Dynamic heat dissipation control method and system for intelligent electric welding machine

Abstract

The invention relates to the technical field of electric welding machines, in particular to a dynamic heat dissipation control method and system for an intelligent electric welding machine. A heat load real-time calculation and heat accumulation trend prediction step; a dynamic collaborative heat dissipation strategy generation and execution step: dynamically generating and executing an air cooling heat dissipation strategy, an air cooling and liquid cooling collaborative heat dissipation strategy or a protective heat dissipation strategy according to a comparison result; wherein the control parameters of the air cooling heat dissipation strategy are determined according to the difference value between the predicted temperature and the corresponding threshold value and the environment temperature; the control parameters of the air cooling and liquid cooling collaborative heat dissipation strategy are determined according to the difference value between the predicted temperature and the corresponding threshold value and the environment humidity; and a heat dissipation effect feedback and self-adaptive calibration step. Real-time sensing, thermal load prediction, a dynamic heat dissipation strategy and feedback calibration are combined, the heat dissipation strategy can be dynamically adjusted according to the working environment and the operation state of the electric welding machine, and the stability and reliability of equipment are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric welding machines, and in particular to a dynamic heat dissipation control method and system for an intelligent electric welding machine. BACKGROUND

[0002] An electric welding machine is a device that uses the high-temperature electric arc generated when the positive and negative poles are instantaneously short-circuited to melt the welding material on the welding electrode and the material being welded, so as to combine the contacted objects. The core of the electric welding machine is a high-power transformer, which relies on the inductance to generate a huge voltage change when turned on and off, and uses the high-voltage electric arc generated during the instantaneous short circuit to melt the welding material and achieve atomic combination. During the operation of the electric welding machine, a large amount of heat is generated due to the passage of a large current through the internal coil and power device. If this heat cannot be dissipated in time, the internal temperature of the device will rise sharply, which will affect the welding quality, shorten the service life of the device, and even cause the insulation layer of the coil to melt, the components to burn out, and other safety accidents.

[0003] The heat dissipation methods of traditional electric welding machines mainly include air cooling, water cooling, and a combination of the two. Air cooling mainly relies on the built-in fan to force convection, which carries away the heat inside the electric welding machine and discharges it to the external environment. Water cooling passes the circulating cooling liquid through the surface of the heat generating components, and the liquid absorbs and carries away the heat due to its high specific heat capacity. The existing heat dissipation technology has the following problems:

[0004] Uneven heat dissipation and local overheating: The air flow organization is often unreasonable, and it is difficult to cover all heat source areas, which is easy to form heat accumulation in key parts such as the transformer, reactor, and power device of the electric welding machine, resulting in local overheating.

[0005] Low heat dissipation efficiency: The single heat dissipation method has limited capacity. The efficiency of air cooling drops sharply at high ambient temperatures, and the water cooling system is complex and has the risk of cooling liquid leakage. The simple combination of the two does not achieve efficient cooperation and cannot meet the stringent requirements of high-load continuous welding scenarios for heat dissipation capacity.

[0006] High energy consumption and high noise: The heat dissipation system often runs at maximum power, for example, the fan runs at full speed, regardless of the actual heat load, causing waste of electrical energy. At the same time, the airflow noise generated by the high-speed fan also affects the working environment.

[0007] Lack of intelligent adjustment: The traditional heat dissipation control strategy is simple, usually only based on a single temperature threshold to start and stop, and cannot dynamically adjust according to multiple parameters such as real-time changing welding current, voltage, ambient temperature and humidity, and thermal characteristics of different welding materials. This results in a lag in the response of the heat dissipation system, which cannot effectively prevent heat accumulation, or causes energy waste when full-power heat dissipation is not needed. SUMMARY

[0008] In order to achieve the above purpose, the application provides a dynamic heat dissipation control method and system for an intelligent electric welding machine.

[0009] The welding process multi-parameter real-time sensing and collecting step: the welding current value, the welding voltage value, the internal core temperature of the electric welding machine, the ambient temperature value, the ambient humidity value, and the airflow pressure value in the heat dissipation air duct are monitored and collected in real time; the internal core temperature of the electric welding machine is collected by a temperature sensor array distributed in the main heat source area of the electric welding machine;

[0010] The heat load real-time calculation and heat accumulation trend prediction step: the instantaneous heat power of the electric welding machine is calculated according to the real-time collected welding current value, welding voltage value, and arc duration; the short-term heat accumulation amount is calculated based on the instantaneous heat power and historical core temperature data; the change trend of the internal core temperature of the electric welding machine in the future period of time is predicted by using a heat accumulation trend prediction model;

[0011] The dynamic cooperative heat dissipation strategy generation and execution step: the predicted core temperature value is compared with the preset multi-level temperature threshold value; according to the comparison result, the air cooling heat dissipation strategy, the air cooling and liquid cooling cooperative heat dissipation strategy, or the protective heat dissipation strategy is dynamically generated and executed; wherein the control parameters of the air cooling heat dissipation strategy are determined according to the difference between the predicted temperature and the corresponding threshold value and the ambient temperature; the control parameters of the air cooling and liquid cooling cooperative heat dissipation strategy are determined according to the difference between the predicted temperature and the corresponding threshold value and the ambient humidity;

[0012] The heat dissipation effect feedback and self-adaptive calibration step: after the heat dissipation strategy is executed, the actual change of the internal core temperature of the electric welding machine is monitored, and the deviation between the actual cooling rate and the predicted cooling rate is calculated; if the deviation continuously exceeds the acceptable range, the parameters in the heat accumulation trend prediction model are fine-tuned and calibrated.

