CN113343391B - Control method, device and equipment for scraper material taking system - Google Patents
Control method, device and equipment for scraper material taking system Download PDFInfo
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- RHZUVFJBSILHOK-UHFFFAOYSA-N anthracen-1-ylmethanolate Chemical compound C1=CC=C2C=C3C(C[O-])=CC=CC3=CC2=C1 RHZUVFJBSILHOK-UHFFFAOYSA-N 0.000 description 1
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F30/17—Mechanical parametric or variational design
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
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- G—PHYSICS
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
- G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
Abstract
The invention discloses a control method of a scraper reclaiming system, which comprises the steps of obtaining a target environment variable; determining an optimal input quantity through an offline operation list according to the target environment variable; the offline operation list is a list of optimal input amounts corresponding to different environment variables determined according to preset target output amounts and historical operation data; and controlling the scraper reclaiming system according to the optimal input quantity. According to the invention, the target output quantity and the optimal input quantity are in one-to-one correspondence under different environment variables through the pre-stored offline operation list, so that the corresponding optimal input quantity can be directly called from the offline operation list, the automatic control of the scraper material taking system under different conditions is realized, the response speed and the transportation efficiency are improved, the manual operation is omitted, and the production cost is greatly reduced. The invention also provides a scraper reclaiming system control device, equipment and a computer readable storage medium with the beneficial effects.
Description
Technical Field
The present invention relates to the field of raw material transportation, and in particular, to a method, apparatus, device, and computer readable storage medium for controlling a scraper reclaiming system.
Background
For an automatic control system of a scraper stacker-reclaimer, the existing control method mainly models the imaging of a material pile through a three-dimensional laser scanner, and a sensing device is arranged on the stacker-reclaimer so as to realize automatic control. However, for different characteristics of different stockpiles, different stockpile heights on site and different material taking modes, the control parameters of the scraper stacker-reclaimer are basically the same, so that the scraper stacker-reclaimer cannot be optimized for different stockpiles, the conditions of unstable scraper current value, blockage caused by overlarge belt material flow and the like often occur in the actual operation process of different stockpiles, the transportation efficiency is not ideal, and a method for manually modifying the control parameters by means of manual experience also exists in the prior art, but the method is highly dependent on the experience and quality of operators, so that the transportation efficiency is greatly fluctuated, the cost is high, the safety operation is not facilitated, and the transportation regulation efficiency is low.
Therefore, how to improve the transportation operation efficiency and reduce the production cost is a problem to be solved by those skilled in the art.
Disclosure of Invention
The invention aims to provide a scraper reclaiming system control method, a scraper reclaiming system control device, scraper reclaiming system control equipment and a computer readable storage medium, so as to solve the problems of high production cost and low transportation efficiency in the prior art.
In order to solve the technical problems, the invention provides a control method of a scraper reclaiming system, which comprises the following steps:
acquiring a target environment variable;
determining an optimal input quantity through an offline operation list according to the target environment variable; the offline operation list is a list of optimal input amounts corresponding to different environment variables determined according to preset target output amounts and historical operation data;
and controlling the scraper reclaiming system according to the optimal input quantity.
Optionally, in the method for controlling a scraper reclaiming system, the method for obtaining the offline operation list includes:
determining optimal input quantities corresponding to different environment variables through a data mining algorithm according to the target output quantity and the historical operation data;
and determining the offline operation list according to the optimal input quantity corresponding to different environment variables.
Optionally, in the method for controlling a scraper reclaiming system, the method for obtaining the offline operation list includes:
determining the optimal input quantity corresponding to different environment variables through a genetic algorithm according to the target output quantity and a pre-trained scraper material taking model; the scraper material taking model is built through machine learning according to the historical operation data;
and determining the offline operation list according to the optimal input quantity corresponding to different environment variables.
Optionally, in the method for controlling a scraper reclaiming system, the method for establishing a scraper reclaiming model includes:
acquiring the historical operation data;
and establishing the scraper material taking model through a least square support vector machine according to the historical operation data.
