Magnetic sensor array optimized layout for magnetic flux leakage detection systems

Abstract

The application relates to the technical field of ferromagnetic material detection, and discloses a magnetic sensor array optimized layout magnetic flux leakage detection system. The system comprises five modules, namely a magnetic sensor array layout feature extraction module, a region detection completeness analysis module, a multi-stage optimized layout generation module, an array reconstruction interference analysis module and an optimal layout decision updating module. The magnetic sensor array layout feature extraction module acquires spatial magnetic field distribution data of a ferromagnetic material surface in real time, and constructs a defect and detection environment feature information set; the region detection completeness analysis module analyzes detection completeness in combination with initial layout parameters, a detection path and a working state; the multi-stage optimized layout generation module generates a multi-stage optimized scheme set based on defect features when completeness is lower than a threshold value; the array reconstruction interference analysis module calculates a reconstruction interference value when layout switching occurs; and the optimal layout decision updating module filters an optimal scheme according to the interference value and updates the detection path. The system can dynamically optimize array layout and a detection path, and improve magnetic flux leakage detection performance.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ferromagnetic material detection, in particular to a magnetic sensor array optimized layout magnetic flux leakage detection system. BACKGROUND

[0002] In the field of industrial production and equipment operation, ferromagnetic materials are widely used in the manufacture of key equipment such as pipelines, pressure vessels, and mechanical parts due to their high strength and good magnetic conductivity. However, these materials are susceptible to environmental corrosion, load effects, and fatigue damage, which can cause surface and internal defects such as cracks, holes, and rust. If these defects are not detected and addressed in a timely manner, they will continue to expand over time.

[0003] To ensure the safe and stable operation of ferromagnetic material equipment, magnetic flux leakage detection technology has become one of the current mainstream detection technologies due to its advantages of not requiring coupling agents, wide detection range, and high sensitivity to surface and near-surface defects. In a magnetic flux leakage detection system, the layout design of the magnetic sensor array directly determines the accuracy, completeness, and efficiency of magnetic field signal acquisition. Traditional magnetic sensor array layouts often use a fixed and uniform distribution approach. This approach only considers general requirements in ideal detection environments and does not fully consider complex factors in actual detection scenarios.

[0004] In actual detection processes, there are many uncertainties in the detection environment, such as surface flatness differences of the detection object, environmental magnetic field interference, and detection path deviations. When using a fixed layout magnetic sensor array for detection, if the detection area has uneven surfaces, some sensors may not be able to maintain a reasonable detection distance from the surface of the detection object, resulting in distorted magnetic field signals and affecting the accuracy of defect identification. If there is external magnetic field interference in the detection environment, a fixed layout sensor array cannot adjust the collection position according to the interference, and may collect a large amount of signals containing interference noise, increasing the difficulty of subsequent signal processing. At the same time, a fixed detection path cannot be dynamically adjusted based on real-time collected defect feature information. When a suspected defect area is detected, the sensor layout and detection path cannot be optimized to focus on re-measuring this area, which may result in missed defects or incomplete detection.

[0005] Traditional magnetic flux leakage detection systems lack flexible layout adjustment capabilities when facing different specifications and different defect types of detection objects. If the sensor array layout is to be redesigned for different detection objects, not only a large amount of time and labor cost will be consumed, but also the detection efficiency will be greatly reduced, which is difficult to meet the needs of industrialized batch detection. With the development of industrial equipment towards large-scale and complex, higher requirements are put forward for the precision and efficiency of magnetic flux leakage detection technology. The limitations of the traditional magnetic sensor array layout method are increasingly prominent, and a magnetic flux leakage detection system capable of dynamically optimizing the sensor array layout according to the detection environment and defect characteristics is needed to improve the accuracy, integrity and adaptability of detection. SUMMARY

[0006] The purpose of the present application is to provide a magnetic flux leakage detection system with optimized magnetic sensor array layout to solve the problems raised in the background art.

