Full-automatic plugging testing machine control system and method

Abstract

The invention relates to the technical field of intelligent control, in particular to a full-automatic plug-in testing machine control system and method, and the system comprises a multi-dimensional data collection module, an abnormal state recognition module, an action termination judgment module, a parameter correction module and a trajectory optimization execution module. According to the invention, based on multi-sensor array fusion, multi-physical quantity cooperative acquisition in equipment operation is realized, vibration, temperature, path and plugging force data are comprehensively calibrated by using a time axis, and through spectral analysis and three-dimensional deviation comparison, fine abnormity and trend change are dynamically revealed, and operation fluctuation and potential abnormity are identified in real time. According to the method, the reasonable interval of the insertion and extraction force is automatically calculated, the action duration and path trend are adjusted, the operation parameters are continuously corrected, equipment abnormity early warning, accurate intervention and multi-parameter linkage adjustment are achieved in the whole process, the test continuity, the execution sensitivity and the prevention and control capacity are improved, misoperation is effectively restrained, abrasion is reduced, and the high-density intelligent test requirement is met.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, in particular to a control system and method for a full-automatic plug-in test machine. Background Art

[0002] The field of intelligent control technology involves the use of computers and automation technologies to control and manage equipment and systems. This area primarily encompasses aspects such as control system design and optimization, sensor technology, actuator control, data acquisition and processing, and the application of intelligent algorithms. Intelligent control technology can improve the automation level of equipment and enhance the precision and reliability of production processes. It is widely used in various fields, including industry, transportation, energy, and healthcare. Core issues include control system modeling and analysis, control algorithm design, and real-time system monitoring and feedback mechanisms. The goal is to enhance the automation and efficiency of equipment or systems through intelligent means.

[0003] Among them, the traditional fully automatic plug-in / plug-out tester control system refers to a device control system used to test plug-in / plug-out performance. This system uses relay control, PLC control, analog circuit control, and other means to operate and manage the equipment. Traditional systems manually set parameters and perform plug-in / plug-out tests, but their degree of automation is low and they cannot monitor and adjust parameters in real time, resulting in low operational efficiency. This is particularly prone to errors and cumbersome operations in high-frequency, large-scale testing environments. To improve test accuracy and operational efficiency, traditional control systems automate plug-in / plug-out tests through fixed program settings and hardware connections. However, this leads to slow equipment response and complex maintenance.

[0004] The existing system only performs single-type parameter setting and action control based on hardware logic units such as relays or PLCs, and lacks multi-variable real-time collaborative monitoring and trend analysis methods. When faced with high-speed or multi-batch testing, it is prone to data lag, feedback blind spots, and untimely abnormality identification. The conventional static parameter operation makes it difficult to actively perceive the actual plugging and unplugging force, displacement, and temperature fluctuations. The equipment cannot correct itself when encountering abnormalities or wear and deformation during operation, which can easily lead to misoperation, equipment damage, and complex maintenance. It is difficult to adapt to the intelligent adjustment requirements in frequent switching or high-intensity environments. Summary of the Invention

[0005] In order to solve the technical problems that the existing system only performs single-type parameter setting and action control based on hardware logic units such as relays or PLCs, lacks multi-variable real-time collaborative monitoring and trend analysis methods, is prone to data lag, feedback blind spots and untimely abnormality identification in the face of high-speed or multi-batch testing, and is difficult to actively perceive the actual plugging force, displacement, and temperature fluctuations using conventional static parameter operations. The equipment cannot correct abnormalities or wear and deformation during operation, which easily leads to malfunctions, equipment damage, and complex maintenance. It is difficult to adapt to the intelligent adjustment requirements in frequent switching or high-intensity environments. The embodiment of the present invention provides a control system and method for a fully automatic plug-in tester. The technical solution is as follows: On the one hand, a fully automatic plug-in test machine control system is provided, which includes: The multi-dimensional data acquisition module collects time series data on acceleration, displacement change, temperature gradient, three-dimensional path coordinates, insertion and removal force, and contact duration through a sensor array. It aligns vibration, temperature, trajectory, and force data by timestamp to generate a synchronized data set and transmits it to the abnormal state recognition module. An abnormal state recognition module receives the synchronous data set, processes the vibration amplitude sequence using fast Fourier transform to extract frequency features, calculates and compares the period amplitude difference and temperature change, combines the trajectory offset comparison, generates a three-dimensional deviation set, and transmits it to the action termination judgment module; The action termination judgment module receives the three-dimensional deviation set, scans and identifies the vibration super-stable band, temperature super-thermal stability range, and trajectory offset super-continuous range time slices. If three consecutive time slices meet the conditions, a stop command is sent, an abnormal trigger parameter is generated, and passed to the parameter correction module; The parameter correction module receives the abnormal trigger parameter, adjusts the insertion and extraction force safety range based on the average vibration amplitude, corrects the dwell time according to the temperature change slope, calculates the path fine-tuning vector according to the trajectory offset direction, generates a correction parameter set, and passes it to the trajectory optimization execution module.

[0006] As a further solution of the present invention, the synchronization data set includes data time alignment results, signal synchronization accuracy indicators and time series integrity verification, the three-dimensional deviation set includes spectrum feature difference values, temperature gradient change amplitude and spatial trajectory deviation, the abnormal trigger parameters include continuous abnormal state judgment identification, equipment protection start signal and abnormal action control code, and the correction parameter set includes plug-in force safety threshold range, temperature change rate correction coefficient and trajectory offset vector adjustment amount.

[0007] As a further solution of the present invention, the multidimensional data acquisition module includes: The vibration temperature acquisition submodule collects time series data on acceleration, displacement change, temperature gradient, three-dimensional path coordinates, insertion and removal force, and contact duration through a sensor array. It aligns the acceleration and temperature gradient data based on timestamps, calculates the correlation between acceleration and temperature change, and generates a heating correlation coefficient. The trajectory data generation submodule calls the displacement change data based on the heating correlation coefficient, calculates the displacement increment and direction vector of each time period, applies coordinate transformation, normalizes the displacement change according to the time series, and generates a three-dimensional path change; The mechanical feature alignment submodule calls the plugging force and contact duration data according to the three-dimensional path change, calculates the incremental change in the plugging force and the relative change in the contact duration, and performs data alignment based on the data relationship between the plugging force and the contact duration to generate a synchronized data set.

[0008] As a further solution of the present invention, the heating correlation coefficient refers to a numerical parameter calculated by a correlation analysis algorithm based on acceleration data and temperature gradient data at the same timestamp; The normalization process refers to using the range normalization method to linearly transform the original three-dimensional path change amount to a unified interval; The three-dimensional path change refers to a vector composed of displacement increments in the X, Y, and Z axes directions calculated at time intervals based on the displacement change data.

[0009] As a further solution of the present invention, the abnormal state identification module includes: The frequency feature extraction submodule extracts the value of each sampling point of the vibration amplitude sequence based on the synchronized data set and establishes an amplitude array in chronological order. It uses fast Fourier transform to calculate the frequency-corresponding amplitude sequence, summarizes the amplitude proportion according to the frequency distribution, and generates the frequency-amplitude distribution coefficient. The frequency amplitude distribution coefficient refers to the parameter obtained by statistically analyzing the proportion of the frequency segment amplitude in the total amplitude after decomposing the vibration signal through FFT; The amplitude-temperature difference calculation submodule calculates the amplitude difference and temperature difference of consecutive cycles according to the frequency amplitude distribution coefficient and the temperature sequence at the same time in the synchronous data set, arranges them accordingly, and integrates them in chronological order to obtain the cycle amplitude-temperature difference ratio; The cycle amplitude temperature difference ratio refers to the ratio of the change in vibration amplitude between consecutive cycles to the change in temperature during the same period; The offset comparison generation submodule calls the periodic amplitude temperature difference ratio and trajectory offset sequence, constructs an offset trend and ratio curve for the same time period, extracts its corresponding amplitude sequence, performs amplitude normalization matching according to the time series and maps it to three-dimensional coordinates, summarizes the coordinate points, and generates a three-dimensional deviation set; The amplitude normalization matching refers to a processing method of mapping the amplitude sequence to a unified interval using the maximum and minimum normalization method, and then performing corresponding matching according to the same time series.

