A smart wind turbine tower welding control system and method

Abstract

This invention relates to the field of wind turbine tower welding technology and discloses an intelligent wind turbine tower welding control system and method, including: collecting raw parameters and determining their validity, performing intelligent geometric feature recognition, executing knowledge graph contextual reasoning, and performing causal tracing and knowledge evolution. This intelligent wind turbine tower welding control system and method ensures reliable raw data quality through real-time acquisition and validity determination of multi-source data; achieves accurate identification of bevel morphology and intelligent differentiation of misaligned edges through three-dimensional point cloud geometric feature depth recognition and deep learning classification, reducing geometric misjudgment rate; integrates process rules and data models through knowledge graph contextual reasoning and hybrid-driven judgment, achieving accurate risk level assessment and context-adaptive decision-making; and constructs a defect root cause chain and continuously optimizes reasoning weights through causal tracing and knowledge evolution mechanisms, realizing a shift from single-point control to systemic prevention, comprehensively improving welding quality consistency and production safety.

Description

Technical Field

[0001] This invention relates to the field of wind turbine tower welding technology, specifically to an intelligent wind turbine tower welding control system and method. Background Technology

[0002] As the core load-bearing structure of wind turbine generators, the stability of the wind turbine tower and the quality of its welding directly affect the safe operation, service life, and power generation efficiency of the entire unit. The intelligent wind turbine tower welding control system and method is a complex system that integrates advanced sensor technology, big data processing, artificial intelligence, robotics, real-time control technology, and Internet of Things technology. Its goal is to achieve high precision, high efficiency, high quality, and high reliability in the wind turbine tower welding process through a closed loop of perception, understanding, decision-making, and execution. It also promotes the transformation of the entire wind power manufacturing industry towards intelligence and digitalization, thereby improving the automation level, process stability, product quality, and production efficiency of wind turbine tower welding, and reducing reliance on operator skills. Existing intelligent wind turbine tower welding control systems and methods lack multi-source data fusion and validity verification mechanisms, making it difficult to guarantee data quality. They also lack deep learning-based intelligent recognition capabilities for geometric features, making it impossible to accurately distinguish between misaligned edges and normal bevels, resulting in a high misjudgment rate. Furthermore, they lack contextual reasoning supported by knowledge graphs, making it difficult to conduct dynamic risk assessments by comprehensively considering the characteristics of the base material and environmental factors. They also lack systematic causal tracing and cross-domain knowledge fusion capabilities, making it impossible to trace the deep-seated root causes such as equipment and materials. Consequently, they struggle to achieve predictive maintenance and continuous process optimization, leading to passive quality control and difficulty in knowledge accumulation, thus limiting their practicality. Summary of the Invention

[0003] This invention provides the following technical solution: an intelligent wind turbine tower welding control method, comprising: S1, collecting raw parameters and determining their validity; S2, performing noise filtering and outlier removal on the point cloud of the bevel area, and segmenting the point cloud into a left parent material area, a right parent material area, a bevel bottom area, and a transition area based on spatial geometric features; S3, extracting slope continuity features; S4, extracting symmetry features; S5, extracting depth consistency features; S6, determining the bevel morphology category; S7, executing a multi-level tracking strategy for verification; S8, embedding process rules for final determination; S9, encapsulating the determination results, key parameters, and decision criteria into a standard output, and performing knowledge graph contextual reasoning.

[0004] Preferably, the knowledge graph contextual reasoning is performed, specifically: determining whether the data is complete; performing data completion or matching the extracted context information with entity nodes in the knowledge graph; determining whether the matching is successful; performing unknown working condition processing or constructing a sub-graph view of the current working condition; checking whether there are absolute prohibitive constraints; identifying whether there are reinforced coupling relationships; determining whether there are risk couplings; and performing risk classification decisions and access determination.

[0005] Preferably, the risk classification decision-making and admission determination are carried out as follows: data-driven probabilistic model inference is performed; risk level and confidence level combination is determined; medium risk review and dynamic adjustment are performed; similar case retrieval is initiated, and inference is made based on the risk level of the most similar case; causal tracing admission and quality score generation are performed; causal tracing and knowledge evolution are performed.

[0006] Preferably, causal tracing and knowledge evolution are performed, specifically: determining the integrity of the input data packet; performing input anomaly handling or determining whether node instantiation was successful; performing image completion or determining whether a traceable path exists; performing special handling of untraceable data or determining whether the tooling status is abnormal; and performing multi-dimensional root cause hierarchical tracing and root cause classification.

[0007] Preferably, multi-dimensional root cause hierarchical tracing and root cause classification are performed, specifically: performing systemic root cause confirmation and early warning push; performing hydraulic system pressure tracing to determine the hydraulic system status; performing systemic root cause confirmation and early warning push or determining the degree of temperature influence; performing systemic root cause confirmation and early warning push or determining whether material properties are abnormal; performing supply chain knowledge embedding and batch control or multi-factor coupling effect identification; performing supply chain risk control and multi-factor coupling time sequence deduction.

[0008] Preferably, the implementation of supply chain risk management and multi-factor coupling time-series analysis includes: implementing supply chain knowledge embedding and batch control: automatically tightening the pairing gap control requirements for subsequent steel plates in the same batch; implementing multi-factor coupling effect identification or implementing closed-loop records and upstream feedback; determining whether there is known reinforcing coupling; determining the adjustment intensity or searching for similar misalignment deviation records; implementing delay defect risk analysis and monitoring enhancement or cross-domain knowledge fusion and standard matching; and implementing delay defect prevention and control and multi-standard compliance access decisions.

[0009] Preferably, the implementation of delayed defect prevention and multi-standard compliance access decision-making includes: analyzing the thermal cycling records and post-weld non-destructive testing results of similar historical cases to determine the presence of delayed cracks or delayed porosity; implementing delayed defect prevention and cycle extension or implementing cross-domain knowledge fusion and standard matching; implementing cross-domain knowledge fusion and standard matching, implementing closed-loop record-keeping and upstream feedback, or assessing the impact on structural safety margin; implementing delayed defect prevention and cycle extension; implementing closed-loop record-keeping and upstream feedback: pushing structured feedback reports to upstream processes, updating the knowledge graph and marking typical samples, or implementing key component identification and differentiation strategies; identifying whether the current weld is a key load-bearing component; implementing final access judgment and knowledge evolution or suspending welding pending adjudication; comprehensively considering all verification results, if all verifications pass, it is determined that entry into the welding process is allowed, and the knowledge graph case library is synchronized and optimized; if there is a minor warning but control measures have been added, it is determined that entry into the welding process is allowed, and it is marked as an observation case and included in the learning library; if it is in a critical state, knowledge graph situational reasoning is re-executed or expert arbitration is triggered.

[0010] A system for implementing intelligent wind turbine tower welding control includes: a data acquisition module: acquiring multi-source heterogeneous data such as 3D point cloud of the weld area, ambient temperature and humidity, and base material properties in real time, performing data integrity verification and anomaly re-sampling, providing a reliable raw data foundation for subsequent analysis; a geometric recognition module: analyzing slope continuity, symmetry, and depth consistency features based on a deep learning classifier, distinguishing between normal bevels and misaligned edges, and verifying through a coarse-fine two-level tracking strategy to achieve intelligent judgment of welding geometric quality; a knowledge reasoning module: constructing a process knowledge graph, integrating contextual information such as base material properties, environmental conditions, and historical cases, performing hard rule red line judgment and data-driven probabilistic reasoning, dynamically assessing the risk level of misaligned edges and generating a visual explanation; a causal tracing module: tracing the systemic root causes of equipment, environment, and materials based on the knowledge graph through reverse links, identifying the enhanced coupling effect of multiple factors, inferring the risk of delayed defects, and achieving a leap from single-point quality control to full-link prevention; and a decision execution module: integrating the results of geometric recognition, knowledge reasoning, and causal tracing, triggering a hierarchical response strategy, and feeding back cases to the knowledge graph for continuous learning and optimization.

[0011] The present invention has the following beneficial effects: 1. By collecting key information such as weld geometry parameters, environmental conditions, and equipment status in real time through multi-source sensors, a raw data acquisition mechanism is established. The system performs integrity verification on the collected data, identifies data loss and anomalies caused by sensor failure or environmental interference, and triggers automatic re-collection or anomaly handling mechanisms. At the same time, the system performs physical rationality judgment on key parameters, removes outliers that exceed reasonable ranges, and ensures that the data entering the subsequent analysis stage is true and reliable. This stage constitutes the basic data layer of intelligent control, providing high-quality data input for upper-level algorithms, avoiding misjudgments and decision-making errors caused by data quality issues, ensuring the authenticity and reliability of raw data, eliminating misjudgments caused by data loss or anomalies, laying a solid foundation for subsequent intelligent analysis, and improving the overall robustness of the system.

[0012] 2. First, deep learning algorithms are used to intelligently identify the geometric features of the bevel, analyzing multi-dimensional features such as slope continuity and symmetry to accurately distinguish between normal bevels and misaligned edges. Based on this, a welding process knowledge graph is constructed, integrating multi-source information such as base material properties, environmental conditions, and historical cases. Hybrid reasoning of hard-coded process red lines and data-driven probabilistic models is executed. The system dynamically assesses the risk level based on the context, generating visual explanations for high-risk conditions and pushing them for manual review, while automatically approving low-risk, high-confidence conditions. This stage achieves a leap from geometric shape understanding to contextual knowledge reasoning, significantly improving the intelligence level of welding quality control, realizing deep integration of geometric shape and process knowledge, improving the accuracy of misaligned edge identification and the reliability of risk assessment, reducing the frequency of manual intervention, and enhancing the consistency of welding quality.

