CN114856604A - Tunneling machine control method, device, equipment and storage medium - Google Patents
Tunneling machine control method, device, equipment and storage medium Download PDFInfo
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- E21D9/00—Tunnels or galleries, with or without linings; Methods or apparatus for making thereof; Layout of tunnels or galleries
- E21D9/10—Making by using boring or cutting machines
- E21D9/108—Remote control specially adapted for machines for driving tunnels or galleries
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Abstract
The application discloses a tunneling machine control method, a tunneling machine control device, tunneling machine control equipment and a storage medium, which relate to the technical field of tunnel construction and comprise the following steps: acquiring monitoring data on a target sensor and a target intelligent instrument which are arranged in each subsystem of the tunneling machine through a lower controller to obtain a target energy efficiency monitoring parameter, and inputting the target energy efficiency monitoring parameter into a target machine learning model which is obtained after an initial parameter optimization model which is constructed based on a machine learning algorithm is trained on the upper controller by using historical engineering data so as to optimize the target energy efficiency monitoring parameter through the target machine learning model to obtain a target optimization parameter; and sending the target optimization parameters to a lower controller and forwarding the target optimization parameters to an execution mechanism so that the execution mechanism can adjust the working state of the heading machine based on the target optimization parameters. According to the method and the device, the control parameters of the heading machine can be adjusted in a self-adaptive mode according to the whole machine energy efficiency monitoring data and based on the model created by the machine learning algorithm, the energy consumption of the heading machine is reduced, and the energy efficiency is maximized.
Description
Technical Field
The application relates to the technical field of tunnel construction, in particular to a heading machine control method, a heading machine control device, heading machine control equipment and a storage medium.
Background
Currently, there is a significant increase in roadheader loader power over the previous in the roadheader manufacturing industry, for example, nearly doubling its loader power over earlier machines of the same diameter and type. This increase is mainly due to the higher torque, speed and thrust configurations of modern heading machines, and the significant increase in the number of auxiliary equipment installed on the rear equipment. However, such increased power or equipment is only needed in some cases, and in most cases only a small portion thereof is needed to meet operational requirements. This results in the power consumption of the roadheader being higher than actually required most of the time during the tunneling process. Therefore, how to effectively reduce the energy consumption of the high-power equipment and maximize the energy efficiency is crucial under the background of energy shortage and energy conservation and emission reduction at present.
At present, a heading machine control system is mainly adopted for solving the green energy-saving problem of the heading machine, and specifically, the actual value of a parameter of the control system is compared with a preset ideal value of the parameter, and then the actual value is adjusted in a feedback control mode. However, the above method has disadvantages in that: the optimal preset parameters of each device in the system need to be calculated firstly, but the optimal parameters are related to the load in the actual work, and the load is changed in real time, so that the calculation is difficult, and a fixed and unchangeable preset parameter cannot meet the energy-saving requirement of the whole project. In addition, the heading machine control system is complex and diverse and has strong coupling, the association of the optimal preset parameters and a plurality of variables is difficult to obtain through simple analysis, and the currently proposed solution does not explain how to effectively acquire the preset parameters.
In summary, how to maximize the energy efficiency of the heading machine on the premise of not affecting the construction effect is a problem to be further solved at present.
Disclosure of Invention
In view of the above, an object of the present application is to provide a method, an apparatus, a device and a storage medium for controlling a heading machine, which can reduce energy consumption of the heading machine and maximize energy efficiency of the heading machine without affecting construction effects. The specific scheme is as follows:
in a first aspect, the application discloses a heading machine control method, which includes:
monitoring the complete machine energy efficiency of the heading machine through a target sensor and a target intelligent instrument which are pre-installed in each subsystem of the heading machine, and acquiring monitoring data on the target sensor and the target intelligent instrument through a lower controller to obtain a target energy efficiency monitoring parameter;
inputting the target energy efficiency monitoring parameter into a target machine learning model obtained after training on an upper controller, so that the target energy efficiency monitoring parameter is optimized through the target machine learning model to obtain a target optimization parameter; the target machine learning model is obtained by training an initial parameter optimization model constructed based on a machine learning algorithm by using historical engineering data;
and sending the target optimization parameters to the lower controller, and forwarding the target optimization parameters to an executing mechanism through the lower controller so that the executing mechanism can adjust the working state of the heading machine based on the target optimization parameters.
Optionally, the acquiring, by the lower controller, the monitoring data on the target sensor and the target smart meter to obtain a target energy efficiency monitoring parameter includes:
and acquiring monitoring data on the target sensor and the target intelligent instrument through a lower controller to obtain initial energy efficiency monitoring parameters, and screening out key parameters in the operation process of each subsystem from the initial energy efficiency monitoring parameters to obtain target energy efficiency monitoring parameters.
