A control system of a vehicle-mounted circuit board processing production line

Abstract

The application discloses a kind of control systems of vehicle-mounted circuit board processing production line, it is related to the technical field of automobile electronics manufacturing, including interference perception modeling module, interference feature extraction module, signal discrimination evaluation module, control adjustment response module, interference event record module and self-learning optimization module: interference perception modeling module, based on multipoint distributed collection mechanism, the control signal of key node, power voltage and space electric field data are collected, the three-dimensional coupling distribution diagram of interference sensitive area is constructed, as the benchmark model of interference identification.The application realizes the dynamic perception and intelligent response of control system to interference by distributed collection, interference modeling, feature extraction and self-learning mechanism, constructs closed-loop anti-interference control system, effectively reduces false trigger rate and product defect rate, improves production stability and consistency, and has significant engineering application value.

Description

Technical Field

[0001] This invention relates to the field of automotive electronics manufacturing technology, and more specifically to a control system for an automotive circuit board processing production line. Background Technology

[0002] The control system of an automotive circuit board manufacturing production line refers to an integrated and intelligent control platform applied in the manufacturing process of automotive electronic products. It is primarily used for unified scheduling, real-time monitoring, and automated collaborative management of various processing stages within the production line (such as surface mount technology, soldering, testing, depaneling, and packaging). By collecting the operating status, process parameters, and product information of each piece of equipment, the system achieves precise control and process optimization of the processing flow, ensuring that automotive circuit boards efficiently complete production tasks under the requirements of high speed, high precision, and high reliability. Its core functions typically include production cycle control, equipment interlocking and fault diagnosis, parameter traceability and process data management. Furthermore, it can be combined with technologies such as industrial bus, PLC, embedded controllers, MES systems, and sensor networks to build an advanced manufacturing system with intelligent scheduling, adaptive control, and data closed-loop feedback capabilities, serving as a crucial support for intelligent manufacturing applications in the automotive electronics field.

[0003] The existing technology has the following shortcomings: In automotive circuit board manufacturing lines, due to the high-density integration, high-speed operation, and complex electrical connections of the production equipment, the control system is often in a high electromagnetic noise environment. Especially in scenarios involving frequent switching of high-speed devices, interface plugging and unplugging operations while energized, and the release of static electricity by operators or equipment (such as static electricity from the human body or materials), strong transient electromagnetic interference signals are easily generated. These interference signals can intrude into the control system through various paths, including power lines, signal lines, control buses, or spatial radiation coupling, affecting the recognition process of low-voltage control signals and causing logical misjudgments or false signal triggering in the control unit.

[0004] For example, in the surface mount or soldering process, execution instructions that originally relied on precise trigger signals may be prematurely or abnormally activated after interference, leading to problems such as mounting errors, soldering sequence disorders, and misaligned transport cycles. This type of electromagnetic interference is sudden and unpredictable, and is difficult to completely isolate using traditional shielding or filtering methods. Once it occurs, it will seriously disrupt the rhythm consistency and process stability of the production process, resulting in batch defects, equipment malfunctions, or even production interruptions, affecting the overall reliability of the system and the consistency of finished products.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a control system for an automotive circuit board processing production line to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a control system for an automotive circuit board processing production line, comprising an interference perception modeling module, an interference feature extraction module, a signal discrimination and evaluation module, a control adjustment and response module, an interference event recording module, and a self-learning optimization module. The interference perception modeling module, based on a multi-point distributed acquisition mechanism, collects control signals, power supply voltage, and spatial electric field data of key nodes to construct a three-dimensional coupled distribution map of the interference-sensitive area, which serves as a benchmark model for interference identification. The interference feature extraction module performs dynamic difference analysis on the three-dimensional coupling distribution map to extract abrupt change amplitude, nonlinear jitter and cross-device synchronization offset features, forming a set of interference weight parameters; The signal discrimination and evaluation module constructs a control signal discrimination framework based on the interference weight parameter set and dynamically sets the interference filtering threshold by combining historical operating parameters. The control adjustment response module performs synchronous control adjustments based on the interference filtering threshold, including dynamic optimization of trigger edge, time redundancy and command confirmation mechanism; The interference event recording module writes the identified interference characteristics, control adjustment records, and response results into the interference behavior database after the control action command adjustment is completed, forming a structured record. The self-learning optimization module implements a self-learning strategy based on the interference behavior database, periodically optimizing the interference weight parameters and control adjustment logic to achieve dynamic adaptation and continuous prevention and control of high-frequency interference.

[0008] Preferably, the interference perception modeling module includes the following steps: Multiple data collection points are set up in the surface mount assembly area, reflow soldering area, online inspection area, automatic board splitting area, finished product packaging area and material conveying intersection area; At each acquisition point, control signal parameters, power supply voltage parameters, and spatial electric field strength parameters are collected respectively. Based on the three-dimensional coordinate information of each collection point, the collected data is projected onto the three-dimensional spatial model of the processing production line and spatial interpolation is performed. A three-dimensional coupled distribution map of the interference-sensitive area is generated based on the spatial model and visualized, forming a reference for subsequent interference identification and response adjustment.

[0009] Preferably, the interference feature extraction module includes the following steps: The control signal waveform data of each acquisition point are synchronously unfolded according to the spatial coordinate sequence, and key points are marked. Identify voltage mutation behavior in control signals and establish a mutation point distribution table; Detect nonlinear jitter in the edge time of the control signal and generate a jitter feature dataset; The trigger time difference of the rising edge of the control signal between adjacent acquisition points is analyzed and the synchronization offset parameter is extracted to form a set of interference weight parameters.

[0010] Preferably, the signal discrimination and evaluation module includes the following steps: Establish a one-to-one correspondence between control signal paths and interference characteristic parameters; Collect real-time parameters of the control signal and calculate the current credibility score; The current credibility score is compared and analyzed with historical credibility data to calculate the interference filtering threshold; Based on the comparison between the current credibility score and the interference filtering threshold, it is determined whether the control signal enters the control execution channel.

[0011] Preferably, the control adjustment response module includes the following steps: Adjust the trigger edge recognition conditions of the control signal according to the interference filtering threshold; After trigger edge recognition, perform time redundancy verification and verify signal stability; Receive the target device's readiness feedback signal after the signal is confirmed to be valid but before executing the action command; The parameters of the edge recognition, voltage verification, and action confirmation mechanisms are dynamically adjusted according to the interference level to achieve synchronous and coordinated adjustment of the control triggering process.

