Stirring head power consumption detection method, server, storage medium and program product

Abstract

The invention discloses a stirring head power consumption detection method, a server, a storage medium and a program product, and relates to the field of welding. According to the method, the server collects the sound field vibration signals in the welding process through the sound field sensor and conducts wavelet transformation, main frequency band energy and phase information are obtained, then the sound field distortion factor and the sound field rotation angular speed are calculated, and the sound field distortion factor and the sound field rotation angular speed are input into the power consumption calculation model to obtain effective power consumption of the stirring head. Compared with traditional power consumption monitoring which only depends on current, voltage and other electrical parameters, the method can accurately reflect the complex dynamic state of mechanical contact, and the working state of the stirring head is monitored in a multi-dimensional mode. The uniformity of sound field energy distribution is quantified (abnormal contact of the stirring head is found in time, workpiece damage or excessive loss of the stirring head caused by energy concentration is avoided, and the welding quality stability is improved), the sound field energy transmission efficiency is tracked, and the accuracy and real-time performance of power consumption calculation are improved.

Description

Technical Field

[0001] This application relates to the field of welding, and particularly to a method for detecting the power consumption of a stirring head, a server, a storage medium, and a program product. Background Art

[0002] With the wide application of friction stir welding technology in high-end manufacturing fields such as aerospace and rail transit, people have put forward higher and higher requirements for the power consumption control of the stirring head during the welding process. The power consumption of the stirring head during the welding process directly affects the weld quality and the service life of the equipment. Therefore, it is of great significance to achieve accurate detection of the power consumption of the stirring head.

[0003] The related technology mainly collects the current value and rotation speed value of the stirring head motor during operation through a current sensor and an angular velocity sensor, and then inputs the collected current value and rotation speed value into a set power calculation formula to obtain the power consumption value of the stirring head, and adjusts the welding process parameters accordingly.

[0004] However, due to the complex and variable state of the workpiece material during the welding process, it is difficult to accurately reflect the actual energy transfer situation between the stirring head and the workpiece only relying on the parameters of the stirring head motor. Under high-speed welding conditions, the softening degree and flow state of the workpiece material will change rapidly, resulting in a large deviation between the calculated power consumption value and the actual power consumption. Summary of the Invention

[0005] This application provides a method for detecting the power consumption of a stirring head, a server, a storage medium, and a program product, which is used to improve the accuracy of detecting the power consumption of the stirring head.

[0006] In a first aspect, this application provides a method for detecting the power consumption of a stirring head, which is applied to a server. The method includes: collecting the sound field vibration signal during the welding process through the sound field sensors evenly arranged in the circumferential direction of the stirring head, and the sound field vibration signal is used to represent the sound field energy distribution of the contact surface between the stirring head and the workpiece; performing wavelet transform on the sound field vibration signal to obtain the main frequency band energy and phase information corresponding to the sound field vibration signal, where the main frequency band energy is used to represent the intensity of the sound field vibration signal, and the phase information is used to represent the spatial position of the sound field vibration signal in the circumferential direction of the stirring head; calculating the sound field distortion factor based on the main frequency band energy, and the sound field distortion factor is used to represent the uniformity of the sound field energy distribution; calculating the phase difference between adjacent sound field sensors according to the phase information and establishing a phase difference matrix, and calculating the sound field rotation angular velocity based on the phase difference matrix, where the phase difference is used to represent the offset amount of the sound field vibration position, and the sound field rotation angular velocity is used to characterize the energy transfer rate of the stirring head during the stirring process; inputting the sound field distortion factor and the sound field rotation angular velocity into the power consumption calculation model to obtain the effective power consumption of the stirring head.

[0007] By adopting the above technical solution, the server collects the acoustic field vibration signals during the welding process through an acoustic field sensor and performs wavelet transform to obtain the main frequency band energy and phase information, and then calculates the acoustic field distortion factor and the acoustic field rotation angular velocity, and inputs them into the power consumption calculation model to obtain the effective power consumption of the stirring head. Compared with the traditional power consumption monitoring that only relies on electrical parameters such as current and voltage, this method can accurately reflect the complex dynamics of mechanical contact, monitor the working state of the stirring head in multiple dimensions, quantify the uniformity of the acoustic field energy distribution (promptly detect abnormal contact of the stirring head, avoid workpiece damage or excessive wear of the stirring head caused by energy concentration, and improve the stability of welding quality), track the acoustic field energy transfer efficiency, and improve the accuracy and real-time performance of power consumption calculation.

[0008] Combined with some embodiments of the first aspect, in some embodiments, wavelet transform is performed on the acoustic field vibration signal to obtain the main frequency band energy and phase information corresponding to the acoustic field vibration signal, which specifically includes: performing wavelet decomposition on the acoustic field vibration signal to obtain the wavelet coefficients corresponding to each frequency band, reconstructing the wavelet coefficients to obtain the reconstructed signal of each frequency band; based on the energy distribution characteristics of the reconstructed signal, determining the main frequency band range, calculating the energy value within the main frequency band range to obtain the main frequency band energy; performing Hilbert transform on the reconstructed signal within the main frequency band range to obtain an analytical signal; calculating the phase angle based on the real part and the imaginary part of the analytical signal to obtain the phase information.

[0009] By adopting the above technical solution, the server performs wavelet decomposition and reconstruction on the acoustic field vibration signal, which can effectively separate the energy distribution characteristics of different frequency bands, facilitate accurately locating the main frequency band range that has the greatest impact on power consumption, and avoid the interference of other frequency bands. The server demodulates the reconstructed signal within the main frequency band range through Hilbert transform, converts the time-domain signal into an analytical signal containing phase information, and accurately calculates the phase angle using the real part and the imaginary part of the analytical signal, which can capture the dynamic spatial position deviation of the acoustic field vibration signal in the circumferential direction of the stirring head in real time, providing high-precision energy intensity and spatial distribution data support for the calculation of the acoustic field distortion factor and the acoustic field rotation angular velocity.