Optionally, the monitoring of the complete machine energy efficiency of the heading machine is performed by target sensors and target intelligent instruments which are pre-installed in each subsystem of the heading machine, and monitoring data on the target sensors and the target intelligent instruments are collected by a lower controller to obtain target energy efficiency monitoring parameters, including:
monitoring the energy efficiency of a propelling system of a heading machine through a target sensor and an electric meter which are pre-installed in the propelling system of the heading machine, and acquiring monitoring data on the target sensor and the electric meter through a lower controller to obtain a target energy efficiency monitoring parameter; the target energy efficiency monitoring parameters comprise any of soil bin pressure, oil cylinder penetration, cutter head rotating speed, soil bin position, soil bin temperature, propelling speed and ammeter parameters.
Optionally, the acquiring, by the lower controller, the monitoring data on the target sensor and the electricity meter to obtain a target energy efficiency monitoring parameter further includes:
and processing the target energy efficiency monitoring parameter in real time through an automatic control program of the lower controller to obtain a first propulsion speed, and sending the first propulsion speed to a propulsion oil cylinder of the propulsion system so that the propulsion oil cylinder can propel the tunneling machine according to the first propulsion speed.
Optionally, the obtaining process of the target machine learning model includes:
acquiring historical geological exploration data including a tunnel face stratum type, development machine manufacturing experience data and historical engineering project parameters, and dividing the historical geological exploration data, the development machine manufacturing experience data and the historical engineering project parameters into a target training set and a target testing set;
inputting the target training set into the initial parameter optimization model constructed based on a machine learning algorithm, training the initial parameter optimization model by using a reinforcement learning algorithm, and verifying the accuracy of the trained initial parameter optimization model by using the target test set to obtain the target machine learning model.
Optionally, the optimizing the target energy efficiency monitoring parameter through the target machine learning model to obtain a target optimization parameter includes:
optimizing the propulsion speed in the target energy efficiency monitoring parameters through the target machine learning model to obtain a second propulsion speed;
comparing the second propulsion speed with the propulsion speed in the target test set to obtain a comparison result, and judging whether the comparison result meets a preset difference threshold condition;
and if the comparison result meets the preset difference threshold condition, taking the second propulsion speed as a target optimization parameter.
Optionally, the method for controlling a heading machine further includes:
acquiring the historical engineering data of the development machine, and dividing the historical engineering data into a training set and a testing set to obtain a training sample set and a testing sample set;
comparing the target optimization parameter with the test sample set to obtain a first comparison error;
repeatedly inputting the target energy efficiency monitoring parameters into the target machine learning model to obtain new optimization parameters, and comparing the new optimization parameters with the test sample set to obtain a second comparison error;
and subtracting the first comparison error from the second comparison error, judging whether the difference value is a negative value, optimizing the target machine learning model by using the target energy efficiency monitoring parameter as a training set if the difference value is the negative value, adjusting the working state of the heading machine by using the new optimization parameter, and deleting the new optimization parameter if the difference value is the positive value.
In a second aspect, the present application discloses a heading machine control apparatus comprising:
the data acquisition module is used for monitoring the complete machine energy efficiency of the heading machine through a target sensor and a target intelligent instrument which are pre-installed in each subsystem of the heading machine, and acquiring monitoring data on the target sensor and the target intelligent instrument through a lower controller so as to obtain a target energy efficiency monitoring parameter;
the parameter optimization module is used for inputting the target energy efficiency monitoring parameters into a target machine learning model obtained after training on an upper controller so as to optimize the target energy efficiency monitoring parameters through the target machine learning model to obtain target optimization parameters; the target machine learning model is obtained by training an initial parameter optimization model constructed based on a machine learning algorithm by using historical engineering data;
and the parameter adjusting module is used for sending the target optimization parameters to the lower controller and forwarding the target optimization parameters to the executing mechanism through the lower controller so that the executing mechanism can adjust the working state of the heading machine based on the target optimization parameters.
In a third aspect, the present application discloses an electronic device comprising a processor and a memory; the processor implements the heading machine control method when executing the computer program stored in the memory.
In a fourth aspect, the present application discloses a computer readable storage medium for storing a computer program; wherein the computer program when executed by a processor implements the aforementioned ripper control method.
Therefore, the whole machine energy efficiency of the heading machine is monitored by the target sensor and the target intelligent instrument which are pre-installed in each subsystem of the heading machine, and the lower controller collects the monitoring data on the target sensor and the target intelligent instrument to obtain a target energy efficiency monitoring parameter, then the target energy efficiency monitoring parameters are input into a target machine learning model obtained by training an initial parameter optimization model constructed based on a machine learning algorithm by using historical engineering data on an upper controller, so as to optimize the target energy efficiency monitoring parameters through the target machine learning model to obtain target optimization parameters, then send the target optimization parameters to the lower controller, and forward the target optimization parameters to the executing mechanism through the lower controller, so that the actuating mechanism adjusts the working state of the heading machine based on the target optimization parameters. Therefore, according to the model created based on the machine learning algorithm and the complete machine energy efficiency monitoring data, the control parameters of the heading machine can be adjusted in a self-adaptive mode on the premise that the construction effect is not influenced, the energy consumption of the heading machine is reduced, the heading machine can be enabled to be efficient to the maximum, the heading machine is enabled to be green and energy-saving in the whole heading process, and meanwhile the construction cost is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a flowchart of a tunneling machine control method disclosed in the present application;
fig. 2 is a schematic diagram of a specific hardware architecture of a heading machine control system disclosed in the present application;
FIG. 3 is a flow chart of a specific development machine control method disclosed herein;
fig. 4 is a schematic structural diagram of a heading machine control device disclosed in the present application;
fig. 5 is a block diagram of an electronic device disclosed in the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The embodiment of the application discloses a heading machine control method, and as shown in fig. 1, the method comprises the following steps:
step S11: the method comprises the steps of monitoring the whole machine energy efficiency of the heading machine through a target sensor and a target intelligent instrument which are installed in each subsystem of the heading machine in advance, and acquiring monitoring data on the target sensor and the target intelligent instrument through a lower controller to obtain target energy efficiency monitoring parameters.
