CN115437248A - Equipment operation control method, device and equipment based on deep Q learning algorithm - Google Patents
Equipment operation control method, device and equipment based on deep Q learning algorithm Download PDFInfo
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Abstract
The disclosure provides a device operation control method and device based on a deep Q learning algorithm. The method comprises the following steps: obtaining target operational data for a plurality of coal equipment, wherein the plurality of coal equipment comprises: the method comprises the steps of determining a target function for controlling the operation of a plurality of coal mining devices according to target operation data, processing the target operation data and the target function based on a depth Q learning algorithm to obtain target operation control parameters, and controlling the operation of the scraper conveyor, the coal mining device and the hydraulic support based on the target operation control parameters, so that the cooperative control of the coal mining device, the scraper conveyor, the hydraulic support and other devices can be realized, the necessary safety characteristics of water prevention, dust prevention, explosion prevention and the like of the individual devices during deep mining in a coal mine well can be met, and the cooperative control among multiple devices determines the cooperative working efficiency among the devices and the safety of the whole working face.
Description
Technical Field
The disclosure relates to the technical field of intelligent mines, in particular to a method, a device and equipment for controlling equipment operation based on a deep Q learning algorithm.
Background
Along with the advance of underground deep mining work of coal mines, the research and development of unmanned and intelligent mining equipment have important roles and practical significance, and finally, an intelligent and unmanned working face is also an important target for the advance of coal mining technology.
In the related art, coal mining machines, scraper conveyors, hydraulic supports and other equipment effectively improve the efficiency and the safety of coal mining to a certain extent, but the research and development of intelligent equipment except that the necessary safety characteristics such as waterproof, dustproof and explosion-proof of individual equipment when the deep portion of coal mine was mined in the pit need satisfying, cooperative control between the multiple equipment has decided the collaborative work efficiency between each equipment and the security of whole working face, and from this, need urgently to provide an equipment operation control method based on degree of depth Q learning algorithm to carry out cooperative control to coal mining machines, scraper conveyors, hydraulic supports and other equipment.
Disclosure of Invention
The present disclosure is directed to solving, at least to some extent, one of the technical problems in the related art.
Therefore, the purpose of the disclosure is to provide an equipment operation control method, an equipment operation control device, electronic equipment and a storage medium based on a deep Q learning algorithm, so that cooperative control of equipment such as a coal mining machine, a scraper conveyor and a hydraulic support can be realized, safety characteristics such as water resistance, dust resistance and explosion resistance required by single equipment during deep mining in a coal mine well can be met, and cooperative control among multiple equipment determines cooperative work efficiency among the equipment and safety of an overall working face.
The device operation control method based on the deep Q learning algorithm provided by the embodiment of the first aspect of the disclosure comprises the following steps: obtaining target operational data for a plurality of coal equipment, wherein the plurality of coal equipment comprises: scraper conveyors, coal mining machines and hydraulic supports; determining a target function for controlling the operation of a plurality of coal mining devices according to the target operation data; processing the target operation data and the target function in a deep Q learning algorithm to obtain target operation control parameters; and controlling the scraper conveyor, the coal mining machine and the hydraulic support to operate based on the target operation control parameters.
In an apparatus operation control method based on a deep Q learning algorithm according to an embodiment of the first aspect of the present disclosure, target operation data of a plurality of coal apparatuses is obtained, where the plurality of coal apparatuses includes: the method comprises the steps of determining a target function for controlling the operation of a plurality of coal mining devices according to target operation data, processing the target operation data and the target function based on a depth Q learning algorithm to obtain target operation control parameters, and controlling the operation of the scraper conveyor, the coal mining device and the hydraulic support based on the target operation control parameters, so that the cooperative control of the coal mining device, the scraper conveyor, the hydraulic support and other devices can be realized, the necessary safety characteristics of water prevention, dust prevention, explosion prevention and the like of the individual devices during deep mining in a coal mine well can be met, and the cooperative control among multiple devices determines the cooperative working efficiency among the devices and the safety of the whole working face.
The device operation control device based on the deep Q learning algorithm provided by the embodiment of the second aspect of the disclosure comprises: an acquisition module configured to acquire target operational data for a plurality of coal equipment, wherein the plurality of coal equipment includes: scraper conveyors, coal mining machines and hydraulic supports; the determining module is used for determining a target function for controlling the operation of the plurality of coal mining devices according to the target operation data; the processing module is used for processing the target operation data and the target function based on a deep Q learning algorithm so as to obtain target operation control parameters; and the control module is used for controlling the scraper conveyor, the coal mining machine and the hydraulic support to operate based on the target operation control parameters.
