High-density four-row connector layout intelligent optimization method

Abstract

The application relates to the technical field of connector layout optimization, and discloses a high-density four-row connector layout intelligent optimization method. The method collects design parameters of four-row connectors on a high-density circuit board, including connector position coordinates, pin numbers and electrical impedance values, and related environmental data, including temperature distribution, electromagnetic interference intensity and vibration frequency; the design parameters and the environmental data are standardized and aligned to form a layout data set with unified dimensions; the layout data set is processed by using a layout optimization model to generate an initial layout scheme, the initial layout scheme including connector arrangement order and position adjustment suggestions; real-time monitoring of change data of the working state of the circuit board, including current fluctuation and thermal distribution update; based on the change data, layout abnormal conditions are identified, the initial layout scheme is iteratively corrected according to the layout abnormal conditions, and a final optimized layout scheme is output. The method can dynamically adapt to the working state of equipment and improve layout rationality.

Description

Technical Field

[0001] The present invention relates to the technical field of connector layout optimization, and in particular to a high-density four-row connector layout intelligent optimization method. Background Art

[0002] As electronic devices evolve towards miniaturization and higher density, the layout design of connectors on circuit boards faces increasingly severe challenges. This is especially true for four-row connectors, as their dense arrangement, high number of pins, and complex electrical connections directly impact the performance of the entire electronic system.

[0003] Traditional connector layout design relies heavily on engineers' experience and judgment, with layout plans often completed by manually adjusting positions and arrangement sequences. This approach is not only time-consuming and labor-intensive, but also fails to fully consider the impact of multiple factors. For example, when dealing with temperature distribution issues, relying solely on experience often fails to accurately predict connector heat dissipation under different operating conditions, potentially leading to localized overheating and affecting device stability.

[0004] Electromagnetic interference (EMI) is another prominent issue. Signal transmission between four rows of connectors is prone to mutual interference. Traditional layout methods make it difficult to accurately assess the EMI intensity under different arrangements, often leading to signal transmission errors or attenuation. Furthermore, the impact of vibration on connector layout cannot be ignored. Connectors with improperly positioned connectors can experience poor contact under long-term vibration, reducing equipment reliability.

[0005] As circuit board integration continues to increase, the number of connector pins continues to grow, making electrical impedance matching increasingly difficult. Traditional layout solutions lack a systematic analysis of electrical impedance values, making them prone to signal reflections caused by impedance mismatches, impacting the integrity of data transmission. Furthermore, when devices are in dynamic operation, current fluctuations and changes in thermal distribution further exacerbate layout defects. Traditional methods are unable to respond to these changes in real time, making it difficult to adjust the layout in a timely manner.

[0006] Some existing layout optimization methods attempt to introduce simple algorithmic models. However, these models often optimize only a single factor, ignoring the correlation between design parameters and environmental data, resulting in limited optimization results. When faced with complex layout scenarios like high-density four-row connectors, they cannot develop a comprehensive, dynamic optimization solution, making it difficult to meet the high performance and high reliability requirements of modern electronic devices. Summary of the Invention

[0007] The object of the present invention is to provide a high-density four-row connector layout intelligent optimization method to solve the problems raised in the above background technology.

[0008] To achieve the above objectives, the present invention provides a high-density four-row connector layout intelligent optimization method, the method comprising:

[0009] Collecting design parameters and related environmental data for four-row connectors on a high-density circuit board. The design parameters include connector location coordinates, number of pins, and electrical impedance values. The environmental data includes temperature distribution, electromagnetic interference intensity, and vibration frequency.

[0010] Performing standardized alignment processing on the design parameters and the environmental data to form a layout data set of unified dimensions;

[0011] Processing the layout data set using a layout optimization model to generate an initial layout solution, wherein the initial layout solution includes a connector arrangement sequence and position adjustment suggestions;

[0012] Real-time monitoring of circuit board operating status changes, including current fluctuations and thermal distribution updates;

[0013] Identify layout anomalies based on the change data, iteratively modify the initial layout solution according to the layout anomalies, and output a final optimized layout solution.

[0014] Preferably, the standardization and alignment processing of the design parameters and the environmental data includes:

[0015] extracting the connector position coordinates in the design parameters and the temperature distribution information in the environmental data, and converting the unstructured coordinate data into a structured grid mapping;

[0016] Performing time alignment on the design parameters and the environmental data based on timestamps to ensure that data points correspond to each other in the same time frame;

[0017] Remove conflicting or redundant fields and retain parameter values ​​with high source priority to form a cleaned layout dataset;

[0018] The layout dataset is converted into a matrix format for subsequent model input.

[0019] Preferably, the processing of the layout dataset using the layout optimization model includes:

[0020] Configuring an input layer of the layout optimization model to receive the layout data set in the matrix format;

[0021] Executing a connector position optimization algorithm within the layout optimization model to calculate an adjustment vector of the position coordinates;

[0022] Generate a connector arrangement sequence based on the adjustment vector and evaluate the matching degree of pin quantity and electrical impedance value;

[0023] An initial layout solution including position adjustment suggestions is output, wherein the position adjustment suggestions include new coordinate values ​​and arrangement priorities.

