of the pixel.
And step S103, performing image mosaic processing on the fused image information to obtain spectrum image information.
In the embodiment of the application, the image mosaic processing method for the fused image information comprises the following steps: and selecting one image with uniform brightness and color from the image information to be fused as a reference image of mosaic, and performing mosaic on other images from near to far according to the reference image.
A possible implementation manner of the embodiment of the present application, step S13 further includes step S31, step S32, and step S33, where,
step S31, weight information and travel gradient information are acquired.
The weight information comprises mine car weight information and ore weight information, the ore weight information is used for representing ore weight information corresponding to rolling ore information, and the running gradient information is used for representing gradient information of a current running road of the mine car.
Specifically, the weight information of the mine car is weight information after the mine car is measured in advance through the ground pump, and the ore weight information is a gravity sensing device arranged on the carrying mechanical arm and used for sensing the ore weight carried by the carrying mechanical arm.
S32, calculating the ore bearing weight of the mine car according to the running gradient information and the mine car weight information to obtain a standard bearing weight;
and step S33, judging whether the ore weight information exceeds the standard bearing weight, if so, generating alarm information and sending the alarm information to a terminal of the mine car.
In one possible implementation manner of the embodiment of the present application, step S32 specifically includes steps S321 and S322, where,
and S321, calculating gradient branch weights of the mine car under different gradients based on the trigonometric function and the weight information of the mine car.
In the embodiment of the application, the gradient of the road on which the current mine car runs is determined by adopting a level meter device, and then the weight branch information of the current mine car, which is parallel to the current gradient, is calculated by adopting a trigonometric function calculation formula.
In step S322, the gradient in the driving gradient information is matched with different gradients to obtain the standard bearing weight.
In one possible implementation manner of the embodiment of the present application, step S13 further includes: acquiring historical blasting information and current blasting information, wherein the historical blasting information is blasting information for mining mine cars of different materials in different degrees in a preset historical time period; creating a blasting network model, and iterating the blasting network model based on an artificial fish swarm algorithm and a parameter type sequence in historical blasting information to obtain an iterated blasting network model; and inputting the historical blasting information into the blasting network model for training to obtain a trained blasting network model, and inputting the current blasting information into the trained blasting network model for iterative recognition according to the parameter type sequence to obtain the current blasting parameters.
In one possible implementation manner of the embodiment of the present application, step S13 further includes: finely classifying and taking out ore identification information to obtain optimized identification information, importing corresponding spatial data to the optimized identification information based on real-time image information to obtain coordinate identification information, carrying out grid-to-vector conversion on sitting identification information to obtain vector identification information, judging whether preset abnormality exists in the vector identification information, and if so, generating intervention information to inform staff of carrying out intervention correction on the vector identification information.
Specifically, the preset anomalies include: vector recognition classification errors due to algorithm inaccuracy.
The above embodiment describes an artificial intelligence based blasting drilling method from the viewpoint of a method flow, and the following embodiment describes an artificial intelligence based blasting drilling device from the viewpoint of a virtual module or a virtual unit, specifically the following embodiment.
An embodiment of the present application provides an artificial intelligence based blasting and drilling device 20, as shown in fig. 2, the blasting and drilling device 20 based on artificial intelligence may specifically include: an information acquisition module 21, an identification processing module 22, a rolling judgment module 23, and an ore handling module 24, wherein,
the information acquisition module 21 is used for acquiring road image information and mine car running tracks, the road image information is used for representing road image information positioned in a preset range in front of the mine car, and the mine car running tracks are used for representing actual running tracks of mine car wheels;
the recognition processing module 22 is used for performing training recognition processing on the road image information to obtain ore recognition information;
the rolling judgment module 23 is used for determining whether ore rolling contact exists when the mine car runs within a preset range based on the ore identification information and the mine car running track;
the ore handling module 24 is configured to determine crushed ore information based on the ore identification information when there is ore crushing contact when the mine car travels to a preset range, and determine whether the crushed ore information meets preset standard ore information, if not, generate an ore handling instruction, and control the handling mechanical arm to remove ore corresponding to the crushed ore information from the travel track of the mine car.
In one possible implementation manner of the embodiment of the present application, when the recognition processing module 22 performs training recognition processing on the road image information to obtain the ore recognition information, the recognition processing module is specifically configured to:
preprocessing road image information to obtain spectrum image information;
the spectrum image information is input into a trained classification and identification model for training, so that ore image information and labeling vector information corresponding to the ore image information are obtained, and the labeling vector information is used for representing coordinate information of each ore in the ore image information;
and correspondingly binding the ore image information with the labeling vector information to obtain ore identification information.
