CN115238564A - Method for automatically matching and optimizing blasting parameters based on drilling parameters and overbreak and undermining results - Google Patents
Method for automatically matching and optimizing blasting parameters based on drilling parameters and overbreak and undermining results Download PDFInfo
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- 238000005422 blasting Methods 0.000 title claims abstract description 103
- 238000005553 drilling Methods 0.000 title claims abstract description 88
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Abstract
The invention discloses a method for automatically matching and optimizing blasting parameters based on drilling parameters and an overbreak and undermining result, which comprises the following steps: and inputting drilling parameters and tunnel section parameters, automatically matching blasting parameters by an expert system, and optimizing the blasting parameters according to the overbreak and undermine parameters after blasting. The expert system is obtained by training the following steps: acquiring a training set, wherein the training set comprises acquired tunnel section parameters, drilling parameters and overbreak and underbreak parameters; and inputting the training set into a constructed expert system, and constructing the mapping relation between the tunnel section parameters, the drilling parameters and the overbreak parameters and the blasting method, the blast hole distribution position and the loading amount. According to the method, the blasting scheme is automatically matched according to the tunnel section parameters, the drilling parameters of the rock drilling jumbo drill and the upper circulation overbreak and underbreak parameters, after the blasting scheme is implemented, the lower circulation blasting parameters can be optimized according to the overbreak and underbreak scanning result, the adaptability of the blasting scheme to the current surrounding rock and geological environment is ensured, the overbreak and underbreak square amount is reduced, the processing cost is reduced, and the construction speed and the construction quality are improved.
Description
Technical Field
The invention relates to a method for automatically matching and optimizing blasting parameters based on drilling parameters and an overbreak result, and belongs to the technical field of blasting.
Background
High speed and high speed railway roads in mountain environments often require tunnel excavation to shorten the route length. At present, in the process of excavating the mountain tunnel, in order to improve the safety and reliability of the road, the drilling and blasting method is still the most commonly used construction scheme, so that the establishment of parameters such as a hole distribution scheme and the loading quantity which influence the drilling and blasting effect in the construction process is the content which needs to be determined in the construction process.
In the construction process of the drilling and blasting method, a designer needs to design parameters such as the number of blast holes, the spacing, the loading amount and the like according to a plurality of factors such as geological conditions, surrounding rock grades, surrounding rock types, surrounding rock physical and mechanical properties, hydrogeological conditions, tunnel face sizes and the like, before a tunnel is constructed, the designer can design a blasting scheme which substantially meets the geological conditions of the tunnel according to geological data, the selection of the blasting scheme is influenced by the factors, different blasting schemes are selected according to different geological conditions, in the current blasting construction process, the evaluation of blasting effects and the optimization and correction of blasting parameters mainly depend on the experience and the knowledge of field constructors, if the experience of the field constructors is rich, reasonable optimization and correction can be performed according to the past construction experience, but the experience and the knowledge of the field constructors are different, each constructor can not be guaranteed to perform reasonable judgment, so that the current tunnel blasting construction often has a large overbreak condition, the processing cost is increased and the post-event processing cost is reduced, and the quality of the tunnel is often influenced.
In addition, the rock mass is a material with non-homogeneity, anisotropy and discontinuity, a tunnel with the length of several kilometers may even pass through different rock strata, the surrounding rock mass and geological conditions can be obviously changed in the construction process, and when the geological conditions are changed and the blasting scheme is not updated and optimized in time, the quality and effect of field blasting can be hardly ensured, so that the drilling and blasting construction cost is increased and the construction safety risk is improved.
Disclosure of Invention
The technical problem to be solved by the invention is to overcome the defects of the prior art and provide a method for automatically matching and optimizing blasting parameters based on drilling parameters and an over-under excavation result, wherein pre-drilling is firstly carried out, pressure sensors, flow sensors and torque sensors are arranged on a rock drill of a rock drilling trolley, the sensors can acquire basic situation parameters and mechanical parameters of tunnel surrounding rocks in the pre-drilling process, an expert system can automatically match a drilling and blasting scheme suitable for current tunnel construction based on the parameters and pre-input parameters such as tunnel face size, and the like, and drilling and blasting construction can be carried out according to the drilling and blasting scheme after a constructor confirms that the drilling and blasting scheme is correct.
In order to achieve the purpose, the invention provides an optimized drilling and blasting method based on an automatic matching expert system, which comprises the following steps:
scanning the section of the tunnel after the upper-cycle blasting construction to obtain an overbreak parameter;
collecting drilling parameters of the drill jumbo;
and inputting the drilling parameters and the overbreak and underbreak scanning results into an expert system to obtain blasting parameters.