The device operation control device based on the deep Q learning algorithm provided by the embodiment of the second aspect of the present disclosure obtains target operation data of a plurality of coal equipments, where the plurality of coal equipments include: the method comprises the steps of determining a target function for controlling the operation of a plurality of coal mining devices according to target operation data, processing the target operation data and the target function based on a depth Q learning algorithm to obtain target operation control parameters, and controlling the operation of the scraper conveyor, the coal mining device and the hydraulic support based on the target operation control parameters, so that the cooperative control of the coal mining device, the scraper conveyor, the hydraulic support and other devices can be realized, the necessary safety characteristics of water prevention, dust prevention, explosion prevention and the like of the individual devices during deep mining in a coal mine well can be met, and the cooperative control among the multiple devices determines the cooperative work efficiency among the devices and the safety of the whole working face.
An embodiment of a third aspect of the present disclosure provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where when the processor executes the computer program, the method for controlling the operation of the device based on the deep Q learning algorithm as set forth in the embodiment of the first aspect of the present disclosure is implemented.
A fourth aspect of the present disclosure provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a device operation control method based on a deep Q learning algorithm as set forth in the first aspect of the present disclosure.
An embodiment of a fifth aspect of the present disclosure provides a computer program product, which, when executed by an instruction processor in the computer program product, executes the method for controlling the operation of a device based on a deep Q learning algorithm as set forth in an embodiment of the first aspect of the present disclosure.
Additional aspects and advantages of the disclosure will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the disclosure.
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The foregoing and/or additional aspects and advantages of the present disclosure will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a schematic flowchart of an apparatus operation control method based on a deep Q learning algorithm according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of an apparatus operation control method based on a deep Q learning algorithm according to another embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an apparatus operation control device based on a deep Q learning algorithm according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an apparatus operation control device based on a deep Q learning algorithm according to another embodiment of the present disclosure;
FIG. 5 illustrates a block diagram of an exemplary electronic device suitable for use in implementing embodiments of the present disclosure.
Detailed Description
Reference will now be made in detail to the embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of illustrating the present disclosure and should not be construed as limiting the same. On the contrary, the embodiments of the disclosure include all changes, modifications and equivalents coming within the spirit and terms of the claims appended hereto.
Fig. 1 is a schematic flowchart of an apparatus operation control method based on a deep Q learning algorithm according to an embodiment of the present disclosure.
It should be noted that the execution subject of the device operation control method based on the deep Q learning algorithm in this embodiment is a device operation control apparatus based on the deep Q learning algorithm, and the apparatus may be implemented in a software and/or hardware manner, and the apparatus may be configured in an electronic device, and the electronic device may include, but is not limited to, a terminal, a server, and the like.
As shown in fig. 1, the method for controlling the operation of the device based on the deep Q learning algorithm includes:
s101: obtaining target operational data for a plurality of coal equipment, wherein the plurality of coal equipment comprises: scraper conveyor, coal-winning machine and hydraulic support.
The method for controlling the operation of the equipment based on the deep Q learning algorithm described in the embodiment of the present disclosure may be, for example, used for controlling the operation of the coal equipment, and the following description of the embodiment of the present disclosure illustrates examples of the coal equipment.
In an embodiment of the present disclosure, a coal machine may include: the plate conveyor, the coal mining machine and the hydraulic support, that is, the method for controlling the operation of the equipment based on the deep Q learning algorithm described in the embodiments of the present disclosure, may support the cooperative operation control of the plate conveyor, the coal mining machine and the hydraulic support, which is not limited thereto.
The coal mining equipment may have some relevant data during operation, which may be referred to as target operation data, and the target operation data may be equipment parameters of the coal mining equipment during operation of the coal mining equipment, which is not limited herein.
In some embodiments, the obtaining of the target operation data of the coal equipment may be, for the equipment operation control device based on the deep Q learning algorithm, providing a corresponding data transmission interface in advance, obtaining various data of the coal equipment in the operation process through the data transmission interface, and using the data as the target operation data of the coal equipment, which is not limited herein.
In the embodiment of the disclosure, the target operation data of the coal mining machine equipment may be acquired by using a sensor to acquire operation data of a plate conveyor, a coal mining machine and a hydraulic support in an operation process of the coal mining machine equipment, and the operation data is used as the target operation data, which is not limited to this.