[0013] Preferably, in the welding process multi-parameter real-time sensing and collecting step, the welding current value and the welding voltage value are collected by the current sensor and the voltage sensor installed on the circuit board of the electric welding machine, and the collected current and voltage data are processed after analog-to-digital conversion;

[0014] The sampling frequency of the current sensor and the voltage sensor can be dynamically adjusted according to the welding process type, and the collected data is stored in a temporary buffer area; the ambient temperature value and the ambient humidity value are collected by the temperature and humidity sensor installed near the air inlet of the electric welding machine; the airflow pressure value is collected by the micro-pressure difference sensor installed in the heat dissipation air duct, which is used to indirectly reflect the degree of air duct smoothness;

[0015] The duration of the electric arc is obtained by the control system of the welding machine based on the welding current signal; the temperature sensor array adopts a high-temperature resistant insulating package and its arrangement covers the main heat source areas inside the welding machine, including transformers, reactors and power devices.

[0016] Preferably, in the real-time calculation of heat load and prediction of heat accumulation trend, when calculating the instantaneous heat power, the differences in arc energy conversion efficiency and thermal conductivity characteristics of different welding materials are taken into account. The arc energy conversion efficiency is determined by the factory calibration test of the welding machine, and the thermal conductivity data of different welding materials are pre-stored in the welding machine control system.

[0017] The short-term heat accumulation is calculated using a rolling time window method. The length of the rolling time window can be preset or dynamically adjusted according to the welding process and material type. The input of the heat accumulation trend prediction model includes at least the instantaneous heat power, the current core temperature, the ambient temperature, and the historical temperature change rate. The model is trained using historical operating data, and its internal parameters can be updated during operation through the adaptive calibration step.

[0018] Preferably, in the dynamic collaborative heat dissipation strategy generation and execution step, the air cooling heat dissipation strategy is implemented by controlling the EC fan, and the speed of the EC fan is determined by querying a preset fuzzy control rule table based on the difference between the predicted temperature and the air cooling start threshold and the ambient temperature.

[0019] The air-cooling and liquid-cooling synergistic heat dissipation strategy activates the liquid-cooling system on the basis of air cooling. The flow rate and velocity of the liquid-cooling system are determined by querying the fuzzy control rule table based on the difference between the predicted temperature and the liquid-cooling activation threshold and the ambient humidity. When the ambient humidity is high, the liquid-cooling flow rate is appropriately reduced to prevent condensation.

[0020] When the ambient humidity is low, the liquid cooling flow rate is increased to enhance the heat dissipation effect; the fuzzy control rule table takes the difference between the predicted temperature and the threshold, the ambient temperature or the ambient humidity as input, and obtains the control parameter adjustment amount through fuzzy inference.

[0021] Preferably, in the dynamic collaborative heat dissipation strategy generation and execution step, if the predicted temperature reaches or exceeds the equipment safety threshold, a protective heat dissipation strategy is generated and executed to control the air cooling system and liquid cooling system to operate at maximum power, while reducing the output power of the welding machine or triggering a pause in welding operations until the core temperature drops below the safety threshold.

[0022] The reduction in the output power of the welding machine is determined based on the degree to which the predicted temperature exceeds the safety threshold, and is achieved through either stepped or linear adjustment. The duration of the welding operation pause is calculated based on the amount of heat accumulation and the ambient temperature.

[0023] Preferably, the method further includes an intelligent cleaning step for the heat dissipation duct. During the intermittent period of the heat dissipation system or when the welding machine is on standby, the system determines whether the duct is blocked based on historical data of the airflow pressure value. If it is determined that the duct may be blocked, the system starts the automatic cleaning program for the duct and controls the fan to alternately rotate forward and reverse to blow away the dust.

[0024] If the airflow pressure value still does not return to the normal range after the fan alternates between forward and reverse rotation, a small mechanical cleaning device is controlled to extend into the air duct for auxiliary cleaning; the cleaning frequency and cleaning stroke of the small mechanical cleaning device are set according to the degree to which the airflow pressure value deviates from the normal range; the airflow pressure value is continuously monitored during the cleaning process until it returns to the normal range.

[0025] Preferably, in the heat dissipation effect feedback and adaptive calibration step, heat dissipation effect data under different welding materials, welding processes and environmental conditions are recorded to optimize the parameters of the preset fuzzy control rule table and the heat accumulation trend prediction model.

[0026] The optimization process is based on machine learning algorithms. By comparing the deviation between the predicted temperature and the actual temperature, the model parameters and control rules are continuously adjusted to improve the system's adaptability in different application scenarios.

[0027] Preferably, the method further includes an initialization configuration step, which guides the user to set basic parameters when the welding machine is used for the first time or when the main welding components are replaced, or automatically learns and initializes the basic parameters of the heat accumulation trend prediction model and the basic rules of the fuzzy control rule table through trial operation.

[0028] The trial operation process includes simulating different welding conditions within a safe range and recording temperature change data, and establishing initial model parameters and control rules based on this data.

[0029] Preferably, the multi-level temperature thresholds include air-cooled start-up thresholds, liquid-cooled start-up thresholds, and equipment safety thresholds. The specific values ​​of these thresholds are determined by calibration tests conducted on the welding machine according to its model and specifications before it leaves the factory, or by the user manually setting them within the allowable range based on actual welding conditions and experience.

[0030] The calibration test includes testing the temperature rise characteristics of the welding machine under different loads and environmental conditions to determine the threshold range that ensures safety without excessively affecting welding efficiency.

[0031] Accordingly, embodiments of the present invention also provide a dynamic heat dissipation control system for an intelligent welding machine, including a memory configured to store instructions, a processor configured to call the instructions from the memory, and capable of implementing any of the dynamic heat dissipation control methods for an intelligent welding machine as described in any embodiment of the present invention when executing the instructions.

[0032] The beneficial effects of this invention are:

[0033] 1. This invention optimizes the synergy between air-cooling and water-cooling systems, combining intelligent control technology and refined airflow design to achieve uniform coverage of the heat source area throughout the welding machine. Specifically, specialized airflow guiding devices are designed for key components such as transformers, reactors, and power devices to ensure that heat is effectively removed from high-temperature areas and discharged in a timely manner, thereby effectively preventing localized overheating.