[0010] As a further solution of the present invention, the action termination judgment module includes: The deviation identification submodule obtains the acceleration deviation, temperature deviation, and trajectory offset values ​​in the three-dimensional deviation set, determines whether each time slice exceeds the corresponding threshold based on the vibration identification threshold, temperature identification threshold, and trajectory offset threshold, extracts the abnormal deviation time slice data, and generates the abnormal deviation amplitude interval; The vibration identification threshold is the upper limit of the absolute value or change of acceleration, and the time slice exceeding this value is determined to be a vibration abnormality; The temperature identification threshold is a critical value set for temperature deviation. Time slices exceeding this temperature difference are judged as temperature anomalies. The trajectory deviation threshold is the maximum allowable deviation distance between the trajectory point and the expected trajectory. Time slices exceeding this value are judged as trajectory anomalies. The stable area scanning submodule, based on the abnormal deviation amplitude interval, calls the vibration amplitude value, temperature change range and trajectory deviation rate in the corresponding time slice, and selects the time slices that meet the stability conditions according to the vibration stability judgment interval, thermal stability interval and trajectory continuity range, counts the number of consecutive groups, and generates the number of continuous stable segments; The instruction generation submodule determines whether there are three groups of adjacent time slices that simultaneously meet the three stability conditions based on the number of continuous stable segments. If so, the instruction generation submodule extracts the corresponding deviation data and generates an abnormal trigger parameter.

[0011] As a further solution of the present invention, the parameter correction module includes: The abnormality identification submodule obtains the vibration amplitude data, temperature acquisition data and trajectory offset vector value in the abnormal trigger parameters, calculates the average amplitude of the vibration time series, obtains the temperature change value per unit time to calculate the slope, calculates the offset direction based on the trajectory point displacement, and generates the abnormality feature judgment value; The abnormal characteristic judgment value refers to the judgment result used to quantify the abnormality degree of the current state after comprehensive analysis of parameters such as the average vibration amplitude, temperature change slope and trajectory deviation direction; The safety interval adjustment submodule calls the average vibration amplitude in the abnormal characteristic judgment value, sets the upper and lower limits of the initial safety interval of the plugging and unplugging force, calculates the percentage correction amplitude, and generates the plugging and unplugging force correction interval after adjusting the boundary value; The correction set generation submodule calls the boundary value of the insertion and extraction force correction interval, combines the temperature change slope and the trajectory offset direction scalar in the abnormal feature judgment value, adjusts the dwell time according to the temperature slope, sets the path adjustment vector according to the offset direction, and generates a correction parameter set.

[0012] As a further solution of the present invention, the trajectory optimization execution module receives the correction parameter set, uses a spline interpolation function to smooth the trajectory data, replans the insertion and extraction path sequence, adjusts the speed curve and contact timing, and the actuator verifies the three-dimensional deviation state of the new trajectory to generate an optimized trajectory execution instruction; The optimized trajectory execution instruction includes spline interpolation curve parameters, speed curve shape adjustment parameters and contact time sequence optimization arrangement.

[0013] As a further solution of the present invention, the trajectory optimization execution module includes: The trajectory reconstruction submodule uses a spline interpolation function to interpolate the three-dimensional coordinates of the insertion and removal nodes in the original trajectory data based on the position offset and angle change values ​​in the correction parameter set, recalculates the time interval and position change between nodes, adjusts the smoothness of the trajectory curve, and obtains a trajectory smoothness and continuity measurement value; The trajectory smoothness and continuity metric is a quantitative indicator that measures whether the velocity and acceleration changes between trajectory nodes are continuous and smooth; a path adjustment submodule that rearranges the path order between trajectory nodes according to the trajectory smoothness continuity metric and the speed limit threshold in the correction parameter set, calls the spatial distance and contact order between trajectory nodes, adjusts the speed rhythm of the path based on the speed change, and generates a path order matching deviation rate; The path order matching deviation rate represents the deviation ratio of the node spatial order after trajectory reconstruction relative to the original path; The speed limit threshold is the maximum safe movement speed allowed during trajectory planning; The error correction submodule matches the deviation rate with the interpolated trajectory data according to the path sequence, compares the three-dimensional trajectory deviation degree according to the actuator's current position deviation and speed trajectory deviation data, selects trajectory nodes that exceed the speed change reference value, and generates optimized trajectory execution instructions; The speed change reference value is a reference threshold for determining whether the speed change of adjacent trajectory nodes is abnormal.

[0014] On the other hand, a control method for a fully automatic plugging and unplugging test machine is provided. The control method is performed based on the above-mentioned fully automatic plugging and unplugging test machine control system and includes the following steps: S1: Collect time series data of acceleration, displacement change, temperature gradient, three-dimensional path coordinates, insertion and removal force, and contact duration through the sensor array, align vibration, temperature, trajectory, and force data by timestamp, and generate a synchronized data set; S2: calling the synchronized data set, processing the vibration amplitude sequence using fast Fourier transform to extract frequency features, calculating and comparing the period amplitude difference and temperature variation, and combining the trajectory offset comparison to generate a three-dimensional deviation set; S3: Receive the three-dimensional deviation set, scan and identify the vibration super-stable band, temperature super-thermal stability range, and trajectory offset super-continuous range time slices. If three consecutive time slices meet the conditions, send a stop command and generate an abnormal trigger parameter; S4: Receive the abnormal trigger parameter, adjust the insertion and extraction force safety range based on the average vibration amplitude, correct the dwell time according to the temperature change slope, calculate the path fine-tuning vector according to the trajectory offset direction, and generate a correction parameter set; S5: Call the correction parameter set, use the spline interpolation function to perform a smoothing operation on the three-dimensional path, re-plan the plug-in path sequence, call the speed curve to adjust the running rate parameters, rearrange the contact timing to obtain the corresponding plug-in path, verify the three-dimensional deviation state of the corrected trajectory, and output the optimized trajectory execution instruction.

[0015] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least: Based on the fusion of multi-sensor arrays, the coordinated collection of multiple physical quantities in the operation of the equipment is realized, and the interrelated vibration, temperature, path and plug-in force data are fully calibrated on the time axis. Through spectrum analysis and three-dimensional deviation comparison, subtle anomalies and trend changes are dynamically revealed, and operation fluctuations and potential abnormal states are identified in real time. The reasonable range of plug-in force is automatically calculated based on the deviation parameters, the duration of the action and the direction of the path are adjusted, and the operation parameters are continuously corrected. The whole process realizes equipment abnormality early warning, precise intervention and multi-parameter linkage adjustment, which significantly improves the test continuity, execution sensitivity and abnormality prevention and control capabilities, effectively suppresses false operations and reduces equipment wear, and meets the intelligent matching needs in high-density test environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 Schematic diagram of a control system for a fully automatic plug-in test machine provided by an embodiment of the present invention; Figure 2Schematic diagram of the system framework of the present invention; Figure 3 This is a flow chart of the multidimensional data acquisition module in the present invention; Figure 4 This is a flow chart of the abnormal state identification module in the present invention; Figure 5 This is a flow chart of the action termination judgment module in the present invention; Figure 6 This is a flow chart of the parameter correction module in the present invention; Figure 7 This is a flow chart of the trajectory optimization execution module in the present invention; Figure 8 This is a flow chart of a control method for a fully automatic plug and unplug testing machine provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0019] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0020] In the embodiments of the present invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same. The terms "of," "corresponding," and "corresponding" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same.

