CN112440152B - Milling control method and device, expert database model training method and device - Google Patents
Milling control method and device, expert database model training method and device Download PDFInfo
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- CN112440152B CN112440152B CN202011242490.8A CN202011242490A CN112440152B CN 112440152 B CN112440152 B CN 112440152B CN 202011242490 A CN202011242490 A CN 202011242490A CN 112440152 B CN112440152 B CN 112440152B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
- B23Q15/00—Automatic control or regulation of feed movement, cutting velocity or position of tool or work
- B23Q15/007—Automatic control or regulation of feed movement, cutting velocity or position of tool or work while the tool acts upon the workpiece
- B23Q15/013—Control or regulation of feed movement
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
- B23Q1/00—Members which are comprised in the general build-up of a form of machine, particularly relatively large fixed members
- B23Q1/0009—Energy-transferring means or control lines for movable machine parts; Control panels or boxes; Control parts
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
- B23Q15/00—Automatic control or regulation of feed movement, cutting velocity or position of tool or work
- B23Q15/007—Automatic control or regulation of feed movement, cutting velocity or position of tool or work while the tool acts upon the workpiece
- B23Q15/08—Control or regulation of cutting velocity
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/17—Mechanical parametric or variational design
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
Abstract
The application provides a milling control method, which solves the problem of low working efficiency of equipment. The milling control method comprises the following steps: acquiring a first operation parameter of the equipment in operation in real time; calculating the actual power of a first module according to the first operating parameter and preset data when the equipment operates, wherein the preset data comprises inherent attribute data of the equipment; calculating the maximum theoretical power of a second module according to the total output power of the equipment and the actual power of the first module; inputting the first operating parameter, the total output power of the equipment, the actual power of the first module and the maximum theoretical power of the second module into an expert library model to generate a reference operating parameter, wherein the expert library model is configured to enable the theoretical efficiency of the equipment when the equipment operates at the reference operating parameter to reach a preset threshold; and determining a final operating parameter according to the reference operating parameter.
Description
Technical Field
The application relates to the technical field of intelligent control, in particular to a milling control method and device, an expert database model training method and device, electronic equipment and a computer readable storage medium.
Background
The double-wheel slot milling machine is special equipment for underground diaphragm wall construction, has higher operation difficulty and has high requirements on a manipulator for operating the equipment. The main working modules of the double-wheel slot milling machine are a slurry pump and a milling wheel module, the milling wheel module is used for milling a stratum, and the slurry pump is used for discharging milled slag and stone slurry. At present, the operation parameters of the equipment are adjusted mainly by depending on the operation experience of a manipulator, the intelligent adjustment of the operation parameters cannot be realized, and the working efficiency of the equipment is low.
Disclosure of Invention
In view of this, the present application provides a milling control method and apparatus, an expert database model training method and apparatus, an electronic device, and a computer-readable storage medium, which solve the problem of low working efficiency of the device.
In a first aspect, the present application provides a milling control method, including: acquiring a first operation parameter of the equipment in operation in real time; calculating the actual power of a first module according to the first operating parameter and preset data when the equipment operates, wherein the preset data comprises inherent attribute data of the equipment; calculating the maximum theoretical power of a second module according to the total output power of the equipment and the actual power of the first module; inputting the first operating parameter, the total output power of the equipment, the actual power of the first module and the maximum theoretical power of the second module into an expert library model to generate a reference operating parameter, wherein the expert library model is configured to enable the theoretical efficiency of the equipment when the equipment operates at the reference operating parameter to reach a preset threshold; and determining a final operating parameter according to the reference operating parameter.
With reference to the first aspect, in a possible implementation manner, the determining a final operating parameter according to the reference operating parameter includes: operating the device according to the reference operating parameter; adjusting the reference operation parameter to obtain an intermediate operation parameter; enabling the equipment to operate according to the intermediate operation parameters, and collecting the operation efficiency of the equipment; and determining the intermediate operation parameter when the operation efficiency of the equipment reaches the maximum value in a preset time period as a final operation parameter.
With reference to the first aspect, in a possible implementation manner, the operating the device according to the reference operating parameter includes: operating the apparatus in accordance with a reference second module feed force and a reference second module rotational speed, wherein the reference operating parameters include the reference second module feed force and the reference second module rotational speed; wherein the determining the intermediate operation parameter when the efficiency of the device during operation reaches the maximum value within the preset time period as the final operation parameter comprises: and determining the intermediate operation parameters when the efficiency of the equipment in operation reaches the maximum value in the preset time period as the final second module feeding force and the final second module rotating speed, wherein the intermediate operation parameters comprise the intermediate second module feeding force and the intermediate second module rotating speed.
With reference to the first aspect, in a possible implementation manner, the calculating actual power of a first module according to the first operating parameter and preset data during the operation of the device includes: calculating the formation hardness according to the first operation parameter when the equipment operates; determining a first module theoretical flow rate according to the formation hardness and the preset data, wherein the preset data further comprises a corresponding relation between the formation hardness and the first module theoretical flow rate; and calculating the actual power of the first module according to the theoretical flow rate of the first module, the first operating parameter of the equipment and the preset data.
