CN114722697A - Method and device for determining control parameters of heading machine based on machine learning - Google Patents
Method and device for determining control parameters of heading machine based on machine learning Download PDFInfo
- Publication number
- CN114722697A CN114722697A CN202210221954.XA CN202210221954A CN114722697A CN 114722697 A CN114722697 A CN 114722697A CN 202210221954 A CN202210221954 A CN 202210221954A CN 114722697 A CN114722697 A CN 114722697A
- Authority
- CN
- China
- Prior art keywords
- data
- machine
- control
- parameter
- determining
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 61
- 238000010801 machine learning Methods 0.000 title claims abstract description 27
- 238000004088 simulation Methods 0.000 claims abstract description 47
- 238000012549 training Methods 0.000 claims abstract description 34
- 230000005641 tunneling Effects 0.000 claims abstract description 26
- 238000013528 artificial neural network Methods 0.000 claims abstract description 24
- 238000013527 convolutional neural network Methods 0.000 claims abstract description 23
- 238000012545 processing Methods 0.000 claims abstract description 21
- 230000001174 ascending effect Effects 0.000 claims abstract description 18
- 238000011161 development Methods 0.000 claims abstract description 10
- 238000010276 construction Methods 0.000 claims abstract description 8
- 239000011435 rock Substances 0.000 claims description 34
- 238000009412 basement excavation Methods 0.000 claims description 21
- 230000008569 process Effects 0.000 claims description 15
- 238000005096 rolling process Methods 0.000 claims description 14
- 230000035515 penetration Effects 0.000 claims description 10
- 230000002159 abnormal effect Effects 0.000 claims description 6
- 238000001914 filtration Methods 0.000 claims description 6
- 238000013075 data extraction Methods 0.000 claims description 4
- 125000004122 cyclic group Chemical group 0.000 claims description 3
- 230000000630 rising effect Effects 0.000 claims description 3
- 230000008030 elimination Effects 0.000 claims description 2
- 238000003379 elimination reaction Methods 0.000 claims description 2
- 230000000694 effects Effects 0.000 abstract description 3
- 238000003860 storage Methods 0.000 description 9
- 238000010586 diagram Methods 0.000 description 6
- 230000002093 peripheral effect Effects 0.000 description 6
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 238000013473 artificial intelligence Methods 0.000 description 3
- 230000006978 adaptation Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000004590 computer program Methods 0.000 description 2
- 238000005520 cutting process Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 210000002569 neuron Anatomy 0.000 description 2
- 230000003252 repetitive effect Effects 0.000 description 2
- 238000013526 transfer learning Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000008014 freezing Effects 0.000 description 1
- 238000007710 freezing Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000005055 memory storage Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000009877 rendering Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Mathematical Physics (AREA)
- Computational Linguistics (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Data Mining & Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Informatics (AREA)
- Computer Hardware Design (AREA)
- Geometry (AREA)
- Feedback Control In General (AREA)
Abstract
The invention relates to the field of engineering construction of development machines, in particular to a development machine control parameter determination method and a development machine control parameter determination device based on machine learning; the method comprises the following steps: constructing a simulation system of the development machine; acquiring control parameter data to be simulated; inputting the control parameters to be simulated into a simulation system for circulation to obtain tunneling simulation data; processing the tunneling simulation data, and extracting data of an ascending section and a stable section of each cycle; determining a CNN neural network structure, and training the CNN neural network based on the input parameter characteristics and the output parameter characteristics to obtain a control instruction; inputting the control instruction as control parameter data to be simulated into a simulation system for circulation and training until control parameters meeting preset conditions are obtained; according to the invention, the problems of too few training samples and low model accuracy caused by using simulation data as training data are solved, and the technical effect of simply, efficiently and accurately optimizing the control parameters is achieved.
Description
Technical Field
The invention relates to the field of engineering construction of heading machines, in particular to a method and a device for determining control parameters of a heading machine based on machine learning.