[0024] Preferably, identifying a layout anomaly based on the change data includes:

[0025] Real-time acquisition of current fluctuation data and thermal distribution update data from the change data of the circuit board's working state;

[0026] Comparing the current fluctuation data with a preset threshold value to detect abnormal fluctuation points;

[0027] Analyzing the changing trend of the thermal distribution update data and identifying thermal anomaly areas;

[0028] Abnormal fluctuation points and thermal anomaly areas are integrated to generate a layout anomaly report.

[0029] Preferably, the iteratively correcting the initial layout solution according to the abnormal layout situation includes:

[0030] Extracting the coordinates of the abnormal points and the affected range in the layout abnormality report;

[0031] Positioning the affected connectors in the initial layout solution and recalculating adjustment vectors of the position coordinates;

[0032] Update the connector arrangement order and position adjustment suggestions to form an intermediate optimization plan;

[0033] Verifying whether the intermediate optimization solution satisfies the constraints of electrical impedance value and number of pins;

[0034] Output the final optimized layout solution that has passed verification.

[0035] Preferably, the connector position optimization algorithm is executed within the layout optimization model, and the execution steps are:

[0036] Initialize the search space of connector position parameters and define the feasible range of position coordinates;

[0037] An evolutionary strategy algorithm is used to iteratively generate candidate position solutions in the search space;

[0038] Evaluating an objective function value for each candidate position solution, wherein the objective function is calculated based on electromagnetic interference intensity and vibration frequency;

[0039] The best candidate position solution is selected as the adjustment vector input.

[0040] Preferably, the verifying whether the intermediate optimization solution satisfies the constraints of electrical impedance value and number of pins includes:

[0041] Extract position adjustment suggestions and pin quantity data from the intermediate optimization solutions;

[0042] Calculate the correlation matrix between position coordinates and electrical impedance values;

[0043] Check whether the correlation matrix is ​​within the preset tolerance range;

[0044] If not satisfied, the adjustment vector for recalculating the position coordinates is returned;

[0045] If satisfied, the output is the final optimized layout solution.

[0046] Preferably, the real-time monitoring of the change data of the circuit board working status includes:

[0047] Deploy a sensor network to collect current fluctuation data and thermal distribution update data;

[0048] Compare the collected data with the electrical impedance values ​​in the design parameters in real time;

[0049] Generate a stream of change data containing timestamps;

[0050] The change data stream is input into the anomaly detection module.

[0051] Preferably, generating a layout anomaly report includes:

[0052] Aggregate coordinate data of abnormal fluctuation points and thermal anomaly areas;

[0053] Calculate the electromagnetic interference intensity and vibration frequency influence factors of abnormal points;

[0054] Generate anomaly level labels based on impact factors;

[0055] Outputs a labeled layout exception report.

[0056] Preferably, the outputting of the final optimized layout solution includes:

[0057] Integrate position adjustment suggestions and connector arrangement order to generate a layout coordinate diagram;

[0058] Convert the layout coordinate diagram into an executable design file;

[0059] Send design files to the circuit board manufacturing system.

[0060] Compared with the prior art, the present invention has the following beneficial effects:

[0061] By collecting multi-dimensional design parameters and environmental data, we achieve comprehensive coverage of factors influencing layout. Design parameters such as connector location coordinates, pin count, and electrical impedance are combined with environmental data such as temperature distribution, electromagnetic interference intensity, and vibration frequency to provide a wealth of basic information for generating layout solutions. This allows layout design to move beyond the consideration of a single factor and instead balance all factors.

[0062] Standardized alignment integrates different types of data into a unified layout dataset, eliminating analytical barriers caused by differences in data formats and enabling the layout optimization model to process information more efficiently. This data processing approach ensures the consistency and accuracy of model inputs, laying a solid foundation for subsequent layout solution generation. This allows the model to fully explore the inherent connections between data and enhance the scientific nature of layout solutions.

[0063] The layout optimization model generates an initial layout plan, including connector order and position adjustment suggestions. This breaks away from the traditional layout model's reliance on experience. Leveraging the model's analytical capabilities, it can explore more layout possibilities that balance multiple factors. This intelligent solution generation reduces the limitations of human judgment and makes layout design more objective and reasonable.

[0064] Real-time monitoring of changing data on the PCB's operating status, including current fluctuations and thermal distribution updates, allows layout optimization to closely track the device's dynamic operation. By capturing these real-time changes, problems with the layout can be promptly identified, avoiding the mismatch between static layout and dynamic operating conditions encountered in traditional methods.

[0065] Based on change data, layout anomalies are identified and iteratively corrected, allowing the layout plan to continuously adapt to changes in the equipment's operating status. This dynamic adjustment mechanism ensures that the layout plan remains reasonable throughout the equipment's lifecycle, adapting to changing requirements at different operating stages. Through continuous iteration, the layout plan is gradually refined, better coordinating the positional relationships between connectors and reducing various issues caused by improper layout.