In another possible implementation manner of the embodiment of the present application, when the recognition processing module 22 performs preprocessing on the road image information to obtain the spectral image information, the recognition processing module is specifically configured to:
performing geometric correction processing on the road image information to obtain corrected image information;
performing image fusion processing on the corrected image information and the multispectral image to obtain fused image information;
and performing image mosaic processing on the fused image information to obtain spectrum image information.
Another possible implementation manner of the embodiment of the present application, an artificial intelligence based blasting and drilling device 20 further includes: the weight acquisition module, the bearing calculation module and the bearing judgment module, wherein,
the weight acquisition module is used for acquiring weight information and running gradient information, wherein the weight information comprises mine car weight information and ore weight information, the ore weight information is used for representing ore weight information corresponding to rolling ore information, and the running gradient information is used for representing gradient information of a current running road of the mine car;
the bearing calculation module is used for calculating the ore bearing weight of the mine car according to the running gradient information and the mine car weight information to obtain the standard bearing weight;
and the bearing judgment module is used for judging whether the ore weight information exceeds the standard bearing weight, if so, generating alarm information and sending the alarm information to the mine car terminal.
In another possible implementation manner of the embodiment of the present application, the load-bearing calculation module is specifically configured to, when calculating the ore load weight of the mine car according to the running gradient information and the weight information of the mine car to obtain the standard load weight:
calculating gradient branch weights of the mine car under different gradients based on the trigonometric function and the weight information of the mine car;
and matching the gradient in the driving gradient information with different gradients to obtain the standard bearing weight.
Another possible implementation manner of the embodiment of the present application, an artificial intelligence based blasting and drilling device 20 further includes: a blasting acquisition module, a model creation module, a model training module and an iteration identification module, wherein,
the explosion acquisition module is used for acquiring historical explosion information and current explosion information, wherein the historical explosion information is explosion information for mining mine cars of different materials to different degrees in a preset historical time period;
the model creation module is used for creating a blasting network model, and iterating the blasting network model based on an artificial fish swarm algorithm and a parameter type sequence in the historical blasting information to obtain an iterated blasting network model;
the model training module is used for inputting the historical blasting information into the blasting network model for training to obtain a trained blasting network model;
the iteration identification module is used for inputting the current blasting information into the trained blasting network model in a traversing manner according to the parameter type sequence for iteration identification, and obtaining the current blasting parameters.
Another possible implementation manner of the embodiment of the present application, an artificial intelligence based blasting and drilling device 20 further includes: a classification and identification module, a space import module, a vector processing module and a vector judgment module, wherein,
the classification and identification module is used for carrying out fine crushing classification and taking out on the ore identification information to obtain optimized identification information;
the space importing module is used for importing corresponding space data of the optimized identification information based on the real-time image information to obtain coordinate identification information;
the vector processing module is used for carrying out grid-to-vector conversion processing on the coordinate identification information to obtain vector identification information;
the vector judgment module is used for judging whether the vector identification information has preset abnormality or not, and if so, generating intervention information so as to inform staff of carrying out intervention correction on the vector identification information.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In an embodiment of the present application, as shown in fig. 3, an electronic device 300 shown in fig. 3 includes: a processor 301 and a memory 303. Wherein the processor 301 is coupled to the memory 303, such as via a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that, in practical applications, the transceiver 304 is not limited to one, and the structure of the electronic device 300 is not limited to the embodiment of the present application.
The processor 301 may be a CPU (Central Processing Unit ), general purpose processor, DSP (Digital Signal Processor, data signal processor), ASIC (Application Specific Integrated Circuit ), FPGA (Field Programmable Gate Array, field programmable gate array) or other programmable logic device, transistor logic device, hardware components, or any combination thereof. Which may implement or perform the various exemplary logic blocks, modules and circuits described in connection with this disclosure. Processor 301 may also be a combination that implements computing functionality, e.g., comprising one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.
Bus 302 may include a path to transfer information between the components. Bus 302 may be a PCI (Peripheral Component Interconnect, peripheral component interconnect Standard) bus or an EISA (Extended Industry Standard Architecture ) bus, or the like. Bus 302 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in fig. 3, but not only one bus or one type of bus.
The Memory 303 may be, but is not limited to, a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory ) or other type of dynamic storage device that can store information and instructions, an EEPROM (Electrically Erasable Programmable Read Only Memory ), a CD-ROM (Compact Disc Read Only Memory, compact disc Read Only Memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.
