Control method and system of multifunctional gas shielded welding machine

Abstract

The invention provides a control method and system of a multifunctional gas shielded welding machine. The method comprises the steps that first gas attribute information corresponding to a current first welding / cutting mode is determined, and second gas attribute information corresponding to a second welding / cutting mode to be switched is determined; performing feature extraction on the first gas attribute information and the second gas attribute information by using a first convolutional network to obtain current gas features and target gas features, and performing analysis processing on the current gas features and the target gas features by using a waiting duration prediction model to obtain switching waiting duration; and after a switching signal corresponding to the second welding / cutting mode is received, timing is carried out until the switching waiting duration is reached, and the electrical equipment corresponding to the second welding / cutting mode is switched on. According to the control method of the multifunctional gas shielded welding machine, the waiting duration of the welding machine during switching of different welding / cutting modes can be accurately determined, and safety and stability of mode switching are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of welding machines, and in particular to a control method and system for a multifunctional gas shielded welding machine. Background Art

[0002] In the field of modern industrial manufacturing, welding and cutting are extremely important processing technologies, which are widely used in many industries such as machinery manufacturing, automobile industry, aerospace, etc. Traditional welding equipment and cutting equipment often have single functions, and one device can only realize one function of welding or cutting. This not only requires enterprises to purchase multiple equipment, increasing equipment costs and floor space, but also in actual operation, frequent equipment replacement also reduces work efficiency.

[0003] In order to solve these problems, multifunctional welding machines came into being. This type of welding machine integrates multiple functions such as DC manual welding, argon arc welding, gas shielded welding, AC argon arc welding and cutting machine, which greatly improves the utilization rate of the equipment, reduces the equipment investment cost, and provides users with a more convenient and efficient processing solution. However, there are many challenges in the switching process between welding / cutting modes of multifunctional welding machines, among which safety control during mode switching and determination of appropriate waiting time become key issues.

[0004] When switching from welding mode to cutting mode, or switching between different welding modes, if there is no appropriate waiting mechanism, a series of problems will arise. For example: (1) In some welding processes, shielding gas is used to prevent oxidation of the welded parts, but shielding gas may not be required or a different gas environment may be required during cutting. After switching from welding mode to cutting mode, it is necessary to wait for a period of time for the original shielding gas in the pipeline to be discharged and for the new gas environment to stabilize to ensure that the cutting effect is not affected by the residual shielding gas during the cutting process. For example, when cutting with oxygen, if there is residual shielding gas such as argon in the pipeline, the oxygen purity may be insufficient, affecting the cutting speed and quality.

[0005] (2) There are specific requirements for gas flow during cutting. After switching modes, the gas supply system needs time to adjust the flow to the appropriate value required for cutting. If the cutting mode is turned on immediately, the pressure and flow rate of the cutting oxygen flow may be inappropriate due to unstable gas flow, resulting in uneven cutting surfaces and cutting marks.

[0006] Existing switching waiting times are determined based on empirical values ​​to ensure safety, but they are generally set to be relatively long, which requires operators to wait for too long, which is not conducive to improving work efficiency.

[0007] Therefore, how to determine the appropriate switching waiting time for the multifunctional welding machine is a technical problem that needs to be solved at present. Summary of the invention

[0008] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a control method, system, electronic equipment, computer storage medium and computer program product of a multifunctional gas shielded welding machine.

[0009] The present invention provides a control method for a multifunctional gas shielded welding machine, the method comprising the following steps: Determine first gas attribute information corresponding to the current first welding / cutting mode, the first gas attribute information including first gas type information, first gas flow information, and first gas pressure information; and determine second gas attribute information corresponding to the second welding / cutting mode to be switched, the second gas attribute information including second gas type information, second gas flow information, and second gas pressure information; Using a first convolutional network to extract features from the first gas attribute information and the second gas attribute information to obtain current gas features and target gas features, and using a waiting time prediction model to analyze and process the current gas features and the target gas features to obtain a switching waiting time; After receiving the switching signal corresponding to the second welding / cutting mode, timing is performed until the switching waiting time is reached, and then the electrical device corresponding to the second welding / cutting mode is turned on.