[0034] 2. This invention utilizes an intelligent heat dissipation control system to dynamically adjust the operating modes and intensities of air cooling and water cooling based on multiple parameters such as actual current, voltage, and ambient temperature and humidity during the welding process. This avoids the problem of drastic efficiency drops in traditional air cooling under high-temperature environments. The combination of the water cooling and air cooling systems employs a more efficient heat exchange design, and through an improved fluid circulation scheme, it reduces the risk of coolant leakage, significantly improving heat dissipation capacity and meeting the stringent heat dissipation requirements of high-load continuous welding scenarios.

[0035] 3. This invention employs an intelligent control system that dynamically adjusts the operating status of the fan and water pump based on real-time welding conditions, avoiding the continuous operation of traditional cooling systems when unnecessary, thus effectively reducing energy waste. Furthermore, by optimizing fan speed and airflow design, airflow noise is reduced, improving the working environment and making the equipment more energy-efficient and quieter during operation.

[0036] 4. The intelligent heat dissipation control system of this invention can monitor welding current, voltage, temperature and humidity, and the thermal characteristics of different welding materials in real time, and automatically adjust the heat dissipation strategy according to changes in these parameters. This dynamic adjustment enables the heat dissipation system to respond quickly and prevent heat accumulation, thereby avoiding the problem of lag in response of traditional heat dissipation systems and avoiding the waste of energy when full heat dissipation is not required. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in this invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0038] Fig. 1 This is a flowchart of the steps of the method of the present invention;

[0039] Fig. 2 This is a flowchart illustrating the steps involved in generating and executing the dynamic collaborative heat dissipation strategy according to the present invention.

[0040] Fig. 3 This is a flowchart illustrating the steps of intelligent cleaning of the heat dissipation air duct in the method of the present invention. Detailed Implementation

[0041] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. It should also be noted that, to make the embodiments more comprehensive, the following embodiments are the best and preferred embodiments, and those skilled in the art can use other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0042] Please see Figs. 1-3 This invention provides a dynamic heat dissipation control method for an intelligent welding machine. First, the welding machine uses an array of temperature sensors arranged in key internal heat source areas to monitor various data generated during the welding process in real time, including welding current, voltage, internal core temperature, ambient temperature and humidity, and airflow pressure. This data is then fed back to the control system in real time through high-frequency sampling and transmission, providing accurate data for subsequent heat load calculations and heat dissipation strategy formulation.

[0043] This step enables a comprehensive understanding of the welding machine's operating status during the welding process, especially the core temperature and the influence of the environment. It provides strong data support for precise thermal management and prevents uneven heat dissipation or overheating caused by incomplete information collection.

[0044] Based on real-time monitoring of welding current and voltage values, as well as the duration of the electric arc, the system can calculate the instantaneous heat power of the welding machine. By combining historical core temperature data, the system can calculate the short-term heat accumulation and use a heat accumulation trend prediction model to predict the future temperature change trend of the welding machine's internal components. This process not only predicts short-term temperature changes but also provides early warnings of potential overheating problems.

[0045] By dynamically predicting temperature change trends, the problem of delayed response in traditional welding machine heat dissipation systems can be effectively avoided, allowing for advance adjustment of heat dissipation strategies and preventing safety hazards or performance degradation caused by overheating.

[0046] By comparing the predicted core temperature with preset temperature thresholds, the system generates and executes different heat dissipation strategies based on different temperature ranges. For example, when the temperature approaches the set threshold, the system may activate an air-cooling strategy; if the temperature approaches a higher threshold, it will switch to a combined air-cooling and liquid-cooling strategy. In extreme cases, a protective heat dissipation strategy can also be implemented to ensure that the device is not overheated.

[0047] The system dynamically adjusts the heat dissipation mode based on real-time temperature changes and environmental conditions, avoiding the problems of traditional heat dissipation methods being singular and inefficient. This allows the heat dissipation system to automatically adjust when the load changes, ensuring maximum heat dissipation efficiency while reducing energy waste.

[0048] After the heat dissipation strategy is implemented, the system will monitor the core temperature change of the welding machine in real time and calculate the deviation between the actual cooling rate and the predicted cooling rate. If the deviation exceeds the acceptable range, the system will fine-tune and calibrate the parameters in the heat accumulation trend prediction model to optimize the subsequent prediction accuracy and heat dissipation effect.

[0049] This invention combines real-time sensing, heat load prediction, dynamic heat dissipation strategy, and feedback calibration to form a highly efficient and intelligent heat dissipation control system. It can dynamically adjust the heat dissipation strategy according to the working environment and operating status of the welding machine, solving problems such as uneven heat dissipation, low efficiency, high energy consumption, and lag response in traditional heat dissipation technologies, and improving the stability, reliability, and service life of the equipment.

[0050] In one possible implementation, current and voltage sensors are mounted on the circuit board of the welding machine. These sensors are responsible for acquiring welding current and voltage values ​​in real time. The sensors convert the acquired analog signals into digital signals via an analog-to-digital converter for subsequent processing and analysis. In this way, the system can monitor current and voltage changes during the welding process in real time, providing accurate data support for heat load calculation.

[0051] This high-precision data acquisition method ensures that the current and voltage information during the welding process can be monitored and analyzed in real time, thereby improving the response speed and accuracy of the entire heat dissipation control system and avoiding the risk of overheating due to data lag.

[0052] The sampling frequencies of the current and voltage sensors can be dynamically adjusted depending on the welding process. This flexible sampling frequency setting optimizes the data acquisition process based on the specific welding conditions, ensuring optimal sampling results across different processes. The acquired data is stored in a temporary buffer for quick access and processing.

[0053] Dynamically adjusting the sampling frequency allows the system to adapt to various welding processes, avoiding redundancy or inadequacy caused by fixed-frequency sampling, thereby improving the efficiency and accuracy of data acquisition.