For example, in the embodiment of the disclosure, the sensors are adopted to collect the hydraulic supports (the number of the hydraulic supports is N) in the operation process of the coal mining equipment v The number of the hydraulic supports v can be determined, and the included angle alpha between the base and the horizontal plane measured by the inclination angle sensor of each hydraulic support is collected v Where v represents the hydraulic mount, v = [ N = 1 N 2 ...N v ]And collecting the included angle beta between the front connecting rod and the horizontal plane measured by the tilt angle sensor of each hydraulic support v And collecting the tilt sensor measurements of each hydraulic mountThe included angle delta between the top beam and the horizontal plane v And collecting the tracking speed V of each hydraulic support v Then, whether the working state of each hydraulic support is abnormal or not is detected, and a binary representation mode is adopted and marked as A v E {0,1}, wherein 0 is a normal working state and 1 is an abnormal working state.
The method comprises collecting operation data of a coal mining machine (denoted by symbol M) during operation of the coal mining machine by using sensors, such as determining cutting speed V of the coal mining machine when cutting coal m The limit speed of the coal cutter cutting coal under the current working face
Cutting propulsion attitude pitch angle phi of coal mining machine m With the direction above the advancing direction being positive, the coal cutter advances the attitude yaw angle
The right side of the advancing direction is taken as the positive side, the cutting advancing attitude of the coal mining machine is inclined at a side angle gamma m If the working state of the coal mining machine is abnormal or not by changing the clockwise rotation in the propelling direction to be positive, a binary representation mode is adopted and is marked as A M The element belongs to {0,1}, wherein 0 is a normal working state, and 1 is an abnormal working state.
The method comprises the steps of collecting operation data of a scraper conveyor (the scraper conveyor is represented by an S symbol) in the operation process of coal equipment by adopting a sensor, and determining the proportion L of the current of a motor of the scraper conveyor to the rated current s And determining whether the working state of the scraper conveyor is abnormal or not, and adopting a binary representation mode, and recording the binary representation mode as A s E {0,1}, wherein 0 is a normal working state and 1 is an abnormal working state.
S102: and determining an objective function for controlling the operation of a plurality of coal mining devices according to the target operation data.
In the embodiment of the disclosure, after the target operation data of the coal mining machine is acquired, the target functions of a plurality of coal mining devices can be constructed according to the target operation data.
The objective function may be used for assisting in cooperative control of multiple coal mining devices, which is not limited herein.
In some embodiments, the objective function of the multiple coal machines is constructed according to the target operation data, the target operation data may be analyzed after the target operation data of the coal machines is obtained, so as to determine an association relationship between the target operation data, and then the objective function may be constructed based on the association relationship between the target operation data, or any other possible method may be adopted to implement the construction of the objective function of the multiple coal machines according to the target operation data, such as a parameterized modeling method and an engineering algorithm method, which is not limited to this.
Optionally, in this embodiment of the present disclosure, an objective function of the multiple coal mining devices is constructed according to the target operation data, where the objective function may be constructed according to the target operation data, and the constraint conditions of the operation states of the multiple coal mining devices, the constraint conditions of the cutting speed of the coal mining machine, and the constraint conditions of the working power of the scraper conveyor, and the objective function is constructed according to the constraint conditions of the operation states of the multiple coal mining devices, the constraint conditions of the cutting speed of the coal mining machine, and the constraint conditions of the working power of the scraper conveyor.
The scraper conveyor, the coal mining machine and the hydraulic support need to ensure that each device is in a non-abnormal working state, and the constraint conditions of the running states of a plurality of coal mining machines are as follows:
wherein, A v Indicating the operating state of the hydraulic support, A m Indicating whether the working state of the mining machine is abnormal, A s Indicating whether the working state of the scraper conveyor is abnormal or not.
In the embodiment of the disclosure, the cutting speed of the coal mining machine needs to be less than the maximum speed of the equipment to prevent overload, and the constraint condition of the cutting speed of the coal mining machine is as follows:
wherein, V m Represents the cutting speed of the coal cutter when cutting coal,
representing a cutting speed threshold value when the coal mining machine cuts coal;
in the embodiment of the disclosure, the working power of the scraper conveyor needs to be less than the maximum rated power (1), and the constraint condition of the working power of the scraper conveyor is as follows:
L s ≤1；
wherein L is s The ratio of the scraper conveyor motor current to the rated current is shown.
In the disclosed embodiments, the objective of cooperative control is to maximize mining efficiency when the three constraints are met, and thus, the objective may be to maximize the mining efficiency
L S And
performing normalization, which may be specifically expressed as:
wherein Mean represents taking the Mean of the offline data, and Std represents solving the standard deviation of the offline data.