In the embodiment, referring to fig. 2, in terms of hardware design of the heading machine, the whole heading machine control system adopts an upper and lower two-stage control structure, including an upper controller 1 and a lower controller 2; the upper Controller 1 is a non-real-time Controller with high computational power, such as an industrial computer, and the lower Controller 2 is a real-time Controller, such as a Programmable Logic Controller (PLC). And various types of sensors 3 and intelligent instruments 4 are pre-installed in each subsystem of the heading machine and are used for monitoring the complete machine energy efficiency of the heading machine. In the operation process of the heading machine, the data monitored by the various types of sensors 3 and the intelligent instrument 4 can be collected by the lower controller 2 to obtain target energy efficiency monitoring parameters. It should be noted that the various types of sensors 3 and the intelligent instrument 4 should be selected according to specific equipment and application scenarios, for example, a soil bin temperature sensor, a position sensor, a speed sensor, a pressure sensor, an electric meter and the like can be selected for a propulsion system with high energy consumption in the heading machine; the various types of sensors 3 include, but are not limited to, pressure sensors, position sensors, energy consumption sensors, speed sensors, and the like. The upper controller 1 and the lower controller 2 can transmit data through a Transmission Control Protocol/Internet Protocol (TCP/IP), the lower controller 2 is connected with the executing mechanism 5 to be used for issuing an executing task, and the lower controller 2 and the sensors 3 with various types can communicate with the intelligent instrument 4 through a Modbus TCP communication Protocol.
In this embodiment, the acquiring, by the lower controller, the monitoring data on the target sensor and the target smart meter to obtain a target energy efficiency monitoring parameter may specifically include: and acquiring monitoring data on the target sensor and the target intelligent instrument through a lower controller to obtain initial energy efficiency monitoring parameters, and screening out key parameters in the operation process of each subsystem from the initial energy efficiency monitoring parameters to obtain target energy efficiency monitoring parameters. Specifically, monitoring data of a target sensor and a target intelligent instrument which are pre-installed in each subsystem of the heading machine can be collected through a lower controller, so that a corresponding initial energy efficiency monitoring parameter is obtained, further, the initial energy efficiency monitoring parameter can be screened in a targeted manner in order to accelerate the processing efficiency, and for example, a key parameter of each subsystem in the operation process is screened from the initial energy efficiency monitoring parameter, so that a target energy efficiency monitoring parameter is obtained.
Step S12: inputting the target energy efficiency monitoring parameter into a target machine learning model obtained after training on an upper controller, so that the target energy efficiency monitoring parameter is optimized through the target machine learning model to obtain a target optimization parameter; the target machine learning model is obtained by training an initial parameter optimization model constructed based on a machine learning algorithm by using historical engineering data.
In this embodiment, after acquiring monitoring data on the target sensor and the target intelligent instrument by the lower controller to obtain a target energy efficiency monitoring parameter, the target energy efficiency monitoring parameter is input into a target machine learning model obtained by training an initial parameter optimization model constructed based on a machine learning algorithm by using historical engineering data on the upper controller, and the target energy efficiency monitoring parameter is optimized by the target machine learning model to obtain a target optimization parameter; the historical engineering data comprises but is not limited to historical geological exploration data, heading machine manufacturing experience data, historical engineering project parameters and the like; the historical geological exploration data refers to data obtained by a construction project bureau in a geological exploration process, and the historical engineering project parameters refer to construction data of each device collected in an existing engineering project.
Step S13: and sending the target optimization parameters to the lower controller, and forwarding the target optimization parameters to an executing mechanism through the lower controller so that the executing mechanism can adjust the working state of the heading machine based on the target optimization parameters.
In this embodiment, after the target energy efficiency monitoring parameter is optimized through the target machine learning model to obtain a target optimization parameter, the target optimization parameter is further sent to the lower controller, and the target optimization parameter is forwarded to a corresponding execution mechanism through the lower controller, and the execution mechanism can adjust the current working state of the heading machine according to the target optimization parameter.