[0021] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0022] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0023] The embodiment of the present invention provides a fully automatic plug-in test machine control system, such as Figure 1-2 The control system diagram of the fully automatic plug-in test machine shown in the figure includes: The multi-dimensional data acquisition module collects time series data on acceleration, displacement change, temperature gradient, three-dimensional path coordinates, insertion and removal force, and contact duration through a sensor array. It aligns vibration, temperature, trajectory, and force data by timestamp to generate a synchronized data set and transmits it to the abnormal state recognition module. The abnormal state recognition module receives the synchronous data set, uses fast Fourier transform to process the vibration amplitude sequence to extract frequency features, calculates and compares the period amplitude difference and temperature change, and combines the trajectory offset comparison to generate a three-dimensional deviation set, which is passed to the action termination judgment module; The action termination judgment module receives the three-dimensional deviation set, scans and identifies the vibration super-stable band, temperature super-thermal stability range, and trajectory offset super-continuous range time slices. If three consecutive time slices meet the conditions, a stop command is sent, an abnormal trigger parameter is generated, and passed to the parameter correction module; The parameter correction module receives the abnormal trigger parameters, adjusts the insertion and extraction force safety range based on the average vibration amplitude, corrects the dwell time according to the temperature change slope, calculates the path fine-tuning vector according to the trajectory offset direction, generates a correction parameter set, and passes it to the trajectory optimization execution module; The trajectory optimization execution module receives the correction parameter set, uses the spline interpolation function to smooth the trajectory data, replans the insertion and removal path sequence, adjusts the speed curve and contact timing, and the actuator verifies the three-dimensional deviation state of the new trajectory to generate the optimized trajectory execution instruction; The synchronization data set includes data time alignment results, signal synchronization accuracy indicators and time series integrity verification; the three-dimensional deviation set includes spectral feature difference values, temperature gradient change amplitude and spatial trajectory deviation; the abnormal trigger parameters include continuous abnormal state judgment identification, equipment protection start signal and abnormal action control code; the correction parameter set includes plug-in force safety threshold range, temperature change rate correction coefficient and trajectory offset vector adjustment amount; the optimized trajectory execution instructions include spline interpolation curve parameters, velocity curve shape adjustment parameters and contact time series optimization arrangement.

[0024] Specifically, if Figure 2 、 3 As shown, the multi-dimensional data acquisition module includes: The vibration temperature acquisition submodule collects time series data on acceleration, displacement change, temperature gradient, three-dimensional path coordinates, insertion and removal force, and contact duration through a sensor array. It aligns the acceleration and temperature gradient data based on timestamps, calculates the correlation between acceleration and temperature change, and generates a heating correlation coefficient. The vibration temperature acquisition submodule collects time series data of acceleration, displacement change, temperature gradient, three-dimensional path coordinates, plug-in force and contact duration through the sensor array. First, the six-axis inertial measurement unit (IMU) is called to record the real-time three-dimensional acceleration and angular velocity. For example, the ADXL355 series accelerometer is used with an acquisition frequency of 100Hz per second and an acquisition time of 60 seconds, obtaining 6000 sets of acceleration data records. Then, the linear displacement sensor (such as LVDT) is enabled to record the displacement change data. The acquisition frequency is set to 50 times per second, matching the 20mm stroke displacement sampling during the plug-in process, for a total of 1000 sets of displacement data. The temperature gradient information is collected by multiple thermocouples (such as K-type thermocouples) distributed around the plug-in contact. The arrangement positions are the center of the pin, the edge of the jack, and the connector shell. The sampling frequency is set to 1Hz to record the temperature differences at different points in the whole process. At the same time, the three-dimensional path is obtained through a three-dimensional trajectory sensor (such as an optical tracking module). Coordinates, record the change value of the path coordinates during the displacement process, set the starting point coordinates to (0, 0, 0), accumulate each displacement increment vector point by point, and generate the path coordinates; the insertion and extraction force is recorded at the contact point by an embedded force sensor (such as a piezoelectric sensor), with a maximum measurement range of 0-50N and a sampling frequency of 200Hz. The contact duration is detected by the system timer and trigger linkage, and the start and end time of the contact is recorded and converted into the number of seconds. For example, the starting contact time is 2.34s and the end time is 2.91s, then the contact time is 0.57s. Then, based on the timestamp information of the data record, the collected data is uniformly organized according to the second-level timestamp, and a two-way matching search mechanism is used to pair the acceleration and temperature gradient data according to the closest timestamp, such as the acceleration data with a timestamp of 2.500s is paired with the temperature data of 2.498s. The total number of matching pairs is determined based on the error less than 0.005s. Then, each group of paired data is traversed and each pair of acceleration data is called With temperature gradient The Pearson correlation coefficient is calculated after normalization of the values, that is, according to the formula: ; in and are the mean values ​​of acceleration and temperature gradient respectively, and n is the number of paired data groups. The actual calculation example is as follows: Assume that the acceleration in the five groups of sampled data is [0.98, 1.02, 1.00, 1.01, 0.99] , the temperature gradient is [2.5, 2.6, 2.4, 2.5, 2.3] , after calculating the mean, they are 、 , substitute the formula and calculate item by item: ; The correlation coefficient result is 0.444, which is between the interval of [0.3, 0.6]. According to the actual standard definition, this value is classified as "moderate correlation". Therefore, this dataset indicates a moderate linear correlation between acceleration and temperature gradient during the test phase. This result is the heating correlation coefficient. The threshold setting range of the correlation coefficient here is: weak correlation [0–0.3], moderate correlation [0.3–0.6], and strong correlation [0.6–1.0]. The reference source is set to the statistical distribution range of the average correlation degree of the original multi-batch test data, and the grading is based on the IEEE engineering experiment standard.

[0025] Table 1: Acceleration and temperature gradient acquisition example

[0026] As shown in Table 1, the acceleration and temperature gradient data corresponding to five key time points in a certain plugging and unplugging process are listed. After normalization and correlation calculation, the heating correlation value with medium linear correlation is obtained.

[0027] The trajectory data generation submodule calls the displacement change data based on the heating correlation coefficient, calculates the displacement increment and direction vector of each time period, applies coordinate transformation, normalizes the displacement change according to the time series, and generates the three-dimensional path change; The trajectory data generation submodule first retrieves the displacement change data based on the above-mentioned heating correlation coefficient, and reads the displacement change corresponding to each timestamp in the time series database recorded by the system. The data structure is defined as a three-dimensional vector form. , each vector represents the three-dimensional position increment between two adjacent time stamps, such as at time point and The displacement coordinates recorded by the detector change from Change to , then the three-dimensional increment during this period is , and then the incremental vector is unitized in three-dimensional space, that is, it is divided by the vector modulus to obtain the direction vector. The calculation process of the modulus is: ; The direction vector is , which represents the normalized direction of motion within the time period. Then the normalization process is called to uniformly convert the three-dimensional displacement changes to the [0, 1] interval. The normalization method uses the range normalization method, that is, the displacement change of each axis is transformed using the following linear transformation formula: ; Assume that during the complete test cycle, the minimum X-direction displacement is 9.5 mm, the maximum is 19.5 mm, and the current displacement is 13.1 mm. The normalized value is: ; Similarly, after processing the changes in Y and Z direction coordinates, the normalized coordinate triplet is obtained. Through this process, the original path change value of the time period is unified to the standard scale interval, thereby eliminating the influence of the absolute dimension difference of the path coordinates; further, in order to make the normalized result have dynamic adjustment ability, the heating correlation coefficient obtained in the previous section is introduced As an adjustment factor, the weight of each displacement change is adjusted. The operation method is as follows: The value of is used as a weighting factor to multiply the normalized displacement result of each segment. For example, the normalized result of the current path point is ,correspond , then the weighted coordinates are: ; The weighted normalized vectors of each time period are stored in the path array in sequence and accumulated, that is, the overall three-dimensional path change is generated by accumulation. For example, the starting point is the origin The first time period is , the second paragraph is , then the coordinates of the second path are , and so on to obtain a complete path sequence; the path array represents the standardized motion trajectory of the connector terminal in three-dimensional space during the entire plugging and unplugging process. This array serves as an important coordinate reference for subsequent alignment with the mechanical characteristic data.