[0010] Optionally, the waiting time prediction model includes a GRU-based feature extraction unit and a waiting time prediction unit; The input of the feature extraction unit is connected to the output of the first convolutional network to perform secondary feature extraction on the first current gas feature and the first target gas feature to obtain a second current gas feature and a second target gas feature; The waiting time prediction unit analyzes and processes the second current gas characteristic and the second target gas characteristic to obtain the switching waiting time.

[0011] Optionally, the waiting time prediction unit includes a feature fusion layer, a fully connected layer, an attention mechanism layer, and an output layer.

[0012] Optionally, the waiting time prediction unit analyzes and processes the second current gas characteristic and the second target gas characteristic to obtain the switching waiting time, including: The waiting time prediction unit analyzes and processes the second current gas characteristic and the second target gas characteristic to obtain a first switching waiting time; The current temperature, the current humidity, and the historical temperature sequence and the historical humidity sequence within a set period of time detected by the temperature sensor and the humidity sensor of the receiving welding machine are respectively detected, and the historical temperature sequence and the historical humidity sequence are respectively extracted with a second convolutional network to obtain historical temperature features and historical humidity features; The current temperature and the historical temperature features are merged into a temperature feature, the current humidity and the historical humidity features are merged into a humidity feature, and the temperature feature and the humidity feature are analyzed and processed using an environmental impact model to obtain an environmental impact value; A second switching waiting time is calculated according to the environmental impact value and the first switching waiting time.

[0013] Optionally, the method further includes: outputting the switching waiting time through an interactive interface of the welding machine.

[0014] The present invention also provides a control system for a multifunctional gas shielded welding machine, the system comprising a processing device and a storage device, the processing device executing a computer program in the storage device to implement the following steps: Determine first gas attribute information corresponding to the current first welding / cutting mode, the first gas attribute information including first gas type information, first gas flow information, and first gas pressure information; and determine second gas attribute information corresponding to the second welding / cutting mode to be switched, the second gas attribute information including second gas type information, second gas flow information, and second gas pressure information; Using a first convolutional network to extract features from the first gas attribute information and the second gas attribute information to obtain current gas features and target gas features, and using a waiting time prediction model to analyze and process the current gas features and the target gas features to obtain a switching waiting time; After receiving the switching signal corresponding to the second welding / cutting mode, timing is performed until the switching waiting time is reached, and then the electrical device corresponding to the second welding / cutting mode is turned on.

[0015] Optionally, the waiting time prediction model includes a GRU-based feature extraction unit and a waiting time prediction unit; The input of the feature extraction unit is connected to the output of the first convolutional network to perform secondary feature extraction on the first current gas feature and the first target gas feature to obtain a second current gas feature and a second target gas feature; The waiting time prediction unit analyzes and processes the second current gas characteristic and the second target gas characteristic to obtain the switching waiting time.

[0016] Optionally, the waiting time prediction unit includes a feature fusion layer, a fully connected layer, an attention mechanism layer, and an output layer.

[0017] Optionally, the waiting time prediction unit analyzes and processes the second current gas characteristic and the second target gas characteristic to obtain the switching waiting time, including: The waiting time prediction unit analyzes and processes the second current gas characteristic and the second target gas characteristic to obtain a first switching waiting time; The current temperature, the current humidity, and the historical temperature sequence and the historical humidity sequence within a set period of time detected by the temperature sensor and the humidity sensor of the receiving welding machine are respectively detected, and the historical temperature sequence and the historical humidity sequence are respectively extracted with a second convolutional network to obtain historical temperature features and historical humidity features; The current temperature and the historical temperature features are merged into a temperature feature, the current humidity and the historical humidity features are merged into a humidity feature, and the temperature feature and the humidity feature are analyzed and processed using an environmental impact model to obtain an environmental impact value; A second switching waiting time is calculated according to the environmental impact value and the first switching waiting time.

[0018] Optionally, the method further includes: outputting the switching waiting time through an interactive interface of the welding machine.

[0019] The present invention also provides an electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor calls the executable program code stored in the memory to execute any of the methods described above.

[0020] The present invention also provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, any of the above methods is executed.

[0021] The present invention also provides a computer program product, which includes a computer program stored in a computer storage medium, and when the computer program is executed by a processor of an electronic device, it implements any of the methods described above.