[0007] To achieve the above purpose, the present application provides a magnetic flux leakage detection system with optimized magnetic sensor array layout, which comprises:

[0008] A magnetic sensor array layout feature extraction module is used to obtain spatial magnetic field distribution data of the surface of a ferromagnetic material in real time through a magnetic sensor array, construct a defect feature information set and a detection environment feature information;

[0009] A regional detection integrity analysis module is used to obtain the regional detection integrity corresponding to the defect feature information set based on the detection environment feature information, in combination with the initial layout parameters of the magnetic sensor array, the current detection path and the working state information of the magnetic sensor array at the current time;

[0010] A multi-level optimization layout generation module is used to generate a multi-level optimization layout scheme set based on the defect feature information set when the regional detection integrity is lower than a preset abnormal threshold;

[0011] An array reconstruction interference analysis module is used to calculate the array reconstruction interference value corresponding to the working state switching of the magnetic sensor array at the current time to the initial layout parameters in combination with the detection environment feature information;

[0012] An optimal layout decision update module is used to filter the optimal layout scheme of the magnetic sensor array based on the array reconstruction interference value and update the current detection path of the magnetic sensor array.

[0013] Preferably, in the magnetic sensor array layout feature extraction module, the defect feature information set includes the defect position, size and magnetic field strength gradient extracted based on the spatial magnetic field distribution data;

[0014] The detection environment feature information includes the material thickness and environmental magnetic noise intensity of different regions of the surface of the ferromagnetic material.

[0015] Preferably, the region detection integrity analysis module determines the region detection integrity by the minimum coverage distance between the current detection path and the defect position, the magnetic field intensity gradient variation of the defect size, and the deviation degree of the environmental magnetic noise intensity from the preset reference noise.

[0016] Preferably, the multi-level optimization layout generation module obtains the boundary topological nodes of each defect in the defect feature information set when the region detection integrity is lower than the preset abnormal threshold.

[0017] The safe layout region without magnetic field interference is divided based on the boundary topological nodes, and a multi-level optimization layout scheme set of the magnetic sensor array is generated by a multi-level optimization algorithm.

[0018] Preferably, the array reconstruction interference analysis module comprises:

[0019] A state switching cost calculation unit is configured to obtain an instruction execution sequence of switching the magnetic sensor array from a current working state to initial layout parameters.

[0020] A dynamic interference quantification unit is configured to calculate an array reconstruction interference value according to the parameter reconstruction frequency in the instruction execution sequence and the magnetic field calibration delay time.

[0021] Preferably, the optimal layout decision updating module performs the following operations on each scheme in the multi-level optimization layout scheme set:

[0022] According to the array reconstruction interference value and the material thickness in the detection environment feature information, a comprehensive interference influence value of each layout scheme is calculated.

[0023] A gradient optimization algorithm is used to select the layout scheme with the minimum comprehensive interference influence value as the optimal layout scheme.

[0024] Preferably, the optimal layout decision updating module generates a defect analysis report containing the defect feature information set, the detection environment feature information, and the optimal layout scheme after updating the current detection path of the magnetic sensor array.

[0025] Preferably, the array reconstruction interference analysis module calculates the array reconstruction interference value based on the offset between the magnetic field calibration delay time and the parameter reconstruction frequency.

[0026] Preferably, the safe layout region division process of the multi-level optimization layout generation module comprises:

[0027] A magnetic field attenuation safe boundary is generated according to the defect boundary topological nodes;

[0028] The current position of the magnetic sensor array is connected with each magnetic field attenuation safe boundary to form a continuous region without defect magnetic field intersection.

[0029] Preferably, the optimal layout decision updating module determines the comprehensive interference influence value by correlating the interference value, the material thickness and the ratio of the current detection path to the path length of the optimal layout scheme.

[0030] Compared with the prior art, the present application has the following advantages:

[0031] Through the cooperative work of each functional module, the problems of fixed magnetic sensor array layout, poor adaptability, insufficient detection integrity and the like in traditional magnetic flux leakage detection systems are effectively solved, and the present application exhibits significant advantages in detection accuracy, environmental adaptability, detection efficiency and system flexibility and the like.

[0032] In terms of detection data acquisition and defect information acquisition, the system is provided with a magnetic sensor array layout feature extraction module, which can acquire spatial magnetic field distribution data of the surface of the ferromagnetic material in real time through the magnetic sensor array, and construct a defect feature information set and a detection environment feature information. Compared with the traditional fixed layout which can only acquire magnetic field signals of a single fixed region, the module can capture dynamic magnetic field changes in the detection process in real time, comprehensively collect feature parameters related to defects, and record key influencing factors in the detection environment, thereby providing rich and accurate basic data for subsequent detection analysis and layout optimization, and avoiding the problem of defect misjudgment or omission caused by incomplete initial data acquisition.