[0028] The mechanical feature alignment submodule uses the insertion and extraction force and contact duration data based on the 3D path change, calculates the incremental insertion and extraction force change and the relative change in contact duration, and aligns the data based on the relationship between the insertion and extraction force and contact duration to generate a synchronized data set. The mechanical feature alignment submodule first calls the insertion and extraction force and contact duration data based on the previously calculated three-dimensional path change, and arranges the insertion and extraction force change and contact duration at each time point in timestamp order to ensure that the data is aligned with the path data. For example, at timestamp When the plugging and unplugging force recorded by the system is , the contact duration is , the change of the three-dimensional path at the same time point is Next, to align the data, the incremental change in insertion and extraction force and the relative change in contact duration are calculated. It can be obtained by the difference in insertion and extraction force between two adjacent time points: ; Similarly, the contact duration increment Calculated by the difference in duration between adjacent time points: ; Next, the data is aligned based on the relationship between the insertion and removal force and the contact duration, and the time alignment accuracy between the data is ensured to reach 0.01 seconds through dynamic time warping (DTW) or interpolation methods. At this time, the insertion and removal force and contact time are synchronized with the path change at the same time interval, ensuring that the physical characteristics and trajectory changes during the entire insertion and removal process are reflected simultaneously, forming the final synchronized data set in time series for subsequent analysis and prediction. For example, in the timestamp When , the generated synchronization data set contains the following information: ; In this way, the synchronization and correlation between data can be ensured, thereby providing reliable input data for subsequent analysis, modeling and prediction.

[0029] Specifically, if Figure 2 、 4 As shown, the abnormal state recognition module includes: The frequency feature extraction submodule extracts the value of each sampling point of the vibration amplitude sequence based on the synchronous data set and establishes an amplitude array in chronological order. It uses fast Fourier transform to calculate the frequency-corresponding amplitude sequence, summarizes the amplitude proportion according to the frequency distribution, and generates the frequency-amplitude distribution coefficient. The frequency feature extraction submodule is based on the synchronous data set. It first extracts the vibration amplitude sequence associated with the plugging and unplugging process, and arranges the vibration amplitude value of each sampling point in the order of timestamp to form a one-dimensional array. Assuming that the sampling period is 0.01 seconds and the data collection of 500 sampling points is completed within 5 seconds, the amplitude array constructed is in the form of , each of which Indicates at a point in time The instantaneous vibration amplitude of the sequence is obtained by quantifying the maximum acceleration amplitude detected by the vibration sensor at each time point, such as at time point The detection value is ,exist At that time , thus forming the continuous vibration response data of the entire plugging and unplugging process, and then performing a fast Fourier transform operation on the amplitude array, performing a frequency domain conversion on the original time domain signal, and obtaining its response amplitude at the frequency point. The transformation process requires calculating the complex modulus values ​​of the real and imaginary parts and recording their corresponding frequency intervals. In this embodiment, the frequency distribution is from 0Hz to 250Hz, and the frequency corresponding amplitude sequence is obtained after conversion. , each of which Indicates the corresponding frequency The signal energy at the frequency band is calculated, and the percentage of the total amplitude energy in the frequency band is counted. The frequency amplitude distribution coefficient is extracted by the amplitude ratio of the frequency distribution. This process requires dividing the frequency band into several sub-intervals and calculating the total energy ratio of each interval. For example, 0-250Hz is divided into five sub-intervals: ; Calculate the ratio of the sum of the amplitudes in each subinterval to the overall amplitude sum. For example, the sum of the amplitudes in the first interval is , the sum of all frequency amplitudes is , then the frequency amplitude distribution coefficient of this interval is , and so on to calculate the proportion of all frequency intervals, and finally expressed in vector form as The frequency-amplitude distribution coefficient vector is used as the frequency domain feature input. The following table lists the frequency, corresponding amplitude, and calculated proportion of the frequency band under the sample data, which is used to show the typical results during the execution of this step.

[0030] Table 2: Frequency amplitude distribution table

[0031] As shown in Table 2, the frequency bands with larger total amplitudes are concentrated in 100–150 Hz and 200–250 Hz, indicating that the main energy in the plugging and unplugging action is concentrated in these two frequency bands. The amplitude coefficient of the 150–200 Hz segment is 0.201, which belongs to the normal energy segment, while the coefficient of the 0–50 Hz segment is 0.153, slightly lower than that of the subsequent frequency bands. If the coefficient of this segment is lower than 0.05 in the remaining samples, it should be judged as a low-frequency response area and further classified as an edge feature frequency band.

[0032] The amplitude-temperature difference calculation submodule calculates the amplitude difference and temperature difference of consecutive cycles according to the frequency amplitude distribution coefficient and the temperature sequence at the same time in the synchronous data set, arranges them accordingly, and integrates them in chronological order to obtain the cycle amplitude-temperature difference ratio; After completing the extraction of the frequency amplitude distribution coefficient, the amplitude-temperature difference calculation submodule calls the synchronized time series temperature data and matches the frequency amplitude vector corresponding to each moment with the temperature value collected at the same moment one by one. For example, at time point At , the frequency amplitude distribution coefficient vector is: , the corresponding temperature value is ,Then the system calls the corresponding parameters of the next sampling period, for example The frequency amplitude vector at time is , the corresponding temperature is , compare the amplitude and temperature at the two time points, and calculate the change in the sum of the frequency amplitude:

[0033] The corresponding temperature difference is , thus the amplitude difference and temperature difference ratio of this period is obtained as: ; Similarly, in the entire sampling period, this type of operation is performed on adjacent time periods in sequence to extract the amplitude difference and temperature difference ratio sequence between adjacent sampling points, forming a set of array data sorted by time. The data structure is: ; Each value can specifically identify the sensitivity of the frequency response intensity to temperature changes at different stages. It should be noted that during the calculation process, the temperature changes The period less than the set threshold should be excluded from the ratio calculation to avoid the denominator approaching 0, which will lead to incomparability of the calculation results. In this embodiment, the threshold is set to When the temperature difference is lower than this value, the system sets the ratio item to invalid or skips it directly. For example, when a certain temperature change is , the system does not perform the ratio operation of this section. When constructing the complete amplitude difference and temperature difference ratio sequence, the valid ratio points are retained and stored in conjunction with the original timestamp to form a ratio curve sequence with time attributes. The final result is an amplitude-temperature ratio array that changes with time, which is used for subsequent mapping and fusion analysis with the trajectory offset trend.

[0034] The offset comparison generation submodule calls the periodic amplitude temperature difference ratio and trajectory offset sequence, constructs the offset trend and ratio curve for the same time period, extracts its corresponding amplitude sequence, performs amplitude normalization matching according to the time series and maps it to three-dimensional coordinates, summarizes the coordinate points, and generates a three-dimensional deviation set; Amplitude normalization matching refers to a processing method that uses the maximum and minimum normalization method to map the amplitude sequence to a unified interval and then performs corresponding matching according to the same time series; The offset comparison generation submodule accesses the calculated amplitude difference temperature difference ratio sequence and the previously obtained trajectory offset sequence, and performs a point-by-point matching operation on the two data sets in the same time period. First, the system calculates the ratio corresponding to the time point in the amplitude temperature ratio sequence. Offset from track One-to-one alignment, such as at a point in time When the amplitude-temperature ratio is , the trajectory offset is , at a subsequent time point When the corresponding ratio is , the offset is , the system combines the ratio and offset data to form a binary sequence , then the amplitude sequences of the comparison value and the offset are normalized to the maximum and minimum respectively, specifically, the ratio Mapped to the interval [0, 1], the normalization method is to subtract the minimum value of the sequence from the current value and divide it by the difference between the maximum and minimum values. For example, if the maximum value of the ratio sequence is 0.078 and the minimum value is 0.032, then the ratio of 0.054 is normalized to: ; The offset is normalized in the same way to obtain a normalized ratio sequence and a normalized offset sequence. The system aligns and matches the two sets of normalized data to form a normalized mapping pair. For example, if the normalized ratio is 0.478 and the normalized offset is 0.513, the normalized matching result at this time point is the three-dimensional coordinate point , where time is used as the horizontal axis input, and a complete time-amplitude-offset three-dimensional mapping set is constructed in sequence. If the normalized ratio and the normalized offset change trend are inconsistent within a certain time period, for example, the ratio increases while the offset decreases, the system needs to record this section as a reverse offset interval, and further analyze the relationship between its occurrence time and mechanical behavior. Finally, the time point matching results are summarized to form a complete three-dimensional offset set. The data structure is ,in is the normalized ratio, It is a normalized offset used for subsequent data support.