From this, the objective function can be determined as shown in the following equation:
wherein N represents the number of hydraulic supports on the working face,
representing the cutting speed at which the shearer cuts the coal at time t,
representing the chasing speed of the hydraulic support at the time t,
the ratio of the scraper conveyor motor current to the rated current at the time t is shown,
the working state of the hydraulic support at the time t is shown,
showing whether the working state of the coal mining machine is abnormal at the time t,
indicating whether the working state of the scraper conveyor is abnormal at the time t,
representing the cutting speed at which the shearer cuts the coal at time t,
representing the limit speed at which the shearer cuts coal under the current face.
The ratio of the motor current of the scraper conveyor to the rated current at time t is shown.
S103: and processing the target operation data and the target function based on a deep Q learning algorithm to obtain target operation control parameters.
In the embodiment of the disclosure, after the target operation data is obtained and the target function of the multiple coal mining devices is determined, the target function may be an equipment operation control target based on a deep Q learning algorithm, and the deep Q learning algorithm is adopted to perform offline training on the target operation data so as to obtain an operation control parameter for performing cooperative control on the scraper conveyor, the coal mining machine and the hydraulic support, where the operation control parameter may be referred to as a target operation control parameter, and is not limited thereto.
S104: and controlling the scraper conveyor, the coal mining machine and the hydraulic support to operate based on the target operation control parameters.
In the embodiment of the disclosure, after the target operation control parameters for performing cooperative control on the scraper conveyor, the coal mining machine and the hydraulic support are obtained, the scraper conveyor, the coal mining machine and the hydraulic support may be controlled to operate based on the target operation control parameters.
That is to say, in the embodiment of the present disclosure, the target operation control parameter may be issued to the scraper conveyor, the coal mining machine, and the hydraulic support to control the scraper conveyor, the coal mining machine, and the hydraulic support to operate according to the target operation control parameter, so that cooperative control of the coal mining machine, the scraper conveyor, the hydraulic support, and other devices may be implemented, safety characteristics such as water resistance, dust resistance, and explosion resistance necessary for a single device during deep mining in a coal mine may be satisfied, and cooperative control among multiple devices determines cooperative work efficiency among the devices and safety of an entire working face.
In this embodiment, target operation data of a plurality of coal equipment is obtained, where the plurality of coal equipment includes: the method comprises the steps of determining a target function for controlling the operation of a plurality of coal mining devices according to target operation data, processing the target operation data and the target function based on a depth Q learning algorithm to obtain target operation control parameters, and controlling the operation of the scraper conveyor, the coal mining device and the hydraulic support based on the target operation control parameters, so that the cooperative control of the coal mining device, the scraper conveyor, the hydraulic support and other devices can be realized, the necessary safety characteristics of water prevention, dust prevention, explosion prevention and the like of the individual devices during deep mining under a coal mine can be met, and the cooperative control among the multiple devices determines the cooperative work efficiency among the devices and the safety of the whole working face.
Fig. 2 is a schematic flow chart of an apparatus operation control method based on a deep Q learning algorithm according to another embodiment of the present disclosure.
As shown in fig. 2, the method for controlling the operation of the device based on the deep Q learning algorithm includes:
s201: obtaining target operational data for a plurality of coal equipment, wherein the plurality of coal equipment comprises: scraper conveyor, coal-winning machine and hydraulic support.
S202: and determining an objective function for controlling the operation of the plurality of coal mining devices according to the target operation data.
For the description of S201 to S202, reference may be made to the above embodiments, and details are not repeated herein.
S203: and determining a reward function of the deep Q learning algorithm according to the target operation data.
In the embodiment of the disclosure, after the target operation data of the coal equipment is acquired, the reward function of the deep Q learning algorithm can be determined according to the target operation data.
The reward function may be expressed as:
wherein, reward represents a reward function,
the working speed of the coal mining machine is shown,
the speed of the hydraulic support and the machine is shown,
a prize value corresponding to the scraper conveyor power is indicated,
a reward value representing the speed difference between the coal machine and the hydraulic support,
representing a prize value corresponding to the speed differential of the face conveyor and the hydraulic mount,
representing the reward value, A, corresponding to the difference in speed between the shearer and the scraper conveyor t Indicating the reward value corresponding to the abnormality of the equipment at the current time.
In the embodiment of the present disclosure, the calculation manner of each parameter in the reward function may be specifically expressed as:
wherein, V v Indicating the chasing speed, V, of each hydraulic carriage m Indicates the cutting speed, L, of the coal cutter when cutting coal s Indicating scraper conveyor motor current occupancyRatio phi of rated current m The pitch angle of the cutting and propelling attitude of the coal mining machine is shown, the upper part of the propelling direction is taken as the positive,
the yaw angle of the cutting and propelling attitude of the coal mining machine is shown, the right side of the propelling direction is positive, gamma m Indicating the cutting advance attitude of the coal mining machine, rotating clockwise in the advance direction to positive Act _ V v An action parameter, act _ V, representing the chasing speed of each hydraulic support m An action parameter, act _ L, representing the cutting speed of the shearer when cutting coal s An operation parameter Act _ phi representing the ratio of the motor current of the scraper conveyor to the rated current m Representing the action parameters of the pitch angle of the cutting and propelling attitude of the coal mining machine,
an action parameter, act _ Gamma, representing the cutting propulsion attitude yaw angle of the coal mining machine m And the action parameters represent the cutting advancing attitude roll angle of the coal mining machine.