[0054] Ambient temperature and humidity values ​​are collected using temperature and humidity sensors installed near the air inlet of the welding machine. Simultaneously, airflow pressure values ​​are collected using micro-differential pressure sensors installed in the cooling duct. These pressure sensors indirectly reflect the unobstructed flow of the airflow, ensuring smooth circulation of cooling air.

[0055] By monitoring ambient temperature, humidity, and airflow pressure in real time, the system can comprehensively consider the impact of environmental factors on heat dissipation, adjust heat dissipation strategies, and ensure stability and safety during the welding process.

[0056] The control system of the welding machine uses the welding current signal to time and obtain the duration of the electric arc. The temperature sensor array, using high-temperature resistant insulated encapsulation, is placed inside the welding machine in the main heat source areas, such as transformers, reactors, and power devices. These sensors can monitor temperature changes of critical components in real time.

[0057] Real-time acquisition of arc duration helps calculate heat load and optimize heat dissipation strategies. Meanwhile, a high-temperature resistant temperature sensor array covers all critical heat source areas, ensuring comprehensive and effective heat dissipation control and preventing localized overheating.

[0058] By employing advanced sensor technology and dynamic data acquisition methods, comprehensive monitoring and intelligent control of the welding process of the welding machine are achieved. This precise and flexible control method not only improves the efficiency and stability of the heat dissipation system, but also maintains optimal performance under different welding environments and processes, thereby extending the service life of the equipment and ensuring operational safety.

[0059] In one possible implementation, the system first calculates the instantaneous thermal power using real-time data on current, voltage, and arc duration. During the calculation, the arc energy conversion efficiency, i.e., the proportion of heat energy actually generated by the arc to the total input electrical energy, is considered. This conversion efficiency is obtained through factory calibration tests of the welding machine and stored in the control system. Simultaneously, for different welding materials, such as steel, aluminum, and copper, the system pre-stores the thermal conductivity parameters of each material and corrects for these parameters when calculating the thermal power, thereby obtaining an instantaneous thermal power value that better reflects the actual working conditions.

[0060] By considering the energy conversion efficiency of the electric arc and the material properties, the differences in heat input during the welding process can be accurately reflected, avoiding the deviation in temperature rise calculation caused by ignoring material properties, and improving the accuracy of heat load prediction.

[0061] The system uses a rolling time window method to calculate short-term heat accumulation, that is, to accumulate instantaneous heat power to obtain short-term heat load within a fixed or dynamically adjusted time interval. The length of the rolling time window can be preset according to the welding process type and material heat capacity, or dynamically adjusted according to real-time operating data, to adapt to the thermal response characteristics under different welding conditions.

[0062] The rolling window method can smooth out instantaneous power fluctuations, reflect the trend of heat accumulation in the short term, and enable the system to capture changes in heat load in a timely manner, preventing local overheating of key components.

[0063] The system utilizes a heat accumulation trend prediction model trained on historical operational data. Inputs include instantaneous thermal power, current core temperature, ambient temperature, and historical temperature change rates. During operation, the model updates its internal parameters through adaptive calibration steps to continuously optimize prediction accuracy. In this way, the system can predict temperature change trends over a future period, allowing for proactive adjustments to cooling strategies.

[0064] The heat accumulation trend prediction model can predict the temperature changes of the core components inside the welding machine in advance. Combined with the adaptive calibration mechanism, it ensures the accuracy and reliability of the prediction model in long-term operation, thereby optimizing heat dissipation parameters such as fan speed and coolant flow rate, and realizing intelligent and dynamic thermal management.

[0065] By considering arc efficiency, material properties, short-term heat accumulation, and trend prediction from multiple dimensions, real-time and precise control of the welding machine's thermal state is achieved, significantly improving the response speed and protection capability of the heat dissipation system, extending the service life of key components of the welding machine, and ensuring the stability and safety of the welding process.

[0066] In one possible implementation, the air-cooling strategy is achieved by controlling the speed of the EC fan. The EC fan is a high-efficiency, adjustable-speed fan whose speed is determined based on the difference between the predicted temperature and the air-cooling start-up threshold, as well as the ambient temperature. Specifically, when the welding machine core temperature approaches or exceeds the air-cooling start-up threshold, the fan speed gradually increases to enhance airflow and accelerate heat dissipation. The speed adjustment is based on a pre-defined fuzzy control rule table, and the appropriate fan speed is derived through fuzzy inference.

[0067] This air-cooling strategy can dynamically adjust the fan speed, achieving precise heat dissipation management based on the actual working status of the welding machine and environmental changes, avoiding excessive or insufficient fan operation, thereby improving heat dissipation efficiency and reducing energy waste.

[0068] Building upon the air-cooling system, when the welding machine's core temperature is high, the system activates the liquid cooling system, combining air and liquid cooling to achieve more efficient heat dissipation. The coolant flow rate and velocity of the liquid cooling system are adjusted based on the difference between the predicted temperature and the liquid cooling activation threshold, as well as the ambient humidity. By querying a fuzzy control rule table, the system can adjust the liquid cooling flow rate according to the current ambient humidity. If the ambient humidity is high, the liquid cooling flow rate will be appropriately reduced to prevent condensation; conversely, when the ambient humidity is low, the liquid cooling flow rate will be increased to enhance the heat dissipation effect.

[0069] The combined use of liquid cooling and air cooling can provide more efficient heat dissipation in high-temperature working environments. The liquid cooling system plays a stronger role in heat dissipation when air cooling is insufficient. At the same time, humidity sensing avoids condensation problems, improving the stability and safety of the system.

[0070] All heat dissipation strategy adjustments are made through a fuzzy control rule table. Input parameters include the difference between the predicted temperature and the threshold, ambient temperature, and humidity. Fuzzy inference derives control strategies based on these input parameters, such as adjustments to fan speed and liquid cooling flow rate. Through fuzzy control, the system can flexibly adjust the heat dissipation strategy based on a comprehensive judgment of multiple factors, ensuring that the welding machine maintains optimal heat dissipation performance under different operating conditions.

