Motor welding spot control method and system

Abstract

The invention discloses a motor welding spot control method and system, particularly relates to the field of motor welding spots, and comprises a data batch division step, a data acquisition step, a data analysis step, a data comprehensive evaluation step and a real-time feedback monitoring step. In the data acquisition step, a welding process data acquisition unit and a welding spot quality data acquisition unit are included and used for acquiring target data in real time, and the welding process data acquisition unit is used for acquiring welding energy data, welding pressure data and welding temperature data; the welding spot quality data acquisition unit is used for acquiring welding spot geometrical shape data, welding spot internal structure data and welding spot electrical performance data; a welding process data analysis unit and a welding spot quality data analysis unit are included in the data analysis step; according to the method, the data of the motor welding spots are divided into different batches, so that the accuracy and practicability of the data of the welding spots are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of motor welding spots, and more particularly, to a method and system for controlling motor welding spots. Background Art

[0002] With the continuous development of industrial production and increasing levels of automation, motors are increasingly used in various fields, and the requirements for their quality and performance are becoming increasingly stringent. Due to its critical impact on the overall performance of motors, quality control technology for motor solder joints has brought new challenges and opportunities to motor production. Precise solder joint control ensures the stability of the motor's electrical connections and mechanical strength, meeting the demand for high-quality motor production.

[0003] The existing motor welding solder joint control method mainly includes a solder joint data acquisition module, a data transmission module, a solder joint quality analysis module, and a control feedback module. Through the solder joint data acquisition module, high-precision sensors are installed at each key solder joint position in the motor welding production line to collect data such as solder joint temperature, pressure, welding time, and solder consumption, thereby realizing real-time monitoring of the welding process. Through the data transmission module, the collected data is securely transmitted using an encryption algorithm to ensure the accuracy and integrity of the data during the transmission process. Through the solder joint quality analysis module, the transmitted data is decrypted and analyzed to discover possible solder joint defect information and quality anomalies, providing data support for the stability of motor welding quality. Through the control feedback module, technicians can intuitively understand the status of the welding process, realize remote adjustment and control of welding parameters, and promptly correct the operating parameters of the welding equipment to improve the reliability and stability of the motor solder joint quality.

[0004] However, this approach still has some shortcomings in practical applications. For example, in data collection, due to limitations in sensor accuracy and sensitivity, some weld data may be collected with deviations, resulting in incomplete data and adversely affecting subsequent weld quality analysis. Furthermore, in weld quality analysis, the massive amount of welding data and complex quality assessment algorithms can lead to inaccurate and incomplete analysis results. This can make it difficult to detect potential weld quality issues in a timely manner, reducing the ability to control motor production quality.

[0005] Therefore, there is an urgent need to provide a motor welding spot control method and system to solve the problems of incomplete data collection and inaccurate analysis results of the existing motor welding spot control system, and further improve the quality and reliability of motor production. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a motor welding spot control method and system, which solves the problems raised in the above-mentioned background technology through the following solutions.

[0007] To achieve the above objectives, the present invention provides the following technical solutions: a motor welding spot control method, comprising: S1. Data batch division step: determining the data to be collected as target data, dividing the target data into different batches in an equal time division manner, and marking them as 1, 2, ..., n in sequence; S2. Data collection step: This step includes a welding process data collection unit and a solder joint quality data collection unit, which are used to collect target data in real time and transmit the collected data to the data analysis step. The welding process data collection unit is used to collect welding energy data, welding pressure data, and welding temperature data; the solder joint quality data collection unit is used to collect solder joint geometry data, solder joint internal structure data, and solder joint electrical performance data; S3. Data analysis step: This step includes a welding process data analysis unit and a solder joint quality data analysis unit, which are used to collect target data in real time and transmit the collected data to the data comprehensive evaluation step. The welding process data analysis unit includes a welding energy data analysis node, a welding pressure data analysis node, and a welding temperature data analysis node; the solder joint quality data analysis unit includes a solder joint geometry data analysis node, a solder joint internal structure data analysis node, and a solder joint electrical performance data analysis node. S4, a data comprehensive evaluation step: comprising a motor welding spot data analysis unit, configured to comprehensively analyze the analysis results transmitted from the data analysis step, and transmit the analysis results to the real-time feedback monitoring step; S5. Real-time feedback monitoring step: used to establish a preset value of the motor welding spot comprehensive evaluation index, judge the motor welding spot comprehensive evaluation index value according to the preset value of the motor welding spot comprehensive evaluation index, and send a corresponding signal according to the judgment result.

[0008] Preferably, the welding energy data includes the welding current rising slope Ws, the welding current pulse flat top time fluctuation value Wf, and the inverse of the welding energy pulse trailing edge falling time Ec; the welding pressure data includes the welding pressure loading acceleration Pa, the welding process pressure center offset Op, and the welding pressure unloading vibration frequency Pf; the welding temperature data includes the welding preheating zone temperature gradient Wt, the welding molten pool center and edge temperature difference Ed, and the welding cooling phase change temperature interval residence time Cp; the solder joint geometry data includes the solder joint three-dimensional shape fractal dimension Ds, the solder joint contour least squares ellipse fitting major axis to minor axis ratio Mr, and the solder joint surface micro-convex average spacing Js; the solder joint internal structure data includes the solder joint internal dislocation density Sd and the interface energy Se of different crystal phases inside the solder joint; the solder joint electrical performance data includes the high-frequency skin depth change rate Rf, the temperature and thermoelectric potential change Tp, and the solder joint and ground loop parasitic inductance Gp.

[0009] Preferably, the welding energy data analysis node is used to establish a welding energy data calculation model, import the welding energy data transmitted in the data acquisition step into the welding energy data calculation model, and obtain the welding energy quality coefficient value. The welding energy data calculation model is specifically expressed as: , in, Indicates the welding energy quality coefficient value calculated for the i-th time, Ws i Indicates the rising slope of the welding current collected for the i-th time, Wf i Indicates the flat-top time fluctuation value of the welding current pulse collected for the i-th time, Ec i represents the inverse of the falling time of the welding energy pulse trailing edge collected for the i-th time, σ represents the standard deviation of the falling time of the welding energy pulse trailing edge, and μ represents the mean of the falling time of the welding energy pulse trailing edge.

