CN117900725A - Intelligent welding machine operation control method and system - Google Patents
Intelligent welding machine operation control method and system Download PDFInfo
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- CN117900725A CN117900725A CN202410289096.1A CN202410289096A CN117900725A CN 117900725 A CN117900725 A CN 117900725A CN 202410289096 A CN202410289096 A CN 202410289096A CN 117900725 A CN117900725 A CN 117900725A
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- B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
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Abstract
The invention relates to the field of remote monitoring and discloses an intelligent welding machine operation control method and system, wherein the method comprises the following steps: analyzing a welding machine component of the welding machine, identifying component function indexes of the welding machine component, and constructing a welding path of a welding target; collecting welding data of a welding machine on a welding target, analyzing a current welding path of the welding machine, and calculating a deviation coefficient of the welding path and the current welding path; encoding welder parameters of a welder to obtain encoded welder parameters, and constructing a welder encoding parameter set of the welder; and calculating the welding fitness of the encoding parameter set of the welding machine, determining the target encoding parameter set of the welding machine, decoding the target encoding parameter set to obtain a decoding encoding parameter set, adjusting the welding machine parameters of the welding machine to obtain adjusted welding machine parameters, and realizing the operation control of the welding machine based on the adjusted welding machine parameters. The invention can improve the control effect on the operation of the welding machine.
Description
Technical Field
The invention relates to the field of remote monitoring, in particular to an intelligent welding machine operation control method and system.
Background
Welder operation control refers to the process of managing and directing the welder to complete a welding job through a series of control systems and programs. This typically includes precise control of welding parameters (e.g., current, voltage, speed, welding power, etc.), as well as optimal management of the welding process (e.g., welding speed, welding sequence, welding path, etc.). The aim of the operation control of the welding machine is to ensure welding quality, improve production efficiency and reduce production cost.
At present, the operation control of the welding machine is realized mainly by setting welding parameters after analyzing a welding target of the welding machine, and the special conditions such as clamping and deviation of the welding machine cannot be repaired in time in the mode, so that the welding risk of the welding machine is caused, and the operation control effect of the welding machine is reduced.
Disclosure of Invention
The invention provides an intelligent welding machine operation control method and system, and mainly aims to improve the control effect on the operation of a welding machine.
In order to achieve the above object, the present invention provides an intelligent welding machine operation control method, including:
Acquiring welder data and welding target data of a welder, analyzing a welder component of the welder based on the welder data, identifying component function indexes of the welder component, and constructing a cooperative compensation network of the welder component based on the component function indexes;
Identifying welding target characteristics of a welding target corresponding to the welding machine based on the welding target data, and constructing a welding path of the welding target based on the welding target characteristics;
Collecting welding data of the welding machine on the welding target by utilizing the cooperative compensation network based on the welding path, analyzing the current welding path of the welding machine based on the welding data, and calculating the deviation coefficient of the welding path and the current welding path;
when the deviation coefficient is larger than a preset deviation threshold, encoding welder parameters of the welder to obtain encoded welder parameters, and constructing a welder encoding parameter set of the welder based on the encoded welder parameters;
and calculating the welding fitness of the encoding parameter set of the welding machine, determining the target encoding parameter set of the welding machine based on the welding fitness, decoding the target encoding parameter set to obtain a decoding encoding parameter set, adjusting the welding machine parameters of the welding machine based on the decoding encoding parameter set to obtain an adjusted welding machine parameter, and realizing the operation control of the welding machine based on the adjusted welding machine parameter.
Optionally, the analyzing the welder component of the welder based on the welder data includes:
preprocessing the welder data to obtain preprocessed welder data;
carrying out statistical analysis on the data of the pretreatment welding machine to obtain a data statistical analysis result;
Analyzing the data association coefficient of the preprocessing welding machine data based on the data statistics analysis result;
and determining a welder component of the welder based on the data association coefficient and the data statistics analysis result.
Optionally, the constructing a collaborative compensation network for the welder component based on the component function index includes:
Identifying a collaborative compensation demand for the welder component based on the component functional index;
Analyzing a demand complexity coefficient of the collaborative compensation demand;
determining a collaborative compensation strategy for the welder component based on the demand complexity factor;
And constructing a cooperative compensation network of the welding machine assembly based on the cooperative compensation strategy.