[0071] Fuzzy control rule tables can provide more refined and dynamic heat dissipation strategies by handling changing environmental conditions and equipment states, avoiding over- or under-adjustment caused by simple threshold control, and improving the intelligence and adaptability of the system.

[0072] By combining air cooling and liquid cooling methods and intelligently adjusting the heat dissipation strategy using a fuzzy control rule table, the welding machine can achieve efficient heat dissipation in various environments. This dynamic and coordinated heat dissipation strategy not only improves the heat dissipation effect but also avoids instability in the heat dissipation system caused by environmental changes, thereby enhancing the overall performance and reliability of the welding machine.

[0073] In one possible implementation, when the predicted core temperature of the welding machine reaches or exceeds a set safety threshold, the system will activate a protective heat dissipation strategy. This strategy includes:

[0074] The system controls both the air-cooling and liquid-cooling systems to operate at maximum power simultaneously to rapidly reduce equipment temperature. The air-cooling system enhances airflow by outputting maximum air volume; the liquid-cooling system improves heat exchange efficiency by increasing the coolant flow rate.

[0075] To reduce heat generation in the welding machine, the system will appropriately reduce its output power. The extent of the power reduction is adjusted based on the predicted temperature exceeding the safety threshold. If the temperature exceedance is minor, only a small power reduction may be implemented; however, if the exceedance is severe, a more significant power reduction will occur. This adjustment can be achieved through a stepped approach (gradually reducing power in stages) or a linear adjustment approach (reducing power linearly according to the degree of temperature rise).

[0076] In certain situations, when the temperature is too high and cannot be effectively and quickly cooled down by the cooling system, the system will trigger a pause in the welding operation, temporarily halting welding work until the core temperature drops below a safe threshold. The duration of the welding pause will depend on the heat accumulation of the welding machine and the ambient temperature; typically, the pause time will be longer when the ambient temperature is higher.

[0077] The system monitors how much the core temperature of the welding machine exceeds a safe threshold and determines the extent to which the output power should be reduced. If the temperature slightly exceeds the threshold, the output power will be reduced slightly to balance heat dissipation and the need for continued welding; however, if the temperature significantly exceeds the threshold, the system will drastically reduce the power to slow the rate of temperature increase, and may even reduce the output power to a minimum to prevent equipment damage.

[0078] This method of gradually adjusting power can effectively prevent welding instability caused by sudden and significant power drops, while maintaining sufficient heat dissipation to ensure that the equipment is not damaged due to overheating.

[0079] When the system triggers a pause in welding operations, it calculates the pause duration based on the current heat accumulation and ambient temperature. If the heat accumulation is high or the ambient temperature is high, the system will decide on a longer pause to ensure that the core temperature has enough time to drop back to a safe range. If the heat accumulation is low or the ambient temperature is low, the pause time will be correspondingly shorter.

[0080] By adjusting the pause time based on heat accumulation and ambient temperature, the system can flexibly respond to the heat dissipation requirements under different working environments, avoid excessive downtime that would reduce production efficiency, and at the same time ensure that the welding machine receives sufficient cooling.

[0081] Through a sophisticated temperature prediction and dynamic adjustment mechanism, the system can respond quickly when the welding machine temperature exceeds the safety threshold. It protects the machine from overheating damage through multiple measures, including a maximum-power cooling system, adjusting output power, and pausing welding operations. This approach not only improves equipment safety but also effectively extends the welding machine's lifespan and ensures continuous stability in welding operations.

[0082] In one possible implementation, during periods of inactivity in the cooling system or when the welding machine is in standby mode, the system analyzes historical airflow pressure data to determine if there is a risk of duct blockage. The airflow pressure value is monitored in real time by sensors. When abnormal fluctuations in airflow pressure occur, the system compares these fluctuations with historical data to determine if duct blockage is possible. If a blockage is suspected, the system initiates an automatic duct cleaning procedure.

[0083] By continuously monitoring the airflow pressure value, the system can detect potential problems before the air duct is completely blocked, thus avoiding poor heat dissipation caused by complete blockage of the air duct and protecting the normal operation of the welding machine.

[0084] After the automatic cleaning program is activated, the system controls the fan to alternately rotate forward and reverse. This utilizes the reverse impact force of the airflow to effectively blow away dust and debris in the duct. The alternating forward and reverse operation helps to clear the duct, improve airflow, and thus restore the duct to its normal working condition.

[0085] This simple and efficient cleaning method can complete the initial cleaning of the air duct in a short time, avoiding excessive dust accumulation and maintaining high heat dissipation performance.

[0086] If the airflow pressure does not return to the normal range after the fan alternates between forward and reverse rotation, it indicates that there may be stubborn dirt or blockage in the air duct. In this case, the system will control a small mechanical cleaning device to extend into the air duct for deeper cleaning. The small mechanical cleaning device uses a brush head or other cleaning tools to physically clean and remove dust and debris from inside the air duct. The cleaning frequency and stroke of the cleaning device will be dynamically adjusted according to the degree to which the airflow pressure deviates from the normal range.

[0087] This multi-stage cleaning method can address blockages of varying degrees. When the air duct is severely blocked, the mechanical cleaning device can thoroughly clean the air duct, restore good airflow channels, and ensure the long-term effective operation of the welding machine's heat dissipation system.

[0088] During the cleaning process, the system continuously monitors the airflow pressure until it returns to the normal range. This means that the system will adjust the cleaning strategy in real time according to the cleaning progress and automatically stop the cleaning operation when the airflow returns to normal.

[0089] By continuously monitoring the airflow pressure value, the system can ensure that the cleaning work is completed accurately and effectively, avoiding over-cleaning or unnecessary cleaning operations, while ensuring that the cleanliness of the air duct is at its best, and avoiding poor heat dissipation caused by airflow problems.