[0010] Combined with some embodiments of the first aspect, in some embodiments, based on the main frequency band energy, the acoustic field distortion factor is calculated. The acoustic field distortion factor is used to represent the uniformity of the acoustic field energy distribution, which specifically includes: determining the maximum main frequency band energy and the minimum main frequency band energy in the main frequency band energy; dividing the difference between the maximum main frequency band energy and the minimum main frequency band energy by the maximum main frequency band energy to obtain the acoustic field distortion factor.

[0011] By adopting the above technical solution, when the sound field energy distribution on the contact surface between the stirring head and the workpiece is more uniform, the difference between the maximum main frequency band energy and the minimum main frequency band energy is smaller, and the calculated sound field distortion factor is also smaller. This calculation method intuitively reflects the uniformity of energy transfer during the stirring process, can timely detect abnormal conditions such as the eccentricity and wear of the stirring head, and provides an important reference index for welding quality control.

[0012] Combined with some embodiments of the first aspect, in some embodiments, the power consumption calculation model is: P = k×ω×(1 - θ); where k is used to represent the characteristic coefficient corresponding to the geometric size of the stirring head, ω is used to represent the angular velocity of sound field rotation, and θ represents the sound field distortion factor.

[0013] By adopting the above technical solution, the power consumption calculation model takes into account multiple influencing factors such as the physical characteristics of the stirring head, the sound field energy transfer rate, and the uniformity of the sound field energy distribution, making the calculation result closer to the actual working conditions. When the sound field distortion factor is smaller, it indicates that the sound field energy distribution is more uniform, and the effective power consumption of the stirring head is greater; conversely, the non-uniform sound field energy distribution will reduce the effective power consumption of the stirring head. This power consumption calculation model can accurately reflect the actual energy transfer situation between the stirring head and the workpiece, providing a scientific basis for process parameter optimization.

[0014] Combined with some embodiments of the first aspect, in some embodiments, after the step of inputting the sound field distortion factor and the angular velocity of sound field rotation into the power consumption calculation model to obtain the effective power consumption of the stirring head, the method further includes: determining a power consumption range according to the effective power consumption of the stirring head, and the power consumption range includes a normal power consumption range, an overload power consumption range, and a critical power consumption range; when the power consumption range is the critical power consumption range, determining the eccentricity degree of the stirring head according to the sound field distortion factor; when the eccentricity degree of the stirring head exceeds a preset first range, triggering automatic correction of the stirring head trajectory.

[0015] By adopting the above technical solution, when the effective power consumption of the stirring head is in the critical power consumption range, the server will judge the eccentricity degree of the stirring head according to the sound field distortion factor, and automatically trigger the correction of the stirring head trajectory when the eccentricity degree of the stirring head exceeds the preset first range. This automatic adjustment mechanism can effectively prevent the stirring head from entering the overload power consumption range, avoiding equipment damage and welding quality problems. At the same time, the server monitors the change of the sound field distortion factor in real time, can timely detect the abnormality of the stirring head movement trajectory, and ensure the stability of the welding process.

[0016] In some embodiments in combination with some embodiments of the first aspect, after the step of inputting the sound field distortion factor and the angular velocity of sound field rotation into the power consumption calculation model to obtain the effective power consumption of the stirring head, the method further includes: plotting a curve of the change trend of the effective power consumption of the stirring head, and determining the slope of the change trend curve as the power consumption change rate; when the effective power consumption of the stirring head exceeds the upper limit value of the normal power consumption range and the power consumption change rate is greater than a preset first threshold, predicting the remaining time until the effective power consumption of the stirring head reaches the lower limit value of the overload power consumption range; within the remaining time, reducing the rotation speed of the stirring head in accordance with a preset step size until the power consumption change rate is less than a preset second threshold, and the preset second threshold is less than the preset first threshold.

[0017] By adopting the above technical solution, the server plots and analyzes the curve of the change trend of the effective power consumption of the stirring head in real time to establish a control strategy for actively preventing overload. When the effective power consumption of the stirring head exceeds the upper limit value of the normal power consumption range and the power consumption change rate is greater than the preset first threshold, the server will predict the remaining time to reach the overload power consumption range, and during this period, control the power consumption growth by gradually reducing the rotation speed of the stirring head. This progressive speed regulation method based on trend prediction avoids the process impact that may be brought about by sudden speed reduction, ensures the stability of the welding quality, and effectively extends the service life of the equipment.

[0018] In some embodiments in combination with some embodiments of the first aspect, after the step of collecting the sound field vibration signal during the welding process by the sound field sensors uniformly arranged in the circumferential direction of the stirring head, the method further includes: establishing a heat map of the sound field energy distribution according to the sound field vibration signal, and the heat map of the sound field energy distribution is used to represent the energy distribution density of the contact surface between the stirring head and the workpiece; based on the heat map of the sound field energy distribution, calculating the energy density gradient in the circumferential direction of the stirring head; when the energy density gradient is greater than a preset third threshold, calculating the worn part and the degree of wear of the stirring head based on the direction and magnitude of the energy density gradient; when the degree of wear exceeds a preset second range, calculating the compensation inclination angle corresponding to the worn part, and adjusting the posture of the stirring head according to the compensation inclination angle.

[0019] By adopting the above technical solution, the server establishes a heat map of the sound field energy distribution, realizing the visual representation of the energy distribution of the contact surface between the stirring head and the workpiece. The server calculates the energy density gradient in the circumferential direction of the stirring head, which can accurately locate and evaluate the wear condition of the stirring head. For example, when the energy density gradient is abnormal, the server can calculate the specific worn part and the degree of wear according to its direction and magnitude, and automatically calculate the compensation inclination angle for posture adjustment. This wear monitoring and compensation mechanism based on the energy distribution characteristics can effectively extend the service life of the stirring head, ensure the stability of the welding quality, and at the same time reduce the equipment maintenance cost.