Optionally, the analyzing the demand complexity factor of the collaborative compensation demand includes:
Identifying a demand type of the collaborative compensation demand;
Calculating the demand weight of the demand type;
calculating a demand complexity factor for the collaborative compensation demand based on the demand type and the demand weight using the formula:
；
wherein, A demand complexity factor representing the demand for collaborative compensation,Representing a model of the identification of the need,The t-th type of demand is set,Representing the demand weight corresponding to the t-th demand type,A demand correlation coefficient representing a t-th demand type and a demand type,Representing the number of demand types.
Optionally, the constructing a welding path of the welding target based on the welding target feature includes:
constructing a target three-dimensional model of the welding target based on the welding target characteristics;
calculating a model complexity coefficient of the target three-dimensional model;
Determining a path optimization algorithm of the target three-dimensional model based on the model complexity coefficient;
and calculating the welding path of the welding target based on the path optimization algorithm.
Optionally, the calculating, based on the path optimization algorithm, a welding path of the welding target includes:
Marking welding nodes and welding source points of the welding targets;
Based on the path optimization algorithm, calculating the shortest path length of the welding node and the welding source point by using the following formula:
；
wherein, Representing weld source to weld nodeIs provided for the shortest path of the (c) signal,Representing the shortest path length of the weld node and the weld source point,Representing edgesIs used for the weight of the (c),Represent the firstA plurality of the welding nodes are arranged on the welding frame,Represent the firstWelding nodes;
and planning a welding path of the welding target based on the shortest path length.
Optionally, the calculating a deviation coefficient of the welding path and the current welding path includes:
Mapping the welding path and the current welding path to obtain a mapped welding path;
Identifying a deviation zone of the mapped welding path;
Identifying a departure distance and a departure angle of the departure area;
And calculating the deviation coefficient of the welding path and the current welding path based on the deviation distance and the deviation angle.
Optionally, the calculating the welding fitness of the welder coding parameter set includes:
determining welding optimization indexes of the welding machines corresponding to the welding machine coding parameter sets;
Calculating the index weight of the welding optimization index;
constructing an adaptability function of the welding machine coding parameter set based on the welding optimization index and the index weight;
and calculating the welding fitness of the welding machine coding parameter set based on the fitness function.
Optionally, the calculating the welding fitness of the welder coding parameter set based on the fitness function includes:
based on the fitness function, the welding fitness of the welder coding parameter set is calculated using the following formula:
；
wherein, Indicating the weld suitability of the welder encoding parameter set,Coding parameter group of welderThe index weight of each welding optimization index,Coding parameter group of welderThe fitness function of the individual welding optimization indicators,Representing a set of welder encoding parameters.
In order to solve the above problems, the present invention further provides an intelligent welding machine operation control system, the system comprising:
The collaborative network construction module is used for acquiring welding machine data and welding target data of a welding machine, analyzing a welding machine component of the welding machine based on the welding machine data, identifying component function indexes of the welding machine component, and constructing a collaborative compensation network of the welding machine component based on the component function indexes;
The welding path construction module is used for identifying welding target characteristics of a welding target corresponding to the welding machine based on the welding target data and constructing a welding path of the welding target based on the welding target characteristics;
The path deviation analysis module is used for acquiring welding data of the welding machine on the welding target by utilizing the cooperative compensation network based on the welding path, analyzing the current welding path of the welding machine based on the welding data, and calculating deviation coefficients of the welding path and the current welding path;
The encoding parameter set construction module is used for encoding the welder parameters of the welder to obtain encoding welder parameters when the deviation coefficient is larger than a preset deviation threshold value, and constructing a welder encoding parameter set of the welder based on the encoding welder parameters;
and the welding machine operation control module is used for calculating the welding fitness of the welding machine coding parameter set, determining the target coding parameter set of the welding machine based on the welding fitness, decoding the target coding parameter set to obtain a decoding coding parameter set, adjusting the welding machine parameters of the welding machine based on the decoding coding parameter set to obtain an adjusted welding machine parameter, and realizing the operation control of the welding machine based on the adjusted welding machine parameter.