Background
The construction of the heading machine has the advantages of safety, high efficiency, environmental protection and the like, and becomes a preferred construction method for excavation construction. Due to the fact that the geological adaptability of the tunneling machine is poor, operation and control of the tunneling machine in tunneling are greatly dependent on personal experience of a main driver, and the problems that in the tunneling process, state change of rock mass equipment is not sensed timely, decision is not scientific, the tunneling process is not economical and efficient and the like generally exist. When the control parameters are not properly set, serious engineering accidents can even be caused. Thrust and torque are the most important performance parameters of the tunnel boring machine and are also important factors limiting the boring of the tunnel boring machine. Predicting performance parameters of the tunnel boring machine under appropriate control parameters can provide important references for the operation of the primary driver.
At present, a great deal of research work is done by numerous scholars and experts on rock excavating performance evaluation and tunnel boring machine performance prediction models. The difference of the models and the equipment parameters of the tunnel boring machine in different engineering projects causes the problem that a single model is difficult to be commonly used in different projects. The transfer learning method can retrain the generated model on the small sample set data in a mode of freezing partial parameters, and can effectively avoid the over-fitting problem caused by training a new model on the small sample set.
And because the transfer learning method needs a large amount of training data and only depends on the data collected from the heading machine, the accuracy of the model is far from sufficient.
Disclosure of Invention
The embodiment of the application provides a method for determining the control parameters of a heading machine based on machine learning, which is used for determining the control parameters of the heading machine so as to solve the technical problem that the existing control system cannot be simply and accurately optimized in parameters. In order to achieve the above purpose, the embodiments of the present application adopt the following technical solutions:
in a first aspect, a method for determining control parameters of a heading machine based on machine learning includes the following steps: constructing a simulation system of the development machine; acquiring control parameter data to be simulated; inputting the control parameters to be simulated into a simulation system for circulation to obtain tunneling simulation data; processing the tunneling simulation data, and extracting data of an ascending section and a stable section of each cycle; determining an input parameter characteristic and an output parameter characteristic based on the ascending segment and the stable segment; determining a CNN neural network structure, and training the CNN neural network based on the input parameter characteristics and the output parameter characteristics to obtain a control instruction; and inputting the control instruction serving as control parameter data to be simulated into a simulation system for circulation and training until the control parameters meeting preset conditions are obtained.
With reference to the first aspect, a first implementation possibility is provided, where the tunneling simulation data includes at least one of system internal operating environment data and system external operating environment data.
With reference to the second implementation possibility, a third implementation possibility is proposed, in which the system internal operating environment data includes: at least one of the propelling speed, the cutter head rotating speed, the propelling speed preset value, the cutter head rotating speed preset value, the cutter head penetration degree, the single-cutter thrust, the single-cutter rolling force and the cutter head power; the system external operating environment data includes: at least one of a rock excavation index and a rock machinability index.
In combination with the first implementation possibility, a fourth implementation possibility is provided, and the processing of the tunneling simulation data includes: determining data of a cyclic ascending section and a stable section; removing abnormal values of the stable segment data; performing median filtering processing on the stable segment data; coding the stable segment data after the abnormal value elimination and the median filtering; normalized rise data and plateau data are obtained.
With reference to the third implementation possibility, a fifth implementation possibility is provided, where the training of the CNN neural network based on the input parameter features and the output parameter features to obtain a control instruction includes: generating a machine learning training set for the input parameter features and the output parameter features; and taking the machine learning training set as training data to train the CNN neural network.
And combining the fourth realization possibility to provide a sixth realization possibility, wherein the input and output characteristic parameters comprise a propulsion speed of the ascending section, a cutter disc rotating speed, a propulsion speed preset value, a cutter disc rotating speed preset value, a cutter disc penetration degree, a single-cutter thrust force, a single-cutter rolling force, a rock excavation index, a rock machinability index, a propulsion speed average value of the stable section, a cutter disc rotating speed average value, a single-cutter thrust average value, a single-cutter rolling force average value, a rock excavation index average value and a rock machinability index average value.
And in combination with the fifth implementation possibility, a seventh implementation possibility is provided, and the machine method is to establish a full connection layer I and a full connection layer II on the basis of the CNN neural network, and splice the full connection layer I and the full connection layer II to obtain a full connection layer III.
And combining the sixth realization possibility to provide an eighth realization possibility, wherein the input of the full connection layer I is the propulsion speed, the cutter head rotating speed, the propulsion speed preset value, the cutter head rotating speed preset value, the cutter head penetration degree, the single-cutter thrust, the single-cutter rolling power, the rock excavation index and the rock machinability index of the ascending section.