[0090] By implementing an intelligent cleaning function in the cooling air ducts, the long-term stable operation of the welding machine's cooling system is effectively ensured. The automated cleaning process not only promptly detects and resolves air duct blockages but also adjusts the cleaning strategy based on the actual blockage situation, guaranteeing the equipment's efficient heat dissipation performance. Through this method, the welding machine can maintain good heat dissipation for an extended period, thereby improving the equipment's lifespan and working efficiency.

[0091] In one possible implementation, during actual welding, variations in welding materials, welding processes, and environmental conditions directly affect the heat dissipation effect. Therefore, the system records heat dissipation data under different application scenarios. This data includes, but is not limited to, the type of welding material, welding speed, temperature setting, ambient temperature, and humidity. This data not only reflects changes in heat load during welding but also helps the system identify differences in the performance of the heat dissipation system under different conditions.

[0092] By collecting multi-dimensional heat dissipation data, the system can gain a more comprehensive understanding of the impact of various factors on the performance of the heat dissipation system, providing sufficient data support for the subsequent optimization process.

[0093] By using the recorded data, the system optimizes the parameters of a pre-set fuzzy control rule table and a heat accumulation trend prediction model. The fuzzy control rule table adjusts the heat dissipation strategy based on real-time monitored temperature and welding conditions, while the heat accumulation trend prediction model predicts the temperature change trend during welding. Through machine learning algorithms, the system can optimize these parameters based on historical data, making them better adaptable to different welding materials, processes, and environmental conditions.

[0094] Optimized control rules and prediction models can improve the system's response speed and accuracy, enabling it to maintain efficient and stable heat dissipation under different welding conditions, and avoiding equipment damage or welding quality problems caused by excessively high or low temperatures.

[0095] The system continuously analyzes the deviation between predicted and actual temperatures using machine learning algorithms. Whenever a deviation occurs, the system automatically adjusts model parameters and control rules to gradually optimize the accuracy of temperature prediction. This optimization process is achieved by comparing the differences between historical and real-time data, thereby continuously improving the accuracy of the prediction model and making the system more adaptable to different application scenarios.

[0096] Machine learning's adaptive adjustments enable the system to self-optimize in constantly changing working environments, avoiding the tedious process of manual intervention and adjustments, and improving the overall intelligence level of the system. Over time, the system can automatically adjust the optimal heat dissipation strategy based on accumulated data and experience, greatly improving the stability and reliability of heat dissipation.

[0097] Through the above optimization process, the system can automatically adjust its heat dissipation strategy under different welding materials, processes, and environmental conditions, ensuring that the welding machine is always in the best heat dissipation state. Whether in high-temperature and high-humidity environments, or when using different materials and welding processes, the system can flexibly cope with these conditions, avoiding equipment damage and reduced welding quality caused by excessive temperature.

[0098] This adaptive control method, based on machine learning optimization, not only improves the operating efficiency of the equipment but also ensures the consistency and stability of welding quality, reduces malfunctions and maintenance caused by improper temperature control, and extends the service life of the welding equipment.

[0099] Through machine learning algorithms and data feedback mechanisms, the heat dissipation system achieves self-optimization and adaptive adjustment. This enables the welding machine to maintain efficient heat dissipation in various application scenarios, enhancing the system's flexibility and long-term stability, thereby improving equipment performance and lifespan.

[0100] In one possible implementation, when the welding machine is used for the first time or when major welding components are replaced, the system first guides the user through basic parameter settings. These parameters typically include basic information related to the welding process, such as welding material type, welding current, and welding voltage. Through these settings, the system can initially obtain key information about the welding process, providing basic data for subsequent heat dissipation control.

[0101] Based on the parameters input by the user, the system can quickly establish an initial control model that adapts to the current working environment, providing a suitable starting point for the heat dissipation control of the welding machine.

[0102] If the user does not manually configure the system, it can automatically learn and initialize the basic parameters of the heat accumulation trend prediction model and the basic rules of the fuzzy control rule table during trial operation. During the trial operation, the welding machine will simulate different welding conditions (such as different currents and materials) within a safe range and record temperature change data during the welding process. This data will be used to analyze the heat dissipation requirements of the welding machine under different conditions, and based on this data, the initial heat accumulation trend prediction model and fuzzy control rules will be gradually established.

[0103] Through trial operation and automatic learning, the system can automatically adjust the initial control rules and model parameters based on data from the actual welding process. This method avoids the limitations of relying on manual settings, allowing the equipment to be adjusted according to the actual situation upon first use, thus improving the system's intelligence level and ease of use.

[0104] During trial operation, the system will simulate different welding conditions, such as different welding currents, welding voltages, and the use of different welding materials, and record the temperature changes under these conditions. Based on the real-time temperature change data, the system will predict the heat dissipation demand and establish a preliminary heat accumulation trend prediction model through data analysis.

[0105] By simulating different welding conditions, the system can establish accurate initial temperature prediction models under various conditions, which helps to more precisely control temperature changes under different conditions. With the support of multi-condition data, the system can more flexibly respond to different welding needs in actual use, improving welding quality and equipment efficiency.

[0106] Based on recorded temperature change data, the system establishes an initial heat accumulation trend prediction model and a fuzzy control rule table. These initial model and rule settings enable the welding machine to control temperature during normal operation and adjust its heat dissipation strategy according to real-time data, ensuring that overheating or insufficient heat dissipation does not occur during welding.

[0107] The establishment of the initial model and control rules provides a solid foundation for the heat dissipation control of the welding machine. This process can automatically configure a suitable heat dissipation control strategy when the welding machine is used for the first time or after the main welding components are replaced, ensuring that the welding machine can reach its optimal working state in a short time without the need for tedious manual adjustments.

[0108] By combining automatic learning and user settings, the intelligent configuration of the welding machine's heat dissipation control system is achieved. Through trial runs simulating different welding conditions and recording temperature changes, the system provides a reasonable temperature control model for the initial use of the welding machine. This allows the equipment to adaptively adjust its heat dissipation strategy under different operating conditions, ensuring the high efficiency and stability of the equipment. This method reduces the complexity of manual settings and improves the debugging efficiency of the welding machine during its first use.

