Ultrasonic power supply frequency locking method and device, electronic equipment and storage medium
By predicting the resonant frequency of ultrasonic welding through machine learning, the problem of long frequency locking time in the existing technology is solved, a fast and stable welding process is achieved, and the welding quality is improved.
Patent Information
- Application Number
- CN202510900104.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-01
- Publication Date
- 2025-09-30
AI Technical Summary
In existing ultrasonic welding, digital phase-locked loop technology takes a long time to lock when the resonant frequencies differ greatly, resulting in inconsistent welding time, affecting welding stability and product quality.
A machine learning method is used to obtain real-time temperature, voltage and current data based on the welding response. The resonant frequency is predicted by the recursive least squares method, and the frequency is locked based on this data to avoid the sweep time of the intermediate non-resonant frequency.
The frequency locking time is greatly shortened, which improves the stability and consistency of welding and ensures product quality.
Smart Images
Figure CN120715367A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of tool control technology, and specifically to an ultrasonic power supply frequency locking method, device, electronic device and storage medium. Background Art
[0002] Ultrasonic welding, as an efficient, fast, energy-saving and environmentally friendly welding method, is widely used in the fields of metal and plastic welding. To ensure consistent welding quality during the welding process, the digital power supply needs to quickly and accurately lock the resonant frequency of the transducer. In existing frequency locking technologies, digital phase-locked loop (PLL) technology is usually used for frequency locking. In other words, the frequency is locked by determining the phase difference between voltage and current. However, this method has a major disadvantage. When the resonant frequency difference is large, it takes more time to correctly lock the resonant frequency. If the frequency locking time is inconsistent during each welding, the welding time will be inconsistent, affecting welding stability and failing to guarantee product quality. Summary of the Invention
[0003] In order to solve the technical problems existing in the prior art, the embodiments of the present application provide an ultrasonic power supply frequency locking method, device, electronic device and storage medium, which can achieve rapid locking of the resonant frequency by using a machine learning method.
[0004] In order to achieve the above objectives, the technical solutions adopted in the embodiments of the present application are as follows:
[0005] In a first aspect, a method for locking the frequency of an ultrasonic power supply is provided, which is applied to a transducer system. The method includes: obtaining the real-time temperature of the current transducer based on the welding response, and comparing the real-time temperature with the adjacent historical temperature to determine the temperature difference; when the temperature value is greater than a preset threshold, the real-time temperature, real-time resonant frequency, real-time voltage data and real-time current data are input into a prediction model that has been trained to converge to obtain a predicted resonant frequency, and the predicted resonant frequency is determined as the starting scanning frequency and the frequency is locked.
[0006] In some specific implementations, the method further includes: when the temperature difference is less than a preset threshold, obtaining the historical welding resonant frequency as the starting scanning frequency, and locking the frequency based on the phase difference between the historical voltage data, historical current data and the real-time voltage data, real-time current data.
[0007] In some specific implementations, the method further includes: when the temperature value is greater than a preset threshold, determining whether the prediction model has converged; when the prediction model has not converged, obtaining a historical welding resonant frequency as a starting scanning frequency, and locking the frequency based on the historical welding resonant frequency.
[0008] In some specific implementations, the prediction model is constructed based on the recursive least squares method and is expressed based on the following formula: in, To predict the resonant frequency, w0 is the real-time resonant frequency, w1 is the temperature coefficient, w2 is the temperature change rate coefficient, w3 is the power coefficient, T(t) is the real-time temperature, is the temperature difference.
[0009] In some specific implementations, when the covariance value of the prediction model is less than 1, the prediction model is in a convergence state.
[0010] In a second aspect, an ultrasonic power supply frequency locking device is provided, which is applied to a transducer system, and the device includes: a data processing module, which is used to obtain the real-time temperature of the current transducer based on the welding response, and compare the real-time temperature with the adjacent historical temperature to determine the temperature difference; a first execution module, which is used to input the real-time temperature, real-time resonant frequency, real-time voltage data and real-time current data into a prediction model that has been trained to converge to obtain a predicted resonant frequency when the temperature value is greater than a preset threshold, and determine the predicted resonant frequency as the starting scanning frequency and perform frequency locking.
[0011] In some specific implementations, the device also includes a switching module, which is used to switch the current first execution module to the second execution module, and the second execution module is used to execute the frequency locking task when the temperature difference is less than a preset threshold value, specifically including: when the temperature difference is less than the preset value threshold, the historical welding resonant frequency is obtained as the starting scanning frequency, and the frequency is locked based on the phase difference between the historical voltage data, historical current data and the real-time voltage data, real-time current data.