S204: and determining the action parameters of the coal equipment according to the target operation data.
After determining the reward function of the deep Q learning algorithm, the embodiment of the disclosure may determine the action parameters of a plurality of coal mining devices according to the target operation data, where the action parameters may be related parameters adjusted according to the operation parameters of the scraper conveyor, the coal mining machine, and the hydraulic support, and are not limited thereto.
In the embodiment of the present disclosure, the action parameters of the multiple coal mining devices may be represented as:
wherein, act _ V v An action parameter, act _ V, representing the chasing speed of each hydraulic support m An action parameter, act _ L, representing the cutting speed of the shearer when cutting coal s An operation parameter Act _ phi representing the ratio of the motor current of the scraper conveyor to the rated current m Representing the action parameters of the pitch angle of the cutting and propelling attitude of the coal mining machine,
an action parameter, act _ Gamma, representing the cutting propulsion attitude yaw angle of the coal mining machine m And the action parameters represent the cutting advancing attitude roll angle of the coal mining machine.
S205: and acquiring target operation control parameters according to the reward function, the action parameters and the target function.
Optionally, in some embodiments, the obtaining of the target operation control parameter according to the reward function, the action parameter and the target function may be determining a state parameter of the multiple coal mining devices according to the reward function and the action parameter, and obtaining the target operation control parameter according to the state parameter, the reward function, the action parameter and the target function.
In the embodiment of the present disclosure, the state parameters of the multiple coal equipment may be determined according to the reward function and the action parameter, and the state parameter table may be represented as:
that is, in the embodiment of the present disclosure, a deep Q learning step α e (0, 1) may be set, a value matrix Q (State, action, W) is initialized arbitrarily for all states, actions, W, then, for each mining process, the State matrix is initialized, each equipment in the mining process is regulated and controlled, a strategy obtained from the value matrix Q and a weight W is used, an Action is selected at the State, the Action is executed, a reward and a State' are obtained through calculation, a weight coefficient W is finally obtained through training, in an actual application process, each time data acquired by a sensor is returned to a calculation unit, the calculation unit counts according to the weight coefficient W to output a corresponding target operation control parameter, and then, based on the target operation control parameter, the coordinated operation of a scraper conveyor, a coal mining machine and a hydraulic support may be controlled, which may specifically refer to the subsequent embodiments.
S206: and respectively transmitting the target operation control parameters to the scraper conveyor, the coal mining machine and the hydraulic support based on a Controller Area Network (CAN).
After the target operation control parameters are determined, calculation CAN be carried out by means of a general Personal Computer (PC) of a ground central station and an underground substation, monitoring software is configured, actual data transmission is completed by a Controller Area Network (CAN) interface card and a CAN bus in an intermediate layer, detection equipment and various sensors are hung on the CAN bus through a CAN _ input module, monitoring data such as the speed of a coal mining machine, the posture of the coal mining machine, the chasing speed of a hydraulic support, the number of the hydraulic support, the current power of a scraper, the working state of the coal mining machine, the working state of the scraper conveyor, the working state of the hydraulic support and the like are transmitted to the underground substation through the CAN bus, and the plurality of substations transmit and forward safety monitoring information between the underground control substation and an upper Computer of the ground central station through a shielded twisted pair and the CAN communication card. Two ends of the CAN bus are connected with 120 ohm terminal matching resistors for suppressing reflection so as to improve the anti-interference performance and reliability of communication.
Therefore, the sensor data of the coal mining machine, the scraper conveyor and the hydraulic support are synchronized with the time difference not more than 50ms, calculation is carried out in an underground substation, action parameters are fed back through a CAN bus, and three-machine integrated cooperative control is realized.
S207: and controlling the scraper conveyor, the coal mining machine and the hydraulic support to operate according to the target operation control parameters.
The embodiment of the disclosure CAN control the scraper conveyor, the coal mining machine and the hydraulic support to operate with the target operation control parameters after the target operation control parameters are respectively transmitted to the scraper conveyor, the coal mining machine and the hydraulic support based on the controller area network CAN.