[0109] In one possible implementation, the air-cooling start-up threshold refers to the temperature threshold at which the air-cooling system should activate when the welding machine reaches a certain temperature during the welding process. The air-cooling system relies on airflow to remove heat and lower the welding machine's temperature. This threshold setting needs to ensure that the air-cooling system can activate promptly when the welding machine is under light load, preventing overheating.

[0110] The liquid cooling start-up threshold refers to the temperature range at which the air-cooling system will activate when the welding machine is under heavy load or operating continuously, as the air-cooling system may not be sufficient to handle excessively high temperatures. The liquid cooling system efficiently removes heat from the welding machine's interior by circulating coolant, making it suitable for high-load or long-duration welding operations. The liquid cooling start-up threshold is typically set in a higher temperature range than the air-cooling start-up threshold to ensure effective cooling even in high-temperature environments.

[0111] The equipment safety threshold is the maximum safe temperature threshold for the welding machine. When the welding machine temperature reaches this threshold, the system will automatically cut off the power or activate emergency cooling measures to prevent the equipment from being damaged due to overheating. The setting of the safety threshold ensures that the welding machine can take timely measures even in extreme situations to avoid equipment damage or safety accidents.

[0112] Before a welding machine leaves the factory, the manufacturer conducts calibration tests according to its model and specifications. These tests include evaluating the temperature rise characteristics of the welding machine under different load conditions. The main steps of the calibration test are as follows:

[0113] By adjusting the workload of the welding machine (such as different current or voltage settings), its temperature changes under various load conditions are recorded. This process can identify the heat dissipation requirements of the welding machine under different loads, providing a basis for determining appropriate temperature thresholds.

[0114] The tests are also conducted under different ambient temperatures and humidity conditions to simulate various working environments that the welding machine may face. These tests help the system identify how the temperature rise characteristics of the welding machine change under extreme environments such as high temperature and high humidity, ensuring that the temperature threshold can adapt to various actual working conditions.

[0115] Based on test results under load and environmental conditions, the temperature rise characteristics of the welding machine are analyzed. This method allows for the determination of a threshold range that ensures safe operation of the welding machine while avoiding excessive impact on welding efficiency. For example, excessively high temperatures may decrease welding efficiency or even cause the welding machine to malfunction; conversely, excessively low temperatures may lead to insufficient heat dissipation, also affecting welding efficiency.

[0116] In some cases, users can manually set these temperature thresholds based on their actual welding conditions and experience. By setting temperature thresholds, users can personalize adjustments according to the specific welding environment. For example, in high-temperature environments, users may want to lower the liquid cooling start-up threshold to activate the liquid cooling system earlier and prevent equipment overheating. When users manually set temperature thresholds, the system should provide a reasonable range to prevent users from setting excessively high or low temperature values, ensuring safe equipment operation.

[0117] By setting reasonable multi-level temperature thresholds, equipment damage due to overheating can be effectively prevented. Setting equipment safety thresholds allows for timely power cut-off or other emergency measures to prevent potential safety hazards.

[0118] By combining multi-level temperature threshold settings, calibration tests, and manual adjustments, the welding machine can maintain optimal heat dissipation under different welding conditions, improving equipment safety and efficiency, and ensuring long-term stable operation. This technical feature effectively enhances the welding machine's intelligence level and user experience.

[0119] Accordingly, embodiments of the present invention also provide a dynamic heat dissipation control system for an intelligent welding machine, including a memory configured to store instructions, a processor configured to call the instructions from the memory, and capable of implementing any of the dynamic heat dissipation control methods for an intelligent welding machine as described in any embodiment of the present invention when executing the instructions.

[0120] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0121] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A dynamic heat dissipation control method for an intelligent welding machine, characterized in that, Includes the following steps: The welding process involves real-time sensing and acquisition of multiple parameters: real-time monitoring and acquisition of welding current, welding voltage, core temperature inside the welding machine, ambient temperature, ambient humidity, and airflow pressure in the heat dissipation duct; the core temperature inside the welding machine is acquired by an array of temperature sensors distributed in the main heat source areas inside the welding machine. Steps for real-time calculation of heat load and prediction of heat accumulation trend: Calculate the instantaneous heat power of the welding machine based on the real-time collected welding current value, welding voltage value, and arc duration; calculate the short-term heat accumulation based on the instantaneous heat power and historical core temperature data. Using a heat accumulation trend prediction model, the changing trend of the core temperature inside the welding machine can be predicted over a period of time. The dynamic collaborative heat dissipation strategy generation and execution steps are as follows: The predicted core temperature value is compared with the preset multi-level temperature thresholds; based on the comparison results, an air cooling heat dissipation strategy, an air cooling and liquid cooling collaborative heat dissipation strategy, or a protective heat dissipation strategy are dynamically generated and executed; among them, the control parameters of the air cooling heat dissipation strategy are determined based on the difference between the predicted temperature and the corresponding threshold and the ambient temperature; the control parameters of the air cooling and liquid cooling collaborative heat dissipation strategy are determined based on the difference between the predicted temperature and the corresponding threshold and the ambient humidity. Heat dissipation effect feedback and adaptive calibration steps: After the heat dissipation strategy is implemented, the actual change in the core temperature inside the welding machine is monitored, and the deviation between the actual cooling rate and the predicted cooling rate is calculated; if the deviation continues to exceed the acceptable range, the parameters in the heat accumulation trend prediction model are fine-tuned and calibrated.