It can be understood that after the target optimization parameters are obtained, the target optimization parameters can be further analyzed, and the effective target optimization parameters are used as a new training set to train the target machine learning model, so that the target machine learning model is continuously corrected and optimized in the actual construction project.
In this embodiment, the method for controlling a heading machine may further include: acquiring the historical engineering data of the development machine, and dividing the historical engineering data into a training set and a testing set to obtain a training sample set and a testing sample set; comparing the target optimization parameter with the test sample set to obtain a first comparison error; repeatedly inputting the target energy efficiency monitoring parameters into the target machine learning model to obtain new optimization parameters, and comparing the new optimization parameters with the test sample set to obtain a second comparison error; and subtracting the first comparison error from the second comparison error, judging whether the difference value is a negative value, optimizing the target machine learning model by using the target energy efficiency monitoring parameter as a training set if the difference value is the negative value, adjusting the working state of the heading machine by using the new optimization parameter, and deleting the new optimization parameter if the difference value is the positive value. In this embodiment, after the historical engineering data of the heading machine is obtained, the historical engineering data of the heading machine may be further divided into a training set and a testing set to obtain a corresponding training sample set and a corresponding testing sample set, and then the training sample set is used to train an initial parameter optimization model constructed based on a machine learning algorithm to obtain a target machine learning model. Further, after the target machine learning model is used for optimizing the target energy efficiency monitoring parameters acquired in real time to obtain the target optimization parameters, and the target optimization parameters are used for adjusting the working state of the heading machine, the target optimization parameters can be compared with the test sample set to obtain corresponding first comparison errors, then the target energy efficiency monitoring parameters are input into the target machine learning model again, the target machine learning model optimizes the target energy efficiency monitoring parameters again to obtain new optimization parameters, then the new optimization parameters are compared with the test sample set to obtain corresponding second comparison errors, further, the second comparison errors are subtracted from the first comparison errors to obtain corresponding difference values, and whether the difference values are negative values is judged, and if the difference value is a negative value, the parameter obtained after the second optimization is closer to the historical engineering project parameter, namely the parameter has a control parameter better than the control parameter obtained by the first optimization, the target energy efficiency monitoring parameter is used as a training set to optimize the target machine learning model, the new optimization parameter is used for adjusting the current working state of the heading machine, if the difference value is a positive value, the new optimization parameter is deleted, and the process of obtaining the new optimization parameter is continuously repeated.
Therefore, the embodiment of the application monitors the whole energy efficiency of the heading machine through the target sensors and the target intelligent instruments which are pre-installed in each subsystem of the heading machine, and the lower controller collects the monitoring data on the target sensor and the target intelligent instrument to obtain a target energy efficiency monitoring parameter, then the target energy efficiency monitoring parameters are input into a target machine learning model obtained by training an initial parameter optimization model constructed based on a machine learning algorithm by using historical engineering data on an upper controller, so as to optimize the target energy efficiency monitoring parameters through the target machine learning model to obtain target optimization parameters, then send the target optimization parameters to the lower controller, and forward the target optimization parameters to the executing mechanism through the lower controller, so that the actuating mechanism adjusts the working state of the heading machine based on the target optimization parameters. Therefore, according to the embodiment of the application, the control parameters of the heading machine can be adaptively adjusted on the premise of not influencing the construction effect according to the whole machine energy efficiency monitoring data and the model created based on the machine learning algorithm, so that the energy consumption of the heading machine is reduced, the efficiency of the heading machine is maximized, the heading machine is green and energy-saving in the whole heading process, and the construction cost is reduced.
The embodiment of the application discloses a specific heading machine control method, and as shown in fig. 3, the method comprises the following steps:
step S21: monitoring the energy efficiency of a propelling system of a heading machine through a target sensor and an electric meter which are pre-installed in the propelling system of the heading machine, and acquiring monitoring data on the target sensor and the electric meter through a lower controller to obtain a target energy efficiency monitoring parameter; the target energy efficiency monitoring parameters comprise any of soil bin pressure, oil cylinder penetration, cutter head rotating speed, soil bin position, soil bin temperature, propelling speed and ammeter parameters.
In this embodiment, the energy efficiency of the propulsion system of the heading machine is monitored by various types of target sensors and electric meters which are pre-installed in the propulsion system of the heading machine, and then data monitored by the various types of target sensors are sent to a lower controller, and simultaneously, electric meter parameters acquired by the electric meter can be sent to the lower controller through a network cable, and the data received by the lower controller from the various types of target sensors and the electric meter include, but are not limited to, any of soil bin pressure, oil cylinder penetration, cutter head rotation speed, soil bin position, soil bin temperature, propulsion speed, electric meter parameters and the like. It should be noted that, in the sensor for monitoring the energy efficiency of the propulsion system of the heading machine, besides the sensor equipped in the conventional heading machine, an additional soil bin temperature sensor is added for monitoring the temperature of the soil bin during the operation of the heading machine.