Optionally, in the method for controlling a scraper reclaiming system, after acquiring the historical operation data, the method further includes:
obtaining steady-state historical data through a sliding discrimination method according to the historical operation data;
correspondingly, the scraper material taking model is built through a least square support vector machine according to the steady historical data.
Optionally, in the method for controlling a scraper reclaiming system, before the scraper reclaiming model is built by a least square support vector machine according to the steady-state historical data, the method further includes:
and eliminating outlier data from the steady-state historical data.
A scraper reclaiming system control apparatus comprising:
the acquisition module is used for acquiring the target environment variable;
the input determining module is used for determining the optimal input quantity through an offline operation list according to the target environment variable; the offline operation list is a list of optimal input amounts corresponding to different environment variables determined according to preset target output amounts and historical operation data;
and the control module is used for controlling the scraper reclaiming system according to the optimal input quantity.
Optionally, in the scraper reclaiming system control device, the method for obtaining the offline operation list includes:
the data mining module is used for determining the optimal input quantity corresponding to different environment variables through a data mining algorithm according to the target output quantity and the historical operation data;
and the first list determining module is used for determining the offline operation list according to the optimal input quantity corresponding to different environment variables.
A screed reclaiming system control apparatus comprising:
a memory for storing a computer program;
and the processor is used for realizing the steps of the scraper reclaiming system control method when executing the computer program.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of a method of controlling a screed take-off system as described in any one of the above.
According to the control method of the scraper reclaiming system, provided by the invention, the target environment variable is obtained; determining an optimal input quantity through an offline operation list according to the target environment variable; the offline operation list is a list of optimal input amounts corresponding to different environment variables determined according to preset target output amounts and historical operation data; and controlling the scraper reclaiming system according to the optimal input quantity. According to the invention, the target output quantity and the optimal input quantity are in one-to-one correspondence under different environment variables through the pre-stored offline operation list, so that when the system receives the target environment variable of the material pile to be taken currently, the corresponding optimal input quantity can be directly fetched from the offline operation list, and the scraper material taking system is controlled according to the optimal input quantity, thereby realizing the automatic control of the scraper material taking system under different conditions, improving the response speed and the transportation efficiency, saving the manual operation and greatly reducing the production cost. The invention also provides a scraper reclaiming system control device, equipment and a computer readable storage medium with the beneficial effects.
Drawings
For a clearer description of embodiments of the invention or of the prior art, the drawings that are used in the description of the embodiments or of the prior art will be briefly described, it being apparent that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained from them without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of an embodiment of a method for controlling a scraper reclaiming system according to the present invention;
FIG. 2 is a schematic flow chart of a method for obtaining an offline operation list in a specific embodiment of a method for controlling a scraper reclaiming system according to the present invention;
FIG. 3 is a schematic flow chart of a method for obtaining an offline operation list in another embodiment of a method for controlling a scraper reclaiming system according to the present invention;
FIG. 4 is a schematic structural view of an embodiment of a control device for a scraper reclaiming system according to the present invention;
fig. 5 is a schematic structural view of a material taking apparatus in the prior art.
Detailed Description
In order to better understand the aspects of the present invention, the present invention will be described in further detail with reference to the accompanying drawings and detailed description. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Firstly, introducing a scraper material taking system, wherein the scraper material taking system is generally used for material pile material taking, taking coal pile material taking of a coal yard as an example, a scraper running, pitching mechanism and slewing mechanism are combined in the material taking process of a circular coal yard scraper material stacker to conduct layered material taking, the scraper pitching mechanism is used for layering, and the height of a material taking arm is positioned. The material taking port seat frame revolves left and right around the middle private upright post to drive the material taking scraping plate mechanism to operate, the scraping plate mechanism is started to pitch to a certain angle, a plurality of scraping plates which do reciprocating motion on the material taking port seat frame are utilized to scrape materials layer by layer to the lower center funnel, the funnel transfers the materials to the ground belt conveyor, and the ground belt system is started to convey the materials to an appointed position outside a material yard.