[0013] 3. Based on the knowledge graph reverse linking mechanism, the system traces back from the current defect node to potential root causes such as tooling, hydraulic system, and material batches, constructing a complete causal chain. Through multi-factor coupling analysis, it identifies the interactive effects of environment, materials, and processes, and assesses the risk of delayed defects by combining temporal causal inference. At the same time, it integrates the requirements of multiple standard systems across domains, correlates design load data to assess the impact on structural safety, and finally feeds the analysis results back to upstream processes to trigger preventive maintenance or incoming material interception. Typical cases are incorporated into the knowledge graph for continuous learning, optimizing inference weights and process rules to form a quality control closed loop. This achieves the transformation from single-point defect control to systemic prevention, blocks defect propagation paths, establishes a continuous learning mechanism, and promotes the continuous evolution and optimization of welding process knowledge. Attached Figure Description

[0014] Figure 1 This is a flowchart of the intelligent wind turbine tower welding control method of the present invention; Figure 2 This is a system block diagram of the intelligent wind turbine tower welding control method of the present invention. Detailed Implementation

[0015] Example 1: A smart wind turbine tower welding control method, see reference. Figure 1 ,include: S1. Collect raw parameters and determine their validity; S2. Extract the valid dataset, i.e., the valid geometric parameters are encapsulated into a standard data structure, noise filtering and outlier removal are performed on the point cloud of the bevel area, and the point cloud is segmented into the left parent material area, right parent material area, bevel bottom area, and transition area based on spatial geometric features, establishing a spatial benchmark for feature extraction: Input valid point cloud: ; Apply statistical outlier filtering: Region segmentation based on normal vector clustering: The segmentation criterion is the normal vector of each point. With the preset bevel direction vector The included angle : ;in, This is a collection of valid point clouds, used to store all valid 3D point data retained after the first layer of filtering. The number of points in the point cloud represents the total number of points in the point cloud being processed, used for traversal and counting. This is the index of the point, used to identify and traverse the sequence number of each point in the point cloud. This is a set of filtered point clouds used to store point cloud data that has been filtered to remove noise and outliers. For point of Average height of neighboring buildings Standard deviation, The nearest neighbor parameter specifies the number of neighboring points to consider when calculating the local mean, and is usually a fixed value (such as 10 or 20). This is the point set for the left parent material region, used to store all points belonging to the left parent material after region segmentation. This is the point set for the right-side parent material region, used to store all points belonging to the right-side parent material after region partitioning. ， This is a point set for the bottom region of the bevel, used to store all points belonging to the bottom (root) of the bevel. S3 is a set of transition region points used to store transition region points located between the base material and the bottom of the bevel; S4 calculates the slope distribution on both sides of the bevel, analyzes the continuous change of slope along the weld direction, and extracts the slope continuity characteristics. A normal bevel exhibits stable slope characteristics, while abrupt changes or discontinuities in slope occur at misaligned edges: for the left side region and the right side area Fit the slope separately (taking the slope fitting of the left region as an example, perform the same operation for the slope fitting of the right region): along the weld direction (set as...) (axis) by window length Sliding, calculate local slope: ; Calculate the slope along Rate of change of direction: ; Calculate the slope continuity score: ;when When it is determined to be continuous, The mutation was determined to exist at that time; among them, This is the left slope angle, used to represent the angle of inclination of the left side of the base material wall relative to the horizontal plane. The coordinate position along the weld direction is used to indicate the position of the first [element] in the longitudinal direction (Y-axis) of the weld. The position of each sampling window This represents the change in height on the left side, specifically the difference between the maximum and minimum heights within the left-side region, used to reflect changes in the vertical direction. This represents the change in horizontal distance on the left side, that is, the span of the left region in the horizontal direction (X-axis). This represents the height of the point set in the left region, i.e., the set of Z coordinates of all points within the left parent material region. This is a maximum value function used to extract the maximum value from a set of data to calculate the height range. This is a minimum value function used to extract the minimum value from a set of data to calculate the height range. This is the horizontal distance, i.e., the fixed span of the window in the X direction. The slope change rate (gradient) indicates how quickly the slope changes along the weld direction and is used to detect abrupt changes. The increment in the y-direction, i.e., the distance between adjacent sampling windows, is used to calculate the rate of change. This represents the maximum rate of change of slope on the left side, i.e., the value where the slope changes most drastically at all locations on the left, indicating the greatest degree of discontinuity. This represents the maximum rate of change of slope on the right side, i.e., the value where the slope changes most drastically at all locations on the right side. The slope change threshold is the dividing line for determining whether the slope is continuous, used for normalization calculation scoring; S4, analyze the symmetry of the parent material on both sides of the bevel relative to the centerline, extract symmetry features. A normal bevel is mirror symmetrical on both sides, while misalignment is manifested as height asymmetry or angle asymmetry: take the fitted plane of the upper surface of the parent material on both sides. and Angle bisector plane, establish a reference plane for the centerline of the bevel. ; Calculate the distance from the average height of the two parent materials to the centerline: ; High symmetry deviation: ; Calculate the angle between the slopes on both sides and the centerline: ; ; Angular symmetry deviation: Overall symmetry score: ;in, This is a distance function used to calculate the perpendicular distance from a point to a plane or the spatial distance between two points. This represents the average position / center point of the left-side base material, i.e., the geometric center of all points in the left-side base material area, signifying the overall position of the left side. This represents the average position / center point of the right-side base material, i.e., the geometric center of all points in the right-side base material area, signifying the overall position of the right side. This is the normal vector of the left-side plane, i.e., the unit vector perpendicular to the left-side base material surface, representing the orientation of the left-side wall. This is the normal vector of the centerline / center plane, i.e., the normal vector of the bevel center reference plane, used to calculate symmetry. The normal vector of the right-side plane, i.e., the unit vector perpendicular to the surface of the right-side base material, represents the orientation of the right-side wall. This is the square of the height deviation, i.e., the square of the difference in distance from the two parent materials to the centerline, used to quantify the degree of height asymmetry. The variance is the square of the standard deviation of height, a measure of the uncertainty in height measurement, used to normalize the effects of bias. This is the square of the angular deviation, i.e., the square of the difference between the slopes on both sides and the centerline, used to quantify angular asymmetry. The variance is the square of the standard deviation of the angle, and it is a measure of the uncertainty in angle measurement. The exponential function (natural exponent) is used to convert the deviation into a score between 0 and 1, achieving a non-linear mapping; S5, evaluate the uniformity of the groove depth along the weld direction, extract the depth consistency feature. Normal groove depth is consistent, while misalignment or poor processing will cause abrupt or gradual changes in depth: calculate the local groove depth. Local bevel depth This refers to the vertical distance from the lower surface to the upper surface of the parent material on both sides of the centerline; calculate the coefficient of variation of the depth sequence: Calculate the depth consistency score: Detecting deep mutation points: ;like Mark this location as a depth anomaly location; if If the depth change at that location is smooth and without abrupt changes, it is considered to have normal depth and is not marked as a depth anomaly location; among which, This represents the total number of data points or the number of points within the window. It is the number of depth data points sampled along the weld direction and is used for statistical calculations. The index is used to iterate through the sampling points along the weld direction. The average depth, which is the arithmetic mean of the bevel depths at all sampling locations, represents the overall depth level. This is a minimum function used to limit the maximum value of the coefficient of variation in deep consistency scoring to no more than 1. The coefficient of variation threshold is a standard value used to determine whether the depths are consistent, in order to perform normalized scoring. The second-order difference (curvature) of depth is used to measure depth at a given location. The degree of mutation at a location, detecting local depth discontinuities. For position The depth value at that location, i.e., the bevel depth at the next location after the current sampling point. For position The depth value at the current sampling point is the slope depth at a given location. S6. Combine the slope continuity, symmetry, and depth consistency features into a feature vector, input it into a pre-trained deep learning classifier, and determine the slope morphology category: Construct a comprehensive feature vector: Combine the eight feature parameters—slope continuity score, symmetry score, depth consistency score, height symmetry deviation, angle symmetry deviation, depth variation coefficient, average depth, and average slope angle—into a feature vector for subsequent classification processing; Feature standardization and classification network input: Standardize the constructed feature vector to eliminate the influence of different feature dimensions, and input the standardized feature vector... The input is a multilayer perceptron classification network, which consists of three linear transformation layers: the first layer performs a weighted summation of the input features and adds a bias, introducing non-linearity through an activation function; the second layer again performs a weighted summation and bias adjustment on the output of the first layer, and then passes it through an activation function again; the third layer performs the final weighted summation and bias adjustment to obtain the original output value of the classification network; Softmax probability calculation: the original output value of the classification network is subjected to an exponential transformation, converting the output value of each class into an exponential form with the natural constant as the base, the sum of the exponential values ​​of all classes is calculated, and the probability of the sample belonging to each class is obtained by dividing the exponential value of each class by the sum. The sum of probabilities for all categories is 1; Classification and confidence output: Compare the probability values ​​of the sample belonging to each category, select the category with the highest probability value as the predicted category, and output the confidence value of this classification for subsequent decision-making; S7, Execute a multi-level tracking strategy for verification. When the initial classification is a misclassification, start a coarse-fine two-level tracking strategy for verification to reduce the risk of misjudgment by a single sensor: Height difference calculation in the coarse tracking stage: Use low-resolution laser scanning data to calculate the average height values ​​of the left and right parent material areas respectively, and take the absolute value of the difference between the two as the macro height difference for quick identification of obvious height differences. Misalignment situation; Coarse tracking judgment condition: Compare the calculated macro height difference with 15% of the plate thickness value. If the macro height difference is greater than 15% of the plate thickness value, the coarse tracking judgment is marked as passed, and a suspected misalignment is considered to exist; otherwise, it is marked as normal; Fine tracking stage height difference calculation: Fine tracking is only started when the coarse tracking judgment is passed. High-resolution laser scanning data is used to take the median height values ​​of all points in the left parent material area and the median height values ​​of all points in the right parent material area, and the difference between the two is calculated as the precise height difference; Fine tracking judgment condition: Compare the precise height difference with 10% of the plate thickness value, and at the same time check whether the confidence level of S5 output is greater than 0.85. Fine tracking judgment passes only when both conditions are met simultaneously; Consistency verification: Compare the coarse tracking judgment result and the fine tracking judgment result. Consistency is confirmed only when both are judged to have misaligned edges; if the two results are inconsistent, a conservative strategy is adopted or manual verification is triggered; S8. After verification confirmation, the embedded process rules are used for final judgment, distinguishing between tolerable deviations and serious misaligned edges that must be intervened: Key process parameter extraction: The precise height difference calculated in the fine tracking stage is used as the final height difference, and together with the plate thickness value, it is used as a key process parameter for subsequent rule judgment; Hard rule judgment: Calculate the ratio of the final height difference to the plate thickness value. If the ratio is greater than 20%, the hard-coded rule is triggered, and it is judged as a serious misaligned edge. At this time, it is immediately suspended. The welding correction action is stopped, triggering a level 3 alarm. This forces a switch to manual intervention mode or a reassembly, and sets a process termination flag, preventing further steps. Soft rule judgment: If the ratio of the final height difference to the plate thickness is between 10% and 20%, a soft rule judgment is triggered, indicating a tolerable deviation. In this case, the process is not terminated, but parameters are adaptively adjusted: welding speed is reduced proportionally to the height difference, heat input is increased proportionally to the height difference, and the weld is marked as a state requiring attention. The risk factor is then incorporated into subsequent knowledge graph contextual reasoning for prompts. Normal pass judgment: If the ratio of the final height difference to the plate thickness is less than or equal to 10%, and the deep learning classifier predicts a normal bevel, then passes steps S2-S9, and the process is set to proceed to the knowledge graph contextual reasoning stage. The inference in the map recognition context is marked as true, and a geometric quality score is calculated. This score is the average of the slope continuity score, symmetry score, and depth consistency score multiplied by the classification confidence. S9. The judgment results, key parameters, and decision criteria are encapsulated into standard output and synchronously recorded in the database for subsequent tracking and learning optimization: Judgment result generation: Based on the judgment results of the preceding steps, hierarchical judgment results are generated: If a hard rule judgment is triggered, the result is termination; if a soft rule judgment is triggered, the result is adaptive; if a normal pass judgment is passed, the result is pass. Visual interpretation data generation: Visual data containing spatial coordinates and color codes is generated, where the color codes are based on a comprehensive analysis of three parameters: height symmetry deviation, angle symmetry deviation, and slope continuity score. The mapped data is used to highlight abnormal areas in the 3D point cloud model. Confidence and quality score calculation: If a soft rule judgment is triggered, the classification confidence of S5 is multiplied by 0.5 to obtain the final confidence score; if the judgment passes normally, the classification confidence of S5 is directly used as the final confidence score. The final quality score is calculated based on the judgment result: for normal passing, the geometric quality score is used; for adaptive states, half of the geometric quality score is used. Time-series record generation: A complete record is generated containing information such as timestamp, judgment result, final height difference, plate thickness value, height difference to plate thickness ratio, slope continuity score, symmetry score, depth consistency score, final confidence score, and executed actions. This record is used for subsequent tracing and learning optimization, and for performing knowledge graph contextual reasoning.