Step S22: and processing the target energy efficiency monitoring parameter in real time through an automatic control program of the lower controller to obtain a first propulsion speed, and sending the first propulsion speed to a propulsion oil cylinder of the propulsion system so that the propulsion oil cylinder can propel the tunneling machine according to the first propulsion speed.
In this embodiment, after the lower controller collects monitoring data on the target sensor and the electric meter to obtain a target energy efficiency monitoring parameter, the target energy efficiency monitoring parameter may be further processed in real time by an automatic control program of the lower controller to obtain a first propulsion speed, and the first propulsion speed is sent to a propulsion cylinder of the propulsion system, and after receiving the first propulsion speed, the propulsion cylinder may propel the heading machine according to the first propulsion speed.
Step S23: inputting the target energy efficiency monitoring parameter into a target machine learning model obtained after training on an upper controller, so that the target machine learning model can optimize the propulsion speed in the target energy efficiency monitoring parameter to obtain a second propulsion speed; the target machine learning model is obtained by training an initial parameter optimization model constructed based on a machine learning algorithm by using historical engineering data.
In this embodiment, after the first propulsion speed is sent to the propulsion system to propel, the target energy efficiency monitoring parameter is input to a target machine learning model obtained after training on an upper controller, and then the propulsion speed in the target energy efficiency monitoring parameter is optimized through the target machine learning model to obtain a second propulsion speed.
In this embodiment, the process of obtaining the target machine learning model of the propulsion system of the heading machine may specifically include: acquiring historical geological exploration data including a tunnel face stratum type, development machine manufacturing experience data and historical engineering project parameters, and dividing the historical geological exploration data, the development machine manufacturing experience data and the historical engineering project parameters into a target training set and a target testing set; inputting the target training set into the initial parameter optimization model constructed based on a machine learning algorithm, training the initial parameter optimization model by using a reinforcement learning algorithm, and verifying the accuracy of the trained initial parameter optimization model by using the target test set to obtain the target machine learning model. It can be understood that the heading machine as a large-scale mechanical device is composed of a plurality of different systems and devices, and therefore when a target machine learning model is constructed for different systems and devices in the heading machine, historical engineering data corresponding to the different systems and devices should be collected as a training set of the target machine learning model. For example, aiming at a propulsion system of a heading machine, historical geological exploration data including a tunnel face stratum type, heading machine manufacturing experience data and historical engineering project parameters which are acquired by a construction engineering bureau can be used as a training set; the historical engineering project parameters comprise but are not limited to soil bin pressure, oil cylinder penetration, cutter head rotating speed, soil bin position, soil bin temperature, propelling speed, electric meter parameters and the like monitored by various sensors and electric meters arranged in a propelling system of the heading machine.
Further, after the historical geological exploration data, the heading machine manufacturing experience data and the historical engineering project parameters are obtained, the historical geological exploration data, the heading machine manufacturing experience data and the historical engineering project parameters can be divided into corresponding target training sets and target test sets, then the target training sets are input into the initial parameter optimization model constructed based on a machine Learning algorithm, the initial parameter optimization model is trained by a Reinforcement Learning (RL) algorithm to obtain a trained initial parameter optimization model, then the accuracy of the trained initial parameter optimization model is verified by the target test sets, and the target machine Learning model with higher accuracy is obtained. The reinforcement learning algorithm includes, but is not limited to, a Q-learning algorithm, a Sarsa algorithm, a Deep Q Network algorithm, a Policy Gradient algorithm, an Actor Critic algorithm, and the like. It should be noted that, in addition to the reinforcement learning algorithm, other algorithms, such as a supervised learning algorithm, may be used to train the initial parameter optimization model.
Step S24: and comparing the second propulsion speed with the propulsion speed in the target test set to obtain a comparison result, and judging whether the comparison result meets a preset difference threshold condition.
In this embodiment, after the target machine learning model optimizes the propulsion speed in the target energy efficiency monitoring parameters to obtain a second propulsion speed, the second propulsion speed is compared with the propulsion speed in the target test set to obtain a corresponding comparison result, and then whether the comparison result meets a preset difference threshold condition is determined. For example, in the case of the same or similar input parameters, it is calculated whether the difference between the second propulsion speed output after passing through the target machine learning model and the propulsion speeds recorded in the target test set satisfies a preset difference threshold condition.
Step S25: and if the comparison result meets the preset difference threshold condition, taking the second propulsion speed as a target optimization parameter.
In this embodiment, if the comparison result meets the preset difference threshold condition, the second propulsion speed may be used as a target optimization parameter.
Step S26: and sending the target optimization parameters to the lower controller, and forwarding the target optimization parameters to an executing mechanism through the lower controller so that the executing mechanism can adjust the working state of the heading machine based on the target optimization parameters.
In this embodiment, if the comparison result meets the preset difference threshold condition, the second propulsion speed is used as a target optimization parameter, and then the target optimization parameter is sent to the lower controller, and is forwarded to the propulsion cylinder in the propulsion system by the lower controller, and the propulsion cylinder can propel the operation of the heading machine according to the target optimization parameter after receiving the target optimization parameter.
