CN115963723A - Method for automatically adjusting and controlling operation of intelligent electromechanical system equipment - Google Patents
Method for automatically adjusting and controlling operation of intelligent electromechanical system equipment Download PDFInfo
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Abstract
The invention relates to the field of equipment control, and discloses a method for automatically adjusting and controlling the running of intelligent electromechanical system equipment and electronic equipment, wherein the method comprises the following steps: the method comprises the steps of obtaining electromechanical equipment to be operated, identifying a control object of the electromechanical equipment, configuring an actual control strategy of the control object, and collecting machine parameters and environment parameters of the control object; inputting the actual control strategy, the machine parameters and the environment parameters into the trained fuzzy neural controller to execute fuzzy decoding processing to obtain control decoding parameters; the blur decoding operation includes: carrying out parameter fuzzy processing on the machine parameters and the environment parameters by using a membership function of a fuzzy neural controller to obtain fuzzy parameters; carrying out control simulation reasoning on the fuzzy parameters by using a reasoning function of the fuzzy neural controller to obtain a simulation control strategy; carrying out deblurring calculation on the analog control strategy by using a deblurring device of the fuzzy neural network controller to obtain a control decoding parameter; and executing intelligent control on the control object according to the control decoding parameters.
Description
Technical Field
The invention relates to the field of equipment control, in particular to an automatic operation adjusting and controlling method for intelligent electromechanical system equipment.
Background
The electromechanical equipment generally refers to machinery, electrical equipment and electrical automation equipment, and in a building, the electromechanical equipment is generally called machinery and pipeline equipment except for earthwork, carpentry, reinforcing steel bars and muddy water. The intelligent control of electromechanical equipment can be more accurate to equipment control management, and the system can in time report the position that breaks down after breaking down, and convenient maintenance can go the life of guarantee equipment better, makes things convenient for administrator's use, can clearly know the user state of equipment, improves work efficiency.
The existing intelligent control method of the electromechanical device is realized through an expert system, namely, the control of the electromechanical device is realized through pre-configuring control rules of the electromechanical device, but in an actual service scene, the environment where the electromechanical device is located is complex and changeable, so that newly added variables can appear in the actual operation process of the electromechanical device, the expert system cannot well learn the newly added variables, the newly added variables cannot be converted into an actual control strategy, the control of the electromechanical device is not intelligent enough, and the control efficiency of the electromechanical device can be influenced.
Disclosure of Invention
The invention provides a method for automatically adjusting and controlling the operation of intelligent electromechanical system equipment, and mainly aims to realize the control intellectualization of the electromechanical equipment and improve the control efficiency of the electromechanical equipment.
In order to achieve the above object, the method for automatically adjusting and controlling the operation of the intelligent electromechanical system device and the electronic device provided by the present invention comprise:
the method comprises the steps of obtaining electromechanical equipment to be operated, identifying a control object of the electromechanical equipment, retrieving an operation log of the control object, and configuring an actual control strategy of the control object according to the operation log;
collecting machine parameters and environment parameters of the control object, uploading the machine parameters and the environment parameters to a trained fuzzy neural controller, and carrying out parameter fuzzy processing on the machine parameters and the environment parameters by using a membership function of the fuzzy neural controller to obtain fuzzy parameters;
performing control simulation reasoning on the fuzzy parameters by using a reasoning function of the fuzzy neural controller through the actual control strategy to obtain a simulation control rule;
and carrying out deblurring calculation on the simulation control rule by using a deblurring device of the fuzzy neural network controller to obtain a control decoding parameter, and executing intelligent control on the control object according to the control decoding parameter.
Optionally, the identifying a control object of the mechatronic device includes:
retrieving whether the mechatronic device is in an operational state; in that
Scanning a project progress of the electromechanical device when the electromechanical device is in an operable state;
and determining the control object of the electromechanical device according to the project progress.
Optionally, the configuring, according to the operation log, an actual control policy of the control object includes:
performing data sorting on the operation log to obtain target operation data;
calculating the operation logic of the target operation data through a preset logic reasoning function;
configuring an actual control strategy of the control object according to the operation logic;
wherein the preset logical inference function comprises:
wherein it is present>
Representing the run logic, x representing the target run data, and e representing the infinite acyclic fraction.
Optionally, the uploading the machine parameters and the environment parameters to a trained fuzzy neural controller includes:
acquiring sensors for acquiring the machine parameters and the environment parameters, and retrieving a data storage module in the fuzzy neural controller;
constructing a data transmission channel of the sensor and the data storage module;
transmitting the machine parameters and the environmental parameters to the fuzzy neural controller through the data transmission channel.