[0033] In terms of detection region integrity guarantee, the region detection integrity analysis module analyzes the region detection integrity corresponding to the defect feature information set based on the detection environment feature information, in combination with the initial layout parameters of the magnetic sensor array, the current detection path and the working state information. This process breaks the mode of relying on fixed paths to complete detection and then evaluating the integrity in the traditional detection, and can judge in real time whether there is a detection blind area or information missing in the current detection region during the detection process. By finding the problem of insufficient detection integrity in time, a clear direction is provided for subsequent layout optimization, the risk of defect omission caused by incomplete detection is effectively reduced, and it is ensured that the detection result can truly reflect the defect distribution condition of the surface of the ferromagnetic material.

[0034] In terms of dynamic optimization and adaptability of layout, when the region detection completeness is lower than the preset abnormal threshold, the multi-level optimization layout generation module generates a set of multi-level optimization layout schemes based on the defect feature information set. This multi-level optimization method is not simply adjusting the sensor position, but according to the type, size, distribution density of the defect and the specific situation of the detection environment, different priority and adjustment amplitude layout schemes are formulated, so that the layout optimization is more targeted. Compared with the limitations of the traditional fixed layout that cannot adapt to complex environments and various defects, this module can make the magnetic sensor array flexibly adjust the layout according to the actual detection situation, whether it is a detection object with uneven surface or an environment with external magnetic field interference, the optimal signal acquisition position of the sensor can be ensured through optimized layout, and the accuracy and reliability of magnetic field signal acquisition are improved.

[0035] In terms of array reconstruction stability and detection continuity, the array reconstruction interference analysis module calculates the array reconstruction interference value corresponding to the initial layout parameters when the current working state is switched. This design fully considers the influence of the interference generated in the layout switching process on the stability of the detection system. By calculating the interference value in advance, the influence of interference factors can be avoided or reduced during layout adjustment, avoiding the interruption or distortion of detection signals caused by layout switching, and ensuring the continuity and stability of the detection process. The traditional detection system often ignores the interference problem in the switching process when adjusting the sensor layout, which is easy to cause detection data fault, while the system effectively solves this problem and ensures the smooth and orderly operation of the detection work.

[0036] In terms of optimal layout decision and detection path optimization, the optimal layout decision update module selects the optimal layout scheme based on the array reconstruction interference value and updates the current detection path. This module does not simply select a single optimization scheme, but considers the layout optimization effect and reconstruction interference degree to select the optimal scheme that has the least impact on system stability while ensuring detection accuracy. At the same time, combined with the optimal layout scheme, the detection path is dynamically updated, so that the detection path can be optimized in real time according to the defect distribution and layout adjustment, and the suspected defect area can be retested, and the defect-free area can be reasonably shortened. The detection path, while improving detection accuracy, effectively reduces unnecessary detection processes and improves overall detection efficiency. Whether it is a single defect or a multi-defect distribution detection object, the system can realize efficient and accurate detection through optimal layout and path adjustment, greatly improving the applicability and practicality of the system in different detection scenarios. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 The timing diagram of the magnetic sensor array optimization layout magnetic flux leakage detection system described in the present application;

[0038] Figure 2 a flow chart for region detection completeness analysis;

[0039] Figure 3 a flow chart for multi-level optimization layout generation;

[0040] Figure 4 a flow chart for array reconstruction interference analysis;

[0041] Figure 5 a flow chart for security layout region division. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0043] Please refer to Figure 1 The present application provides a magnetic sensor array optimization layout magnetic flux leakage detection system, which comprises:

[0044] The system obtains spatial magnetic field distribution data of the surface of the ferromagnetic material in real time, constructs a defect feature information set and a detection environment feature information. Based on the detection environment feature information, the initial layout parameters of the magnetic sensor array, the current detection path and the working state information of the magnetic sensor array at the current time are combined to obtain the region detection completeness corresponding to the defect feature information set. When the region detection completeness is lower than a preset abnormal threshold, a multi-level optimization layout scheme set is generated based on the defect feature information set. Combined with the detection environment feature information, the array reconstruction interference value corresponding to the working state switching of the magnetic sensor array at the current time to the initial layout parameters is calculated. Based on the array reconstruction interference value, the optimal layout scheme of the magnetic sensor array is screened, and the current detection path of the magnetic sensor array is updated. The system realizes dynamic adjustment of the sensor layout in a complex detection environment, and improves the accuracy and efficiency of defect detection.