[0066] This method combines design parameters, environmental data, and real-time operating status to form a closed-loop optimization system. Within this system, all links work together, from data collection to solution generation and dynamic correction, forming a complete layout optimization process. This approach can address the complexity of high-density four-row connector layouts, adapt to the trend of miniaturization and high density in electronic equipment, and improve the overall adaptability of connector layouts. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 This is a working principle diagram of the high-density four-row connector layout intelligent optimization method according to the present invention;

[0068] Figure 2 Flowchart for standardizing the alignment process for design parameters and environmental data;

[0069] Figure 3 A flowchart for iteratively revising the initial layout plan based on layout anomalies;

[0070] Figure 4 Flowchart for connector position optimization algorithm implementation;

[0071] Figure 5 This is a flow chart for real-time monitoring of circuit board working status change data. DETAILED DESCRIPTION

[0072] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0073] See also Figure 1 The present invention provides a high-density four-row connector layout intelligent optimization method, the method comprising:

[0074] Step 1: Collect design parameters and related environmental data for four-row connectors on a high-density PCB. Design parameters include connector location coordinates, pin count, and electrical impedance. Environmental data includes temperature distribution, electromagnetic interference intensity, and vibration frequency. This data is acquired through a multi-source sensor network and PCB design files to ensure data integrity and real-time performance.

[0075] Step 2: Standardize and align design parameters and environmental data to form a unified layout dataset. Data cleaning and format conversion techniques are used to eliminate data redundancy and conflicts, ensuring compatibility with subsequent model processing.

[0076] Step 3: Use the layout optimization model to process the layout dataset and generate an initial layout solution. The layout optimization model is based on a multi-objective optimization algorithm and outputs connector arrangement and position adjustment suggestions, including new coordinate values ​​and priority ranking.

[0077] Step 4: Monitor the PCB's operating status in real time, including current fluctuations and thermal distribution updates. Dynamically collect data through embedded sensors and monitoring modules and compare it with the initial design parameters.

[0078] Step 5: Identify layout anomalies based on the change data and iteratively modify the initial layout plan accordingly. A feedback mechanism is used to adjust connector positions and order, ultimately outputting an optimized layout that meets electrical and mechanical constraints.

[0079] Example 1: See Figure 2 ,This embodiment focuses on the standardized alignment process of ,design parameters and environmental data, and the core operating mechanism of ,layout optimization model.

[0080] When the standardized alignment process is initiated, the system first parses the input design parameters and environmental data sources. The connector location coordinates in the design parameters are presented in an unstructured format, consisting of a mixture of polar and rectangular coordinates. The temperature distribution in the environmental data is presented as thermal image point cloud data. The processing module invokes a spatial grid mapping engine to divide the circuit board into a 0.5mm×0.5mm grid of cells. Bilinear interpolation is used to relocate the original coordinate data to the grid intersections. For the temperature distribution data, the inverse distance weighted method is used to convert discrete temperature points into a grid temperature matrix, with each grid cell assigned a value representing the average temperature of that area. In the temporal dimension, all data streams are timestamped with nanosecond-level precision. The timing alignment engine creates a 10ms time window, forcing missing data within the window to be filled using linear interpolation from adjacent time points. If there is a conflict between the pin count in the design parameters and the pin signals collected by the environmental sensors, the conflict is resolved based on the data source reliability level: EDA design file parameters are prioritized as level 1, online monitoring data as level 2, and historical data as level 3. After conflict resolution, the dataset undergoes field simplification, removing redundant information such as low-frequency harmonic components in the vibration frequency, while retaining valid vibration spectrum data from 50Hz to 10kHz. The final output layout dataset is converted into an N×M matrix format, where N represents the total number of grid cells in the circuit board (a standard 25cm² board corresponds to approximately 25,000 grid cells). The M-dimensional data contains six parameters: the X / Y coordinates of the grid center, the measured electrical impedance, the temperature gradient, the peak electromagnetic field intensity, the main vibration frequency, and the connector presence flag.

[0081] When the layout optimization model starts processing, the input layer is set to a receiver structure with 25,000 nodes and 6 channels, with each node corresponding to a cell in the grid matrix. During the model initialization phase, a constraint rule library is preloaded to define the feasible region for connector placement: a 1.2mm keepout zone is maintained from the board edge, and the minimum center-to-center distance between connectors is set to 3.8mm. During the placement optimization algorithm, an initial population of 200 candidate solutions is created, each representing a connector distribution state across 25,000 grid cells. During the iterative calculation process, the objective function evaluates the quality of candidate solutions in three stages: an electromagnetic interference intensity assessment phase uses a field strength simulator based on Maxwell's equations to calculate the electromagnetic coupling coefficient between adjacent connectors; a vibration sensitivity assessment phase uses finite element modal analysis to predict the displacement response at resonant frequencies of the 1st to 5th order; and a pin density match score is calculated based on the deviation between the number of grid cells covered by the connector and the theoretical value. The top 20 solutions are retained in each iteration, and 150 progeny solutions are generated through a two-point crossover operation. Mutation uses an adaptive mutation rate strategy, with a base mutation rate of 0.05 that is increased to 0.15 when optimization stalls. If the improvement in the optimal solution over ten consecutive generations is less than 0.1%, the algorithm terminates and outputs an adjustment vector. This vector contains coordinate offset instructions for each grid cell. The offsets are smoothed using a Kalman filter to eliminate sudden changes. The connector arrangement order reconstruction module analyzes the adjusted position matrix and establishes connector indexes according to the natural order of the grid cell numbers. It also automatically prioritizes connectors with impedance differences exceeding 15Ω between adjacent cells. The output initial layout solution is encoded as a binary data stream. Each connector records three types of information: the starting coordinates of the grid it occupies, its coverage area, and its recommended arrangement order. The solution package size is kept below 2MB to accommodate the transmission requirements of industrial control systems. The processing flow incorporates a three-step re-verification mechanism: a coordinate conversion module detects grid out-of-bounds conditions, an impedance matching module verifies the logical relationship between pin number and impedance value, and a thermal coupling module predicts the interaction between temperature gradients and electromagnetic fields. On a standard server configuration, a single complete processing step takes no more than 12 seconds, with 85% of this time spent on electromagnetic field simulation. The data persistence layer employs a tiered storage strategy, storing the original design parameters and the final optimized solution in a relational database. Intermediate process data is transferred to a time series database to support retrospective analysis. When grid cells conflict, the system automatically initiates a secondary arbitration protocol: high-impedance connector locations are first allocated based on impedance priority, and the remaining areas are re-allocated based on vibration sensitivity indicators. A conflict resolution log is then generated for designer review. During the optimization process, environmental data fluctuations are continuously monitored. When temperature distribution or electromagnetic field intensity changes exceed limits, a data re-collection process is triggered to maintain the validity of the underlying computational data. The connector special processing module recognizes multi-pin (>80 pin) connectors and automatically activates a quadrilateral mesh splitting mode instead of a standard rectangular mesh, ensuring that optimization accuracy in high-pin density areas is controlled within a 0.1mm positioning error.