Optionally, the performing parameter fuzzy processing on the machine parameter and the environment parameter by using the membership function of the fuzzy neural controller to obtain fuzzy parameters includes:
integrating the machine parameters and the environment parameters to obtain integrated parameters, and configuring the membership degree of the integrated parameters;
according to the membership degree, fuzzy calculation is carried out on the integration parameters through the membership degree function to obtain fuzzy parameters;
wherein the membership function comprises:
Optionally, the performing, by the actual control strategy, a control simulation inference on the fuzzy parameter by using an inference function of the fuzzy neural controller to obtain a simulation control rule includes:
mapping the actual control strategy according to the fuzzy parameters to obtain a target control rule;
according to the target control rule, the fuzzy parameters are subjected to control simulation reasoning by using the reasoning function to obtain a simulation control rule;
wherein, the inference function comprises:
wherein said->
Representing a simulated control rule, f () inference function, based on>
A target control rule is represented by a target control rule,
denotes a fuzzy parameter, n denotes the number of target control rules, and e denotes an infinite acyclic decimal.
Optionally, the obtaining, by using a deblurring unit of the fuzzy neural network controller, a control decoding parameter of the simulation control rule through deblurring calculation includes:
analyzing the rule attribute of the simulation control rule through an analysis layer in the defuzzifier, and configuring a defuzzification rule through the rule attribute;
according to the de-fuzzy rule, performing de-fuzzy calculation on the simulation control rule through a de-fuzzy function in the de-fuzzifier to obtain the control decoding parameter;
wherein the deblurring function comprises: g (x, y) = f (x, y) × h (x, y) wherein g (x, y) represents a control decoding parameter, f (x, y) represents a de-blurring rule, and h (x, y) represents an analog control rule.
Optionally, the performing intelligent control on the control object according to the control decoding parameter includes:
analyzing the control decoding parameters to control the control part of the control object;
according to the control decoding parameters, identifying the operation behavior executed by the control part;
and executing intelligent control of the electromechanical equipment according to the control part and the operation behavior.
In order to solve the above problems, the present invention further provides an automatic operation adjustment control device for an intelligent electromechanical system device, including:
the actual control strategy configuration module is used for acquiring electromechanical equipment to be operated, identifying a control object of the electromechanical equipment, retrieving an operation log of the control object, configuring an actual control strategy of the control object according to the operation log, and acquiring machine parameters and environment parameters of the control object;
the parameter fuzzy processing module is used for inputting the actual control strategy, the machine parameters and the environment parameters into a trained fuzzy neural controller to execute fuzzy decoding processing to obtain control decoding parameters; the blur decoding operation includes: carrying out parameter fuzzy processing on the machine parameters and the environment parameters by utilizing a membership function of the fuzzy neural controller to obtain fuzzy parameters;
the simulation control strategy reasoning module is used for carrying out control simulation reasoning on the fuzzy parameters by using a reasoning function of the fuzzy neural controller through the actual control strategy to obtain a simulation control strategy;
the decoding parameter acquisition module is used for carrying out deblurring calculation on the simulation control strategy by using a deblurring device of the fuzzy neural network controller to obtain a control decoding parameter;
and the intelligent control module is used for executing intelligent control on the control object according to the control decoding parameter.
In order to solve the above problem, the present invention also provides an electronic device, including:
at least one processor; and (c) a second step of,
a memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,
the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to implement the method for operating automatic adjustment control of the smart electromechanical systems device as described above.
In order to solve the above problem, the present invention further provides a computer-readable storage medium, in which at least one computer program is stored, and the at least one computer program is executed by a processor in an electronic device to implement the method for automatic adjustment control of operation of an intelligent electromechanical systems device described above.
It can be seen that, in the embodiment of the present invention, firstly, by identifying a control object of an electromechanical device to be operated, retrieving an operation log of the control object to configure an actual control strategy of the control object, data acquisition and data feedback can be directly performed on a corresponding control object, so as to ensure a control premise of a subsequent electromechanical device, and by retrieving an operation log of the control object, an actual control strategy of the control object can be configured, so that data support can be provided for intelligent control of the control object by using an equipment operation experience, and the stability of operation can be improved; secondly, the machine parameters and the environmental parameters of the control object are collected and uploaded to a trained fuzzy neural controller, so that parameter fuzzy processing is performed on the machine parameters and the environmental parameters by using a membership function of the fuzzy neural controller to obtain fuzzy parameters, fuzzy data can be provided for subsequent fuzzy reasoning operation, the electromechanical equipment can be guaranteed to learn newly added variables in the actual operation process, and intelligent control of the electromechanical equipment is realized; furthermore, the embodiment of the invention utilizes the inference function of the fuzzy neural controller to control and simulate the fuzzy parameters through an actual control strategy to obtain a simulated control rule, and utilizes the fuzzy resolver of the fuzzy neural network controller to perform fuzzy calculation on the simulated control rule to obtain the control decoding parameters so as to execute intelligent control on a control object, realize intelligent control on electromechanical equipment and improve the control efficiency of the electromechanical equipment. Therefore, the method for automatically adjusting and controlling the operation of the intelligent electromechanical system equipment, which is provided by the embodiment of the invention, can realize the control intellectualization of the electromechanical equipment and improve the control efficiency of the electromechanical equipment.