[0012] In some specific implementations, the switching module is also used to switch the current first execution module to the third execution module, and the third control module is used to perform a frequency locking task when the prediction model has not converged, specifically including: when the prediction model has not converged, obtaining a historical welding resonant frequency as the starting scanning frequency, and locking the frequency based on the historical welding resonant frequency.
[0013] According to a third aspect, an electronic device is provided, comprising: a memory and a processor coupled to the memory, wherein the processor is configured to execute any one of the above-mentioned ultrasonic power frequency locking methods based on instructions stored in the memory.
[0014] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that when the program is executed by a processor, the steps of the ultrasonic power supply frequency locking method as described in any one of the above are implemented.
[0015] In the technical solution provided in the embodiments of the present application, the method obtains the real-time temperature of the current transducer based on the welding response, and compares the real-time temperature with the adjacent historical temperatures to determine the temperature difference. When the temperature value is greater than a preset threshold, the real-time temperature, real-time resonant frequency, real-time voltage data, and real-time current data are input into a prediction model that has been trained to convergence to obtain a predicted resonant frequency, and the predicted resonant frequency is determined as the starting scanning frequency and frequency locking is performed. This technical solution uses the recursive least squares algorithm in machine learning to first predict a possible resonant frequency, and then starts frequency tracking based on this predicted frequency, avoiding the sweep time of intermediate non-resonant frequencies and significantly shortening the frequency locking time. For different transducer systems, the software algorithm can automatically update the parameters of the software model, improving the versatility of the algorithm application. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.
[0017] The methods, systems, and / or programs in the accompanying drawings will be further described according to exemplary embodiments. These exemplary embodiments will be described in detail with reference to the drawings. These exemplary embodiments are non-limiting exemplary embodiments, wherein example numerals represent similar structures in the various views of the drawings.
[0018] Figure 1 This is a flow chart of an ultrasonic power supply frequency locking method provided in an embodiment of the present application.
[0019] Figure 2 Schematic diagram of the structure of the frequency locking device provided in the embodiment of the present application.
[0020] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION
[0021] In order to better understand the above technical solution, the technical solution of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.
[0022] In the following detailed description, numerous specific details are set forth by way of example in order to provide a thorough understanding of the relevant teachings. However, it will be apparent to one skilled in the art that the present application can be practiced without these details. In other instances, well-known methods, procedures, systems, compositions, and / or circuits have been described at a relatively high level, without detail, to avoid unnecessarily obscuring aspects of the present application.
[0023] Flowcharts are used in this application to illustrate the execution processes performed by the system according to the embodiments of the present application. It should be clearly understood that the execution processes of the flowcharts may not be executed in sequence. Instead, these execution processes may be executed in reverse order or simultaneously. In addition, at least one additional execution process may be added to the flowchart. One or more execution processes may be deleted from the flowchart.
[0024] Before further explaining the embodiments of the present invention in detail, the nouns and terms involved in the embodiments of the present invention are explained. The nouns and terms involved in the embodiments of the present invention are subject to the following interpretations.
[0025] (1) In response to, it is used to indicate the conditions or states on which the executed operations depend. When the dependent conditions or states are met, one or more operations executed may be in real time or with a set delay. Unless otherwise specified, there is no restriction on the order in which the multiple operations executed are executed.
[0026] (2) Based on, used to indicate the conditions or states on which the executed operations depend. When the dependent conditions or states are met, one or more operations executed may be in real time or with a set delay. Unless otherwise specified, there is no restriction on the order in which the multiple operations executed are executed.
[0027] The present application provides a method for locking the frequency of an ultrasonic power supply. In existing digital ultrasonic power supplies, the frequency locking is typically achieved by continuously adjusting the output voltage frequency by collecting the phase difference between the voltage and current at the output terminal to keep the transducer system in a resonant state. As welding progresses, the transducer becomes increasingly hot, and the resonant frequency of the entire transducer system also decreases. However, the decrease in resonant frequency is nonlinear with temperature. If the transducer cools down, due to the large difference in resonant frequency, it takes a long time to lock the resonant frequency, while the actual welding time is reduced, resulting in inconsistent welding results and product quality issues. How to quickly lock the resonant frequency becomes a problem with existing digital ultrasonic power supplies. Furthermore, when the transducer temperature differs significantly, for example, when the transducer is taken from room temperature to a high-temperature environment before welding, the temperature will slowly rise, or when it is taken to a low-temperature operating condition, or when it stops working after a period of operation, the temperature will slowly drop. In these cases, directly using the old frequency locking algorithm would take a long time.