Combining the seventh realization possibility, and proposing a ninth realization possibility, wherein the input of the full connection layer II is the average value of the rock excavation performance index, the average value of the rock machinability index, the average value of the propulsion speed and the average value of the cutter head rotating speed of the current excavation cycle stable section of the previous excavation cycle stable section; and the output of the CNN neural network is the average value of the single-blade thrust and the average value of the single-blade rolling force of the current tunneling cycle.
In a second aspect, an embodiment of the present application provides a heading machine control parameter determining device based on machine learning, including:
the construction module is used for constructing a simulation system of the development machine; the control parameter acquisition module to be simulated is used for acquiring control parameter data to be simulated; the circulation module is used for inputting the control parameters to be simulated into the simulation system for circulation to obtain tunneling simulation data; the data extraction module is used for processing the tunneling simulation data and extracting data of an ascending section and a stable section of each cycle; a parameter feature determination module for determining an input parameter feature and an output parameter feature based on the ascending segment and the stable segment; the control instruction determining module is used for determining a CNN neural network structure, training the CNN neural network based on the input parameter characteristics and the output parameter characteristics, and obtaining a control instruction; and the training module is used for inputting the control instruction as control parameter data to be simulated into the simulation system for circulation and training until the control parameter meeting the preset condition is obtained.
According to an aspect of the embodiments of the present application, there is provided a computer device, including a processor and a memory, where at least one instruction, at least one program, a code set, or a set of instructions is stored in the memory, and the at least one instruction, the at least one program, the code set, or the set of instructions is loaded and executed by the processor to implement the above-mentioned repetitive information reminding method.
According to an aspect of the embodiments of the present application, there is provided a computer-readable storage medium having at least one instruction, at least one program, a code set, or a set of instructions stored therein, the at least one instruction, the at least one program, the code set, or the set of instructions being loaded and executed by a processor to implement the above-mentioned repetitive information reminding method.
According to an aspect of embodiments herein, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instruction from the computer readable storage medium, and executes the computer instruction, so that the computer device executes the repeated information reminding method.
The method and the device for determining the control parameters of the heading machine based on machine learning solve the problems that the labor cost is too high due to the fact that parameter adjustment needs to be manually carried out in the prior art, training samples are too few due to the fact that actual operation data serve as training data, and the model accuracy is low, and achieve the technical effect of simply, efficiently and accurately optimizing the control parameters.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
The methods, systems, and/or processes of the figures are further described in accordance with the exemplary embodiments. These exemplary embodiments will be described in detail with reference to the drawings. These exemplary embodiments are non-limiting exemplary embodiments in which example numerals represent similar mechanisms throughout the various views of the drawings.
Figure 1 is a block diagram of a machine learning based ripper control parameter determination system provided in accordance with an embodiment of the present application;
FIG. 2 is a flow chart of a method of machine learning based determination of heading machine control parameters provided according to an embodiment of the present application;
figure 3 is a block diagram of a machine learning based ripper control parameter determination apparatus provided in accordance with an embodiment of the present application;
FIG. 4 is a block diagram of a computer device provided in accordance with an embodiment of the present application.
Detailed Description
In order to better understand the technical solutions, the technical solutions of the present application are described in detail below with reference to the drawings and specific embodiments, and it should be understood that the specific features in the embodiments and examples of the present application are detailed descriptions of the technical solutions of the present application, and are not limitations of the technical solutions of the present application, and the technical features in the embodiments and examples of the present application may be combined with each other without conflict.
In the following detailed description, numerous specific details are set forth by way of examples in order to provide a thorough understanding of the relevant guidance. It will be apparent, however, to one skilled in the art that the present application may be practiced without these specific details. In other instances, well-known methods, procedures, systems, components, and/or circuits have been described at a relatively high-level, without detail, in order to avoid unnecessarily obscuring aspects of the present application.
Flowcharts are used herein to illustrate the implementations performed by systems according to embodiments of the present application. It should be expressly understood that the processes performed by the flowcharts may be performed out of order. Rather, these implementations may be performed in the reverse order or simultaneously. In addition, at least one other implementation may be added to the flowchart. One or more implementations may be deleted from the flowchart.