[0082] Example 2: See Figure 3 ,This embodiment focuses on the real-time monitoring and processing mechanism of ,the circuit board working status change data, and the dynamic ,correction process for layout anomalies.

[0083] The system uses a distributed monitoring network to track current fluctuations. An array of Hall-effect current sensors is deployed in key circuit boards, with a fixed sampling frequency of 1kHz. Sensor measurements are transmitted to the central processing unit via an isolation amplifier circuit. Current data stream processing utilizes a three-layer filtering architecture: The raw sampled values ​​are filtered through an FIR digital filter to suppress high-frequency noise. A sliding window is then used to extract the effective fluctuation characteristics within the current 5 milliseconds. Finally, this characteristic is compared with a threshold comparator calculated using an impedance matching model. This threshold is not a fixed value but is calculated in real time based on the electrical impedance of the corresponding area: increasing impedance increases the upper tolerance limit, while decreasing impedance tightens the floating range. If the instantaneous fluctuation peak exceeds the dynamic threshold three times in a row, the system marks the current waveform segments within 10 microseconds before and after that point in time, compresses and encodes them, and stores them in a temporary cache queue.

[0084] Thermal distribution data acquisition uses a non-contact infrared sensor matrix to refresh the surface temperature distribution map of the circuit board at a cycle of 30 milliseconds. The thermal data processing engine converts the original image pixels into a temperature value matrix through coordinate mapping. The row coordinates of the matrix correspond to the X-axis position of the circuit board, and the column coordinates correspond to the Y-axis position. Each unit stores the Celsius temperature value. Change trend detection uses an incremental analysis method to perform a differential calculation on the current temperature matrix and the matrix copies of the previous ten cycles to generate a temperature gradient matrix. This gradient matrix is ​​partitioned and scanned using a morphological algorithm to identify continuous areas where the temperature rise rate exceeds the preset critical value - defined as supercritical hot spots. The boundary coordinates of the hot spot are extracted using an edge tracking algorithm, and the core temperature point coordinates are taken as the geometric center position of the area, together with the area and average temperature rise rate, and written into the thermal anomaly feature table.

[0085] The anomaly comprehensive processing module receives the current anomaly mark and thermal anomaly feature table and performs spatial correlation mapping operations. The system creates an association map of the circuit board layout coordinate system, rendering the current anomaly point to the nearby area with the weight of the electromagnetic radiation impact factor, and the thermal anomaly area is vector-filled with the temperature gradient weight. The two types of anomaly data are superimposed in the spatial grid to calculate the composite intensity value. When the composite intensity exceeds the threshold, the layout anomaly report is generated. The report is constructed in an object format: the primary key of the anomaly point is the circuit board coordinate code, and the attribute list includes the anomaly type identification code (current / thermal / combined type), intensity value, influence radius calculation value (the current influence radius is calculated according to the electromagnetic field attenuation formula, and the thermal influence radius is deduced according to the thermal conductivity characteristics), timestamp, and anomaly confidence score.

[0086] Upon receiving a layout anomaly report, the iterative correction engine initiates the initial layout plan adjustment process. The system retrieves all connector numbers within the radius of the anomaly coordinates from the layout database and loads their original design parameters and location information. The adjustment vector recalculation process utilizes constrained quadratic optimization: the distance constraint between the new connector position and the original position is set at 20% of the maximum movement threshold. The permutation optimization objective function incorporates an anomaly intensity penalty term, with electromagnetic-related anomalies weighted for position dispersion and thermal-related anomalies prioritized for heat dissipation. For high-power connectors, the system establishes a thermal load prediction model. When the probability of a thermal anomaly exceeds a critical threshold, the connector is automatically moved to a nearby low-temperature reserve area. During the generation of the intermediate layout plan after the repositioning, the system performs pin continuity checks: a bidirectional linked list maintains the connector pin index relationship and hash index remapping is initiated when a discontinuous jump in the pin number sequence occurs. Electrical impedance tolerance is verified by comparing impedance distribution maps. The difference between the theoretical impedance field distribution in the new layout and the measured field is calculated using a matrix dot product. Regions exceeding the 5% deviation threshold are marked as red defect areas. If the area of ​​the red defect detected exceeds 0.3% of the layout area, the system automatically returns to the adjustment vector calculation phase and initiates a new iteration. When the tolerance conditions are met, the correction engine generates a final layout solution version number and appends an integrity check code. The entire correction cycle utilizes a timeout mechanism; a single iteration timeout of 180 seconds triggers a manual intervention flag. An exception handling log records the time points of all correction operations, snapshots of key parameters, and decision paths, with a log depth of up to 15 levels of operation backtracking.