[0035] Specifically, if Figure 2 、 5 As shown, the action termination judgment module includes: The deviation identification submodule obtains the acceleration deviation, temperature deviation, and trajectory offset values ​​from the three-dimensional deviation set. Based on the vibration identification threshold, temperature identification threshold, and trajectory offset threshold, it determines whether each time slice exceeds the corresponding threshold, extracts the abnormal deviation time slice data, and generates the abnormal deviation amplitude interval. The deviation identification submodule extracts the acceleration deviation value, temperature deviation value and trajectory offset value corresponding to each time slice from the three-dimensional deviation set, and performs independent threshold judgment operation on each parameter. First, the system calls the acceleration deviation sequence after timestamp alignment. , temperature deviation series and trajectory offset sequence , set the vibration recognition threshold to , the temperature recognition threshold is , the trajectory deviation threshold is , and then for each time slice Perform the following judgment: If , then it is marked that the time slice has abnormal vibration deviation. If , then it is marked that the time slice has abnormal temperature deviation. If , then the time slice is marked as having a trajectory deviation anomaly; in the specific execution, for example, for the time slice , if the three data are 、 、 , the system first compares , meet the vibration deviation abnormal conditions, and then compare , meeting the temperature deviation abnormality condition, but due to , does not meet the trajectory deviation abnormality condition, then mark the time slice abnormality type as "vibration + temperature"; the system traverses the time slices in turn, executes the above three types of judgment logic for each segment, obtains the time index set of each type of abnormality, and further generates three types of abnormality marking matrices. Then, according to the abnormality type and amplitude value marked in each time slice, calculate its abnormal amplitude range. For example, the amplitude is defined as the maximum relative deviation among the three types of abnormal items. If the corresponding time slice 、 、 , then the corresponding relative deviation is: , , ; If the maximum value of the three is 0.5, the abnormal amplitude is marked as "0.5", and the corresponding segment enters the abnormal amplitude interval "0.5 gear". By performing such calculations on the segments, the system constructs a complete set of abnormal amplitude intervals. ,in Indicates a combination of abnormal types, such as "vibration + temperature + trajectory", Indicates the corresponding maximum relative deviation amplitude value, such as "0.5". Finally, the time slices that meet the conditions for exceeding any type of abnormal threshold are extracted to form an abnormal deviation time slice sequence, which is used for stability analysis and trigger logic identification in the next stage. The data examples referenced in this step are as follows: Table 3: Abnormal deviation judgment data table

[0036] As shown in Table 3, by comparing the difference ratios between each parameter type and its corresponding identification threshold, the maximum relative deviation value is calculated and used to archive the abnormal amplitude level of the time slice, forming a set of abnormal deviation amplitude intervals, providing data for subsequent stability identification. This result shows that the system achieves abnormal identification and partitioning of time slices by calculating the deviation ratios for each acceleration, temperature, and trajectory value and obtaining the abnormal level based on the maximum value.

[0037] The stable zone scanning submodule, based on the abnormal deviation amplitude interval, calls the vibration amplitude value, temperature change range and trajectory deviation rate in the corresponding time slice. According to the vibration stability judgment interval, thermal stability interval and trajectory continuity range, it selects the time slices that meet the stability conditions, counts the number of consecutive groups, and generates the number of continuous stable segments. After receiving the abnormal deviation amplitude interval, the stable area scanning submodule calls the vibration amplitude value, temperature change range and trajectory deviation rate in the corresponding time slice for each time segment marked as abnormal, and corresponds to the absolute value of acceleration respectively. , temperature gradient , first-order difference of displacement , and compared with the three stability judgment standards in turn, the vibration stability judgment interval is set to The temperature stability range is , the trajectory continuous range is set to ,In the specific implementation, the system first obtains the acceleration values ​​of the current time point and ,the adjacent time points before and after each time slice, and ,calculates the vibration amplitude using a sliding window method, that is, the maximum value of the ,acceleration of the three points in the window is taken as the vibration index of the ,current segment. If the maximum value is lower than 0.35, the vibration stability condition ,is met, for example, the time slice , the acceleration before and after it is The maximum value of the three is 0.31, which is less than the upper limit of judgment and meets the vibration stability condition; the temperature change range is calculated by taking the temperature values ​​at adjacent time points to calculate the absolute value of the difference, for example, the temperature before and after the time slice is and , corresponding to The value difference is , which is less than the threshold , it is judged that the temperature is stable; the trajectory deviation rate is calculated by dividing the trajectory difference between two adjacent points by the sampling time interval, and the sampling frequency is set to 100Hz, that is, the time interval is , if the position difference of the two point trajectories is , then the migration rate is , which is less than the set threshold , satisfying the trajectory continuity condition, the system judges that all three conditions of the time slice are satisfied, then the time slice is recorded as a "stable segment". If there are three consecutive time slices (such as ) all meet the above three conditions, the system records the group as a "continuous stable segment group" and continues to slide the statistics to the end of the sequence to obtain a complete continuous stable group sequence. For example, if the number of groups that meet the conditions continuously is 5, the system will mark the result value as "5" and output the start and end marks of the segment on the original timeline for subsequent matching operations.

[0038] The instruction generation submodule determines whether there are three groups of adjacent time slices that meet the three stability conditions at the same time based on the number of continuous stable segments. If so, it extracts the corresponding deviation data and generates the abnormal trigger parameter; The instruction generation submodule obtains the number of continuous stable segment groups output in the previous step and determines whether there are at least three groups of adjacent time slices that meet the three stability conditions at the same time. The system sets the judgment criteria as follows: three segment groups appear continuously in the stable segment sequence and the time difference is less than , that is, each group of time spans is , the start and end times of the three adjacent groups should be as follows 、 、 During the execution of the system, the stable fragment group index is first grouped and compared to obtain the starting time interval between adjacent groups. If the intervals are , it is determined to be "three consecutive groups" and the next step of data extraction is performed. The system extracts the abnormal deviation data within the three time periods, including the maximum vibration deviation value, maximum temperature fluctuation value and maximum trajectory deviation rate value contained in the segment. For example, the maximum acceleration recorded in the three time periods is , temperature difference for , the trajectory rate is , the system records the three values ​​as trigger parameters in sequence, and then combines the three values ​​to form an "abnormal trigger parameter set", which includes a time stamp , vibration trigger value , Temperature trigger value , trajectory trigger value The parameter set is pushed to the upper control system for linkage response triggering. In the stable segment group, the system loops through three groups of adjacent structure combinations and counts the number of time periods that meet the trigger conditions. For example, if three consecutive groups that meet the conditions are detected, the output contains three abnormal trigger parameter sets, each set corresponding to a stability mutation trigger state. The result will be used for further action judgment.