[0012] Preferably, the interference event recording module includes the following steps: Collect the amplitude of sudden changes, nonlinear jitter and synchronization offset features identified in the current control cycle, and generate a disturbance feature field structure; Record the edge recognition adjustment, redundancy verification extension and action confirmation mechanism changes that occur within the current control cycle, and generate the control strategy adjustment field structure; Collect control action response results, including signal trigger status, feedback time and abnormality indicator, and generate control response fields; After binding the interference feature field, control strategy adjustment field, and control response field with the control cycle number and timestamp, write them into the interference behavior database.

[0013] Preferably, the self-learning optimization module includes the following steps: Set a fixed operating cycle and extract interference event records within that cycle from the interference behavior database; The interference event records are aggregated according to the control channel number, the frequency of interference types is statistically analyzed, and the control response results are categorized. The interference weight parameters are updated based on the interference frequency, intensity, and response impact to form an updated parameter configuration table; Write the updated parameter configuration table to the configuration storage unit and set it to take effect in the next control cycle.

[0014] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention establishes an interference spatial distribution model through multi-point distributed data acquisition, enabling the control system to possess structured perception capabilities of interference sources, intensity, and paths. Furthermore, feature extraction and weight calculation enhance the system's ability to distinguish different interference types and its targeted response. Combined with dynamically set interference filtering thresholds and control signal discrimination mechanisms, it achieves refined adjustment of anti-interference triggering strategies. Finally, by integrating interference event recording and self-learning optimization mechanisms, it constructs a continuously evolving closed-loop control system, allowing the control system to continuously adapt to changing environmental interference trends during long-term operation. In summary, this solution not only breaks through the traditional anti-interference approach relying on hardware shielding but also achieves intelligent control optimization through software-hardware collaboration and data-driven processes. While ensuring stable operation of key processes such as surface mount technology (SMT), soldering, and testing, it significantly reduces the error rate and product defect rate, improving overall production line efficiency and product consistency, demonstrating significant engineering application value and promising industrial application prospects. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0016] Figure 1 This is a schematic diagram of the control system of an in-vehicle circuit board processing production line according to the present invention. Detailed Implementation

[0017] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0018] This invention provides, for example Figure 1 The control system of the vehicle-mounted circuit board processing production line shown includes an interference perception modeling module, an interference feature extraction module, a signal discrimination and evaluation module, a control adjustment and response module, an interference event recording module, and a self-learning optimization module. The interference perception modeling module, based on a multi-point distributed acquisition mechanism, collects control signal waveforms, power supply voltage stability parameters, and spatial electric field intensity data of multiple key nodes in the automotive circuit board processing production line, and generates a three-dimensional coupling distribution map of interference-sensitive areas covering the entire production environment, which is used as a reference benchmark for subsequent interference identification. To address the issue of electromagnetic interference (EMI) susceptibility in automotive circuit board manufacturing production lines, a three-dimensional electromagnetic environment modeling based on a multi-point distributed acquisition mechanism is proposed for effective identification and dynamic control of potential interference sources. By deploying sensing and acquisition devices at multiple key nodes along the production line, control signals, power status, and electromagnetic environment parameters are acquired in real time. This allows for the construction of a three-dimensional coupled distribution map of interference-sensitive areas, providing fundamental data support for subsequent interference identification and control strategies. The method mainly includes the following steps: In the automotive circuit board manufacturing production line, based on the equipment layout and the physical structure of the process flow, several interference-sensitive key locations, including the surface mount assembly area, reflow soldering area, online inspection area, automatic board separation area, finished product packaging area, and material handling and conveying intersection area, are designated as data acquisition points. Each acquisition point is equipped with three types of acquisition devices: first, a control signal acquisition device, employing a high-speed analog-to-digital converter with differential input capability, to acquire indicators such as voltage amplitude, rise and fall time, signal width, and periodic stability of the control signal at that location; second, a power monitoring device, using a precision voltage detection chip with a wide voltage sampling range, to monitor in real time the average DC voltage, voltage fluctuation amplitude, number of voltage drops, and duration of each drop on the power supply line in that area; and third, a spatial electric field strength acquisition device, using an electric field probe array, to acquire the electric field strength change amplitude, electric field direction change rate, and duration of electric field disturbance per unit time at that location. These three devices are connected to the host processing unit via an industrial Ethernet interface or RS485 interface, configured with a periodic data upload mechanism to ensure synchronous acquisition of the electrical and electromagnetic states of all areas of the entire production line.

[0019] After collecting the above data, the control signal, power supply status, and electric field intensity data were preprocessed. The control signal data underwent noise reduction filtering to extract time-domain features such as edge jitter, amplitude drift range, and repetition period stability. The power supply status data underwent voltage stabilization trend analysis to extract its upper and lower voltage limits, average value fluctuation range, voltage jump frequency distribution, and load transient response characteristics. The spatial electric field intensity data was based on continuous multi-frame sampling values, extracting its maximum electric field change amplitude per second, interference duration, and gradient direction and rate of change in space. All of the above data were appended with sampling timestamp information and the three-dimensional coordinates of the collected points within the processing production line, forming a dataset with complete temporal and spatial coordinate correspondence, which was stored in a data cache queue as the data input source for subsequent modeling.

[0020] The processed multi-source data is fused and projected onto a 3D spatial model of the automotive circuit board manufacturing production line to establish a 3D coupling distribution map of interference-sensitive areas. The 3D spatial model uses the actual production line layout as a reference, dividing the entire production line into regular spatial volume units, each corresponding to a certain range of equipment and space. Based on the 3D coordinates of each acquisition point, the collected control signal stability parameters, power fluctuation parameters, and electric field disturbance intensity parameters are assigned and mapped to their respective spatial units. Through spatial interpolation between adjacent acquisition points, the electromagnetic state index values ​​for areas without acquisition points are calculated, ensuring the integrity and continuity of the overall spatial model. After projection and interpolation, all indices of all spatial units are normalized to form a physically meaningful data distribution map.

[0021] Based on the modeling results, a visualized interference coupling distribution map is generated. This map, grounded in a three-dimensional coordinate system, uses different colors or grayscale levels to represent the relative levels of control signal stability, power supply voltage disturbance, and spatial electric field strength, thus intuitively reflecting the interference intensity and distribution patterns in various areas of the production line. For example, darker areas represent higher levels of interference and a greater likelihood of false triggering of control signals. This map can be used by engineers to visually identify high-risk interference areas and also serves as a reference for setting control judgment thresholds, identifying interference characteristics, and generating response adjustment strategies. The interference coupling distribution map can be updated periodically and dynamically adjusted according to changes in the production line status, exhibiting good timeliness and scalability.