[0045] Embodiment 1: Please refer to Figure 2Embodiments of the magnetic flux leakage detection system with optimized magnetic sensor array layout involve the specific operation procedures of the magnetic sensor array layout feature extraction module and the region detection completeness analysis module. The system initiates the detection process by real-time acquisition of spatial magnetic field distribution data on the surface of ferromagnetic materials. The magnetic sensor array moves on the material surface according to the preset scanning path, and each sensor node captures magnetic field strength data at a specific sampling frequency. After removing high-frequency noise through the signal conditioning circuit, these raw data are sent to the data processing unit for spatial interpolation calculation to generate a complete two-dimensional or three-dimensional magnetic field distribution map. During data processing, the system uses a sliding window algorithm to identify magnetic field anomaly regions and preliminarily judges the positions of possible defects by comparing the magnetic field strength differences between adjacent regions.

[0046] The construction of the defect feature information set is based on the analyzed spatial magnetic field distribution data. The system determines the extreme points in the magnetic field distribution, which usually correspond to the center regions of defects. By calculating the spatial variation rate of magnetic field strength, the system obtains the gradient distribution characteristics of each suspected defect region. The estimation of defect size uses the contour analysis method to outline the defect boundary profile with a specific magnetic field strength value as the threshold. At the same time, the system records the peak magnetic field strength and the maximum gradient value of each defect region, which together constitute the magnetic field feature fingerprint of the defect. The acquisition of environmental feature information is carried out synchronously. The material thickness data is obtained through the ultrasonic thickness measurement module integrated on the sensor array, which measures the thickness changes of different regions of the material in a non-contact manner. The environmental magnetic noise intensity is monitored by special reference sensors placed outside the detection area to record the fluctuation of the background magnetic field.

[0047] The region detection completeness analysis module evaluates and calculates based on multiple source information. This module receives the defect feature information set from the feature extraction module and the detection environment feature information, as well as the initial layout parameters of the magnetic sensor array, including the initial position coordinates, spacing configuration, and orientation angle of the sensors. The current detection path information is obtained from the motion control module in real time, including the motion trajectory coordinate sequence of the sensor array and the timestamp data of the detection points. The working state information of the magnetic sensor array is provided by the state monitoring unit, including the calibration state, sensitivity setting, and current sampling frequency of each sensor. These data are sent to the completeness evaluation algorithm for processing.

[0048] The integrity evaluation algorithm calculates the detection integrity of the region by multi-parameter fusion. The algorithm calculates the minimum coverage distance between the current detection path and the defect position. A spatial nearest neighbor search algorithm is used to traverse all points on the path and the geometric distance of each defect position. The minimum value is taken as the coverage distance index. This index reflects the coverage degree of the detection path to the defect region. A larger minimum coverage distance means that there may be a detection blind area. The algorithm analyzes the magnetic field strength gradient change of the defect size. By comparing the gradient feature change amplitude of the same defect in the current detection period and the historical period, the stability of the defect feature is evaluated. The deviation degree of environmental magnetic noise intensity from the preset reference noise is calculated using the standard deviation analysis method. The real-time monitored noise data is compared with the reference noise spectrum stored in the database, and the statistical deviation value is calculated.

[0049] These calculated parameters generate the region detection integrity index through a weighted fusion algorithm. The weighting coefficients are dynamically adjusted according to the characteristics of the detection environment. For example, in a high noise environment, the noise deviation degree is given a higher weight. The final region detection integrity is a dimensionless numerical index. The lower the value, the worse the detection integrity. The abnormal threshold preset by the system is determined based on a large amount of historical data statistical analysis. When the calculated integrity value is lower than this threshold, the system will trigger the optimization layout process. The entire evaluation process uses a real-time pipeline architecture to ensure continuous dynamic evaluation of detection integrity during the detection process. In the specific implementation process, the magnetic sensor array uses a matrix arrangement structure. Each sensor node has independent signal acquisition and processing capability. The array is connected to the main control unit through a communication bus and uploads the collected magnetic field data in real time. The data preprocessing steps include signal filtering, baseline correction, and temperature compensation to eliminate the influence of environmental factors on the measurement results. The defect feature extraction algorithm uses a multi-scale analysis method to identify defect features of different sizes. The detection environment monitoring module uses multi-sensor fusion technology to integrate ultrasonic thickness measurement data and magnetic noise monitoring data to build a complete environmental feature model.