[0087] Example 3: See Figure 4 , this embodiment elaborates on the core operation mechanism and execution process of the connector position optimization algorithm. The algorithm adopts an improved evolutionary strategy as the calculation framework, and its search space initialization phase is strictly limited to the effective layout area of ​​the circuit board. The system first loads the mechanical structure constraint database of the circuit board, which contains three types of forbidden area definitions: the physical edge forbidden area is a closed polygon formed by shrinking the circuit board outline inward by 1.2mm; the fixed hole forbidden area is a circular area with a diameter of 3mm, and the coordinates of the center of the circle are taken from the assembly drawing; the heat dissipation channel forbidden area is a polygonal forbidden area formed by extending the projection outline of the heat sink outward by 0.8mm. The set of feasible connector positions is generated by computational geometry methods. After the effective area of ​​the circuit board is gridded, the ray method is used to screen out all the grid center point coordinates that do not overlap with other forbidden areas to form a discrete expression of the initial solution space.

[0088] When the evolutionary population is initialized, a population of 50 candidate solutions is created, and each candidate solution uses binary coding to represent the connector distribution status. The coding structure is divided into a position segment and an attribute segment: the position segment occupies 20 bits of binary to represent the X / Y coordinates of the connector in the grid coordinate system, of which the first 10 bits encode the X-axis coordinate value and the last 10 bits encode the Y-axis coordinate value; the attribute segment occupies 8 bits to represent the connector type identification (3 bits), pin density level (3 bits) and thermal sensitivity flag (2 bits) respectively. Latin hypercube sampling is used in the population generation process to ensure that the initial solutions are evenly distributed in the search space to avoid premature convergence. The quality evaluation of each candidate solution is calculated through a multi-objective fitness function, which integrates three indicators: electromagnetic compatibility, mechanical stability and thermal management:

[0089]

[0090] in represents the electromagnetic radiation intensity coefficient of the i-th connector, is the Euclidean distance between connectors i and j, is the amplitude coefficient of the j-th vibration mode, represents the mechanical resonance sensitivity of the kth connector, is the temperature gradient modulus at position (x, y). Weight coefficient 、 、 Dynamic adjustment based on environmental monitoring data: When the electromagnetic interference sensor reading exceeds the warning value Automatically increase by 30%; when the vibration accelerometer detects an abnormal spectrum Increase by 25%; when the infrared thermal imager shows a local over-temperature area Linear growth.

[0091] The evolutionary process uses a hybrid strategy: During the selection phase, the top 10% of solutions are retained for direct advancement to the next generation. A crossover operation randomly pairs the remaining 90% of solutions, performing a two-point crossover with dynamically changing crossover points. A base crossover probability of 0.65 is set. Mutation uses an adaptive mutation rate mechanism, with a base mutation probability of 0.05 increasing to 0.12 when population diversity falls below a threshold. Mutation involves bit-flipping the position segment encoding and introducing Gaussian noise to perturb the thermal sensitivity flag in the attribute segment. Solution validity is verified after each iteration: a table lookup confirms that the new coordinates fall within the feasible grid, that the pin density level matches the connector model, and that the thermal sensitivity flag is consistent with the current temperature distribution. Invalid solutions are discarded and replaced by randomly generated new ones.

[0092] The algorithm termination condition sets two judgment criteria: the main termination condition is that the improvement of the optimal solution for 15 consecutive generations is less than 0.08%; the auxiliary termination condition is that the total number of iterations reaches 200. When any of the conditions is met, the system extracts a non-dominated solution set from the final population, and the solution set is screened out by Pareto frontier analysis to select the three most representative candidate solutions: electromagnetic compatibility optimal solution selection The one with the highest score will be selected as the optimal solution for mechanical stability The one with the highest score will be selected as the optimal thermal management solution The three solutions are fed into the decision engine for a comprehensive evaluation, which takes into account the current operating state of the circuit board: thermal management solutions are given a 40% higher weight in high-temperature conditions, mechanical stability solutions are given a 35% higher priority in high-vibration environments, and electromagnetic compatibility solutions receive an additional 50% weighted score in high-electromagnetic interference scenarios.

[0093] Before outputting the optimal solution, coordinate refinement is performed: discrete grid coordinates are converted to precise values ​​in a continuous coordinate system, and the optimal connector positioning points are calculated through quadratic interpolation. The adjustment vector generation module calculates the displacement components of each connector from the initial position to the optimized position. This vector field is represented in complex form: the real part records the X-axis displacement, the imaginary part records the Y-axis displacement, the modulus represents the distance moved, and the argument indicates the direction of movement. The vector field data is smoothed using a Kalman filter to eliminate sudden changes and maintain displacement continuity. The final output adjustment solution includes the new connector position coordinates, a movement priority flag (ranked 1-5), and an estimated improvement benefit index, which is a weighted composite of the relative improvements in the three fitness components. The solution data is packaged in a proprietary binary format, including a version header, a checksum, and a compressed coordinate data block. The file size of a single solution is kept within 128KB to accommodate the storage limitations of industrial control systems. A comprehensive operation log is generated throughout the optimization process, recording the characteristic parameters, fitness distribution, and selection pressure curves of each generation of the population. This log data is used for subsequent algorithm parameter tuning and anomaly diagnosis and analysis.