[0022] The purpose of this step is to establish a comprehensive, accurate, and spatially resolvable electromagnetic interference (EMI) sensing model for the control system in the automotive circuit board manufacturing production line. This enables the quantitative identification and dynamic localization of interference source distribution, interference intensity, and interference impact paths. In actual manufacturing processes, equipment such as pick-and-place machines, soldering equipment, testing instruments, and depaneling devices operate at high frequencies, high speeds, and high power, generating strong EMI. Simultaneously, electrostatic discharge, load fluctuations, and signal coupling during operation create complex interference fields in the production environment. These interferences often exhibit transient, nonlinear, and spatially non-uniform distribution characteristics, making it impossible for traditional single-point acquisition and static protection methods to comprehensively and accurately capture their temporal and spatial relationships.

[0023] By deploying multi-point distributed acquisition devices at key nodes throughout the production line, control signal waveforms, power supply voltage fluctuations, and spatial electric field strength are collected in real time. Combined with three-dimensional coordinate positioning processing based on the process layout, a spatially comprehensive interference sensitivity distribution map covering the entire production line can be established. Mapping this electromagnetic state data onto the actual production line structure using a unified coordinate system and fusing it into a three-dimensional coupled distribution map not only reveals the true distribution of interference sources in physical space but also analyzes their propagation paths and potential control channels. This provides a scientific and accurate basis for subsequent signal reliability assessment, trigger mechanism optimization, and dynamic adjustment of control strategies. Therefore, the fundamental role of this step is to structure, model, and spatialize the electromagnetic state information in the production environment, laying the data foundation and spatial awareness capability for the entire anti-interference control mechanism. It is a crucial preliminary step in the entire interference prevention and control scheme.

[0024] The interference feature extraction module applies a dynamic differential analysis algorithm to the three-dimensional coupled distribution map of the interference-sensitive area to extract the abrupt amplitude features, nonlinear jitter features, and cross-device synchronization offset features generated by the control signal in the time-series changes, forming a set of interference weight parameters for interference discrimination. To accurately identify the type and degree of interference received by control signals in the automotive circuit board manufacturing line, and to provide a quantitative basis for subsequent control signal reliability assessment and control strategy optimization, based on the aforementioned constructed three-dimensional coupling distribution map of interference-sensitive areas, the electrical parameter data contained in this distribution map are further processed to extract representative interference behavior features, which are then transformed into a parameter set for interference intensity assessment. This process includes the following steps: The data in the constructed 3D coupling distribution map of the interference-sensitive area is unfolded according to spatial coordinates, and the control signal waveform data recorded by each spatial acquisition point under a unified time reference is extracted. This signal waveform data is a sequence of analog voltage values ​​acquired by signal detection devices installed at various key locations, converted from analog to digital, and recorded at fixed time intervals (e.g., 1 millisecond). To ensure time consistency of data from different acquisition points, all waveform data are synchronized according to a unified system clock, and missing data points are filled in using linear interpolation. Based on this, key points such as the rising edge start position, voltage rise rate, peak voltage, signal duration, and falling edge occurrence time are further extracted from each waveform data, and their positions on the time axis are marked, laying the foundation for subsequent interference characteristic determination.

[0025] The voltage abrupt change characteristics in the control signal waveform are identified. This step involves calculating the voltage change at two sampling points within each consecutive time interval and comparing this change with the maximum allowable rate of change for that channel under standard operating conditions. Taking the control signal of a pick-and-place machine as an example, under normal operating conditions, the voltage change per sampling interval does not exceed 0.5 volts. When a voltage change exceeding this threshold is detected at a certain moment, that moment is marked as an abrupt change point. The same process is repeated for all sampling points, and the number of abrupt change points occurring per unit time at each spatial location is counted, with this number serving as the abrupt change density index. Simultaneously, each abrupt change point is assigned its corresponding spatial coordinates and time label to establish an abrupt change point distribution table for subsequent identification of areas with concentrated interference.

[0026] After identifying abrupt changes, the temporal stability of the control signal is tested to determine if edge drift or periodic instability exists. The procedure involves selecting multiple consecutive periods from the acquired signal data, recording the first occurrence time of the rising edge of the control signal within each period, and calculating the range of variation of this time across different periods. If the rising edge trigger times of multiple periods exhibit irregular fluctuations—for example, the rising edge appears at 10 milliseconds in the first period, 13 milliseconds in the second period, and then earlier at 9 milliseconds in the third period—and the fluctuation range exceeds 2 milliseconds (i.e., exceeds the controller's tolerance threshold), then the signal is considered to exhibit nonlinear jitter behavior. Simultaneously, the maximum offset time, average offset time, and offset variation trend of the signal are recorded and combined with its spatial location to form a jitter feature dataset.

[0027] Analyze the temporal consistency of control signal changes between different spatial acquisition points to determine if there is a synchronization offset between devices. Specifically, select two spatially adjacent acquisition points or those with a logically dependent relationship. Extract their control signal waveforms within the same time period, identify the rise time of each, and calculate the difference between their trigger times. For example, acquisition point A in the patch area and acquisition point B in the soldering area should logically trigger control signals with a 1-millisecond time interval. However, in actual testing, a drift of more than 3 milliseconds is found between the two points, and this drift occurs repeatedly. This indicates a synchronization offset interference problem. The offset time, drift direction (delay or advance), and number of occurrences are used as synchronization offset parameters and stored together with the aforementioned abrupt change and jitter characteristics to form a complete set of interference characteristics.

[0028] Through the above steps, three representative types of interference behaviors can be extracted from the temporal behavior characteristics of the control signal: first, instantaneous voltage mutation characteristics, reflecting signal disturbances caused by high-frequency interference sources; second, nonlinear jitter characteristics, revealing timing drift caused by unstable power supply or electric field interference; and third, cross-regional synchronization offset characteristics, characterizing the existence of coupling interference or propagation delay in the signal link. These three types of characteristics are quantified numerically according to the acquisition frequency, influence intensity, and spatial range of occurrence, establishing a unified data structure to form a set of interference weight parameters, providing a basic reference for subsequent control signal confidence assessment and trigger mechanism adjustment.

[0029] This step involves in-depth timing characteristic analysis of the large number of electrical signals collected in the automotive circuit board manufacturing production line. It identifies and extracts core characteristic parameters representing electromagnetic interference behavior, and based on this, constructs a set of weighted parameters for interference discrimination. This provides solid data support for subsequent signal reliability judgment, control command triggering adjustment, and dynamic interference response in the control system. In modern automotive electronics manufacturing, the stability of control signals plays a decisive role in surface mount accuracy, soldering quality, and overall process cycle coordination. However, in the high-density wiring, high-speed operation, and frequent circuit switching environment, control signals are often affected by multi-source interference from switching devices, power supply disturbances, and changes in the spatial electric field. This interference often manifests as signal abrupt changes, edge jitter, or triggering asynchrony between devices.