In an embodiment of the present disclosure, target operation data of a plurality of coal mining devices is obtained, where the plurality of coal mining devices include: the method comprises the steps of determining a target function for controlling the operation of a plurality of coal mining devices according to target operation data, determining a reward function of a depth Q learning algorithm according to the target operation data, determining the reward function of the depth Q learning algorithm according to the target operation data, obtaining target operation control parameters according to the reward function, action parameters and the target function, respectively transmitting the target operation control parameters to a scraper conveyor, a coal mining machine and a hydraulic support based on a Controller Area Network (CAN), and controlling the scraper conveyor, the coal mining machine and the hydraulic support to operate according to the target operation control parameters, so that the cooperative control of the coal mining machine, the scraper conveyor and the hydraulic support and other devices CAN be realized, the necessary safety characteristics of water prevention, dust prevention, explosion prevention and the like of the individual devices during deep mining under a coal mine CAN be met, and the cooperative control among the multiple devices determines the cooperative work efficiency among the devices and the safety of the whole working face.
Fig. 3 is a schematic structural diagram of an apparatus operation control device based on a deep Q learning algorithm according to an embodiment of the present disclosure.
As shown in fig. 3, the apparatus operation control device 30 based on the deep Q learning algorithm includes:
an obtaining module 301, configured to obtain target operation data of a plurality of coal equipment, where the plurality of coal equipment includes: scraper conveyors, coal mining machines and hydraulic supports;
a determining module 302, configured to determine, according to the target operation data, an objective function for performing operation control on multiple coal mining equipment;
the processing module 303 is configured to process the target operation data and the target function based on a deep Q learning algorithm to obtain a target operation control parameter;
and the control module 304 is used for controlling the operation of the scraper conveyor, the coal mining machine and the hydraulic support based on the target operation control parameters.
In some embodiments of the present disclosure, as shown in fig. 4, fig. 4 is a schematic structural diagram of an apparatus operation control device based on a deep Q learning algorithm according to another embodiment of the present disclosure, and the determining module 302 is further configured to:
according to the target operation data, constructing constraint conditions of the operation states of a plurality of coal mining machines, constraint conditions of the cutting speed of the coal mining machine and constraint conditions of the working power of the scraper conveyor;
and constructing an objective function according to the constraint conditions of the running states of a plurality of coal mining machines, the constraint conditions of the cutting speed of the coal mining machine and the constraint conditions of the working power of the scraper conveyor.
In some embodiments of the present disclosure, constraints on operating conditions of a plurality of coal equipment are as follows:
wherein A is v Indicating the operating state of the hydraulic support, A m Indicating whether the working state of the mining machine is abnormal, A s Indicating whether the working state of the scraper conveyor is abnormal or not.
The constraint condition of the cutting speed of the coal mining machine is shown as the following formula:
wherein, V m Which represents the cutting speed of the shearer when cutting coal,
representing a cutting speed threshold value when the coal mining machine cuts coal;
the constraint condition of the working power of the scraper conveyor is shown as the following formula:
L s ≤1；
wherein L is s The ratio of the scraper conveyor motor current to the rated current is shown.
In some embodiments of the present disclosure, the objective function is as follows:
wherein the content of the first and second substances,
representing the cutting speed at which the shearer cuts the coal at time t,
representing the chasing speed of the hydraulic support at the time t,
represents the proportion of the current of the motor of the scraper conveyor to the rated current at the time t,
showing the working state of the hydraulic support at the moment t,
showing whether the working state of the coal mining machine is abnormal at the moment t,
indicates whether the working state of the scraper conveyer is abnormal at the time t,
represents the cutting speed of the shearer when cutting coal at the time t,
representing the cutting speed threshold at which the shearer cuts the coal.
The ratio of the scraper conveyor motor current to the rated current is shown.
In some embodiments of the present disclosure, the processing module 303 includes:
the first determining submodule 3031 is used for determining a reward function of the deep Q learning algorithm according to the target operation data; wherein the reward function is represented by the following formula:
wherein, reward represents a reward function,
the working speed of the coal mining machine is shown,
the speed of the hydraulic support and the machine is shown,
a prize value corresponding to the scraper conveyor power is indicated,
a reward value representing the speed difference between the coal machine and the hydraulic support,
representing a prize value corresponding to the speed differential of the face conveyor and the hydraulic mount,
representing the reward value, A, corresponding to the difference in speed between the shearer and the scraper conveyor t The reward value corresponding to the abnormity of the coal machine equipment at the current moment is represented;
a second determining submodule 3032, configured to determine, according to the target operation data, an action parameter of the multiple coal mining devices;
and an obtaining submodule 3033, configured to obtain the target operation control parameter according to the reward function, the action parameter, and the target function.