2. The intelligent welding machine dynamic heat dissipation control method according to claim 1, characterized in that, In the real-time sensing and acquisition step of the welding process, the welding current value and welding voltage value are acquired by current sensor and voltage sensor installed on the circuit board of the welding machine. The acquired current and voltage data are processed after analog-to-digital conversion. The sampling frequency of the current sensor and voltage sensor can be dynamically adjusted according to the welding process type, and the collected data is stored in a temporary buffer area; the ambient temperature and ambient humidity values ​​are collected by temperature and humidity sensors installed near the air inlet of the welding machine; the airflow pressure value is collected by a micro differential pressure sensor installed in the heat dissipation duct, which is used to indirectly reflect the unobstructedness of the airflow duct. The duration of the electric arc is obtained by the control system of the welding machine based on the welding current signal; the temperature sensor array adopts a high-temperature resistant insulating package and its arrangement covers the main heat source areas inside the welding machine, including transformers, reactors and power devices.

3. The intelligent welding machine dynamic heat dissipation control method according to claim 1, characterized in that, In the real-time calculation of heat load and prediction of heat accumulation trend, when calculating the instantaneous heat power, the differences in arc energy conversion efficiency and thermal conductivity of different welding materials are taken into account. The arc energy conversion efficiency is determined by the factory calibration test of the welding machine, and the thermal conductivity data of different welding materials are pre-stored in the welding machine control system. The short-term heat accumulation is calculated using a rolling time window method, and the length of the rolling time window can be preset or dynamically adjusted according to the welding process and material type. The inputs to the heat accumulation trend prediction model include at least the instantaneous thermal power, the current core temperature, the ambient temperature, and the historical temperature change rate. The model is trained using historical operating data, and its internal parameters can be updated during operation through the adaptive calibration step.

4. The dynamic heat dissipation control method for an intelligent welding machine according to claim 1, characterized in that, In the dynamic collaborative heat dissipation strategy generation and execution step, the air cooling heat dissipation strategy is implemented by controlling the EC fan. The speed of the EC fan is determined by querying a preset fuzzy control rule table based on the difference between the predicted temperature and the air cooling start threshold and the ambient temperature. The air-cooling and liquid-cooling synergistic heat dissipation strategy activates the liquid-cooling system on the basis of air cooling. The flow rate and velocity of the liquid-cooling system are determined by querying the fuzzy control rule table based on the difference between the predicted temperature and the liquid-cooling activation threshold and the ambient humidity. When the ambient humidity is high, the liquid-cooling flow rate is appropriately reduced to prevent condensation. When the ambient humidity is low, the liquid cooling flow rate is increased to enhance the heat dissipation effect; the fuzzy control rule table takes the difference between the predicted temperature and the threshold, the ambient temperature or the ambient humidity as input, and obtains the control parameter adjustment amount through fuzzy inference.

5. The dynamic heat dissipation control method for an intelligent welding machine according to claim 4, characterized in that, In the dynamic collaborative heat dissipation strategy generation and execution step, if the predicted temperature reaches or exceeds the equipment safety threshold, a protective heat dissipation strategy is generated and executed, controlling the air cooling system and liquid cooling system to operate at maximum power, while reducing the output power of the welding machine or triggering a pause in welding operations until the core temperature drops below the safety threshold. The reduction in the output power of the welding machine is determined based on the degree to which the predicted temperature exceeds the safety threshold, and is achieved through either stepped or linear adjustment. The duration of the welding operation pause is calculated based on the amount of heat accumulation and the ambient temperature.

6. The dynamic heat dissipation control method for an intelligent welding machine according to claim 1, characterized in that, The method also includes an intelligent cleaning step for the heat dissipation air duct. During the intermittent period of the heat dissipation system or when the welding machine is on standby, the system determines whether the air duct is blocked based on historical data of the airflow pressure value. If it is determined that the air duct may be blocked, the system starts the automatic cleaning program for the air duct and controls the fan to alternately rotate forward and reverse to blow away the dust. If the airflow pressure value still does not return to the normal range after the fan alternates between forward and reverse rotation, a small mechanical cleaning device is controlled to extend into the air duct for auxiliary cleaning; the cleaning frequency and cleaning stroke of the small mechanical cleaning device are set according to the degree to which the airflow pressure value deviates from the normal range; the airflow pressure value is continuously monitored during the cleaning process until it returns to the normal range.

7. The intelligent welding machine dynamic heat dissipation control method according to claim 1, characterized in that, In the heat dissipation effect feedback and adaptive calibration step, heat dissipation effect data under different welding materials, welding processes and environmental conditions are recorded to optimize the parameters of the preset fuzzy control rule table and the heat accumulation trend prediction model. The optimization process is based on machine learning algorithms. By comparing the deviation between the predicted temperature and the actual temperature, the model parameters and control rules are continuously adjusted to improve the system's adaptability in different application scenarios.

8. The intelligent welding machine dynamic heat dissipation control method according to claim 1, characterized in that, The method also includes an initialization configuration step, which guides the user to set basic parameters when the welding machine is used for the first time or when the main welding components are replaced, or automatically learns and initializes the basic parameters of the heat accumulation trend prediction model and the basic rules of the fuzzy control rule table through trial operation. The trial operation process includes simulating different welding conditions within a safe range and recording temperature change data, and establishing initial model parameters and control rules based on this data.

9. The dynamic heat dissipation control method for an intelligent welding machine according to claim 1, characterized in that, The multi-level temperature thresholds include air-cooled start-up threshold, liquid-cooled start-up threshold, and equipment safety threshold. The specific values ​​of these thresholds are determined by calibration tests conducted on the welding machine according to its model and specifications before it leaves the factory, or by the user manually setting them within the allowable range based on actual welding conditions and experience. The calibration test includes testing the temperature rise characteristics of the welding machine under different loads and environmental conditions to determine the threshold range that ensures safety without excessively affecting welding efficiency.

10. A dynamic heat dissipation control system for an intelligent welding machine, characterized in that, The system includes a memory configured to store instructions, a processor configured to retrieve the instructions from the memory, and, when executing the instructions, to implement the dynamic heat dissipation control method for an intelligent welding machine as described in any one of claims 1-9.

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