Preferably, the expert system is trained by the steps comprising:
acquiring a training set, wherein the training set comprises tunnel section parameters, drilling parameters and upper circulation overbreak and underbreak parameters;
inputting the tunnel section parameters, the drilling parameters and the upper-cycle overbreak and underbreak parameters into an expert system, and constructing the mapping relation among the tunnel section parameters, the drilling parameters and the overbreak parameters, the blasting method, the blast hole distribution position and the explosive loading;
and when the expert system loss function of the training set is converged to the range of [0,1e-7], the training is finished, and the obtained expert system is output.
Preferably, the expert system loss function is:
in the formula, x i Inputting parameters for an expert system, wherein the input parameters comprise tunnel section parameters, drilling parameters, surrounding rock condition parameters and surrounding rock mechanical parameters, and f (x) i ) Predicting the result for the expert system, y i Is the actual result corresponding to the input parameter,zthe error between the predicted result and the actual result of the expert system is defined, and Loss (z) is the Loss function result of the expert system.
Preferably, the neural network comprises an input layer, an output layer and a plurality of hidden layers, and the input layer, the hidden layers and the output layer are connected in sequence.
Preferably, the training acquired expert system is tested, comprising the steps of:
acquiring a test set;
inputting the test set into an expert system obtained by training to obtain a prediction test result;
comparing the predicted test result with the actual blasting method, the blast hole distribution position and the loading amount corresponding to the test set, if the accuracy of the predicted test result is not less than the set qualified rate, indicating that the expert system is qualified for training, and ending the training; and if the accuracy of the prediction test result is lower than the set qualified rate, increasing the total amount of the training set and continuing to train the expert system.
Preferably, the obtained blasting parameters are optimized, comprising the following steps:
if the over-under-excavation result shows that under-excavation exists in the blasting section of the current tunnel, the blasting effect of the blasting area is poor, and the blast hole distribution position of the current blasting area deviates outwards and the loading amount of the blast hole distribution position is increased; and if the overbreak and underbreak scanning result shows that the overbreak phenomenon exists in the blasting area, shifting the blast hole distribution position of the blasting area to the inner side and reducing the explosive loading of the blast hole.
Preferentially, the drilling parameters and the overbreak and underbreak scanning results are stored and uploaded to the cloud, an expert system database is enriched, and an expert system algorithm is optimized; the expert system can receive data transmitted from the cloud server periodically, and complete system algorithm upgrading and optimization.
An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of any of the above methods when executing the program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any of the above.
The invention achieves the following beneficial effects:
the invention provides a method for automatically matching and optimizing blasting parameters based on drilling parameters and an overbreak and underbreak result, wherein on-site pre-drilling can be carried out before drilling construction, and collected drilling parameters are input into an expert system, so that current surrounding rock condition parameters and surrounding rock mechanical parameters can be obtained;
according to the surrounding rock condition parameters and the surrounding rock mechanical parameters, the expert system can automatically match a suggested blasting scheme, and drilling and blasting construction can be carried out according to the scheme after the expert system is confirmed by a designer. After the explosive blasting construction is finished, constructors adopt a three-dimensional scanner to obtain an overbreak and underbreak scanning result after tunnel blasting; and according to the overbreak and undermine scanning result, the expert system optimizes the blast hole distribution position and the loading amount of the subsequent blasting scheme. Through the application of the expert system, the rock drilling trolley can automatically match a suitable blasting scheme according to the current surrounding rock condition parameters and the surrounding rock mechanical parameters, and can continuously optimize the blasting scheme according to the over-under-excavation scanning result after blasting, so that the adaptability of the blasting scheme to the current surrounding rock and geological environment is ensured, the over-under-excavation square amount is reduced, the processing cost is reduced, and the construction speed and the quality are improved.
Drawings
FIG. 1 is a schematic diagram of a neural network;
FIG. 2 is a diagram of an application scenario of the expert system;
fig. 3 is a flow chart of the expert system operation.
Reference sign, 11-1-acquisition terminal; 11-2-three-dimensional scanner; 12-1-device data storage; 12-2-cloud total data storage; 13-a data analysis terminal; 14-result confirmation terminal; 15-executing the terminal.
Detailed Description
The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
The method for automatically matching and optimizing the blasting parameters based on the drilling parameters and the overbreak and undermining results is characterized by comprising the following steps of:
scanning the section of the tunnel after the upper-cycle blasting construction to obtain an overbreak parameter;
collecting drilling parameters of the drill jumbo;
and inputting the drilling parameters and the overbreak and underbreak scanning results into an expert system to obtain blasting parameters.