[0022] The beneficial effect of the present invention is at least that: the control method of the multifunctional gas shielded welding machine of the present invention can accurately determine the waiting time of the welding machine when switching between different welding / cutting modes, thereby ensuring the safety and stability of the mode switching. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 It is a schematic diagram of a control method for a multifunctional gas shielded welding machine disclosed in an embodiment of the present invention.

[0025] Figure 2 It is a structural diagram of the waiting time prediction model disclosed in an embodiment of the present invention.

[0026] Figure 3 It is a structural schematic diagram of a waiting time prediction unit disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0028] For the above technical issues, please refer to Figure 1 The embodiment of the present invention discloses a control method for a multifunctional gas shielded welding machine, the method comprising the following steps: Determine the first gas attribute information corresponding to the current first welding / cutting mode, the first gas attribute information including first gas type information, first gas flow information, and first gas pressure information; and determine the second gas attribute information corresponding to the second welding / cutting mode to be switched, the second gas attribute information including second gas type information, second gas flow information, and second gas pressure information.

[0029] In this step, when the welder is working, different welding / cutting modes require different gas environments to ensure the quality of welding or cutting. Determine the first gas attribute information corresponding to the current first welding / cutting mode, and the second gas attribute information corresponding to the second welding / cutting mode to be switched. These attribute information include gas type, flow rate and pressure, among which the second gas flow rate information and the second gas pressure information are the flow rate and pressure data when the gas enters a stable state in the new mode, which directly affects the metallurgical reaction, protection effect and energy transfer during welding or cutting. For example, argon arc welding usually uses argon as the shielding gas, and gas shielded welding may use a mixture of carbon dioxide and argon. Different gas types and ratios, as well as different flow and pressure settings, can adapt to different welding materials and process requirements.

[0030] Use the first convolutional network to extract features of the first gas attribute information and the second gas attribute information to obtain current gas features and target gas features, use the waiting time prediction model to analyze and process the current gas features and the target gas features to obtain the switching waiting time.

[0031] In this step, since the convolutional network is suitable for extracting key features from data with a spatial or sequence structure, the present invention uses the first convolutional network to extract features from the first gas attribute information and the second gas attribute information, thereby obtaining the current gas feature and the target gas feature.

[0032] After receiving the switching signal corresponding to the second welding / cutting mode, timing is performed until the switching waiting time is reached, and then the electrical device corresponding to the second welding / cutting mode is turned on.

[0033] In this step, the waiting time prediction model is trained based on a large amount of experimental data and training data obtained from theoretical analysis. It can accurately predict the waiting time required to switch from the current mode to the target mode based on the current gas state and the target gas state. In this process, the waiting time prediction model takes into account the differences in the physical properties of different gases, such as density, viscosity, etc., as well as the flow characteristics of the gas in the pipeline, including the influence of factors such as the diameter, length and material of the pipeline on gas exhaust and the new gas reaching a stable flow rate, so as to give a reasonable waiting time prediction value.

[0034] When the user needs to switch the function of the welding machine, he operates on the control panel. At this time, the controller of the welding machine receives the corresponding switching signal, and the welding machine starts timing. The corresponding electrical equipment is not connected until the switching waiting time is reached. The welding machine officially switches to the second welding / cutting mode and starts a new workflow. Before the timing reaches the predicted switching waiting time, the electrical equipment corresponding to the second welding / cutting mode is not connected temporarily to give the gas system enough time to empty the original gas in the pipeline and allow the new gas to reach a stable flow and pressure to meet the requirements of the second welding / cutting mode.

[0035] The control method of the multifunctional gas shielded welding machine of the present invention can accurately determine the waiting time of the welding machine when switching between different welding / cutting modes, thereby ensuring the safety and stability of the mode switching.

[0036] Alternatively, if Figure 2 As shown, the waiting time prediction model includes a GRU-based feature extraction unit and a waiting time prediction unit; The input of the feature extraction unit is connected to the output of the first convolutional network to perform secondary feature extraction on the first current gas feature and the first target gas feature to obtain a second current gas feature and a second target gas feature; The waiting time prediction unit analyzes and processes the second current gas characteristic and the second target gas characteristic to obtain the switching waiting time.