The memory 303 is used for storing application program codes for executing the inventive arrangements and is controlled to be executed by the processor 301. The processor 301 is configured to execute the application code stored in the memory 303 to implement what is shown in the foregoing method embodiments.
Among them, electronic devices include, but are not limited to: mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and stationary terminals such as digital TVs, desktop computers, and the like. But may also be a server or the like. The electronic device shown in fig. 3 is merely an example and should not be construed to limit the functionality and scope of use of the disclosed embodiments.
Embodiments of the present application provide a computer-readable storage medium having a computer program stored thereon, which when run on a computer, causes the computer to perform the corresponding method embodiments described above. Compared with the related art, in the embodiment of the application, in the process that the mine car is loaded with the blasting drilling equipment and goes to the preset blasting position, the road image information and the mine car running track are acquired, then the road image information is subjected to training recognition processing to obtain the ore recognition information, whether ore rolling contact exists when the mine car runs to the preset range is determined based on the ore recognition information and the mine car running track, when the mine car runs to the preset range, the ore rolling contact exists, the ore rolling information is determined based on the ore recognition information, whether the ore rolling information accords with the preset standard ore information is judged, if not, an ore carrying instruction is generated, and the carrying mechanical arm is controlled to carry away the ore corresponding to the ore rolling information from the mine car running track, so that the occurrence of rollover of the mine car due to ore rolling is avoided, and the transportation safety coefficient of the mine car is improved.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.
The foregoing is only a partial embodiment of the present application, and it should be noted that it will be apparent to those skilled in the art that modifications and adaptations can be made without departing from the principles of the present application, and such modifications and adaptations should and are intended to be comprehended within the scope of the present application.
Claims (10)
1. An artificial intelligence based blasting drilling method, comprising:
acquiring road image information and mine car running tracks, wherein the road image information is used for representing road image information positioned in a preset range in front of a mine car, and the mine car running tracks are used for representing actual running tracks of mine car wheels;
training and identifying the road image information to obtain ore identification information;
determining whether ore rolling contact exists when the mine car runs within the preset range or not based on the ore identification information and the mine car running track;
if the mine car runs within the preset range and has ore rolling contact, determining rolling ore information based on the ore identification information, judging whether the rolling ore information accords with preset standard ore information, if not, generating an ore conveying instruction, and controlling a conveying mechanical arm to convey ore corresponding to the rolling ore information away from the mine car running track;
the control carrying mechanical arm carries ore corresponding to the crushed ore information away from the mine car running track, and then the control carrying mechanical arm further comprises:
acquiring weight information and running gradient information, wherein the weight information comprises mine car weight information and ore weight information, the ore weight information is used for representing ore weight information corresponding to the rolling ore information, and the running gradient information is used for representing gradient information of a current running road of the mine car;
calculating the ore bearing weight of the mine car according to the running gradient information and the mine car weight information to obtain a standard bearing weight;
judging whether the ore weight information exceeds the standard bearing weight, if so, generating alarm information, and sending the alarm information to a mine car terminal.
2. The artificial intelligence based blasting and drilling method according to claim 1, wherein the training and identifying the road image information to obtain ore identification information comprises:
preprocessing the road image information to obtain spectrum image information;
inputting the spectrum image information into a trained classification and identification model for training to obtain ore image information and labeling vector information corresponding to the ore image information, wherein the labeling vector information is used for representing coordinate information of each ore in the ore image information;
and correspondingly binding the ore image information with the labeling vector information to obtain ore identification information.
3. The artificial intelligence based blasting and drilling method according to claim 2, wherein the preprocessing the road image information to obtain spectral image information comprises:
performing geometric correction processing on the road image information to obtain corrected image information;
performing image fusion processing on the corrected image information and the multispectral image to obtain fusion image information;
and performing image mosaic processing on the fused image information to obtain spectrum image information.
4. An artificial intelligence based blasting and drilling method according to claim 1, wherein the calculating the ore bearing weight of the mine car according to the driving gradient information and the weight information of the mine car to obtain a standard bearing weight comprises:
calculating gradient branch weights of the mine car under different gradients based on the trigonometric function and the weight information of the mine car;
and matching the gradient in the driving gradient information with the different gradients to obtain the standard bearing weight.
5. An artificial intelligence based blast drilling method according to claim 1, wherein the method further comprises:
acquiring historical blasting information and current blasting information, wherein the historical blasting information is blasting information of mining mine cars with different materials in different degrees in a preset historical time period;
creating a blasting network model, and iterating the blasting network model based on an artificial fish swarm algorithm and a parameter type sequence in the historical blasting information to obtain an iterated blasting network model;
inputting the historical blasting information into the blasting network model for training to obtain a trained blasting network model;
and traversing and inputting the current blasting information into the trained blasting network model according to the parameter type sequence for iterative recognition to obtain the current blasting parameters.