Referring to fig. 1, a block diagram of a machine learning-based heading machine control parameter determination system according to an embodiment of the present application is shown. The heading machine control parameter determination system includes at least one computer device, such as a terminal 110, a network 120, and a server 130.
The terminal 110 may be a terminal in which a heading machine control parameter determination method exists.
In this embodiment, the terminal 110 is preferably arranged in a control center of the heading machine, that is, the method for determining heading machine control parameters provided in this embodiment is preferably configured on the terminal 110 in the control center of the heading machine.
The server 130 is a background server for providing a configuration file of the heading machine control parameter determination method. The server 130 may be an independent physical server, may also be a server cluster or a distributed system formed by a plurality of physical servers, and may also be a cloud server (cloud computing service center) that provides basic cloud computing services such as cloud service, a cloud database, cloud computing, a cloud function, cloud storage, web service, cloud communication, middleware service, domain name service, security service, CDN (Content Delivery Network), and a big data and artificial intelligence platform. The terminal 110 may be connected to the server 130 through the network 120. The network 120 may be a wired network or a wireless network. The server 130 is configured to provide a background service for the terminal 110, for example, the server 130 may transmit a configuration file of the to-be-repeated-information reminder to the terminal 110, and the terminal 110 completes the method for determining the heading machine control parameter.
Optionally, the server 130 provides background services for a plurality of terminals 110 at the same time. The terminal 110 and the server 130 may be directly or indirectly connected through wired or wireless communication, and the application is not limited herein.
Referring to fig. 2, a flow chart of a method provided by an embodiment of the present application is shown. The execution subject of the method may be the terminal 110 in fig. 1, and the method may include the following steps:
and step 210, constructing a development machine simulation system.
And simulating the working process of the heading machine on the basis of the working process of the heading machine on the simulation software, so as to determine the parameters of the heading machine in the simulation process.
And step 220, acquiring control parameter data to be simulated.
In this embodiment, the data of the control parameters to be simulated are also the main control parameters in the working process of the heading machine, and the acquisition of the data can be determined based on the main process and state of daily work.
And 230, inputting the control parameters to be simulated into the simulation system for circulation to obtain tunneling simulation data.
In this embodiment, the tunneling simulation data includes at least one of system internal operating environment data and system external operating environment data.
In this embodiment, the system internal operating environment data includes: the propelling speed, the cutter head rotating speed, the propelling speed preset value, the cutter head rotating speed preset value, the cutter head penetration degree, the single-cutter thrust, the single-cutter rolling force and the cutter head power.
The system external operating environment data includes: at least one of a rock excavation index and a rock machinability index.
And 240, processing the tunneling simulation data, and extracting data of the ascending section and the stable section of each cycle.
The method specifically comprises the following steps: processing the tunneling simulation data, wherein the processing process comprises the following steps;
and 241, determining data of a cyclic ascending section and a stable section.
In the present embodiment, the extraction of the rising and stationary sections is mainly based on the movement state of the heading machine. The method specifically comprises the following steps: and determining the data of the circular ascending section and the stable section based on whether the heading machine is in a heading state and whether the cutter head is in a stable heading state after contacting with the tunnel face.
And 242, removing abnormal values of the stable segment data.
And removing the data which obviously exceeds the conventional working state of the heading machine, wherein the removing mode comprises the step of setting a data threshold value, and removing the abnormal data when the data is larger than or smaller than the threshold value.
And 243, performing median filtering processing on the stable section data.
In this embodiment, the data of the stationary segment is subjected to median filtering processing to filter out part of the noise in the stationary segment.
And 244, encoding the processed stable segment data.
Step 245, obtaining the normalized rise data and the stabilized section data.
In this embodiment, the input and output characteristic parameters include ten parameters of the thrust speed v, the cutter rotational speed n, the thrust speed preset value, the cutter rotational speed preset value, the cutter penetration P, the single-cutter thrust Fn, the single-cutter rolling force Fr, the rock excavation index, the rock machinability index, the cutter power of the ascending section within 60s from the moment when the cutter contacts the face, and seven parameters of the thrust speed mean value, the cutter rotational speed mean value, the single-cutter thrust mean value, the single-cutter rolling force mean value, the rock excavation index mean value, the rock machinability index mean value, and the cutter power mean value of the stable section.