[0050] The region detection integrity analysis module uses a distributed computing architecture to distribute the calculation tasks to multiple processing units for parallel execution. The minimum coverage distance calculation uses a spatial index acceleration algorithm to improve search efficiency. The magnetic field gradient change analysis uses a time series comparison method to establish an evolution model of the defect feature. The noise deviation calculation uses a frequency domain analysis method to convert the noise signal to the frequency domain for feature extraction and comparison. The weighted fusion algorithm uses an adaptive weight adjustment mechanism to dynamically optimize the weight distribution according to the changes in environmental conditions.

[0051] The system emphasizes the real-time and accuracy of data during implementation. The sensor data collection uses synchronous sampling technology to ensure the time consistency of spatial magnetic field data. The defect feature extraction algorithm uses iterative optimization to gradually refine the feature parameters. The environmental feature monitoring uses continuous sampling to ensure that the instantaneous changes of environmental conditions can be captured. The integrity evaluation algorithm uses a sliding window update mechanism to continuously update the evaluation results to reflect the latest detection status. The entire implementation process emphasizes the collaborative work between modules, and ensures smooth information transmission through unified data interfaces and communication protocols.

[0052] Embodiment 2: refer to Figure 3 and Figure 4 The implementation of the magnetic flux leakage detection system with optimized magnetic sensor array layout involves the specific operation processes of the multi-level optimized layout generation module and the array reconstruction interference analysis module. When the evaluation value output by the regional detection integrity analysis module is lower than the preset abnormal threshold value, the multi-level optimized layout generation module starts to perform its function. This module extracts the boundary topology nodes of each defect from the defect feature information set. These nodes are a set of feature points obtained by morphological processing of spatial magnetic field distribution data, reflecting the key geometric features of the defect outline. The generation of boundary topology nodes uses the Delaunay triangulation algorithm to discretize the continuous curve of the defect edge into a series of feature points with topological relationships. These points not only contain spatial coordinate information, but also have magnetic field gradient vector data at that position.

[0053] Based on these boundary topology nodes, the system begins to divide the safe layout area without magnetic field interference. This process uses a region division method based on potential field theory, considering each defect as a potential field source that generates magnetic field interference. The safe area is determined by calculating the composite potential field value of each point in space. The system generates a magnetic field attenuation safety boundary, which is composed of points that meet certain potential field threshold conditions. The shape and range of this boundary are determined by the magnetic field intensity distribution characteristics of the defect. The current position of the magnetic sensor array is connected with each magnetic field attenuation safety boundary in space, and the visibility graph algorithm is used to generate a continuous area of intersection of non-defect magnetic fields. This area ensures that the sensor receives the least magnetic field interference when working in this range.

[0054] The multi-level optimization algorithm then generates a set of layout schemes within this safe region. The first level of the algorithm employs a global exploration strategy, using an improved genetic algorithm to conduct a broad search in the solution space. The chromosome coding uses real number coding, with each gene representing the position coordinates and orientation angle of a sensor. The fitness function takes into account multiple factors such as coverage range, signal strength, and interference level. The second level of optimization employs a local fine search strategy, using a sequential quadratic programming algorithm to refine the preliminary scheme, adjusting the micro-position and orientation of the sensor nodes to further improve detection performance. The final output multi-level optimization layout scheme set contains several Pareto optimal solutions, each corresponding to a complete set of sensor array configuration parameters.

[0055] The array reconstruction interference analysis module begins to evaluate the cost required to switch from the current state to each candidate scheme. The state switching cost calculation unit analyzes the differences between the current working state and the target state, generating a detailed instruction execution sequence. This sequence includes movement instructions for sensor physical positions, reset instructions for working parameters, and start instructions for calibration procedures. Each instruction has a timestamp and execution priority information, forming a directed acyclic graph execution plan. The dynamic interference quantification unit calculates the interference degree during the reconstruction process based on this execution plan, mainly focusing on two core indicators: parameter reconstruction frequency and magnetic field calibration delay time.