For more specific processing procedures of the above steps, reference may be made to corresponding contents disclosed in the foregoing embodiments, and details are not repeated here.
It can be seen that in the embodiment of the application, the energy efficiency of the propulsion system of the heading machine is monitored through a target sensor and an electric meter which are pre-installed in the propulsion system of the heading machine, monitoring data on the target sensor and the electric meter are collected through a lower controller to obtain a target energy efficiency monitoring parameter, then the target energy efficiency monitoring parameter is processed in real time through an automatic control program of the lower controller to obtain a first propulsion speed, the first propulsion speed is sent to a propulsion oil cylinder of the propulsion system, the target energy efficiency monitoring parameter is input into a target machine learning model obtained after training on an upper controller to obtain a second propulsion speed, and finally the working state of the heading machine is adjusted through the second propulsion speed. Therefore, according to the energy efficiency monitoring data of the propulsion system and the model created based on the machine learning algorithm, the control parameters of the propulsion cylinder of the heading machine can be adjusted in a self-adaptive mode on the premise that the construction effect is not influenced, and the energy consumption of the propulsion system of the heading machine is reduced.
Correspondingly, the embodiment of the application also discloses a heading machine control device, and as shown in fig. 4, the device comprises:
the data acquisition module 11 is used for monitoring the complete machine energy efficiency of the heading machine through a target sensor and a target intelligent instrument which are pre-installed in each subsystem of the heading machine, and acquiring monitoring data on the target sensor and the target intelligent instrument through a lower controller to obtain a target energy efficiency monitoring parameter;
the parameter optimization module 12 is configured to input the target energy efficiency monitoring parameter into a target machine learning model obtained after training on an upper controller, so that the target energy efficiency monitoring parameter is optimized through the target machine learning model to obtain a target optimization parameter; the target machine learning model is obtained by training an initial parameter optimization model constructed based on a machine learning algorithm by using historical engineering data;
and the parameter adjusting module 13 is configured to send the target optimization parameter to the lower controller, and forward the target optimization parameter to the executing mechanism through the lower controller, so that the executing mechanism adjusts the working state of the heading machine based on the target optimization parameter.
For the specific work flow of each module, reference may be made to corresponding content disclosed in the foregoing embodiments, and details are not repeated here.
Therefore, in the embodiment of the application, the whole machine energy efficiency of the heading machine is monitored by the target sensors and the target intelligent instruments which are pre-installed in each subsystem of the heading machine, and the lower controller collects the monitoring data on the target sensor and the target intelligent instrument to obtain a target energy efficiency monitoring parameter, then the target energy efficiency monitoring parameters are input into a target machine learning model obtained by training an initial parameter optimization model constructed based on a machine learning algorithm by using historical engineering data on an upper controller, so as to optimize the target energy efficiency monitoring parameters through the target machine learning model to obtain target optimization parameters, then send the target optimization parameters to the lower controller, and forward the target optimization parameters to the executing mechanism through the lower controller, so that the actuating mechanism adjusts the working state of the heading machine based on the target optimization parameters. Therefore, according to the embodiment of the application, the control parameters of the heading machine can be adjusted in a self-adaptive manner on the premise of not influencing the construction effect according to the whole machine energy efficiency monitoring data and the model created based on the machine learning algorithm, so that the energy consumption of the heading machine is reduced, the efficiency of the heading machine is maximized, the heading machine is green and energy-saving in the whole heading process, and the construction cost is reduced.
In some specific embodiments, the data acquisition module 11 may specifically include:
the first data acquisition unit is used for acquiring monitoring data on the target sensor and the target intelligent instrument through a lower controller to obtain an initial energy efficiency monitoring parameter;
and the data screening unit is used for screening out key parameters in the operation process of each subsystem from the initial energy efficiency monitoring parameters to obtain target energy efficiency monitoring parameters.
In some specific embodiments, the data acquisition module 11 may specifically include:
the second data acquisition unit is used for monitoring the energy efficiency of a propulsion system of the heading machine through a target sensor and an electric meter which are pre-installed in the propulsion system of the heading machine, and acquiring monitoring data on the target sensor and the electric meter through a lower controller to obtain target energy efficiency monitoring parameters; the target energy efficiency monitoring parameters comprise any of soil bin pressure, oil cylinder penetration, cutter head rotating speed, soil bin position, soil bin temperature, propelling speed and ammeter parameters.
In some specific embodiments, after the acquiring, by the lower controller, the monitoring data on the target sensor and the electric meter to obtain the target energy efficiency monitoring parameter, the method may further include:
and the first development machine propulsion unit is used for processing the target energy efficiency monitoring parameters in real time through an automatic control program of the lower controller to obtain a first propulsion speed, and sending the first propulsion speed to a propulsion oil cylinder of the propulsion system so that the propulsion oil cylinder can propel the development machine according to the first propulsion speed.