[0030] This step involves systematically synchronizing, spatially corresponding, and numerically analyzing the control signal waveform data, voltage change data, and electric field intensity data in the three-dimensional coupled distribution diagram. This process extracts three typical interference behavior parameters: abrupt amplitude characteristics, nonlinear jitter characteristics, and synchronization offset characteristics. These parameters not only reflect the intensity and frequency of the interference but also reveal its propagation path and affected area, possessing clear physical significance and engineering value. By quantifying and structuring these characteristics to form a unified set of interference weight parameters, not only is interference identification provided with a data foundation but the control system can also dynamically respond and adjust its strategies based on quantified indicators when facing complex dynamic interference environments. This significantly improves the stability, reliability, and intelligence of the entire control process. Therefore, this step occupies a crucial, pivotal position in the entire anti-interference control method, serving as an indispensable core component for achieving highly robust production scheduling and accurate signal identification.

[0031] The signal discrimination and evaluation module constructs a control signal discrimination framework based on the interference weight parameter set, evaluates the credibility of the control signal in multiple dimensions, and dynamically calculates the interference filtering threshold corresponding to each control path in combination with the historical operating parameter trend of the control system, so as to improve the anti-interference capability of the control signal triggering judgment process. To improve the accuracy and anti-interference capability of control signal triggering determination in the automotive circuit board processing production line, after completing interference feature extraction, a control signal discrimination framework based on a set of interference weight parameters is further constructed. This discrimination framework uses features such as abrupt amplitude, nonlinear jitter, and cross-device synchronization offset as input parameters. By analyzing the deviation between the real-time state of the control signal and its historical operating trend, it dynamically calculates the interference filtering threshold corresponding to each control path, thereby achieving real-time evaluation of the reliability of the control signal. The implementation process mainly includes the following steps: A binding relationship is established between each control signal path and interference characteristic parameters. Specifically, based on the production line layout diagram and signal channel numbers, the actual transmission path of each control signal is mapped to a spatial cell in the aforementioned constructed 3D coupled interference distribution map. For example, the placement machine loading control signal channel numbered C-01 passes through spatial cells numbered Z3 to Z7. The system uses the maximum amplitude of the sudden change, the average value of the nonlinear jitter, and the maximum delay value of the synchronization offset corresponding to the spatial cell associated with this channel as the environmental interference background for that control signal channel, and stores them in the corresponding data structure. This data structure includes fields such as control signal number, spatial coordinates, sudden change amplitude (in volts), periodic jitter amplitude (in milliseconds), and synchronization offset time difference (in milliseconds), achieving a precise one-to-one correspondence between control paths and interference characteristics.

[0032] The current state of control signals is evaluated in real time to determine their reliability. During production, each control signal undergoes real-time parameter acquisition before triggering. The acquired data includes the current signal voltage value, signal change rate, edge trigger time, cycle interval, and timing deviation from the most recent valid trigger signal. Taking the surface mount control signal as an example, if the system detects a voltage change rate of 1.2 volts per millisecond and a rise time 1.8 milliseconds earlier than the previous cycle, significantly deviating from the historical normal offset tolerance (±0.5 milliseconds), it is marked as a low-reliability signal. During the evaluation process, each abnormal indicator is assigned a fixed interference risk weight value, and the current reliability score (out of 100 points) is calculated based on the number of anomalies and the risk level. For example, if the sudden voltage exceedance is greater than 0.5 volts and lasts for more than two cycles, the risk score is 40 points; if the jitter duration exceeds the standard range by more than 1 millisecond, 20 points are deducted, resulting in a final comprehensive score of 40 points, which is lower than the system's set reliability threshold of 60 points.

[0033] The current assessment results are compared with the historical operational data of the signal to determine the interference filtering strategy to be adopted in this cycle. Historical data mainly includes the average reliability, standard deviation of reliability, minimum reliability, and the lowest occurrence cycle difference of the trigger signal in the past 100 consecutive effective triggers of the channel. The system compares the deviation between the current cycle's reliability score and the historical average. If the deviation exceeds 15 points and shows a downward trend compared to the minimum historical reliability, it is judged as a potential interference risk signal, and the trigger judgment threshold for the channel is immediately increased. For example, if the historical average reliability of channel C-01 is 80 points, the current cycle's is 40 points, and the last two cycles have both been below 45 points, the system raises its filtering threshold from 60 points to 75 points, thereby preventing the signal from entering the action triggering link. Simultaneously, this anomaly record is written to a temporary cache for subsequent analysis.

[0034] The aforementioned dynamic threshold control mechanism is used to make real-time judgments on the control signal triggering process. Upon receiving each control signal request, the controller first extracts the signal's current confidence score and compares it with the dynamic filtering threshold. If the current confidence score is higher than the threshold (e.g., 82 points for a threshold of 75 points), it is determined to be an acceptable signal and allowed to enter the control execution channel. If the current confidence score is lower than the threshold (e.g., 52 points for a threshold of 75 points), the signal is temporarily discarded, or it is transferred to an alternative path for secondary verification by auxiliary logic, such as resampling, cross-confirmation, or combining with other control signals to determine whether to merge the trigger. This control method dynamically adjusts the control signal triggering conditions under different interference backgrounds, enhancing the control system's ability to cope with sudden electromagnetic interference and preventing equipment malfunctions due to short-term abnormal signals, thereby ensuring the cycle accuracy and consistency of key processes such as mounting, soldering, and testing.

[0035] This step aims to establish a dynamic correlation mechanism between control signals and environmental interference characteristics, enabling real-time reliability assessment of control signal effectiveness. It also dynamically sets the signal trigger threshold for each control path based on historical operating patterns and current interference conditions, significantly enhancing the anti-interference capability and execution stability of the entire automotive circuit board manufacturing production line control system. In circuit board manufacturing, control signals are fundamental to the coordinated operation of key actions (such as placement, soldering, testing, and depaneling). Any mis-triggered, delayed, or premature signal can lead to misaligned cycles, disordered processing sequences, and consequently, large-scale process defects and product consistency issues. However, due to the complex industrial environment, control signals are susceptible to various factors during transmission, including sudden interference from high-frequency switching devices, jitter interference caused by power fluctuations, and synchronization offset interference from the superposition of spatial electric fields. Traditional static threshold determination methods struggle to address these nonlinear and dynamically changing interference patterns.