[0020] In a second aspect, an embodiment of the present application provides a server, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the server to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product containing instructions. When the above computer program product runs on a server, it causes the above server to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, including instructions. When the above instructions run on a server, it causes the above server to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0023] It can be understood that the server provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiments of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which will not be elaborated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By adopting the above technical solution, the server collects the sound field vibration signal during the welding process through a sound field sensor and performs wavelet transform to obtain the main frequency band energy and phase information. Then, the sound field distortion factor and the sound field rotation angular velocity are calculated, and they are input into the power consumption calculation model to obtain the effective power consumption of the stirring head. Compared with the traditional power consumption monitoring that only relies on electrical parameters such as current and voltage, this method can accurately reflect the complex dynamics of mechanical contact, monitor the working state of the stirring head in multiple dimensions, quantify the uniformity of the sound field energy distribution (promptly detect abnormal contact of the stirring head, avoid workpiece damage or excessive wear of the stirring head caused by energy concentration, and improve the stability of welding quality), track the sound field energy transfer efficiency, and improve the accuracy and real-time performance of power consumption calculation.

[0025] 2. By adopting the above technical solution, the server performs wavelet decomposition and reconstruction on the sound field vibration signal, which can effectively separate the energy distribution characteristics of different frequency bands, facilitating the accurate positioning of the main frequency band range that has the greatest impact on power consumption and avoiding interference from other frequency bands. The server demodulates the reconstructed signal within the main frequency band range through Hilbert transform, converts the time-domain signal into an analytic signal containing phase information, and precisely calculates the phase angle using the real and imaginary parts of the analytic signal, enabling the real-time capture of the dynamic spatial position deviation of the sound field vibration signal in the circumferential direction of the stirring head, providing high-precision energy intensity and spatial distribution data support for the calculation of the sound field distortion factor and the angular velocity of the sound field rotation.

[0026] 3. By adopting the above technical solution, the power consumption calculation model takes into account multiple influencing factors such as the physical characteristics of the stirring head, the sound field energy transfer rate, and the uniformity of the sound field energy distribution, making the calculation result closer to the actual working conditions. When the sound field distortion factor is smaller, it indicates that the sound field energy distribution is more uniform, and the effective power consumption of the stirring head is greater; conversely, uneven sound field energy distribution will reduce the effective power consumption of the stirring head. This power consumption calculation model can accurately reflect the actual energy transfer situation between the stirring head and the workpiece, providing a scientific basis for the optimization of process parameters. Description of the Drawings

[0027] Figure 1 is a schematic flowchart of a method for detecting the power consumption of a stirring head in an embodiment of the present application; Figure 2 is another schematic flowchart of a method for detecting the power consumption of a stirring head in an embodiment of the present application; Figure 3 is a schematic structural diagram of a physical device of a server in an embodiment of the present application. Detailed Embodiments

[0028] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification of the present application, the singular forms "a", "an", "the above", "the", and "this" are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations including one or more of the listed items.

[0029] Hereinafter, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0030] The following describes the process of the method provided in this embodiment. Please refer to Figure 1 , which is a schematic flowchart of a method for detecting the power consumption of a stirring head in an embodiment of the present application.

[0031] S101. Collect the acoustic field vibration signals during the welding process through the acoustic field sensors uniformly arranged in the circumferential direction of the stirring head. The acoustic field vibration signals are used to represent the acoustic field energy distribution on the contact surface between the stirring head and the workpiece. Among them, the stirring head refers to the main tool for friction stir welding, which has a cylindrical structure and specific threads or patterns at the bottom; the acoustic field sensor refers to a sensing device that can convert acoustic wave signals into electrical signals, usually using piezoelectric crystals as sensitive elements; the acoustic field vibration signal refers to the acoustic wave signal formed by the mechanical vibration generated on the contact surface between the stirring head and the workpiece propagating in the medium, including characteristics such as amplitude, frequency, and phase; the workpiece contact surface refers to the surface area where the stirring head actually rubs against the workpiece to be welded; the acoustic field energy distribution is used to represent the change in the energy density of the acoustic field vibration signal in space and can be represented by an energy density contour map.

[0032] Specifically, the server confirms that multiple acoustic field sensors at preset positions in the circumferential direction of the stirring head are in a normal working state, and then synchronously collects the acoustic field vibration signals of each acoustic field sensor at a preset sampling frequency (such as 1 kHz). During the collection process, the server preprocesses the acoustic field vibration signals, including filtering out power frequency interference and eliminating random noise, etc. The server arranges the processed acoustic field vibration signals according to the spatial positions of the acoustic field sensors.

[0033] The following lists a specific example of friction stir welding. Assume that 8 acoustic field sensors (numbered T1 - T8, with an angular interval of 45° between adjacent acoustic field sensors) are uniformly arranged in the circumferential direction of a stirring head with a diameter of 20 mm. When the stirring head performs welding at a rotational speed of 2000 rpm, the server performs the following operations: (1) Sensor status confirmation: T1 status: Normal, position: 0°; T2 status: Normal, position: 45°; T3 status: Normal, position: 90°; T4 status: Normal, position: 135°; T5 status: Normal, position: 180°; T6 status: Normal, position: 225°; T7 status: Normal, position: 270°; T8status: Normal, position: 315°; (2)Signal acquisition (using a sampling frequency of 1 kHz to acquire a 1 ms data segment): Original data of T1: [0.82, 0.85, 0.79, 0.83, ……] mV; Original data of T2: [0.75, 0.78, 0.73, 0.76, ……] mV; Original data of T3: [0.79, 0.81, 0.77, 0.80, ……] mV; Original data of T4: [0.77, 0.79, 0.75, 0.78, ……] mV; Original data of T5: [0.81, 0.84, 0.78, 0.82, ……] mV; Original data of T6: [0.76, 0.78, 0.74, 0.77, ……] mV; Original data of T7: [0.80, 0.83, 0.78, 0.81, ……] mV; Original data of T8: [0.78, 0.80, 0.76, 0.79, ……] mV; (3)Signal preprocessing (power frequency interference filtering (50Hz notch filtering) and random noise elimination (wavelet threshold denoising)): Processed data of T1: [0.80, 0.80, 0.80, 0.81, ……] mV; Processed data of T2: [0.75, 0.75, 0.75, 0.76, ……] mV; Processed data of T3: [0.78, 0.78, 0.79, 0.79, ……] mV; Processed data of T4: [0.77, 0.77, 0.77, 0.78, ……] mV; Processed data of T5: [0.80, 0.80, 0.81, 0.81, ……] mV; Processed data of T6: [0.76, 0.76, 0.76, 0.77, ……] mV; Processed data of T7: [0.79, 0.79, 0.80, 0.80, ……] mV; Processed data of T8: [0.77, 0.77, 0.78, 0.78, ……] mV; (4)Data arrangement (data matrix organized according to the spatial positions of the acoustic field sensors): Table 1 Data acquisition table of acoustic field sensors