With reference to the first aspect, in a possible implementation manner, the determining a first module theoretical flow rate according to the formation hardness and the preset data includes: determining the stratum category according to the stratum hardness and the preset data, wherein the preset data further comprises the corresponding relation between the stratum hardness and the stratum category; and determining the theoretical flow rate of the first module according to the stratum category and the preset data, wherein the preset data further comprises the corresponding relation between the stratum category and the theoretical flow rate of the first module.
With reference to the first aspect, in a possible implementation manner, the calculating, according to the total output power of the device and the actual power of the first module, a maximum theoretical power of a second module includes: calculating the total output power of the equipment according to the first operation parameter when the equipment operates; and calculating the difference value between the total output power of the equipment and the actual power of the first module to obtain the maximum theoretical power of the second module.
With reference to the first aspect, in a possible implementation manner, the calculating a total output power of the device according to the first operation parameter when the device is operating includes: collecting real-time power factors of an engine of the device in real time, wherein the first operating parameter comprises the real-time power factors; and calculating the total output power of the equipment according to the real-time power factor and preset data of the engine.
In a second aspect, the present application provides a training method for expert database models, including: establishing an initial expert database model according to the first operating parameter for training, the total output power of the equipment for training, the actual power of the first module for training, the maximum theoretical power of the second module for training and the corresponding final operating parameter for training; acquiring a first operation parameter, total output power of the equipment, actual power of a first module, maximum theoretical power of a second module and a corresponding final operation parameter of the equipment in operation in real time; and updating the initial expert database model according to the first operating parameter, the total output power of the equipment, the actual power of the first module, the maximum theoretical power of the second module and the corresponding final operating parameter when the equipment operates, and generating an expert database model, wherein the expert database model is used for generating a reference operating parameter according to the first operating parameter, the total output power of the equipment, the actual power of the first module and the maximum theoretical power of the second module.
In a third aspect, the present application provides a milling control device, including: the acquisition module is configured to acquire a first operation parameter of the equipment in real time; a first module actual power determining module, configured to calculate a first module actual power according to the first operating parameter and preset data during operation of the device, where the preset data includes inherent attribute data of the device; a second module maximum theoretical power calculation module configured to calculate a second module maximum theoretical power according to the total output power of the device and the first module actual power; a reference operating parameter generating module configured to input the first operating parameter, the total output power of the equipment, the actual power of the first module, and the maximum theoretical power of the second module into an expert database model to generate a reference operating parameter, wherein the expert database model is configured to enable the theoretical efficiency of the equipment when the equipment operates with the reference operating parameter to reach a preset threshold; and a final operating parameter determining module configured to determine a final operating parameter based on the reference operating parameter.
In a fourth aspect, the present application provides an expert database model training device, including: the initial expert database model building module is configured to build an initial expert database model according to the first operating parameters for training, the total output power of the equipment for training, the actual power of the first module for training, the maximum theoretical power of the second module for training and the corresponding final operating parameters for training; the parameter acquisition module is configured to acquire a first operation parameter, total output power of the equipment, actual power of the first module, maximum theoretical power of the second module and a corresponding final operation parameter in real time when the equipment runs; and an expert database model generation module configured to update the initial expert database model according to the first operating parameter, the total output power of the equipment, the first module actual power, the second module maximum theoretical power and the corresponding final operating parameter when the equipment is operated, and generate an expert database model, wherein the expert database model is used for generating a reference operating parameter according to the first operating parameter, the total output power of the equipment, the first module actual power and the second module maximum theoretical power.
In a fifth aspect, the present application provides an electronic device, comprising: a processor; and a memory for storing the processor-executable instructions; the processor is configured to execute the milling control method in any one of the foregoing implementation manners.
In a sixth aspect, the present application provides a computer-readable storage medium storing a computer program for executing the milling control method described in any one of the above-described implementation manners.
The milling control method and device, the expert database model training method and device, the electronic equipment and the computer readable storage medium provided by the embodiment of the application acquire the first operation parameter of the equipment in operation in real time, then calculating the maximum theoretical power of the second module, thereby obtaining the maximum power which can be utilized by the second module, then the maximum theoretical power of the second module is processed by an expert database model to generate a reference operation parameter, so that the theoretical efficiency of the equipment when the equipment runs by the reference operation parameter reaches the preset threshold value, and when the equipment runs according to the reference operation parameter, can fully utilize the total output power of the equipment, provide reference operation parameters for the equipment, and enable the theoretical efficiency of the equipment to reach a preset threshold value, the final operating parameter is then determined based on the reference operating parameter, thereby improving the efficiency of the plant operating at the final operating parameter.
Drawings
Fig. 1 is a schematic flow chart of a milling control method according to an embodiment of the present application.
Fig. 2 is a schematic flow chart of a milling control method according to another embodiment of the present application.
Fig. 3 is a schematic flow chart of a milling control method according to another embodiment of the present application.
Fig. 4 is a schematic flow chart of a milling control method according to another embodiment of the present application.
Fig. 5 is a schematic flow chart of a milling control method according to another embodiment of the present application.
Fig. 6 is a schematic flow chart of a milling control method according to another embodiment of the present application.
Fig. 7 is a schematic flow chart of a milling control method according to another embodiment of the present application.
Fig. 8 is a schematic flowchart illustrating an expert database model training method according to an embodiment of the present application.