[0036] This step establishes a control signal reliability scoring mechanism by constructing a set of interference weight parameters centered on abrupt change amplitude, nonlinear jitter, and synchronization offset, and combining this with the historical performance of each control signal channel during actual operation. Then, by comparing the deviation between the current period and historical trends, the reliability threshold required for control signals to enter the execution path is dynamically adjusted, enabling immediate interception or processing of suspicious signals. Specifically, when the system detects that the current reliability of a signal channel is significantly lower than the normal fluctuation range, the trigger threshold for that channel will be automatically increased to prevent equipment malfunctions caused by interference signals entering the execution path. Conversely, in stable environments, the threshold will be appropriately lowered to improve system response speed and efficiency. This method not only enables adaptive adjustment based on real-time operating conditions but also gradually forms a more accurate judgment model and response strategy through long-term accumulation of historical data, constructing a signal discrimination mechanism with continuous learning capabilities.

[0037] Therefore, the core function of this step is to transform the judgment of the effectiveness of control signals from the traditional single threshold and rule-driven mode to a multi-dimensional comprehensive evaluation mechanism that integrates environmental data, historical behavior and real-time performance. This enables the entire control system to have dynamic response capabilities against electromagnetic interference, effectively improving the reliability, stability and process consistency of the entire production line under high interference conditions. It is a key foundation for ensuring the long-term stable operation of intelligent manufacturing systems.

[0038] The control adjustment response module executes a synchronous control adjustment process based on the interference filtering threshold, dynamically adjusting the trigger edge conditions, time redundancy period, and action command confirmation mechanism of the control action command to avoid erroneous execution caused by suspicious interference signals. To prevent erroneous execution of control actions in automotive circuit board manufacturing production lines under electromagnetic interference, the triggering mechanism of control commands is dynamically adjusted based on the calculated control signal reliability and interference filtering threshold. Specifically, this includes strengthening the identification conditions for control signal trigger edges, confirming the time redundancy of control signal states, confirming the bidirectional execution of action commands, and ensuring synchronous and coordinated execution of the entire process. This implementation achieves effective isolation and response control of interference risk signals by constructing a control logic processing flow with real-time adaptability. The process includes the following steps: The trigger edge recognition mechanism of the control signal is adjusted based on the interference filtering threshold. In traditional control methods, the control signal is usually triggered by a level change, for example, a control action is triggered when the voltage jumps from a low level (0V) to a high level (5V). However, in scenarios with frequent interference, such edge changes may be caused by interference pulses rather than genuine command signals, thus requiring multiple recognition conditions. In this embodiment, the level change of the control signal must remain stable for a specified duration. For example, when the control signal voltage jumps from 0V to 5V, the system not only detects the edge, but also confirms that the high-level state is maintained for no less than 5 milliseconds, and that the voltage fluctuation range during this period does not exceed ±0.3V. Only when all these conditions are met does the system determine that the edge is a valid trigger event, thereby significantly reducing the risk of misidentification caused by electromagnetic interference.

[0039] After successful edge detection, a time-redundant verification mechanism is added to further verify the stability of the control signal. This mechanism works as follows: after detecting a valid edge, the controller does not immediately execute a control action. Instead, it sets a fixed signal verification period, such as 10 milliseconds, and continuously samples the control signal at 1-millisecond intervals within this period. If all 10 samples remain stable (i.e., the voltage value remains between 5V ± 0.3V without significant abrupt changes, drops, or discontinuities), the signal is considered a valid trigger signal. If voltage deviation, noise interference, or level instability occurs at any point during the verification period, the current trigger action is canceled, and the signal is marked as a suspicious event. This method, through time-based signal stability verification, effectively prevents malfunctions caused by transient interference and improves the accuracy of control command response.

[0040] After confirming the signal's validity, an action confirmation mechanism is introduced before executing the action command to ensure that the equipment is ready and the control conditions are met before initiating the actual action. In this embodiment, after issuing a pre-execution signal for the control action, the controller waits for the target device to return a "ready" response signal. This feedback signal is generated by the device's internal status detection device, detecting information such as the position status of the execution components, the presence of mechanical interference in the execution path, and whether the current load is within the allowable range. For example, before the placement equipment executes the placement command, it needs to confirm that the placement arm is in its initial position, the placement nozzle's vacuum state is normal, and the movement trajectory is not obstructed by other mechanisms. Only when all detection conditions are met and a confirmation signal is returned within a set time does the controller formally issue the action execution command. If no confirmation signal is received, the execution is automatically canceled, and the event is recorded for subsequent fault analysis. This confirmation mechanism constructs a safety barrier before the control command is executed, further improving the overall system's execution reliability.

[0041] The aforementioned edge recognition, redundancy verification, and action confirmation mechanisms are integrated into a workflow and linked with the interference weight evaluation mechanism to achieve dynamic synchronous adjustment of the control triggering process. To achieve adaptive control, the specific parameters of each mechanism are adjusted according to the interference level: when the system assesses the current interference intensity as high, for example, an interference mutation amplitude exceeding 1.2V or a nonlinear jitter duration exceeding 10 milliseconds, the system extends the voltage stabilization time for edge recognition from 5 milliseconds to 10 milliseconds, the redundancy verification cycle from 10 milliseconds to 20 milliseconds, increases the sampling frequency to once every 0.5 milliseconds, and simultaneously extends the action confirmation feedback waiting time to 200 milliseconds; when the interference level decreases, the system gradually restores to the default parameters to improve response efficiency. This synchronous linkage mechanism enables the control process to have environmental awareness, enhancing robustness in high-interference environments and ensuring cycle efficiency in low-interference environments.

[0042] The purpose of this step is to further construct a control triggering mechanism with dynamic adjustment capabilities based on the reliability assessment results of control signal interference. This ensures that the control system will not easily trigger actions in the presence of suspicious interference signals, thereby effectively preventing equipment malfunctions, cycle disruptions, and product quality defects caused by false signals. In high-cycle, high-precision processes such as chip mounting, soldering, testing, and depaneling, automotive circuit board processing production lines have extremely high requirements for the accuracy of control signals. Once a control action is erroneously triggered by an interference signal—for example, the mounting head descends prematurely, the soldering arm moves out of sequence, or the conveyor path switches incorrectly—it will not only cause the product to fail but may also lead to cycle disruptions throughout the entire production line, equipment damage, or even batch scrapping. Traditional control methods often use fixed trigger levels for judgment, failing to consider the fluctuations in signal behavior under electromagnetic interference, making it difficult to fundamentally isolate false triggers caused by interference signals.