[0010] Preferably, the welding pressure data analysis node is used to establish a welding pressure data calculation model, import the welding pressure data transmitted in the data acquisition step into the welding pressure data calculation model, and obtain the welding pressure stability coefficient value. The welding pressure data calculation model is specifically expressed as: , in, Indicates the welding pressure stability coefficient value calculated for the i-th time, Pa i Op represents the welding pressure loading acceleration collected for the i-th time, i Indicates the pressure center offset of the welding process collected for the i-th time, Pf i represents the welding pressure unloading vibration frequency collected for the i-th time.

[0011] Preferably, the welding temperature data analysis node is used to establish a welding temperature data calculation model, import the welding temperature data transmitted in the data acquisition step into the welding temperature data calculation model, and obtain a welding temperature rationality coefficient value. The welding temperature data calculation model is specifically expressed as: , in, Indicates the reasonableness coefficient of the welding temperature calculated for the i-th time, Wt i represents the temperature gradient of the welding preheating zone collected for the i-th time, Ed i represents the temperature difference between the center and edge of the welding pool collected for the i-th time, Cp i It represents the dwell time in the welding cooling phase transition temperature interval collected for the i-th time.

[0012] Preferably, the solder joint geometry data analysis node is used to establish a solder joint geometry data calculation model, import the solder joint geometry data transmitted in the data acquisition step into the solder joint geometry data calculation model, and obtain the solder joint geometry quality characteristic value. The solder joint geometry data calculation model is specifically expressed as: , in, Denotes the geometric quality characteristic value of the solder joint calculated for the i-th time, Ds i represents the fractal dimension of the three-dimensional shape of the solder joint collected for the i-th time, Mr i It represents the ratio of the major axis to the minor axis of the least squares ellipse fitting of the solder joint contour collected for the i-th time, Represents the ratio of the major axis to the minor axis of the least squares ellipse fitting of the standard solder joint contour, Js i It represents the average spacing of micro-asperities on the solder joint surface collected for the i-th time.

[0013] Preferably, the solder joint internal structure data analysis node is used to establish a solder joint internal structure data calculation model, import the solder joint internal structure data transmitted in the data acquisition step into the solder joint internal structure data calculation model, and obtain the solder joint internal microstructure excellence coefficient value. The solder joint internal structure data calculation model is specifically expressed as: , in, Indicates the excellent coefficient value of the internal microstructure of the solder joint calculated for the i-th time, Sd i represents the internal dislocation density of the solder joint collected for the i-th time, Se i Represents the interfacial energy of different crystal phases inside the solder joint collected for the i-th time.

[0014] Preferably, the solder joint electrical performance data analysis node is used to establish a solder joint electrical performance data calculation model, import the solder joint electrical performance data transmitted in the data acquisition step into the solder joint electrical performance data calculation model, and obtain the solder joint electrical performance excellence coefficient value. The solder joint electrical performance data calculation model is specifically expressed as: , in, Indicates the excellent coefficient value of the electrical performance of the solder joint calculated for the i-th time, Rf i Indicates the rate of change of high-frequency skin depth collected for the i-th time, Tp i Indicates the temperature and thermoelectric potential changes collected for the i-th time, Gp i represents the parasitic inductance between the solder joint and the ground loop collected for the i-th time.

[0015] Preferably, the motor welding spot data analysis unit is used to establish a motor welding spot data calculation model, and import the welding energy quality coefficient value, welding pressure stability coefficient value, welding temperature rationality coefficient value, welding spot geometry quality characteristic value, welding spot internal microstructure excellence coefficient value, and welding spot electrical performance excellence coefficient value transmitted in the data analysis step into the motor welding spot data calculation model to obtain a motor welding spot comprehensive evaluation index value. The motor welding spot data calculation model is specifically expressed as: , Where A represents the calculated comprehensive evaluation index value of the motor welding spot, , , represents the welding energy quality coefficient value calculated for the i-th time, represents the welding pressure stability coefficient value calculated for the i-th time, Indicates the reasonableness coefficient of the welding temperature calculated for the i-th time, represents the geometric quality characteristic value of the solder joint calculated for the i-th time, Indicates the minimum value of the calculated solder joint geometry quality characteristic, Indicates the maximum value of the calculated geometric quality characteristics of the solder joint, represents the excellent coefficient value of the internal microstructure of the solder joint calculated for the i-th time, Indicates the minimum value of the calculated internal microstructure excellence coefficient of the solder joint, Indicates the maximum value of the calculated internal microstructure excellence coefficient of the solder joint, It represents the excellent coefficient value of the electrical performance of the solder joint calculated for the i-th time, Indicates the minimum value of the calculated solder joint electrical performance coefficient. Indicates the maximum value of the calculated solder joint electrical performance coefficient, i indicates starting from the i-th number, and n indicates ending at the n-th number.

[0016] Preferably, a motor welding spot control system is characterized by comprising: Data batch division module: used to determine the data to be collected as target data, divide the target data into different batches according to the equal time division method, and mark them as 1, 2, ..., n in sequence; Data acquisition module: includes a welding process data acquisition unit and a solder joint quality data acquisition unit, which are used to collect target data in real time and transmit the collected data to the data analysis module. The welding process data acquisition unit is used to collect welding energy data, welding pressure data, and welding temperature data; the solder joint quality data acquisition unit is used to collect solder joint geometry data, solder joint internal structure data, and solder joint electrical performance data; Data Analysis Module: This module includes a welding process data analysis unit and a solder joint quality data analysis unit, which are used to collect target data in real time and transmit the collected data to the data comprehensive evaluation module. The welding process data analysis unit includes welding energy data analysis nodes, welding pressure data analysis nodes, and welding temperature data analysis nodes; the solder joint quality data analysis unit includes solder joint geometry data analysis nodes, solder joint internal structure data analysis nodes, and solder joint electrical performance data analysis nodes. Data comprehensive evaluation module: including the motor welding spot data analysis unit, which is used to comprehensively analyze the analysis results transmitted by the data analysis module and transmit the analysis results to the real-time feedback monitoring module; Real-time feedback monitoring module: used to establish a preset value of the motor welding spot comprehensive evaluation index, judge the motor welding spot comprehensive evaluation index value according to the preset value of the motor welding spot comprehensive evaluation index, and send a corresponding signal according to the judgment result.