Based on the welder data, the welder components of the welder are analyzed, so that support can be provided for component control of the welder in the later stage; according to the embodiment of the invention, based on the component function index, the cooperative compensation network for constructing the welding machine component can realize the accurate control of the welding machine component through the network; optionally, the embodiment of the invention constructs the welding path of the welding target based on the characteristics of the welding target, and can plan the welding route of the welding machine, thereby improving the welding efficiency of the welding machine; further, based on the welding data, the embodiment of the invention analyzes the current welding path of the welding machine, and can compare the recorded path with the welding path to identify whether the working of the welding machine has deviation, thereby improving the welding control effect of the welding machine; according to the embodiment of the invention, when the deviation coefficient is larger than the preset deviation threshold value, the welder parameters of the welder are encoded, so that the encoded welder parameters can be used as the basis for optimizing and selecting the welder parameters. Therefore, the intelligent welding machine operation control method and system provided by the invention can improve the control effect on the operation of the welding machine.
Drawings
FIG. 1 is a flow chart of an intelligent welder operation control method according to an embodiment of the present invention;
FIG. 2 is a functional block diagram of an intelligent welder operation control system according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an electronic device of an intelligent welder operation control system according to an embodiment of the present invention;
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides an intelligent welding machine operation control method. The execution main body of the intelligent welding machine operation control method comprises, but is not limited to, at least one of a server, a terminal and the like which can be configured to execute the electronic equipment of the method provided by the embodiment of the application. In other words, the intelligent welder operation control method may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The service end 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 cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Referring to fig. 1, a flow chart of an intelligent welding machine operation control method according to an embodiment of the invention is shown. In this embodiment, the intelligent welder operation control method includes:
s1, acquiring welding machine data and welding target data of a welding machine, analyzing a welding machine component of the welding machine based on the welding machine data, identifying component function indexes of the welding machine component, and constructing a cooperative compensation network of the welding machine component based on the component function indexes.
In the embodiment of the invention, the welding machine data refers to data describing the welding machine, such as welding machine model, welding machine component data and the like, and the welding target data refers to data describing a welding object of the welding machine, such as welding object material, welding object shape and the like.
Further, the embodiment of the invention can provide support for carrying out component control on the welding machine in a later stage by analyzing the welding machine component of the welding machine based on the welding machine data. The welder assembly refers to various parts and assemblies used in the welding process, such as assemblies of a welding power source, a control device, a welding transformer, and the like.
As one embodiment of the present invention, the analyzing a welder component of the welder based on the welder data includes: preprocessing the welder data to obtain preprocessed welder data; carrying out statistical analysis on the data of the pretreatment welding machine to obtain a data statistical analysis result; analyzing the data association coefficient of the preprocessing welding machine data based on the data statistics analysis result; and determining a welder component of the welder based on the data association coefficient and the data statistics analysis result.
The data statistical analysis result refers to an analysis result obtained after the statistical analysis of the data of the pre-processing welding machine, such as an internal structure of the welding machine, a welding index and the like, and the data association coefficient refers to an association degree between the data of the pre-processing welding machine.
Optionally, in the embodiment of the present invention, the component function index refers to a function index that may be implemented by the welding machine component, for example, an index of power supply, a control index, and the like.
According to the embodiment of the invention, based on the component function index, the cooperative compensation network for constructing the welding machine component can realize the accurate control of the welding machine component through the network. Wherein the collaborative compensation network refers to a network that controls the welder components.
As an embodiment of the present invention, the constructing a collaborative compensation network for the welder component based on the component function index includes: identifying a collaborative compensation demand for the welder component based on the component functional index; analyzing a demand complexity coefficient of the collaborative compensation demand; determining a collaborative compensation strategy for the welder component based on the demand complexity factor; and constructing a cooperative compensation network of the welding machine assembly based on the cooperative compensation strategy.
The collaborative compensation requirement refers to a requirement for realizing the mutual assistance work of the welding machine component, the requirement complexity coefficient refers to a complexity degree of the requirement for realizing the mutual assistance work of the welding machine component, and the collaborative compensation strategy refers to a strategy for realizing the mutual assistance work requirement of the welding machine component, for example, realizing data transmission of the component.
Optionally, as an optional embodiment of the present invention, the analyzing the requirement complexity factor of the collaborative compensation requirement includes: identifying a demand type of the collaborative compensation demand; calculating the demand weight of the demand type; calculating a demand complexity factor for the collaborative compensation demand based on the demand type and the demand weight using the formula:
；
wherein, A demand complexity factor representing the demand for collaborative compensation,Representing a model of the identification of the need,The t-th type of demand is set,Representing the demand weight corresponding to the t-th demand type,A demand correlation coefficient representing a t-th demand type and a demand type,Representing the number of demand types.