[0043] This step protects the control signal triggering process through three core mechanisms: First, it strengthens edge recognition, requiring not only level transitions but also transition slopes and high-level durations to meet set conditions. Second, it introduces a time redundancy confirmation mechanism, which verifies the stability of identified trigger signals over multiple sampling cycles; only when the waveform is stable within a certain time is it considered genuine and valid. Third, it introduces a device status feedback confirmation mechanism before the action command is executed; the controller only issues the execution command after the device returns a ready signal. These three mechanisms together form a "multi-layered protection barrier" before the control action is executed. When an increase in interference level or a decrease in reliability is detected, the trigger threshold is automatically raised by extending the signal confirmation period, increasing the signal slope judgment threshold, and strengthening the device status confirmation requirements, forming a dynamic hardening strategy. When the environment is stable, the original fast response strategy is automatically restored to ensure efficiency.

[0044] Therefore, the essential function of this step is to transform the control command triggering mechanism from a traditional static, single-edge recognition mechanism into a dynamic control process that integrates signal stability confirmation, time continuity verification, and equipment response feedback. This not only enhances the system's anti-interference capability in complex electromagnetic environments but also ensures the accuracy and timing consistency of action execution. It holds crucial technical value for applications highly dependent on process control, such as automotive electronics manufacturing, and is an important component in building safe and intelligent control systems.

[0045] The interference event recording module, after the control action command adjustment is completed, synchronously writes the interference feature information identified in the current cycle, the control strategy adjustment record and the control response result data into the interference behavior database to form a structured record of interference events; To enhance the response and recording capabilities of control systems in automotive circuit board manufacturing production lines to interference events, and to achieve systematic accumulation of interference impacts and subsequent strategy optimization, a method based on a mechanism for writing information on the entire process of interference behavior within a control cycle is proposed. This method involves structured collection, encoding, archiving, and writing of interference characteristics, control strategy adjustment behaviors, and control response states occurring in each control cycle into a database. This forms a continuous, complete record of interference events with time-series indexing capabilities, providing data support for intelligent diagnosis, self-learning adjustment, and fault tracing of the control system. The specific process includes the following steps: The system collects and organizes the interference characteristics identified in the current control cycle and structures the fields. Specifically, during the control signal reliability analysis phase, the system has identified multiple interference behavior indicators, including abrupt change amplitude, nonlinear jitter, and cross-device synchronization offset. In this step, these characteristics need to be converted into standardized data fields and written into the database. Taking abrupt change amplitude as an example, the recorded fields include "control channel number" (e.g., CH-04), "maximum voltage change value" (in volts, e.g., 1.2V), "time of abrupt change" (relative to the start time of the control cycle, in milliseconds), and "spatial acquisition point number" (e.g., PZ-07). For nonlinear jitter, the recorded fields include "maximum edge offset time" (e.g., 2.4 milliseconds), "number of consecutive cycles" (e.g., 3 cycles), and "standard deviation value" (e.g., 0.7 milliseconds). The synchronization offset field is recorded as "master channel number," "slave channel number," "trigger time difference" (e.g., 1.6 milliseconds), and "number of synchronization offset durations" (e.g., 2 times). These fields are uniformly packaged and encoded by the data processing unit to form a complete interference characteristic field structure.

[0046] The dynamic adjustment behavior of the control strategy within the current cycle is collected and encoded to form a control strategy adjustment field structure. In the preceding steps, if a control signal path triggers operations such as edge recognition delay, redundancy verification cycle extension, or action verification mechanism enhancement due to interference intensity exceeding a set threshold, this behavior must be fully recorded. For example, for control channel CH-04, in control cycle number T-2025080501, the minimum edge recognition duration is adjusted from the original 5 milliseconds to 10 milliseconds, the redundancy verification cycle is extended from 10 milliseconds to 20 milliseconds, and the action execution requires waiting for device feedback signal, which is changed from "True" to "True+ReadyState=1". These parameter adjustment information must be recorded as fields, including "Adjustment Parameter Name", "Original Value", "Adjusted Value", "Adjustment Timestamp", and "Adjustment Trigger Reason Code" (e.g., interference confidence level is lower than the set threshold). All field information is structured and stored in a data structure named "Strategy Adjustment Record Table", and associated one-to-one with the corresponding control channel number.

[0047] Collect and record the final response results of control actions to construct a closed-loop response chain for interference. Even if the system employs identification and protection strategies for control signals, it is essential to clearly record whether the control signal actually triggered a device action and whether the action was completed as scheduled. Taking the execution signal of a pick-and-place machine as an example, the recorded fields include "signal trigger status" (e.g., Executed / Blocked), "trigger confirmation time" (e.g., 78 milliseconds), "execution feedback code" (e.g., 200 for success, 503 for no device response), "device ID" (e.g., PMX-02), and "abnormal flag" (e.g., 1 for an abnormality). If the action is not triggered, the "interception reason" (e.g., insufficient credibility, abnormal device status) must also be recorded for subsequent statistics. All response data is entered into the "Control Response Record Table" according to the fields and associated with the control cycle number and control channel number to form a complete behavioral response data chain.

[0048] The three data structures mentioned above—interference feature field, control strategy adjustment field, and control response field—are uniformly written into the interference behavior database and bound with a unique control cycle number and timestamp to generate structured records of interference events. Data writing adopts a standard relational structure definition. The main table is named "Interference Event Record Table," and each record contains an "Event Number," "Control Cycle Number," "Control Channel Number," "Event Timestamp," "Interference Feature Field ID," "Strategy Adjustment Field ID," and "Control Response Field ID." Integrity constraints are set for all sub-table fields to ensure that the data is searchable, comparable, and reusable. This database supports combined searches based on multi-dimensional conditions such as control channel, interference type, response result, and time interval. It can also be used as an input data source for control strategy optimization algorithms or as a basis for subsequent manual analysis and report export by system maintenance personnel.

[0049] The purpose of this step is to structurally record the identification, response, and execution results of interference within a complete control cycle of the control system, and write them into an interference behavior database. This constructs an interference event archive system with temporal continuity, parameter integrity, and spatial positioning capabilities, thereby providing fundamental data support for the long-term evolution, self-learning optimization, and anomaly tracing of the control system. In automotive circuit board manufacturing lines, the control system must maintain high stability and accuracy in complex electromagnetic interference environments. Its response to interference must not only be real-time and effective but also possess traceability and knowledge accumulation capabilities, enabling the system to make better judgments when facing similar interference scenarios. Traditional control systems often remain at the immediate processing stage, only roughly recording the result status, lacking the ability to record and analyze the entire process of interference impact, thus limiting the system's development in intelligent evolution, precise decision-making, and long-term reliability.