[0017] The technical effects and advantages of the present invention are as follows: 1. This invention effectively improves the accuracy and practicality of motor solder joint data by dividing it into batches. By collecting multi-dimensional data on welding energy, pressure, temperature, geometry, internal structure, and electrical properties, this multifaceted data collection model significantly overcomes the limitations of traditional solder joint inspection data, which relies on a single source. It provides a sufficient and reliable information foundation for subsequent solder joint quality assessment. 2. The present invention conducts in-depth analysis of the collected data through data evaluation, and then calculates the welding energy quality coefficient value, welding pressure stability coefficient value, welding temperature rationality coefficient value, solder joint geometry quality characteristic value, solder joint internal microstructure excellence coefficient value and solder joint electrical performance excellence coefficient value, and clearly points out the factors that may affect the quality of the solder joint; 3. By comprehensively weighing and deeply integrating data evaluation results, the accuracy and effectiveness of the assessment results are significantly enhanced. Through real-time monitoring and continuous tracking, an alarm signal is immediately issued once a weld anomaly is detected. This allows technicians and managers to quickly understand the motor welding status and potential risks, and take timely action to adjust and optimize welding processes and parameters. This provides a strong guarantee for achieving high-precision motor welding joint control and scientific and reasonable decision-making and deployment. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 Schematic diagram of the method structure of the present invention; Figure 2 Schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0020] As attached Figure 1 The motor welding spot control method shown specifically includes: a data batch division step, a data collection step, a data analysis step, a data comprehensive evaluation step, and a real-time feedback monitoring step.

[0021] S1. Data batch division step: used to determine the data to be collected as target data, divide the target data into different batches in an equal time division manner, and mark them as 1, 2, ..., n in sequence.

[0022] In this embodiment, it should be specifically explained that the method of dividing by equal time is based on the characteristics of each target data. According to the real-time requirements of each target data, the database is run by the method to collect data once at equal intervals, and the data is divided into different collection times.

[0023] S2. Data Collection Step: This includes a welding process data collection unit and a solder joint quality data collection unit, which are used to collect target data in real time and transmit the collected data to the data analysis step. The welding process data collection unit is used to collect welding energy data, welding pressure data, and welding temperature data; the solder joint quality data collection unit is used to collect solder joint geometry data, solder joint internal structure data, and solder joint electrical performance data.

[0024] In this embodiment, it is specifically necessary to explain that: the welding energy data includes the welding current rising slope Ws, the welding current pulse flat top time fluctuation value Wf, and the inverse of the welding energy pulse trailing edge falling time Ec; the welding pressure data includes the welding pressure loading acceleration Pa, the welding process pressure center offset Op, and the welding pressure unloading vibration frequency Pf; the welding temperature data includes the welding preheating zone temperature gradient Wt, the welding pool center and edge temperature difference Ed, and the welding cooling phase transition temperature interval residence time Cp; the solder joint geometry data includes the solder joint three-dimensional shape fractal dimension Ds, the solder joint contour least squares ellipse fitting major axis to minor axis ratio Mr, and the solder joint surface micro-convex average spacing Js; the solder joint internal structure data includes the solder joint internal dislocation density Sd and the interface energy Se of different crystal phases inside the solder joint; the solder joint electrical performance data includes the high-frequency skin depth change rate Rf, the temperature and thermoelectric potential change Tp, and the solder joint and ground loop parasitic inductance Gp.

[0025] The welding current rise slope determines how quickly energy is injected into the weld within a short period of time. The greater the slope, the faster the weld material acquires sufficient heat to begin melting, helping to improve welding efficiency. However, if the slope is too large, it can cause local overheating of the weld material, resulting in defects such as spatter. This is crucial for uniform initial melting of the weld. A suitable slope ensures uniform heating of all parts of the weld during the initial stages, laying the foundation for subsequent good fusion. This current data is collected by connecting a high-precision, high-bandwidth current sensor in series within the welding circuit and transmitting the real-time current data to a high-speed data acquisition system. The acquisition system acquires data on the current's change over time at an extremely high sampling frequency. This data is then differentiated using specialized digital signal processing algorithms to determine the welding current rise slope.

[0026] The flat-top time fluctuation value of the welding current pulse refers to the length of time that the welding current remains relatively stable after reaching its peak value. The flat-top time fluctuation value reflects the accuracy of this stable stage. During this stage, the welding material continues to absorb energy to melt and fuse. A smaller fluctuation value means that the energy is continuously and stably input, which can ensure the uniformity of the internal structure of the weld and form a high-quality weld. If the fluctuation value is too large, local overheating or insufficient energy areas may appear inside the weld, resulting in inconsistent weld strength, internal voids and other problems. The collection method is: using a high-precision time counter combined with a current threshold judgment method, that is, when the welding current rises to the set flat-top current threshold, the time counter starts timing, and when the current drops below the threshold, the timing ends. The flat-top time fluctuation value of the welding current pulse is obtained through measurement and statistical analysis of multiple welding processes.

[0027] The inverse of the falling time of the welding energy pulse's trailing edge is related to the rate of energy withdrawal at the end of welding. During the solidification process of the weld, an appropriate energy withdrawal rate helps control the material's crystallization morphology. A faster withdrawal rate may cause the weld to solidify quickly, but it may also lead to excessive internal stress. On the other hand, an excessively slow withdrawal rate may subject the weld to unnecessary heat during solidification, affecting its microstructure and performance. By controlling this parameter, the crystallization conditions during the weld solidification process can be optimized, improving the quality and reliability of the weld. The acquisition method is as follows: First, the time of the welding current's falling phase is accurately measured. This requires the use of high-precision time measurement equipment with a resolution of microseconds. Timing is started when the welding current begins to decline from its peak value and stopped when the current drops to near zero. The inverse of the measured falling time is then taken to obtain the inverse of the falling time of the welding energy pulse's trailing edge.

[0028] The welding pressure loading acceleration determines the dynamic characteristics of the pressure application. When the electrode and the weld part begin to contact, the magnitude of the acceleration directly affects the impact force between them. Appropriate acceleration ensures good contact between the electrode and the weld part, ensuring that the initial contact resistance is within a reasonable range. Excessive acceleration may cause surface damage to the weld part, affecting subsequent weld quality; while excessive acceleration may lead to poor contact between the electrode and the weld part, resulting in uneven current conduction and, in turn, affecting weld formation. The acquisition method is to install a high-precision accelerometer on the pressure application device of the welding electrode, with a measurement accuracy of ±0.1N / ms². The accelerometer directly measures the acceleration changes during the pressure application process and transmits the analog signal to the signal conditioning circuit. The signal conditioning circuit amplifies, filters, and processes the signal before converting it into a digital signal and transmitting it to the control system for storage and analysis. When the accelerometer is installed, it is tightly connected to the pressure application device, with its measurement direction aligned with the pressure application direction, to measure the pressure loading acceleration.