In the process of taking the coal pile, as the bottom of the coal pile is a certain distance away from the blanking port of the central column, the coal pile needs to be opened, and as the materials are scraped to the central hopper, the peak of the material pile is gradually scraped off, and the space between the mountain leg and the central hopper is gradually filled. When judging that the existing coal falls into the belt (the current of the belt increases instantaneously), finishing slope repairing. The operating parameters at this stage are different from those at the time of cyclic material taking.
After the pile opening operation is finished or the coal pile from which part of coal is taken is subjected to material taking operation, which is called cyclic material taking operation. Firstly, taking materials at the highest layer at the end of a material pile, wherein a material taking arm is driven by a pitching device to descend to a certain depth in the material pile, a scraping plate runs, and a revolving device drives a portal to revolve, so that the first layer material taking operation is completed; when the first layer of material taking is completed, the pitching device of the material taking arm continuously lowers the scraping plate to a certain depth according to the adjustment value, the scraping plate enters the next material layer, and the material taking arm reversely rotates to complete the material taking operation of the second layer; and so on, taking materials layer by layer. In the material taking process, the current of the scraping plate reflects the depth of the scraping plate entering the coal pile, so that the load of the scraping plate and the material taking amount are reflected; the flow of the belt reflects the amount of coal scraped by the scraper blade entering the belt and mainly reflects the material taking flow; the two parameters are the result data of attention of the material taking process, and the scraper current and the belt flow are kept at the optimal values under the condition of ensuring safe and efficient operation.
The automatic control system of the scraper stacker-reclaimer is widely used at present, the coal pile is imaged and modeled mainly through a three-dimensional laser scanner, sensing equipment is arranged on the stacker-reclaimer, and automatic control is realized through an automatic positioning technology and an anti-collision technology. However, most of automatic control systems in the current stage have lower material taking operation efficiency compared with the manual material taking mode, and the main reason is that the operation mode of the automatic material taking process is fixed, namely, the single descending angle of the material taking mechanism, the material taking rotation speed, the material taking scraper moving speed and other parameters are fixed values, and the conditions of coal blockage and the like caused by unstable scraper current values and overlarge belt material flow in the actual operation process of different coals are usually caused, so that the operation parameters are often required to be manually modified by experience in the operation process. In order to improve the operation efficiency and obtain accurate material taking control parameters, an operation offline optimal control parameter library within the range of the whole coal type needs to be established, and the control parameters can be automatically optimized when the control system operates, so that an efficient and safe operation mode is realized.
The invention provides a control method of a scraper reclaiming system, a flow diagram of one specific embodiment of which is shown in fig. 1, which is called as a specific embodiment one, comprising:
s101: and obtaining a target environment variable.
The environmental variables are inherent properties of the pile, for example, the pile of coal, which may include the heating value of the coal, the pile angle of the pile of coal, the moisture, ash and volatiles of the coal, etc., each of which may be referred to as a condition.
S102: determining an optimal input quantity through an offline operation list according to the target environment variable; the offline operation list is a list of optimal input amounts corresponding to different environment variables determined according to preset target output amounts and historical operation data.
The input quantity refers to system control parameters for controlling the scraper reclaiming system, such as the rotation speed, the scraper speed, the single dip angle and the like of the scraper reclaiming system, and the output quantity refers to system parameters related to the transportation efficiency, such as the scraper current, the belt flow and the like. I.e. changing the input will affect the output.
S103: and controlling the scraper reclaiming system according to the optimal input quantity.
And under the condition of the optimal input quantity, the output quantity of the scraper reclaiming system is consistent with the target output quantity.