[0094] Example 4: See Figure 5 This embodiment details the verification mechanism for the intermediate optimization solution and the operational process of the real-time monitoring system. The verification system performs multi-dimensional compliance checks on the connector configuration after layout adjustments, while also establishing a dynamic data acquisition network to continuously track the operating status of the circuit board. The following demonstrates the complete workflow using a specific example:

[0095] A high-density PCB model HDC-4200 contains four rows of 36 connectors. After optimization in Example 3, an intermediate layout solution was generated. The verification system first analyzed the new coordinate data of the connectors in the solution and extracted the key parameter samples shown in Table 1 for preliminary analysis. The table shows some examples of verification parameters for connectors:

[0096] Table 1 Example of connector layout verification parameters

[0097] Connector ID Original X coordinate (mm) New X coordinate (mm) Original Y coordinate (mm) New Y coordinate (mm) Number of pins Impedance reference value (Ω) Measured impedance (Ω) Temperature (℃) Status Marker CN-12 45.2 48.7 102.4 98.6 64 50.0 51.3 68 qualified CN-18 88.5 85.1 76.3 79.8 48 75.0 72.4 72 warn CN-23 132.7 129.5 54.9 58.2 32 100.0 104.7 65 defect CN-31 156.8 152.3 112.7 115.4 56 62.5 60.1 70 qualified

[0098] After the impedance matching verification module is activated, the system reads the actual impedance values ​​of each connector loop from the circuit board test interface. For the measured value of 104.7Ω displayed by the CN-23 connector, the system calculates that its deviation from the baseline value of 100.0Ω is 4.7%, exceeding the preset 3% tolerance limit. The verification engine immediately marks the connector as "defective" and traces its distribution to adjacent connectors in the new layout. Inspection reveals that the connector has been adjusted to shorten the distance to the high-frequency signal source CN-29 to 2.1mm, triggering electromagnetic coupling effects. The system automatically generates impedance correction suggestions: move CN-23 at least 3.5mm in the positive direction of the Y axis, or insert a ground shield grid between it and CN-29.

[0099] Pin continuity testing utilizes a hierarchical verification strategy. For example, the CN-18 connector has 48 pins, numbered P1-P48 in the original layout. After loading the new coordinates, the system uses a 3D projection algorithm to simulate the actual layout and detects a 0.3mm overlap risk in the pin group P23-P27. This overlap could result in assembly interference, so the system marks the connector as "Warning" and highlights the risk area in the visualization interface. Corrective measures include rotating the connector 15 degrees or selecting a staggered pin arrangement while maintaining the original pin count.

[0100] The temperature field monitoring system acquires real-time data from 16 digital temperature sensors distributed across the circuit board surface. For the 72°C reading displayed by CN-18 in Table 1, the system retrieved the historical temperature profile for that area and found an 8°C increase compared to before optimization. The thermal analysis module attributed this phenomenon to the connector being relocated downstream in the heat dissipation path and recommended two adjustments: Option A involves adding a micro heat sink to the new location, while Option B involves shifting the connector coordinates 4mm to the left of the airflow path. The system prioritizes Option B to avoid adding additional components.

[0101] The real-time monitoring network operates using a distributed architecture and comprises three functional layers: a data acquisition layer consisting of Hall effect sensors and temperature sensors installed in the power circuit, sampling at a 1kHz frequency; a transport layer that packages sensor data using the CAN bus protocol; each packet includes a timestamp, sensor ID, and calibrated physical value; and an analysis layer that employs an anomaly detection algorithm, using a sliding window to compare current readings against pre-optimized baseline parameters. If the current fluctuation in the CN-31 connector circuit exceeds two standard deviations of the historical mean for five consecutive times, the system automatically logs the event and triggers a secondary verification process.

[0102] The defect traceability system initiated a root cause analysis for the CN-23 connector, marked as "defective." The system retrieved the layout adjustment log and discovered that during the third iteration, the connector was forced to shorten its spacing from the CN-29 to avoid a vibration-sensitive area. The vibration analysis module reassessed the modal response under the current layout and confirmed that resonance risk persisted at a frequency of 125 Hz. After comprehensive considerations, the system generated a compromise solution: shifting the CN-23 3.2 mm in the +Y direction and applying a damping coating to the CN-29. This solution reduced the impedance deviation to 2.1% while keeping the resonance amplitude within a safe threshold.

[0103] A status marking system assigns a final classification to each connector based on the verification results: "Pass" indicates passing all checks; "Warning" indicates minor deviations within acceptable limits; and "Defective" requires mandatory correction. The system devises different handling strategies for each status: Passing connector coordinates are directly written into the final solution; Warning connectors generate a monitoring list for subsequent tracking; Defective connectors trigger an automated correction cycle. All processing results are stored in a version-controlled layout database, preserving a complete modification history.

[0104] The visual interface presents this verification process in an interactive format. Designers can view a 3D thermal map of the connector, where red areas indicate impedance anomalies, blue areas indicate temperature anomalies, and yellow areas indicate vibration risks. Users can manually adjust specific connector positions, and the system previews the resulting parameter changes in real time. When the operator drags the CN-18 to the recommended 79.8mm position, the interface immediately updates the impedance prediction and temperature distribution simulation results.