[0029] The pressure center offset during the welding process reflects the uniformity of pressure distribution on the contact surface between the welding electrode and the weld. Ideally, the pressure should be evenly distributed on the contact surface so that each point in the weld is subjected to the same pressure. However, due to factors such as the electrode shape, installation accuracy, and surface flatness of the welding material, the pressure center may be offset. A large offset will cause excessive pressure on one side of the weld and insufficient pressure on the other side, resulting in good fusion on one side of the weld and poor fusion on the other side, seriously affecting the quality and strength of the weld. The acquisition method is as follows: multiple miniature pressure sensors are evenly distributed around the welding electrode to form a sensor array. During the welding process, each sensor measures the pressure value at its location in real time and transmits the data to the control system. Based on the position information and pressure data of the sensor, the control system calculates the position of the pressure center through a complex algorithm, and compares it with the geometric center of the electrode to obtain the pressure center offset.

[0030] The welding pressure unloading vibration frequency indicates that when the pressure is unloaded, the stress inside the weld will be redistributed. If the vibration frequency is too high, tiny cracks may be generated inside the weld. These cracks may expand during subsequent use, causing the weld to fail. By monitoring and controlling this vibration frequency, the stress state of the weld during the pressure unloading process can be evaluated, and corresponding measures can be taken to reduce the risk of microcracks in the weld. The acquisition method is: using a pressure sensor to collect pressure signals during the unloading process. The collected pressure signal is an analog signal that varies with time, which is transmitted to the signal conditioning circuit for pre-processing such as amplification and filtering. Then, a spectrum analysis device is used to perform spectrum analysis on the pre-processed signal. The spectrum analysis device has high frequency resolution, with a resolution of ±0.1kHz. By analyzing the characteristics of the pressure signal in the frequency domain, the vibration frequency component is extracted, thereby obtaining the vibration frequency during the welding pressure unloading process.

[0031] The temperature gradient in the welding preheating zone is a key parameter. It reflects how quickly the temperature in the preheating area changes with distance. A reasonable temperature gradient can enable the welding material to achieve uniform thermal expansion during the preheating process, avoiding thermal stress concentration caused by excessive temperature differences. If the temperature gradient is uneven, thermal stress may be generated inside the welding material, affecting the quality of the solder joint during subsequent welding, such as causing cracks or deformation in the solder joint. The collection method is as follows: multiple miniature thermocouples are arranged at a certain interval around the welding area to form a temperature gradient measurement network. The measurement accuracy of the thermocouple is ±0.5℃ / mm. Each thermocouple measures the temperature at its location in real time and transmits the temperature data to the control system. Based on the position information of the thermocouple and the measured temperature value, the control system calculates the temperature difference between adjacent thermocouples through numerical calculation methods to obtain the temperature gradient.

[0032] The temperature difference between the center and edge of the weld pool significantly influences the direction and speed of liquid flow within the pool. During the welding process, liquid flow within the pool affects the distribution of elements within the weld. A large temperature difference causes strong convection from the center to the edge of the pool, which redistributes elements within the weld material. By monitoring and controlling this temperature difference, the distribution of elements within the weld pool can be optimized, improving the quality and performance of the weld, such as its strength and corrosion resistance. This is achieved by capturing images of the weld pool using a high-resolution infrared thermal imager. The infrared thermal imager has a temperature resolution of ±2°C, enabling clear discrimination of the temperature distribution at the center and edge of the weld pool. During the capture process, ensure that the thermal imager lens is perpendicular to the weld pool surface and at an appropriate distance to obtain accurate temperature images. After processing the captured thermal image, an image analysis algorithm is used to extract the temperature values ​​at the center and edge of the weld pool, and the temperature difference between the two is calculated.

[0033] The dwell time in the soldering cooling phase transition temperature range represents the critical phase transition temperature range where the solder material transitions from liquid to solid. This dwell time plays a decisive role in determining the proportion and morphology of the crystalline phases. Different crystalline phases have different properties. An appropriate dwell time can help the solder joint form an ideal crystalline structure, improving its strength, toughness, and other properties. If the dwell time is too short, incomplete crystallization may result in a loose structure; while if the dwell time is too long, grain growth may occur, degrading solder joint performance. The data is collected by embedding micro-temperature sensors within the solder joint with a measurement accuracy of ±0.1s. These sensors measure temperature changes within the solder joint in real time. Based on the research and determination of the phase transition temperature range of the solder material, the control system begins timing when the solder joint temperature enters this phase transition temperature range. Timing stops when the temperature leaves this phase transition temperature range, and the resulting time is the dwell time in the phase transition temperature range.

[0034] The fractal dimension of the solder joint's three-dimensional shape describes the complexity of the solder joint's surface. The fractal dimension is closely related to the mechanical engagement and electrical contact performance of the solder joint. A higher fractal dimension indicates a more complex microstructure on the solder joint surface, which helps increase the mechanical engagement area between the solder joint and other components, improving the connection strength. A complex surface structure also increases electrical contact points, reduces contact resistance, and improves electrical performance. The fractal dimension is acquired by scanning the solder joint surface using a 3D laser scanner. The scanner acquires 3D coordinate information of the solder joint surface with extremely high precision (accuracy of ±0.001). The resulting 3D data point cloud is transferred to a computer and processed using specialized fractal analysis algorithms. These algorithms, based on fractal geometry theory, determine the fractal dimension of the solder joint by calculating characteristics such as coverage and roughness at different scales.

[0035] The ratio of the major axis to the minor axis of the least squares ellipse fitting of the weld profile reflects the ellipticity of the weld. Ideally, the weld shape should be symmetrical, but due to various factors during the welding process, the weld may exhibit a certain degree of elliptical deformation. Abnormal variations in this ratio can be used to determine whether there are asymmetric factors in the welding process. If the ratio deviates from the normal range, it may mean that the weld is subjected to uneven force or energy in a certain direction, which can affect the quality and performance of the weld, such as reducing the strength of the weld in certain directions. The acquisition method involves first acquiring weld profile data using a 3D profilometer. The 3D profilometer uses contact or non-contact measurement methods to accurately obtain the coordinate information of each point on the weld profile. The measured profile data is transmitted to a computer and processed using a least squares ellipse fitting algorithm. This algorithm finds the ellipse that best fits the weld profile, calculates the lengths of the major and minor axes of the ellipse, and then determines the ratio of the major axis to the minor axis.