The material taking system comprises material taking equipment, a control module, a detection module, a data interaction module and an operation control optimal parameter library. The material taking equipment is shown in fig. 5, the rotary rail controls the rotation action of the scraper machine, and the control system controls the rotation speed; pulling the steel wire rope to control the scraper machine to perform pitching motion along the hinged point of the scraper machine and the door frame; the scraper blade is installed on the scraper machine, and the scraper blade rotates in a reciprocating mode along the scraper machine in a circulating mode, so that the scraper blade is used for scraping materials downwards to take materials. The detection module comprises a laser scanner for detecting the geographic morphology of the geographic morphology features of the whole material yard, a rotary encoder for detecting the rotary angle of the material taking equipment, a pitching encoder for detecting the pitching angle of the scraper machine, a measuring device for detecting the scraper speed, a belt flow measuring instrument for detecting the amount of materials on a belt and a scraper current measuring device for measuring the scraper load. The control module comprises a data server and a calculation module, wherein the calculation module applies an artificial intelligence algorithm to perform data modeling so as to obtain optimal control parameters; the data interaction module enables the control system to conduct data interaction with the fuel data platform, obtains characteristic parameters of the material coal types of the current coal yard, and is used for data modeling and optimizing through the calculation module. The operation control optimal parameter library is to extract operation parameters of different coal types under stable working conditions from a historical database as successful operation cases, so as to establish an operation offline control parameter library within the range of the whole coal types. The detection module transmits the detected data information to the control module, the control module stores the received data information in the data server, updates and calculates the data information in real time, extracts optimal control parameters from the optimal parameter library, and modifies the control parameters in the control module, so that a high-efficiency stable material taking operation mode is realized.
The invention provides a scheme for automatically searching optimal control parameters (i.e. input quantity) in real time aiming at different stockpiles and different operation conditions (i.e. different environmental parameters), and the final aim is to construct an optimal strategy library of the material taking operation parameters of a scraper stacker-reclaimer, taking coal stacking of a power plant as an example, the system can give out optimal set values of various operation variables according to the working conditions of different coal types and different operation modes, and the optimal set values can be used for automatically adjusting the control parameters of the material taking process, improving the operation efficiency and reducing the potential safety hazard.
The offline operation list in the specific embodiment can be established by searching the optimal input quantity for different environment variables in the historical data; the machine learning model can be constructed through the historical operation data, and then the optimal input quantity corresponding to the target output quantity can be obtained through the machine learning model.
According to the control method of the scraper reclaiming system, provided by the invention, the target environment variable is obtained; determining an optimal input quantity through an offline operation list according to the target environment variable; the offline operation list is a list of optimal input amounts corresponding to different environment variables determined according to preset target output amounts and historical operation data; and controlling the scraper reclaiming system according to the optimal input quantity. According to the invention, the target output quantity and the optimal input quantity are in one-to-one correspondence under different environment variables through the pre-stored offline operation list, so that when the system receives the target environment variable of the material pile to be taken currently, the corresponding optimal input quantity can be directly fetched from the offline operation list, and the scraper material taking system is controlled according to the optimal input quantity, thereby realizing the automatic control of the scraper material taking system under different conditions, improving the response speed and the transportation efficiency, saving the manual operation and greatly reducing the production cost.
On the basis of the first embodiment, the obtaining manner of the offline operation list is further limited, so as to obtain a second embodiment, a flow diagram of which is shown in fig. 2, and the obtaining method of the offline operation list includes:
s201: and determining the optimal input quantity corresponding to different environment variables through a data mining algorithm according to the target output quantity and the historical operation data.
The historical operation data is the corresponding relation between the input quantity and the output quantity under the condition that the historical operation data comprises a plurality of environmental variables in the historical operation of the system, and the optimal input quantity under each environmental variable in the historical operation data is determined through a data mining algorithm, namely, the output quantity corresponding to the optimal input quantity is closest to the target output quantity.
S202: and determining the offline operation list according to the optimal input quantity corresponding to different environment variables.
In this embodiment, the method for obtaining the offline operation list is provided, that is, in the historical operation data of the system, an optimal input amount corresponding to each environmental variable is selected and stored in an offline database, and the PLC control system selects the target environmental variable to select the input amount according to the offline database. Because the data mining algorithm is adopted to select the optimal solution from the existing historical operation data, the required calculation force is small, the optimal input quantity corresponding to each environment transformation can be rapidly screened from a large amount of historical operation data, the requirement on hardware is low, and the universality is strong.
On the basis of the first embodiment, another method for obtaining the offline operation list is provided for limiting, so as to obtain a third embodiment, a flow chart of which is shown in fig. 3, and the method for obtaining the offline operation list comprises the following steps:
s301: determining the optimal input quantity corresponding to different environment variables through a genetic algorithm according to the target output quantity and a pre-trained scraper material taking model; the scraper material taking model is built through machine learning according to the historical operation data.