[0056] The parameter reconstruction frequency reflects the number of sensor parameters that need to be adjusted, including the number of changes to configurable parameters such as position coordinates, sampling frequency, and sensitivity. The magnetic field calibration delay time includes hardware response delay and software processing delay, with the hardware delay depending on the mechanical motion characteristics of the sensor and the software delay related to the complexity of the parameter configuration algorithm. These indicators are converted into an array reconstruction interference value through a comprehensive mathematical model, which quantifies the degree of influence on system detection performance during state switching. The calculation of the array reconstruction interference value uses the following formula:

[0057]

[0058] Where: represents the array reconstruction interference value, represents the total number of parameters that need to be reconstructed, is the reconstruction weight coefficient of the th parameter, is the reconstruction difficulty factor of the th parameter, is the calibration time weight coefficient, represents the calibration delay time of the th sensor. The parameter reconstruction weight coefficient is dynamically adjusted according to the parameter type and importance, with the weight of the position parameter usually higher than that of other configuration parameters. The reconstruction difficulty factor The physical implementation complexity of parameter adjustment, such as the distance of sensor movement, affects the difficulty of reconstructing the position parameters. Calibration time weight coefficient According to the real-time requirements of the system, the higher the requirements, the greater the weight. In the specific implementation process, the multi-level optimization layout generation module adopts a distributed computing architecture, decomposes the optimization task to multiple computing nodes for parallel processing. Each computing node is responsible for the scheme generation of a sub-region, and the scheme fusion is performed through the coordination node. The processing of the boundary topology node adopts point cloud data processing technology, and uses KD tree data structure for fast spatial retrieval and nearest neighbor query. The division of the safety area uses computational geometry method, and the optimal sensor arrangement area is determined by constructing convex hull and Voronoi diagram.

[0059] The array reconstruction interference analysis module adopts an event-driven architecture to perform evaluation tasks. When a new candidate layout scheme is generated, an evaluation event is triggered to start the cost calculation process. The generation of instruction execution sequence adopts a rule-based expert system to automatically generate the optimal execution steps according to factors such as sensor type, movement distance, parameter adjustment amplitude, etc. The dynamic interference quantization unit adopts time series analysis method to predict the time consumption and resource occupation of each execution step, so as to accurately estimate the overall interference degree.

[0060] This embodiment focuses on the real-time and accuracy of each processing link. The multi-level optimization algorithm adopts an adaptive convergence strategy to dynamically adjust the number of iterations and search range according to the complexity of the detection environment. The boundary topology processing introduces multi-resolution analysis technology to extract defect features at different scales, ensuring that the generated safety area can avoid interference without losing detection coverage. The interference analysis module adopts an incremental update mechanism, which can quickly re-evaluate the interference value when the environmental conditions change, without the need for complete recalculation. The whole implementation process emphasizes the collaborative work between subsystems. The multi-level optimization layout generation module and the feature extraction module keep the data synchronized to ensure that the defect information used is the latest. The array reconstruction interference analysis module and the system state monitoring module interact closely to obtain the current working state and performance parameters of the sensor in real time. All computing processes use algorithms with good numerical stability to avoid rounding errors and numerical overflow problems, ensuring the reliability of the calculation results. Data storage uses time series database technology to record all intermediate results and final schemes in the optimization process, providing a data basis for subsequent analysis.

[0061] Embodiment 3: The implementation of the magnetic sensor array optimized layout's MFL detection system involves the complete operation process of the optimal layout decision update module. This module receives the scheme set from the multi-level optimized layout generation module and the evaluation data from the array reconstruction interference analysis module, and starts to perform comprehensive decision analysis. For each candidate scheme in the multi-level optimized layout scheme set, the system calculates its comprehensive interference impact value, which reflects the degree of interference the system as a whole receives when adopting this layout scheme. The calculation process takes into account the array reconstruction interference value, the material thickness parameter in the detection environment feature information, and the degree of path matching between the current detection path and the optimal layout scheme. The material thickness data affects the magnetic field propagation characteristics, and thicker materials require higher sensor sensitivity, which can amplify the interference effects generated during layout switching. The calculation of the comprehensive interference impact value uses a multi-factor weighted model, which is quantified by the following formula:

[0062]

[0063] Where: represents the comprehensive interference impact value, is the array reconstruction interference value, is the material thickness, is the thickness reference constant, represents the path adjustment time cost, and are the weight coefficients of the interference term and the path term, respectively. The thickness reference constant is set according to the material type and detection standards. The path adjustment time cost is obtained by calculating the difference between the execution time of the new path and the current path time. The weight coefficients are dynamically adjusted according to the priority of the detection task, and tasks with high real-time requirements are given higher weights to the path term.

[0064] The gradient optimization algorithm finds the optimal solution with the smallest comprehensive interference impact value among all candidate schemes. The algorithm uses the conjugate gradient method for iterative optimization, with the initial point set to the parameter value corresponding to the current layout scheme. In each iteration, the algorithm calculates the gradient direction of the objective function at the current point and performs one-dimensional search along this direction to find the optimal step length. To avoid falling into local optima, the algorithm introduces a momentum term and an adaptive learning rate mechanism. When the target function value changes for consecutive iterations are less than a set threshold, the algorithm terminates and outputs the current optimal solution. The finally selected layout scheme not only needs to satisfy the minimum comprehensive interference impact value, but also needs to pass the feasibility verification, including sensor movement range limitations, energy consumption constraints, and real-time requirements, etc.

[0065] After selecting the optimal layout scheme, the system updates the current detection path of the magnetic sensor array. The path update process uses a smooth transition strategy to calculate the difference between the current path and the new path, generating a gradual path adjustment sequence. The new detection path needs to avoid known defect areas while ensuring complete coverage of undetected areas. The path planning algorithm uses an improved A* algorithm, which uses the magnetic field intensity distribution as part of the heuristic function, to preferentially select paths with less magnetic field interference. The updated path information is sent to the actuator through the motion control interface, and the path database in the system is also updated.

[0066] The generation of defect analysis reports is an important part of the implementation process. The report content integrates all relevant data from the start of detection to the current time, including the original defect feature information set, environmental monitoring data, layout optimization process record, and the final optimal layout scheme. The defect feature information set includes detailed parameters for each defect, such as spatial coordinates, size measurement values, magnetic field intensity extreme value, and gradient distribution characteristics. The detection environment feature information records material thickness distribution, environmental magnetic noise spectrum characteristics, and auxiliary parameters such as temperature and humidity. The optimal layout scheme part details the final configuration of the sensor array, including the position coordinates of each sensor, the orientation angle, the working parameter setting, and the expected detection performance indicators.

[0067] The report generation module uses a structured data storage format, and all data is organized in chronological order. The report output supports multiple formats, including readable text summaries, detailed data tables, and intuitive graphical displays. The graphical display part includes magnetic field distribution cloud maps, defect location marker maps, sensor layout schematics, and detection path trajectory maps. These visual elements help users intuitively understand the detection results and system optimization process. The report part includes data quality evaluation indicators, which explain the reliability of each data and the possible error range. In the specific implementation process, the calculation of comprehensive interference influence values uses a distributed parallel computing architecture. Each candidate scheme's calculation task is assigned to different processing units, and the MapReduce framework is used to achieve efficient large-scale data processing. The gradient optimization algorithm uses an incremental calculation mode, using historical optimization data to speed up the convergence process. The path update algorithm introduces a real-time obstacle avoidance mechanism, which can dynamically adjust the path to respond to sudden environmental changes.

[0068] The generation of defect analysis reports uses a templated design to ensure the standardization and consistency of the output format. The data verification step ensures that all data included in the report has undergone integrity checks and logical consistency verification. The report storage uses a version control mechanism, preserving historical versions for tracing and comparison. The entire implementation process focuses on data traceability, with each calculation step and decision result being timestamped and having execution context information, forming a complete audit trail.