[0016] The process involves collecting raw parameters and determining their validity. Specifically, this includes: performing multi-source data acquisition and fusion on the weld seams of the target wind turbine tower; activating laser scanning sensors, arc sensors, and contact thickness measurement devices to simultaneously collect spatial coordinate information, electrical signal characteristics, and physical thickness values ​​of the weld seam area; and performing timestamp alignment and spatial registration on the multi-source data to unify it under the tower's global coordinate system. Let the point cloud set acquired by the laser scanning be... The arc sensing signal is Contact thickness measurement value The fused dataset is obtained after coordinate transformation. ;in ， For the first A point, or a single data point in a point cloud, contains three-dimensional coordinate information. , , For the first The coordinates of a point in the horizontal transverse direction (usually pointing towards the bevel width), the horizontal longitudinal direction (usually along the weld direction), and the vertical direction. The total number of points in the point cloud, i.e., the total number of points in the point cloud acquired by laser scanning. This serves as an index, used to iterate through the points in the point cloud. This is the original fusion dataset, used to store the integrated data after fusion of multiple sensor sources, including spatial, temporal, electrical signal, and thickness information. For the first A time point, i.e., a timestamp at a specific sampling moment. This is a time set, i.e., the set of all sampling times, used for time alignment; Calculate the effective coverage of the point cloud data, identify void areas caused by arc interference, splash occlusion, or sensor malfunction, statistically analyze the spatial distribution density of effective points, and determine whether the data integrity meets the requirements for subsequent processing through point cloud quality assessment: Define the effective point judgment function: Efficiency in calculating point clouds: ; Calculate the point cloud missing rate: Data missing detection function ;in, This is the minimum effective height, i.e., the lower limit of the effective height of point cloud data. Values ​​below this are considered invalid or background noise. This is the maximum effective height, i.e., the upper limit of the effective height of point cloud data. Anything exceeding this value is considered invalid or intrusive. For point The measurement confidence level reflects the reliability of the measurement quality at that point, and is determined by the inherent accuracy of the sensor. This is the confidence threshold, the minimum confidence level used to determine whether a point is valid; points below this value are considered invalid. This refers to the number of points in the point cloud, i.e., the total number of points included in the statistics. This is the index used to iterate through the sequence number of each point; if the data missing detection function outputs 1, that is... The function determines that data is missing or abnormal, and classifies the parameters as invalid geometric parameters; if the data missing determination function outputs 0, the data is missing normally and the data integrity requirements are met. Perform geometric feature extraction of the bevel, planar fitting and edge detection on the effective point cloud, identify the upper surface contour lines of the parent material on both sides of the bevel, calculate the height difference, included angle features and root distance of the two contour lines, and extract key geometric parameters; perform region segmentation on the point cloud to obtain the point set of the left parent material. and the right-side parent material point set Fit the plane equations of the upper surfaces on both sides respectively: ; Calculate key geometric parameters, including the height difference between the two parent materials, bevel angle, root gap, and plate thickness: Height difference between the two parent materials: ;Bevel angle: ; Root gap: Plate thickness: ;in, This is the left-side fitted plane, which is the mathematical plane obtained by fitting the upper surface of the left-side parent material using the least squares method. This is the fitted plane on the right, which is the mathematical plane obtained by fitting the upper surface of the right parent material using the least squares method. , , , These are the coefficients of the left-hand plane equation, used to define the position and orientation of the left-hand plane in space. The average height on the left side is the average of the Z-coordinates of all points on the upper surface of the left-side substrate, representing the overall height of the left side. The average height on the right side is the average of the Z-coordinates of all points on the upper surface of the right-side substrate, representing the overall height of the right side. The left-side normal vector is formed by the plane coefficients. The resulting vector is perpendicular to the left side of the surface. The right-hand normal vector is formed by the plane coefficients. The vector formed is perpendicular to the right surface. , For point and points , are points from the left and right sides respectively, used to calculate the minimum distance. , For point and points The horizontal coordinate is used for distance calculation. , For point and points The vertical coordinate is used for distance calculation. , As an index, it is used to identify the points on the left and right sides respectively. This refers to the thickness measurement taken by the upper contact sensor, i.e., the plate thickness data measured above the bevel. This refers to the thickness measurement value of the lower contact type, that is, the plate thickness data measured by the contact sensor below the bevel (or the data on the other side). By verifying the physical rationality of the parameters, the extracted geometric parameters are compared with physical common sense and process specifications to identify outliers caused by measurement errors or data anomalies. The logical consistency between parameters is checked, and contradictory data combinations are eliminated. Physical constraint intervals for each parameter are defined, including height difference constraint intervals, bevel angle constraint intervals, root gap constraint intervals, and plate thickness consistency constraint intervals. A joint validity judgment function is established. ;like If any parameter exceeds the constraint, it is considered an abnormal parameter and is deemed an invalid geometric parameter; if If all physical constraints are met (height difference, bevel angle, root gap, and plate thickness consistency are all within reasonable ranges), then the parameters are considered normal; the determination of each sub-item is as follows: ; ; ; ;in, The design plate thickness is the theoretical plate thickness specified in the tower design drawings, used to verify the rationality of the measured values. This is an index used to iterate through the sequence numbers of the four physical constraints. The multiplication symbol represents a logical AND operation of the four component results; all conditions must be met simultaneously. To determine whether the height difference is within its physical constraint range, Whether the bevel angle is within its physical constraint range. To determine whether the root gap is within its physical constraint range. Whether the plate thickness consistency is within its physical constraint range; The valid geometric parameters are encapsulated into a standard data structure. After data integrity and parameter rationality checks pass, the extracted geometric parameters are encapsulated into a standard data structure, marked with a timestamp and quality level: generating a valid parameter set. ;in, For timestamps, The specific formula for scoring data quality is as follows: ;in, This represents the angle tolerance / threshold, i.e., the acceptable fluctuation range of the bevel angle, used to calculate the angle stability score. , , The weighting coefficients correspond to the weights of the three indicators: data effectiveness, height stability, and angle stability, respectively, satisfying the following conditions: ;like If so, then perform intelligent geometric feature recognition and deep learning classification; if If it is marked as "needs attention", the confidence weight will be reduced when performing intelligent geometric feature recognition; if If the parameter is abnormal, it is determined to be an invalid geometric parameter; where, This is the minimum quality score threshold, representing the lowest acceptable standard for data quality. Data below this value is considered unreliable. This is the quality score threshold, which serves as the standard for high-quality data. Data exceeding this value is considered high-quality and can be used directly. Output standard data structures and perform intelligent geometric feature recognition for subsequent geometric morphology analysis; implement anomaly handling and recovery mechanisms for invalid geometric parameters; for cases of missing data or abnormal parameters, initiate a tiered response strategy: attempt data repair and sensor resampling for minor anomalies; suspend the system and trigger manual checks for severe anomalies; define anomaly levels: ;like If so, local data interpolation repair is initiated, uncertain areas are marked, and intelligent geometric feature recognition continues but with reduced confidence; if This triggers the sensor self-test program. After re-collecting the data, the original parameters are re-collected, and the validity is determined; if If the fault occurs, welding should be immediately suspended, and the system should switch to backup sensor or manual mode, outputting a fault code. The sensor self-test procedure is as follows: .