Drawings
Fig. 1 is a schematic flow chart of a method for automatically adjusting and controlling the operation of an intelligent electromechanical system device according to an embodiment of the present invention;
fig. 2 is a schematic block diagram of an automatic operation adjustment control device of an intelligent electromechanical system device according to an embodiment of the present invention;
fig. 3 is a schematic internal structural diagram of an electronic device implementing a method for automatically adjusting and controlling operation of an intelligent electromechanical system device according to an embodiment of the present invention;
the implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the invention provides a method for automatically adjusting and controlling the operation of intelligent electromechanical system equipment and electronic equipment. The execution subject of the method for operating the automatic adjustment control by the intelligent electromechanical system device includes, but is not limited to, at least one of the electronic devices that can be configured to execute the method provided by the embodiment of the present invention, such as a server, a terminal, and the like. In other words, the method for operating the automatic adjustment control by the intelligent electromechanical system device may be executed by software or hardware installed in the terminal device or the server device, where the software may be a block chain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.
Referring to fig. 1, a schematic flow chart of a method for automatically adjusting and controlling an operation of an intelligent electromechanical system device according to an embodiment of the present invention is shown. In an embodiment of the present invention, the method for automatically adjusting and controlling the operation of the intelligent electromechanical system device includes:
s1, obtaining electromechanical equipment to be operated, identifying a control object of the electromechanical equipment, retrieving an operation log of the control object, configuring an actual control strategy of the control object according to the operation log, and collecting machine parameters and environment parameters of the control object.
According to the embodiment of the invention, by acquiring the electromechanical equipment to be operated, the control object of the electromechanical equipment can be identified, and data acquisition and data feedback can be directly carried out on the corresponding control object, so that the control premise of the subsequent electromechanical equipment is guaranteed. The control object is a module which needs to be controlled in the electromechanical equipment, such as soybean paste manufacturing equipment, the control object can be a soybean screening module, a soybean paste packaging module or automobile manufacturing equipment, and the control object can be a frame welding module or a paint spraying module.
As an embodiment of the present invention, the identifying a control object of the mechatronic device includes: retrieving whether the mechatronic device is in an operational state; scanning a project progress of the mechatronic device while in an operational state at the mechatronic device; and determining the control object of the electromechanical device according to the project progress.
The operable state refers to a state that the electromechanical device can normally work, the project schedule refers to a step to which the electromechanical device works, for example, a thick broad-bean sauce manufacturing device manufactures thick broad-bean sauce, and the project schedule can be selected, cooked, stirred, dried, packaged and the like.
Further, in an optional implementation of the present invention, the scanning of the electromechanical device for project schedules may be implemented by a driver.
Furthermore, according to the embodiment of the invention, by retrieving the operation log of the control object and configuring the actual control strategy of the control object according to the operation log, the equipment operation experience can be utilized to provide data support for the intelligent control of the control object, so that the stability of operation is improved. The operation log refers to a data set of operation steps and operation logics of the control object in previous work, the actual control strategy refers to a set of rules for controlling the control object generated according to the operation log, for example, when manufacturing thick broad-bean sauce by thick broad-bean sauce manufacturing equipment, the control rules are that raw materials and water need 1:1 proportion, when the raw material is 1 kg, a water injection machine automatically injects 1 kg of water; when the raw material is 2 kg, the water injector automatically injects 2 kg of water.
As an embodiment of the invention, the running log for retrieving the control object can be extracted by accessing the background database of the electromechanical device through a script.
Further, as an embodiment of the present invention, the configuring an actual control policy of the control object according to the operation log includes: performing data sorting on the operation log to obtain target operation data; calculating the operation logic of the target operation data through a preset logic reasoning function; and configuring the actual control strategy of the control object according to the operation logic.
The target operation data refers to data obtained by processing the operation log by one or more of error correction, repeated item deletion, unified specification, correction logic, conversion structure, data compression, residual/empty value complementation, data/variable discarding and the like, and the operation logic refers to an operation process in which the control object completes a series of operation actions through a series of data instructions.
According to the embodiment of the invention, the target operation data is logically calculated through a preset logical reasoning function, so that the obtained operation logic can configure a corresponding rule through the operation logic, a data basis is provided for an intelligent control mechanism, and the intelligence is improved.
Further, in an optional implementation of the present invention, the preset logical inference function includes:
According to the embodiment of the invention, the accuracy of data in intelligent control calculation can be ensured by collecting the machine parameters and the environmental parameters of the control object. The machine parameters refer to parameters fed back by the current control object, for example, the food conveyor belt feedback parameters may be parameters such as conveyor belt rotating speed and food weight on the conveyor belt; the environmental parameter may be a temperature, a wind speed, or the like.
As an embodiment of the present invention, the machine parameters and the environmental parameters of the control object may be acquired by a sensor, wherein the sensor converts a specific measured signal into a certain "usable signal" according to a certain rule through a sensing element and a conversion element, and outputs the "usable signal" to meet the requirements of information transmission, processing, recording, displaying, controlling, and the like.