In the step, a scraper material taking model is established through a machine learning algorithm according to the historical operation data, wherein the scraper material taking model is a prediction model reflecting the relation among the input quantity, the environment variable and the output quantity.
S302: and determining the offline operation list according to the optimal input quantity corresponding to different environment variables.
When designing the ideal operation list, the primary task is to establish a scraper material taking model, wherein the model mainly describes characteristics and correlation among a plurality of input variables and output variables of the scraper material taking system, and meanwhile, the operation safety of the material taking system is ensured. And establishing a model by using an artificial intelligent algorithm, and combining the model with a PLC of a control system to form an operation offline control parameter library within the range of the whole coal.
Further, the method for establishing the scraper material taking model comprises the following steps:
a1: and acquiring the historical operation data.
A2: and establishing the scraper material taking model through a least square support vector machine according to the historical operation data.
Due to the complexity of the blade reclaimer system, blade current and belt flow are affected by a number of factors and exhibit significant non-linear, strong coupling characteristics. According to the method, a Least Square Support Vector Machine (LSSVM) is used for establishing a scraper material taking system model, the modeling method can be directly applied to real operation data of a system, the complex connection relation between input variables and output variables is not needed to be known, and modeling is carried out only by using information contained in the operation data, so that the method is very suitable for a modeling process which is complex and has no knowledge of mechanisms.
As a preferred embodiment, after the historical operation data is acquired, further comprising:
obtaining steady-state historical data through a sliding discrimination method according to the historical operation data;
correspondingly, the scraper material taking model is built through a least square support vector machine according to the steady historical data.
And extracting steady-state working points from the original data by adopting a sliding discrimination method, and judging whether each data point is in a steady-state section or not by sliding along a time window in a point-by-point traversing mode of the groups by the sliding discrimination algorithm so as to remove non-steady-state points.
Still further, before the scraper material taking model is built by the least squares support vector machine according to the steady state historical data, the method further comprises:
and eliminating outlier data from the steady-state historical data.
The extracted steady-state operating point has a point which is far away from other steady-state values and is called an outlier. The invention mainly adopts the similarity between working conditions as the basis, and samples with lower similarity are selected from steady-state operation data and removed.
For models built using historical data of operation, the accuracy of the model may be affected by the quality of the data. Because the equipment is interfered by human, special conditions and the like in the production and operation process, the quality of the measured data is easily affected, and abnormal values often appear. In addition, due to the influence of the dynamic operation of the equipment, when the operation data deviate from a steady state value, the corresponding data cannot accurately establish a model of the material taking system. Therefore, preprocessing such as steady-state operating point extraction, outlier removal, filtering noise reduction and the like is needed for the historical data before modeling.
The following describes a specific example of the process of establishing the scrapper material taking model, including:
in a sample extraction method based on operation data, a sliding discrimination method uses the heat value of coal as a characteristic variable, and data is normalized to a [0,1] interval by using the following method:
wherein x is a variable value; x is x max And x min Is the maximum and minimum of the variables.
The number of discrimination points 2N is 20, the steady-state threshold Kt is 0.95, and the weight threshold ρ is 0.5. Finally, 268 sets of data with the most tabulation are selected, 216 sets of data are used as training data, and the other 52 sets are used as test data.
In order to make the model have a more representative full coal type, the training data comprises working conditions of lignite, bituminous coal, anthracite, non-caking coal, coking coal and weak caking coal. And establishing a scraper reclaiming system model by taking the selected 216 groups of data as modeling data. The model can respectively predict the current value of the scraping plate and the flow value of the belt when the system takes coal.
The optimal value I of the scraper current can be obtained through a test of early manual operation under the safe and efficient operation state in the material taking process of the scraper stacker-reclaimer best And optimum value F of belt flow best According to the invention, the genetic algorithm is utilized to carry out off-line optimization on each coal working condition of the established scraper extracting system model, and the equivalent values of the scraper current and the belt flow are constrained on the premise of ensuring safe operation, so that the off-line optimal values of each adjusting variable (extracting arm rotation speed, scraper speed and each extracting pitching descending angle) of the system under the working conditions when the scraper current and the belt flow are optimal are formed, and an operation parameter optimal strategy library is formed.