[0017] Example 2 is an improvement on Example 2. This intelligent wind turbine tower welding control method performs knowledge graph contextual reasoning, specifically: extracting contextual information, including base material grade, plate thickness, ambient temperature, humidity, welding position, wind speed, historical welding records, etc.; determining data completeness: if the four key pieces of information—base material grade, plate thickness, temperature, and humidity—are successfully extracted and there are no missing values, the data is considered complete; if any item is missing or marked as invalid, the data is considered incomplete; if the data is incomplete, data completion is performed: if the missing item can be obtained from a backup sensor, the backup data source is switched to re-extract the data; if the missing item can be inferred from historical records, it is filled based on the historical average of similar working conditions and marked as an inferred value; if the missing item cannot be completed and is a key parameter, the current working condition is considered uncontrollable, triggering a safety shutdown; if the missing item cannot be completed but is a non-key parameter, the most conservative default value is adopted, a high uncertainty label is added, and the subsequent confidence weight is reduced; Example For example, if the main humidity sensor suddenly malfunctions during welding, resulting in data loss, the system automatically switches to the backup sensor to read the values. If the backup sensor also fails, the system consults the historical database and uses the average humidity of the same period over the previous three days as the inferred value and marks it as an inference. If the key parameter of the base material grade is completely unavailable, the system determines that the operating condition is uncontrollable and immediately shuts down. If only secondary parameters such as ambient wind speed are missing, the system uses a conservative default value of level zero wind, adds a high uncertainty marker, and continues execution, but reduces the weight of this parameter in subsequent inferences. If the data is complete, the extracted context information is matched with entity nodes in the knowledge graph: the base material grade matches the material attribute node, the plate thickness matches the geometric constraint node, the ambient temperature matches the environmental condition node, and the humidity matches the atmosphere control node. The entity nodes in the knowledge graph refer to the vertices in the graph data structure representing specific objects in the real world, such as "Q390 high-strength steel", "plate thickness 40mm", "ambient temperature 5℃", etc., and each node contains attribute values ​​(such as yield strength and chemical composition). The matching process is as follows: If all key parameters are successfully matched to their corresponding nodes, the matching is considered successful; if any parameters cannot be matched (e.g., a new steel grade not yet recorded), the matching is considered unsuccessful. If the matching is unsuccessful, unknown working conditions are handled: If the unknown parameters are within the predefined extended set, extrapolation is performed based on the physical model, and the condition is marked as an extrapolation working condition; if the unknown parameters are completely beyond the scope of knowledge, an expert consultation request is triggered, and complete data samples are collected simultaneously for subsequent knowledge graph updates; if in an emergency production state, the most conservative general process specifications are adopted, with full manual monitoring and recording of complete process data; for example, when welding a new type of Q500 high-strength steel for the first time, which has not yet been recorded in the knowledge graph, the system identifies it as an unknown working condition. Since Q500 and Q390 have similar performance, the system extrapolates the preheating temperature requirements based on the physical metallurgical model, marks it as an extrapolation working condition, and continues welding. If the material has no reference data and is outside the extended set, a remote consultation request from a material expert is immediately triggered. If the project is urgent and cannot be completed in time, the system will proceed with the extrapolation. While awaiting expert feedback, the most conservative general process specifications (maximum preheating temperature, minimum welding speed) are forcibly adopted. The entire process is manually monitored and recorded, with complete process data used for subsequent knowledge graph updates. If a match is successful, the associated process rule edges and constraint edges are activated to construct a sub-graph view of the current working condition: First, the real-time collected context parameters (base material grade, plate thickness, temperature and humidity) are used as query conditions to match the corresponding entity nodes in the knowledge graph. Then, all process rule edges and constraint edges connected to these nodes are activated. Finally, the local network composed of these related nodes and edges is extracted to form a customized knowledge sub-graph for the current specific working condition. For example, if the current welding is of Q390 steel, plate thickness 45mm, temperature 5℃, and humidity 80%, the system matches four nodes: "material-Q390", "geometry-thick plate", "environment-low temperature", and "environment-high humidity". Edges such as "Q390-low temperature-requires preheating" and "thick plate-high humidity-high risk of hydrogen-induced cracking" are activated, and a sub-graph containing these associations is constructed for subsequent reasoning. In the activated subgraph, hard-coded rule nodes are retrieved. By judging the combination of plate thickness and misalignment, it is checked whether there is an absolute prohibition constraint, and the corresponding execution operation for each combination is determined: if the plate thickness is greater than or equal to 40 mm and the misalignment exceeds 2 mm, an absolute prohibition constraint is determined to exist, and a safety shutdown is directly triggered, outputting a termination command; if the plate thickness is less than 40 mm, or the plate thickness is greater than or equal to 40 mm but the misalignment does not exceed 2 mm, an absolute prohibition constraint is determined to exist, and A1-A2 are executed; if the plate thickness is in the critical range (35 to 40 mm), enhanced monitoring is executed: if the misalignment is close to the 2 mm threshold, hard rule judgment is executed according to the 40 mm plate thickness standard; if the misalignment is small, additional... The online inspection frequency is as follows: a geometric check is performed every 50 mm of weld. If the plate thickness measurement value fluctuates and crosses the critical line during the welding process, the judgment standard is switched in real time and the switching time is recorded. For example, if the current tower plate thickness is detected to be 38 mm, which is in the critical range of 35 to 40 mm, and the measured misalignment is 1.9 mm, which is close to the 2 mm threshold, the system will immediately start enhanced monitoring and treat this weld as a 40 mm thick plate standard to implement strict control. At the same time, the inspection interval is shortened, and a geometric dimension check is paused every 50 mm of welding. If the real-time plate thickness measurement value fluctuates and crosses 41 mm due to thermal deformation during the welding process, the system will immediately switch to the thick plate judgment standard and record the switching time for quality traceability. A1. Retrieve multi-factor interaction edges in the knowledge graph within the current context to identify whether reinforcing coupling relationships exist. A2. Determine if risk coupling exists: If there is known reinforcing coupling between the base material properties and environmental conditions (e.g., high-strength steel and low-temperature environment, high-humidity environment and thick plate combination), mark it as a highly sensitive working condition; if there is no reinforcing coupling, mark it as a standard working condition; if there is an unknown combination of parameters, perform coupling effect assessment: if a physical model exists to describe the combination, predict the coupling strength based on simulation calculations; if a physical model is lacking but some similar cases exist, infer based on case similarity weighting; if there is no reference at all, design a rapid verification experiment, conduct test plate verification before formal welding, dynamically update the knowledge graph based on the verification results, and execute subsequent steps; perform risk classification decision-making and access determination.