S2, inputting the actual control strategy, the machine parameters and the environment parameters into a trained fuzzy neural controller to execute fuzzy decoding processing to obtain control decoding parameters; the blur decoding operation includes: and carrying out parameter fuzzy processing on the machine parameters and the environment parameters by utilizing the membership function of the fuzzy neural controller to obtain fuzzy parameters.
In the embodiment of the invention, the actual control strategy, the machine parameters and the environment parameters are input into the trained fuzzy neural controller to execute the fuzzy decoding processing, so that the machine parameters and the environment parameters can be automatically analyzed and learned by the fuzzy neural controller in the obtained control decoding parameters, and an execution command is output, thereby enabling the equipment control to be more intelligent. The fuzzy neural controller is a trained controller used for receiving the acquisition parameters of the control object and feeding back the quality of the next operation through a series of calculations.
Further, in an optional implementation of the present invention, retrieving the data storage module in the fuzzy neural controller is performed by using an Nmap tool, and the construction and transmission of the data transmission channel may be implemented by using a PeerConnection channel.
Furthermore, the embodiment of the invention carries out parameter fuzzy processing on the machine parameters and the environment parameters by utilizing the membership function of the fuzzy neural controller to obtain fuzzy parameters so as to provide fuzzy data for subsequent fuzzy reasoning operation, ensure that the electromechanical equipment can learn newly added variables in the actual operation process and realize the intelligent control of the electromechanical equipment. The fuzzy parameter refers to a data set which is fuzzy through a fuzzy means.
As an embodiment of the present invention, the performing parameter fuzzy processing on the machine parameter and the environment parameter by using the membership function of the fuzzy neural controller to obtain fuzzy parameters includes: integrating the machine parameters and the environment parameters to obtain integrated parameters, and configuring the membership degree of the integrated parameters; and carrying out fuzzy calculation on the integration parameters through the membership function according to the membership to obtain fuzzy parameters.
The membership degree refers to a variable describing the degree of the elements belonging to the fuzzy set, and the value range of the variable is a closed interval [0,1], which is one of the cores of fuzzy mathematics.
According to the embodiment of the invention, the integrated function is subjected to fuzzy calculation through the preset membership function, so that the integrated function can be fuzzified, and data support is provided for subsequent fuzzy reasoning operation.
Further, in an optional implementation of the present invention, the membership function includes:
And S3, performing control simulation reasoning on the fuzzy parameters by using a reasoning function of the fuzzy neural controller through the actual control strategy to obtain a simulation control rule.
Furthermore, the embodiment of the invention utilizes the inference function of the fuzzy neural controller to carry out control simulation inference on the fuzzy parameters through the actual control strategy to obtain a simulation control rule, and can calculate the currently most applicable control scheme through the inference function, thereby improving the intelligence of the equipment under the condition of ensuring the intelligent operation of the equipment. The simulation control rule refers to a control rule which is calculated by the fuzzy parameter through a reasoning function according to the actual control strategy and is used for carrying out the next operation on the control object, and it needs to be stated that the simulation control rule can be identified by the control object only by the fuzzy resolving process.
As an embodiment of the present invention, the performing, by the actual control strategy, control simulation reasoning on the fuzzy parameter by using the reasoning function of the fuzzy neural controller to obtain a simulation control rule includes: mapping the actual control strategy according to the fuzzy parameters to obtain a target control rule; and according to the target control rule, performing control simulation reasoning on the fuzzy parameter by using the reasoning function to obtain a simulation control rule.
The target control rule is a control rule which has reference meaning for uploading the control object of the corresponding parameter.
Further, in an optional implementation of the present invention, the mapping the actual control strategy may be implemented by an ETL data mapping tool.
According to the embodiment of the invention, the fuzzy parameters are subjected to reasoning calculation through the reasoning function, so that the control rule which is very suitable for the current control object can be calculated, the intelligence of equipment is improved, and the working efficiency is improved.
Further, in an optional implementation of the present invention, the inference function includes:
And S4, carrying out deblurring calculation on the simulation control rule by using a deblurring device of the fuzzy neural network controller to obtain a control decoding parameter.
In the embodiment of the invention, the fuzzy resolver of the fuzzy neural network controller is used for carrying out fuzzy calculation on the simulation control rule to obtain the control decoding parameter, so that the control decoding parameter can be received and identified by the control object, the intelligent control of electromechanical equipment is realized, and the control efficiency of the electromechanical equipment is improved. The control decoding parameters refer to parameter control values which are generated after the simulation rule control is deblurred by a deblurring device and can be identified by the control object.
As an embodiment of the present invention, the obtaining, by a deblurring unit of the fuzzy neural network controller, a control decoding parameter of the simulation control rule through a deblurring calculation includes: analyzing the rule attribute of the simulation control rule through an analysis layer in the defuzzifier, and configuring a defuzzification rule through the rule attribute; and according to the de-fuzzy rule, performing de-fuzzy calculation on the simulation control rule through a de-fuzzy function in the de-fuzzifier to obtain the control decoding parameter.