The optimization problem can be described by the following formula:
minf(DV,MV)＝λ 1 |I-I best |+λ 2 |F-F best |
[MV] min ≤MV≤[MV] max
I min ≤I≤I max
F min ≤F≤F max
coefficient lambda in 1 、λ 2 In order to pay attention to the degree coefficient of attention to the scraper blade current value and belt flow, the coefficient can be adjusted under different safety requirements or operating efficiency; DV and MV are disturbance variables and operation variables of the system respectively, namely input variables of the model; f represents the relation between each controlled variable and the characteristic variable; [ MV ]] min And [ MV ]] max For the change range of the operation variable (namely the input quantity), the change range of each operation variable at each working condition point (namely each environment variable) is between the maximum value and the minimum value in a set formed by 15 data points before and after the coal type point, so that the influence of the optimizing result on the actual operation of the system is reduced; i max And I min F for maximum and minimum allowable blade current max And F min Is the maximum and minimum allowable belt flow value.
When optimizing at each working point, the coal type and the operation mode are determined, so that only 3 MVs of rotation speed of the material taking arm, speed of the scraping plate and pitch descending angle of material taking are required to be optimized each time. The optimization problem described above adopts a genetic algorithm to perform off-line optimization on the operation variable at the normal operation working point, finds the off-line optimal value of MVs of each working point, and constructs an off-line optimal database. And solving the parameter optimization problem of the scraper material taking model by adopting a real number coding method, a self-adaptive variation rate, a reserved optimal strategy and other methods, so that the value of each MVs in the offline optimal database is more accurate.
It should be noted that, the method for obtaining an offline manifest provided in this embodiment may be used in combination with the method for obtaining an offline manifest in the second embodiment, that is, the initial offline manifest is obtained by screening historical operation data through a data mining algorithm, but the scratch board material taking model also exists at the same time, if an environmental variable (such as a coal pile of a new coal) that does not appear in the historical operation data is encountered, the target output quantity and the new environmental variable are input into the scratch board material taking model, then the corresponding optimal input quantity is calculated through the scratch board material taking model, and the corresponding relationship among the newly obtained environmental variable, the optimal input quantity and the target output quantity is added into the offline manifest.
The following describes the control device of the scraper reclaiming system provided by the embodiment of the invention, and the control device of the scraper reclaiming system and the control method of the scraper reclaiming system described below can be referred to correspondingly.
Fig. 4 is a block diagram of a control device for a scraper reclaiming system according to an embodiment of the present invention, and referring to fig. 4, the control device for a scraper reclaiming system may include:
an acquisition module 100 for acquiring a target environment variable;
an input determining module 200, configured to determine an optimal input amount according to the target environment variable through an offline operation list; the offline operation list is a list of optimal input amounts corresponding to different environment variables determined according to preset target output amounts and historical operation data;
and the control module 300 is used for controlling the scraper reclaiming system according to the optimal input quantity.
As a preferred embodiment, the method for obtaining the offline operation list includes:
the data mining module is used for determining the optimal input quantity corresponding to different environment variables through a data mining algorithm according to the target output quantity and the historical operation data;
and the first list determining module is used for determining the offline operation list according to the optimal input quantity corresponding to different environment variables.
As a preferred embodiment, the method for obtaining the offline operation list includes:
the model calculation module is used for determining the optimal input quantity corresponding to different environment variables through a genetic algorithm according to the target output quantity and the pre-trained scraper reclaiming model; the scraper material taking model is built through machine learning according to the historical operation data;
and the second list determining module is used for determining the offline operation list according to the optimal input quantity corresponding to different environment variables.
As a preferred embodiment, the method for establishing the scraper reclaiming model includes:
the history acquisition module is used for acquiring the history operation data;
and the LSSVM module is used for establishing the scrapping plate material taking model through a least square support vector machine according to the historical operation data.