[0028] To address the above issues, an embodiment of the present application provides a machine learning-based method for predicting the resonant frequency, and uses this predicted frequency as a benchmark for frequency tracking, thereby avoiding the sweep time of intermediate non-resonant frequencies and significantly shortening the frequency locking time.
[0029] For details about this method, please refer to Figure 1 As shown, the following steps are included:
[0030] Step S11: Acquire the real-time temperature of the current transducer based on the welding response, and compare the real-time temperature with adjacent historical temperatures to determine a temperature difference.
[0031] In this embodiment, the adjacent historical temperature values are the temperature data collected during the last welding, that is, the temperature data corresponding to the transducer in this welding is compared with the temperature data collected during the last welding to determine the temperature difference between the two temperature data.
[0032] Step S12. When the temperature value is greater than a preset threshold, the real-time temperature, real-time resonant frequency, real-time voltage data and real-time current data are input into a prediction model that has been trained to converge to obtain a predicted resonant frequency, and the predicted resonant frequency is determined as the starting scanning frequency and frequency locked.
[0033] The temperature difference corresponding to the two comparisons is compared with a preset temperature threshold. When the temperature difference is greater than the preset threshold, the real-time temperature, real-time resonant frequency, real-time voltage data, and real-time current data corresponding to the current transducer are obtained.
[0034] Among them, the real-time resonant frequency is the frequency of the output excitation signal, and the real-time voltage data and real-time current data are the voltage data and current data collected by the feedback loop at the output end of the transducer, wherein the corresponding frequency data can be obtained for the voltage data and current data.
[0035] The above three data are input into the prediction model to obtain the predicted resonant frequency, and then the predicted resonant frequency is used as the starting scanning frequency. The phase difference of voltage and current is used to lock the frequency, thereby quickly locking the system resonant frequency. Among them, the process of locking the resonant frequency by voltage and current difference is as follows: the digital power supply outputs an excitation signal with an initial frequency, collects the voltage and current signals at the output end, and calculates the phase difference and lead-lag relationship of the signals at both ends of the transducer through the phase difference calculation circuit. If the phase difference value is large, the transducer system is not resonant, and the frequency of the output signal needs to be changed. The frequency of the signal is increased or decreased according to the lead-lag relationship of the phase. If the phase difference value is small, it means that the transducer system is in a resonant state. The output signal frequency at this time is the resonant frequency of the transducer, indicating that the digital power supply has successfully locked the frequency.
[0036] The prediction model is constructed based on the recursive least squares method in this embodiment and is expressed based on the following formula: in, To predict the resonant frequency, w0 is the real-time resonant frequency, w1 is the temperature coefficient, w2 is the temperature change rate coefficient, w3 is the power coefficient, T(t) is the real-time temperature, is the temperature difference.
[0037] Step S13. When the temperature difference is less than a preset threshold, the historical welding resonant frequency is obtained as the starting scanning frequency, and the frequency is locked based on the phase difference between the historical voltage data, historical current data and the real-time voltage data, real-time current data.
[0038] Step S14: When the temperature value is greater than a preset threshold, determine whether the prediction model has converged; when the prediction model has not converged, obtain a historical welding resonant frequency as a starting scanning frequency, and lock the frequency based on the historical welding resonant frequency.
[0039] In this embodiment, step S13 and step S14 are frequency-locking control decisions made when neither the temperature nor the prediction model meets the required conditions.
[0040] In this embodiment, whether the training of the prediction model is converged is judged based on whether the covariance value of the prediction model is less than 1. When the covariance value is less than 1, the prediction model is in a converged state.
[0041] Specifically, this embodiment further provides a prediction model training method, wherein when training the prediction model, it is necessary to determine the contribution ratio of each input data to the model weight update, which is represented by the Kalman gain in this embodiment, wherein the Kalman gain is expressed based on the following formula: in The input feature vector is used to map the input data to the model input space, which includes temperature, temperature change rate, and power key features.