[0034] S102. Perform wavelet transform on the acoustic field vibration signal to obtain the main frequency band energy and phase information corresponding to the acoustic field vibration signal. The main frequency band energy is used to represent the intensity of the acoustic field vibration signal, and the phase information is used to represent the spatial position of the acoustic field vibration signal in the circumferential direction of the stirring head. Among them, wavelet transform refers to a multi-resolution signal analysis method that can perform local analysis on the signal simultaneously in the time domain and frequency domain. By translating and scaling the basic wavelet function (mother wavelet), the time-frequency decomposition of the signal can be realized. Compared with the traditional Fourier transform, wavelet transform has better time-frequency localization characteristics and can effectively extract the non-stationary characteristics of the signal. For example, the commonly used discrete wavelet transform can decompose the signal into high-frequency detail components and low-frequency approximation components step by step.

[0035] The main frequency band energy refers to the total energy within the frequency range where the signal energy is mainly concentrated after wavelet transform. For the friction stir welding process, this main frequency band energy usually corresponds to the frequency range where the stirring head rotation speed and its multiples are located, reflecting the main energy components in the welding process. The main frequency band range can be determined by calculating the energy ratio of different frequency bands.

[0036] The phase information refers to the relative change characteristics of the acoustic field vibration signal in the time domain and spatial domain. In the circumferential direction of the stirring head, the phase information reflects the propagation delay and relative relationship of the acoustic field vibration at different positions. By analyzing the phase difference of the signals detected by adjacent acoustic field sensors, the propagation direction and propagation speed of the acoustic field energy can be inferred.

[0037] The intensity of the acoustic field vibration signal is used to quantify the amplitude change degree of the acoustic field vibration and directly reflects the magnitude of the force between the stirring head and the workpiece.

[0038] The spatial position refers to the angular coordinate in the circumferential direction of the stirring head and is used to locate the specific position of the acoustic field vibration signal. By establishing the corresponding relationship between the arrangement position of the acoustic field sensors and the signal phase, the distribution change of the acoustic field energy in the circumferential direction can be traced.

[0039] Optionally, generally, performing wavelet transform on the acoustic field vibration signal to obtain the main frequency band energy and phase information corresponding to the acoustic field vibration signal can be achieved in the following ways (not limited here): perform wavelet decomposition on the acoustic field vibration signal to obtain the wavelet coefficients corresponding to each frequency band, reconstruct the wavelet coefficients to obtain the reconstructed signals of each frequency band; based on the energy distribution characteristics of the reconstructed signals, determine the main frequency band range, calculate the energy value within the main frequency band range to obtain the main frequency band energy; perform Hilbert transform on the reconstructed signals within the main frequency band range to obtain the analytic signal; calculate the phase angle based on the real part and imaginary part of the analytic signal to obtain the phase information.

[0040] Specifically, the server selects a suitable wavelet basis function (such as the Daubechies wavelet) to perform wavelet transform processing on the sound field vibration signal: perform multi-layer wavelet decomposition on the sound field vibration signal to obtain high-frequency detail components and low-frequency approximation components in different frequency bands. After decomposition, the server analyzes the energy distribution ratio of each frequency band, and determines the main frequency band range containing the fundamental frequency and its harmonics in combination with the rotation speed of the stirring head. Reconstruct the decomposition result to obtain the signal within the main frequency band range, and calculate the energy value within the main frequency band range.

[0041] Next, the server performs Hilbert transform on the reconstructed main frequency band signal to extract the phase information of the main frequency band signal. The server corresponds the obtained phase information to the arrangement positions of each sound field sensor in the circumferential direction of the stirring head to establish a phase-position mapping relationship. By analyzing the phase difference and phase change law between adjacent sound field sensors, the server determines the propagation characteristics of the sound field vibration signal in the circumferential direction of the stirring head, thus realizing the energy intensity characterization and spatial position positioning of the sound field vibration signal.

[0042] Continuing from step S101, the server selects the db4 (Daubechies-4) wavelet basis function and performs 3-layer wavelet decomposition on the data after signal preprocessing in step S101: The first layer of decomposition obtains the detail coefficient d1 in the 250 - 500 Hz frequency band; the second layer of decomposition obtains the detail coefficient d2 in the 125 - 250 Hz frequency band; the third layer of decomposition obtains the detail coefficient d3 in the 62.5 - 125 Hz frequency band and the approximation coefficient a3 in the 0 - 62.5 Hz frequency band; Taking the data [0.80, 0.80, 0.80, 0.81, ……] mV after T1 processing as an example, the energy distribution ratio of each frequency band obtained after decomposition is: Table 2 Energy Distribution Ratio Table

[0043] Since the rotation speed of the stirring head is 2000 rpm (fundamental frequency 33.33 Hz), it is determined that the main frequency band range is 30 - 100 Hz, mainly included in the d3 and a3 coefficients. Calculate the energy value of each sound field sensor within this main frequency band range: Table 3 Main Frequency Band Energy Table

[0044] The server performs Hilbert transform on the main frequency band signal to obtain the phase information: Table 4 Phase Information Table

[0045] S103. Calculate the sound field distortion factor based on the main frequency band energy. The sound field distortion factor is used to represent the uniformity of the sound field energy distribution; Among them, the main frequency band energy refers to the energy value within the main frequency band obtained through wavelet transform; the sound field distortion factor is a dimensionless parameter representing the degree of non-uniformity of the sound field energy distribution; the uniformity of the sound field energy distribution is used to represent the degree of consistency of the sound field energy distribution in space; the maximum main frequency band energy refers to the maximum value among the main frequency band energies measured by all sound field sensors; the minimum main frequency band energy refers to the minimum value among the main frequency band energies measured by all sound field sensors.