The calculation formula of the single-blade thrust Fn is as follows:
in this embodiment, FnFor single-blade thrust, F denotes total thrust, FfTo the development machine during movementN represents the number of cutterheads rolled.
Wherein, the single-blade rolling force FrThe calculation formula of (2) is as follows:
in this embodiment, FrIs single-cutter rolling force, T is cutter torque, TsThe idle torque of the rotation of the cutter head is R, and the diameter of the cutter head is R.
The formula for calculating the rock tunneling index FPI is as follows:
in this embodiment, P represents the knife penetration.
In this embodiment, the rock machinability index TPI is calculated by the formula:
and 260, determining the structure of the CNN neural network, and training the CNN neural network based on the input parameter characteristics and the output parameter characteristics to obtain a control instruction.
In this embodiment, the CNN neural network includes a full link layer I and a full link layer II, and the full link layer I and the full link layer II are spliced to obtain a full link layer iii. The method includes that neurons of a full connection layer I and neurons of a full connection layer II are combined into a full connection layer in a matrix form, then the combined full connection layer is connected to a full connection layer III, and the combination method can adopt a matrix addition mode in practical application.
The input of the full connecting layer I is propulsion speed, cutter head rotating speed, propulsion speed preset value, cutter head rotating speed preset value, cutter head penetration P, single-cutter thrust Fn, single-cutter rolling force Fr, cutter head power, rock cutting performance index FPI and rock cutting performance index TPI in the ascending section 30 s.
In this embodiment, the input of the full connection layer II is the average value of the rock excavation performance index of the stable section of the previous excavation cycle, the average value of the rock machinability index, the average value of the propulsion speed, the cutter head rotation speed, and the cutter head power of the stable section of the current excavation cycle, and the output of the excavation performance parameter prediction model is the average value of the single-blade thrust and the average value of the single-blade rolling force of the current excavation cycle.
In this embodiment, the input of the full-connection layer II is the average value of the rock excavation performance index of the stable section of the previous excavation cycle and the average value of the rock machinability index, and the output of the control parameter prediction model is the average value of the propulsion speed and the average value of the cutter head rotation speed of the current excavation cycle.
And 270, inputting the control instruction serving as control parameter data to be simulated into the simulation system for circulation and training until the control parameters meeting the preset conditions are obtained.
In this embodiment, based on the control instruction processed by the neural network, the processed control instruction is used as simulation data to perform simulation, so as to repeat simulation and training, thereby obtaining the control parameters meeting the requirements.
The problem that the current human cost that parameter adjustment leads to needs to be carried out by the manual work is too high is solved through above-mentioned mode to this embodiment to and, training sample that leads to as training data through actual operation data is too few, and the problem that the model degree of accuracy is low has reached simple high efficiency and has carried out the technological effect of accurate control parameter optimization.
Referring to fig. 3, a block diagram of an apparatus 300 provided in one embodiment of the present application is shown. The device has the function of realizing the method. The apparatus 300 comprises: the simulation system comprises a construction module 310, a to-be-simulated control parameter acquisition module 320, a circulation module 330, a data extraction module 340, a parameter characteristic determination module 350, a control instruction determination module 360 and a training module 370.
In this embodiment, the building module 310 is configured to build a simulation system of the heading machine.
And a to-be-simulated control parameter obtaining module 320, configured to obtain data of a to-be-simulated control parameter.
And the circulation module 330 is configured to input the control parameters to be simulated into the simulation system for circulation to obtain tunneling simulation data.
And the data extraction module 340 is configured to process the tunneling simulation data and extract data of an ascending section and a stable section of each cycle.
A parameter feature determination module 350 for determining an input parameter feature and an output parameter feature based on the rising segment and the stable segment.
And a control instruction determining module 360, configured to determine a CNN neural network structure, train the CNN neural network based on the input parameter characteristics and the output parameter characteristics, and obtain a control instruction.
And the training module 370 is configured to input the control instruction as control parameter data to be simulated into the simulation system for circulation and training until a control parameter meeting a preset condition is obtained.