In some embodiments of the present disclosure, the obtaining sub-module 303 is further configured to:
determining state parameters of a plurality of coal equipment according to the reward function and the action parameters;
and acquiring target operation control parameters according to the state parameters, the reward function, the action parameters and the target function.
In some embodiments of the present disclosure, the control module 304 is further configured to:
respectively transmitting target operation control parameters to a scraper conveyor, a coal mining machine and a hydraulic support based on a Controller Area Network (CAN);
and controlling the scraper conveyor, the coal mining machine and the hydraulic support to operate according to the target operation control parameters.
In some embodiments of the present disclosure, the obtaining module 301 is further configured to:
acquiring initial operation data of a plurality of coal equipment, wherein the initial operation data of each coal equipment has corresponding sampling frequency information;
and adjusting the sampling frequency information corresponding to the initial operation data of the hydraulic support and the scraper conveyor according to the corresponding sampling frequency information of the coal mining machine.
Corresponding to the device operation control method based on the deep Q learning algorithm provided in the embodiments of fig. 1 to 2, the present disclosure also provides a device operation control device based on the deep Q learning algorithm, and since the device operation control device based on the deep Q learning algorithm provided in the embodiments of the present disclosure corresponds to the device operation control method based on the deep Q learning algorithm provided in the embodiments of fig. 1 to 2, the embodiment of the device operation control method based on the deep Q learning algorithm is also applicable to the device operation control device based on the deep Q learning algorithm provided in the embodiments of the present disclosure, and is not described in detail in the embodiments of the present disclosure.
In this embodiment, target operation data of a plurality of coal mining devices is obtained, where the plurality of coal mining devices include: the method comprises the steps of determining a target function for controlling the operation of a plurality of coal mining devices according to target operation data, processing the target operation data and the target function based on a depth Q learning algorithm to obtain target operation control parameters, and controlling the operation of the scraper conveyor, the coal mining device and the hydraulic support based on the target operation control parameters, so that the cooperative control of the coal mining device, the scraper conveyor, the hydraulic support and other devices can be realized, the necessary safety characteristics of water prevention, dust prevention, explosion prevention and the like of the individual devices during deep mining in a coal mine well can be met, and the cooperative control among the multiple devices determines the cooperative work efficiency among the devices and the safety of the whole working face.
In order to implement the above embodiments, the present disclosure also provides an electronic device, including: the device comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein when the processor executes the program, the device running control method based on the deep Q learning algorithm is realized.
In order to achieve the above embodiments, the present disclosure also proposes a non-transitory computer-readable storage medium on which a computer program is stored, which when executed by a processor implements the device operation control method based on the deep Q learning algorithm as proposed by the foregoing embodiments of the present disclosure.
In order to implement the foregoing embodiments, the present disclosure further proposes a computer program product, which when executed by an instruction processor in the computer program product, executes the device operation control method based on the deep Q learning algorithm as proposed by the foregoing embodiments of the present disclosure.
FIG. 5 illustrates a block diagram of an exemplary electronic device suitable for use in implementing embodiments of the present disclosure. The electronic device 12 shown in fig. 5 is only an example and should not bring any limitations to the functionality and scope of use of the embodiments of the present disclosure.
As shown in FIG. 5, electronic device 12 is embodied in the form of a general purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
Electronic device 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by electronic device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
Although not shown in FIG. 5, a disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a Compact disk Read Only Memory (CD-ROM), a Digital versatile disk Read Only Memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the disclosure.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally perform the functions and/or methodologies of the embodiments described in this disclosure.
Electronic device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with electronic device 12, and/or with any devices (e.g., network card, modem, etc.) that enable electronic device 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Also, the electronic device 12 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public Network such as the Internet) via the Network adapter 20. As shown, the network adapter 20 communicates with the other modules of the electronic device 12 over the bus 18. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, and data backup storage systems, among others.
The processing unit 16 executes various functional applications and data processing by executing programs stored in the system memory 28, for example, implementing the device operation control method based on the deep Q learning algorithm mentioned in the foregoing embodiments.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
It should be noted that, in the description of the present disclosure, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Further, in the description of the present disclosure, "a plurality" means two or more unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and the scope of the preferred embodiments of the present disclosure includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present disclosure.
It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried out in the method for implementing the above embodiment may be implemented by hardware that is related to instructions of a program, and the program may be stored in a computer readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiment.
In addition, functional units in the embodiments of the present disclosure may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present disclosure have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure, and that changes, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present disclosure.