[0037] In this embodiment, the first convolutional network has advantages in processing static data features and can extract spatial feature patterns from gas attribute information. However, in different welding / cutting mode switching scenarios, the changes in gas may be very complex and affected by many factors. The features extracted by the first convolutional network may have defects such as insufficient richness and insufficient depth. GRU is a special recurrent neural network that can effectively capture long-term dependencies in data through a gating mechanism and is good at processing sequence data. Therefore, the present invention further captures the changing trend of gas features over time and the dynamic correlation between features through secondary feature extraction by the feature extraction unit. The combination of the two makes the expression of gas features more comprehensive and in-depth, providing richer and more valuable information for accurately predicting waiting time.

[0038] Therefore, the present invention realizes the switching waiting time by the collaboration of the first convolutional network, the feature extraction unit based on GRU (Gated Recurrent Unit) and the waiting time prediction unit.

[0039] Alternatively, if Figure 3 As shown, the waiting time prediction unit includes a feature fusion layer, a fully connected layer, an attention mechanism layer, and an output layer.

[0040] In this embodiment, the waiting time prediction unit of the present invention is an improved neural network embedded with an attention mechanism, and the main functions of each functional layer are as follows: Feature fusion layer: The feature vectors are connected in sequence into a longer vector by splicing. For example, if the second current gas feature is a vector of length n1 and the second target gas feature is a vector of length n2, then the length of the fused feature vector is n1+n2.

[0041] Fully connected layer: usually composed of multiple neurons, each neuron is connected to all neurons in the previous layer. For the input vector x, the output y of the fully connected layer can be calculated by the following formula: y=f(Wx+b), where W is the weight matrix, b is the bias vector, and f is the activation function, such as the ReLU function.

[0042] Attention mechanism layer: Its function is to weight the features output by the fully connected layer so that the model can pay more attention to the features that are more important for predicting the switching waiting time. First, the attention score of the input feature is calculated, usually through a small neural network or linear transformation; then, the attention score is normalized to obtain the attention weight; finally, the attention weight is multiplied by the input feature to obtain the weighted feature.

[0043] Output layer: The output layer calculates and outputs the final switching waiting time based on the weighted features output by the attention mechanism layer. This layer usually has only one neuron, and its output value is the predicted waiting time. The output features of the attention mechanism layer can be multiplied by a weight vector and a bias term by linear transformation to obtain the final prediction result.

[0044] Optionally, the waiting time prediction unit analyzes and processes the second current gas characteristic and the second target gas characteristic to obtain the switching waiting time, including: The waiting time prediction unit analyzes and processes the second current gas characteristic and the second target gas characteristic to obtain a first switching waiting time; The current temperature, the current humidity, and the historical temperature sequence and the historical humidity sequence within a set period of time detected by the temperature sensor and the humidity sensor of the receiving welding machine are respectively detected, and the historical temperature sequence and the historical humidity sequence are respectively extracted with a second convolutional network to obtain historical temperature features and historical humidity features; The current temperature and the historical temperature features are merged into a temperature feature, the current humidity and the historical humidity features are merged into a humidity feature, and the temperature feature and the humidity feature are analyzed and processed using an environmental impact model to obtain an environmental impact value; A second switching waiting time is calculated according to the environmental impact value and the first switching waiting time.

[0045] In this embodiment, the waiting time prediction unit first analyzes and processes the second current gas characteristic and the second target gas characteristic to preliminarily obtain a switching waiting time, namely, a first switching waiting time.

[0046] The above first switching waiting time is predicted based on the characteristics and state changes of the gas itself, without considering the influence of environmental factors. However, the ambient temperature and humidity of the welding machine will also affect the time required for the residual gas in the gas pipeline to be emptied and the new gas to stabilize. Specific examples are as follows: 1) The ambient temperature will affect the viscosity of the residual gas in the gas pipeline. As the temperature rises, the gas viscosity increases, and as the temperature decreases, the gas viscosity decreases. Higher viscosity will increase the resistance of the gas flow in the pipeline, which is not conducive to the discharge of residual gas. Therefore, in a low temperature environment, the residual gas may be easier to discharge due to its relatively low viscosity.

[0047] When the ambient temperature rises, the volume of the gas will expand. If the pipeline system does not have a corresponding pressure regulation mechanism, the pressure in the pipeline will increase, and it will take longer to adjust the pressure and flow of the newly entering gas to a stable state. Conversely, when the temperature drops, the gas volume shrinks, which will also affect the stability of the pressure and flow, and it will take extra time to achieve stability.