The blasting scheme provided by the expert system is a proposal, and after the constructor confirms that no fault exists, the drilling trolley carries out drilling operation according to the proposal. The blasting method comprises a blast hole method, deep hole blasting, differential blasting, smooth blasting and presplitting blasting.
The expert system is obtained by training the following steps of:
acquiring a training set, wherein the training set comprises tunnel section parameters, drilling parameters and upper circulation overbreak and underbreak parameters;
inputting the tunnel section parameters, the drilling parameters and the upper-circulation over-under-excavation parameters into an expert system, and constructing mapping relations among the tunnel section parameters, the drilling parameters, the over-under-excavation parameters, the blasting method, the blast hole distribution position and the explosive loading;
if the blasting is the first circulation construction of the tunnel, and no upper circulation overbreak result exists temporarily, the expert system can also directly give blasting parameters according to the section parameters of the tunnel and drilling parameters.
And when the expert system loss function of the training set converges to the range of [0,1e-7], the training is finished, and the obtained expert system is output.
Preferably, the expert system loss function is:
in the formula, x i Inputting parameters for the expert system, wherein the input parameters comprise tunnel section parameters, drilling parameters, surrounding rock condition parameters and surrounding rock mechanical parameters, f (x) i ) Predicting the result for the expert system, y i For the actual result corresponding to the input parameter,zthe error between the predicted result and the actual result of the expert system is defined, and Loss (z) is the Loss function result of the expert system.
The samples in the training set are obtained by the following method: drilling parameters are collected through a pressure sensor, an oil flow sensor, an oil pressure sensor and a torque sensor which are arranged on the drill jumbo, and sufficient overbreak and underexcavation data corresponding to the drilling parameters are obtained through a three-dimensional scanner.
An expert system for test training, comprising:
and step 3: and (3) importing the test sample into an expert system, comparing the result predicted and output by the expert system with the actual surrounding rock condition parameters and the actual mechanical parameters, testing the learning result of the expert system, finishing learning if the accuracy of the result predicted and output by the expert system is not less than the set qualified rate, indicating that the training of the expert system is qualified, and increasing the total amount of the learning sample to continue training the expert system if the accuracy of the result predicted and output by the expert system is less than the set qualified rate.
The expert system is applied to the drill jumbo, the drill jumbo can perform trial drilling at any point on a tunnel face before each cycle of operation starts, the expert system can determine surrounding rock structure parameters and mechanical parameters of the current construction cycle according to the drilling parameters in the trial drilling process and forecast possible safety risks in advance, in addition, the expert system can also provide a suggested blasting scheme according to the current surrounding rock structure and mechanical parameters and in combination with tunnel section parameters and upper cycle overbreak parameters, and the scheme can be used as a construction scheme to guide the drill jumbo to perform drilling operation after on-line confirmation of a designer. After blasting work is finished, the expert system can carry out targeted improvement to lower circulation blasting scheme according to the super undermining parameter, optimizes the blasting parameter, reduces the super undermining square amount, reduces construction cost, improves the efficiency of construction.
Optimizing blasting parameters, including:
when the over-under-excavation result shows that under-excavation exists in a certain blasting area in the current blasting section, the blasting effect at the position is poor, the hole distribution position of the blast holes at the current position needs to be shifted outwards, the loading amount of the blast holes is increased, the blasting capacity of the area is improved, and the under-excavation square amount is reduced; on the contrary, if the over-and-under excavation result shows that the over-excavation phenomenon exists in a certain area, the hole distribution positions of the blast holes in the area need to be correspondingly shifted inwards, the explosive loading of the blast holes in the area needs to be correspondingly reduced, the blasting capacity of the area is reduced, and the over-excavation square volume is reduced.
The types of the components of the rock drilling jumbo which can be adopted in the prior art are many, and those skilled in the art can select the appropriate type according to actual requirements, and the embodiment is not illustrated.
The neural network comprises an input layer, an output layer and a plurality of hidden layers, wherein the input layer is composed of input parameters, the output layer refers to a calculation result output through a neural network algorithm, the hidden layers are determined through the neural network, the relation between the input layer and the output layer is determined, the output layer result can be obtained after the input layer parameters are calculated through the hidden layers, the hidden layers are influenced by the training degree, theoretically, the richer the training set, the more the training times, the more sensitive the hidden layers are, and the more reliable the output layer result is.