The demand recognition model is a model for recognizing the complexity of the collaborative compensation demand, and can be constructed through a Convolutional Neural Network (CNN) or a Support Vector Machine (SVM), the demand association coefficient is the association degree between the demand types, and the demand weight is the importance degree of the demand types in realizing the collaborative compensation demand.
S2, based on the welding target data, identifying welding target characteristics of a welding target corresponding to the welding machine, and based on the welding target characteristics, constructing a welding path of the welding target.
In the embodiment of the present invention, the welding target feature refers to a feature attribute of the welding target, for example, a feature such as a welding target material, a welding target shape, and the like.
Optionally, according to the embodiment of the invention, based on the characteristics of the welding target, the welding route of the welding machine can be planned by constructing the welding path of the welding target, so that the welding efficiency of the welding machine is improved. The welding path is a path for welding the welding target by the welding machine.
Optionally, as an embodiment of the present invention, the constructing a welding path of the welding target based on the welding target feature includes: constructing a target three-dimensional model of the welding target based on the welding target characteristics; calculating a model complexity coefficient of the target three-dimensional model; determining a path optimization algorithm of the target three-dimensional model based on the model complexity coefficient; and calculating the welding path of the welding target based on the path optimization algorithm.
The target three-dimensional model is a model constructed by three-dimensional modeling of the welding target, the model complexity coefficient is the construction complexity of the target three-dimensional model corresponding to the welding target, and the path optimization algorithm is an algorithm for calculating an optimal welding path of the target three-dimensional model, such as Dijkstra algorithm or AI algorithm.
Optionally, as an optional embodiment of the present invention, the calculating, based on the path optimization algorithm, a welding path of the welding target includes: marking welding nodes and welding source points of the welding targets; based on the path optimization algorithm, calculating the shortest path length of the welding node and the welding source point by using the following formula:
；
wherein, Representing weld source to weld nodeIs provided for the shortest path of the (c) signal,Representing the shortest path length of the weld node and the weld source point,Representing edgesIs used for the weight of the (c),Represent the firstA plurality of the welding nodes are arranged on the welding frame,Represent the firstWelding nodes;
and planning a welding path of the welding target based on the shortest path length.
The welding source point refers to a starting point for welding the welding target, and the welding node refers to a key node for welding the welding target, such as a welding target corner point.
S3, based on the welding path, acquiring welding data of the welding machine on the welding target by utilizing the cooperative compensation network, analyzing the current welding path of the welding machine based on the welding data, and calculating the deviation coefficient of the welding path and the current welding path.
In the embodiment of the invention, the welding data refers to data related to the welding of the welding machine to the welding target, such as welding speed, current, voltage, temperature, welding seam quality, welding environment factors (such as gas flow, humidity and wind speed) and the like.
Further, the embodiment of the invention analyzes the current welding path of the welding machine based on the welding data, and can compare the recorded path with the welding path to identify whether the working of the welding machine is deviated, thereby improving the welding control effect of the welding machine. The current welding path refers to a welding route of the welding machine to the welding target. In detail, the current welding path may be recorded by a sensor.
Optionally, the embodiment of the invention can timely repair the path by calculating the deviation coefficient of the welding path and the current welding path, thereby improving the operation control effect of the welding machine. Wherein the deviation coefficient refers to a degree of deviation between the welding path and the current welding path.
Optionally, as an embodiment of the present invention, the calculating a deviation coefficient of the welding path and the current welding path includes: mapping the welding path and the current welding path to obtain a mapped welding path; identifying a deviation zone of the mapped welding path; identifying a departure distance and a departure angle of the departure area; and calculating the deviation coefficient of the welding path and the current welding path based on the deviation distance and the deviation angle.
The mapping welding path refers to a path obtained by corresponding overlapping of the welding path and the current welding path, the deviation area refers to an area where no overlapping occurs on the mapping welding path, and the deviation distance and the deviation angle refer to the length of the deviation area and the deviation angle.
And S4, when the deviation coefficient is larger than a preset deviation threshold, encoding the welder parameters of the welder to obtain encoded welder parameters, and constructing a welder encoding parameter set of the welder based on the encoded welder parameters.
According to the embodiment of the invention, when the deviation coefficient is larger than the preset deviation threshold, the welder parameters of the welder are encoded, so that the encoded welder parameters can be used as the optimization selection basis of the welder parameters. Wherein, the encoding welding machine parameters refer to encoding welding parameters (such as current, voltage, welding speed and the like) into parameter forms suitable for genetic algorithm. In detail, the encoding welder parameters may be encoded by binary encoding or real encoding.