The analysis layer refers to a special rule attribute used for analyzing the simulation control rule, such as a threshold value, a weight coefficient, and the like. The deblur rule is a rule for assisting the deblur calculation configuration, for example, if the threshold is greater than 1, the minimum value is output, and if the threshold is less than 1, the maximum value is output.
Further, in the embodiment of the present invention, the fuzzy resolving function in the fuzzy resolving device is used to perform the fuzzy resolving calculation on the simulation control rule, so as to output an accurate value for the control object to identify.
Further, in an optional implementation of the present invention, the deblurring function includes: g (x, y) = f (x, y) × h (x, y)
Wherein g (x, y) represents a control decoding parameter, f (x, y) represents a deblurring rule, and h (x, y) represents an analog control rule.
And S5, executing intelligent control on the control object according to the control decoding parameters.
According to the embodiment of the invention, the intelligent control on the control object is executed according to the control decoding parameter, so that the intelligent control on the electromechanical equipment is realized.
As an embodiment of the present invention, the performing of the intelligent control of the control object according to the control decoding parameter includes: analyzing the control decoding parameters to control the control part of the control object; according to the control decoding parameters, identifying the operation behavior executed by the control part; and executing intelligent control of the electromechanical equipment according to the control part and the operation behavior.
Wherein the control part is a component which needs to be operated by the control object; the operation behavior refers to action behavior required to be performed by the control part.
Further, in an optional implementation of the present invention, the analyzing the control decoding parameter to control the control portion of the control object may be performed by a Ketchup tool.
Further, in an optional implementation of the present invention, the performing intelligent control of the electromechanical device according to the control portion and the operation behavior includes: positioning a node to be operated of the electromechanical device according to the control part; configuring an operation log of the electromechanical device according to the operation behavior; and executing intelligent control of the electromechanical equipment according to the node to be operated and the operation log.
The node to be operated refers to a part needing to be operated in the electromechanical device, and the operation log refers to a log integrated by the operation flows of the node to be operated, which are generated according to the operation behavior.
It can be seen that, in the embodiment of the present invention, firstly, by identifying a control object of an electromechanical device to be operated, retrieving an operation log of the control object to configure an actual control strategy of the control object, data acquisition and data feedback can be directly performed on a corresponding control object, ensuring a control premise of a subsequent electromechanical device, and by retrieving the operation log of the control object, the actual control strategy of the control object can be configured, and data support can be provided for intelligent control of the control object by using a device operation experience, thereby improving the stability of operation; secondly, the machine parameters and the environmental parameters of the control object are collected and uploaded to a trained fuzzy neural controller, so that parameter fuzzy processing is performed on the machine parameters and the environmental parameters by using a membership function of the fuzzy neural controller to obtain fuzzy parameters, fuzzy data can be provided for subsequent fuzzy reasoning operation, the electromechanical equipment can be guaranteed to learn newly added variables in the actual operation process, and intelligent control of the electromechanical equipment is realized; furthermore, the embodiment of the invention utilizes the inference function of the fuzzy neural controller to carry out control simulation inference on the fuzzy parameters through an actual control strategy to obtain a simulation control rule, and utilizes the defuzzifier of the fuzzy neural network controller to carry out defuzzification calculation on the simulation control rule to obtain a control decoding parameter so as to execute intelligent control on a control object, realize intelligent control on electromechanical equipment and improve the control efficiency of the electromechanical equipment. Therefore, the method for automatically adjusting and controlling the operation of the intelligent electromechanical system equipment and the electronic equipment provided by the embodiment of the invention can realize the control intellectualization of the electromechanical equipment and improve the control efficiency of the electromechanical equipment.
Fig. 2 is a functional block diagram of the automatic operation regulation control device for the intelligent electromechanical system equipment according to the present invention.
The automatic operation regulation control device 100 for the intelligent electromechanical system equipment can be installed in electronic equipment. According to the realized functions, the automatic adjustment control device for the operation of the intelligent electromechanical system equipment can comprise an actual control strategy configuration module 101, a parameter fuzzy processing module 102, a simulation control strategy reasoning module 103, a decoding parameter obtaining module 104 and an intelligent control module 105. A module according to the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and which are stored in a memory of the electronic device.
In the present embodiment, the functions of the respective modules/units are as follows:
the actual control strategy configuration module 101 is configured to acquire an electromechanical device to be operated, identify a control object of the electromechanical device, retrieve an operation log of the control object, configure an actual control strategy of the control object according to the operation log, and acquire a machine parameter and an environmental parameter of the control object;
the parameter fuzzy processing module 102 is configured to input the actual control strategy, the machine parameter, and the environmental parameter into a trained fuzzy neural controller to perform fuzzy decoding processing, so as to obtain a control decoding parameter; the blur decoding operation includes: carrying out parameter fuzzy processing on the machine parameters and the environment parameters by utilizing a membership function of the fuzzy neural controller to obtain fuzzy parameters;
the simulation control strategy reasoning module 103 is used for performing control simulation reasoning on the fuzzy parameters by using a reasoning function of the fuzzy neural controller through the actual control strategy to obtain a simulation control strategy;
the device intelligent control module 104 is configured to perform deblurring calculation on the analog control strategy by using a deblurring unit of the fuzzy neural network controller to obtain a control decoding parameter;
the intelligent control module 105 is configured to perform intelligent control on the control object according to the control decoding parameter.