Referring to fig. 4, a block diagram of a computer device according to an embodiment of the present application is shown. The computer device may be an electronic device such as a multimedia playing device, a mobile phone, a tablet computer, a game console, and the like. The computer device is used for implementing the interface display method provided in the above embodiment. Specifically, the method comprises the following steps:
generally, a computer device includes: a processor and a memory.
The processor may include one or more processing cores, such as a 4-core processor, an 8-core processor, and so forth. The processor may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (field Programmable Gate Array), PLA (Programmable Logic Array). The processor may also include a main processor and a coprocessor, where the main processor is a processor for processing data in an awake state, and is also called a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state. In some embodiments, the processor may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content that the display screen needs to display. In some embodiments, the processor may further include an AI (Artificial Intelligence) processor for processing computing operations related to machine learning.
The memory may include one or more computer-readable storage media, which may be non-transitory. The memory may also include high speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In some embodiments, a non-transitory computer readable storage medium in memory is used to store at least one instruction, at least one program, set of codes, or set of instructions configured to be executed by one or more processors to implement the above-described method.
In some embodiments, the computer device may further optionally include: a peripheral interface and at least one peripheral. The processor, memory and peripheral interface may be connected by bus or signal lines. Each peripheral may be connected to the peripheral interface via a bus, signal line, or circuit board. Specifically, the peripheral device includes: at least one of a radio frequency circuit, a display screen, a camera assembly, an audio circuit, a positioning assembly, and a power source.
Those skilled in the art will appreciate that the configuration shown in FIG. 4 is not intended to be limiting of computer devices and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components may be used.
In an exemplary embodiment, a computer readable storage medium is also provided, having stored therein at least one instruction, at least one program, set of codes or set of instructions, which when executed by a processor, implements the above method.
Optionally, the computer-readable storage medium may include: ROM (Read Only Memory), RAM (Random Access Memory), SSD (Solid State drive), or optical disc. The Random Access Memory may include a ReRAM (resistive Random Access Memory) and a DRAM (Dynamic Random Access Memory).
The above disclosure of the embodiments of the present application will be apparent to those skilled in the art from the above disclosure. It should be understood that the process of deriving and analyzing technical terms, which are not explained, by those skilled in the art based on the above disclosure is based on the contents described in the present application, and thus the above contents are not an inventive judgment of the overall scheme.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered merely illustrative and not restrictive of the broad application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present application and thus fall within the spirit and scope of the exemplary embodiments of the present application.
Also, this application uses specific terminology to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various portions of this specification are not necessarily all referring to the same embodiment. Furthermore, certain features, structures, or characteristics may be combined as suitable in at least one embodiment of the application.
In addition, those skilled in the art will recognize that the various aspects of the application may be illustrated and described in terms of several patentable species or contexts, including any new and useful combination of procedures, machines, articles, or materials, or any new and useful modifications thereof. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as a "unit", "component", or "system". Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in at least one computer readable medium.
Additionally, the order of the process elements and sequences described herein, the use of numerical letters, or other designations are not intended to limit the order of the processes and methods unless otherwise indicated in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it should be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware means, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
It should also be appreciated that in the foregoing description of embodiments of the present application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of at least one embodiment of the invention. However, this method of disclosure is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
Claims (10)
1. A method for determining control parameters of a heading machine based on machine learning is characterized by comprising the following processes:
constructing a simulation system of the development machine;
acquiring control parameter data to be simulated;
inputting the control parameters to be simulated into a simulation system for circulation to obtain tunneling simulation data;
processing the tunneling simulation data, and extracting data of an ascending section and a stable section of each cycle;
determining an input parameter characteristic and an output parameter characteristic based on the rising segment and the stable segment;
determining a CNN neural network structure, and training the CNN neural network based on the input parameter characteristics and the output parameter characteristics to obtain a control instruction;
and inputting the control instruction serving as control parameter data to be simulated into a simulation system for circulation and training until the control parameters meeting preset conditions are obtained.
2. The machine-learning based heading machine control parameter determination method of claim 1, wherein the heading simulation data includes at least one of system internal operating environment data and system external operating environment data.
3. The machine-learning based heading machine control parameter determination method of claim 2, wherein the system internal operating environment data comprises: at least one of the propelling speed, the cutter head rotating speed, the propelling speed preset value, the cutter head rotating speed preset value, the cutter head penetration degree, the single-cutter thrust, the single-cutter rolling force and the cutter head power; the system external operating environment data includes: at least one of a rock excavation ability index and a rock machinability index.