[0018] Specifically, the risk classification decision-making and access determination are as follows: Data-driven probabilistic model inference is performed: based on historical case databases and real-time parameters, the misalignment risk level and confidence score of the current working condition are calculated. This involves inputting parameters such as the carbon equivalent of the base material, ambient temperature and humidity, historical defect rates of similar welds, and assembly accuracy into a probabilistic graphical model (e.g., a Bayesian network) to infer the probability of defect occurrence as the risk level. The confidence score is then calculated based on data integrity and historical case matching. Risk level determination: when the calculated defect probability is higher than a preset safety threshold (e.g., the medium-high risk boundary), it is considered high risk; when it is lower, it is considered low risk. Confidence level determination: when data integrity is high and the features match historical cases well, the model is confident in the reliability of the prediction, resulting in high confidence; when the data contains noise or is in the decision boundary region, the model is uncertain, resulting in low confidence. Determine the combination of risk level and confidence level: For highly sensitive operating conditions, increase the risk weight; for standard operating conditions, use the baseline weight. If the risk level is high but the confidence level is low, perform medium-risk review and dynamic adjustment. If the risk level is low and the confidence level is high, perform causal tracing access and quality score generation. If the risk level is low but the confidence level is low, perform medium-risk review and dynamic adjustment. If the risk level is high and the confidence level is high, perform high-risk confirmation and visual interpretation: Generate a detailed visual interpretation report, highlighting abnormal areas in the 3D point cloud model, annotating key indicators such as height difference values, symmetry deviation, and environmental parameter deviations, providing risk source analysis and suggested measures. If the system determines the current situation to be high-risk, the analysis shows the root cause is " The combination of 85% ambient humidity and Q390 high-strength steel as the base material significantly increases the risk of hydrogen-induced cracking. Recommended measures include: immediately extending the flux drying time to 4 hours, increasing the preheating temperature to 150 degrees Celsius, and reducing the welding speed by 20%. If the current mode is automatic and the risk level is extremely high, a forced switch to manual review will be implemented, and the process will be paused pending confirmation. If the current mode is semi-automatic, a warning message will be sent and manual confirmation will be recommended. If the operator does not respond within the specified time, a conservative strategy will be implemented by default, i.e., reducing the welding speed and increasing the monitoring frequency, implementing causal traceability access and quality score generation, and attaching risk markers. The automatic and semi-automatic modes are preset by the operator through the human-machine interface before welding begins. Perform medium-risk review and dynamic adjustment: For situations with insufficient confidence or ambiguous risk levels, a dynamic adjustment mechanism is activated. If the low confidence is due to data noise (such as strong electromagnetic interference causing periodic fluctuations in current sensor values ​​and unstable feature extraction), sensor re-sampling and feature re-extraction are triggered, and knowledge graph contextual reasoning is re-executed. If the low confidence is due to insufficient historical cases (such as welding a new type of high-manganese steel for the first time in an extreme day-night temperature difference environment in the desert, with no such combination record in the knowledge base), a similar case retrieval is initiated, and inference is made based on the risk level of the most similar case: the current working condition feature vector (base material, plate thickness, environmental parameters) is extracted, the similarity with the current vector is calculated in the knowledge graph historical case database, several historical cases with the highest similarity are selected, the risk level distribution of these cases is statistically analyzed, the risk level with the highest frequency is used as the inferred level of the current working condition, and adjustment suggestions are given based on the process parameters of these cases. If the risk level is in the boundary range (such as the calculated risk probability is exactly 0.49, while the high-low risk dividing threshold is 0.5, which is in an ambiguous zone), a conservative estimate is adopted, and high-risk confirmation and visualization interpretation are performed according to the higher risk level. The process of causal tracing admission and quality score generation involves synthesizing the knowledge graph reasoning results to generate a final quality score and risk-carrying parameters. First, a base score is formed by summarizing the matching degrees of various rules in the knowledge graph (e.g., hard rule compliance, multi-factor coupling risk coefficient, environmental compensation factor, and historical case similarity). Then, a weighted adjustment is performed based on the confidence level. Finally, a comprehensive quality score is generated, and the risk weight coefficients (e.g., hydrogen-induced cracking risk weight) and environmental compensation parameters (e.g., preheating temperature compensation value) to be carried into causal tracing and knowledge evolution are determined. If the quality score is higher than the admission threshold and there are no unprocessed warnings, then the causal tracing and knowledge evolution are carried out with the knowledge graph reasoning path record, risk weight coefficients, and environmental compensation parameters to ensure the successful execution of causal tracing. When tracing the root causes of knowledge evolution, it can fully trace back the reasoning logic and risk assessment details of the knowledge graph contextual reasoning, achieving hierarchical information integration and coherent decision-making. If the quality score is higher than the admission threshold but there is a warning mark, additional monitoring requirements are set to perform causal tracing and knowledge evolution, and the real-time verification frequency is increased. This allows the process to continue to ensure production efficiency while strengthening the monitoring of potential risks by increasing the detection frequency, preventing minor hidden dangers from evolving into actual defects. If the quality score is at a critical value, the data-driven probabilistic model reasoning is re-executed to avoid blindly approving quality judgments in a critical ambiguity state. The accuracy of the judgment is ensured by recalculating through algorithms or introducing manual experience for review, avoiding quality accidents caused by system uncertainty.

[0019] Example 3 is an improvement upon Example 3. In this example, causal tracing and knowledge evolution are performed, specifically: extracting quality scores, risk weight coefficients, environmental compensation parameters, and knowledge graph reasoning path records; determining the completeness of the input data packet: if any item is missing or the format is abnormal, the input data packet is considered abnormal; if the three core data items—quality score, risk weight coefficient, and reasoning path record—are all present and in valid format, the input data packet is considered normal; if the input data packet is abnormal, input abnormality handling is performed: a repair mechanism is initiated; if the missing data can be recovered from the sensor cache or database backup, it is completed and the knowledge graph contextual reasoning is re-executed for verification; if it cannot be recovered and is a core parameter (quality score or reasoning path), the knowledge graph contextual reasoning output is considered invalid, and the knowledge graph contextual reasoning is re-executed; if it cannot be recovered... If the recovered parameter is a non-core parameter, the default value is used (a conservative low value is used for quality scoring, and the maximum value is used for risk weight), a high uncertainty marker is added, and the input data packet is judged to be normal, but the whole process is manually monitored; if the input data packet is normal, based on the received parameters, the complete node set of the current welding condition is instantiated in the knowledge graph: receiving the four types of parameters output from the first layer, creating geometric state nodes (storing height difference, angle, etc.), material property nodes (storing steel type, carbon equivalent, etc.), environmental condition nodes (storing temperature, humidity, etc.), and equipment status nodes (storing pressure, accuracy, etc.) in the knowledge graph, assigning real-time attributes to each node, establishing the association edges between nodes (such as material-environment-defect risk edges), forming a complete sub-graph of the current working condition; among them, the complete node set contains four major categories of entity nodes: geometric state, material property, environmental condition, and equipment status; Determining Node Instantiation Success: If all four types of nodes are successfully created and their attributes are fully assigned, node instantiation is considered successful. If any node creation fails or attribute assignment is missing, node instantiation fails. If node instantiation fails, image completion is executed: The completion mechanism is activated. If the missing node represents the environment or equipment status and a backup sensor data source exists, the data source is switched and instantiation is re-established. If the missing node represents a material property, an emergency material re-inspection process is triggered, and the process is paused to await the re-inspection results. If completion is not possible and the missing node is a critical decision-making basis, the current working condition is deemed unmodelable, triggering manual intervention. Triggering the emergency material re-inspection process includes immediately stopping the current welding operation, taking samples from the batch of steel plates (or retrieving retained samples from the same batch), and sending them to the relevant authorities. The laboratory conducts chemical composition and mechanical property retesting and awaits the retest results. If the retest is qualified, the material node attributes in the knowledge graph are updated and welding continues; if it is unqualified, the batch is deemed scrapped, triggering the supplier quality traceability process; if the node instantiation is successful, the knowledge graph reverse link query is activated starting from the current misaligned edge risk node, tracing upstream to potential root cause nodes: starting from the "misaligned edge defect" node, all causal relationship edges pointing to this node in the knowledge graph (i.e., the reverse of the "cause" relationship) are queried, and the nodes are traversed upstream along the edges to find the tooling nodes (check pressure records), hydraulic system nodes (check response delay), environmental nodes (check temperature anomalies), and material nodes (check thickness unevenness), constructing a complete causal chain from the root cause to the defect; Determine if a traceable path exists: If the reverse link query returns a valid upstream node (at least one of the following: tooling assembly, hydraulic system, environmental records, or material batch), then a traceable path exists; if the query result is empty (no historical associated data), then no traceable path exists. Here, a reverse link query refers to a graph traversal operation that searches backward from the result node to find the cause node. A valid upstream node refers to an upstream entity node that has a direct or indirect causal relationship with the current defect and has queryable historical data (such as the maintenance record of a specific hydraulic station). If no traceable path exists, special handling for no traceable data is performed: Special handling is initiated if it is the first batch of welding for a brand new project (…). If there is no historical data, the accelerated learning mode is activated, the sampling period is shortened to 50% of the standard value, and intensive monitoring is carried out throughout the process. This batch is used as a key sample for knowledge accumulation. If the query fails due to a data link failure, the system switches to a backup database or local cache for retry. If the retry is successful, it is determined that there is a traceable path. If it fails, it is marked as a data missing risk. The most conservative strategy (assuming that all root causes may exist) is adopted to implement the closed-loop record of handling delay defect prevention and multi-standard compliance access decision-making and upstream feedback. If there is a traceable path, the system queries the historical records of the tooling nodes of the group pair and extracts the hydraulic pressure curves, positioning accuracy data, and clamping force uniformity index of the three most recent group pair operations.

[0020] Determine if the tooling status is abnormal: If the hydraulic pressure fluctuation exceeds 15% of the rated value or the positioning accuracy deviation is greater than 0.5 mm, the tooling status is determined to be abnormal and marked as the root cause of the tooling abnormality; if the hydraulic pressure fluctuation does not exceed 15% of the rated value and the positioning accuracy deviation is less than or equal to 0.5 mm, the tooling status is determined to be abnormal; if the tooling status is normal, perform hydraulic system pressure tracing in multi-dimensional root cause hierarchical tracing and root cause classification judgment; if the tooling status is abnormal, perform systemic root cause confirmation and early warning push in multi-dimensional root cause hierarchical tracing and root cause classification judgment; perform multi-dimensional root cause hierarchical tracing and root cause classification judgment.