[0046] Specifically, the server finds the maximum and minimum values among the main frequency band energies collected by all sound field sensors, then calculates their difference and divides it by the maximum value to obtain the sound field distortion factor. The closer the value of the sound field distortion factor is to 0, the more uniform the sound field energy distribution is, and the closer it is to 1, the more non-uniform the sound field energy distribution is. The server compares the calculated sound field distortion factor with a preset reference range (such as 0.1 - 0.3) to determine whether the energy transfer during the current welding process is uniform, providing a basis for subsequent power consumption calculation and process parameter adjustment.

[0047] Optionally, generally, based on the main frequency band energy, calculating the sound field distortion factor, which is used to represent the uniformity of the sound field energy distribution, can be achieved in the following way, which is not limited here: determine the maximum main frequency band energy and the minimum main frequency band energy in the main frequency band energy; divide the difference between the maximum main frequency band energy and the minimum main frequency band energy by the maximum main frequency band energy to obtain the sound field distortion factor.

[0048] Following step S102, as shown in Table 3, it can be determined that: Maximum main frequency band energy: at positions T1 and T5, 0.60 mV²; Minimum main frequency band energy: at position T2, 0.53 mV²; Calculate the sound field distortion factor θ: θ = (maximum main frequency band energy - minimum main frequency band energy) / maximum main frequency band energy = (0.60 - 0.53) / 0.60 = 0.117.

[0049] S104. Calculate the phase difference between adjacent sound field sensors according to the phase information, establish a phase difference matrix, and calculate the sound field rotation angular velocity based on the phase difference matrix. The phase difference is used to represent the offset amount of the sound field vibration position, and the sound field rotation angular velocity is used to characterize the energy transfer rate of the stirring head during the stirring process; Among them, the phase difference refers to the phase angle difference between the acoustic field vibration signals detected by two adjacent acoustic field sensors; the phase difference matrix refers to the two-dimensional array representation of the phase differences between all pairs of adjacent acoustic field sensors; the acoustic field vibration position offset is used to represent the relative displacement of the acoustic field vibration in space; the acoustic field rotation angular velocity refers to the rotation rate of the acoustic field energy distribution in the circumferential direction, with the unit of radian per second; the energy transfer rate refers to the amount of energy transferred from the stirring head to the workpiece per unit time; adjacent acoustic field sensors refer to two acoustic field sensors arranged adjacent to each other in the circumferential direction of the stirring head.

[0050] Specifically, the server calculates the phase difference between each pair of adjacent acoustic field sensors according to the spatial numbering order of the acoustic field sensors. For n acoustic field sensors, an n×n phase difference matrix is formed. The element in the i-th row and j-th column of the phase difference matrix represents the phase difference between the i-th acoustic field sensor and the j-th acoustic field sensor. Then, the server calculates the rotation direction and angular velocity of the acoustic field by analyzing the variation law of the elements in the phase difference matrix. When calculating, the spatial distribution angle and sampling time interval of the acoustic field sensors are considered, and the phase difference is converted into an angular velocity value. If a sudden change or discontinuity in the phase difference between adjacent acoustic field sensors is detected, the server will perform an anomaly marking and trigger a verification process.

[0051] S105: Input the acoustic field distortion factor and the acoustic field rotation angular velocity into the power consumption calculation model to obtain the effective power consumption of the stirring head.

[0052] Among them, the power consumption calculation model refers to the mathematical expression used to calculate the effective power consumption of the stirring head. The power consumption calculation model is P = k×ω×(1 - θ), where k is used to represent the characteristic coefficient corresponding to the geometric size of the stirring head, ω is used to represent the acoustic field rotation angular velocity, and θ represents the acoustic field distortion factor. The effective power consumption of the stirring head refers to the energy consumption actually used by the stirring head for material plasticization and stirring.

[0053] Specifically, the server looks up a table or calculates the characteristic coefficient according to the geometric size of the stirring head (such as the shoulder diameter, the needle length, etc.). Then, the server substitutes the acoustic field distortion factor, the acoustic field rotation angular velocity, and the characteristic coefficient into the power consumption calculation model to calculate the effective power consumption of the stirring head at the current moment.

[0054] By adopting the above technical solution, the server collects the acoustic field vibration signals during the welding process through the acoustic field sensor and performs wavelet transform to obtain the main frequency band energy and phase information. Then, the acoustic field distortion factor and the acoustic field rotation angular velocity are calculated and input into the power consumption calculation model to obtain the effective power consumption of the stirring head. Compared with the traditional power consumption monitoring that only relies on electrical parameters such as current and voltage, this method can accurately reflect the complex dynamics of mechanical contact, monitor the working state of the stirring head in multiple dimensions, quantify the uniformity of the acoustic field energy distribution (promptly detect abnormal contact of the stirring head, avoid workpiece damage or excessive wear of the stirring head caused by energy concentration, and improve the stability of welding quality), track the acoustic field energy transfer efficiency, and improve the accuracy and real-time performance of power consumption calculation.

[0055] The following is a further and more specific process description of the method provided in this embodiment. Please refer to Figure 2 , which is another process schematic diagram of the stirring head power consumption detection method in the embodiment of the present application.

[0056] After step S101, the following steps may also be executed, or may not be executed, and this is not limited here: S201. According to the acoustic field vibration signal, establish a thermal map of the acoustic field energy distribution, which is used to represent the energy distribution density of the contact surface between the stirring head and the workpiece.