6. The artificial intelligence based blasting and drilling method according to claim 1, wherein the training and identifying the road image information to obtain ore identification information further comprises:
finely crushing and classifying the ore identification information to obtain optimized identification information;
based on the road image information, carrying out corresponding spatial data import on the optimized identification information to obtain coordinate identification information;
performing grid vector conversion processing on the coordinate identification information to obtain vector identification information;
judging whether the vector identification information has preset abnormality or not, if so, generating intervention information to inform staff to perform intervention correction on the vector identification information.
7. An artificial intelligence based blasting drilling device, comprising:
the information acquisition module is used for acquiring road image information and mine car running tracks, wherein the road image information is used for representing the road image information positioned in a preset range in front of a mine car, and the mine car running tracks are used for representing the actual running tracks of the wheels of the mine car;
the recognition processing module is used for carrying out training recognition processing on the road image information to obtain ore recognition information;
the rolling judgment module is used for determining whether ore rolling contact exists when the mine car runs within the preset range or not based on the ore identification information and the mine car running track;
and the ore carrying module is used for determining the rolling ore information based on the ore identification information when ore rolling contact exists when the mine car runs to the preset range, judging whether the rolling ore information accords with preset standard ore information, if not, generating an ore carrying instruction, and controlling the carrying mechanical arm to carry ore corresponding to the rolling ore information away from the mine car running track.
8. The apparatus further comprises: the weight acquisition module, the bearing calculation module and the bearing judgment module, wherein,
the weight acquisition module is used for acquiring weight information and running gradient information, the weight information comprises mine car weight information and ore weight information, the ore weight information is used for representing ore weight information corresponding to the rolled ore information, and the running gradient information is used for representing gradient information of a current running road of the mine car;
the bearing calculation module is used for calculating the ore bearing weight of the mine car according to the running gradient information and the mine car weight information to obtain a standard bearing weight;
the bearing judgment module is used for judging whether the ore weight information exceeds the standard bearing weight, if so, generating alarm information and sending the alarm information to the mine car terminal.
9. An electronic device, comprising:
one or more processors;
a memory;
one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications configured to: performing an artificial intelligence based blast drilling method according to any of claims 1-6.
10. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the artificial intelligence based blast drilling method of any of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310919396.9A CN116664553B (en) | 2023-07-26 | 2023-07-26 | Explosion drilling method, device, equipment and medium based on artificial intelligence |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310919396.9A CN116664553B (en) | 2023-07-26 | 2023-07-26 | Explosion drilling method, device, equipment and medium based on artificial intelligence |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116664553A CN116664553A (en) | 2023-08-29 |
| CN116664553B true CN116664553B (en) | 2023-10-20 |
Family
ID=87715623
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310919396.9A Active CN116664553B (en) | 2023-07-26 | 2023-07-26 | Explosion drilling method, device, equipment and medium based on artificial intelligence |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116664553B (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101531713B1 (en) * | 2015-04-22 | 2015-06-25 | (주) 코스텍 | Apparatus for Measuring Load for Vehicle and Method thereof |
| KR20180032138A (en) * | 2016-09-21 | 2018-03-29 | 주식회사 지오제니컨설턴트 | Method for Surveying and Monitoring Mine Site by using Virtual Reality and Augmented Reality |
| WO2020049517A1 (en) * | 2018-09-07 | 2020-03-12 | Stone Three Digital (Pty) Ltd | Monitoring ore |
| CN111731242A (en) * | 2020-08-06 | 2020-10-02 | 北汽福田汽车股份有限公司 | Automatic emergency braking method and device and vehicle |
| WO2021228147A1 (en) * | 2020-05-15 | 2021-11-18 | 长沙智能驾驶研究院有限公司 | Mine car transportation and driving control method and device, and mine car and storage medium |