Claims (10)
1. A device operation control method based on a deep Q learning algorithm is characterized by comprising the following steps:
obtaining target operational data for a plurality of coal mining equipment, wherein the plurality of coal mining equipment comprises: scraper conveyors, coal mining machines and hydraulic supports;
determining a target function for controlling the operation of a plurality of coal mining devices according to the target operation data;
processing the target operation data and the target function based on the deep Q learning algorithm to obtain target operation control parameters;
and controlling the scraper conveyor, the coal mining machine and the hydraulic support to operate based on the target operation control parameters.
2. The method of claim 1, wherein said determining an objective function for operational control of a plurality of said coal mining equipment based on said target operational data comprises:
constructing constraint conditions of a plurality of coal mining machine equipment operation states, constraint conditions of the cutting speed of the coal mining machine and constraint conditions of the working power of the scraper conveyor according to the target operation data;
and constructing the objective function according to the constraint conditions of the running states of the coal mining machines, the constraint conditions of the cutting speed of the coal mining machine and the constraint conditions of the working power of the scraper conveyor.
3. The method of claim 2, wherein the constraints on the operating conditions of the plurality of the coal equipment are as follows:
wherein A is v Indicating the operating state of the hydraulic support, A m Indicating whether the working state of the mining machine is abnormal, A s Indicating whether the working state of the scraper conveyor is abnormal.
The constraint condition of the cutting speed of the coal mining machine is shown as follows:
wherein, V m Which represents the cutting speed of the shearer when cutting coal,
representing a cutting speed threshold value when the coal mining machine cuts coal;
the constraint condition of the working power of the scraper conveyor is as follows:
L s ≤1；
wherein L is s The ratio of the motor current of the scraper conveyor to the rated current is shown.
4. The method of claim 2, wherein the objective function is represented by the following equation:
wherein the content of the first and second substances,
representing the cutting speed at which the shearer cuts the coal at time t,
shows the chasing speed of the hydraulic support at the time t,
represents the proportion of the current of the motor of the scraper conveyor to the rated current at the time t,
the working state of the hydraulic support at the time t is shown,
showing whether the working state of the coal mining machine is abnormal at the moment t,
indicating whether the working state of the scraper conveyor is abnormal at the time t,
representing the cutting speed at which the shearer cuts the coal at time t,
representing the cutting speed threshold at which the shearer cuts the coal.
The ratio of the motor current of the scraper conveyor to the rated current is shown.
5. The method of claim 1, wherein the processing the target operational data and the target function based on the deep Q learning algorithm to obtain target operational control parameters comprises:
determining a reward function of the deep Q learning algorithm according to the target operation data; wherein the reward function is represented by the following equation:
wherein, reward represents a reward function,
the working speed of the coal mining machine is shown,
the speed of the hydraulic support and the speed of the machine are shown,
a prize value corresponding to the power of the face conveyor is indicated,
a reward value representing the speed difference between the coal machine and the hydraulic support,
indicating scraper conveyer and hydraulic supportThe prize value corresponding to the speed difference of the rack,
representing the reward value, A, corresponding to the difference in speed of the shearer and the scraper conveyor t The reward value corresponding to the abnormity of the coal machine equipment at the current moment is represented;
determining action parameters of a plurality of coal mining devices according to the target operation data;
and acquiring the target operation control parameters according to the reward function, the action parameters and the target function.
6. The method of claim 5, wherein obtaining the target operational control parameter based on the reward function, the action parameter, and the objective function comprises:
determining state parameters of a plurality of coal equipment according to the reward function and the action parameters;
and acquiring the target operation control parameters according to the state parameters, the reward function, the action parameters and the target function.
7. The method of claim 1, wherein the controlling the operation of the face conveyor, the shearer loader, and the hydraulic mount based on the target operational control parameters comprises:
transmitting the target operation control parameters to the scraper conveyor, the coal mining machine and the hydraulic support respectively based on a Controller Area Network (CAN);
and controlling the scraper conveyor, the coal mining machine and the hydraulic support to operate according to the target operation control parameters.
8. An apparatus operation control device based on a deep Q learning algorithm, comprising:
an acquisition module for acquiring target operational data for a plurality of coal mining equipment, wherein the plurality of coal mining equipment comprises: scraper conveyors, coal mining machines and hydraulic supports;
the determining module is used for determining a target function for controlling the operation of the coal mining equipment according to the target operation data;
the processing module is used for processing the target operation data and the target function based on the deep Q learning algorithm to obtain target operation control parameters;
and the control module is used for controlling the scraper conveyor, the coal mining machine and the hydraulic support to operate based on the target operation control parameters.
9. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.
10. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-7.
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