[0048] 2) When the ambient humidity increases, the water vapor in the air mixes with the residual gas in the gas pipeline, causing its density and viscosity to increase, thereby increasing the flow resistance of the residual gas in the pipeline and slowing down the emptying speed.

[0049] The temperature and humidity sensors (preferably detecting the temperature and humidity around the gas pipeline) equipped with the welding machine detect the current temperature and humidity in real time, and record the historical temperature sequence and historical humidity sequence within the set time period (a time period close to the current time, such as 5 minutes, 10 minutes). These environmental data will affect the flow, exhaust and stable state of the gas in the pipeline.

[0050] The second convolutional network (which may be the same as or different from the first convolutional network) is used to extract features from the historical temperature sequence and the historical humidity sequence respectively, so as to find out the changing rules and periodicity of the temperature and humidity of the environment where the welding machine is located over time, so as to obtain the fluctuation rules in the current period of the welding machine, i.e., the historical temperature features and the historical humidity features. Then, the current temperature is fused with the historical temperature features to form a more comprehensive temperature feature, and the current humidity is fused with the historical humidity features to form a humidity feature to more accurately reflect the actual changing characteristics of the current environment.

[0051] The present invention also pre-constructs and trains an environmental impact model. The trained environmental impact model has the ability to accurately analyze the relationship between environmental factors (temperature and humidity) and the switching waiting time. Therefore, by analyzing and processing the temperature characteristics and humidity characteristics, an environmental impact value can be obtained. This coefficient reflects the degree of influence of the current environmental conditions on the switching waiting time. The obtained environmental impact value and the first switching waiting time are comprehensively calculated to obtain the final second switching waiting time. Among them, the environmental impact value can be a coefficient (a value near 1) or a specific value. Correspondingly, the training method and training data of the environmental impact model are also different. When it is a coefficient, the first switching waiting time is multiplied by the environmental impact value. When it is a value, the first switching waiting time is added to the environmental impact value. The environmental impact model is preferably constructed based on Transformer.

[0052] The second switching waiting time obtained through this embodiment comprehensively considers the characteristics of the gas itself and the influence of environmental factors, and can accurately predict the switching waiting time under various environmental conditions, thereby ensuring the safety and stability of welding machine mode switching.

[0053] Optionally, the method further includes: outputting the switching waiting time through an interactive interface of the welding machine.

[0054] In this embodiment, after the user triggers the mode switching button on the control panel, the welding machine in the present invention needs to wait for the switching waiting time before actually connecting to the electrical equipment corresponding to the target mode, and the existence of the switching waiting time may cause the user to have a wrong understanding of the welding machine failure or malfunction. To avoid this situation, the present invention is also configured to output the switching waiting time through the interactive interface of the welding machine to prompt the user of the existence of the switching waiting time, so as to avoid the user from performing other wrong operations on the welding machine due to the above-mentioned wrong understanding.

[0055] The embodiment of the present invention further provides a control system for a multifunctional gas shielded welding machine, the system comprising a processing device and a storage device, the processing device executing a computer program in the storage device to implement the following steps: Determine first gas attribute information corresponding to the current first welding / cutting mode, the first gas attribute information including first gas type information, first gas flow information, and first gas pressure information; and determine second gas attribute information corresponding to the second welding / cutting mode to be switched, the second gas attribute information including second gas type information, second gas flow information, and second gas pressure information; Using a first convolutional network to extract features from the first gas attribute information and the second gas attribute information to obtain current gas features and target gas features, and using a waiting time prediction model to analyze and process the current gas features and the target gas features to obtain a switching waiting time; After receiving the switching signal corresponding to the second welding / cutting mode, timing is performed until the switching waiting time is reached, and then the electrical device corresponding to the second welding / cutting mode is turned on.

[0056] An embodiment of the present invention further provides an electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor calls the executable program code stored in the memory to execute the method described in any of the above embodiments.

[0057] An embodiment of the present invention further provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method described in any of the above embodiments is executed.

[0058] An embodiment of the present invention further provides a computer program product, which includes a computer program stored in a computer storage medium, and when the computer program is executed by a processor of an electronic device, the method described in any of the above embodiments is implemented.