Fig. 9 is a schematic structural diagram of a milling control device according to an embodiment of the present application.
Fig. 10 is a schematic structural diagram of a milling control device according to another embodiment of the present application.
Fig. 11 is a schematic structural diagram of an expert database model training device according to an embodiment of the present application.
Fig. 12 is a schematic structural diagram of an electronic device according to an embodiment of 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.
Fig. 1 is a schematic flow chart of a milling control method according to an embodiment of the present application. As shown in fig. 1, the milling control method includes the following steps:
step 101: and acquiring a first operation parameter of the equipment in operation in real time.
Specifically, the equipment can be a double-wheel slot milling machine or other equipment needing power distribution, and the type of the equipment is not particularly limited in the application. The plant may be commissioned based on first operating parameters, which may be empirically determined by the plant operator. The first operation parameter may be a rotation speed of the apparatus, a feeding force of the apparatus, a flow rate of the apparatus, etc., and the first operation parameter may be a different parameter according to a type of the apparatus, and the first operation parameter is not specifically limited in the present application.
Step 102: and calculating the actual power of the first module according to a first operating parameter and preset data during the operation of the equipment, wherein the preset data comprises inherent attribute data of the equipment.
Specifically, the first module actual power may be a function of the first operating parameter and the predetermined data, for example, when the apparatus is a two-wheel slot milling machine, the first module may be a mud pump, and the actual power of the mud pump may be calculated from a mud density, a mud flow rate, a gravitational acceleration, a mud pump pipe diameter, and a mud lift height. The first operating parameter may be mud density, mud flow rate and mud lift height, i.e. mud density and mud flow rate may change accordingly depending on the formation being milled, and mud lift height may change accordingly depending on the depth being milled. The preset data can be gravity acceleration and slurry pump pipe diameter, namely, the preset data can not change according to the operation environment of the equipment and are inherent parameters or inherent properties of the equipment.
Step 103: and calculating the maximum theoretical power of the second module according to the total output power of the equipment and the actual power of the first module.
Specifically, the second module maximum theoretical power may be the maximum power to which the second module may be allocated in an ideal state (excluding transmission-induced power loss, friction-induced power loss, etc.). The device may comprise a first module and a second module, the total power of the device may be allocated to the first module and the second module, and the maximum theoretical power of the second module may be calculated when the total output power of the device and the actual power of the first module are known. For example, when the apparatus is a two-wheel slot milling machine, the first module may be a mud pump and the second module may be a cutterhead module, and the maximum power that the cutterhead module can dispense may be the total output power of the apparatus minus the actual power of the mud pump.
Step 104: and inputting the first operating parameter, the total output power of the equipment, the actual power of the first module and the maximum theoretical power of the second module into an expert library model to generate a reference operating parameter, wherein the expert library model is configured to enable the theoretical efficiency of the equipment when the equipment runs by the reference operating parameter to reach a preset threshold value.
Specifically, the expert database model may match a reference operating parameter according to the input first operating parameter, the total output power of the device, the actual power of the first module, and the maximum theoretical power of the second module, so that the theoretical efficiency of the device when the device operates with the reference operating parameter reaches a preset threshold. The predetermined threshold may be the maximum efficiency that the plant can theoretically achieve when the plant is operating according to the reference operating parameter, or may be a value that is less than the maximum efficiency that the plant can theoretically achieve, for example, the maximum efficiency of the plant is 95% and the predetermined threshold may be 90%.
Step 105: and determining the final operation parameter according to the reference operation parameter.
Specifically, the reference operation parameter may be adjusted to obtain a final operation parameter, so that the efficiency of the equipment operating with the final operation parameter is greater than or equal to the efficiency of the equipment operating with the reference operation parameter.
Therefore, according to the milling control method provided by the embodiment of the application, the maximum theoretical power of the second module is calculated by acquiring the first operating parameter of the equipment in operation in real time, the maximum power which can be utilized by the second module is obtained, the maximum theoretical power of the second module is processed by the expert library model, the reference operating parameter is generated, the theoretical efficiency of the equipment in operation with the reference operating parameter reaches the preset threshold value, the total output power of the equipment can be fully utilized when the equipment operates according to the reference operating parameter, the reference operating parameter which can enable the theoretical efficiency of the equipment to reach the preset threshold value is provided for the equipment, and the final operating parameter is determined according to the reference operating parameter, so that the efficiency of the equipment in operation with the final operating parameter is improved.
Fig. 2 is a schematic flow chart of a milling control method according to another embodiment of the present application. As shown in fig. 2, determining the final operating parameter based on the reference operating parameter includes the steps of:
step 201: the device is operated according to the reference operating parameter.
Specifically, the equipment is operated according to the reference operating parameters generated by the expert library model, and an operating parameter which is relatively consistent with the stratum currently milled is provided for the equipment.
Step 202: and adjusting the reference operation parameter to obtain an intermediate operation parameter.
In particular, the reference operating parameter may comprise a plurality of parameters, such as a reference rotational speed of the device, a reference feed force of the device, etc. The reference operation parameter is adjusted, which may be a certain parameter of the reference operation parameter, or may be multiple parameters of the reference operation parameter, and the number of the adjusted reference operation parameters is not specifically limited in the present application.
Step 203: and operating the equipment according to the intermediate operation parameters, and acquiring the operation efficiency of the equipment.
In particular, the efficiency of the plant is collected when the plant is operating with intermediate operating parameters.
Step 204: and determining the intermediate operation parameter when the operation efficiency of the equipment reaches the maximum value in the preset time period as the final operation parameter.
Specifically, the preset time period is a preset time period, and can be set according to specific situations. The method comprises the steps of adjusting reference operation for multiple times within a preset time period to obtain multiple intermediate operation parameters, operating equipment according to the multiple intermediate operation parameters, acquiring multiple efficiencies of the corresponding equipment, and determining the intermediate operation parameter corresponding to the maximum value of the efficiencies in the multiple efficiency values as a final operation parameter, so that the efficiency of the equipment when the final operation parameter is operated is greater than or equal to the efficiency of the equipment when the reference operation parameter is operated, and further improving the efficiency of the equipment.
Fig. 3 is a schematic flow chart of a milling control method according to another embodiment of the present application. As shown in fig. 3, operating the apparatus according to the reference operating parameter comprises the steps of:
step 301: operating the apparatus based on the reference second module feed force and the reference second module rotational speed, wherein the reference operating parameters include the reference second module feed force and the reference second module rotational speed.
Specifically, the reference operating parameter may include a reference second module feed force and a reference second module rotational speed.
The method for determining the intermediate operation parameter as the final operation parameter when the efficiency of the equipment in operation within the preset time period reaches the maximum value comprises the following steps:
step 302: and determining an intermediate operation parameter when the efficiency of the equipment in operation reaches the maximum value in a preset time period as a final second module feeding force and a final second module rotating speed, wherein the intermediate operation parameter comprises the intermediate second module feeding force and the intermediate second module rotating speed.
In particular, the intermediate second module feed force is obtained by adjusting the reference second module feed force. The intermediate second module speed is obtained by adjusting the reference second module speed. The equipment can be operated according to the feeding force of the middle second module and the rotating speed of the reference second module, the efficiency of the equipment during operation is acquired, the equipment can be operated according to the feeding force of the reference second module and the rotating speed of the middle second module, the efficiency of the equipment during operation is acquired, and the equipment can be operated according to the feeding force of the middle second module and the rotating speed of the middle second module, and the efficiency of the equipment during operation is acquired. According to the collected multiple efficiencies of the equipment, determining the middle second module feeding force and the middle second module rotating speed corresponding to the maximum value of the efficiencies in the multiple efficiency values as the final second module feeding force and the final second module rotating speed, so that the efficiency of the equipment in the operation with the final operation parameters is greater than or equal to the efficiency of the equipment in the operation with the reference operation parameters, and further improving the efficiency of the equipment.
Fig. 4 is a schematic flow chart of a milling control method according to another embodiment of the present application. As shown in fig. 4, calculating the actual power of the first module according to the first operating parameter and the preset data during the operation of the device includes the following steps:
step 401: and calculating the formation hardness according to the first operation parameter when the equipment operates.
Specifically, the actual power of the second module, the feeding force of the second module, and the feeding speed of the second module may be calculated according to the first operating parameter, and the formation hardness may be a function of the actual power of the second module, the feeding force of the second module, and the feeding speed of the second module, that is, a numerical value of the formation hardness may be calculated according to the actual power of the second module, the feeding force of the second module, and the feeding speed of the second module, that is, the formation hardness may be calculated according to the first operating parameter.
Step 402: and determining the theoretical flow rate of the first module according to the formation hardness and preset data, wherein the preset data further comprises the corresponding relation between the formation hardness and the theoretical flow rate of the first module.
Specifically, after the formation hardness is known, a first module theoretical flow rate corresponding to the formation hardness can be obtained according to preset data.
Step 403: and calculating the actual power of the first module according to the theoretical flow rate of the first module, the first operating parameter of the equipment and preset data.
Specifically, the first module actual power may be a function of the first module theoretical flow rate, the first operating parameter of the plant, and the preset data, that is, the first module actual power may follow a change when the first module theoretical flow rate and the first operating parameter of the plant change.
For example, when the apparatus is a two-wheel slot milling machine, the first module theoretical flow rate may be a mud pump theoretical flow rate, the first operating parameter of the apparatus may be mud density, mud flow rate, and mud lift height, and the preset data may be gravity acceleration and mud pump pipe diameter.
By calculating the formation hardness, then determining the theoretical flow rate of the first module, and then calculating the actual power of the first module according to the theoretical flow rate of the first module, the calculation of the actual power of the first module is more accurate, so that the power distribution is more accurate, and the operation efficiency of the equipment is further improved.
Fig. 5 is a schematic flow chart of a milling control method according to another embodiment of the present application. As shown in fig. 5, determining the first module theoretical flow rate based on the formation hardness and the preset data comprises the following steps:
step 501: and determining the stratum type according to the stratum hardness and preset data, wherein the preset data further comprises the corresponding relation between the stratum hardness and the stratum type.
Specifically, the formation types may be classified into a plurality of levels according to specific values of the formation hardness, for example, the formation types may be classified from small to large according to the values of the formation hardness, that is, the formation hardness is from soft to hard: soft soil layer, hard soil layer, soil and stone mixing layer, soft rock layer, hard rock layer.
Step 502: and determining the theoretical flow rate of the first module according to the stratum category and preset data, wherein the preset data further comprises the corresponding relation between the stratum category and the theoretical flow rate of the first module.
Specifically, each formation category corresponds to a first module theoretical flow rate.
The stratum category is determined according to the stratum hardness, so that each stratum category corresponds to one first module theoretical flow rate, the first module theoretical flow rate can be determined more quickly, and the real-time performance of power distribution is improved.
Fig. 6 is a schematic flow chart of a milling control method according to another embodiment of the present application. As shown in fig. 6, calculating the maximum theoretical power of the second module according to the total output power of the device and the actual power of the first module includes the following steps:
step 601: and calculating the total output power of the equipment according to the first operation parameter when the equipment operates.
Specifically, the operating environment of the device is different, the first operating parameter of the device during operation may also change correspondingly, and the total output power of the device may also change accordingly, for example, the ambient temperature is low, and the first operating parameter of the device during operation, such as the output power of the transmission efficiency or the engine, may be lower, which may result in a reduction in the total output power of the device.
Step 602: and calculating the difference value of the total output power of the equipment and the actual power of the first module to obtain the maximum theoretical power of the second module.
Specifically, when the device includes only the first module and the second module, the difference between the total output power of the device and the actual power of the first module is the maximum theoretical power of the second module.
The total output power of the equipment is calculated according to the first operation parameter when the equipment operates, so that the calculation of the total output power of the equipment is more accurate, the maximum theoretical power of the second module is obtained through difference calculation, the calculation of the maximum theoretical power of the second module is simpler, the real-time performance of power distribution is further improved, and the efficiency of the equipment is further improved.
Fig. 7 is a schematic flow chart of a milling control method according to another embodiment of the present application. As shown in fig. 7, calculating the total output power of the device according to the first operating parameter when the device is operating includes the following steps:
step 701: real-time power factors of an engine of the device are collected in real-time, wherein the first operating parameter includes the real-time power factors.
Specifically, the power factor of the engine is the ratio of the power output by the engine and the power rating of the engine. The power factor of the engine may vary due to the engine operating in different environments or due to the varying time of use of the engine.
Step 702: and calculating the total output power of the equipment according to the real-time power factor and preset data of the engine.
Specifically, the real-time power factor of the engine is acquired in real time and then multiplied by the rated power of the engine, so that the total output power of the engine is obtained.
The total output power of the equipment is calculated according to the real-time power factor of the engine, so that the calculation of the total output power is more accurate, the accuracy of power distribution is further improved, and the efficiency of the equipment is further improved.
Fig. 8 is a schematic flowchart illustrating an expert database model training method according to an embodiment of the present application. As shown in fig. 8, the expert database model training method includes the following steps:
step 801: and establishing an initial expert database model according to the first operating parameter for training, the total output power of the equipment for training, the actual power of the first module for training, the maximum theoretical power of the second module for training and the corresponding final operating parameter for training.
Specifically, the initial expert database model is a learning model, which can be continuously refined according to continuously updated parameters. And operating the equipment by using the first operation parameter for training, the total output power of the equipment for training, the actual power of the first module for training, the maximum theoretical power of the second module for training and the corresponding final operation parameter for training, wherein the actual power of the equipment is greater than or equal to a preset threshold value.
Step 802: and acquiring a first operation parameter, the total output power of the equipment, the actual power of the first module, the maximum theoretical power of the second module and a corresponding final operation parameter during the operation of the equipment in real time.
Step 803: updating an initial expert database model according to a first operation parameter, total output power of the equipment, actual power of a first module, maximum theoretical power of a second module and a corresponding final operation parameter when the equipment operates, and generating an expert database model, wherein the expert database model is used for generating a reference operation parameter according to the first operation parameter, the total output power of the equipment, the actual power of the first module and the maximum theoretical power of the second module.
Specifically, a first operation parameter, a total output power of the equipment, an actual power of a first module, a maximum theoretical power of a second module and a corresponding final operation parameter during the operation of the equipment are collected in real time, the parameters are used as training parameters of an expert database model to update an initial expert database model, so that the expert database model is generated, the expert database model is continuously perfected, the theoretical efficiency of the equipment during the operation of the equipment with reference operation parameters reaches a preset threshold, the reference operation parameter which can reach the preset threshold of the efficiency is provided for the operation of the equipment, the time for the equipment to reach the maximum efficiency is shortened, and the instantaneity for the equipment to reach the maximum efficiency is improved.
Fig. 9 is a schematic structural diagram of a milling control device according to an embodiment of the present application. As shown in fig. 9, the milling control apparatus 900 includes: the device comprises an acquisition module 901, a first module actual power determination module 902, a second module maximum theoretical power calculation module 903, a reference operation parameter generation module 904 and a final operation parameter determination module 905.
The acquisition module 901 is configured to: and acquiring a first operation parameter of the equipment in operation in real time.
The first module real power determination module 902 is configured to: and calculating the actual power of the first module according to a first operating parameter and preset data during the operation of the equipment, wherein the preset data comprises inherent attribute data of the equipment.
The second module maximum theoretical power calculation module 903 is configured to: and calculating the maximum theoretical power of the second module according to the total output power of the equipment and the actual power of the first module.
The reference operating parameter generation module 904 is configured to: and inputting the first operating parameter, the total output power of the equipment, the actual power of the first module and the maximum theoretical power of the second module into an expert library model to generate a reference operating parameter, wherein the expert library model is configured to enable the theoretical efficiency of the equipment when the equipment runs by the reference operating parameter to reach a preset threshold value.
The final operating parameter determination module 905 is configured to: and determining the final operation parameter according to the reference operation parameter.
Fig. 10 is a schematic structural diagram of a milling control device according to another embodiment of the present application. As shown in fig. 10, the final operating parameter determination module 905 includes: a parameter providing unit 9051, an intermediate operation parameter determining unit 9052, an efficiency acquiring unit 9053, and a final operation parameter determining unit 9054.
The parameter providing unit 9051 is configured to: the device is operated according to the reference operating parameter.
The intermediate operation parameter determination unit 9052 is configured to: and adjusting the reference operation parameter to obtain an intermediate operation parameter.
The efficiency acquisition unit 9053 is configured to: and operating the equipment according to the intermediate operation parameters, and acquiring the operation efficiency of the equipment.
The final operation parameter determination unit 9054 is configured to: and determining the intermediate operation parameter when the operation efficiency of the equipment reaches the maximum value in the preset time period as the final operation parameter.
The parameter providing unit 9051 is further configured to: operating the apparatus based on the reference second module feed force and the reference second module rotational speed, wherein the reference operating parameters include the reference second module feed force and the reference second module rotational speed.
The final operating parameter determination unit 9054 is further configured to: and determining an intermediate operation parameter when the efficiency of the equipment in operation reaches the maximum value in a preset time period as a final second module feeding force and a final second module rotating speed, wherein the intermediate operation parameter comprises the intermediate second module feeding force and the intermediate second module rotating speed.
The first module real power determination module 902 includes: the device comprises a formation hardness calculation unit 9021, a first module theoretical flow rate determination unit 9022 and a first module actual power calculation unit 9023.
The formation hardness calculation unit 9021 is configured to: and calculating the formation hardness according to the first operation parameter when the equipment operates.
The first module theoretical flow rate determination unit 9022 is configured to: and determining the theoretical flow rate of the first module according to the formation hardness and preset data, wherein the preset data further comprises the corresponding relation between the formation hardness and the theoretical flow rate of the first module.
The first module real power calculation unit 9023 is configured to: and calculating the actual power of the first module according to the theoretical flow rate of the first module, the first operating parameter of the equipment and preset data.
The first module theoretical flow rate determination unit 9022 includes: a formation type determination subunit 90221 and a first module theoretical flow rate determination subunit 90222.
The stratum category determination subunit 90221 is configured to: and determining the stratum type according to the stratum hardness and preset data, wherein the preset data further comprises the corresponding relation between the stratum hardness and the stratum type.
The first module theoretical flow rate determining subunit 90222 is configured to: and determining the theoretical flow rate of the first module according to the stratum category and preset data, wherein the preset data further comprises the corresponding relation between the stratum category and the theoretical flow rate of the first module.
The second module maximum theoretical power calculation module 903 comprises: a total output power calculation unit 9031 and a second module maximum theoretical power calculation unit 9032.
The total output power calculation unit 9031 is configured to: and calculating the total output power of the equipment according to the first operation parameter when the equipment operates.
The second module maximum theoretical power calculation unit 9032 is configured to: and calculating the difference value of the total output power of the equipment and the actual power of the first module to obtain the maximum theoretical power of the second module.
The total output power calculation unit 9031 includes: a real-time power factor acquisition subunit 90311 and a total output power calculation subunit 90312.
The real-time power factor acquisition subunit 90311 is configured to: real-time power factors of an engine of the device are collected in real-time, wherein the first operating parameter includes the real-time power factors.
The total output power calculation subunit 90312 is configured to: and calculating the total output power of the equipment according to the real-time power factor and preset data of the engine.
Fig. 11 is a schematic structural diagram of an expert database model training device according to an embodiment of the present application. As shown in fig. 11, the expert database model training device 110 includes: the system comprises an initial expert database model establishing module 1101, a parameter collecting module 1102 and an expert database model generating module 1103.
The initial expert library model setup module 1101 is configured to: and establishing an initial expert database model according to the first operating parameter for training, the total output power of the equipment for training, the actual power of the first module for training, the maximum theoretical power of the second module for training and the corresponding final operating parameter for training.
The parameter acquisition module 1102 is configured to: and acquiring a first operation parameter, the total output power of the equipment, the actual power of the first module, the maximum theoretical power of the second module and a corresponding final operation parameter during the operation of the equipment in real time.
The expert library model generation module 1103 is configured to: updating an initial expert database model according to a first operation parameter, total output power of the equipment, actual power of a first module, maximum theoretical power of a second module and a corresponding final operation parameter when the equipment operates, and generating an expert database model, wherein the expert database model is used for generating a reference operation parameter according to the first operation parameter, the total output power of the equipment, the actual power of the first module and the maximum theoretical power of the second module.
Next, an electronic apparatus according to an embodiment of the present application is described with reference to fig. 12. Fig. 12 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
As shown in fig. 12, the electronic device 120 includes one or more processors 1201 and memory 1202.