[0105] The final output module integrates all verification results into a layout file that complies with industry standards. This file includes a connector coordinate table, a bill of materials, and special process instructions. For CN-23 connectors, the file additionally specifies the required damping treatment requirements; for CN-18 connectors, it includes recommended assembly tolerance control instructions. A complete audit trail is established throughout the entire verification process, documenting the input parameters, processing logic, and output results at each decision point to support subsequent quality traceability and process improvement.

[0106] Example 5: This example describes in detail the generation mechanism of the layout anomaly report and the output process of the final optimization solution.

[0107] The anomaly report generation system receives raw anomaly data from the monitoring network, including the spatial coordinates of abnormal current fluctuations and thermal distribution areas. The data processing engine first performs an anomaly cluster analysis using a density-based spatial clustering algorithm, setting the neighborhood radius to 1.5 times the minimum connector spacing on the PCB and the minimum number of anomaly points to 5. This algorithm automatically filters out isolated noise points and identifies three anomaly clusters with practical engineering significance: a power module anomaly cluster near coordinates (45.7, 88.3), a signal conversion area anomaly cluster at coordinates (102.6, 54.1), and a high-speed interface anomaly cluster around coordinates (155.3, 112.9). For each anomaly cluster, the system invokes an impact factor calculation module, which includes an electromagnetic field propagation model and a mechanical vibration transfer function library. The electromagnetic impact factor calculation considers the dielectric constant distribution of the PCB stackup structure and applies skin effect correction to high-frequency interference signals. The vibration impact factor adjusts the damping coefficient based on the material properties of the mounting bracket. The calculated results are converted into a five-level impact index, with values ​​below 0.3 designated as blue, low-risk; 0.3-0.6 as yellow, observation; 0.6-0.8 as orange, warning; and 0.8 and above as red, emergency. Anomaly reports utilize a structured document format. The main document section contains the coordinates of the anomaly cluster center, the radius of the affected area, the dominant anomaly type identifier, the impact index value, and the confidence score. The attachment section stores raw waveform fragments and compressed thermal image data.

[0108] The process of constructing the final optimized layout plan begins with solution synthesis, systematically integrating all verified connector repositioning suggestions to generate a layout coordinate diagram with version labels. This coordinate diagram is created using a layered vector drawing mechanism. The bottom layer is the circuit board outline layer, which accurately depicts machining boundaries and keep-out areas. The middle layer is the electrical connection layer, using different line widths to distinguish power and signal traces. The top layer is the component location layer, where connectors are marked with color symbols. The symbol color is based on the risk level in the exception report: blue symbols correspond to low-risk connectors, yellow symbols require special process control, orange symbols require design redundancy, and red symbols require position adjustment. The connector arrangement order is achieved through a numbering rule. The first digit indicates the priority sequence, the second digit identifies the connector type code, and the last digit is the serial number within the category. For example, A-CN15 represents the highest priority standard connector No. 15, while B-HD08 represents the medium priority high-speed data connector No. 8.

[0109] The conversion process of executable design files is implemented in three stages. First, data format conversion is performed to process the vector coordinate diagram into a DXF graphic file that conforms to international standards, including layer attributes and entity parameters. The design attribute processing module adds manufacturing process instructions: for connectors whose position changes by more than 10% of the original position, dimension tolerance annotations are added to the corresponding layer; for connectors affected by thermal anomalies, heat dissipation path annotations are added; for connectors that have been corrected after three iterations, structural reinforcement symbols are added. The file generation module encapsulates the data package according to the enterprise standard. The main file is a text list containing the final coordinates of the connector, and the auxiliary files contain assembly schematics, material handling instructions, and a list of process control points. Before sending to the manufacturing system, the system activates the digital twin verification process. This process loads the three-dimensional model of the circuit board and performs virtual assembly simulation, focusing on detecting conflicts in three aspects: the spatial interference verification of the connector pins and routing vias uses three-dimensional Boolean operations, the mechanical vibration modal analysis calls the finite element solver, and the thermal distribution prediction is based on the computational fluid dynamics model. Potential conflicts discovered during simulation are recorded as correction logs. For example, the system identifies that connector CN-23 may have a 0.2mm interference risk with adjacent capacitors under vibration conditions, and then adds a 0.5mm displacement compensation value to the connector coordinate data.

[0110] The manufacturing system interconnection utilizes industry-standard communication protocols, with design files transmitted over a secure, encrypted channel. The transmission data packet structure consists of a header, a payload, and a checksum. The header records the sending time, file type, and version information; the payload encapsulates the design file in ZIP compression format; and the checksum is generated using a cyclic redundancy check algorithm. An automatic parsing module is installed on the receiving end of the manufacturing system to extract connector coordinate data and convert it into machine control instructions. This conversion process preserves the original data accuracy, and the instruction file controls the coordinate positioning system with a step accuracy of 0.01mm. For critical connectors identified in anomaly reports, the manufacturing system activates special monitoring procedures during the assembly phase: For connectors with an orange risk rating or higher, the entire installation process is video recorded; for connectors with a red risk rating, an electrical parameter review step is added. A comprehensive operation logging mechanism is established throughout the implementation process, maintaining seven types of log files: an anomaly point collection log records the time series of raw sensor data; a cluster analysis log records algorithm parameter settings; a report generation log tracks the source of each data field; a file conversion log verifies the integrity of each conversion step; a digital simulation log stores conflict detection details; a transmission log records the packet transmission path; and a manufacturing feedback log collects production line execution data. Log data is uniformly stored in a time series database, supporting multi-dimensional retrieval by time range, device number, connector type, etc.