In the embodiment of the invention, the welder coding parameter set refers to control parameters of the welder for constructing a set by the coding welder parameters.
S5, calculating welding fitness of the welding machine coding parameter set, determining a target coding parameter set of the welding machine based on the welding fitness, decoding the target coding parameter set to obtain a decoding coding parameter set, adjusting welding machine parameters of the welding machine based on the decoding coding parameter set to obtain an adjusted welding machine parameter, and realizing operation control of the welding machine based on the adjusted welding machine parameter.
Optionally, the embodiment of the invention can select the welding fitness of the welding machine coding parameter set as the basis for selecting the welding machine parameters by calculating the welding fitness. And the welding fitness is the fitness of the welding machine coding parameter set in the current environment.
Optionally, as an embodiment of the present invention, the calculating the welding fitness of the welder coding parameter set includes: determining welding optimization indexes of the welding machines corresponding to the welding machine coding parameter sets; calculating the index weight of the welding optimization index; constructing an adaptability function of the welding machine coding parameter set based on the welding optimization index and the index weight; and calculating the welding fitness of the welding machine coding parameter set based on the fitness function.
The welding optimization index refers to a target for repairing the current deviation state of the welding machine, such as a welding seam speed, a welding seam angle and the like, the index weight refers to the importance degree of the welding optimization index in the repairing process of the current deviation state of the welding machine, and the fitness function refers to a comprehensive effect function reflecting the welding quality, efficiency, cost and other engineering considerations of the coding parameter set of the welding machine.
Optionally, as an optional embodiment of the present invention, the calculating, based on the fitness function, a welding fitness of the welder coding parameter set includes: based on the fitness function, the welding fitness of the welder coding parameter set is calculated using the following formula:
；
wherein, Indicating the weld suitability of the welder encoding parameter set,Coding parameter group of welderThe index weight of each welding optimization index,Coding parameter group of welderThe fitness function of the individual welding optimization indicators,Representing a set of welder encoding parameters.
Further, in the embodiment of the present invention, the target encoding parameter set refers to a set of encoding parameters that is optimal for repairing the path of the welding machine and is selected from the set of encoding parameters of the welding machine. In detail, the target encoding parameter set may be selected by selecting the parameter set having the highest welding fitness. The decoding encoding parameter set refers to a parameter set after decoding the target encoding parameter set, and in detail, the decoding encoding parameter set may be implemented by real number decoding.
Optionally, the embodiment of the invention adjusts the welder parameters of the welder based on the decoding encoding parameter set, and the welder parameters can be adjusted by adjusting the welder parameters, thereby improving the control effect on the operation of the welder. The welding machine parameter adjustment refers to a new welding machine parameter obtained after the welding machine parameter is adjusted according to the decoding encoding parameter set.
Finally, the embodiment of the invention realizes the operation control of the welding machine based on the parameter adjustment of the welding machine and realizes the efficient control of the operation of the welding machine.
Based on the welder data, the welder components of the welder are analyzed, so that support can be provided for component control of the welder in the later stage; according to the embodiment of the invention, based on the component function index, the cooperative compensation network for constructing the welding machine component can realize the accurate control of the welding machine component through the network; optionally, the embodiment of the invention constructs the welding path of the welding target based on the characteristics of the welding target, and can plan the welding route of the welding machine, thereby improving the welding efficiency of the welding machine; further, based on the welding data, the embodiment of the invention analyzes the current welding path of the welding machine, and can compare the recorded path with the welding path to identify whether the working of the welding machine has deviation, thereby improving the welding control effect of the welding machine; according to the embodiment of the invention, when the deviation coefficient is larger than the preset deviation threshold value, the welder parameters of the welder are encoded, so that the encoded welder parameters can be used as the basis for optimizing and selecting the welder parameters. Therefore, the intelligent welding machine operation control method provided by the invention can improve the control effect on the operation of the welding machine.
FIG. 2 is a functional block diagram of an intelligent welder operation control system according to an embodiment of the present invention.