4. The machine-learning-based method of determining heading control parameters of claim 3, wherein processing heading simulation data comprises:
determining data of a cyclic ascending section and a stable section;
removing abnormal values of the stable segment data;
carrying out median filtering processing on the stable segment data;
coding the stable segment data after the abnormal value elimination and the median filtering;
normalized rise data and plateau data were obtained.
5. The machine-learning-based method for determining the control parameters of the heading machine according to claim 1, wherein the CNN neural network comprises a fully-connected layer I and a fully-connected layer II, and the fully-connected layer I and the fully-connected layer II are spliced to obtain a fully-connected layer iii.
6. The machine-learning-based method for determining control parameters of a heading machine according to claim 5, wherein training a CNN neural network based on the input parameter features and the output parameter features to obtain control commands comprises:
generating a machine learning training set for the input parameter features and the output parameter features; and taking the machine learning training set as training data to train the CNN neural network.
7. The machine-learning based method of determining control parameters of a heading machine of claim 6, wherein the input and output characteristic parameters include a thrust speed for up leg, a cutter head rotational speed, a thrust speed preset value, a cutter head rotational speed preset value, a cutter head penetration, a single blade thrust, a single blade roll force, a rock machinability index, and a thrust speed mean for steady leg, a cutter head rotational speed mean, a single blade thrust mean, a single blade roll force mean, a rock machinability index mean.
8. The machine-learning based heading machine control parameter determination method of claim 7, wherein the input to the fully-connected layer I is a thrust speed, a cutter head rotation speed, a thrust speed preset value, a cutter head rotation speed preset value, a cutter head penetration, a single cutter thrust, a single cutter roll power, a rock excavatability index, a rock machinability index of the up section.
9. The machine-learning-based method of determining control parameters of a heading machine according to claim 8, wherein the inputs to the fully-connected layer II are a mean value of rock excavation performance indicators, a mean value of rock machinability indicators, a mean value of propulsion speed and a mean value of cutterhead rotation speed of a stable segment of a previous heading cycle; and the output of the CNN neural network is the average value of the single-blade thrust and the average value of the single-blade rolling force of the current tunneling cycle.
10. A heading machine control parameter determination device based on machine learning, comprising:
the construction module is used for constructing a simulation system of the development machine;
the control parameter acquisition module to be simulated is used for acquiring control parameter data to be simulated;
the circulation module is used for inputting the control parameters to be simulated into the simulation system for circulation to obtain tunneling simulation data;
the data extraction module is used for processing the tunneling simulation data and extracting data of an ascending section and a stable section of each cycle;
a parameter feature determination module for determining an input parameter feature and an output parameter feature based on the ascending segment and the stable segment;
the control instruction determining module is used for determining a CNN neural network structure and training the CNN neural network based on the input parameter characteristics and the output parameter characteristics to obtain a control instruction;
and the training module is used for inputting the control instruction as control parameter data to be simulated into the simulation system for circulation and training until the control parameter meeting the preset condition is obtained.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210221954.XA CN114722697A (en) | 2022-03-09 | 2022-03-09 | Method and device for determining control parameters of heading machine based on machine learning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210221954.XA CN114722697A (en) | 2022-03-09 | 2022-03-09 | Method and device for determining control parameters of heading machine based on machine learning |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114722697A true CN114722697A (en) | 2022-07-08 |
Family
ID=82236750
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210221954.XA Pending CN114722697A (en) | 2022-03-09 | 2022-03-09 | Method and device for determining control parameters of heading machine based on machine learning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114722697A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115268272A (en) * | 2022-08-11 | 2022-11-01 | 北京交通大学 | TBM control parameter decision method and device based on tunneling load prediction |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103870677A (en) * | 2014-02-07 | 2014-06-18 | 上海交通大学 | Setting method for tunneling parameters of tunneling machine |