The processor 1201 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device 120 to perform desired functions.
Memory 1202 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. Volatile memory can include, for example, Random Access Memory (RAM), cache memory (or the like). The non-volatile memory may include, for example, Read Only Memory (ROM), a hard disk, flash memory, and the like. One or more computer program instructions may be stored on a computer readable storage medium and executed by the processor 1201 to implement the milling control methods of the various embodiments of the present application described above or other desired functions. Various content such as milling parameters may also be stored in the computer readable storage medium.
In one embodiment, the electronic device 120 may be a two-wheel slot milling machine.
In one embodiment, the electronic device 120 may further include: an input device 1203 and an output device 1204, which are interconnected by a bus system and/or other form of connection mechanism (not shown).
The input device 1203 may include, for example, a keyboard, a mouse, and the like.
The output device 1204 may output various information including the determined exercise data and the like to the outside. The output 1204 may include, for example, a display, a communication network, a remote output device connected thereto, and so forth.
Of course, for simplicity, only some of the components of the electronic device 120 relevant to the present application are shown in fig. 12, and components such as buses, input/output interfaces, and the like are omitted. In addition, the electronic device 120 may include any other suitable components, depending on the particular application.
In addition to the above-described methods and apparatus, embodiments of the present application may also be a computer program product comprising computer program instructions which, when executed by a processor, cause the processor to perform the steps in the milling control method according to various embodiments of the present application described in the present specification.
The computer program product may include program code for carrying out operations for embodiments of the present application in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present application may also be a computer-readable storage medium having stored thereon computer program instructions, which, when executed by a processor, cause the processor to perform the steps in the milling control method of the present specification according to various embodiments of the present application.
A computer-readable storage medium may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing describes the general principles of the present application in conjunction with specific embodiments, however, it is noted that advantages, effects, etc. mentioned in the present application are only embodiments and are not limiting, and they should not be considered essential to the various embodiments of the present application. Furthermore, the foregoing disclosure of specific details is merely for purposes of example and not for purposes of limitation, and the present disclosure is not limited to the specific details set forth herein as they may suggest or render expedient.
The block diagrams of devices, apparatuses, systems referred to in this application are only given as illustrative examples and are not intended to require or imply that the connections, arrangements, configurations, etc. must be made in the manner shown in the block diagrams. These devices, apparatuses, devices, systems may be connected, arranged, configured in any manner, as will be appreciated by those skilled in the art. Words such as "including," "comprising," "having," and the like are open-ended words that mean "including, but not limited to," and are used interchangeably therewith. The words "or" and "as used herein mean, and are used interchangeably with, the word" and/or, "unless the context clearly dictates otherwise. The word "such as" is used herein to mean, and is used interchangeably with, the phrase "such as but not limited to".
It should also be noted that in the devices, apparatuses, and methods of the present application, the components or steps may be decomposed and/or recombined. These decompositions and/or recombinations are to be considered as equivalents of the present application.
The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the application. Thus, the present application is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modifications, equivalents and the like that are within the spirit and principle of the present application should be included in the scope of the present application.
Claims (10)
1. A milling control method, characterized by comprising:
acquiring a first operation parameter of the equipment in operation in real time;
calculating the actual power of a first module according to the first operating parameter and preset data when the equipment operates, wherein the preset data comprises inherent attribute data of the equipment;
calculating the maximum theoretical power of a second module according to the total output power of the equipment and the actual power of the first module;
inputting the first operating parameter, the total output power of the equipment, the actual power of the first module and the maximum theoretical power of the second module into an expert library model to generate a reference operating parameter, wherein the expert library model is configured to enable the theoretical efficiency of the equipment when the equipment operates at the reference operating parameter to reach a preset threshold; and
determining a final operation parameter according to the reference operation parameter;
wherein, the calculating the actual power of the first module according to the first operating parameter and the preset data during the operation of the equipment comprises:
calculating to obtain the actual power of a second module, the feeding force of the second module and the feeding speed of the second module according to the first operation parameter when the equipment operates;
calculating to obtain the formation hardness according to the second module actual power, the second module feeding force and the second module feeding speed;
determining a first module theoretical flow rate according to the formation hardness and the preset data, wherein the preset data further comprises a corresponding relation between the formation hardness and the first module theoretical flow rate; and
calculating a first module actual power according to the first module theoretical flow rate, the first operating parameter of the equipment and the preset data;
wherein determining a first module theoretical flow rate based on the formation hardness and the preset data comprises:
determining the stratum category according to the stratum hardness and the preset data, wherein the preset data further comprises the corresponding relation between the stratum hardness and the stratum category; and
and determining the theoretical flow rate of the first module according to the stratum category and the preset data, wherein the preset data further comprises the corresponding relation between the stratum category and the theoretical flow rate of the first module.
2. The milling control method of claim 1, wherein determining a final operating parameter from the reference operating parameter comprises:
operating the device according to the reference operating parameter;
adjusting the reference operation parameter to obtain an intermediate operation parameter;
enabling the equipment to operate according to the intermediate operation parameters, and collecting the operation efficiency of the equipment; and
and determining the intermediate operation parameter when the operation efficiency of the equipment reaches the maximum value in a preset time period as a final operation parameter.