The intelligent welder operation control system 200 of the present invention may be installed in an electronic device. Depending on the functions implemented, the intelligent welder operation control system 200 may include a collaborative network construction module 201, a welding path construction module 202, a path deviation analysis module 203, an encoding parameter set construction module 204, and a welder operation control module 205. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the present embodiment, the functions concerning the respective modules/units are as follows:
The collaborative network construction module 201 is configured to obtain welder data and welding target data of a welder, analyze a welder component of the welder based on the welder data, identify a component function index of the welder component, and construct a collaborative compensation network of the welder component based on the component function index;
The welding path construction module 202 is configured to identify a welding target feature of a welding target corresponding to the welding machine based on the welding target data, and construct a welding path of the welding target based on the welding target feature;
The path deviation analysis module 203 is configured to collect welding data of the welding machine on the welding target by using the cooperative compensation network based on the welding path, analyze a current welding path of the welding machine based on the welding data, and calculate a deviation coefficient of the welding path and the current welding path;
The encoding parameter set construction module 204 is configured to encode a welder parameter of the welder to obtain an encoding welder parameter when the deviation coefficient is greater than a preset deviation threshold, and construct a welder encoding parameter set of the welder based on the encoding welder parameter;
The welder operation control module 205 is configured to calculate a welding fitness of the welder encoding parameter set, determine a target encoding parameter set of the welder based on the welding fitness, decode the target encoding parameter set to obtain a decoded encoding parameter set, adjust welder parameters of the welder based on the decoded encoding parameter set to obtain an adjusted welder parameter, and implement operation control of the welder based on the adjusted welder parameter.
In detail, each module in the intelligent welder operation control system 200 in the embodiment of the present invention adopts the same technical means as the intelligent welder operation control method in the drawings when in use, and can produce the same technical effects, which are not described herein.
The embodiment of the invention provides electronic equipment for realizing an intelligent welding machine operation control method.
Referring to fig. 3, the electronic device may include a processor 30, a memory 31, a communication bus 32, and a communication interface 33, and may further include a computer program stored in the memory 31 and executable on the processor 30, such as a smart welder operation control method program.
The processor may be formed by an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be formed by a plurality of integrated circuits packaged with the same function or different functions, including one or more central processing units (Central Processing Unit, CPU), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor is a Control Unit (Control Unit) of the electronic device, connects various components of the entire electronic device using various interfaces and lines, and executes various functions of the electronic device and processes data by running or executing programs or modules stored in the memory (e.g., executing an intelligent welder running Control program, etc.), and calling data stored in the memory.
The memory includes at least one type of readable storage medium including flash memory, removable hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory may in some embodiments be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD), etc. that are provided on the electronic device. Further, the memory may also include both internal storage units and external storage devices of the electronic device. The memory can be used for storing application software installed in the electronic equipment and various data, such as codes based on an intelligent welding machine operation control program and the like, and can be used for temporarily storing data which is output or is to be output.
The communication bus may be a peripheral component interconnect standard (PERIPHERAL COMPONENT INTERCONNECT, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, or the like. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory and at least one processor or the like.
The communication interface is used for communication between the electronic equipment and other equipment, and comprises a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), or alternatively 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, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.
For example, although not shown, the electronic device may further include a power source (such as a battery) for powering the respective components, and preferably, the power source may be logically connected to the at least one processor through a power management system, so as to perform functions of charge management, discharge management, and power consumption management through the power management system. The power supply may also include one or more of any of a direct current or alternating current power supply, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and the like. The electronic device may further include various sensors, bluetooth modules, wi-Fi modules, etc., which are not described herein.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The intelligent welder operation control program stored in the memory of the electronic device is a combination of a plurality of instructions, and when the intelligent welder operation control program is operated in the processor, the intelligent welder operation control program can realize:
Acquiring welder data and welding target data of a welder, analyzing a welder component of the welder based on the welder data, identifying component function indexes of the welder component, and constructing a cooperative compensation network of the welder component based on the component function indexes;
Identifying welding target characteristics of a welding target corresponding to the welding machine based on the welding target data, and constructing a welding path of the welding target based on the welding target characteristics;
Collecting welding data of the welding machine on the welding target by utilizing the cooperative compensation network based on the welding path, analyzing the current welding path of the welding machine based on the welding data, and calculating the deviation coefficient of the welding path and the current welding path;
when the deviation coefficient is larger than a preset deviation threshold, encoding welder parameters of the welder to obtain encoded welder parameters, and constructing a welder encoding parameter set of the welder based on the encoded welder parameters;
and calculating the welding fitness of the encoding parameter set of the welding machine, determining the target encoding parameter set of the welding machine based on the welding fitness, decoding the target encoding parameter set to obtain a decoding encoding parameter set, adjusting the welding machine parameters of the welding machine based on the decoding encoding parameter set to obtain an adjusted welding machine parameter, and realizing the operation control of the welding machine based on the adjusted welding machine parameter.