In detail, when the modules in the automatic operation adjustment control apparatus 100 for an intelligent electromechanical system device in the embodiment of the present invention are used, the same technical means as the above-mentioned method for automatically adjusting and controlling an intelligent electromechanical system device in fig. 1 is adopted, and the same technical effects can be produced, which is not described herein again.
Fig. 3 is a schematic structural diagram of an electronic device 1 for implementing the method for automatically adjusting and controlling the operation of the intelligent electromechanical system device according to the present invention.
The electronic device 1 may include a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further include a computer program, such as an intelligent electromechanical system device running automatic adjustment control program, stored in the memory 11 and executable on the processor 10.
In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions, and includes one or more Central Processing Units (CPUs), a microprocessor, a digital Processing chip, a graphics processor, a combination of various control chips, and the like. The processor 10 is a Control Unit of the electronic device 1, connects various components of the electronic device 1 by using various interfaces and lines, and executes various functions and processes data of the electronic device 1 by running or executing programs or modules stored in the memory 11 (for example, executing an intelligent electromechanical system device running automatic adjustment Control program, etc.), and calling data stored in the memory 11.
The memory 11 includes at least one type of readable storage medium including flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 can be used not only for storing application software installed in the electronic device 1 and various types of data, such as codes of an automatic adjustment control program run by the intelligent electromechanical system device, but also for temporarily storing data that has been output or will be output.
The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
The communication interface 13 is used for communication between the electronic device 1 and other devices, and includes a network interface and an employee interface. Optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a bluetooth interface, etc.), which are generally used to establish a communication connection between the electronic device 1 and another electronic device 1. The employee interface may be a Display (Display), an input unit, such as a Keyboard (Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable for displaying information processed in the electronic device 1 and for displaying a visual staff interface.
Fig. 3 only shows the electronic device 1 with components, and it will be understood by those skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device 1 may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so as to implement functions of charge management, discharge management, power consumption management, and the like through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device 1 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
It is to be understood that the embodiments described are for illustrative purposes only and that the scope of the claimed invention is not limited to this configuration.
The intelligent electromechanical system device running automatic adjustment control program stored in the memory 11 of the electronic device 1 is a combination of a plurality of computer programs, and when running in the processor 10, can realize that:
the method comprises the steps of obtaining electromechanical equipment to be operated, identifying a control object of the electromechanical equipment, retrieving an operation log of the control object, configuring an actual control strategy of the control object according to the operation log, and collecting machine parameters and environment parameters of the control object;
inputting the actual control strategy, the machine parameters and the environment parameters into a trained fuzzy neural controller to execute fuzzy decoding processing to obtain control decoding parameters; the blur decoding operation includes: carrying out parameter fuzzy processing on the machine parameters and the environment parameters by utilizing a membership function of the fuzzy neural controller to obtain fuzzy parameters;
performing control simulation reasoning on the fuzzy parameters by using a reasoning function of the fuzzy neural controller through the actual control strategy to obtain a simulation control strategy;
carrying out deblurring calculation on the simulation control strategy by using a deblurring device of the fuzzy neural network controller to obtain a control decoding parameter;
and executing intelligent control on the control object according to the control decoding parameters.
Specifically, the processor 10 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1 for a specific implementation method of the computer program, which is not described herein again.
Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may be stored in a non-volatile computer-readable storage medium. The computer readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM).
The present invention also provides a computer-readable storage medium, storing a computer program which, when executed by a processor of an electronic device 1, may implement:
the method comprises the steps of obtaining electromechanical equipment to be operated, identifying a control object of the electromechanical equipment, retrieving an operation log of the control object, configuring an actual control strategy of the control object according to the operation log, and collecting machine parameters and environment parameters of the control object;
inputting the actual control strategy, the machine parameters and the environment parameters into a trained fuzzy neural controller to execute fuzzy decoding processing to obtain control decoding parameters; the blur decoding operation includes: carrying out parameter fuzzy processing on the machine parameters and the environment parameters by utilizing a membership function of the fuzzy neural controller to obtain fuzzy parameters;
performing control simulation reasoning on the fuzzy parameters by using a reasoning function of the fuzzy neural controller through the actual control strategy to obtain a simulation control strategy;
carrying out deblurring calculation on the simulation control strategy by using a deblurring device of the fuzzy neural network controller to obtain a control decoding parameter;
and executing intelligent control on the control object according to the control decoding parameters.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The embodiment of the invention can acquire and process related data based on an artificial intelligence technology. Among them, artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
Furthermore, it will be obvious that the term "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A method for automatically adjusting and controlling the operation of intelligent electromechanical system equipment is characterized by comprising the following steps:
the method comprises the steps of obtaining electromechanical equipment to be operated, identifying a control object of the electromechanical equipment, retrieving an operation log of the control object, configuring an actual control strategy of the control object according to the operation log, and collecting machine parameters and environment parameters of the control object;
inputting the actual control strategy, the machine parameters and the environment parameters into a trained fuzzy neural controller to execute fuzzy decoding processing to obtain control decoding parameters; wherein: carrying out parameter fuzzy processing on the machine parameters and the environment parameters by utilizing a membership function of the fuzzy neural controller to obtain fuzzy parameters; performing control simulation reasoning on the fuzzy parameters according to the actual control strategy by using a reasoning function of the fuzzy neural controller to obtain a simulation control strategy; carrying out deblurring calculation on the simulation control strategy by using a deblurring device of the fuzzy neural controller to obtain the control decoding parameters;
and executing intelligent control on the control object according to the control decoding parameters.