[0036] The average spacing between the micro-bumps on the solder joint surface directly affects the actual contact area and contact resistance of the solder joint. At the microscopic level, the solder joint surface is not completely smooth, but rather contains many micro-bumps. The spacing between these micro-bumps determines the number and distribution of actual contact points. A smaller average spacing means more contact points, which can increase the effective contact area between the solder joint and other components, reduce contact resistance, and improve electrical performance. At the same time, a suitable spacing between micro-bumps also helps to better disperse stress under mechanical pressure and improve the mechanical properties of the solder joint. The acquisition method is to scan the solder joint surface using an atomic force microscope. The atomic force microscope uses the interaction force between the probe and the atoms on the solder joint surface to obtain microscopic surface morphology information. Its vertical resolution can reach ±0.1nm, which can clearly distinguish the height and position of the micro-bumps. After processing the scanned surface morphology data, the micro-bumps are identified using an image analysis algorithm, and the average spacing between them is calculated.

[0037] The dislocation density within a solder joint is closely related to its strength and plastic deformation capacity. Dislocations are defects in the atomic arrangement of crystalline materials that cause them to move and interact when subjected to stress. A higher dislocation density means that dislocations are more likely to move when stressed, resulting in a solder joint with better plastic deformation capacity and the ability to absorb more energy without breaking. However, excessively high dislocation density can also lead to stress concentration within the material, reducing the solder joint's strength. Therefore, controlling and understanding the dislocation density within a solder joint is crucial for evaluating solder joint performance. This is achieved by observing ultrathin sections of the solder joint using a transmission electron microscope. Transmission electron microscopy uses the principle of an electron beam penetrating a sample and forming an image on a fluorescent screen or detector, providing high-resolution microstructural images. Specialized sectioning techniques are required to ensure that the sections are sufficiently thin and that the internal structure of the solder joint is not damaged during the sectioning process. The prepared ultrathin sections are placed on the sample stage of a transmission electron microscope. By adjusting the electron beam parameters and the microscope's focus, a clear image of the solder joint's internal microstructure is obtained. Specialized dislocation counting software is then used to identify and count the dislocations in the image and calculate the dislocation density.

[0038] The interfacial energy of different crystalline phases within the solder joint affects the bonding strength and stability between them. During the solidification process of a solder joint, multiple different crystalline phases may form, and the interfaces between these phases are the weak links within the solder joint. The magnitude of the interfacial energy determines the bonding strength between the crystalline phases. Lower interfacial energy means that the crystalline phases are more likely to bond, forming a stable structure, thereby improving the overall strength and stability of the solder joint. Conversely, high interfacial energy can easily cause the crystalline phases to separate, reducing solder joint performance. The interfacial energy of different crystalline phases within the solder joint is measured using high-resolution scanning probe microscopy combined with thermodynamic analysis methods. Scanning probe microscopy acquires surface physical property information through the interaction between the probe and the sample surface. First, the solder joint surface is scanned using a scanning probe microscope to identify the boundary regions between different crystalline phases. Then, by varying parameters such as the distance between the probe and the sample and the applied force, the energy changes at the crystalline phase boundaries are measured. Simultaneously, the measured data is analyzed and calculated using thermodynamic theory and models to determine the interfacial energy of different crystalline phases.

[0039] The high-frequency skin depth change rate reflects the solder joint's adaptability to high-frequency signals. A suitable change rate ensures uniform current distribution during high-frequency operation, reduces local overheating, lowers resistance losses, improves electrical performance and stability, prevents solder joint aging and cracking, and ensures the reliability of high-frequency motor operation. The data acquisition method involves applying a high-frequency pulse current to the solder joint and measuring it with a high-frequency magnetic field sensor with an accuracy of ±0.01% / MHz around the solder joint. As the current passes through, the sensor collects data on the change in magnetic field strength with frequency. Using a data acquisition system and algorithm, the skin depth at different frequencies is calculated based on the relationship between the magnetic field and skin depth, and the change rate is then derived.

[0040] The temperature and thermoelectric potential changes indicate that the solder joints are affected by temperature changes during motor operation. Thermoelectric potential is the electromotive force generated by the temperature gradient. If this parameter is too large, it will introduce additional voltage signals into the circuit, resulting in measurement errors or interference with other signals, affecting the normal operation of the motor. Monitoring this parameter can evaluate the electrical stability of the solder joints under temperature changes, ensure good electrical performance at different temperatures, and reduce electrical failures. The collection method is: test the solder joints with a special thermoelectric potential measuring device. Place the solder joints in an environment where the temperature can be precisely controlled, such as a constant temperature box or thermal cycler, and measure the thermoelectric potential by connecting two different metal wires at the solder joints. The measurement accuracy is ±0.1μV / ℃. Change the temperature and record the thermoelectric potential value to obtain the thermoelectric potential-temperature curve, and calculate the change in different temperature ranges.

[0041] The parasitic inductance between solder joints and the ground loop describes a non-ideal electrical parameter of solder joints in a circuit. In high-frequency circuits or those requiring high electromagnetic compatibility, this parameter can affect the circuit's high-frequency response, causing signal distortion and reducing interference immunity. Measuring and controlling this parameter can optimize circuit design, improve high-frequency performance and electromagnetic compatibility, and ensure stable motor operation in complex electromagnetic environments. The parasitic inductance is measured using a vector network analyzer and a specialized inductance measurement fixture. The solder joint and the ground loop are connected to the fixture. The analyzer sends test signals of varying frequencies to the circuit, measuring the reflected and transmitted signal characteristics. Combined with the fixture's calibration parameters, the parasitic inductance is calculated using an internal algorithm.

[0042] S3. Data Analysis Step: This includes a welding process data analysis unit and a solder joint quality data analysis unit, which are used to collect target data in real time and transmit the collected data to the data comprehensive evaluation step. The welding process data analysis unit includes welding energy data analysis nodes, welding pressure data analysis nodes, and welding temperature data analysis nodes; the solder joint quality data analysis unit includes solder joint geometry data analysis nodes, solder joint internal structure data analysis nodes, and solder joint electrical performance data analysis nodes.

[0043] In this embodiment, it should be specifically explained that the welding energy data analysis node is used to establish a welding energy data calculation model, import the welding energy data transmitted in the data acquisition step into the welding energy data calculation model, and obtain the welding energy quality coefficient value. The welding energy data calculation model is specifically expressed as: , in, Indicates the welding energy quality coefficient value calculated for the i-th time, Ws i Indicates the rising slope of the welding current collected for the i-th time, Wf i Indicates the flat-top time fluctuation value of the welding current pulse collected for the i-th time, Ec i represents the inverse of the falling time of the welding energy pulse trailing edge collected for the i-th time, σ represents the standard deviation of the falling time of the welding energy pulse trailing edge, and μ represents the mean of the falling time of the welding energy pulse trailing edge.

[0044] In this embodiment, it should be specifically explained that the welding pressure data analysis node is used to establish a welding pressure data calculation model, import the welding pressure data transmitted in the data acquisition step into the welding pressure data calculation model, and obtain the welding pressure stability coefficient value. The welding pressure data calculation model is specifically expressed as: , in, Indicates the welding pressure stability coefficient value calculated for the i-th time, Pa i Op represents the welding pressure loading acceleration collected for the i-th time, i Indicates the pressure center offset of the welding process collected for the i-th time, Pf i represents the welding pressure unloading vibration frequency collected for the i-th time.

[0045] In this embodiment, it should be specifically noted that the welding temperature data analysis node is used to establish a welding temperature data calculation model, import the welding temperature data transmitted in the data acquisition step into the welding temperature data calculation model, and obtain a welding temperature rationality coefficient value. The welding temperature data calculation model is specifically expressed as: , in, Indicates the reasonableness coefficient of the welding temperature calculated for the i-th time, Wt i represents the temperature gradient of the welding preheating zone collected for the i-th time, Ed i represents the temperature difference between the center and edge of the welding pool collected for the i-th time, Cp i It represents the dwell time in the welding cooling phase transition temperature interval collected for the i-th time.

[0046] In this embodiment, it should be specifically explained that the solder joint geometry data analysis node is used to establish a solder joint geometry data calculation model, import the solder joint geometry data transmitted in the data acquisition step into the solder joint geometry data calculation model, and obtain the solder joint geometry quality characteristic value. The solder joint geometry data calculation model is specifically expressed as: , in, Denotes the geometric quality characteristic value of the solder joint calculated for the i-th time, Ds i represents the fractal dimension of the three-dimensional shape of the solder joint collected for the i-th time, Mr i It represents the ratio of the major axis to the minor axis of the least squares ellipse fitting of the solder joint contour collected for the i-th time, Represents the ratio of the major axis to the minor axis of the least squares ellipse fitting of the standard solder joint contour, Js i It represents the average spacing of micro-asperities on the solder joint surface collected for the i-th time.

[0047] In this embodiment, it should be specifically noted that the solder joint internal structure data analysis node is used to establish a solder joint internal structure data calculation model, import the solder joint internal structure data transmitted in the data acquisition step into the solder joint internal structure data calculation model, and obtain the solder joint internal microstructure excellence coefficient value. The solder joint internal structure data calculation model is specifically expressed as: , in, Indicates the excellent coefficient value of the internal microstructure of the solder joint calculated for the i-th time, Sd i represents the internal dislocation density of the solder joint collected for the i-th time, Se i Represents the interfacial energy of different crystal phases inside the solder joint collected for the i-th time.

[0048] In this embodiment, it should be specifically noted that the solder joint electrical performance data analysis node is used to establish a solder joint electrical performance data calculation model, import the solder joint electrical performance data transmitted in the data acquisition step into the solder joint electrical performance data calculation model, and obtain a solder joint electrical performance excellence coefficient value. The solder joint electrical performance data calculation model is specifically expressed as: , in, Indicates the excellent coefficient value of the electrical performance of the solder joint calculated for the i-th time, Rf i Indicates the rate of change of high-frequency skin depth collected for the i-th time, Tp i Indicates the temperature and thermoelectric potential changes collected for the i-th time, Gp i represents the parasitic inductance between the solder joint and the ground loop collected for the i-th time.

[0049] S4. Data comprehensive evaluation step: including a motor welding spot data analysis unit, which is used to comprehensively analyze the analysis results transmitted in the data analysis step and transmit the analysis results to the real-time feedback monitoring step.

[0050] In this embodiment, it should be specifically explained that the motor welding spot data analysis unit is used to establish a motor welding spot data calculation model, and imports the welding energy quality coefficient value, welding pressure stability coefficient value, welding temperature rationality coefficient value, welding spot geometry quality characteristic value, welding spot internal microstructure excellence coefficient value, and welding spot electrical performance excellence coefficient value transmitted in the data analysis step into the motor welding spot data calculation model to obtain a motor welding spot comprehensive evaluation index value. The motor welding spot data calculation model is specifically expressed as follows: , Where A represents the calculated comprehensive evaluation index value of the motor welding spot, , , represents the welding energy quality coefficient value calculated for the i-th time, represents the welding pressure stability coefficient value calculated for the i-th time, Indicates the reasonableness coefficient of the welding temperature calculated for the i-th time, represents the geometric quality characteristic value of the solder joint calculated for the i-th time, Indicates the minimum value of the calculated solder joint geometry quality characteristic, Indicates the maximum value of the calculated geometric quality characteristics of the solder joint, represents the excellent coefficient value of the internal microstructure of the solder joint calculated for the i-th time, Indicates the minimum value of the calculated internal microstructure excellence coefficient of the solder joint, Indicates the maximum value of the calculated internal microstructure excellence coefficient of the solder joint, It represents the excellent coefficient value of the electrical performance of the solder joint calculated for the i-th time, Indicates the minimum value of the calculated solder joint electrical performance coefficient. Indicates the maximum value of the calculated solder joint electrical performance coefficient, i indicates starting from the i-th number, and n indicates ending at the n-th number.

[0051] S5. Real-time feedback monitoring step: used to establish a preset value of the motor welding spot comprehensive evaluation index, judge the motor welding spot comprehensive evaluation index value according to the preset value of the motor welding spot comprehensive evaluation index, and send a corresponding signal according to the judgment result.

[0052] In this embodiment, it should be specifically explained that the preset value of the comprehensive evaluation index of the motor welding spot is marked as A def , when A defWhen it is ≤ A, a normal signal is sent. This signal indicates that the preset value of the comprehensive evaluation index of the motor welding solder joints is less than or equal to the value of the comprehensive evaluation index of the motor welding solder joints, indicating that the welding state of the motor solder joints is normal; when it is def > A, a warning signal is sent to relevant technical management personnel. This signal indicates that the preset value of the comprehensive evaluation index of the motor welding solder joints is greater than the value of the comprehensive evaluation index of the motor welding solder joints, indicating that the welding state of the motor solder joints is poor.

[0053] Based on the above solution and appendix Figure 2 , this application embodiment also discloses a control system for motor welding solder joints, including: Data batch division module: used to determine the data to be collected as target data, divide the target data into different batches in an equal-time division manner, and sequentially mark them as 1, 2,..., n; Data acquisition module: includes a welding process data acquisition unit and a solder joint quality data acquisition unit, used to collect the target data in real time and transmit the collected data to the data analysis module. The welding process data acquisition unit is used to collect welding energy data, welding pressure data, and welding temperature data; the solder joint quality data acquisition unit is used to collect solder joint geometry data, solder joint internal structure data, and solder joint electrical performance data; Data analysis module: includes a welding process data analysis unit and a solder joint quality data analysis unit, used to collect the target data in real time and transmit the collected data to the data comprehensive evaluation module. The welding process data analysis unit includes a welding energy data analysis node, a welding pressure data analysis node, and a welding temperature data analysis node; the solder joint quality data analysis unit includes a solder joint geometry data analysis node, a solder joint internal structure data analysis node, and a solder joint electrical performance data analysis node; Data comprehensive evaluation module: includes a motor welding solder joint data analysis unit, used to comprehensively analyze the analysis results transmitted by the data analysis module and transmit the analysis results to the real-time feedback monitoring module; Real-time feedback monitoring module: used to establish a preset value of the comprehensive evaluation index of the motor welding solder joints, judge the value of the comprehensive evaluation index of the motor welding solder joints according to the preset value of the comprehensive evaluation index of the motor welding solder joints, and send corresponding signals according to the judgment results.

[0054] The present invention effectively improves the accuracy and practicality of the solder joint data by dividing the data of the motor solder joints into batches. By collecting welding energy data, welding pressure data, welding temperature data, solder joint geometry data, solder joint internal structure data, and solder joint electrical performance data in multiple dimensions, this multi-faceted data acquisition mode has greatly overcome the limitations of the single source of traditional solder joint detection data, and provides a sufficient and reliable information foundation for subsequent solder joint quality assessment. Through data assessment, the collected data is deeply analyzed, and then the welding energy quality coefficient value, welding pressure stability coefficient value, welding temperature rationality coefficient value, solder joint geometry quality characteristic value, solder joint internal microstructure excellence coefficient value, and solder joint electrical performance excellence coefficient value are calculated, clearly pointing out the factors that may affect the quality of the solder joint. By comprehensively weighing and deeply integrating the data evaluation results, the accuracy and effectiveness of the evaluation results are significantly enhanced. Through real-time monitoring and continuous tracking, once an abnormal weld spot is detected, an alarm signal will be immediately issued. Relevant technicians and managers can quickly understand the status and potential risks of motor welding and take timely actions to adjust and optimize welding processes and parameters, providing strong guarantees for achieving high-precision motor welding spot control and scientific and reasonable decision-making and deployment.

[0055] Secondly: The drawings of the embodiments disclosed in the present invention only involve structures related to the embodiments disclosed in the present invention. Other structures may refer to conventional designs. The same embodiment and different embodiments of the present invention may be combined with each other without conflict. Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A motor welding spot control method, characterized in that: include: S1, data batch division step: used to determine the data to be collected as target data, divide the target data into different batches in an equal time division manner, and mark them as 1, 2, ..., n in sequence; S2, data acquisition step: including a welding process data acquisition unit and a solder joint quality data acquisition unit, which are used to acquire target data in real time and transmit the acquired data to the data analysis step, wherein the welding process data acquisition unit is used to acquire welding energy data, welding pressure data and welding temperature data; the solder joint quality data acquisition unit is used to acquire solder joint geometry data, solder joint internal structure data and solder joint electrical performance data; S3, data analysis step: including a welding process data analysis unit and a solder joint quality data analysis unit, which are used to collect target data in real time and transmit the collected data to the data comprehensive evaluation step; the welding process data analysis unit includes a welding energy data analysis node, a welding pressure data analysis node and a welding temperature data analysis node; the solder joint quality data analysis unit includes a solder joint geometry data analysis node, a solder joint internal structure data analysis node and a solder joint electrical performance data analysis node; S4, data comprehensive evaluation step: including a motor welding spot data analysis unit, used for comprehensively analyzing the analysis results transmitted in the data analysis step, and transmitting the analysis results to the real-time feedback monitoring step; S5, real-time feedback monitoring step: used to establish a preset value of the motor welding spot comprehensive evaluation index, judge the motor welding spot comprehensive evaluation index value according to the preset value of the motor welding spot comprehensive evaluation index, and send a corresponding signal according to the judgment result.

2. A motor welding spot control method according to claim 1, characterized in that: The welding energy data includes the welding current rising slope Ws, the welding current pulse flat top time fluctuation value Wf and the inverse of the welding energy pulse trailing edge falling time Ec; the welding pressure data includes the welding pressure loading acceleration Pa, the welding process pressure center offset Op and the welding pressure unloading vibration frequency Pf; the welding temperature data includes the welding preheating zone temperature gradient Wt, the welding molten pool center and edge temperature difference Ed and the welding cooling phase change temperature interval residence time Cp; the solder joint geometry data includes the solder joint three-dimensional shape fractal dimension Ds, the solder joint contour least squares ellipse fitting major axis to minor axis ratio Mr and the solder joint surface micro-convex average spacing Js; the solder joint internal structure data includes the solder joint internal dislocation density Sd and the interface energy Se of different crystal phases inside the solder joint; the solder joint electrical performance data includes the high-frequency skin depth change rate Rf, the temperature and thermoelectric potential change Tp and the solder joint and ground loop parasitic inductance Gp.

3. The motor welding spot control method according to claim 1, characterized in that: The welding energy data analysis node is used to establish a welding energy data calculation model, import the welding energy data transmitted in the data acquisition step into the welding energy data calculation model, and obtain the welding energy quality coefficient value. The welding energy data calculation model is specifically expressed as: ， in, Indicates the welding energy quality coefficient value calculated for the i-th time, Ws i Indicates the rising slope of the welding current collected for the i-th time, Wf i Indicates the fluctuation value of the flat-top time of the welding current pulse collected for the i-th time, Ec i represents the inverse of the falling time of the trailing edge of the welding energy pulse collected for the i-th time, σ represents the standard deviation of the inverse of the falling time of the trailing edge of the welding energy pulse, and μ represents the mean of the inverse of the falling time of the trailing edge of the welding energy pulse.

4. The motor welding spot control method according to claim 1, characterized in that: The welding pressure data analysis node is used to establish a welding pressure data calculation model, import the welding pressure data transmitted in the data acquisition step into the welding pressure data calculation model, and obtain the welding pressure stability coefficient value. The welding pressure data calculation model is specifically expressed as: ， in, Indicates the welding pressure stability coefficient value calculated for the i-th time, Pa i Op represents the welding pressure loading acceleration collected for the i-th time, i Pf represents the pressure center offset of the welding process collected for the i-th time. i Represents the welding pressure unloading vibration frequency collected for the i-th time.

5. The motor welding spot control method according to claim 1, characterized in that: The welding temperature data analysis node is used to establish a welding temperature data calculation model, import the welding temperature data transmitted in the data acquisition step into the welding temperature data calculation model, and obtain the welding temperature rationality coefficient value. The welding temperature data calculation model is specifically expressed as: ， in, Indicates the reasonableness coefficient of the welding temperature calculated for the i-th time, Wt i represents the temperature gradient of the welding preheating zone collected for the i-th time, Ed i represents the temperature difference between the center and edge of the welding pool collected for the i-th time, Cp i It represents the residence time in the welding cooling phase change temperature interval collected for the i-th time.

6. A motor welding spot control method according to claim 1, characterized in that: The solder joint geometry data analysis node is used to establish a solder joint geometry data calculation model, import the solder joint geometry data transmitted in the data acquisition step into the solder joint geometry data calculation model, and obtain the solder joint geometry quality characteristic value. The solder joint geometry data calculation model is specifically expressed as: ， in, Denotes the geometric quality characteristic value of the solder joint calculated for the i-th time, Ds i represents the fractal dimension of the three-dimensional shape of the solder joint collected for the i-th time, Mr i It represents the ratio of the major axis to the minor axis of the least squares ellipse fitting of the solder joint contour collected for the i-th time, Represents the ratio of the major axis to the minor axis of the least squares ellipse fitting of the standard solder joint contour, Js i It represents the average spacing of micro-convex bodies on the solder joint surface collected for the i-th time.

7. The motor welding spot control method according to claim 1, characterized in that: The solder joint internal structure data analysis node is used to establish a solder joint internal structure data calculation model, import the solder joint internal structure data transmitted in the data acquisition step into the solder joint internal structure data calculation model, and obtain the solder joint internal microstructure excellence coefficient value. The solder joint internal structure data calculation model is specifically expressed as: ， in, represents the excellent coefficient value of the internal microstructure of the solder joint calculated for the i-th time, Sd i represents the internal dislocation density of the solder joint collected for the i-th time, Se i Represents the interface energy of different crystal phases inside the solder joint collected for the i-th time.

8. The motor welding spot control method according to claim 1, characterized in that: The solder joint electrical performance data analysis node is used to establish a solder joint electrical performance data calculation model, import the solder joint electrical performance data transmitted in the data acquisition step into the solder joint electrical performance data calculation model, and obtain the solder joint electrical performance excellent coefficient value. The solder joint electrical performance data calculation model is specifically expressed as: ， in, Indicates the excellent coefficient value of the electrical performance of the solder joint calculated for the i-th time, Rf i Tp represents the rate of change of high-frequency skin depth collected for the i-th time. i Gp represents the temperature and thermoelectric potential changes collected for the i-th time. i It represents the parasitic inductance between the solder joint and the ground loop collected for the i-th time.

9. The motor welding spot control method according to claim 1, characterized in that: The motor welding spot data analysis unit is used to establish a motor welding spot data calculation model, and imports the welding energy quality coefficient value, welding pressure stability coefficient value, welding temperature rationality coefficient value, welding spot geometry quality characteristic value, welding spot internal microstructure excellence coefficient value and welding spot electrical performance excellence coefficient value transmitted by the data analysis step into the motor welding spot data calculation model to obtain the motor welding spot comprehensive evaluation index value. The motor welding spot data calculation model is specifically expressed as: ， Where A represents the calculated comprehensive evaluation index value of the motor welding spot, , , represents the welding energy quality coefficient value calculated for the i-th time, represents the welding pressure stability coefficient value calculated for the i-th time, represents the reasonableness coefficient of the welding temperature calculated for the i-th time, represents the geometric quality characteristic value of the solder joint calculated for the i-th time, represents the minimum value of the calculated solder joint geometry quality characteristic, represents the maximum value of the calculated geometric quality characteristics of the solder joint, represents the excellent coefficient value of the internal microstructure of the solder joint calculated for the i-th time, Indicates the minimum value of the calculated solder joint internal microstructure excellence coefficient, Indicates the maximum value of the calculated internal microstructure excellence coefficient of the solder joint. represents the excellent coefficient value of the electrical performance of the solder joint calculated for the i-th time, Indicates the minimum value of the calculated solder joint electrical performance coefficient. It indicates the maximum value of the calculated solder joint electrical performance coefficient, i indicates starting from the i-th number, and n indicates ending at the n-th number.

10. A motor welding spot control system, used to implement a motor welding spot control method as described in any one of claims 1 and 9, characterized in that: include: Data batch division module: used to determine the data to be collected as target data, divide the target data into different batches according to equal time division, and mark them as 1, 2, ..., n in sequence; Data acquisition module: including welding process data acquisition unit and welding spot quality data acquisition unit, used to collect target data in real time and transmit the collected data to the data analysis module; The welding process data acquisition unit is used to collect welding energy data, welding pressure data and welding temperature data; the solder joint quality data acquisition unit is used to collect solder joint geometry data, solder joint internal structure data and solder joint electrical performance data; Data analysis module: including a welding process data analysis unit and a solder joint quality data analysis unit, which are used to collect target data in real time and transmit the collected data to the data comprehensive evaluation module. The welding process data analysis unit includes a welding energy data analysis node, a welding pressure data analysis node, and a welding temperature data analysis node; the solder joint quality data analysis unit includes a solder joint geometry data analysis node, a solder joint internal structure data analysis node, and a solder joint electrical performance data analysis node; Data comprehensive evaluation module: including a motor welding spot data analysis unit, used for comprehensively analyzing the analysis results transmitted by the data analysis module, and transmitting the analysis results to the real-time feedback monitoring module; Real-time feedback monitoring module: used to establish a preset value of the comprehensive evaluation index of the motor welding spot, judge the comprehensive evaluation index value of the motor welding spot according to the preset value of the comprehensive evaluation index of the motor welding spot, and send a corresponding signal according to the judgment result.

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