[0057] Among them, the acoustic field vibration signal refers to the acoustic wave signal formed by the mechanical vibration generated on the contact surface between the stirring head and the workpiece propagating in the medium; the thermal map of the acoustic field energy distribution refers to a two-dimensional graph that visually displays the acoustic field energy density data through a color gradient, usually using different colors to represent different energy intensities; the energy distribution density refers to the magnitude of the acoustic field energy per unit area, reflecting the spatial distribution characteristics of energy transfer during the stirring process; the color gradient refers to a visualization method that uses different shades of color to represent the change in energy magnitude.

[0058] Specifically, the server generates continuous acoustic field energy distribution data for the entire 360° circumference through the interpolation algorithm for the acoustic field energy data of discrete measurement points in the circumferential direction of the stirring head (such as the positions of 8 acoustic field sensors). Then, the server establishes a two-dimensional grid in the rectangular coordinate system, where the abscissa represents the angular position (0 - 360°) in the circumferential direction of the stirring head, and the ordinate represents the radial position (0 - R, where R is the radius of the stirring head). The server maps the continuous acoustic field energy distribution data onto the two-dimensional grid and uses different colors to represent the energy density magnitudes at different positions to obtain the thermal map of the acoustic field energy distribution.

[0059] S202. Based on the thermal map of the acoustic field energy distribution, calculate the energy density gradient in the circumferential direction of the stirring head.

[0060] Among them, the thermogram of the sound field energy distribution refers to a two-dimensional visualization graph representing the energy distribution on the contact surface between the stirring head and the workpiece; the energy density gradient refers to the rate of change of the energy density in space, and its magnitude and direction are represented by a vector.

[0061] Specifically, on the two-dimensional grid in the established rectangular coordinate system, the server calculates the difference in energy density between adjacent grid points. In the circumferential direction of the stirring head, the server calculates the rate of change of the energy density between adjacent angular points; in the radial direction, the server calculates the rate of change of the energy density between adjacent radius points. The rate of change of the energy density in these two directions is synthesized into an energy density gradient vector.

[0062] S203. When the energy density gradient is greater than a preset third threshold, based on the direction and magnitude of the energy density gradient, calculate the worn part and the degree of wear of the stirring head.

[0063] Among them, the energy density gradient is a vector describing the spatial variation characteristics of the energy density; the preset third threshold is the upper limit value of the energy density gradient safety determined according to process experience; the worn part refers to the specific position on the surface of the stirring head where wear occurs; the degree of wear refers to the degree to which the surface topography of the stirring head deviates from the designed profile; the gradient direction is the direction in which the energy density increases fastest; the gradient magnitude is the rate of change of the energy density in space.

[0064] Specifically, the server marks the areas where the energy density gradient is greater than the preset third threshold, and these areas usually correspond to abnormal energy concentration or dissipation. Then, based on the divergence and curl characteristics of the energy density gradient vector, the server analyzes the abnormal mode of energy transfer. The server matches the abnormal mode with the typical wear mode to determine the possible wear positions. For each potential wear position, the server calculates the wear depth according to the magnitude of the energy density gradient and estimates the wear rate considering the cumulative working time.

[0065] S204. When the degree of wear exceeds the preset second range, calculate the compensation inclination angle corresponding to the worn part, and adjust the attitude of the stirring head according to the compensation inclination angle.

[0066] Among them, the preset second range is the allowable range of the degree of wear, and attitude compensation is required when it is exceeded; the compensation inclination angle is the inclination angle of the stirring head that needs to be adjusted to compensate for the influence of wear; the attitude of the stirring head refers to the spatial position and angular relationship of the stirring head relative to the workpiece surface.

[0067] Specifically, the server establishes a mathematical model of the wear profile based on the circumferential distribution characteristics of the worn parts. Based on the wear profile mathematical model, the server calculates the compensation inclination angle that makes the pressure distribution on the contact surface between the stirring head and the workpiece the most uniform. The influence of the geometric characteristics of the stirring head (such as the thread angle) and process parameters (such as the rotation speed and feed rate) is considered during the calculation. After determining the compensation inclination angle, the server decomposes it into the motion commands of each axis of the machine tool and executes the attitude adjustment through the control system to achieve dynamic compensation for wear.

[0068] After step S105, the following steps may also be executed, or may not be executed, which is not limited herein: S205. Determine the power consumption range according to the effective power consumption of the stirring head. The power consumption range includes the normal power consumption range, the overload power consumption range, and the critical power consumption range.

[0069] Among them, the effective power consumption of the stirring head refers to the effective electric power actually consumed by the stirring head during the welding process; the power consumption range refers to the different power consumption level ranges divided according to process requirements; the normal power consumption range refers to the power consumption range of the stirring head under the ideal working state, usually 60%-80% of the rated power; the overload power consumption range refers to the power consumption range exceeding the safe working load, usually higher than 90% of the rated power; the critical power consumption range refers to the transition area between the normal power consumption range and the overload power consumption range, usually 80%-90% of the rated power; the rated power refers to the designed power value of the equipment under standard working conditions.

[0070] Specifically, the server obtains the rated power of the stirring head and then sets the boundary values of the three power consumption ranges. For example, for a stirring head with a rated power of 10 kW, the normal power consumption range is 6-8 kW, the critical power consumption range is 8-9 kW, and the overload power consumption range is above 9 kW. The server compares the effective power consumption of the stirring head monitored in real time with these boundary values to determine the current power consumption range.

[0071] S206. When the power consumption range is the critical power consumption range, determine the eccentricity of the stirring head according to the sound field distortion factor.

[0072] Among them, the eccentricity of the stirring head refers to the deviation degree of the actual rotation axis of the stirring head from the ideal rotation axis; the deviation degree refers to the relative value of the center distance, usually expressed as a percentage.