| CN114721376A (en) * | 2022-03-16 | 2022-07-08 | 山西维度空间信息科技有限公司 | Road condition identification method, device, equipment and medium for unmanned mine car |
| CN115601972A (en) * | 2022-11-28 | 2023-01-13 | 青岛慧拓智能机器有限公司（Cn） | Obstacle processing system for unmanned mine driving area |
| CN115962692A (en) * | 2023-01-31 | 2023-04-14 | 北方***科技有限公司 | Blasting parameter optimization method applied to open blasting engineering |
| CN116168246A (en) * | 2023-02-13 | 2023-05-26 | 中国铁道科学研究院集团有限公司节能环保劳卫研究所 | Method, device, equipment and medium for identifying waste slag field for railway engineering |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR102564023B1 (en) * | 2018-11-09 | 2023-08-07 | 현대자동차주식회사 | Apparatus and method for transmission control of vehicle, and vehicle system |
-
2023
- 2023-07-26 CN CN202310919396.9A patent/CN116664553B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101531713B1 (en) * | 2015-04-22 | 2015-06-25 | (주) 코스텍 | Apparatus for Measuring Load for Vehicle and Method thereof |
| KR20180032138A (en) * | 2016-09-21 | 2018-03-29 | 주식회사 지오제니컨설턴트 | Method for Surveying and Monitoring Mine Site by using Virtual Reality and Augmented Reality |
| WO2020049517A1 (en) * | 2018-09-07 | 2020-03-12 | Stone Three Digital (Pty) Ltd | Monitoring ore |
| WO2021228147A1 (en) * | 2020-05-15 | 2021-11-18 | 长沙智能驾驶研究院有限公司 | Mine car transportation and driving control method and device, and mine car and storage medium |
| CN111731242A (en) * | 2020-08-06 | 2020-10-02 | 北汽福田汽车股份有限公司 | Automatic emergency braking method and device and vehicle |
| CN114721376A (en) * | 2022-03-16 | 2022-07-08 | 山西维度空间信息科技有限公司 | Road condition identification method, device, equipment and medium for unmanned mine car |
| CN115601972A (en) * | 2022-11-28 | 2023-01-13 | 青岛慧拓智能机器有限公司（Cn） | Obstacle processing system for unmanned mine driving area |
| CN115962692A (en) * | 2023-01-31 | 2023-04-14 | 北方***科技有限公司 | Blasting parameter optimization method applied to open blasting engineering |
| CN116168246A (en) * | 2023-02-13 | 2023-05-26 | 中国铁道科学研究院集团有限公司节能环保劳卫研究所 | Method, device, equipment and medium for identifying waste slag field for railway engineering |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116664553A (en) | 2023-08-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11181624B2 (en) | Method and apparatus for calibration between laser radar and camera, device and storage medium | |
| CN109300159B (en) | Position detection method, device, equipment, storage medium and vehicle | |
| EP3096293B1 (en) | Methods for improving the accuracy of dimensioning-system measurements | |
| US9396553B2 (en) | Vehicle dimension estimation from vehicle images | |
| CN113744144B (en) | Remote sensing image building boundary optimization method, system, equipment and storage medium | |
| CN114283155B (en) | Ore image segmentation method, device and computer readable storage medium | |
| CN112534469A (en) | Image detection method, image detection device, image detection apparatus, and medium | |
| Liu et al. | Research on deviation detection of belt conveyor based on inspection robot and deep learning | |
| CN114873465A (en) | High-precision underground monorail crane positioning method and system based on machine vision | |
| CN116664553B (en) | Explosion drilling method, device, equipment and medium based on artificial intelligence | |
| CN115265545A (en) | Map matching navigation method, device, equipment and storage medium based on decision analysis | |
| CN116883611B (en) | Channel silt distribution active detection and identification method combining GIS channel information | |
| Huang et al. | Deep learning–based autonomous road condition assessment leveraging inexpensive rgb and depth sensors and heterogeneous data fusion: Pothole detection and quantification | |
| CN116012422B (en) | Monocular vision-based unmanned aerial vehicle 6D pose estimation tracking method and application thereof | |
| CN115972198B (en) | Mechanical arm visual grabbing method and device under incomplete information condition | |
| CN115116026B (en) | Automatic tracking method and system for logistics transfer robot | |
| CN115170527A (en) | Visual detection method and device for deviation of conveying belt, electronic equipment and storage medium | |
| CN113642961B (en) | Monitoring method and device in cargo handling process | |
| CN112541431B (en) | High-resolution image target detection method and system | |
| CN113781416A (en) | Conveyer belt tearing detection method and device and electronic equipment | |
| CN115544191A (en) | Three-dimensional point cloud crowdsourcing type semantic map updating method and device | |
| CN115583510B (en) | Automatic soil discharging control method and system based on laser scanner | |
| CN116109213B (en) | Project security inspection method and system applied to construction scene and electronic equipment | |
| Wang et al. | A Roadheader Positioning Method Based on Multi-Sensor Fusion | |
| CN114739402B (en) | Fusion positioning method, medium and electronic equipment |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |