Real-time monitoring method for welding state of precision terminal

Abstract

The invention provides a precision terminal welding state real-time monitoring method, which comprises the following steps of: acquiring a buckle root temperature distribution and an opening angle measured value from a target welding station through a thermal imager and a displacement sensor to obtain an initial temperature distribution and an opening angle; if the relevance between the temperature distribution and the structural deformation in the welding process exceeds a preset relevance threshold value, it is judged that the corresponding position of the buckle has the function failure risk and the quality abnormity, and the function failure risk grade and the quality abnormity position are recognized; matching the function failure risk level and the quality abnormality position with a pre-established process adjustment signal library to obtain an adjustment signal type corresponding to the current state; and sending a trigger instruction to a welding station according to the type of the adjusting signal, identifying the adjusting signal of the welding temperature and displacement control process, and adjusting according to the temperature control and displacement control of the buckle function failure risk and the welding quality abnormal point.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information technology, and in particular to a precision terminal welding state real-time monitoring method. BACKGROUND

[0002] Precision terminal welding is a key process in the electronic manufacturing field to ensure connection reliability, and its quality directly affects product performance and safety. In the welding process, high temperature not only completes terminal connection, but also has a complex impact on the surrounding structure, especially on the geometric stability of the buckle structure. As the core component of the terminal locking function, the interaction between the root deformation and the locking performance change of the buckle becomes an important factor restricting the welding quality. In the existing method, when monitoring the welding quality, the temperature or mechanical properties of the welding point itself are usually focused on, and the dynamic changes of the buckle structure under high temperature are ignored, making it difficult to fully capture the multi-dimensional geometric changes caused by the heating of the buckle root. This limitation leads to an inability to accurately predict the failure risk of the buckle locking function, especially under complex working conditions, the deviation of the opening angle or the change of the locking stroke of the buckle is often ignored, affecting the real-time optimization of the welding process. During the welding process, the buckle root will undergo a small but critical deformation after being heated, which directly affects the opening angle and locking stroke of the buckle, and thus weakens the locking function of the terminal. For example, in the welding of automobile wire harness terminals, if the temperature of the buckle root exceeds a certain threshold, it may cause the opening angle to deviate from the design value, resulting in insufficient locking force, causing the terminal to loosen or have poor contact. Therefore, how to real-time grasp the mutual influence between the temperature distribution of the buckle root, the opening angle, the locking stroke and the elastic recovery time during the welding process, and identify the buckle function failure risk through the correlation analysis of these parameters, has become a key problem in the quality control of precision terminal welding. SUMMARY

[0003] The present application provides a precision terminal welding state real-time monitoring method, mainly comprising:

[0004] Collecting the temperature distribution and the opening angle data of the buckle root from the target welding station, obtaining the initial temperature distribution and the opening angle; obtaining the locking travel length and the elastic recovery duration, combining the target station position, obtaining the temperature distribution, the opening angle, the locking travel value and the recovery duration of each station; extracting the temperature peak value of each station from the temperature distribution, determining the geometric size change value according to the opening angle and the locking travel value, evaluating the corresponding relationship between the temperature peak value and the geometric size change value, identifying the relevance of the temperature distribution and the structure deformation in the welding process; determining the functional failure risk level and the quality abnormal position according to the relevance of the functional failure risk and the quality abnormality of the buckle corresponding position; matching the functional failure risk level and the quality abnormal position with the pre-established process adjustment signal library, obtaining the temperature adjustment signal and the displacement adjustment signal; packaging the temperature adjustment signal and the displacement adjustment signal into trigger instructions and sending them to the welding station to adjust the temperature and displacement of the buckle functional failure risk point and the quality abnormal point.

[0005] Further, the collecting the temperature distribution and the opening angle data of the buckle root from the target welding station, obtaining the initial temperature distribution and the opening angle, comprises:

[0006] Scanning the target welding station by a thermal imager, obtaining the thermal field distribution image of the buckle root, converting the pixel gray value of the thermal field distribution image into a temperature value according to a temperature calibration curve, and constructing a temperature value matrix of the buckle root; detecting the edge point coordinates of the buckle opening on both sides by a displacement sensor, calculating the included angle between the edge connecting line and the horizontal reference plane, obtaining the opening angle data, recording the offset and height value of the sensor relative to the center of the station, and establishing the space positioning parameters of the station; associating the temperature value matrix and the opening angle data through a time stamp to form an initial state data group containing the station number, the temperature distribution characteristics and the opening angle.

[0007] Further, the obtaining the locking travel length and the elastic recovery duration, combining the target station position, obtaining the temperature distribution, the opening angle, the locking travel value and the recovery duration of each station, comprises:

[0008] The displacement of the buckle from the initial position to the locked position is measured by a laser ranging sensor, the distance between the start point and the end point coordinates is calculated, the buckle locking stroke length is obtained, the time interval from the maximum compression position to the stable state of the buckle is recorded, and the elastic recovery duration is obtained; the buckle locking stroke length and the elastic recovery duration are paired with the station position coordinates, combined with the initial temperature distribution and the opening angle, to form a comprehensive data record containing station coordinates, locking stroke, recovery duration, temperature and angle; the comprehensive data record is time-aligned, the Kalman filter algorithm is used to estimate the state of the temperature distribution, the opening angle, the locking stroke length and the elastic recovery duration, and the filtered state value is output; the missing data is compensated by linear interpolation of adjacent station parameters, arranged by station number, and the updated temperature distribution, opening angle, locking stroke value and recovery duration of each station are stored.

[0009] Further, the time sequence alignment of the comprehensive data record, the state estimation of the temperature distribution, the opening angle, the locking stroke length and the elastic recovery duration by the Kalman filter algorithm, and the output of the filtered state value, comprise:

[0010] Extract the temperature distribution, opening angle, locking stroke length and elastic recovery duration data of the same timestamp in the comprehensive data record; predict based on the state value and process noise covariance of the previous moment by the Kalman filter algorithm, update combined with the current measurement value and measurement noise covariance, generate the state estimation value of the temperature distribution, the opening angle, the locking stroke length and the elastic recovery duration; group and store the state estimation value according to the station number, update the real-time data of each station.

[0011] Further, the temperature peak value of each station is extracted from the temperature distribution, the geometric size change amount is determined according to the opening angle and the locking stroke value, the corresponding relationship between the temperature peak value and the geometric size change amount is evaluated, and the correlation between the temperature distribution and the structural deformation in the welding process is identified, comprising:

[0012] Traverse the temperature matrix of the temperature distribution, compare adjacent temperature values, extract the local maximum value of each station as the temperature peak value, record the peak timestamp and spatial coordinates; read the opening angle and the locking stroke value at the corresponding moment according to the timestamp, calculate the opening angle deviation value and the locking stroke difference value, and combine them into the geometric size change amount; calculate the linear correlation degree of the temperature peak value and the geometric size change amount by the Pearson correlation coefficient, compare the correlation coefficient with the preset threshold value to determine the corresponding relationship; fit the functional relationship between temperature and deformation by the least squares method, calculate the theoretical deformation value, compare the geometric size change amount with the theoretical deformation value, and identify the correlation between the temperature distribution and the structural deformation.

[0013] Further, the function of determining the functional failure risk level and the quality abnormal position according to the correlation determination of the buckle corresponding position includes:

[0014] Comparing the correlation value of the temperature distribution and the structure deformation with the preset correlation threshold, calculating the exceeding deviation value, extracting the buckle position coordinates of the corresponding station, determining the functional failure risk, determining the low, medium and high functional failure risk level; determining the abnormal part through the buckle position coordinates, marking the root, opening or middle abnormality, generating the identification result of the functional failure risk level and the quality abnormal position.

[0015] Further, the function of determining the functional failure risk level and the quality abnormal position according to the correlation determination of the buckle corresponding position includes:

[0016] Read the buckle position coordinates, compare the buckle position coordinates with the preset range of the buckle root, opening and middle area respectively, mark the abnormal type of the corresponding area; combine the functional failure risk level and the abnormal type to generate the risk level code and the abnormal position coordinates; summarize the risk level code and the abnormal position coordinates to form the identification result data group containing the station number, risk level and abnormal part.

[0017] Further, the function of matching the functional failure risk level and the quality abnormal position with the pre-established process adjustment signal library to obtain the temperature adjustment signal and the displacement adjustment signal includes:

[0018] Construct a query index key value composed of the functional failure risk level and the quality abnormal position, retrieve the process adjustment signal library through the query index key value, extract the temperature control parameter and the displacement control parameter; determine the adjustment amplitude according to the functional failure risk level, generate the temperature adjustment signal containing the target temperature value and the change rate, and the displacement adjustment signal containing the correction amount and the moving direction.

[0019] Further, the function of packaging the temperature adjustment signal and the displacement adjustment signal into trigger instructions and sending to the welding station to adjust the temperature and displacement of the buckle functional failure risk point and the quality abnormal point includes:

[0020] Packaging the temperature adjustment signal and the displacement adjustment signal into control instruction data packet, containing station address code and adjustment parameter value, sending to the target welding station through the communication bus; analyzing the control instruction data packet, extracting the target temperature value, change rate, displacement and moving direction; sending power adjustment instruction to the welding power supply to adjust the temperature, sending position adjustment instruction to the clamp driving device to adjust the buckle position, updating the temperature and displacement of the buckle functional failure risk point and the quality abnormal point.

[0021] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0022] This invention discloses a real-time monitoring method for the welding status of precision terminals. It collects temperature distribution and opening angle data at the root of the welding station's latch using a thermal imager and displacement sensor. The method then performs real-time correlation processing of temperature, angle, locking stroke length, and elastic recovery time to construct a station data group. This addresses the business problem of analyzing the correlation between temperature distribution and structural deformation during welding and identifying functional failure risks. By storing station data in groups, this invention extracts peak temperature and geometric dimensional changes, evaluates their correlation, and determines that stations exceeding preset thresholds have a functional failure risk. High, medium, and low-risk failure points and quality anomaly locations are marked, generating a failure risk list. Furthermore, by matching a process adjustment signal library, temperature and displacement adjustment signals are generated to trigger real-time optimization control of the welding station. This invention achieves precise correlation analysis between temperature and deformation, intelligent risk identification, and adaptive process adjustment, improving welding quality stability and latch function reliability. Attached Figure Description

[0023] Fig. 1 This is a flowchart of a method for real-time monitoring of the welding status of precision terminals according to the present invention.

[0024] Fig. 2 This is a schematic diagram of a method for real-time monitoring of the welding status of precision terminals according to the present invention.

[0025] Fig. 3 This is another schematic diagram of a method for real-time monitoring of the welding status of precision terminals according to the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and thoroughly described below with reference to the accompanying drawings. The described embodiments are merely some embodiments of the present invention.

[0027] like Figs. 1-3 This embodiment of a method for real-time monitoring of the welding status of precision terminals may specifically include:

[0028] Step S101: The measured values ​​of the temperature distribution and opening angle at the root of the buckle are collected from the target welding station using a thermal imager and a displacement sensor to obtain the initial temperature distribution and opening angle.

[0029] A thermal imager scans the target welding station to acquire an image of the thermal field distribution at the base of the buckle, identifying areas of temperature gradient change. Based on the thermal imager's built-in temperature calibration curve, the grayscale value of each pixel in the thermal field distribution image is converted into a corresponding Celsius temperature value, constructing a temperature value matrix for each point at the base of the buckle. A displacement sensor detects the buckle opening, recording the spatial coordinates of the edge points on both sides of the opening. The angle between the line connecting the two edges and the horizontal reference plane is calculated using these coordinates, obtaining the measured opening angle value. Simultaneously, the sensor's offset and height relative to the station center are recorded, establishing spatial positioning parameters for each station. The temperature distribution characteristics of the buckle base region are extracted based on the temperature value matrix. The measured opening angle value is correlated with the spatial positioning parameters of the corresponding station. By matching the temperature distribution characteristics and opening angle data at the same acquisition time using timestamps, an initial state data set containing the station number, temperature distribution characteristics, and opening angle is formed, yielding the initial temperature distribution and opening angle for each target welding station.

[0030] In one embodiment, when the thermal imager scans the welding station, it employs an infrared thermal imaging sensor with a resolution of 320×240 pixels. Each pixel corresponds to an area of ​​approximately 0.5 mm × 0.5 mm on the surface of the buckle root. The thermal imager's built-in temperature calibration curve is established based on the blackbody radiation law. Multiple temperature calibration points are pre-selected within the range of 20℃ to 800℃, and the infrared radiation intensity values ​​corresponding to each calibration point are recorded to form a grayscale-temperature mapping table. The temperature calibration curve is constructed using a piecewise linear interpolation method. In the normal temperature range of 20℃ to 200℃, dense calibration points are used, with one calibration point set every 10℃. In the high temperature range of 200℃ to 800℃, calibration points are set every 50℃. After the thermal imager acquires an infrared image of the buckle root, it reads the 16-bit grayscale value of each pixel and calculates the precise Celsius temperature value by looking up two adjacent calibration points in the mapping table and using linear interpolation.

[0031] For example, a pixel with a grayscale value of 32768, when looked up in the table, is found to be between the grayscale value of 30000 corresponding to 350℃ and the grayscale value of 35000 corresponding to 400℃. Through linear interpolation, the temperature at this point is calculated to be 386.7℃, thus constructing a complete temperature value matrix. The displacement sensor uses the laser triangulation principle, emitting a laser beam to both sides of the buckle opening and receiving the reflected light to determine the three-dimensional coordinates of the edge points. The sensor establishes a coordinate system with the welding station center as the origin, recording the coordinates of the left edge point (x1, y1, z1) and the right edge point (x2, y2, z2).

[0032] In one possible implementation, the opening angle is calculated by the angle between the line connecting the two edge points and the horizontal reference plane. Specifically, the direction vector of the line connecting the two points is first determined, and then the angle between this vector and the XY plane is calculated to obtain the precise value of the opening angle.

[0033] For example, the extraction of temperature distribution features focuses on the key area at the root of the buckle. The 5×5 pixel area closest to the welding point is selected as the core monitoring area, and the average temperature, maximum temperature and temperature gradient value of this area are calculated as feature parameters.

[0034] Preferably, timestamp matching employs millisecond-level precision. The thermal imager and displacement sensor control data acquisition through a unified clock synchronization module, ensuring that the time deviation between temperature and angle data is less than 10 milliseconds. By establishing a circular buffer to store the most recent 100 sets of acquired data, the system can quickly retrieve and match multi-source data at the same time. During the formation of the initial state data set, abnormal data points are automatically eliminated. When the temperature value exceeds a reasonable range or an angle measurement jumps, the average value of the preceding and following times is used for compensation, ensuring data continuity and reliability.

[0035] Step S102: Obtain the buckle locking stroke length and elastic recovery time, and combine them with the location of the target station to perform real-time correlation processing on the initial temperature distribution, opening angle, buckle locking stroke length and elastic recovery time to obtain the temperature distribution, opening angle, buckle locking stroke value and recovery time of each station.

[0036] A laser rangefinder sensor is used to measure the displacement change of the buckle from its initial position to its locked position. The coordinates of the starting and ending positions during the buckle locking process are recorded. The buckle locking stroke length is obtained by calculating the distance between the two points. Simultaneously, a timer is started to record the time interval required for the buckle to recover from its maximum compression position to a stable state, thus obtaining the elastic recovery time value. Based on the buckle locking stroke length and elastic recovery time values, the position coordinates provided by the station encoder are read. The coordinate values ​​of each station are paired and stored with the buckle performance parameters collected at the corresponding time. The initial temperature distribution and opening angle data recorded at each station are retrieved to form a comprehensive data record containing station coordinates, locking stroke, recovery time, temperature, and angle. The comprehensive data record is time-series aligned, and the parameter data under the same timestamp are extracted. A Kalman filter algorithm is used to estimate the state of temperature distribution, opening angle, locking stroke length, and elastic recovery time. The prediction step uses the state value and process noise covariance of the previous time step, and the update step uses the current measurement value and measurement noise covariance. The filtered state estimate value is output. If data for a certain workstation is missing, linear interpolation compensation is performed using the corresponding parameters of adjacent workstations. Based on the complete dataset after compensation, the data is sorted and organized according to the workstation number. The estimated state values ​​are then grouped and stored by workstation to obtain the real-time updated temperature distribution, opening angle, buckle locking stroke value, and recovery time for each workstation.

[0037] In one embodiment, the laser rangefinder sensor uses the triangulation principle to accurately measure the buckle displacement. The sensor emits a 650 nm wavelength laser beam onto the buckle surface, and captures the position of the reflected light spot through a built-in CCD receiver. The target distance is calculated based on the displacement of the light spot on the receiver. During the measurement of the buckle locking stroke length, the sensor continuously records the buckle position data at a sampling frequency of 1000 Hz. When the buckle is in the initial relaxed state, the starting position coordinate value D0 is recorded; under the action of welding thermal stress, the buckle gradually tightens and reaches the maximum compression position, and the ending position coordinate value D1 is recorded; the locking stroke length is obtained by calculating |D1-D0|. Simultaneously, a timer starts timing from when the buckle reaches the maximum compression position, continuously monitoring the buckle position change. When the position change of 10 consecutive sampling points is less than 0.01 mm, it is determined that the buckle has recovered to a stable state, and the elapsed time interval is recorded as the elastic recovery time value. The station encoder uses an absolute value encoding method, with each station configured with a unique 12-bit binary code, and transmits the position coordinates to the data acquisition system in real time via an RS485 bus.

[0038] Preferably, the comprehensive data record is formed using an associative array structure, with the timestamp as the primary key, and the workstation coordinates, locking stroke, recovery time, temperature distribution, and opening angle stored as associated attributes. Each record contains six fields: workstation code, XYZ three-dimensional coordinates, locking stroke value, recovery time, temperature matrix data, and angle measurement value.

[0039] In one possible implementation, the state estimation process of the Kalman filter algorithm is divided into two stages: prediction and update. In the prediction stage, based on the previous state estimate x(k-1) and the state transition matrix F, the prior state estimate x′(k) = F × x(k-1) + w is calculated, where w is the process noise, following a normal distribution with zero mean and covariance Q. The prior error covariance matrix P′(k) = F × P(k-1) × F^T + Q is used to quantify the uncertainty of the prediction. In the update stage, the Kalman gain K(k) = P′(k) × H^T × (H × P′(k) × H^T + R)^(-1) is calculated based on the measured value z(k), where H is the observation matrix and R is the measurement noise covariance. The posterior state estimate x(k) = x′(k) + K(k) × (z(k) - H × x′(k)) integrates the information from the predicted and measured values. The posterior error covariance P(k) = (IK(k) × H) × P′(k) characterizes the estimation accuracy after filtering. Dynamic tracking and noise suppression of temperature distribution, opening angle, locking stroke, and recovery time are achieved through iterative execution of prediction and update steps. The state vector x contains four components: mean temperature, opening angle, locking stroke, and recovery time. The process noise covariance Q is set according to the stability of the welding process; the standard deviation of process noise for the temperature component is 2℃, for the angle component it is 0.5 degrees, for the stroke component it is 0.1 mm, and for the duration component it is 0.05 seconds. The measurement noise covariance R is determined based on sensor accuracy; the standard deviation of measurement noise for the thermal imager is 1℃, and for the displacement sensor it is 0.05 mm. Data missing is determined based on continuity testing; when a station fails to acquire valid data at the expected sampling time, or when the data value exceeds the physically reasonable range, an interpolation compensation mechanism is triggered. Linear interpolation uses the corresponding parameter values ​​of two adjacent stations, weighted according to spatial distance.

[0040] For example, if the temperature data for workstation i is missing, the temperature values ​​T(i-1) and T(i+1) of the adjacent workstations i-1 and i+1 are selected, and the interpolated temperature is calculated based on the distances d1 and d2 between the workstations.

[0041] T(i) = (d2×T(i-1)+d1×T(i+1)) / (d1+d2). Data is grouped and stored using an index structure based on workstation number. Each workstation maintains an independent circular buffer to store the most recent 1000 sets of historical data.

[0042] The collected initial temperature distribution data is grouped and stored according to the workstation number. The opening angle of each workstation is recorded synchronously. The locking stroke length and corresponding elastic recovery time of each buckle are obtained. A table corresponding to the workstation number and temperature value is established. By recording the opening angle of each workstation during the target time period, the real-time change of the opening angle is obtained. Complete data of buckle locking stroke and elastic recovery time are supplemented to obtain a data set containing workstation temperature, opening size, stroke length and recovery time.

[0043] Based on the collected initial temperature distribution data, an index table is established according to the workstation number. Temperature distribution data for each workstation number is grouped and stored. Simultaneously, the opening angle values ​​for each workstation are recorded at the same time. A synchronization relationship between temperature and angle is established using timestamps, resulting in an initial dataset grouped by workstation. For this initial dataset, the locking stroke length measurement value of each latch is read from the displacement sensor, and the corresponding elastic recovery time record is obtained from the timer. A correspondence table between workstation number and temperature value is established, recording the temperature distribution data and timestamp information for each workstation, forming a workstation temperature mapping table. The workstation position is determined using this temperature mapping table. Opening angle data for each workstation is continuously collected within the target time period. The difference between the current angle value and the initial angle value is calculated to obtain the real-time change in opening angle. Simultaneously, data at the interruption points of the latch locking stroke is supplemented, filling in missing time records during the elastic recovery process. Based on the real-time change in opening angle and the supplemented complete data, various parameters are integrated in chronological order, and records with duplicate timestamps are removed, resulting in a data set containing workstation temperature, opening size, stroke length, and recovery time.

[0044] In one embodiment, the index table is implemented using a hash table data structure, with the workstation number as the key and temperature distribution data as the value range. The temperature distribution data for each workstation contains a two-dimensional array, where rows represent sampling points along the length of the clip and columns represent sampling points along the width. Each array element stores the temperature value at the corresponding location. The index table supports fast lookup and update operations, with a lookup time complexity of constant time. The timestamp is based on a unified system clock, achieving millisecond-level precision. During each data acquisition, both the temperature distribution data and the opening angle value are recorded simultaneously and stamped with the same timestamp to ensure data consistency.

[0045] Specifically, establishing time synchronization involves the coordinated operation of multiple sensors. Although the thermal imager and displacement sensor have different sampling frequencies (25Hz for the thermal imager and 100Hz for the displacement sensor), data alignment is achieved through interpolation algorithms. When complete data for a specific moment is needed, the nearest temperature sampling point is first located, and then the corresponding angle value is precisely matched from the high-frequency displacement data. If the temperature sampling moment is t1, and the displacement sensor's sampling moments before and after t1 are t0 and t2 respectively, then the angle value at moment t1 is calculated through linear interpolation: Angle(t1) = Angle(t0) + (Angle(t2) - Angle(t0)) × (t1 - t0) / (t2 - t0). (Angle(t2) - Angle(t0)) represents the change in angle within the time interval [t0, t2]. This interpolation method ensures time alignment of sensor data from different sampling frequencies, forming an initial dataset grouped by workstation and synchronized in time.

[0046] Preferably, the workstation temperature mapping table adopts a three-layer nested structure. The first layer is the workstation index layer, which stores the workstation number; the second layer is the time index layer, which stores data records for each moment in order of timestamp; the third layer is the data storage layer, which contains the temperature distribution matrix, temperature statistical feature values, and associated sensor readings. The temperature statistical feature values ​​include derived data such as maximum temperature, average temperature, and temperature gradient.

[0047] In one possible implementation, the locking stroke length is measured using a laser displacement sensor mounted on the side of the latch, perpendicular to its direction of movement. The elastic recovery time is measured with the assistance of a high-speed camera, recording the complete process of the latch returning to its natural state from its maximum compressed position.

[0048] For example, the data supplementation mechanism handles two scenarios. The first is data loss due to sensor malfunction; when no data input is detected at a certain workstation within a specific time period, cubic spline interpolation is performed using data from previous and subsequent time points. The second is data interruptions caused by communication delays; a buffer is maintained to store the most recent historical data, and when a data sequence discontinuity is detected, data from the corresponding time period is extracted from the buffer to fill the gap. The calculation of the real-time change in the opening angle considers temperature compensation. The snap-fit ​​material undergoes thermal expansion at high temperatures, causing the measured angle value to include a thermal deformation component. Based on the current temperature and the material's coefficient of thermal expansion, the angle offset caused by thermal deformation is calculated, and this offset is subtracted from the measured angle change to obtain the true mechanical deformation angle.

[0049] For example, in a certain welding process, the initial opening angle of station W08 was 30 degrees and the temperature was 25℃. After welding started, the temperature rose to 450℃, and the measured opening angle was 35.2 degrees. Based on the coefficient of thermal expansion of the buckle material of 1.2×10^-5 / ℃, the calculated thermal deformation angle was 1.5 degrees. Therefore, the actual mechanical deformation caused by the angle change was 3.7 degrees.

[0050] Understandably, the deduplication process during data integration is based on the principle of timestamp uniqueness. When multiple records are found to have the same timestamp, the system compares the data integrity of each record, retains the record with the most complete fields, and removes redundant data. The resulting data set is a structured multidimensional dataset, with each workstation corresponding to a complete time series data set, including temperature evolution, angle change trajectory, stroke displacement curve, and elastic recovery characteristics.

[0051] Step S103: Extract the temperature peak value of each station according to the temperature distribution, identify the geometric dimension change according to the opening angle and the clamping stroke value, evaluate the correspondence between the temperature peak value and the geometric dimension change, and identify the correlation between temperature distribution and structural deformation during welding based on the correspondence.

[0052] The temperature matrix of each workstation is traversed from the temperature distribution data. Local maxima are determined by comparing adjacent temperature values. The temperature peak value and its corresponding spatial coordinates for each workstation are extracted, and the timestamp of the peak value is recorded to obtain the workstation temperature peak sequence. Based on the timestamp of the workstation temperature peak sequence, the opening angle value and the locking stroke value at the corresponding moment are read. The deviation of the opening angle from the initial state is calculated as the angle change, and the difference of the locking stroke from the initial stroke is calculated as the stroke change. The angle change and stroke change are combined to obtain the geometric dimension change. Multiple sets of temperature and deformation data records are established for the geometric dimension change and the corresponding temperature peak. The Pearson correlation coefficient is used to calculate the linear correlation between the temperature peak sequence and the geometric dimension change sequence. If the absolute value of the correlation coefficient is greater than a preset threshold, a significant correspondence is determined. Based on this correspondence, the least squares method is used to fit the functional relationship between temperature and deformation. The temperature distribution data of each workstation is substituted into the function to calculate the theoretical deformation value. The consistency between the geometric dimension change and the theoretical deformation value is compared to identify the correlation between temperature distribution and structural deformation during the welding process.

[0053] In one embodiment, the temperature peak is extracted using a local extremum search method. The temperature matrix of each workstation is traversed, and the temperature distribution data is scanned using a 3×3 sliding window. When the temperature value at the center point is greater than the temperature values ​​of its eight neighboring points, that point is determined to be a local peak. The temperature value, two-dimensional coordinates, and timestamp of the acquisition time of the peak point are recorded.

[0054] It should be noted that the change in geometric dimensions includes two components: the change in angle and the change in stroke. The change in angle is obtained by subtracting the initial opening angle from the current opening angle, and the change in stroke is obtained by subtracting the initial locking stroke from the current locking stroke.

[0055] Specifically, the calculation of the Pearson correlation coefficient involves statistical processing of the temperature peak sequence and the geometric dimension change sequence. Let the temperature peak sequence be T = {T1, T2, ..., Tn}, and the geometric dimension change sequence be G = {G1, G2, ..., Gn}, where n is the number of sampling points. Calculate the average value T_avg of the temperature sequence and the average value G_avg of the geometric dimension change sequence. For each data point i in the sequence, calculate the temperature deviation (Ti - T_avg) and the geometric deviation (Gi - G_avg). Sum the products of the deviations of all corresponding points to obtain the numerator of the covariance. Calculate the standard deviations of the temperature and geometric sequences, i.e., the square root of the sum of the squares of their respective deviations. The Pearson correlation coefficient r is equal to the covariance divided by the product of the two standard deviations. When the absolute value of r is greater than 0.7, a strong correlation is considered between the two variables; when the absolute value of r is between 0.3 and 0.7, a moderate correlation is considered; and when it is less than 0.3, a weak or no correlation is considered. The direction of correlation is determined by the sign of the correlation coefficient: a positive value indicates a positive correlation, meaning the geometric dimension increases with increasing temperature; a negative value indicates a negative correlation, meaning the geometric dimension decreases with increasing temperature. When establishing multiple sets of temperature and deformation data records, the system aligns the data from each workstation in chronological order. Each record contains five fields: workstation number, acquisition time, peak temperature, angle change, and stroke change, forming a structured data table.

[0056] In one possible implementation, the least squares fitting process determines the functional relationship between temperature and deformation by minimizing the sum of squared residuals. A linear function model is defined as y = ax + b, where x is the temperature value, y is the deformation value, and a and b are parameters to be determined. Based on n sets of temperature and deformation data points, an error function E = ∑(yi - axi - b) is established. 2 For the i-th data point (xi) i The difference between the observed value yi and the model predicted value (axi+b) is called the residual. By taking the derivative and setting it to zero, we obtain the parameter a = (n∑xiyi-∑xi∑yi) / (n∑xi 2 -(∑xi) 2The parameter b = (∑yi - a∑xi) / n is used as the temperature-deformation mapping model. In calculating the theoretical deformation value, the temperature value of each measuring point in the real-time collected temperature distribution data is substituted into the fitting function to obtain the corresponding predicted deformation value. When the temperature at a certain workstation is 450℃, the theoretical deformation value is calculated to be 5.9 mm according to the fitting function y = 0.012x + 0.5. The consistency comparison uses the relative error judgment method. The difference between the measured geometric dimension change and the theoretical deformation value is calculated and divided by the theoretical deformation value to obtain the relative error percentage. When the relative error is less than 15%, the temperature and deformation are considered to have good consistency.

[0057] For example, in automotive wiring harness terminal welding scenarios, when the temperature at the base of the clip increases from room temperature (25°C) to welding temperature (650°C), the opening angle changes from an initial 30 degrees to 38 degrees, and the locking stroke changes from 5 mm to 7.8 mm. Correlation analysis revealed a strong positive correlation of 0.85 between temperature and deformation. The correlation results were used to determine the rationality of the welding process parameters. When the correlation coefficient between temperature distribution and structural deformation exceeds 0.8 and the consistency error is less than 10%, it indicates that the thermo-mechanical coupling effect during welding is under control, and the clip function will not fail due to thermal deformation.

[0058] Step S104: If the correlation between temperature distribution and structural deformation during welding exceeds a preset correlation threshold, it is determined that there is a risk of functional failure and quality abnormality at the corresponding position of the buckle, and the level of functional failure risk and the position of quality abnormality are identified.

[0059] The correlation values ​​between temperature distribution and structural deformation are obtained and compared with a preset correlation threshold. If the correlation value exceeds the preset threshold, the difference between the correlation value and the threshold is calculated as the out-of-range deviation value. The coordinates of the corresponding workstation's buckle position are extracted, and the location is determined to have a functional failure risk. Based on the magnitude of the out-of-range deviation value, a pre-established three-level risk classification standard is used. When the out-of-range deviation value is less than a first preset value, it is determined to be a low-risk level; when the out-of-range deviation value is between the first and second preset values, it is determined to be a medium-risk level; and when the out-of-range deviation value is greater than the second preset value, it is determined to be a high-risk level, thus obtaining the functional failure risk level. For the functional failure risk level and buckle position coordinates, the specific location of the anomaly is determined by the coordinate values. When the coordinates are within the buckle root area, it is marked as a root anomaly; when they are within the opening area, it is marked as an opening anomaly; and when they are within the middle area, it is marked as a middle anomaly, thus obtaining the functional failure risk level and the identification result of the quality anomaly location.

[0060] In one embodiment, the comparison between the correlation value and the preset correlation threshold uses a difference calculation method. When the correlation value between temperature distribution and structural deformation is 0.82 and the preset correlation threshold is 0.75, the calculated deviation value is 0.07, indicating a risk of functional failure. The three-level risk classification standard is established based on statistical analysis of a large amount of welding test data. The first preset value is set as a deviation value of 0.05, and the second preset value is set as 0.15. The determination of these two thresholds takes into account the elastic limit of the snap-fit ​​material and the influence of welding temperature on material properties. When the deviation value is less than 0.05, it indicates that the thermal deformation is within the elastic range of the material, and the snap-fit ​​function is basically unaffected, which is judged as low risk; when it is between 0.05 and 0.15, the material may undergo local plastic deformation, and the snap-fit ​​locking force will decrease, which is judged as medium risk; when it is greater than 0.15, the material undergoes significant permanent deformation, and the snap-fit ​​function is severely damaged, which is judged as high risk.

[0061] Specifically, the position coordinates of the buckle are obtained by establishing a local coordinate system, with the center of the buckle root as the origin, the X-axis along the buckle length direction, and the Y-axis perpendicular to the buckle plane.

[0062] Preferably, the abnormal location is divided using a proportional segmentation method. The root region is defined as 30% of the total length from the origin to the latch, the middle region is 30% to 70%, and the opening region is 70% to 100%. The specific abnormal location is determined by judging which interval the X-value of the abnormal point's coordinates falls into.

[0063] In one possible implementation, when an out-of-range deviation of 0.08 is detected at a certain workstation, the system determines that the workstation has a medium risk. Simultaneously, the X-coordinate of the abnormal point is located at 85% of the total length of the clip, indicating an abnormal opening. This combined information suggests that the opening of the clip has a medium risk of functional failure due to the heat effects of welding.

[0064] For example, the identification results of the quality anomaly location are output in the form of a tag code, such as "M2-0" indicating a medium-risk level opening anomaly and "H1-R" indicating a high-risk level root anomaly, which facilitates rapid identification and response by the welding process adjustment system.

[0065] Extract the workstation locations where the temperature peak exceeds the normal welding range, filter the buckle numbers with abnormal geometric dimensional changes, determine the overlapping areas where temperature abnormalities and deformation abnormalities occur simultaneously, mark the overlapping areas as high-risk failure points, record the specific coordinates of medium-risk and low-risk failure points, mark the accurate location of quality abnormal points at the root or opening of the buckle, and generate a failure risk list containing risk level codes and abnormal location coordinates.

[0066] The temperature peak data of each workstation is traversed, and the peak value is compared with the preset upper and lower thresholds of normal welding temperature. When the temperature peak value is lower than the lower threshold or higher than the upper threshold, the position code and spatial coordinates of the workstation are extracted to form a list of workstations with abnormal temperature. The change values ​​of each clip are read from the geometric dimension change data and compared with the preset standard deviation range. Clip numbers whose changes exceed the standard deviation range are selected, and the workstation positions corresponding to the clip numbers are recorded to obtain a list of workstations with abnormal deformation. The intersection operation of the list of workstations with abnormal temperature and the list of workstations with abnormal deformation is performed to determine the overlapping area where temperature abnormality and deformation abnormality occur simultaneously. The overlapping area is marked as a high-risk failure point. Workstations with only temperature abnormality are marked as medium-risk failure points, and workstations with only deformation abnormality are marked as low-risk failure points. The specific coordinate values ​​of failure points of each risk level are recorded. The location of the quality anomaly point on the buckle is determined based on the specific coordinate value. When the coordinate is within the preset area at the root of the buckle, it is marked as a root anomaly. When it is within the preset area at the opening, it is marked as an opening anomaly. A coding method combining risk level code and location code is used to summarize the risk level code and anomaly location coordinates of each failure point to generate a failure risk list.

[0067] In one embodiment, the anomaly determination of temperature peaks is based on the temperature range defined by the welding process specifications. The upper threshold for normal welding temperature is set at 680℃, and the lower threshold at 420℃. These thresholds are determined based on the thermal stability of the snap-fit ​​material and welding quality requirements. When traversing the temperature peak data of all workstations, a point-by-point comparison method is used for filtering. The list of workstations with abnormal temperatures is stored using a structured array, with each record containing four fields: workstation number, temperature peak, type of exceedance, and spatial coordinates. Exceedance types are divided into high-temperature anomalies and low-temperature anomalies to facilitate differentiation of different failure modes. The anomaly determination of geometric dimensional changes involves a comprehensive evaluation of two dimensions: angle change and stroke change. The standard deviation range is determined based on the snap-fit's design tolerances; the allowable deviation for angle change is ±3 degrees, and the allowable deviation for stroke change is ±0.8 mm. When either dimension exceeds the deviation range, the snap-fit ​​is determined to have a deformation anomaly. The list of workstations with deformation anomalies records the snap-fit ​​number, change value, deviation degree, and workstation location information for each anomaly point.

[0068] Preferably, the intersection operation uses a hash table for fast matching. A hash table is built using the station numbers in the temperature anomaly station list as keys. When traversing the deformation anomaly station list, a hash lookup is used to determine if a matching station number exists. If it does, the station belongs to an overlapping area and is marked as a high-risk failure point; if it only exists in the temperature anomaly list, it is marked as medium-risk; and if it only exists in the deformation anomaly list, it is marked as low-risk. This classification reflects the degree of impact of different anomaly types on the locking function: dual anomalies of temperature and deformation indicate thermo-mechanical coupling failure, which is the highest risk; a single temperature anomaly may lead to material performance degradation; and a single deformation anomaly mainly affects the mechanical locking function.

[0069] In one possible implementation, the determination of abnormal locations is based on the geometric features of the latch. The pre-defined region at the latch root is defined as extending 35% of the latch's length from the connection point with the base; the pre-defined region at the opening is defined as extending 35% of the latch's length inward from the end; the middle 30% is a transition zone. This division considers the functional characteristics of different parts of the latch: the root primarily bears bending stress and is sensitive to temperature; the opening directly participates in the locking action and is sensitive to deformation.

[0070] For example, the risk level coding uses a combination of letters and numbers. Risk level codes use H, M, and L to represent high, medium, and low risk, respectively; location codes use R, 0, and T to represent the root, opening, and transition zone, respectively. The coding format is "risk level-location-serial number", such as "HR-001" representing the first high-risk root anomaly point. Furthermore, the failure risk list data structure is designed as a multi-level relational table. The main table records basic information about the failure points, including code, workstation number, risk level, and anomaly type; the sub-tables record detailed data, including peak temperature, deformation, coordinate values, and timestamps. Primary key association enables rapid data retrieval and updating.

[0071] For example, during a welding process monitoring, the peak temperature at station W15 reached 712℃, exceeding the upper limit threshold of 32℃; simultaneously, the change in the opening angle at this station was 4.5 degrees, exceeding the allowable deviation of 1.5 degrees. This station was determined to be a high-risk failure point. Coordinate analysis showed that the anomaly was located in the opening area, generating the code "H-0-015" and recording it in the failure risk list.

[0072] Understandably, the real-time update mechanism of the failure risk list ensures the timeliness of welding quality control. After data collection and analysis are completed for each welding cycle, newly identified failure points are added to the end of the list, and statistical information is updated, including the number of failure points at each risk level, the distribution of anomaly types, and the locations of high-incidence anomalies, providing data support for welding process optimization.

[0073] Step S105: Match the functional failure risk level and quality anomaly location with the pre-established process adjustment signal library to obtain the adjustment signal type corresponding to the current state.

[0074] The system reads data on the risk level of functional failure and the location of quality anomalies, constructs a query index key value, which is composed of a risk level code and an anomaly location code. It then accesses a pre-established process adjustment signal library and retrieves the corresponding adjustment parameter set using the index key value. Based on the data in the adjustment parameter set, it extracts temperature control parameters and displacement control parameters. If the risk level is high, it obtains a large adjustment parameter value; if the risk level is medium or low, it obtains a small adjustment parameter value, determining the temperature adjustment range and displacement adjustment range. For each temperature and displacement adjustment range, it generates corresponding control signals. The temperature adjustment signal includes the target value and rate of change of the welding temperature, and the displacement adjustment signal includes the correction amount for the latch position and the direction of movement data, resulting in temperature and displacement adjustment signals corresponding to the current state.

[0075] In one embodiment, the process adjustment signal library uses a relational database structure to store adjustment parameters. The database consists of two parts: a main table and a parameter table. The main table uses the query index key as the primary key and stores risk level codes, abnormal location codes, and parameter set numbers. The parameter table stores the specific temperature control parameters and displacement control parameter values.

[0076] It should be noted that the index key value is constructed by concatenating the risk level code and the abnormal location code. When the risk level is high and the abnormal location is the root, the index key value is "HR"; when the risk level is medium and the abnormal location is the opening, the index key value is "MO".

[0077] Specifically, the adjustment parameter set includes four basic parameters: temperature decrease, temperature increase, displacement forward, and displacement backward. For high-risk levels, the temperature adjustment parameter ranges to ±15% of the reference temperature, and the displacement adjustment parameter is ±2 mm from the reference position. For medium- and low-risk levels, the temperature adjustment parameter range is reduced to ±5%, and the displacement adjustment parameter is ±0.5 mm. This differentiated setting ensures that the adjustment intensity matches the risk level.

[0078] Preferably, during the determination of the temperature adjustment range and the displacement adjustment range, a secondary correction is made based on the location of the abnormality. If the abnormality occurs at the root of the buckle, the temperature adjustment range is multiplied by a correction factor of 1.2; if it occurs at the opening, the displacement adjustment range is multiplied by a correction factor of 1.3.

[0079] In one possible implementation, the temperature adjustment signal is encoded using pulse width modulation (PWM). The target value is encoded as a 16-bit binary number, representing the desired welding temperature; the rate of change data is encoded as an 8-bit number, representing the temperature change per second. The displacement adjustment signal uses a stepper motor control format, with the correction amount converted into a number of steps, and the direction of movement indicated by positive or negative signs.

[0080] For example, when a high-risk root anomaly is detected, a temperature reduction value of 80°C and a displacement backsliding amount of 1.5 mm are extracted from the adjustment parameter set. After root correction, the actual temperature adjustment range is 96°C. The generated temperature adjustment signal contains a control command for a target temperature of 550°C and a cooling rate of 20°C / second.

[0081] Step S106: Send a trigger command to the welding station to adjust the signal type, identify the adjustment signals of the welding temperature and displacement control process, and adjust the temperature control and displacement control for the risk of buckle function failure and abnormal welding quality points.

[0082] The adjustment signal type is encapsulated into a control command data packet and sent to the target welding station via the communication bus. The data packet contains the station address code, command type identifier, and adjustment parameter values. The station controller receives the data packet, verifies and parses it to obtain a trigger command. Based on the content of the trigger command, the specific parameters of the temperature adjustment signal and the displacement adjustment signal are identified. If it is a temperature adjustment signal, the target temperature value and rate of change are extracted; if it is a displacement adjustment signal, the displacement amount and direction of movement are extracted to determine the temperature control parameters and displacement control parameters. For the temperature control parameters and displacement control parameters, a power adjustment command is sent to the welding power source to change the welding temperature, and a position adjustment command is sent to the fixture drive device to change the clamp position. Sensors monitor the actual changes in temperature and position in real time, and adjustments are made to the temperature and displacement at points of risk of clamp failure and welding quality abnormalities.

[0083] In one embodiment, the control command data packet adopts a fixed-length frame format, containing an 8-byte data field. The first two bytes are the station address code, used to identify the target welding station; the third byte is the command type identifier, where 01H indicates temperature adjustment, 02H indicates displacement adjustment, and 03H indicates combined adjustment; subsequent bytes store the specific adjustment parameter values. The communication bus adopts the RS485 serial communication protocol, with a transmission rate set to 9600bps to ensure the real-time performance and reliability of command transmission. Each station controller is configured with a unique address code and receives the corresponding control command through an address matching mechanism.

[0084] Specifically, the execution of temperature control parameters involves pulse width modulation control of the welding power supply. Upon receiving a temperature adjustment signal, the controller converts the target temperature value into a corresponding duty cycle parameter, and changes the average power output by adjusting the on / off time ratio of the welding current.

[0085] For example, when a temperature reduction of 50°C is required, the duty cycle is adjusted from 80% to 65%, which correspondingly reduces the average output power of the welding power source, thereby achieving precise temperature control. The rate of change parameter determines the adjustment step size of the duty cycle, preventing sudden temperature changes from causing thermal shock to the snap-fit ​​material.

[0086] Preferably, displacement control is achieved through a stepper motor drive system. The fixture drive device includes a two-phase hybrid stepper motor. After receiving the position adjustment command, the motor driver calculates the required number of pulses based on the displacement. Each pulse corresponds to a displacement accuracy of 0.01 mm, and the direction of movement is determined by the motor rotation direction control signal.

[0087] In one possible implementation, real-time monitoring employs a closed-loop control architecture. A temperature sensor acquires the actual temperature value every 100 milliseconds, while a displacement encoder provides real-time feedback on the current position of the clamp. The controller compares the measured values ​​with the target values, calculates the deviation, and dynamically adjusts the control parameters based on the magnitude of the deviation. When a high-risk failure point is detected at the base of the clamp, the system simultaneously sends a combined adjustment command for both temperature reduction and displacement retraction. While the welding power supply reduces its output power, the clamp moves backward by 1.2 millimeters, disengaging the clamp from the high-temperature zone and effectively reducing the risk of thermal deformation.

[0088] Understandably, this real-time adjustment can dynamically compensate for temperature and displacement deviations during the welding process, ensuring that the locking function of the buckle is not affected by welding heat and thus does not fail.

[0089] The above-disclosed embodiments are merely preferred embodiments of the present invention and should not be construed as limiting the scope of the invention. Those skilled in the art will understand that implementing all or part of the above-described embodiments and making equivalent changes in accordance with the claims of the present invention are still within the scope of the invention.

Claims

1. A method for real-time monitoring of the welding status of precision terminals, characterized in that, include: Data on the temperature distribution and opening angle at the root of the buckle are collected from the target welding station to obtain the initial temperature distribution and opening angle. The buckle locking stroke length and elastic recovery time are obtained, and combined with the target station position, the temperature distribution, opening angle, locking stroke value, and recovery time of each station are obtained. The temperature peak value of each station is extracted from the temperature distribution, and the geometric dimension change is determined according to the opening angle and the locking stroke value. The correspondence between the temperature peak value and the geometric dimension change is evaluated to identify the correlation between temperature distribution and structural deformation during welding. Based on the correlation, the functional failure risk and quality anomaly at the corresponding position of the buckle are determined, and the functional failure risk level and quality anomaly position are determined. The functional failure risk level and the quality anomaly position are matched with a pre-established process adjustment signal library to obtain temperature adjustment signal and displacement adjustment signal. The temperature adjustment signal and displacement adjustment signal are encapsulated into trigger commands and sent to the welding station to adjust the temperature and displacement of the buckle functional failure risk point and quality anomaly point.

2. The method for real-time monitoring of the welding status of precision terminals according to claim 1, characterized in that, The process of acquiring temperature distribution and opening angle data at the root of the buckle from the target welding station to obtain the initial temperature distribution and opening angle includes: A thermal imager scans the target welding station to obtain an image of the thermal field distribution at the base of the buckle. Based on the temperature calibration curve, the grayscale values ​​of the pixels in the thermal field distribution image are converted into temperature values ​​to construct a temperature value matrix at the base of the buckle. A displacement sensor detects the coordinates of the edge points on both sides of the buckle opening, calculates the angle between the edge line and the horizontal reference plane, obtains the opening angle data, and records the offset and height values ​​of the sensor relative to the center of the station to establish station spatial positioning parameters. The temperature value matrix and the opening angle data are linked by a timestamp to form an initial state data set containing the station number, temperature distribution characteristics, and opening angle.

3. The method for real-time monitoring of the welding status of precision terminals according to claim 1, characterized in that, The process of obtaining the buckle locking stroke length and elastic recovery time, combined with the target station location, yields the temperature distribution, opening angle, locking stroke value, and recovery time for each station, including: The displacement of the latch from its initial position to its locked position is measured using a laser rangefinder sensor. The distance between the starting and ending coordinates is calculated to obtain the latch locking stroke length. The time interval from the maximum compression position to the stable state is recorded to obtain the elastic recovery time. The latch locking stroke length and the elastic recovery time are paired with the workstation position coordinates, combined with the initial temperature distribution and the opening angle, to form a comprehensive data record containing workstation coordinates, locking stroke, recovery time, temperature, and angle. The comprehensive data record is time-series aligned, and a Kalman filter algorithm is used to estimate the state of the temperature distribution, the opening angle, the locking stroke length, and the elastic recovery time, outputting the filtered state values. Missing data is compensated by linear interpolation of adjacent workstation parameters. The data is organized by workstation number, and the updated temperature distribution, opening angle, locking stroke value, and recovery time for each workstation are stored.

4. The method for real-time monitoring of the welding status of precision terminals according to claim 3, characterized in that, The process involves time-series alignment of the integrated data records, employing a Kalman filter algorithm to estimate the state of the temperature distribution, the opening angle, the locking stroke length, and the elastic recovery time, and outputting filtered state values, including: Extract temperature distribution, opening angle, locking stroke length, and elastic recovery time data at the same timestamp from the comprehensive data record; use the Kalman filter algorithm to predict based on the state value and process noise covariance of the previous moment, and update by combining the current measurement value and measurement noise covariance to generate state estimates of the temperature distribution, opening angle, locking stroke length, and elastic recovery time; group and store the state estimates by workstation number, and update the real-time data of each workstation.

5. The method for real-time monitoring of the welding status of precision terminals according to claim 1, characterized in that, The process of extracting the temperature peak values ​​for each station from the temperature distribution, determining the geometric dimensional change based on the opening angle and the locking stroke value, evaluating the correspondence between the temperature peak values ​​and the geometric dimensional change, and identifying the correlation between temperature distribution and structural deformation during welding includes: The temperature matrix of the temperature distribution is traversed, adjacent temperature values ​​are compared, and the local maximum value of each station is extracted as the temperature peak value. The peak value timestamp and spatial coordinates are recorded. The opening angle and locking stroke values ​​at the corresponding time are read according to the timestamp, and the opening angle deviation value and locking stroke difference value are calculated and combined into the geometric dimension change. The linear correlation between the temperature peak value and the geometric dimension change is calculated using the Pearson correlation coefficient. Based on the correlation coefficient and the preset threshold, the correspondence is determined. The least squares method is used to fit the functional relationship between temperature and deformation, the theoretical deformation value is calculated, and the geometric dimension change value is compared with the theoretical deformation value to identify the correlation between temperature distribution and structural deformation.

6. The method for real-time monitoring of the welding status of precision terminals according to claim 1, characterized in that, The step of determining the functional failure risk and quality anomaly at the corresponding position of the buckle based on the correlation, and determining the functional failure risk level and quality anomaly location, includes: The correlation values ​​between the temperature distribution and structural deformation are compared with a preset correlation threshold to calculate the deviation value exceeding the standard. The coordinates of the buckle position at the corresponding workstation are extracted to determine the functional failure risk and identify the low, medium, and high functional failure risk levels. The abnormal parts are identified by the buckle position coordinates, and the root, opening, or middle abnormalities are marked to generate the identification results of the functional failure risk level and the location of the quality abnormality.

7. The method for real-time monitoring of the welding status of precision terminals according to claim 6, characterized in that, The step of determining abnormal locations by using the coordinates of the buckle position, marking abnormalities at the root, opening, or center, and generating identification results for the functional failure risk level and the location of the quality abnormality includes: Read the buckle position coordinates, compare the buckle position coordinates with the preset ranges of the buckle root, opening and middle areas, and mark the abnormality type of the corresponding area; combine the functional failure risk level with the abnormality type to generate a risk level code and abnormal position coordinates; summarize the risk level code and the abnormal position coordinates to form an identification result data group containing workstation number, risk level and abnormal part.

8. The method for real-time monitoring of the welding status of precision terminals according to claim 1, characterized in that, The step of matching the functional failure risk level and the quality anomaly location with a pre-established process adjustment signal library to obtain temperature adjustment signals and displacement adjustment signals includes: Construct a query index key value consisting of the functional failure risk level and the quality anomaly location, retrieve the process adjustment signal library through the query index key value, and extract temperature control parameters and displacement control parameters; determine the adjustment range according to the functional failure risk level, and generate a temperature adjustment signal containing the target temperature value and the rate of change, as well as a displacement adjustment signal containing the correction amount and the direction of movement.

9. The method for real-time monitoring of the welding status of precision terminals according to claim 1, characterized in that, The step of encapsulating the temperature adjustment signal and the displacement adjustment signal into a trigger command and sending it to the welding station to adjust the temperature and displacement of the points at risk of buckle function failure and quality abnormalities includes: The temperature adjustment signal and the displacement adjustment signal are encapsulated into a control command data packet, which includes the station address code and adjustment parameter value, and sent to the target welding station through the communication bus; the control command data packet is parsed to extract the target temperature value, rate of change, displacement amount and direction of movement; a power adjustment command is sent to the welding power source to adjust the temperature, and a position adjustment command is sent to the fixture drive device to adjust the buckle position; the temperature and displacement of the buckle function failure risk point and the quality abnormality point are updated.

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