[0021] The implementation of multi-dimensional root cause stratification and root cause classification involves the following: Systematic root cause confirmation and early warning push: Historical data and environmental monitoring records of the tooling hydraulic system are queried. If pressure fluctuations exceed the rated value by more than 15% in the last three welding operations, or response delays show an increasing trend, or maintenance records show that maintenance has been overdue, it is determined that the equipment performance is deteriorating, i.e., it is confirmed that the equipment is aging or insufficiently maintained. If the real-time temperature is lower than the design lower limit specified in the process specification (e.g., -15 degrees Celsius), or the humidity exceeds the upper limit of the equipment protection level, it is determined that the environment is exceeding the design conditions. If the equipment performance is deteriorating, a preventive maintenance early warning is pushed to the upstream process, triggering the tooling maintenance plan, blocking the subsequent defect propagation path, and implementing closed-loop records and upstream feedback in delayed defect prevention and multi-standard compliance access decisions. If the environment exceeds the operating conditions, environmentally adaptable process adjustment suggestions are pushed, and key component identification and differentiated strategies are implemented in delayed defect prevention and multi-standard compliance access decisions. Perform hydraulic system pressure tracing: query historical data of hydraulic system nodes, analyze pressure stability, response delay, and leakage alarm records within a 24-hour period prior to welding; determine hydraulic system status: if there are records of sudden pressure drops or excessive response delays, the hydraulic system is deemed abnormal and marked as the root cause; if there are no records of sudden pressure drops or excessive response delays, the hydraulic system is deemed normal; if the hydraulic system is abnormal, perform systemic root cause confirmation and early warning push; if the hydraulic system is normal, query environmental monitoring nodes and extract temperature change curves, current real-time temperature, and temperature gradient data for the period before welding and three 72-hour periods. Determine the degree of temperature impact: If the current temperature is below the material's brittle transition temperature or the daily temperature difference exceeds 20 degrees Celsius, the temperature conditions are considered abnormal and marked as an environmental root cause; if the current temperature is above the material's brittle transition temperature and the daily temperature difference does not exceed 20 degrees Celsius, the temperature conditions are considered normal; if the temperature conditions are abnormal, a systemic root cause confirmation and early warning push are executed; if the temperature conditions are normal, the material batch node is queried to obtain the steel plate rolling record, chemical composition, mechanical properties, thickness tolerance, and warehousing inspection data. Determine if material properties are abnormal: If the positive thickness deviation is concentrated (e.g., more than 50% of batches are greater than the upper limit of the tolerance band 70%) or the yield strength fluctuation exceeds the standard value by more than 10%, the material properties are considered abnormal and marked as the root cause; if the positive thickness deviation is not concentrated (e.g., more than 50% of batches are less than or equal to the upper limit of the tolerance band 70%) and the yield strength fluctuation does not exceed the standard value by more than 10%, the material properties are considered normal; if the material properties are abnormal, execute the supply chain knowledge embedding and batch control in the supply chain risk management and multi-factor coupling time series simulation; if the material properties are normal, execute the multi-factor coupling effect identification in the supply chain risk management and multi-factor coupling time series simulation; execute the supply chain risk management and multi-factor coupling time series simulation.

[0022] Specifically, the implementation of supply chain risk management and multi-factor coupled time-series analysis involves: Implementing supply chain knowledge embedding and batch control: Detected material anomalies are associated with the supply chain knowledge graph, automatically tightening the assembly gap control requirements for subsequent steel plates in the same batch; if a thickness anomaly is detected, the steel plate batch number is extracted (e.g., Batch-2024-05-18-A), the batch node is located in the supply chain knowledge graph, a "current weld-batch" association edge is established, and the detection records of other steel plates in the same batch are queried. If most have similar deviations, the assembly gap control for all steel plates in that batch is automatically tightened. If the requirement is to tighten the thickness (e.g., from the standard 2 mm to 1.5 mm), mark the batch as "observation level" or "interception level" in the graph; if it is a slight thickness deviation (e.g., positive deviation less than 50% of the tolerance zone), dynamically adjust the welding parameters to compensate (e.g., reduce heat input by 5% to 10%), mark the batch as observation level, and perform multi-factor coupling effect identification; if it is a serious material inhomogeneity (e.g., positive deviation exceeds the tolerance zone by 50% and exceeds the standard), trigger incoming material interception, suspend the use of the batch, push a quality warning to the supplier, and implement closed-loop recording and upstream feedback in the delayed defect prevention and multi-standard compliance access decision-making process. Multi-factor coupling effect identification: Retrieve the current working condition node combination, match the multi-factor interaction edges in the knowledge graph, and identify potential reinforcing coupling relationships; Determine if known reinforcing coupling exists: If the base material grade is Q390 or Q420 high-strength steel and the ambient humidity is higher than 70%, it is determined that known reinforcing coupling exists, and the hydrogen-induced cracking reinforcing coupling rule is triggered: For example, when the base material is Q390 / Q420 high-strength steel (high carbon equivalent, large hardening tendency) and the ambient humidity is higher than 70% (the flux is prone to moisture absorption and hydrogen increase), the rule is triggered, forcibly requiring the flux drying temperature to be increased to 350 degrees Celsius and the time to be extended to 4 hours, and the preheating temperature... The temperature is increased to 150 degrees Celsius, and the interpass temperature is strictly controlled. If the base material is a thick plate (greater than 30 mm) and the ambient temperature is below 5 degrees Celsius, a known strengthening coupling is identified, and the cold crack strengthening coupling rule is triggered: when the base material is a thick plate (greater than 30 mm, with high restraint and rapid cooling) and the ambient temperature is below 5 degrees Celsius (further accelerating the cooling rate), this rule is triggered, forcing the preheating temperature to be increased to above 120 degrees Celsius, followed by heat preservation and slow cooling after welding, and post-heat treatment if necessary; otherwise, a known strengthening coupling is identified. If a known strengthening coupling exists, the process compensation parameters are automatically calculated based on the identified coupling type. ;in, This refers to parameter adjustment amounts (such as preheating temperature increments). This is a material dimension weighting coefficient, representing the weight of the influence of material properties on process adjustments. It is used to quantify the contribution of material properties such as the carbon equivalent and strength grade of the base material to the sensitivity of the welding process. This represents the environmental dimension weighting coefficient, indicating the influence of environmental conditions on process adjustments. It is used to quantify the contribution of external conditions such as ambient temperature and humidity to welding quality risks. This is a geometric dimension weighting coefficient, representing the influence of geometric state on process adjustments. It is used to quantify the degree of influence of geometric factors such as plate thickness, misalignment, and constraint on the welding process. This is due to the deviation in carbon equivalent of the material. This represents the actual humidity. As the baseline humidity, For misalignment, each in highly sensitive coupling The value is taken from the upper limit (e.g., 0.8), and the lower limit (e.g., 0.4) is taken when the sensitivity is moderate. Determine the intensity of the adjustment: If it is a highly sensitive coupling (high-strength steel, high humidity, and low temperature), then significantly adjust the process (extend the flux drying time to 4 hours, increase the preheating temperature to 150 degrees Celsius, and reduce the welding speed by 20%), mark the condition as highly sensitive, determine that there is no known reinforcing coupling, and add reinforcement monitoring requirements; if it is a moderately sensitive coupling, then moderately adjust the process (extend the standard drying time to 2.5 hours and increase the preheating temperature to 120 degrees Celsius), determine that there is no known reinforcing coupling; if there is no known reinforcing coupling, then retrieve historical welding data of the same tower section or the same batch of welds to check for similar misalignment deviation records, i.e., whether there are any existing... Regarding historical similar records: if a similar deviation has occurred at the same or adjacent locations before (height difference deviation pattern similarity greater than 80%), then a historical similar record is determined to exist; if no similar deviation has occurred at the same or adjacent locations before (height difference deviation pattern similarity less than or equal to 80%), then a historical similar record is determined not to exist; if a historical similar record exists, then delay defect risk analysis and monitoring enhancement in delay defect prevention and multi-standard compliance access decision-making are implemented; if no historical similar record exists, then cross-domain knowledge fusion and standard matching in delay defect prevention and multi-standard compliance access decision-making are implemented; and delay defect prevention and multi-standard compliance access decision-making is implemented.