In some specific embodiments, the obtaining process of the target machine learning model may specifically include:
the first data acquisition unit is used for acquiring historical geological exploration data including the type of a tunnel face stratum, development machine manufacturing experience data and historical engineering project parameters;
the first data dividing unit is used for dividing the historical geological exploration data, the heading machine manufacturing experience data and the historical engineering project parameters into a target training set and a target testing set;
and the model training unit is used for inputting the target training set into the initial parameter optimization model constructed based on the machine learning algorithm, training the initial parameter optimization model by using a reinforcement learning algorithm, and verifying the accuracy of the trained initial parameter optimization model by using the target test set to obtain the target machine learning model.
In some specific embodiments, the optimizing the target energy efficiency monitoring parameter through the target machine learning model to obtain a target optimization parameter may specifically include:
the propulsion speed optimization unit is used for optimizing the propulsion speed in the target energy efficiency monitoring parameters through the target machine learning model to obtain a second propulsion speed;
the propulsion speed comparison unit is used for comparing the second propulsion speed with the propulsion speed in the target test set to obtain a comparison result;
the first judgment unit is used for judging whether the comparison result meets a preset difference threshold value condition or not;
and the second heading machine propelling unit is used for taking the second propelling speed as a target optimization parameter if the comparison result meets the preset difference threshold condition.
In some embodiments, the heading machine control device may further include:
the second data acquisition unit is used for acquiring the historical engineering data of the heading machine;
the second data dividing unit is used for dividing the historical engineering data into a training set and a testing set to obtain a training sample set and a testing sample set;
the first parameter comparison unit is used for comparing the target optimization parameter with the test sample set to obtain a first comparison error;
the parameter optimization unit is used for repeatedly inputting the target energy efficiency monitoring parameters into the target machine learning model to obtain new optimization parameters;
the second parameter comparison unit is used for comparing the new optimized parameters with the test sample set to obtain a second comparison error;
the second judgment unit is used for subtracting the first comparison error from the second comparison error and judging whether the difference value is a negative value;
the working state adjusting unit is used for optimizing the target machine learning model by taking the target energy efficiency monitoring parameter as a training set and adjusting the working state of the tunneling machine by using the new optimization parameter if the difference value is a negative value;
and the parameter deleting unit is used for deleting the new optimization parameters if the difference is a positive value.
Further, an electronic device is disclosed in the embodiments of the present application, and fig. 5 is a block diagram of the electronic device 20 according to an exemplary embodiment, which should not be construed as limiting the scope of the application.
Fig. 5 is a schematic structural diagram of an electronic device 20 according to an embodiment of the present disclosure. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input output interface 25, and a communication bus 26. The memory 22 is used for storing a computer program, and the computer program is loaded and executed by the processor 21 to implement relevant steps in the heading machine control method disclosed in any one of the foregoing embodiments. In addition, the electronic device 20 in the present embodiment may be specifically an electronic computer.
In this embodiment, the power supply 23 is configured to provide a working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and an external device, and a communication protocol followed by the communication interface is any communication protocol applicable to the technical solution of the present application, and is not specifically limited herein; the input/output interface 25 is configured to obtain external input data or output data to the outside, and a specific interface type thereof may be selected according to specific application requirements, which is not specifically limited herein.
In addition, the storage 22 is used as a carrier for resource storage, and may be a read-only memory, a random access memory, a magnetic disk or an optical disk, etc., and the resources stored thereon may include an operating system 221, a computer program 222, etc., and the storage manner may be a transient storage or a permanent storage.
The operating system 221 is used for managing and controlling each hardware device on the electronic device 20 and the computer program 222, and may be Windows Server, Netware, Unix, Linux, or the like. The computer program 222 may further include a computer program that can be used to perform other specific tasks in addition to the computer program that can be used to perform the heading control method performed by the electronic device 20 disclosed in any of the foregoing embodiments.
Further, the present application also discloses a computer-readable storage medium for storing a computer program; wherein the computer program when executed by a processor implements the heading machine control method disclosed above. For the specific steps of the method, reference may be made to the corresponding contents disclosed in the foregoing embodiments, which are not described herein again.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The method, the device, the equipment and the storage medium for controlling the heading machine provided by the application are described in detail, specific examples are applied to explain the principle and the implementation mode of the application, and the description of the embodiments is only used for helping to understand the method and the core idea of the application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. A heading machine control method is characterized by comprising the following steps:
monitoring the complete machine energy efficiency of the heading machine through a target sensor and a target intelligent instrument which are pre-installed in each subsystem of the heading machine, and acquiring monitoring data on the target sensor and the target intelligent instrument through a lower controller to obtain a target energy efficiency monitoring parameter;
inputting the target energy efficiency monitoring parameter into a target machine learning model obtained after training on an upper controller, so that the target energy efficiency monitoring parameter is optimized through the target machine learning model to obtain a target optimization parameter; the target machine learning model is obtained by training an initial parameter optimization model constructed based on a machine learning algorithm by using historical engineering data;
and sending the target optimization parameters to the lower controller, and forwarding the target optimization parameters to an executing mechanism through the lower controller so that the executing mechanism can adjust the working state of the heading machine based on the target optimization parameters.