As a preferred embodiment, the history acquisition module further includes:
the sliding judging unit is used for obtaining steady-state historical data through a sliding judging method according to the historical operation data;
correspondingly, the LSSVM module comprises a steady-state LSSVM unit, and the LSSVM unit is used for establishing the scraper material taking model through a least square support vector machine according to the steady-state historical data.
As a preferred embodiment, the steady state LSSVM cell further comprises:
and the outlier rejection unit is used for conducting outlier data rejection on the steady historical data.
The control device of the scraper reclaiming system is used for acquiring a target environment variable through the acquisition module 100; an input determining module 200, configured to determine an optimal input amount according to the target environment variable through an offline operation list; the offline operation list is a list of optimal input amounts corresponding to different environment variables determined according to preset target output amounts and historical operation data; and the control module 300 is used for controlling the scraper reclaiming system according to the optimal input quantity. According to the invention, the target output quantity and the optimal input quantity are in one-to-one correspondence under different environment variables through the pre-stored offline operation list, so that when the system receives the target environment variable of the material pile to be taken currently, the corresponding optimal input quantity can be directly fetched from the offline operation list, and the scraper material taking system is controlled according to the optimal input quantity, thereby realizing the automatic control of the scraper material taking system under different conditions, improving the response speed and the transportation efficiency, saving the manual operation and greatly reducing the production cost.
The scraper reclaiming system control device of the present embodiment is configured to implement the foregoing scraper reclaiming system control method, so that the specific implementation of the scraper reclaiming system control device may be found in the foregoing example portions of the scraper reclaiming system control method, for example, the acquisition module 100, the input determination module 200, and the control module 300 are respectively configured to implement steps S101, S102, and S103 in the foregoing scraper reclaiming system control method, so that the specific implementation thereof may refer to the description of the corresponding examples of each portion and will not be repeated herein.
A screed reclaiming system control apparatus comprising:
a memory for storing a computer program;
and the processor is used for realizing the steps of the scraper reclaiming system control method when executing the computer program. According to the control method of the scraper reclaiming system, provided by the invention, the target environment variable is obtained; determining an optimal input quantity through an offline operation list according to the target environment variable; the offline operation list is a list of optimal input amounts corresponding to different environment variables determined according to preset target output amounts and historical operation data; and controlling the scraper reclaiming system according to the optimal input quantity. According to the invention, the target output quantity and the optimal input quantity are in one-to-one correspondence under different environment variables through the pre-stored offline operation list, so that when the system receives the target environment variable of the material pile to be taken currently, the corresponding optimal input quantity can be directly fetched from the offline operation list, and the scraper material taking system is controlled according to the optimal input quantity, thereby realizing the automatic control of the scraper material taking system under different conditions, improving the response speed and the transportation efficiency, saving the manual operation and greatly reducing the production cost.
The invention also provides a computer readable storage medium having a computer program stored thereon, which when executed by a processor, implements the steps of the method for controlling a screed reclaiming system as described in any one of the above. According to the control method of the scraper reclaiming system, provided by the invention, the target environment variable is obtained; determining an optimal input quantity through an offline operation list according to the target environment variable; the offline operation list is a list of optimal input amounts corresponding to different environment variables determined according to preset target output amounts and historical operation data; and controlling the scraper reclaiming system according to the optimal input quantity. According to the invention, the target output quantity and the optimal input quantity are in one-to-one correspondence under different environment variables through the pre-stored offline operation list, so that when the system receives the target environment variable of the material pile to be taken currently, the corresponding optimal input quantity can be directly fetched from the offline operation list, and the scraper material taking system is controlled according to the optimal input quantity, thereby realizing the automatic control of the scraper material taking system under different conditions, improving the response speed and the transportation efficiency, saving the manual operation and greatly reducing the production cost.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, so that the same or similar parts between the embodiments are referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
It should be noted that in this specification, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative elements and steps are described above generally in terms of functionality in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The method, the device, the equipment and the computer readable storage medium for controlling the scraper reclaiming system provided by the invention are described in detail. The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to facilitate an understanding of the method of the present invention and its core ideas. It should be noted that it will be apparent to those skilled in the art that various modifications and adaptations of the invention can be made without departing from the principles of the invention and these modifications and adaptations are intended to be within the scope of the invention as defined in the following claims.