| CN109459941A (en) * | 2018-12-29 | 2019-03-12 | 中铁工程装备集团有限公司 | A kind of shield machine construction process three-dimensional artificial device and emulation mode |
| CN110852423A (en) * | 2019-11-12 | 2020-02-28 | 中铁工程装备集团有限公司 | Tunnel boring machine excavation performance and control parameter prediction method based on transfer learning |
| CN112163316A (en) * | 2020-08-31 | 2021-01-01 | 同济大学 | Tunneling parameter prediction method of hard rock tunnel boring machine based on deep learning |
| CN113104010A (en) * | 2021-05-07 | 2021-07-13 | 的卢技术有限公司 | Vehicle brake control method, system, computer device and storage medium |
| CN114019795A (en) * | 2021-10-15 | 2022-02-08 | 中铁高新工业股份有限公司 | Shield tunneling deviation rectifying intelligent decision-making method based on reinforcement learning |
-
2022
- 2022-03-09 CN CN202210221954.XA patent/CN114722697A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103870677A (en) * | 2014-02-07 | 2014-06-18 | 上海交通大学 | Setting method for tunneling parameters of tunneling machine |
| CN109459941A (en) * | 2018-12-29 | 2019-03-12 | 中铁工程装备集团有限公司 | A kind of shield machine construction process three-dimensional artificial device and emulation mode |
| CN110852423A (en) * | 2019-11-12 | 2020-02-28 | 中铁工程装备集团有限公司 | Tunnel boring machine excavation performance and control parameter prediction method based on transfer learning |
| CN112163316A (en) * | 2020-08-31 | 2021-01-01 | 同济大学 | Tunneling parameter prediction method of hard rock tunnel boring machine based on deep learning |
| CN113104010A (en) * | 2021-05-07 | 2021-07-13 | 的卢技术有限公司 | Vehicle brake control method, system, computer device and storage medium |
| CN114019795A (en) * | 2021-10-15 | 2022-02-08 | 中铁高新工业股份有限公司 | Shield tunneling deviation rectifying intelligent decision-making method based on reinforcement learning |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115268272A (en) * | 2022-08-11 | 2022-11-01 | 北京交通大学 | TBM control parameter decision method and device based on tunneling load prediction |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112649198B (en) | Intelligent fault diagnosis method, system and equipment for quasi-unbalanced rolling bearing and application | |
| CN110852423B (en) | Tunnel boring machine excavation performance and control parameter prediction method based on transfer learning | |
| CN110672323B (en) | Bearing health state assessment method and device based on neural network | |
| CN114722697A (en) | Method and device for determining control parameters of heading machine based on machine learning | |
| CN114282721B (en) | Pollutant forecast model training method and device, electronic equipment and storage medium | |
| CN112559378A (en) | Automatic driving algorithm evaluation method and device and scene library generation method and device | |
| CN114548280A (en) | Fault diagnosis model training method, fault diagnosis method and electronic equipment | |
| CN115272826A (en) | Image identification method, device and system based on convolutional neural network | |
| CN115641549B (en) | Health monitoring method and system for main propulsion diesel unit | |
| CN109902352B (en) | Extraction method and system for key characteristic quantity of transient power angle stability analysis | |
| CN117372944A (en) | Construction site bare soil monitoring method and terminal | |
| CN116962009A (en) | Network attack detection method and device | |
| CN113128568B (en) | Excavator activity recognition method, system, device and storage medium | |
| CN113052060A (en) | Bearing residual life prediction method and device based on data enhancement and electronic equipment | |
| JP2021176091A5 (en) | ||
| CN112231182A (en) | Internet of things equipment working condition data analysis method and device and computer equipment | |
| CN213024908U (en) | Industrial safety accident simulation device | |
| CN111830937A (en) | Vehicle fault identification model construction and identification method and device and management system | |
| CN109062629A (en) | A kind of performance optimization method of flight simulation software | |
| CN113628215B (en) | Image processing method, system, device and storage medium | |
| CN116703001B (en) | Oil consumption prediction method and system of intelligent ship, intelligent ship and medium | |
| CN117215754A (en) | Server response method, device, equipment and storage medium thereof | |
| CN113289346A (en) | Task model training method and device, electronic equipment and storage medium | |
| CN111582446B (en) | System for neural network pruning and neural network pruning processing method | |
| CN112380249B (en) | Coal face period pressure analysis method and device 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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20220708 |
|
| RJ01 | Rejection of invention patent application after publication |