[0050] This step involves comprehensively collecting interference characteristic information, control strategy adjustment behaviors, and action execution feedback results throughout the control cycle. Each interference event is parameterized and structured from three dimensions: "cause of occurrence—response process—execution result," and then uniformly written into the database after the control cycle ends. This database not only possesses fine-grained field-level management but also enables multi-dimensional correlation retrieval through fields such as timestamps, control channel numbers, and spatial node information, meeting various application needs for production management, system maintenance, and engineering improvement. More importantly, these data records provide historical samples for subsequent control strategy self-learning mechanisms. The system can automatically select better strategy parameters based on the interference intensity, response measures, and execution effects in similar past scenarios, achieving dynamic optimization of control logic. Furthermore, in fault diagnosis, production line anomaly analysis, and quality traceability processes, this database also provides a reliable chain of evidence, achieving end-to-end transparency from interference source identification to control behavior correction.

[0051] Therefore, this step not only provides the control system with a high degree of integrity and consistency in data archiving, but also supports the continuous evolution of the entire intelligent manufacturing control system towards "predictability, analyzability, and optimizability" through the structuring and persistence of interference event records. It is an important foundational link in building an intelligent and adaptive industrial control system and has key practical value and technological innovation significance.

[0052] The self-learning optimization module implements a closed-loop self-learning strategy based on the interference behavior database, and periodically updates the generation rules of the interference weight parameter set and the control strategy adjustment logic, thereby realizing the dynamic adaptation and continuous prevention and control capability of the control system to repetitive or high-frequency interference sources during long-term operation. To achieve long-term adaptability of the control system to repetitive electromagnetic interference in the automotive circuit board manufacturing production line, a closed-loop self-learning mechanism based on an interference behavior database is proposed. This mechanism continuously prevents and controls control signal erroneous triggering through periodic extraction of historical data, feature pattern identification, parameter optimization, and strategy updates. The implementation includes the following steps: A fixed operating cycle threshold is set as the trigger condition to extract interference event records within a specific time period from the interference behavior database. Specifically, with a 72-hour operating cycle, a data reading program is automatically executed at the end of each cycle, calling the database interface to retrieve data records related to interference events from all control cycles completed within that cycle. Data retrieval fields include the control channel number (e.g., CH-01 to CH-10), interference type (e.g., sudden amplitude interference, continuous jitter interference, inter-device synchronization offset interference), interference event timestamp, spatial acquisition number, control signal adjustment behavior (e.g., edge recognition delay duration, redundancy verification sampling count, action confirmation waiting period, etc.), and the final control response result (e.g., successful execution, response delay, action cancellation, etc.). After data extraction, the data processing logic performs field integrity verification, removes records with missing information, retains valid interference behavior data, and proceeds to subsequent analysis processes.

[0053] The extracted interference behavior data is statistically analyzed for interference frequency and categorized for response effects. In this step, the system aggregates data according to the control channel number, statistically analyzes the occurrence frequency of each interference type in the specified channel, and quantifies the occurrence time interval, duration, and triggered control adjustment behavior. For example, on channel CH-03, if a sudden amplitude interference is detected an average of 6 times per hour, with each sudden amplitude greater than 1.0 volt, the channel will be marked as a sudden amplitude sensitive path. Simultaneously, the system further compares the corresponding control strategies for this interference type (such as adjusting the minimum edge recognition duration from 5 milliseconds to 8 milliseconds, increasing the redundant verification sampling period, etc.) and analyzes their impact on the control response success rate. If the action response success rate remains above 98% after strategy adjustment, the strategy is marked as a "high reliability measure." During this process, a one-to-one correspondence is established between each interference feature and the control adjustment effect, providing a basis for subsequent parameter optimization.

[0054] The interference weight parameter generation logic is updated based on the classification results to improve the sensitivity of interference identification and the adaptability of the strategy response. In this step, the interference type is reweighted according to its occurrence frequency, interference intensity, and the degree of influence on the control response. For example, if the frequency of abrupt amplitude interference is higher than 10 times per unit time and the average amplitude is greater than 1.2 volts, its weight factor is increased from the default 1.0 to 1.5; correspondingly, the reliability requirement for control signal triggering will be increased, and its filtering threshold setting range will be tightened to prevent false triggering. At the same time, if historical data shows that a certain type of interference (such as cross-device synchronization offset) has no significant impact on the response result, its interference influence factor will be appropriately reduced, thereby reducing its identification sensitivity and improving response efficiency. In addition, the system will also manage the interference data of each process area station, such as the chip mounting station, soldering station, and testing station, based on the process area number, forming a spatial parameter distribution map, which facilitates the personalized adaptation of control strategies to regional differences.

[0055] The updated interference parameter weight table and control strategy adjustment configuration table are written to the system configuration storage unit and set to take effect in the next control cycle. After the parameter update is completed, the system generates a complete parameter change log, recording the original value, adjusted value, adjustment basis (e.g., the sudden change amplitude exceeds the historical average by more than 30%), applicable control channel range, and expected impact fields for each parameter. This log content must be manually reviewed and confirmed by the engineering administrator before activation. After confirmation, the control system will load the new parameter configuration during the next control process initialization, overwriting the original settings. Subsequently, the system automatically starts a new round of operation, continuing to track and record interference behavior and response data, and verifying the performance of the new strategy in the actual environment, including indicators such as response time changes, action execution accuracy, and false trigger rate. If the new strategy shows higher stability and accuracy over multiple control cycles, it is solidified as the default strategy; if the effect is not as expected, it reverts to the previous configuration to ensure that the system's operational reliability is not affected.

[0056] The core function of this step is to endow the control system with the ability to "self-analyze, autonomously optimize, and dynamically evolve" against electromagnetic interference events by introducing a closed-loop self-learning mechanism based on an interference behavior database. This enables long-term adaptation and continuous control against repetitive, high-frequency interference sources. This capability is particularly critical for automotive circuit board manufacturing lines, as their production environment is characterized by high-speed and high-density electrical operations such as surface mount technology (SMT), automated soldering, and testing and handling. These processes not only easily generate complex transient electromagnetic interference but may also cause signal coupling problems between different paths. Traditional interference control methods often rely on manual experience to configure strategies and set static parameters, such as signal trigger levels and time redundancy judgments. This approach struggles to cover the constantly changing interference behavior on-site, lacking both long-term effectiveness and a flexible evolution mechanism.

[0057] This step involves periodically extracting historical data from the interference behavior database to identify which control channels and interference types occur most frequently in specific time periods or spatial nodes. It analyzes the reasons for control strategy adjustments, the specific content of the adjustment measures, and the success or failure of their implementation. Furthermore, based on the statistical results, it optimizes the interference weight parameter setting rules and dynamically adjusts the control signal triggering mechanism, delay strategy, and edge recognition logic. This enables the control system to "memorize" and "adapt" to known interference behavior patterns. In other words, the system can proactively respond to similar interference in advance, without having to relearn and readjust each time.