The acquisition terminal 11-1 comprises a pressure sensor, an oil flow sensor, an oil pressure sensor and a torque sensor which are arranged on the drill jumbo, and a CAN bus 3 and other devices connected with the sensors, the sensors and the devices CAN acquire drilling parameters such as drilling pressure, torque, speed and depth of the drill jumbo in the drilling process and upload the drilling parameters to the device data memory 12-1 and the data analysis terminal 13, and the data memory 12-1 stores the data; the three-dimensional scanner 11-2 can scan the blasting result after blasting is finished to obtain a scanning result, and the scanning result can be processed to obtain the tunnel overbreak and underbreak parameters.
The equipment data storage 12-1 is arranged in the trolley cab 2, can receive and store drilling parameters and over-undermining parameters acquired in the drilling process of the equipment, and in addition, the equipment data storage 12-1 further has networking uploading and downloading functions, can upload the acquired drilling parameters to the cloud total data storage 12-2, and can also regularly download data updating packages from the cloud total data storage 12-2 to update a database of an expert system. The cloud total data memory 12-2 is an expert system total data memory and can receive data collected by the drill jumbo in a networked manner, drilling parameters and an overbreak scanning result are stored and uploaded to the cloud, an expert system database is enriched, and an expert system algorithm is optimized; the expert system can receive data transmitted from the cloud server periodically, and complete system algorithm upgrading and optimization. The continuously updated data can enrich the expert system database and improve the judgment precision of the expert system, in addition, the cloud total data memory 12-2 can also regularly pack and share newly-added data to the data memory 12-1 of each rock drilling trolley device, enrich the device memory database and improve the judgment precision of the expert system on each rock drilling trolley.
The data analysis terminal 13 is an equipment host machine provided with an expert system, when the equipment data storage device uploads input parameters to the expert system, the expert system judges the current surrounding rock condition according to the data and automatically matches a corresponding blasting scheme according to a judgment result, the data analysis terminal has a networking uploading function, and the blasting scheme can be uploaded to a result confirmation terminal 14 of a designer through a network.
The result confirmation end 14 is an equipment host for receiving the output result of the expert system, a designer can receive the current tunnel surrounding rock data and the blasting scheme matched with the current tunnel surrounding rock data through the host, if the designer confirms that the current tunnel surrounding rock data are correct, the construction instruction is sent to the execution terminal 15, if the designer confirms that the parameters are wrong, the output result can be rejected to be analyzed again or directly modified and then sent to the execution terminal 15, and the execution terminal 15 is a drilling trolley.
Further, if the networking condition is not provided in the tunnel, a designer can enter the tunnel along with the drill jumbo and directly check and confirm the explosion scheme at the data analysis terminal 13.
The execution terminal 15 is a three-arm drilling jumbo provided with the system, and the execution terminal 15 can display a blast hole distribution position scheme output by the expert system to guide an operator to complete drilling construction.
Furthermore, the execution end can be divided into a semi-computer version and a full-computer version, and the semi-computer version trolley is provided with a guide system which can guide an operator to complete drilling according to a hole distribution scheme; and the full-computer version can automatically complete the drilling operation according to the hole distribution scheme output by the expert system.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (12)
1. The method for automatically matching and optimizing the blasting parameters based on the drilling parameters and the overbreak and undermining results is characterized by comprising the following steps of:
scanning the section of the tunnel after the upper-cycle blasting construction to obtain an overbreak parameter;
collecting drilling parameters of the drill jumbo;
and inputting the tunnel section parameters, the drilling parameters and the blasting parameters into an expert system to obtain blasting parameters.
2. The method for automatically matching optimized blasting parameters based on drilling parameters and overbreak results according to claim 1,
the expert system is trained by the following steps:
acquiring a training set, wherein the training set comprises tunnel section parameters, drilling parameters and upper circulation overbreak and underbreak parameters;
inputting the tunnel section parameter, the drilling parameter and the upper-cycle overbreak and underbreak parameter into an expert system, and constructing a mapping relation between the tunnel section parameter, the drilling parameter and the upper-cycle overbreak parameter and a blasting method, a blast hole distribution position and a loading amount;
and when the expert system loss function of the training set is converged to the range of [0,1e-7], the training is finished, and the obtained expert system is output.
3. The method for automatically matching optimized blasting parameters based on drilling parameters and overbreak results according to claim 2,
the expert system loss function is:
in the formula, x i Inputting parameters for the expert system, wherein the input parameters comprise tunnel section parameters, drilling parameters, surrounding rock condition parameters and surrounding rock mechanical parameters, f (x) i ) Predicting the result for the expert system, y i For the actual result corresponding to the input parameter,zand the error between the prediction result and the actual result of the expert system is defined, and the Loss (z) is the Loss function result of the expert system.