3. The milling control method of claim 2, wherein operating the device in accordance with the reference operating parameter comprises:
operating the apparatus in accordance with a reference second module feed force and a reference second module rotational speed, wherein the reference operating parameters include the reference second module feed force and the reference second module rotational speed;
wherein the determining the intermediate operation parameter when the efficiency of the device during operation reaches the maximum value within the preset time period as the final operation parameter comprises:
and determining the intermediate operation parameters when the efficiency of the equipment in operation reaches the maximum value in the preset time period as the final second module feeding force and the final second module rotating speed, wherein the intermediate operation parameters comprise the intermediate second module feeding force and the intermediate second module rotating speed.
4. The milling control method of claim 1, wherein calculating a second module maximum theoretical power based on the total output power of the device and the first module actual power comprises:
calculating the total output power of the equipment according to the first operation parameter when the equipment operates; and
and calculating the difference value between the total output power of the equipment and the actual power of the first module to obtain the maximum theoretical power of the second module.
5. The milling control method of claim 4, wherein calculating the total output power of the device as a function of the first operating parameter while the device is operating comprises:
collecting real-time power factors of an engine of the device in real time, wherein the first operating parameter comprises the real-time power factors; and
and calculating the total output power of the equipment according to the real-time power factor and preset data of the engine.
6. An expert database model training method is characterized by comprising the following steps:
establishing an initial expert database model according to the first operating parameter for training, the total output power of the equipment for training, the actual power of the first module for training, the maximum theoretical power of the second module for training and the corresponding final operating parameter for training;
acquiring a first operation parameter, total output power of the equipment, actual power of a first module, maximum theoretical power of a second module and a corresponding final operation parameter of the equipment in operation in real time; and
updating the initial expert database model according to the first operating parameter, the total output power of the equipment, the actual power of the first module, the maximum theoretical power of the second module and the corresponding final operating parameter when the equipment operates, and generating an expert database model, wherein the expert database model is used for generating a reference operating parameter according to the first operating parameter, the total output power of the equipment, the actual power of the first module and the maximum theoretical power of the second module.
7. A milling control device, comprising:
the acquisition module is configured to acquire a first operation parameter of the equipment in real time;
a first module actual power determining module, configured to calculate a first module actual power according to the first operating parameter and preset data during operation of the device, where the preset data includes inherent attribute data of the device;
a second module maximum theoretical power calculation module configured to calculate a second module maximum theoretical power according to the total output power of the device and the first module actual power;
a reference operating parameter generating module configured to input the first operating parameter, the total output power of the equipment, the actual power of the first module, and the maximum theoretical power of the second module into an expert database model to generate a reference operating parameter, wherein the expert database model is configured to enable the theoretical efficiency of the equipment when the equipment operates with the reference operating parameter to reach a preset threshold; and
a final operating parameter determining module configured to determine a final operating parameter according to the reference operating parameter;
wherein the first module actual power determination module comprises:
the stratum hardness calculation unit is configured to calculate and obtain second module actual power, second module feeding force and second module feeding speed according to the first operation parameter when the equipment operates, and calculate and obtain stratum hardness according to the second module actual power, the second module feeding force and the second module feeding speed;
a first module theoretical flow rate determination unit configured to determine a first module theoretical flow rate according to the formation hardness and the preset data, wherein the preset data further includes a corresponding relationship between the formation hardness and the first module theoretical flow rate; and
a first module actual power calculation unit configured to calculate a first module actual power according to the first module theoretical flow rate, the first operating parameter of the device, and the preset data;
wherein the first module theoretical flow rate determination unit includes:
the stratum category determination subunit is configured to: determining the stratum category according to the stratum hardness and preset data, wherein the preset data further comprises the corresponding relation between the stratum hardness and the stratum category;
the first module theoretical flow rate determining subunit is configured to: and determining the theoretical flow rate of the first module according to the stratum category and preset data, wherein the preset data further comprises the corresponding relation between the stratum category and the theoretical flow rate of the first module.
8. An expert database model training device, its characterized in that includes:
the initial expert database model building module is configured to build an initial expert database model according to the first operating parameters for training, the total output power of the equipment for training, the actual power of the first module for training, the maximum theoretical power of the second module for training and the corresponding final operating parameters for training;
the parameter acquisition module is configured to acquire a first operation parameter, total output power of the equipment, actual power of the first module, maximum theoretical power of the second module and a corresponding final operation parameter in real time when the equipment runs; and
and the expert database model generation module is configured to update the initial expert database model according to the first operating parameter, the total output power of the equipment, the first module actual power, the second module maximum theoretical power and the corresponding final operating parameter when the equipment operates, so as to generate an expert database model, wherein the expert database model is used for generating a reference operating parameter according to the first operating parameter, the total output power of the equipment, the first module actual power and the second module maximum theoretical power.
9. An electronic device, characterized in that the electronic device comprises:
a processor; and
a memory for storing the processor-executable instructions;
the processor is configured to execute the milling control method according to any one of claims 1 to 5.
10. A computer-readable storage medium, characterized in that the storage medium stores a computer program for executing the milling control method of any one of the above claims 1 to 5.
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| CN105369817B (en) * | 2014-08-06 | 2018-06-29 | 中联重科股份有限公司 | Slotter milling control process method, apparatus, system and slotter |
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