2. The method for intelligent electromechanical systems device operation autoregulation control according to claim 1, wherein the identifying the control object of the electromechanical device comprises:
retrieving whether the mechatronic device is in an operational state;
scanning a project schedule of the mechatronic device while the mechatronic device is in an operational state;
and determining the control object of the electromechanical device according to the project progress.
3. The method for intelligent electromechanical systems of device operation autoregulation control of claim 1, wherein the configuring the actual control strategy of the control object based on the operation log comprises:
performing data sorting on the operation log to obtain target operation data;
calculating the operation logic of the target operation data through a preset logic reasoning function;
configuring an actual control strategy of the control object according to the operation logic;
wherein the preset logical inference function comprises:
4. The method for implementing automatic tuning control of an intelligent electromechanical systems device according to claim 1, wherein the fuzzy processing of the machine parameter and the environmental parameter using the membership function of the fuzzy neural controller to obtain fuzzy parameters comprises:
integrating the machine parameters and the environment parameters to obtain integrated parameters, and configuring the membership degree of the integrated parameters;
according to the membership degree, fuzzy calculation is carried out on the integration parameters through the membership function to obtain fuzzy parameters;
wherein the membership function comprises:
5. The method for intelligent electromechanical system equipment to run automatic adjustment control as claimed in claim 1, wherein said performing control simulation inference on said fuzzy parameters according to said actual control strategy using inference functions of said fuzzy neural controller to obtain a simulation control strategy comprises:
mapping the actual control strategy according to the fuzzy parameters to obtain a target control rule;
according to the target control rule, performing control simulation reasoning on the fuzzy parameter by using the reasoning function based on a formula (1) to obtain a simulation control rule;
wherein the inference function comprises:
Wherein, the
Representing an analog control rule, f () representing an inference function, based on a comparison of the value of the analog control rule and the value of the reference value>
Indicates the target control rule, <' > is selected>
Denotes a fuzzy parameter, n denotes the number of target control rules, and e denotes an infinite acyclic decimal.
6. The method for the intelligent electromechanical systems device to run automatic adjustment control according to claim 1, wherein the obtaining of the control decoding parameters by the fuzzy algorithm of the simulation control rules by the fuzzy controller of the fuzzy neural network controller comprises:
analyzing the rule attribute of the simulation control rule through an analysis layer in the defuzzifier, and configuring a defuzzification rule through the rule attribute;
according to the de-fuzzy rule, performing de-fuzzy calculation on the simulation control rule through a de-fuzzy function in the de-fuzzifier to obtain the control decoding parameter;
wherein the deblurring function comprises: g (x, y) = f (x, y) × h (x, y)
Wherein g (x, y) represents a control decoding parameter, f (x, y) represents a deblurring rule, and h (x, y) represents an analog control rule.
7. The method for intelligent electromechanical systems device to operate automatic adjustment control as claimed in claim 1, wherein said performing intelligent control of said control object according to said control decoding parameters comprises:
analyzing the control decoding parameters to control the control part of the control object;
according to the control decoding parameters, identifying the operation behavior executed by the control part;
and executing intelligent control of the electromechanical equipment according to the control part and the operation behavior.