[0023] Specifically, the implementation of delayed defect prevention and multi-standard compliance access decisions involves: strengthening delayed defect risk analysis and monitoring; analyzing the thermal cycling records and post-weld non-destructive testing results of similar historical cases to determine the presence of delayed cracks or delayed porosity; retrieving historical cases (matching base material, plate thickness, and environment) similar to the current operating conditions from the knowledge graph, extracting thermal cycling parameters such as welding heat input (kJ / inch), interpass temperature, and cooling rate from these cases, querying the post-weld non-destructive testing records (TOFD or ultrasonic) for 24 and 48 hours, and statistically analyzing the proportion of delayed cracks or delayed porosity; if the proportion of delayed defects in historical cases exceeds a threshold (e.g., 50%), and the current operating condition's thermal input... If the cooling rate is similar to these defect cases (deviation less than 10%), then the historical case is determined to have eventually evolved into a delayed defect, and the current operating conditions have similar heat input and cooling rate. If there are no records of delayed defects in the historical cases, or if the current heat input is significantly lower than that of the defect cases (e.g., more than 20% lower, with slower cooling), then the historical case is determined to have no delayed defect or a different thermal cycle pattern. If the historical case eventually evolved into a delayed defect, and the current operating conditions have similar heat input and cooling rate, then the current situation is determined to be a high-risk precursor to a delayed defect, and delayed defect prevention and control measures and cycle extensions are implemented. If the historical case has no delayed defect or a different thermal cycle pattern, then it is marked as requiring attention but not high-risk, and cross-domain knowledge fusion and standard matching are implemented. Perform cross-domain knowledge fusion and standard matching: Automatically identify the applicable standard system for the project (such as national standards, European standards, and classification society specifications), and retrieve the limit definitions for misalignment in each standard; if the project implements multiple standards and there are differences in limits (e.g., the national standard allows 2 mm while the European standard only allows 1 mm), the strictest rule is triggered (e.g., using the 1 mm limit), the standard source is marked as the European standard, and closed-loop recording and upstream feedback are executed; if the standards are consistent or a single standard is implemented (e.g., only the national standard (GB / T) is implemented, without superimposed European standards or classification society specifications, and the national standard stipulates that the misalignment limit for the plate thickness is 2 mm). If there are no other conflicting standards, and 2 mm is directly used as the judgment threshold, then this limit is directly adopted. The load data in the design stage is correlated to assess the impact on the structural safety margin: the load spectrum (fatigue load, ultimate load) of the weld location is extracted from the BIM model of the design department, a finite element model is established, the current misalignment amount (e.g., 1.5 mm) is input to calculate the local stress concentration factor, and the allowable stress of the material is compared with the fatigue life curve to assess whether the stress increase caused by the current misalignment is within the safety margin range (if the remaining life is still greater than 20 years, it passes; if it is less than 10 years, it is high risk). Implement delayed defect prevention and control and extend the cycle: Based on the high-risk precursors of delayed defect risk analysis and enhanced monitoring, the quality monitoring cycle is forcibly extended; if the risk level is extremely high (e.g., the historical defect rate is greater than 50% and the current thermal cycle is matched), then daily non-destructive testing is forcibly implemented after welding, and double-day (i.e., 48 hours) and triple-day (i.e., 72 hours) follow-up testing are added, and the weld is marked as a full-cycle monitoring object, and closed-loop recording and upstream feedback are implemented; if the risk level is high but not extremely high, daily testing is forcibly implemented, and the weld is marked as a key focus object, and closed-loop recording and upstream feedback are implemented. Execution and handling closed-loop recording and upstream feedback: Based on all the aforementioned tracing, analysis, and judgment results, a complete handling record is generated; if a systemic root cause warning (i.e., systemic root cause confirmation and warning push), incoming material interception (i.e., supply chain knowledge embedding and batch control), or delay defect prevention (i.e., delay defect prevention and cycle extension) has been triggered, a structured feedback report is pushed to the upstream process (assembly, procurement, design); the causal relationship weights in the knowledge graph are updated, and the case is marked as a typical sample; if it is a routine pass or a slight adjustment, it is recorded as a standard case, and key part identification and differentiated strategies are implemented; Implement critical component identification and differentiation strategies: Based on cross-domain fusion design load data, identify whether the current weld is a critical load-bearing component (such as the door frame reinforcement area, flange connection area, or tower diameter change area); if the current weld is a standard component and all verifications have passed, then perform final admission judgment and knowledge evolution; if the current weld is a standard component but there are unclosed warnings, then perform final admission judgment and knowledge evolution after adding monitoring requirements; if the current weld is a critical component and the zero misalignment requirement is not met (e.g., misalignment greater than 0.5 mm), then trigger a rework or concession acceptance review process, suspend welding, and wait for a decision; if the current weld is a critical component but the misalignment meets the zero misalignment requirement, then perform final admission judgment and knowledge evolution. Final admission determination and knowledge evolution: Based on all verification results, if all verifications pass (i.e., no open root causes, no delay risks, standard compliance, and part requirements are met), the case is allowed to enter the welding process, and is simultaneously added to the knowledge graph case library to optimize subsequent reasoning weights; if there is a minor warning but control measures have been added, the case is allowed to enter the welding process, and is marked as an observation case and added to the learning library; if it is in a critical state (such as the quality score is close to the threshold or there are pending warnings), the knowledge graph situational reasoning is re-executed or expert arbitration is triggered.

[0024] Example 4: This example also discloses a system for implementing an intelligent wind turbine tower welding control method, see reference... Figure 2 The system comprises: a data acquisition module, which acquires multi-source heterogeneous data such as 3D point cloud of the weld area, ambient temperature and humidity, and base material properties in real time, performs data integrity verification and anomaly re-sampling, and provides a reliable raw data foundation for subsequent analysis; a geometric recognition module, which analyzes the slope continuity, symmetry, and depth consistency characteristics based on a deep learning classifier, distinguishes between normal bevels and misaligned edges, and performs intelligent judgment of welding geometric quality through a coarse-fine two-level tracking strategy; a knowledge reasoning module, which constructs a process knowledge graph, integrates contextual information such as base material properties, environmental conditions, and historical cases, performs hard rule red line judgment and data-driven probabilistic reasoning, dynamically assesses the risk level of misaligned edges, and generates a visual explanation; a causal tracing module, which traces the systemic root causes of equipment, environment, and materials based on the knowledge graph through reverse links, identifies the enhanced coupling effect of multiple factors, infers the risk of delayed defects, and achieves a leap from single-point quality control to full-link prevention; and a decision execution module, which integrates the results of geometric recognition, knowledge reasoning, and causal tracing, triggers hierarchical response strategies (parameter adaptive adjustment, manual intervention, and process termination), and feeds cases back to the knowledge graph for continuous learning and optimization.

[0025] In this embodiment, real-time acquisition and validity assessment of multi-source data ensure the reliability of raw data quality. Through deep recognition of 3D point cloud geometric features and deep learning classification, accurate identification of bevel morphology and intelligent differentiation of misaligned edges are achieved, reducing the geometric misjudgment rate. Through knowledge graph contextual reasoning and hybrid-driven judgment, process rules and data models are integrated to achieve accurate risk level assessment and context-adaptive decision-making. Through causal tracing and knowledge evolution mechanisms, a defect root cause chain is constructed and reasoning weights are continuously optimized, realizing the transformation from single-point control to systemic prevention, and comprehensively improving welding quality consistency and production safety.

Claims

1. A method for controlling the welding of intelligent wind turbine towers, characterized in that: include: S1. Collect raw parameters and determine their validity; S2. Extract the effective dataset, perform noise filtering and outlier removal on the point cloud of the bevel area, and segment the point cloud into the left parent material area, the right parent material area, the bottom of the bevel area, and the transition area based on spatial geometric features. S3. Calculate the slope distribution on both sides of the bevel, analyze the continuous change of slope along the weld direction, and extract the slope continuity characteristics. S4. Analyze the degree of symmetry of the parent materials on both sides of the bevel relative to the centerline, and extract the symmetry features; S5. Evaluate the uniformity of the groove depth along the weld direction and extract the depth consistency feature; S6. Combine the features of slope continuity, symmetry, and depth consistency into a feature vector, input it into a pre-trained deep learning classifier, and determine the slope morphology category. S7. Perform multi-level tracking strategy review; S8. After verification and confirmation, the embedded process rules are used for final judgment. S9. Encapsulate the judgment results, key parameters, and decision basis into standard output, and perform knowledge graph contextual reasoning.

2. The intelligent wind turbine tower welding control method according to claim 1, characterized in that: Collect raw parameters and determine their validity, specifically as follows: Multi-source data acquisition and fusion were performed on the weld seams of the target wind turbine tower. Calculate the effective coverage of point cloud data, identify void regions, and statistically analyze the spatial distribution density of effective points. Through point cloud quality assessment, determine whether the data integrity meets the requirements. If the data missing judgment function outputs 1, it is determined that the data is missing and the parameter is determined to be an invalid geometric parameter. If the data missing detection function outputs 0, then the data missing is considered normal. Perform bevel geometry feature extraction; By verifying the physical rationality of the parameters, the extracted geometric parameters are compared with physical common sense and process specifications to identify outliers and check the logical consistency between the parameters. Encapsulate valid geometric parameters into standard data structures; Output standard data structures and perform intelligent geometric feature recognition; An exception handling and recovery mechanism is implemented for invalid geometric parameters.

3. The intelligent wind turbine tower welding control method according to claim 1, characterized in that: Performing knowledge graph-based contextual reasoning involves: Extract contextual information; Determine if the data is complete; If the data is incomplete, perform data completion. If the data is complete, the extracted context information will be matched with entity nodes in the knowledge graph; Determine if the match was successful; If the match fails, proceed with the unknown operating condition handling. If a match is successful, the associated process rule edges and constraint edges are activated to construct a sub-graph view of the current working condition. Search for hard-coded rule nodes in the activated subgraph and check for the existence of absolute prohibition constraints; Determine the combination of plate thickness and misalignment, and determine the corresponding operation for each combination; Retrieve multi-factor interaction edges in the knowledge graph under the current context to identify whether there are reinforced coupling relationships; Determine if risk coupling exists; Implement risk-based decision-making and access assessment.

4. The intelligent wind turbine tower welding control method according to claim 3, characterized in that: The implementation of risk classification decisions and access determination is as follows: Perform data-driven probabilistic model inference: Calculate the misalignment risk level and confidence score of the current working condition based on historical case library and real-time parameters; Determine the combination of risk level and confidence level: If the risk level is high but the confidence level is low, then a medium-risk review and dynamic adjustment will be performed. If the risk level is low and the confidence level is high, then causal tracing admission and quality score generation will be performed. If the risk level is low but the confidence level is low, then a medium-risk review and dynamic adjustment will be performed. If the risk level is high and the confidence level is high, then perform high-risk confirmation and visual interpretation. Perform medium-risk review and dynamic adjustment: If the low confidence level is due to data noise, trigger sensor re-sampling and feature re-extraction, and re-execute knowledge graph contextual reasoning; If the low confidence level is due to insufficient historical cases, a similar case search will be initiated, and inferences will be made based on the risk level of the most similar case. If the risk level is within the boundary range, a conservative estimate is adopted, and high-risk confirmation and visualization interpretation are performed according to the higher risk level. Perform causal tracing for admission and quality score generation: Integrate knowledge graph reasoning results to generate the final quality score and risk-carrying parameters; If the quality score is higher than the admission threshold and there are no unprocessed warnings, then causal tracing and knowledge evolution will be performed with the knowledge graph reasoning path record, risk weight coefficient, and environmental compensation parameters. If the quality score is higher than the admission threshold but there is a warning mark, additional monitoring requirements will be set to perform causal tracing and knowledge evolution, and the frequency of real-time verification will be increased. If the quality score is at the critical value, the data-driven probabilistic model inference is re-executed.