[0042] The calculation of covariance is based on the following formula: Covariance is used to indicate the incorrectness of parameter estimation, and the initial value is δ -1 I (δ = 0.01). When the initial value of the covariance is large, the weight w(t) is updated more rapidly and the model converges faster. As time goes by, P(t) gradually decreases, and the weight adjustment becomes smoother, avoiding overshoot. λ∈(0, 1] is the forgetting factor, which controls the speed at which old data is forgotten. The smaller λ is, the faster the weight of old data decays. In this embodiment, it is preferably 0.98.
[0043] The weight update formula is as follows: w(t)=w(t-1)+k(t)(f meas(t)-X T (t)w(t-1)), the weight adjustment amplitude is inversely proportional to the prediction error. If a sudden temperature rise causes frequency offset, the w1 temperature coefficient will be quickly adjusted to compensate for the error.
[0044] The method provided in the embodiment of the present application can predict a possible resonant frequency by using the recursive least squares algorithm in machine learning, and then start frequency tracking based on the predicted frequency, thereby avoiding the frequency sweep time of the intermediate non-resonant frequency and significantly shortening the frequency locking time; for different transducer systems, the software algorithm can automatically update the parameters of the software model, thereby improving the versatility of the algorithm application
[0045] In accordance with the above method, a frequency locking device 200 is also provided in this embodiment. Figure 2 , the device includes the following modules:
[0046] The data processing module 210 is used to obtain the real-time temperature of the current transducer based on the welding response, and compare the real-time temperature with adjacent historical temperatures to determine a temperature difference;
[0047] A switching module 220 is used to switch the execution module;
[0048] A first execution module 230 is configured to input the real-time temperature, real-time resonant frequency, real-time voltage data, and real-time current data into a prediction model that has been trained to convergence to obtain a predicted resonant frequency when the temperature value is greater than a preset threshold, and determine the predicted resonant frequency as the starting scanning frequency and perform frequency locking;
[0049] The second execution module 240 is configured to obtain a historical welding resonant frequency as a starting scanning frequency when the temperature difference is less than a preset threshold value, and lock the frequency based on a phase difference between the historical voltage data, the historical current data and the real-time voltage data, the real-time current data;
[0050] The third execution module 250 is configured to obtain a historical welding resonant frequency as a starting scanning frequency and perform frequency locking based on the historical welding resonant frequency when the prediction model has not converged.
[0051] See Figure 3In other embodiments, the above method can also be integrated into the provided terminal device 30. In view of the fact that the device may have relatively large differences due to different configurations or performance, it can include one or more processors 301 and memory 302. The memory 302 can store one or more application programs or data. Among them, the memory 302 can be a temporary storage or a persistent storage. The application stored in the memory 302 can include one or more modules (not shown in the figure), and each module can include a series of computer-executable instructions in the terminal device. Furthermore, the processor 301 can be configured to communicate with the memory 302, and the terminal device executes the series of computer-executable instructions in the memory 302. The terminal device can also include one or more power supplies 303, one or more wired / wireless network interfaces 304, one or more input / output interfaces 305, one or more keyboards 306, etc.
[0052] In a specific embodiment, the terminal device includes a memory and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer-executable instructions in the terminal device, and the one or more programs are configured to be executed by one or more processors, including computer-executable instructions for performing the following:
[0053] Acquire the real-time temperature of the current transducer based on the welding response, and compare the real-time temperature with adjacent historical temperatures to determine a temperature difference;
[0054] When the temperature value is greater than a preset threshold, the real-time temperature, real-time resonant frequency, real-time voltage data, and real-time current data are input into a prediction model that has been trained to converge to obtain a predicted resonant frequency, and the predicted resonant frequency is determined as the starting scanning frequency and frequency locking is performed;
[0055] When the temperature difference is less than a preset threshold, the historical welding resonant frequency is obtained as the starting scanning frequency, and the frequency is locked based on the phase difference between the historical voltage data, historical current data and the real-time voltage data, real-time current data;
[0056] When the temperature value is greater than a preset threshold, it is determined whether the prediction model has converged. When the prediction model has not converged, a historical welding resonant frequency is obtained as a starting scanning frequency, and frequency locking is performed based on the historical welding resonant frequency.
[0057] The following is a detailed introduction to the various components of the processor:
[0058] In this embodiment, the processor is an application specific integrated circuit (ASIC), or is configured to implement one or more integrated circuits of the embodiments of the present application, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (FPGAs).
[0059] Optionally, the processor can execute various functions by running or executing the software program stored in the memory and calling the data stored in the memory, such as executing the above Figure 1 The method shown.