Specifically, the specific implementation method of the above instruction by the processor may refer to descriptions of related steps in the corresponding embodiment of the drawings, which are not repeated herein.
Further, the electronic device integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or system capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a 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, can implement:
Acquiring welder data and welding target data of a welder, analyzing a welder component of the welder based on the welder data, identifying component function indexes of the welder component, and constructing a cooperative compensation network of the welder component based on the component function indexes;
Identifying welding target characteristics of a welding target corresponding to the welding machine based on the welding target data, and constructing a welding path of the welding target based on the welding target characteristics;
Collecting welding data of the welding machine on the welding target by utilizing the cooperative compensation network based on the welding path, analyzing the current welding path of the welding machine based on the welding data, and calculating the deviation coefficient of the welding path and the current welding path;
when the deviation coefficient is larger than a preset deviation threshold, encoding welder parameters of the welder to obtain encoded welder parameters, and constructing a welder encoding parameter set of the welder based on the encoded welder parameters;
and calculating the welding fitness of the encoding parameter set of the welding machine, determining the target encoding parameter set of the welding machine based on the welding fitness, decoding the target encoding parameter set to obtain a decoding encoding parameter set, adjusting the welding machine parameters of the welding machine based on the decoding encoding parameter set to obtain an adjusted welding machine parameter, and realizing the operation control of the welding machine based on the adjusted welding machine parameter.
In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus, system and method may be implemented in other manners. For example, the system embodiments described above are merely illustrative, e.g., the division of the modules is merely a logical function division, and other manners of division may be implemented in practice.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
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 characteristics 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 application can acquire and process the related data based on the artificial intelligence technology. Wherein artificial intelligence (ARTIFICIAL INTELLIGENCE, AI) is the theory, method, technique, and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend, and expand human intelligence, sense the environment, acquire knowledge, and use knowledge to obtain optimal results.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. Multiple units or systems as set forth in the system claims may also be implemented by means of one unit or system in software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. An intelligent welding machine operation control method is characterized by comprising the following steps:
Acquiring welder data and welding target data of a welder, analyzing a welder component of the welder based on the welder data, identifying component function indexes of the welder component, and constructing a cooperative compensation network of the welder component based on the component function indexes;
Identifying welding target characteristics of a welding target corresponding to the welding machine based on the welding target data, and constructing a welding path of the welding target based on the welding target characteristics;
Collecting welding data of the welding machine on the welding target by utilizing the cooperative compensation network based on the welding path, analyzing the current welding path of the welding machine based on the welding data, and calculating the deviation coefficient of the welding path and the current welding path;
when the deviation coefficient is larger than a preset deviation threshold, encoding welder parameters of the welder to obtain encoded welder parameters, and constructing a welder encoding parameter set of the welder based on the encoded welder parameters;
and calculating the welding fitness of the encoding parameter set of the welding machine, determining the target encoding parameter set of the welding machine based on the welding fitness, decoding the target encoding parameter set to obtain a decoding encoding parameter set, adjusting the welding machine parameters of the welding machine based on the decoding encoding parameter set to obtain an adjusted welding machine parameter, and realizing the operation control of the welding machine based on the adjusted welding machine parameter.
2. The intelligent welder operation control method of claim 1, wherein the analyzing the welder component of the welder based on the welder data comprises:
preprocessing the welder data to obtain preprocessed welder data;
carrying out statistical analysis on the data of the pretreatment welding machine to obtain a data statistical analysis result;
Analyzing the data association coefficient of the preprocessing welding machine data based on the data statistics analysis result;
and determining a welder component of the welder based on the data association coefficient and the data statistics analysis result.
3. The intelligent welder operation control method of claim 1, wherein the constructing the collaborative compensation network for the welder component based on the component function indicator comprises:
Identifying a collaborative compensation demand for the welder component based on the component functional index;
Analyzing a demand complexity coefficient of the collaborative compensation demand;
determining a collaborative compensation strategy for the welder component based on the demand complexity factor;
And constructing a cooperative compensation network of the welding machine assembly based on the cooperative compensation strategy.