[0039] Specifically, if Figure 2 、 6 As shown, the parameter correction module includes: The anomaly identification submodule obtains the vibration amplitude data, temperature acquisition data and trajectory offset vector value in the abnormal trigger parameters, calculates the average amplitude of the vibration time series, obtains the temperature change value per unit time to calculate the slope, calculates the offset direction based on the trajectory point displacement, and generates the abnormal feature judgment value; When the abnormality identification submodule calls the abnormality trigger parameter, it reads three parameter items in sequence, namely vibration amplitude data, temperature acquisition data and trajectory offset vector value. First, for the vibration amplitude data, the system reads the vibration acceleration value sequence corresponding to each time slice in the continuous stable segment group. , where the corresponding values ​​are , by adding the three values ​​in sequence to get the total , and then divide it by the number of sample points 3 to get the average value , thereby obtaining the average value of the vibration amplitude; secondly, for the temperature acquisition data, the system calls the original temperature value data set collected in each time slice , for each pair of adjacent points, the temperature difference is divided by the time interval to calculate the temperature change slope per unit time. The time interval is fixed to , so the two groups of slopes are and , and the slope is calculated by taking the average of the two , and then compare it with the system preset temperature slope reference value By comparison, the relative offset ratio is calculated as , the system marks the temperature slope deviation as "0.5"; the third item processes the trajectory offset vector value, and the system extracts the spatial three-axis trajectory position sequence , construct a trajectory vector set based on each time slice, such as time point 、 、 The corresponding trajectory point coordinates are 、 、 , the system performs vector difference operation on each pair of adjacent time slices and calculates the direction vector as follows: The first segment is , the second paragraph is , then by adding the two vectors and normalizing them, the direction vector is calculated as: ; The unit direction vector is then mapped to an azimuth coding value, and the main direction of the offset is obtained through the spatial quadrant standard conversion. For example, if the main direction corresponds to the first quadrant, it is coded as "D1". The coding result is used as the output of the trajectory offset direction judgment; after processing the three sub-items, the system generates an abnormal feature judgment value set, including the average vibration amplitude , average temperature change slope , the trajectory offset direction vector is encoded as "D1", and this set is used as an input item in subsequent judgment and adjustment steps.

[0040] The safety interval adjustment submodule calls the average vibration amplitude in the abnormal feature judgment value, sets the upper and lower limits of the initial safety interval of the plugging and unplugging force, calculates the percentage correction amplitude, and generates the plugging and unplugging force correction interval after adjusting the boundary value; The safety interval adjustment submodule calls the average value of the vibration amplitude in the abnormal feature judgment value set , the system first sets the upper and lower limits of the initial safety range of the plugging and unplugging force. Assume that the initial setting is that the upper and lower limits are 、 The average value of the vibration amplitude to be called must be consistent with the insertion force fluctuation sensitivity coefficient Linkage correction boundary, setting , the percentage correction is Indicates that is the initial reference vibration value, substitute the numerical value into it: ; Based on the offset amplitude, the system determines that the current vibration value is in the high range and performs a bidirectional symmetrical adjustment of the upper and lower limits of the insertion and extraction force range. That is, the upper limit is adjusted to: ; The lower limit is adjusted to: ; If the vibration amplitude is lower than the reference value, reverse symmetry adjustment is performed. Therefore, the compression process is completed by reducing the upper limit and increasing the lower limit; at the same time, the difference between the upper and lower limits after correction needs to be determined. Is it still greater than the minimum allowable interval width? After confirming that the conditions are met, the system generates a set of modified insertion force interval boundary values If the width requirement is not met, the original set interval is retained and an exception is marked. The correction process also needs to record the adjustment time point to track the policy change history. The example is as follows: Table 4: Insertion and extraction force safety interval adjustment table

[0041] As shown in Table 4, the vibration amplitude is too high, which triggers interval compression. The compression result still meets the minimum allowable interval width requirement, and the system can enter the next correction process normally.

[0042] The correction set generation submodule calls the boundary value of the insertion and extraction force correction interval, combines the temperature change slope and the trajectory offset direction scalar in the abnormal feature judgment value, adjusts the dwell time according to the temperature slope, sets the path adjustment vector according to the offset direction, and generates a correction parameter set; The correction set generation submodule first calls the boundary value of the insertion and extraction force correction interval , combined with the temperature change slope Calculate the parameters with the trajectory offset direction code "D1". The system first processes the temperature slope correction item, adjusts the dwell time based on the temperature slope, and sets the temperature reference slope. , adjustment coefficient , then the corrected stay duration is: ; The original setting of the system is , the corrected residence time is: ; Then the path correction is performed according to the trajectory offset direction. "D1" corresponds to the first quadrant direction. The system reads the corresponding direction vector from the direction encoding library. , after unit normalization, , set the path offset scale factor to , the final path correction vector is: ; The system uses this path offset vector to correct the end-effector trajectory control module, forming path redirection parameters. It also packages the current dwell time, path correction amount, and corrected insertion and extraction force interval into the final correction parameter set: insertion and extraction force lower limit: 12.2664N, insertion and extraction force upper limit: 17.7336N, dwell time: 2.0s, path offset vector: (0.4616, 0.4616, 0.4616)mm. The system synchronously transmits this set to the control logic processing core to complete the dynamic adaptation of the subsequent plugging and unplugging control strategy. This set of correction parameters has a one-to-one correspondence with the judgment values ​​in the previous step. The parameter results are traceable through the formula calculation process, ensuring that the numerical range of the participating items is reasonable and falls within the normal physical range. The plugging and unplugging force correction amount is reduced by 0.533N compared to the original range, the dwell time is shortened by 0.5s, and the path correction direction clearly points to the D1 direction vector, forming a set of dynamic control correction instructions that can be used for actual execution.

[0043] Specifically, if Figure 2 、 7 As shown, the trajectory optimization execution module includes: The trajectory reconstruction submodule uses a spline interpolation function to interpolate the three-dimensional coordinates of the plug-in and pull-out nodes in the original trajectory data based on the position offset and angle change values ​​in the correction parameter set, recalculates the time interval and position change between nodes, adjusts the smoothness of the trajectory curve, and obtains the trajectory smoothness and continuity measurement value; The trajectory reconstruction submodule processes the position offset and angle change value in the correction parameter set. The submodule first reads the three-dimensional coordinate point set of the plug-in and unplug nodes in the original trajectory data of the end effector. For example, the original trajectory contains a node set , where each point , extract the position offset vector from the correction parameter set: ; And act on each plug-in and unplug node one by one, that is, perform vector addition operation on the original coordinates, such as node , the corrected position is: ; The submodule will then perform angle correction tasks and read the angle offset value recorded by the sensor. , and rotate the plug-in and pull-out directions in the neighborhood around each 3D point, for example, by rotating the actuator direction vector around the z axis to change the original direction vector Adjust to , substitute , calculated , then perform spline interpolation on the node sequence after offset and rotation, and use cubic spline for node smoothing, such as at the node and Insert two points between and set them as and , by knowing the node coordinates and timestamp Constructing a spline interpolation function , calculate the coordinates of the newly added interpolation points to make the trajectory change more smoothly during this period of time, repeat this operation until the interpolation reconstruction is completed between the plug-in and unplug nodes, and then use the reconstructed node timestamp set (in Add new nodes for interpolation) Calculate the time interval between adjacent nodes , combined with the node coordinate transformation amplitude Get the change rate of each trajectory , calculate the velocity variance over the entire trajectory To measure the degree of change in trajectory smoothness, this value is used as a continuous measurement of trajectory smoothness and compared with the benchmark continuous measurement standard If the current value is lower than the reference value, the current trajectory is determined to meet the smoothness standard. If it is higher than the reference value, it is recorded as a trajectory segment to be adjusted. For example, the calculation of a group of trajectory sample nodes is shown in the following table: Table 5: Trajectory node interpolation and speed statistics

[0044] As shown in Table 5, the velocity changes after interpolation between nodes of the four trajectory segments are listed, and the trajectory smoothness metric value is further calculated by multi-segment velocity. , the specific calculation is as follows: ; ; The results show that the current trajectory smoothness metric Higher than the set benchmark value Therefore, it is necessary to record this trajectory segment as a node segment that still does not meet the smoothness standard after reconstruction, and enter the next stage path adjustment submodule for sequence rearrangement and speed rhythm control processing.