[0059] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (apparatus), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0060] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0061] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0062] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A control method for a multifunctional gas shielded welding machine, characterized in that: The method comprises the following steps: Determine first gas attribute information corresponding to the current first welding / cutting mode, the first gas attribute information including first gas type information, first gas flow information, and first gas pressure information; and determine second gas attribute information corresponding to the second welding / cutting mode to be switched, the second gas attribute information including second gas type information, second gas flow information, and second gas pressure information; Using a first convolutional network to extract features from the first gas attribute information and the second gas attribute information to obtain current gas features and target gas features, and using a waiting time prediction model to analyze and process the current gas features and the target gas features to obtain a switching waiting time; After receiving the switching signal corresponding to the second welding / cutting mode, timing is performed until the switching waiting time is reached, and then the electrical device corresponding to the second welding / cutting mode is turned on.

2. The control method of a multifunctional gas shielded welding machine according to claim 1, characterized in that: The waiting time prediction model includes a feature extraction unit based on GRU and a waiting time prediction unit; The input of the feature extraction unit is connected to the output of the first convolutional network to perform secondary feature extraction on the first current gas feature and the first target gas feature to obtain a second current gas feature and a second target gas feature; The waiting time prediction unit analyzes and processes the second current gas characteristic and the second target gas characteristic to obtain the switching waiting time.

3. The control method of a multifunctional gas shielded welding machine according to claim 2, characterized in that: The waiting time prediction unit includes a feature fusion layer, a fully connected layer, an attention mechanism layer, and an output layer.

4. The control method of a multifunctional gas shielded welding machine according to claim 2, characterized in that: The waiting time prediction unit analyzes and processes the second current gas characteristic and the second target gas characteristic to obtain the switching waiting time, including: The waiting time prediction unit analyzes and processes the second current gas characteristic and the second target gas characteristic to obtain a first switching waiting time; The current temperature, the current humidity, and the historical temperature sequence and the historical humidity sequence within a set period of time detected by the temperature sensor and the humidity sensor of the receiving welding machine are respectively detected, and the historical temperature sequence and the historical humidity sequence are respectively extracted with a second convolutional network to obtain historical temperature features and historical humidity features; The current temperature and the historical temperature features are merged into a temperature feature, the current humidity and the historical humidity features are merged into a humidity feature, and the temperature feature and the humidity feature are analyzed and processed using an environmental impact model to obtain an environmental impact value; A second switching waiting time is calculated according to the environmental impact value and the first switching waiting time.

5. A control method for a multifunctional gas shielded welding machine according to any one of claims 1 to 4, characterized in that: The method further includes: outputting the switching waiting time through an interactive interface of the welding machine.

6. A control system for a multifunctional gas shielded welding machine, the system comprising a processing device and a storage device, characterized in that: The processing device executes the computer program in the storage device to implement the following steps: Determine first gas attribute information corresponding to the current first welding / cutting mode, the first gas attribute information including first gas type information, first gas flow information, and first gas pressure information; and determine second gas attribute information corresponding to the second welding / cutting mode to be switched, the second gas attribute information including second gas type information, second gas flow information, and second gas pressure information; Using a first convolutional network to extract features from the first gas attribute information and the second gas attribute information to obtain current gas features and target gas features, and using a waiting time prediction model to analyze and process the current gas features and the target gas features to obtain a switching waiting time; After receiving the switching signal corresponding to the second welding / cutting mode, timing is performed until the switching waiting time is reached, and then the electrical device corresponding to the second welding / cutting mode is turned on.

7. A control system for a multifunctional gas shielded welding machine according to claim 6, characterized in that: The waiting time prediction model includes a feature extraction unit based on GRU and a waiting time prediction unit; The input of the feature extraction unit is connected to the output of the first convolutional network to perform secondary feature extraction on the first current gas feature and the first target gas feature to obtain a second current gas feature and a second target gas feature; The waiting time prediction unit analyzes and processes the second current gas characteristic and the second target gas characteristic to obtain the switching waiting time.

8. An electronic device comprising: A memory storing executable program code; A processor coupled to the memory; characterized in that: the processor calls the executable program code stored in the memory to execute the method according to any one of claims 1-5.

9. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is executed.

10. A computer program product, comprising a computer program stored in a computer storage medium, characterized in that: When the computer program is executed by a processor of an electronic device, the method according to any one of claims 1 to 5 is implemented.

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