4. The method for automatically matching optimized blasting parameters based on drilling parameters and overbreak results according to claim 2,
the tunnel section parameters comprise a tunnel section design shape and a tunnel section design size Se;
the drilling parameters include drilling pressure Pd, percussion pressure Ps, whirl torque Tc, drilling velocity Vp, and drilling depth H.
5. The method for automatically matching optimized blasting parameters based on drilling parameters and overbreak results according to claim 2,
the surrounding rock condition parameters comprise surrounding rock types, surrounding rock stability, fault conditions and hydrological conditions;
the mechanical parameters of the surrounding rock comprise the type Rt of the surrounding rock, the stability S of the surrounding rock, the hardness Hr of the surrounding rock, the fault condition Fa and the hydrological condition Hg.
6. The method for automatically matching optimized blasting parameters based on drilling parameters and overbreak results according to claim 2,
the blasting method comprises a blast hole method, deep hole blasting, differential blasting, smooth blasting and presplitting blasting.
7. The method for automatically matching optimized blasting parameters based on drilling parameters and overbreak results according to claim 2,
the neural network comprises an input layer, an output layer and a plurality of hidden layers, wherein the input layer, the hidden layers and the output layer are sequentially connected.
8. The method for automatically matching optimized blasting parameters based on drilling parameters and overbreak results according to claim 2,
an expert system obtained by test training, comprising the steps of:
acquiring a test set;
inputting the test set into an expert system obtained by training to obtain a prediction test result;
comparing the predicted test result with the actual blasting method, the blast hole distribution position and the loading amount corresponding to the test set, if the accuracy of the predicted test result is not less than the set qualified rate, indicating that the expert system is qualified for training, and ending the training; and if the accuracy of the prediction test result is lower than the set qualification rate, increasing the total amount of the training set and continuing to train the expert system.
9. The method for automatically matching optimized blasting parameters based on drilling parameters and overbreak results according to claim 1,
optimizing the obtained blasting parameters, comprising the following steps:
if the upper-cycle overbreak and underbreak parameters show that an underbreak condition exists in an explosion area in an explosion section of the tunnel, the explosion effect of the explosion area is poor, the blast hole distribution position of the explosion area is shifted outwards, and the explosive loading of the blast hole distribution position is increased; and if the overbreak and underbreak scanning result shows that the overbreak phenomenon exists in the blasting area, shifting the blast hole distribution position of the blasting area to the inner side and reducing the explosive loading of the blast hole.
10. The method for automatically matching optimized blasting parameters based on drilling parameters and overbreak results according to claim 1,
and storing the drilling parameters and the overbreak and underbreak scanning results and uploading the results to the cloud.
11. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1 to 10 are implemented when the processor executes the program.
12. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 10.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115900464A (en) * | 2022-12-10 | 2023-04-04 | 深圳市中金岭南有色金属股份有限公司凡口铅锌矿 | Blasting method and device for to-be-blasted area, terminal equipment and storage medium |
| CN117592317A (en) * | 2024-01-19 | 2024-02-23 | 中国铁道科学研究院集团有限公司铁道建筑研究所 | Tunnel wedge-shaped cut blasting design method based on multiple geological information |
| CN118278275A (en) * | 2024-04-01 | 2024-07-02 | 北京交通大学 | Tunnel blasting parameter acquisition method based on artificial intelligence and simulation |
-
2022
- 2022-04-02 CN CN202210342395.8A patent/CN115238564A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115900464A (en) * | 2022-12-10 | 2023-04-04 | 深圳市中金岭南有色金属股份有限公司凡口铅锌矿 | Blasting method and device for to-be-blasted area, terminal equipment and storage medium |
| CN117592317A (en) * | 2024-01-19 | 2024-02-23 | 中国铁道科学研究院集团有限公司铁道建筑研究所 | Tunnel wedge-shaped cut blasting design method based on multiple geological information |
| CN117592317B (en) * | 2024-01-19 | 2024-05-10 | 中国铁道科学研究院集团有限公司铁道建筑研究所 | Tunnel wedge-shaped cut blasting design method based on multiple geological information |
| CN118278275A (en) * | 2024-04-01 | 2024-07-02 | 北京交通大学 | Tunnel blasting parameter acquisition method based on artificial intelligence and simulation |
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