[0111] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0112] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A high-density four-row connector layout intelligent optimization method, characterized in that: The following steps are involved: Collecting design parameters and related environmental data for four-row connectors on a high-density circuit board. The design parameters include connector location coordinates, number of pins, and electrical impedance values. The environmental data includes temperature distribution, electromagnetic interference intensity, and vibration frequency. Performing standardized alignment processing on the design parameters and the environmental data to form a layout data set of unified dimensions; Processing the layout data set using a layout optimization model to generate an initial layout solution, wherein the initial layout solution includes a connector arrangement sequence and position adjustment suggestions; Real-time monitoring of circuit board operating status changes, including current fluctuations and thermal distribution updates; Identifying layout anomalies based on the change data, iteratively correcting the initial layout plan according to the layout anomalies, and outputting a final optimized layout plan; The standardization alignment processing of the design parameters and the environmental data includes: extracting the connector position coordinates in the design parameters and the temperature distribution information in the environmental data, and converting the unstructured coordinate data into a structured grid mapping; Performing time alignment on the design parameters and the environmental data based on timestamps to ensure that data points correspond to each other in the same time frame; Remove conflicting or redundant fields and retain parameter values ​​with high source priority to form a cleaned layout dataset; Converting the layout data set into a matrix format for subsequent model input; The processing of the layout data set using the layout optimization model includes: Configuring an input layer of the layout optimization model to receive the layout data set in the matrix format; Executing a connector position optimization algorithm within the layout optimization model to calculate an adjustment vector of the position coordinates; Generate a connector arrangement sequence based on the adjustment vector and evaluate the matching degree of pin quantity and electrical impedance value; An initial layout solution including position adjustment suggestions is output, wherein the position adjustment suggestions include new coordinate values ​​and arrangement priorities.

2. The intelligent optimization method for high-density four-row connector layout according to claim 1, characterized in that: The identifying of a layout abnormality based on the change data includes: Real-time acquisition of current fluctuation data and thermal distribution update data from the change data of the circuit board's working state; Comparing the current fluctuation data with a preset threshold value to detect abnormal fluctuation points; Analyzing the changing trend of the thermal distribution update data and identifying thermal anomaly areas; Abnormal fluctuation points and thermal anomaly areas are integrated to generate a layout anomaly report.

3. The intelligent optimization method for high-density four-row connector layout according to claim 2, characterized in that: The iteratively correcting the initial layout solution according to the abnormal layout situation includes: Extracting the coordinates of the abnormal points and the affected range in the layout abnormality report; Positioning the affected connectors in the initial layout solution and recalculating adjustment vectors of the position coordinates; Update the connector arrangement order and position adjustment suggestions to form an intermediate optimization plan; Verifying whether the intermediate optimization solution satisfies the constraints of electrical impedance value and number of pins; Output the final optimized layout solution that has passed verification.

4. The intelligent optimization method for high-density four-row connector layout according to claim 3, characterized in that: The connector position optimization algorithm is executed within the layout optimization model, and the execution steps are as follows: Initialize the search space of connector position parameters and define the feasible range of position coordinates; An evolutionary strategy algorithm is used to iteratively generate candidate position solutions in the search space; Evaluating an objective function value for each candidate position solution, wherein the objective function is calculated based on electromagnetic interference intensity and vibration frequency; The best candidate position solution is selected as the adjustment vector input.

5. The intelligent optimization method for high-density four-row connector layout according to claim 4, characterized in that: Verifying whether the intermediate optimization solution satisfies the constraints of electrical impedance value and number of pins includes: Extract position adjustment suggestions and pin quantity data from the intermediate optimization solutions; Calculate the correlation matrix between position coordinates and electrical impedance values; Check whether the correlation matrix is ​​within the preset tolerance range; If not satisfied, the adjustment vector for recalculating the position coordinates is returned; If satisfied, the output is the final optimized layout solution.

6. The intelligent optimization method for high-density four-row connector layout according to claim 5, characterized in that: The real-time monitoring of the change data of the circuit board working status includes: Deploy a sensor network to collect current fluctuation data and thermal distribution update data; Compare the collected data with the electrical impedance values ​​in the design parameters in real time; Generate a stream of change data containing timestamps; The change data stream is input into the anomaly detection module.

7. The intelligent optimization method for high-density four-row connector layout according to claim 6, characterized in that: Generating a layout exception report includes: Aggregate coordinate data of abnormal fluctuation points and thermal anomaly areas; Calculate the electromagnetic interference intensity and vibration frequency influence factors of abnormal points; Generate anomaly level labels based on impact factors; Outputs a labeled layout exception report.

8. The intelligent optimization method for high-density four-row connector layout according to claim 7, characterized in that: The output of the final optimized layout solution includes: Integrate position adjustment suggestions and connector arrangement order to generate a layout coordinate diagram; Convert the layout coordinate diagram into an executable design file; Send design files to the circuit board manufacturing system.

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