[0045] The path adjustment submodule rearranges the path order between trajectory nodes based on the trajectory smoothness continuity metric and the speed limit threshold in the correction parameter set, calls the spatial distance and contact order between trajectory nodes, adjusts the speed rhythm of the path based on the speed change, and generates the path order matching deviation rate; The path adjustment submodule first receives the trajectory smoothness and continuity measurement value passed by the trajectory reconstruction submodule and the speed limit threshold in the correction parameter set , the module judges that the current smoothness does not meet the standard requirements, so it starts the trajectory node rearrangement process and performs the timestamp and the corresponding three-dimensional coordinate points Perform traversal processing and first calculate the spatial distance between adjacent nodes , and then calculate the corresponding time interval , and thus obtain the average speed of multiple segments For example, for a set of nodes , calculated Then the speed is calculated with the preset limit threshold, and the difference between each speed and the threshold is obtained. , respectively 、 、 , then call the corresponding path space distance weight factor between trajectory nodes , local path dynamic adjustment factor , contact sequence influencing factors The three parameters are set as , , , calculate the path sequence matching deviation rate, using the formula: ; in, represents the path sequence matching deviation rate, and Respectively represent The first and The three-dimensional coordinates of adjacent nodes, Indicates the Node and The time interval between nodes, Indicates the speed limit threshold, Indicates the The spatial distance weight factor of each node, Indicates the The local path dynamic adjustment factor of each node, Indicates the The contact order influencing factor of each node, Indicates the trajectory node number in the trajectory data, Indicates the total number of trajectory nodes.

[0046] Calculate the bias weighted terms for each term, which are: Item 1: ; Item 2: ; Item 3: ; After summing, we get: ; This value represents the path sequence matching deviation rate of 1.5216. The system uses this to determine whether the speed fluctuation and spatial arrangement between the current trajectory nodes exceed the expected running rhythm range. If the deviation rate is greater than the matching deviation reference upper limit set by the system , it is confirmed that there is a speed rhythm imbalance problem in the current path, and the order of the path nodes needs to be rearranged. The rearrangement operation is sorted according to the priority of the node speed and contact order factor combination, such as weighted sorting score As the standard, the corresponding weights of the nodes are 0.72, 0.792, and 1.2 respectively. According to the weights, the order of the trajectory nodes is adjusted from high to low to the 3rd, 2nd, and 1st item, and then the time intervals are rearranged. To match the speed and spatial distance requirements, the system outputs the new path node arrangement sequence and the corrected speed distribution data, and recalculates the new path sequence matching deviation rate to confirm that it is lower than the reference value before entering the next stage of the processing flow.

[0047] The error correction submodule matches the deviation rate with the interpolated trajectory data according to the path sequence, compares the three-dimensional trajectory deviation degree based on the actuator's current position deviation and velocity trajectory offset data, filters out trajectory nodes that exceed the speed change reference value, and generates optimized trajectory execution instructions; The error correction submodule is based on the path sequence matching deviation rate The trajectory data after the spline interpolation is processed above, and the current actual posture information of the actuator is first called, that is, the current time point collected in real time by the system Next, get the current position coordinates of the end effector With the current velocity vector , and then read the theoretical position of the corresponding time point in the interpolation trajectory: ; With the theoretical velocity vector , perform position deviation calculation operation , and then calculate the velocity offset , for the spatial position deviation vector modulus , combined with the three-dimensional trajectory offset reference value set by the system , to judge the offset difference, that is, to perform a comparison operation If it is true, the node is marked as an error-exceeding node. Then the system traverses the nodes in the entire trajectory in sequence, and judges the position and speed difference between the actual posture and theoretical posture of each node, and selects the node set that meets the requirement of exceeding the threshold at the same time. , in the current instance, set the trajectory node set to to , where the position deviations are {0.42, 0.28, 0.31, 0.56, 0.48, 0.60, 0.49, 0.52, 0.29, 0.26} (unit: mm), and the system performs batch judgment Operation, where nodes 4, 6, and 8 meet the over-limit condition and form the error node subset , input it into the error correction module, and then execute the instruction optimization operation to calculate the compensation vector of each node position and speed error. Suppose the original theoretical position of the 6th node is , the current location is , the execution position correction vector is , the correction vector is applied to the trajectory point to generate the corrected path point, and the velocity offset vector Execute synchronous adjustment of speed command, for example, if the speed deviation is , the speed compensation adjustment is performed separately for each direction, and the optimized instruction set is in the form of: ; The coordinates are the corrected target point coordinates, and the speed is the adjusted value after difference compensation. Finally, the instructions form a trajectory execution optimization sequence and are output to the actuator control system to replace the execution instructions of the corresponding nodes in the original trajectory.

[0048] See also Figure 8 The control method of the fully automatic plug-in test machine is based on the above-mentioned fully automatic plug-in test machine control system and includes the following steps: S1: Collect time series data of acceleration, displacement change, temperature gradient, three-dimensional path coordinates, insertion and removal force, and contact duration through the sensor array, align vibration, temperature, trajectory, and force data by timestamp, and generate a synchronized data set; S2: Call the synchronized data set, use fast Fourier transform to process the vibration amplitude sequence to extract frequency features, calculate the period amplitude difference and temperature change and perform comparison, combine with the trajectory offset comparison to generate a three-dimensional deviation set; S3: Receive the three-dimensional deviation set, scan and identify the vibration super-stable band, temperature super-thermal stability range, and trajectory offset super-continuous range time slices. If three consecutive time slices meet the conditions, a stop command is sent and an abnormal trigger parameter is generated; S4: Receives abnormal trigger parameters, adjusts the insertion and extraction force safety range based on the average vibration amplitude, corrects the dwell time according to the temperature change slope, calculates the path fine-tuning vector according to the trajectory offset direction, and generates a correction parameter set; S5: Call the correction parameter set, use the spline interpolation function to perform smoothing operations on the three-dimensional path, re-plan the plug-in and pull-out path sequence, call the speed curve to adjust the running rate parameters, rearrange the contact timing to obtain the corresponding plug-in and pull-out path, verify the three-dimensional deviation status of the corrected trajectory, and output the optimized trajectory execution instruction.

[0049] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. The control system of the fully automatic plug-in test machine is characterized by: The system comprises: The multi-dimensional data acquisition module collects time series data on acceleration, displacement change, temperature gradient, three-dimensional path coordinates, insertion and removal force, and contact duration through a sensor array. It aligns vibration, temperature, trajectory, and force data by timestamp to generate a synchronized data set and transmits it to the abnormal state recognition module. An abnormal state recognition module receives the synchronous data set, processes the vibration amplitude sequence using fast Fourier transform to extract frequency features, calculates and compares the period amplitude difference and temperature change, combines the trajectory offset comparison, generates a three-dimensional deviation set, and transmits it to the action termination judgment module; The action termination judgment module receives the three-dimensional deviation set, scans and identifies the vibration super-stable band, temperature super-thermal stability range, and trajectory offset super-continuous range time slices. If three consecutive time slices meet the conditions, a stop command is sent, an abnormal trigger parameter is generated, and passed to the parameter correction module; The parameter correction module receives the abnormal trigger parameter, adjusts the insertion and extraction force safety range based on the average vibration amplitude, corrects the dwell time according to the temperature change slope, calculates the path fine-tuning vector according to the trajectory offset direction, generates a correction parameter set, and passes it to the trajectory optimization execution module.