2. The method for controlling the tunneling machine according to claim 1, wherein the acquiring, by the lower controller, the monitoring data on the target sensor and the target smart meter to obtain the target energy efficiency monitoring parameter comprises:
and acquiring monitoring data on the target sensor and the target intelligent instrument through a lower controller to obtain initial energy efficiency monitoring parameters, and screening out key parameters in the operation process of each subsystem from the initial energy efficiency monitoring parameters to obtain target energy efficiency monitoring parameters.
3. The method for controlling the heading machine according to claim 1, wherein the monitoring of the overall energy efficiency of the heading machine is performed by a target sensor and a target intelligent instrument which are pre-installed in each subsystem of the heading machine, and monitoring data on the target sensor and the target intelligent instrument are collected by a lower controller to obtain a target energy efficiency monitoring parameter, and the method comprises the following steps:
monitoring the energy efficiency of a propelling system of a heading machine through a target sensor and an electric meter which are pre-installed in the propelling system of the heading machine, and acquiring monitoring data on the target sensor and the electric meter through a lower controller to obtain a target energy efficiency monitoring parameter; the target energy efficiency monitoring parameters comprise any of soil bin pressure, oil cylinder penetration, cutter head rotating speed, soil bin position, soil bin temperature, propelling speed and ammeter parameters.
4. The method according to claim 3, wherein after the acquiring, by the lower controller, the monitoring data on the target sensor and the electric meter to obtain the target energy efficiency monitoring parameter, the method further comprises:
and processing the target energy efficiency monitoring parameters in real time through an automatic control program of the lower controller to obtain a first propelling speed, and sending the first propelling speed to a propelling cylinder of the propelling system so that the propelling cylinder propels the tunneling machine according to the first propelling speed.
5. The method of claim 4, wherein the obtaining of the target machine learning model comprises:
acquiring historical geological exploration data including a tunnel face stratum type, development machine manufacturing experience data and historical engineering project parameters, and dividing the historical geological exploration data, the development machine manufacturing experience data and the historical engineering project parameters into a target training set and a target testing set;
inputting the target training set into the initial parameter optimization model constructed based on a machine learning algorithm, training the initial parameter optimization model by using a reinforcement learning algorithm, and verifying the accuracy of the trained initial parameter optimization model by using the target test set to obtain the target machine learning model.
6. The method according to claim 5, wherein the optimizing the target energy efficiency monitoring parameter by the target machine learning model to obtain a target optimization parameter comprises:
optimizing the propulsion speed in the target energy efficiency monitoring parameters through the target machine learning model to obtain a second propulsion speed;
comparing the second propulsion speed with the propulsion speed in the target test set to obtain a comparison result, and judging whether the comparison result meets a preset difference threshold condition;
and if the comparison result meets the preset difference threshold condition, taking the second propulsion speed as a target optimization parameter.
7. The tunneling machine control method according to any one of claims 1 to 6, characterized by further comprising:
acquiring the historical engineering data of the development machine, and dividing the historical engineering data into a training set and a testing set to obtain a training sample set and a testing sample set;
comparing the target optimization parameter with the test sample set to obtain a first comparison error;
repeatedly inputting the target energy efficiency monitoring parameters into the target machine learning model to obtain new optimization parameters, and comparing the new optimization parameters with the test sample set to obtain a second comparison error;
and subtracting the first comparison error from the second comparison error, judging whether the difference value is a negative value, optimizing the target machine learning model by using the target energy efficiency monitoring parameter as a training set if the difference value is the negative value, adjusting the working state of the heading machine by using the new optimization parameter, and deleting the new optimization parameter if the difference value is the positive value.
8. A heading machine control device characterized by comprising:
the data acquisition module is used for monitoring the complete machine energy efficiency of the heading machine through a target sensor and a target intelligent instrument which are pre-installed in each subsystem of the heading machine, and acquiring monitoring data on the target sensor and the target intelligent instrument through a lower controller so as to obtain a target energy efficiency monitoring parameter;
the parameter optimization module is used for inputting the target energy efficiency monitoring parameters into a target machine learning model obtained after training on an upper controller so as to optimize the target energy efficiency monitoring parameters through the target machine learning model to obtain target optimization parameters; the target machine learning model is obtained by training an initial parameter optimization model constructed based on a machine learning algorithm by using historical engineering data;
and the parameter adjusting module is used for sending the target optimization parameters to the lower controller and forwarding the target optimization parameters to the executing mechanism through the lower controller so that the executing mechanism can adjust the working state of the heading machine based on the target optimization parameters.
9. An electronic device comprising a processor and a memory; wherein the processor, when executing the computer program stored in the memory, implements a ripper control method according to any one of claims 1 to 7.
10. A computer-readable storage medium for storing a computer program; wherein the computer program when executed by a processor implements a method of controlling a ripper according to any one of claims 1 to 7.
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