[0058] More importantly, this step eliminates the reliance on manual settings or one-off designs for control strategy updates, instead constructing a self-regulating closed-loop process: from disturbance identification to strategy response, then to feedback evaluation, and finally, optimization. In this way, the system can accumulate increasingly more real-world data on the "disturbance-response-result" cycle over long-term operation, continuously refining its parameter configurations, improving response efficiency and control accuracy, and enhancing overall system stability and robustness. Therefore, this step is not merely a strategy optimization action, but a data-driven control mechanism with self-evolving capabilities. It serves as a fundamental support for achieving long-term operational stability in intelligent manufacturing and automated production lines, demonstrating significant technological innovation and engineering practicality in its logical structure, implementation process, and application value.

[0059] The proposed control system solution effectively improves the control stability and robustness of the automotive circuit board processing production line under high electromagnetic interference environments, enabling dynamic perception, accurate identification, intelligent adjustment, and continuous prevention and control of control signal mis-triggers throughout the entire process. Specifically, this invention establishes an interference spatial distribution model through multi-point distributed data acquisition, giving the control system a structured perception capability of interference sources, intensity, and paths. Furthermore, feature extraction and weight calculation enhance the system's ability to distinguish and respond to different interference types. Combined with dynamically set interference filtering thresholds and control signal discrimination mechanisms, refined anti-interference triggering strategies are adjusted. Finally, by integrating interference event recording and self-learning optimization mechanisms, a continuously evolving closed-loop control system is constructed, allowing the control system to continuously adapt to changing environmental interference trends during long-term operation. In summary, this solution not only breaks through the traditional anti-interference approach relying on hardware shielding but also achieves intelligent control optimization through software-hardware collaboration and data-driven processes. While ensuring stable operation of key processes such as surface mount technology (SMT), soldering, and testing, it significantly reduces the error rate and product defect rate, improving overall production efficiency and product consistency, demonstrating significant engineering application value and promising prospects for industrial promotion.

[0060] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A control system for an automotive circuit board processing production line, characterized in that, It includes an interference perception modeling module, an interference feature extraction module, a signal discrimination and evaluation module, a control and adjustment response module, an interference event recording module, and a self-learning optimization module. The interference perception modeling module, based on a multi-point distributed acquisition mechanism, collects control signals, power supply voltage, and spatial electric field data of key nodes to construct a three-dimensional coupled distribution map of the interference-sensitive area, which serves as a benchmark model for interference identification. The interference feature extraction module performs dynamic difference analysis on the three-dimensional coupling distribution map to extract abrupt change amplitude, nonlinear jitter and cross-device synchronization offset features, forming a set of interference weight parameters; The signal discrimination and evaluation module constructs a control signal discrimination framework based on the interference weight parameter set and dynamically sets the interference filtering threshold by combining historical operating parameters. The control adjustment response module performs synchronous control adjustments based on the interference filtering threshold, including dynamic optimization of trigger edge, time redundancy and command confirmation mechanism; The interference event recording module writes the identified interference characteristics, control adjustment records, and response results into the interference behavior database after the control action command adjustment is completed, forming a structured record. The self-learning optimization module implements a self-learning strategy based on the interference behavior database, periodically optimizing the interference weight parameters and control adjustment logic to achieve dynamic adaptation and continuous prevention and control of high-frequency interference.

2. The control system of a vehicle-mounted circuit board processing production line according to claim 1, characterized in that, The interference sensing modeling module includes the following steps: Multiple data collection points are set up in the surface mount assembly area, reflow soldering area, online inspection area, automatic board splitting area, finished product packaging area and material conveying intersection area; At each acquisition point, control signal parameters, power supply voltage parameters, and spatial electric field strength parameters are collected respectively. Based on the three-dimensional coordinate information of each collection point, the collected data is projected onto the three-dimensional spatial model of the processing production line and spatial interpolation is performed. A three-dimensional coupled distribution map of the interference-sensitive area is generated based on the spatial model and visualized, forming a reference for subsequent interference identification and response adjustment.

3. The control system of a vehicle-mounted circuit board processing production line according to claim 1, characterized in that, The interference feature extraction module includes the following steps: The control signal waveform data of each acquisition point are synchronously unfolded according to the spatial coordinate sequence, and key points are marked. Identify voltage mutation behavior in control signals and establish a mutation point distribution table; Detect nonlinear jitter in the edge time of the control signal and generate a jitter feature dataset; The trigger time difference of the rising edge of the control signal between adjacent acquisition points is analyzed and the synchronization offset parameter is extracted to form a set of interference weight parameters.

4. The control system for a vehicle-mounted circuit board processing production line according to claim 1, characterized in that, The signal discrimination and evaluation module includes the following steps: Establish a one-to-one correspondence between control signal paths and interference characteristic parameters; Collect real-time parameters of the control signal and calculate the current credibility score; The current credibility score is compared and analyzed with historical credibility data to calculate the interference filtering threshold; Based on the comparison between the current credibility score and the interference filtering threshold, it is determined whether the control signal enters the control execution channel.

5. The control system for a vehicle-mounted circuit board processing production line according to claim 1, characterized in that, The control adjustment response module includes the following steps: Adjust the trigger edge recognition conditions of the control signal according to the interference filtering threshold; After trigger edge recognition, perform time redundancy verification and verify signal stability; Receive the target device's readiness feedback signal after the signal is confirmed to be valid but before executing the action command; The parameters of the edge recognition, voltage verification, and action confirmation mechanisms are dynamically adjusted according to the interference level to achieve synchronous and coordinated adjustment of the control triggering process.

6. The control system for a vehicle-mounted circuit board processing production line according to claim 1, characterized in that, The interference event logging module includes the following steps: Collect the amplitude of sudden changes, nonlinear jitter and synchronization offset features identified in the current control cycle, and generate a disturbance feature field structure; Record the edge recognition adjustment, redundancy verification extension and action confirmation mechanism changes that occur within the current control cycle, and generate the control strategy adjustment field structure; Collect control action response results, including signal trigger status, feedback time and abnormality indicator, and generate control response fields; After binding the interference feature field, control strategy adjustment field, and control response field with the control cycle number and timestamp, write them into the interference behavior database.

7. The control system for a vehicle-mounted circuit board processing production line according to claim 1, characterized in that, The self-learning optimization module includes the following steps: Set a fixed operating cycle and extract interference event records within that cycle from the interference behavior database; The interference event records are aggregated according to the control channel number, the frequency of interference types is statistically analyzed, and the control response results are categorized. The interference weight parameters are updated based on the interference frequency, intensity, and response impact to form an updated parameter configuration table; Write the updated parameter configuration table to the configuration storage unit and set it to take effect in the next control cycle.

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