4. The intelligent welder operation control method of claim 3, wherein the analyzing the demand complexity factor of the collaborative compensation demand comprises:
Identifying a demand type of the collaborative compensation demand;
Calculating the demand weight of the demand type;
calculating a demand complexity factor for the collaborative compensation demand based on the demand type and the demand weight using the formula:
；
wherein, Demand complexity factor representing collaborative compensation demand,/>Representing a demand recognition model,/>T-th demand type,/>Representing the corresponding demand weight of the t-th demand type,/>Demand correlation coefficient representing the t-th demand type and demand type,/>Representing the number of demand types.
5. The intelligent welder operation control method of claim 1, wherein the constructing a welding path of the welding target based on the welding target characteristics comprises:
constructing a target three-dimensional model of the welding target based on the welding target characteristics;
calculating a model complexity coefficient of the target three-dimensional model;
Determining a path optimization algorithm of the target three-dimensional model based on the model complexity coefficient;
and calculating the welding path of the welding target based on the path optimization algorithm.
6. The intelligent welder operation control method of claim 5, wherein the calculating a welding path of the welding target based on the path optimization algorithm comprises:
Marking welding nodes and welding source points of the welding targets;
Based on the path optimization algorithm, calculating the shortest path length of the welding node and the welding source point by using the following formula:
；
wherein, Representing weld Source Point to weld node/>Shortest path,/>Representing the shortest path length of a weld node and a weld source point,/>Representing edges/>Weights of/>Represents the/>Welding node,/>Represent the firstWelding nodes;
and planning a welding path of the welding target based on the shortest path length.
7. The intelligent welder operation control method of claim 1, wherein the calculating a deviation coefficient of the welding path and the current welding path comprises:
Mapping the welding path and the current welding path to obtain a mapped welding path;
Identifying a deviation zone of the mapped welding path;
Identifying a departure distance and a departure angle of the departure area;
And calculating the deviation coefficient of the welding path and the current welding path based on the deviation distance and the deviation angle.
8. The intelligent welder operation control method of claim 1, wherein the calculating the weld fitness of the set of welder encoding parameters comprises:
determining welding optimization indexes of the welding machines corresponding to the welding machine coding parameter sets;
Calculating the index weight of the welding optimization index;
constructing an adaptability function of the welding machine coding parameter set based on the welding optimization index and the index weight;
and calculating the welding fitness of the welding machine coding parameter set based on the fitness function.
9. The intelligent welder operation control method of claim 8, wherein the calculating the weld fitness of the set of welder encoding parameters based on the fitness function comprises:
based on the fitness function, the welding fitness of the welder coding parameter set is calculated using the following formula:
；
wherein, Indicating the welding fitness of the encoding parameter set of the welding machine,/>Represents the coding parameter group/>Index weight of each welding optimization index,/>Represents the coding parameter group/>The fitness function of the individual welding optimization indicators,Representing a set of welder encoding parameters.
10. An intelligent welder operation control system for performing the intelligent welder operation control method of any of claims 1-9, the system comprising:
The collaborative network construction module is used for acquiring welding machine data and welding target data of a welding machine, analyzing a welding machine component of the welding machine based on the welding machine data, identifying component function indexes of the welding machine component, and constructing a collaborative compensation network of the welding machine component based on the component function indexes;
The welding path construction module is used for identifying welding target characteristics of a welding target corresponding to the welding machine based on the welding target data and constructing a welding path of the welding target based on the welding target characteristics;
The path deviation analysis module is used for acquiring welding data of the welding machine on the welding target by utilizing the cooperative compensation network based on the welding path, analyzing the current welding path of the welding machine based on the welding data, and calculating deviation coefficients of the welding path and the current welding path;
The encoding parameter set construction module is used for encoding the welder parameters of the welder to obtain encoding welder parameters when the deviation coefficient is larger than a preset deviation threshold value, and constructing a welder encoding parameter set of the welder based on the encoding welder parameters;
and the welding machine operation control module is used for calculating the welding fitness of the welding machine coding parameter set, determining the target coding parameter set of the welding machine based on the welding fitness, decoding the target coding parameter set to obtain a decoding coding parameter set, adjusting the welding machine parameters of the welding machine based on the decoding coding parameter set to obtain an adjusted welding machine parameter, and realizing the operation control of the welding machine based on the adjusted welding machine parameter.
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| CN111360456A (en) * | 2020-03-24 | 2020-07-03 | 上海外高桥造船有限公司 | Welding machine gas collection bag and mobile welding machine box comprising same |
| CN215034944U (en) * | 2021-06-02 | 2021-12-07 | 深圳市艾雷激光科技有限公司 | Welding machine |
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