[0073] Specifically, the server obtains the current sound field distortion factor and establishes a mapping relationship between the sound field distortion factor and the eccentricity. For example, for every 0.1 increase in the sound field distortion factor, the corresponding eccentricity increases by 5%. The server calculates the actual eccentricity of the stirring head through this mapping relationship and compares it with the historical data to analyze the change trend of the eccentricity state. At the same time, the server considers the influence of the power consumption level on the calculation of the eccentricity and makes necessary corrections.

[0074] S207. When the eccentricity of the stirring head exceeds the preset first range, trigger the automatic correction of the stirring head trajectory.

[0075] Among them, the eccentricity of the stirring head refers to the offset of the rotation center of the stirring head relative to the ideal position; the preset first range refers to the allowable maximum eccentricity range, usually 1%-3% of the diameter of the stirring head; the automatic trajectory correction refers to the process of automatically adjusting the movement trajectory of the stirring head; the movement trajectory refers to the spatial path formed during the movement of the stirring head; the correction refers to the control process of adjusting the actual trajectory to the target trajectory.

[0076] Specifically, the server calculates the required compensation amount according to the currently detected eccentricity direction and eccentricity magnitude of the stirring head. The compensation amount is converted into a position adjustment instruction in the machine tool coordinate system and decomposed into incremental movements of each motion axis. The server controls the motors of each axis to execute the motion instructions synchronously to achieve the dynamic correction of the stirring head trajectory. During the correction process, continuously monitor the change of the sound field distortion factor until the eccentricity returns to the allowable range.

[0077] S208. Plot the change trend curve of the effective power consumption of the stirring head and determine the slope of the change trend curve as the power consumption change rate.

[0078] Among them, the change trend curve refers to the graph describing the change law of the effective power consumption of the stirring head over time; the slope refers to the tangent value of the inclination angle of the tangent line of the change trend curve at a certain point to the horizontal line; the power consumption change rate refers to the change amount of the power consumption per unit time, indicating the speed of the power consumption rising or falling.

[0079] Specifically, the server arranges the collected effective power consumption of the stirring head in chronological order and selects a suitable time window (such as 1 second) for data smoothing processing. The server uses methods such as polynomial fitting or spline interpolation to generate a continuous change trend curve of the effective power consumption of the stirring head. The server calculates the local slope of the change trend curve at each sampling point to obtain the power consumption change rate. At the same time, the server calculates the average change rate within a certain time window for trend judgment.

[0080] S209. When the effective power consumption of the stirring head exceeds the upper limit value of the normal power consumption range and the power consumption change rate is greater than the preset first threshold, predict the remaining time when the effective power consumption of the stirring head reaches the lower limit value of the overload power consumption range.

[0081] Among them, the upper limit value of the normal power consumption range refers to the maximum power consumption value of the normal power consumption range; the power consumption change rate refers to the change speed of the power consumption per unit time; the preset first threshold refers to the allowable maximum power consumption change rate; the lower limit value of the overload power consumption range refers to the minimum power consumption value of the overload power consumption range; the remaining time refers to the estimated time required for the effective power consumption of the stirring head to reach the lower limit value of the overload power consumption range at the current moment.

[0082] Specifically, the server establishes a mathematical model of power consumption change based on the effective power consumption of the stirring head, the power consumption change rate, and historical data. By means of this mathematical model of power consumption change, the server predicts the change trend of power consumption, and calculates the time required to increase from the current power consumption level to the lower limit value of the overload power consumption range, that is, the remaining duration. The prediction process takes into account the non-linear characteristics of power consumption change, and adopts an adaptive prediction algorithm to improve the accuracy.

[0083] S210. Within the remaining duration, reduce the rotation speed of the stirring head in accordance with a preset step size until the power consumption change rate is less than a preset second threshold value, and the preset second threshold value is less than the preset first threshold value.

[0084] Wherein, the preset step size refers to the fixed increment of each rotation speed adjustment, usually 50 - 100 rpm; the rotation speed of the stirring head refers to the number of revolutions per minute of the stirring head; the preset second threshold value refers to the target power consumption change rate for speed reduction control.

[0085] Specifically, the server evenly distributes the remaining duration into several speed reduction cycles, and reduces the rotation speed by one preset step size in each cycle. After each speed reduction, the server monitors the response of the power consumption change rate. If the power consumption change rate is still greater than the preset second threshold value, continue to perform the next speed reduction; if the power consumption change rate drops below the preset second threshold value, maintain the current rotation speed. The whole process is ensured to be completed within the remaining duration to avoid the power consumption from entering the overload power consumption range.

[0086] The server in the embodiment of the present invention application will be described from the perspective of hardware processing below. Please refer to Figure 3 which is a schematic structural diagram of an entity device of the server in the embodiment of the present application.

[0087] It should be noted that Figure 3 the structure of the server shown is only an example, and should not bring any limitations to the functions and usage scope of the embodiments of the present invention.

[0088] As Figure 3 shown, the server includes a CPU 301, which can execute various appropriate actions and processes according to the program stored in the read-only memory ROM 302 or the program loaded from the storage part 308 into the random access memory RAM 303, such as executing the method described in the above embodiments. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other through a bus 304. The I / O interface 305 is also connected to the bus 304.

[0089] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a button switch, etc.; an output section 307 including a liquid crystal display (LCD), an audio output device, an indicator light, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. The driver 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is mounted on the driver 310 as needed so that a computer program read therefrom can be installed into the storage section 308 as needed.

[0090] Specifically, according to an embodiment of the present invention, the processes described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product that includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for performing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication section 309, and / or installed from the removable medium 311. When the computer program is executed by the CPU 301, various functions defined in the present invention are executed.

[0091] It should be noted that specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0092] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. Among them, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the above-mentioned module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order from that marked in the accompanying drawings.

[0093] Specifically, the server in this embodiment includes a processor and a memory. A computer program is stored on the memory. When the computer program is executed by the processor, it implements the stirring head power consumption detection method provided in the above embodiment.