Claims (7)
1. A method of controlling a flighting extraction system, comprising:
acquiring a target environment variable;
determining an optimal input quantity through an offline operation list according to the target environment variable; the offline operation list is a list of optimal input quantities corresponding to different environment variables determined under the constraint of scraper current and belt flow values according to preset target output quantities and historical operation data;
controlling the scraper reclaiming system according to the optimal input quantity;
the method for obtaining the offline operation list comprises the following steps:
determining optimal input quantities corresponding to different environment variables through a genetic algorithm based on the attention degree coefficient of the target output quantity according to the optimal value of the target output quantity and a pre-trained scraper reclaiming model; the scraper material taking model is built through machine learning according to the historical operation data;
determining the offline operation list according to the optimal input quantity corresponding to different environment variables;
the optimal input quantity corresponding to different environment variables is determined through a genetic algorithm based on the attention degree coefficient of the target output quantity, and the optimal input quantity is obtained through the following formula:
minf(DV,MV)＝λ 1 |I-I best |+λ 2 |F-F best |
[MV] min ≤MV≤[MV] max
I min ≤I≤I max
F min ≤F≤F max
wherein lambda is 1 、λ 2 DV and MV are disturbance variables and operation variables of the system respectively, F represents the relation between each controlled variable and characteristic variable, [ MV ]] min And [ MV ]] max Is the range of variation of the operating variable; i max And I min F for maximum and minimum allowable blade current max And F min Is the maximum and minimum allowable belt flow value.
2. The method for controlling a scraper reclaiming system as set forth in claim 1, wherein the method for establishing the scraper reclaiming model comprises:
acquiring the historical operation data;
and establishing the scraper material taking model through a least square support vector machine according to the historical operation data.
3. The method of claim 2, further comprising, after obtaining the historical operating data:
obtaining steady-state historical data through a sliding discrimination method according to the historical operation data;
correspondingly, the scraper material taking model is built through a least square support vector machine according to the steady historical data.
4. The method of claim 3, further comprising, prior to establishing the blade take out model by a least squares support vector machine based on the steady state history data:
and eliminating outlier data from the steady-state historical data.
5. A scraper blade reclaiming system control device, comprising:
the acquisition module is used for acquiring the target environment variable;
the input determining module is used for determining the optimal input quantity through an offline operation list according to the target environment variable; the offline operation list is a list of optimal input quantities corresponding to different environment variables determined under the constraint of scraper current and belt flow values according to preset target output quantities and historical operation data;
the control module is used for controlling the scraper reclaiming system according to the optimal input quantity;
the device comprises:
the model calculation module is used for determining the optimal input quantity corresponding to different environment variables through a genetic algorithm based on the attention degree coefficient of the target output quantity according to the optimal value of the target output quantity and a pre-trained scraper material taking model; the scraper material taking model is built through machine learning according to the historical operation data;
the second list determining module is used for determining the offline operation list according to the optimal input quantity corresponding to different environment variables;
the model calculation module is specifically configured to:
determining optimal input quantities corresponding to different environment variables through a genetic algorithm based on the attention degree coefficient of the target output quantity, wherein the optimal input quantities are obtained through the following formula:
minf(DV,MV)＝λ 1 |I-I best |+λ 2 |F-F best |
[MV] min ≤MV≤[MV] max
I min ≤I≤I max
F min ≤F≤F max
wherein lambda is 1 、λ 2 To be a factor of attention to the blade current value and the belt flow rate,DV and MV are disturbance variable and operation variable of the system respectively, F represents the relation between each controlled variable and characteristic variable, [ MV ]] min And [ MV ]] max Is the range of variation of the operating variable; i max And I min F for maximum and minimum allowable blade current max And F min Is the maximum and minimum allowable belt flow value.
6. A blade reclaiming system control apparatus, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the method of controlling a screed reclaiming system as set forth in any one of claims 1 to 4 when executing the computer program.
7. A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the method of controlling a screed take-off system according to any one of claims 1 to 4.
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