2. The fully automatic plug-in test machine control system according to claim 1 is characterized in that: The synchronization data set includes data time alignment results, signal synchronization accuracy indicators and time series integrity verification; the three-dimensional deviation set includes spectrum feature difference values, temperature gradient change amplitudes and spatial trajectory deviations; the abnormal trigger parameters include continuous abnormal state judgment identifiers, equipment protection start signals and abnormal action control codes; the correction parameter set includes plug-in force safety threshold intervals, temperature change rate correction coefficients and trajectory offset vector adjustment amounts.

3. The fully automatic plug-in test machine control system according to claim 1, characterized in that: The multidimensional data acquisition module includes: The vibration temperature acquisition submodule collects time series data on acceleration, displacement change, temperature gradient, three-dimensional path coordinates, insertion and removal force, and contact duration through a sensor array. It aligns the acceleration and temperature gradient data based on timestamps, calculates the correlation between acceleration and temperature change, and generates a heating correlation coefficient. The trajectory data generation submodule calls the displacement change data based on the heating correlation coefficient, calculates the displacement increment and direction vector of each time period, applies coordinate transformation, normalizes the displacement change according to the time series, and generates a three-dimensional path change; The mechanical feature alignment submodule calls the plugging force and contact duration data according to the three-dimensional path change, calculates the incremental change in the plugging force and the relative change in the contact duration, and performs data alignment based on the data relationship between the plugging force and the contact duration to generate a synchronized data set.

4. The fully automatic plug-in test machine control system according to claim 3 is characterized in that: The heating correlation coefficient refers to a numerical parameter calculated by a correlation analysis algorithm based on acceleration data and temperature gradient data at the same time stamp; The normalization process refers to using the range normalization method to linearly transform the original three-dimensional path change amount to a unified interval; The three-dimensional path change refers to a vector composed of displacement increments in the X, Y, and Z axes directions calculated at time intervals based on the displacement change data.

5. The fully automatic plug-in test machine control system according to claim 3, characterized in that: The abnormal state recognition module includes: The frequency feature extraction submodule extracts the value of each sampling point of the vibration amplitude sequence based on the synchronized data set and establishes an amplitude array in chronological order. It uses fast Fourier transform to calculate the frequency-corresponding amplitude sequence, summarizes the amplitude proportion according to the frequency distribution, and generates the frequency-amplitude distribution coefficient. The amplitude-temperature difference calculation submodule calculates the amplitude difference and temperature difference of consecutive cycles according to the frequency amplitude distribution coefficient and the temperature sequence at the same time in the synchronous data set, arranges them accordingly, and integrates them in chronological order to obtain the cycle amplitude-temperature difference ratio; The offset comparison generation submodule calls the periodic amplitude temperature difference ratio and trajectory offset sequence, constructs the offset trend and ratio curve for the same time period and extracts its corresponding amplitude sequence, performs amplitude normalization matching according to the time series and maps it to three-dimensional coordinates, summarizes the coordinate points, and generates a three-dimensional deviation set.

6. The fully automatic plug-in test machine control system according to claim 5, characterized in that: The action termination judgment module includes: The deviation identification submodule obtains the acceleration deviation, temperature deviation, and trajectory offset values ​​in the three-dimensional deviation set, determines whether each time slice exceeds the corresponding threshold based on the vibration identification threshold, temperature identification threshold, and trajectory offset threshold, extracts the abnormal deviation time slice data, and generates the abnormal deviation amplitude interval; The stable area scanning submodule, based on the abnormal deviation amplitude interval, calls the vibration amplitude value, temperature change range and trajectory deviation rate in the corresponding time slice, and selects the time slices that meet the stability conditions according to the vibration stability judgment interval, thermal stability interval and trajectory continuity range, counts the number of consecutive groups, and generates the number of continuous stable segments; The instruction generation submodule determines whether there are three groups of adjacent time slices that simultaneously meet the three stability conditions based on the number of continuous stable segments. If so, the instruction generation submodule extracts the corresponding deviation data and generates an abnormal trigger parameter.

7. The fully automatic plug-in test machine control system according to claim 6, characterized in that: The parameter correction module includes: The abnormality identification submodule obtains the vibration amplitude data, temperature acquisition data and trajectory offset vector value in the abnormal trigger parameters, calculates the average amplitude of the vibration time series, obtains the temperature change value per unit time to calculate the slope, calculates the offset direction based on the trajectory point displacement, and generates the abnormality feature judgment value; The safety interval adjustment submodule calls the average vibration amplitude in the abnormal characteristic judgment value, sets the upper and lower limits of the initial safety interval of the plugging and unplugging force, calculates the percentage correction amplitude, and generates the plugging and unplugging force correction interval after adjusting the boundary value; The correction set generation submodule calls the boundary value of the insertion and extraction force correction interval, combines the temperature change slope and the trajectory offset direction scalar in the abnormal feature judgment value, adjusts the dwell time according to the temperature slope, sets the path adjustment vector according to the offset direction, and generates a correction parameter set.

8. The fully automatic plug-in test machine control system according to claim 1, characterized in that: The trajectory optimization execution module receives the correction parameter set, uses a spline interpolation function to smooth the trajectory data, replans the insertion and removal path sequence, adjusts the speed curve and contact timing, and the actuator verifies the three-dimensional deviation state of the new trajectory to generate an optimized trajectory execution instruction; The optimized trajectory execution instruction includes spline interpolation curve parameters, speed curve shape adjustment parameters and contact time sequence optimization arrangement.

9. The fully automatic plug-in test machine control system according to claim 8, characterized in that: The trajectory optimization execution module includes: The trajectory reconstruction submodule uses a spline interpolation function to interpolate the three-dimensional coordinates of the insertion and removal nodes in the original trajectory data based on the position offset and angle change values ​​in the correction parameter set, recalculates the time interval and position change between nodes, adjusts the smoothness of the trajectory curve, and obtains a trajectory smoothness and continuity measurement value; a path adjustment submodule that rearranges the path order between trajectory nodes according to the trajectory smoothness continuity metric and the speed limit threshold in the correction parameter set, calls the spatial distance and contact order between trajectory nodes, adjusts the speed rhythm of the path based on the speed change, and generates a path order matching deviation rate; The error correction submodule matches the deviation rate with the interpolated trajectory data according to the path sequence, compares the difference of the three-dimensional trajectory deviation degree according to the current position deviation of the actuator and the speed trajectory offset data, selects the trajectory nodes that exceed the speed change reference value, and generates the optimized trajectory execution instruction.

10. A control method for a fully automatic plug-in test machine, characterized in that: The control system of the fully automatic plug and unplug test machine according to any one of claims 1 to 9 comprises the following steps: S1: Collect time series data of acceleration, displacement change, temperature gradient, three-dimensional path coordinates, insertion and removal force, and contact duration through the sensor array, align vibration, temperature, trajectory, and force data by timestamp, and generate a synchronized data set; S2: calling the synchronized data set, processing the vibration amplitude sequence using fast Fourier transform to extract frequency features, calculating and comparing the period amplitude difference and temperature variation, and combining the trajectory offset comparison to generate a three-dimensional deviation set; S3: Receive the three-dimensional deviation set, scan and identify the vibration super-stable band, temperature super-thermal stability range, and trajectory offset super-continuous range time slices. If three consecutive time slices meet the conditions, send a stop command and generate an abnormal trigger parameter; S4: Receive the abnormal trigger parameter, adjust the insertion and extraction force safety range based on the average vibration amplitude, correct the dwell time according to the temperature change slope, calculate the path fine-tuning vector according to the trajectory offset direction, and generate a correction parameter set; S5: Call the correction parameter set, use the spline interpolation function to perform a smoothing operation on the three-dimensional path, re-plan the plug-in path sequence, call the speed curve to adjust the running rate parameters, rearrange the contact timing to obtain the corresponding plug-in path, verify the three-dimensional deviation state of the corrected trajectory, and output the optimized trajectory execution instruction.

Images