[0094] On the other hand, the present invention also provides a computer-readable storage medium. This storage medium may be included in the server described in the above embodiment; or it may exist separately and not be assembled into the server. The above storage medium carries one or more computer programs. When the above one or more computer programs are executed by a processor of the server, the server is enabled to implement the stirring head power consumption detection method provided in the above embodiment.

[0095] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present application.

[0096] As used in the above embodiments, depending on the context, the term "when..." can be interpreted to mean "if...", or "after...", or "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if detecting (the stated condition or event)" can be interpreted to mean "if determining...", or "in response to determining...", or "when detecting (the stated condition or event)", or "in response to detecting (the stated condition or event)".

[0097] Those of ordinary skill in the art can understand all or part of the processes in the methods of the above embodiments. These processes can be completed by relevant hardware instructed by a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. The foregoing storage medium includes: various media such as ROM or random access memory RAM, magnetic disk, or optical disk that can store program codes.

Claims

1. A method for detecting the power consumption of a stirring head, characterized in that, Applied to a server, the method includes: Collecting acoustic field vibration signals during the welding process through acoustic field sensors evenly arranged in the circumferential direction of the stirring head, where the acoustic field vibration signals are used to represent the acoustic field energy distribution at the contact surface between the stirring head and the workpiece; Performing wavelet transform on the acoustic field vibration signals to obtain the main frequency band energy and phase information corresponding to the acoustic field vibration signals. The main frequency band energy is used to represent the intensity of the acoustic field vibration signals, and the phase information is used to represent the spatial position of the acoustic field vibration signals in the circumferential direction of the stirring head; Calculating an acoustic field distortion factor based on the main frequency band energy, where the acoustic field distortion factor is used to represent the uniformity of the acoustic field energy distribution; Calculating the phase difference between adjacent acoustic field sensors according to the phase information and establishing a phase difference matrix. Based on the phase difference matrix, calculating the acoustic field rotation angular velocity. The phase difference is used to represent the offset of the acoustic field vibration position, and the acoustic field rotation angular velocity is used to characterize the energy transfer rate of the stirring head during the stirring process; Inputting the acoustic field distortion factor and the acoustic field rotation angular velocity into a power consumption calculation model to obtain the effective power consumption of the stirring head.

2. The method according to claim 1, characterized in that, The performing wavelet transform on the acoustic field vibration signals to obtain the main frequency band energy and phase information corresponding to the acoustic field vibration signals specifically includes: Performing wavelet decomposition on the acoustic field vibration signals to obtain wavelet coefficients corresponding to each frequency band, and reconstructing the wavelet coefficients to obtain the reconstructed signals of each frequency band; Determining the main frequency band range based on the energy distribution characteristics of the reconstructed signals, calculating the energy value within the main frequency band range, and obtaining the main frequency band energy; Performing Hilbert transform on the reconstructed signals within the main frequency band range to obtain an analytic signal; Calculating the phase angle based on the real part and the imaginary part of the analytic signal to obtain the phase information.

3. The method according to claim 1, characterized in that, The calculating the acoustic field distortion factor based on the main frequency band energy, where the acoustic field distortion factor is used to represent the uniformity of the acoustic field energy distribution specifically includes: Determining the maximum main frequency band energy and the minimum main frequency band energy in the main frequency band energy; Dividing the difference between the maximum main frequency band energy and the minimum main frequency band energy by the maximum main frequency band energy to obtain the acoustic field distortion factor.

4. The method according to claim 1, wherein The power consumption calculation model is: P = k×ω×(1 - θ); Where k is used to represent the characteristic coefficient corresponding to the geometric size of the stirring head, ω is used to represent the acoustic field rotation angular velocity, and θ represents the acoustic field distortion factor.

5. The method according to claim 1, characterized in that After the step of inputting the acoustic field distortion factor and the acoustic field rotation angular velocity into the power consumption calculation model to obtain the effective power consumption of the stirring head, the method further includes: Determining a power consumption interval according to the effective power consumption of the stirring head. The power consumption interval includes a normal power consumption interval, an overload power consumption interval, and a critical power consumption interval; When the power consumption interval is the critical power consumption interval, determining the eccentricity degree of the stirring head according to the acoustic field distortion factor; When the eccentricity degree of the stirring head exceeds a preset first range, triggering automatic correction of the stirring head trajectory.

6. The method according to claim 5, wherein After the step of inputting the acoustic field distortion factor and the acoustic field rotation angular velocity into the power consumption calculation model to obtain the effective power consumption of the stirring head, the method further includes: Plot the change trend curve of the effective power consumption of the stirring head, and determine the slope of the change trend curve as the power consumption change rate; When the effective power consumption of the stirring head exceeds the upper limit value of the normal power consumption range and the power consumption change rate is greater than a preset first threshold, predict the remaining duration until the effective power consumption of the stirring head reaches the lower limit value of the overload power consumption range; Within the remaining duration, reduce the rotation speed of the stirring head at a preset step length until the power consumption change rate is less than a preset second threshold, where the preset second threshold is less than the preset first threshold.

7. The method according to claim 1, characterized in that, After the step of collecting the acoustic field vibration signal during welding by the acoustic field sensors uniformly arranged in the circumferential direction of the stirring head, the method further includes: Based on the acoustic field vibration signal, establish a thermal map of the acoustic field energy distribution, which is used to represent the energy distribution density of the contact surface between the stirring head and the workpiece; Based on the thermal map of the acoustic field energy distribution, calculate the energy density gradient in the circumferential direction of the stirring head; When the energy density gradient is greater than a preset third threshold, calculate the wear position and wear degree of the stirring head based on the direction and magnitude of the energy density gradient; When the wear degree exceeds a preset second range, calculate the compensation inclination angle corresponding to the wear position, and adjust the attitude of the stirring head according to the compensation inclination angle.

8. A server, characterized in that, The server includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the server to execute the method according to any one of claims 1-7.

9. A computer-readable storage medium, comprising instructions, characterized in that, When the instruction runs on the server, cause the server to execute the method according to any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product runs on the server, cause the server to execute the method according to any one of claims 1-7.

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