5. The intelligent wind turbine tower welding control method according to claim 4, characterized in that: Performing causal tracing and knowledge evolution specifically involves: Extract quality scores, risk weight coefficients, environmental compensation parameters, and knowledge graph reasoning path records; Determine the integrity of the input data packet; If the input data packet is abnormal, then execute the input error handling; If the input data packet is normal, then based on the received parameters, instantiate the complete set of nodes for the current welding condition in the knowledge graph; Determine whether node instantiation was successful; If node instantiation fails, perform image completion. If the node is successfully instantiated, the knowledge graph reverse link query is activated, starting from the current faulty edge risk node, to trace the potential root cause node upstream. Determine if a traceable path exists; If no traceable path exists, special handling is performed for untraceable data; If a traceable path exists, query the historical records of the tooling nodes of the pair and extract the hydraulic pressure curves, positioning accuracy data, and clamping force uniformity index of the three most recent pair operations. Determine if the tooling is in an abnormal state; If the tooling is in normal condition, then perform hydraulic system pressure tracing in the multi-dimensional root cause hierarchical tracing and root cause classification judgment. If the tooling status is abnormal, the systemic root cause confirmation and early warning push will be executed in the multi-dimensional root cause hierarchical tracing and root cause classification judgment. Perform multi-dimensional root cause stratification and root cause classification.

6. The intelligent wind turbine tower welding control method according to claim 5, characterized in that: Perform multi-dimensional root cause stratification and root cause classification, specifically as follows: Perform systematic root cause identification and early warning push: If equipment performance deteriorates, push preventive maintenance early warning to upstream processes, trigger tooling maintenance plans, block subsequent defect propagation paths, and execute closed-loop records and upstream feedback in delayed defect prevention and control and multi-standard compliance access decision-making. If the environment exceeds the operating conditions, suggestions for environmentally adaptable process adjustments will be pushed out, and key component identification and differentiated strategies will be implemented in the delay defect prevention and multi-standard compliance access decision-making. Perform hydraulic system pressure traceability: query historical data of hydraulic system nodes and analyze pressure stability, response delay, and leakage alarm records within the unit day before welding; Determine the status of the hydraulic system; If the hydraulic system malfunctions, a systemic root cause analysis and early warning will be performed. If the hydraulic system is normal, query the environmental monitoring node and extract the temperature change curves before welding and three days in advance, the current real-time temperature, and the temperature gradient data. Determine the degree of influence of temperature; If the temperature conditions are abnormal, a systemic root cause identification and early warning will be performed. If the temperature conditions are normal, query the material batch node to obtain the steel plate rolling record, chemical composition, mechanical properties, thickness tolerance, and warehousing inspection data; Determine if the material properties are abnormal; If the material properties are abnormal, then supply chain risk management and multi-factor coupled time-series simulation of supply chain knowledge embedding and batch control will be implemented. If the material properties are normal, then perform the identification of multi-factor coupling effects in supply chain risk management and multi-factor coupling time-series deduction; Implement supply chain risk management and multi-factor coupled time-series simulation.

7. The intelligent wind turbine tower welding control method according to claim 6, characterized in that: The implementation of supply chain risk management and multi-factor coupled time-series analysis is as follows: Implement supply chain knowledge embedding and batch control: Associate detected material anomalies with the supply chain knowledge graph and automatically tighten the assembly gap control requirements for subsequent steel plates in the same batch; If the thickness deviation is slight, the welding parameters are dynamically adjusted to compensate, the batch is marked as observation level, and multi-factor coupling effect identification is performed; If there is a serious material inconsistency, the incoming material will be intercepted, the use of the batch will be suspended, a quality warning will be sent to the supplier, and a closed-loop record of the handling of delay defect prevention and multi-standard compliance access decision-making will be implemented, along with upstream feedback. Perform multi-factor coupling effect identification: retrieve the current working condition node combination, match the multi-factor interaction edges in the knowledge graph, and identify potential reinforcing coupling relationships; Determine if a known reinforcing coupling exists; If known enhanced coupling exists, process compensation parameters are automatically calculated based on the identified coupling type. Determine the intensity of the adjustment; If no known reinforcing coupling exists, retrieve historical welding data for the same tower section or the same batch of welds to check for similar misalignment deviation records. If similar historical records exist, then strengthen the risk analysis and monitoring of delay defects in delay defect prevention and multi-standard compliance access decisions; If no similar historical records exist, then cross-domain knowledge fusion and standard matching will be performed in the process of delay defect prevention and multi-standard compliance access decision-making. Implement delay defect prevention and control and multi-standard compliance access decisions.

8. The intelligent wind turbine tower welding control method according to claim 7, characterized in that: Implementing delay defect prevention and multi-standard compliance access decisions specifically includes: Perform delayed defect risk analysis and enhanced monitoring: analyze the thermal cycling records and post-weld non-destructive testing results of similar historical cases to determine whether delayed cracks or delayed porosity exist; If a historical case eventually evolves into a delay defect, and the current operating conditions have similar heat input and cooling rates, then the current situation is determined to be a high-risk precursor to a delay defect, and delay defect prevention and control measures and cycle extensions are implemented. If historical cases do not show delay defects or different thermal cycling patterns, they are marked as cases that require attention but are not high-risk, and cross-domain knowledge fusion and standard matching are performed. Perform cross-domain knowledge fusion and standard matching: automatically identify the standard system applicable to the project and retrieve the limit definitions of the misalignment of each standard; If a project implements multiple standards and there are differences in the limits, the most stringent rule will be triggered, the standard source will be marked as European standard, and the closed-loop record of the execution and handling will be kept in conjunction with the upstream feedback. If the standards are consistent or a single standard is applied, the limit value is directly adopted, and the load data in the design stage is correlated to assess the impact on the structural safety margin. Implement delayed defect prevention and cycle extension: Based on high-risk precursors identified through delayed defect risk analysis and enhanced monitoring, forcibly extend the quality monitoring cycle; If the risk level is extremely high, then mandatory non-destructive testing will be carried out every unit day after welding, and double and triple unit day follow-up testing will be added. The weld will be marked as a full-cycle monitoring object, and closed-loop record of handling and upstream feedback will be implemented. If the risk level is high but not extremely high, then mandatory unit day testing will be carried out, and the weld will be marked as a key focus object. Closed-loop record of handling and upstream feedback will be implemented. Execution and disposal closed-loop record and upstream feedback: Based on all the aforementioned tracing, analysis and judgment results, a complete disposal record is generated; If a systemic root cause warning, incoming material interception, or delayed defect prevention has been triggered, a structured feedback report is pushed to the upstream process; the causal relationship weights in the knowledge graph are updated, and the case is marked as a typical sample; if it is a routine pass or a minor adjustment, it is recorded as a standard case, and key part identification and differentiation strategies are implemented. Implement critical component identification and differentiation strategies: Based on cross-domain fusion of design load data, identify whether the current weld is a critical load-bearing component; If the current weld is a standard location and all checks have passed, then the final admission decision and knowledge evolution will be performed; if the current weld is a standard location but there are unclosed warnings, then monitoring requirements will be added before the final admission decision and knowledge evolution will be performed; if the current weld is a critical location and the zero misalignment requirement is not met, then the rework or concession acceptance review process will be triggered, and welding will be suspended pending a decision; if the current weld is a critical location but the misalignment meets the zero misalignment requirement, then the final admission decision and knowledge evolution will be performed. Final admission determination and knowledge evolution: Based on the comprehensive verification results, if all verifications pass, it is determined that entry into the welding process is allowed, and the case is simultaneously added to the knowledge graph case library to optimize the subsequent reasoning weights; If a minor warning is issued but control measures have been implemented, it is determined that entry into the welding process is permitted, and the case is marked as an observation case and added to the learning database. If the situation is critical, the knowledge graph contextual reasoning will be re-executed or expert arbitration will be triggered.

9. A system for implementing the intelligent wind turbine tower welding control method of claim 1, characterized in that: include: Data acquisition module: Real-time acquisition of multi-source heterogeneous data such as 3D point cloud of weld area, ambient temperature and humidity, and base material properties; performs data integrity verification and anomaly re-sampling; provides a reliable raw data foundation for subsequent analysis. Geometric recognition module: Based on deep learning classifier, analyze the characteristics of slope continuity, symmetry and depth consistency to distinguish between normal bevel and misaligned edge. Through coarse and fine two-level tracking strategy, realize intelligent judgment of welding geometric quality. Knowledge Reasoning Module: Constructs a process knowledge graph, integrates contextual information such as parent material properties, environmental conditions, and historical cases, executes hard rule red line judgment and data-driven probabilistic reasoning, dynamically assesses the risk level of misalignment and generates a visual explanation; Causal tracing module: Based on knowledge graph reverse linking, trace the systemic root causes of equipment, environment, materials, etc., identify the multi-factor enhanced coupling effect, infer the risk of delayed defects, and realize the leap from single-point quality control to full-chain prevention; Decision execution module: Integrates geometric recognition, knowledge reasoning, and causal tracing results to trigger hierarchical response strategies and feeds cases back to the knowledge graph for continuous learning and optimization.

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