8. An intelligent electromechanical system device operation automatic regulation control device is characterized in that the device comprises:
the actual control strategy configuration module is used for acquiring electromechanical equipment to be operated, identifying a control object of the electromechanical equipment, retrieving an operation log of the control object, configuring an actual control strategy of the control object according to the operation log, and acquiring machine parameters and environment parameters of the control object;
the parameter fuzzy processing module is used for inputting the actual control strategy, the machine parameters and the environment parameters into a trained fuzzy neural controller to execute fuzzy decoding processing to obtain control decoding parameters; the blur decoding operation includes: carrying out parameter fuzzy processing on the machine parameters and the environment parameters by utilizing a membership function of the fuzzy neural controller to obtain fuzzy parameters;
the simulation control strategy reasoning module is used for performing control simulation reasoning on the fuzzy parameters by using a reasoning function of the fuzzy neural controller through the actual control strategy to obtain a simulation control strategy;
the decoding parameter acquisition module is used for carrying out deblurring calculation on the simulation control strategy by using a deblurring device of the fuzzy neural network controller to obtain a control decoding parameter;
and the intelligent control module is used for executing intelligent control on the control object according to the control decoding parameter.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of intelligent mechatronic system device operation autoregulation control according to any of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the method for intelligent electromechanical systems device operation automatic adjustment control of claim 1 to 7.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101063643A (en) * | 2007-02-02 | 2007-10-31 | 北京航空航天大学 | Intelligent diagnostic method for airplane functional failure and system thereof |
| CN102005025A (en) * | 2009-08-31 | 2011-04-06 | 欧姆龙株式会社 | Image processing apparatus |
| CN105743985A (en) * | 2016-03-24 | 2016-07-06 | 国家计算机网络与信息安全管理中心 | Virtual service migration method based on fuzzy logic |
| CN106919982A (en) * | 2017-03-20 | 2017-07-04 | 中国科学院沈阳自动化研究所 | A kind of method for diagnosing faults towards semiconductor manufacturing equipment |
| CN107991982A (en) * | 2017-12-12 | 2018-05-04 | 江苏大学 | A kind of automobile coating production line drying chamber monitoring system and method based on LABVIEW |
| CN108646664A (en) * | 2018-05-15 | 2018-10-12 | 马鞍山中粮生物化学有限公司 | A kind of feed processing control system |
| CN110244559A (en) * | 2019-05-21 | 2019-09-17 | 中国农业大学 | A kind of greenhouse intelligent regulation method based on agriculture solar term empirical data |
| CN112415967A (en) * | 2020-11-17 | 2021-02-26 | 四川大学 | Intelligent management system and method for shoe industry production line |
| CN112631134A (en) * | 2021-01-05 | 2021-04-09 | 华南理工大学 | Intelligent trolley obstacle avoidance method based on fuzzy neural network |
| CN114582161A (en) * | 2022-03-02 | 2022-06-03 | 南京国立资产管理有限责任公司 | Intelligent parking lot entrance and exit system based on self-adaptive fuzzy control algorithm |
| CN114859821A (en) * | 2022-04-25 | 2022-08-05 | 浙江理工大学 | Self-detection, self-analysis and self-adaptive numerical control machine tool fuzzy control system |
-
2023
- 2023-03-17 CN CN202310258507.6A patent/CN115963723A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101063643A (en) * | 2007-02-02 | 2007-10-31 | 北京航空航天大学 | Intelligent diagnostic method for airplane functional failure and system thereof |
| CN102005025A (en) * | 2009-08-31 | 2011-04-06 | 欧姆龙株式会社 | Image processing apparatus |
| CN105743985A (en) * | 2016-03-24 | 2016-07-06 | 国家计算机网络与信息安全管理中心 | Virtual service migration method based on fuzzy logic |
| CN106919982A (en) * | 2017-03-20 | 2017-07-04 | 中国科学院沈阳自动化研究所 | A kind of method for diagnosing faults towards semiconductor manufacturing equipment |
| CN107991982A (en) * | 2017-12-12 | 2018-05-04 | 江苏大学 | A kind of automobile coating production line drying chamber monitoring system and method based on LABVIEW |
| CN108646664A (en) * | 2018-05-15 | 2018-10-12 | 马鞍山中粮生物化学有限公司 | A kind of feed processing control system |
| CN110244559A (en) * | 2019-05-21 | 2019-09-17 | 中国农业大学 | A kind of greenhouse intelligent regulation method based on agriculture solar term empirical data |
| CN112415967A (en) * | 2020-11-17 | 2021-02-26 | 四川大学 | Intelligent management system and method for shoe industry production line |
| CN112631134A (en) * | 2021-01-05 | 2021-04-09 | 华南理工大学 | Intelligent trolley obstacle avoidance method based on fuzzy neural network |
| CN114582161A (en) * | 2022-03-02 | 2022-06-03 | 南京国立资产管理有限责任公司 | Intelligent parking lot entrance and exit system based on self-adaptive fuzzy control algorithm |
| CN114859821A (en) * | 2022-04-25 | 2022-08-05 | 浙江理工大学 | Self-detection, self-analysis and self-adaptive numerical control machine tool fuzzy control system |
Non-Patent Citations (4)
| Title |
|---|
| 乔宝山: "高速公路智能汽车自主换道系统设计", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》, no. 3, pages 038 - 730 * |
| 姚迪: "基于 MATLAB 的烘干车间模糊控制系统仿真设计", 《仪器仪表标准化与计量》, no. 3, pages 20 - 22 * |
| 官泽瑾: "基于模糊理论的多聚焦图像融合研究", 《中国优秀硕士学位论文全文数据库信息科技辑》, no. 5, pages 138 - 1108 * |
| 张炜: "基于PLC的消防自动巡检系统的设计与研究", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》, no. 6, pages 038 - 730 * |
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