[0060] In a specific implementation, as an embodiment, the processor may include one or more microprocessors.
[0061] The memory is used to store the software program for executing the solution of the present application, and the execution is controlled by the processor. The specific implementation method can refer to the above method embodiment and will not be repeated here.
[0062] Alternatively, the memory may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory may be integrated with the processor or exist independently and be coupled to the processing unit through the interface circuit of the processor, and the embodiments of the present application do not specifically limit this.
[0063] It should be noted that the structure of the processor shown in this embodiment does not constitute a limitation on the device. The actual device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.
[0064] In addition, the technical effects of the processor can refer to the technical effects of the method described in the above method embodiment, and will not be repeated here.
[0065] It should be understood that the processor in the embodiments of the present application may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.
[0066] It should also be understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).
[0067] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (such as infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.
[0068] In this application, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.
[0069] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.
[0070] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0071] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.
[0072] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.
[0073] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.
[0074] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
[0075] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.
[0076] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.
Claims
1. An ultrasonic power frequency locking method, characterized in that: Applied to a transducer system, the method comprises: Acquire the real-time temperature of the current transducer based on the welding response, and compare the real-time temperature with adjacent historical temperatures to determine a temperature difference; When the temperature value is greater than a preset threshold, the real-time temperature, real-time resonant frequency, real-time voltage data and real-time current data are input into the prediction model that has been trained to converge to obtain the predicted resonant frequency, and the predicted resonant frequency is determined as the starting scanning frequency and locked.
2. The ultrasonic power frequency locking method according to claim 1, characterized in that: The method further includes: when the temperature difference is less than a preset threshold, obtaining a historical welding resonant frequency as a starting scanning frequency, and locking the frequency based on a phase difference between historical voltage data, historical current data and real-time voltage data, real-time current data.
3. The ultrasonic power frequency locking method according to claim 1, characterized in that: The method further includes: when the temperature value is greater than a preset threshold, determining whether the prediction model has converged; when the prediction model has not converged, obtaining a historical welding resonant frequency as a starting scanning frequency, and performing frequency locking based on the historical welding resonant frequency.
4. The ultrasonic power frequency locking method according to claim 1 or 3, characterized in that: The prediction model is constructed based on the recursive least squares method and is expressed based on the following formula: in, To predict the resonant frequency, w0 is the real-time resonant frequency, w1 is the temperature coefficient, w2 is the temperature change rate coefficient, w3 is the power coefficient, T(t) is the real-time temperature, is the temperature difference.
5. The ultrasonic power frequency locking method according to claim 4, characterized in that: When the covariance value of the prediction model is less than 1, the prediction model is in a convergence state.
6. An ultrasonic power frequency locking device, characterized in that: The device is applied to a transducer system, and comprises: a data processing module, configured to obtain a real-time temperature of the current transducer based on the welding response, and compare the real-time temperature with adjacent historical temperatures to determine a temperature difference; The first execution module is used to input the real-time temperature, real-time resonant frequency, real-time voltage data and real-time current data into a prediction model that has been trained to converge to obtain a predicted resonant frequency when the temperature value is greater than a preset threshold value, and determine the predicted resonant frequency as the starting scanning frequency and perform frequency locking.
7. The ultrasonic power frequency locking device according to claim 6, characterized in that: The device also includes a switching module, which is used to switch the current first execution module to a second execution module, and the second execution module is used to execute a frequency locking task when the temperature difference is less than a preset threshold value. Specifically, when the temperature difference is less than the preset threshold value, a historical welding resonant frequency is obtained as a starting scanning frequency, and frequency locking is performed based on a phase difference between historical voltage data, historical current data and real-time voltage data, real-time current data.
8. The ultrasonic power frequency locking device according to claim 7, characterized in that: The switching module is further configured to switch the current first execution module to a third execution module, and the third control module is configured to execute a frequency locking task when the prediction model has not converged, specifically including: when the prediction model has not converged, obtaining a historical welding resonant frequency as a starting scanning frequency, and performing frequency locking based on the historical welding resonant frequency.
9. An electronic device comprising: A memory and a processor coupled to the memory, wherein the processor is configured to execute the ultrasonic power frequency locking method according to any one of claims 1 to 5 based on instructions stored in the memory.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the ultrasonic power supply frequency locking method according to any one of claims 1 to 5 are implemented.