[0069] The system pays special attention to the real-time performance of the algorithm during implementation. The gradient optimization algorithm uses sparse matrix optimization and parallel computing technology to reduce computation time. The path planning algorithm introduces a preprocessing and caching mechanism to improve path update efficiency. The report generation module uses a streaming processing architecture to achieve real-time data collection and processing. All modules have fault tolerance capabilities and can still function normally under partial data loss or abnormal conditions. Data exchange and communication use a unified protocol standard to ensure seamless collaboration between modules. The gradient optimization algorithm interacts with the parameter constraint management system to ensure that the generated solution meets all physical constraints. The path update module is tightly integrated with the real-time positioning system to ensure the accuracy and executability of the path planning. The report generation module extracts the required information from the data warehouse and generates comprehensive analysis reports through data fusion technology.

[0070] Example 4: Refer to Figure 5 The implementation of the magnetic sensor array optimized layout magnetic flux leakage detection system involves the detailed operation process of the offset calculation method in the array reconstruction interference analysis module and the safety area division process of the multi-level optimized layout generation module. The array reconstruction interference analysis module calculates the final array reconstruction interference value based on the offset of the magnetic field calibration delay time and the parameter reconstruction frequency during interference evaluation. The calculation of the offset requires the establishment of historical reference data, and the system maintains a database containing historical calibration delay time and parameter reconstruction frequency, recording typical values under different detection environments and working conditions. These historical data are processed through time series analysis to extract representative reference values, such as using the moving average method to calculate the reference level of the delay time and determining the normal range of the parameter reconstruction frequency through statistical distribution analysis.

[0071] The magnetic field calibration delay time under the current working condition is obtained through real-time monitoring. The system records the time stamp when each calibration operation starts and records it again after the calibration is completed. The actual delay time is calculated by calculating the difference between the two. The parameter reconstruction frequency is counted through instruction execution sequence analysis. The system analyzes all parameter adjustment instructions required to switch from the current state to the target state and calculates the number of parameter items that need to be modified. After obtaining the current values, the system calculates their relative offset from the corresponding reference values. The delay time offset is expressed as a percentage change rate, and the parameter reconstruction frequency offset is standardized by absolute difference. The two offsets are combined by weighting to form the final array reconstruction interference value. The weight coefficient is dynamically adjusted according to the stability of the detection environment, and the delay time offset is given a higher weight in a more volatile environment.

[0072] The security layout area division process begins with the processing of the defect boundary topology nodes. The system performs spatial clustering analysis on the boundary node set of each defect to identify key nodes that represent the geometric characteristics of the defect. These key nodes are used to generate a magnetic field attenuation security boundary, which is formed by connecting nodes with similar magnetic field attenuation characteristics to form a closed boundary. The boundary generation algorithm considers the spatial distribution of the magnetic field strength and uses contour tracing technology to determine the boundary line of the security area. The line operation between the current position of the magnetic sensor array and each magnetic field attenuation security boundary uses the visibility graph algorithm to calculate whether the direct line from the current position to each security boundary point intersects with other defect areas, and to filter out the collision-free line set.

[0073] Based on these collision-free lines, the system constructs a continuous area of defect-free magnetic field intersection. This area is calculated using the polygon Boolean operation method, which performs a union operation on the security areas corresponding to each security boundary and subtracts the influence range of all defect areas. The final continuous area is one or more polygon regions, representing the spatial range where the sensor can be safely arranged. Within this area, the magnetic field interference level is below the preset threshold, ensuring the reliability of the detection data. The system also performs quality assessment on this continuous area, calculating its area, connectivity, and boundary smoothness, to ensure that the generated area is suitable for sensor layout. In the specific implementation process, the offset calculation uses a sliding window mechanism to update the historical baseline data. The system maintains a fixed-size historical data window, and each time a new detection task is completed, the old data is removed and new data is added to the window, and the baseline value is updated accordingly. This mechanism ensures that the baseline data reflects the recent working state and adapts to the slow changes in system performance. The security area division algorithm uses a multi-resolution processing strategy to quickly determine the approximate security range at a coarse-grained level and perform accurate calculations in the region of interest at a fine-grained level.

[0074] The weight adjustment in the offset calculation process uses a fuzzy logic control method. The system dynamically determines the weight coefficients based on factors such as environmental noise level, material uniformity, and detection speed requirements through fuzzy inference rules. The advantage of this method is that it can handle uncertainty and non-linear relationships, making the weight distribution more reasonable. The security area division introduces machine learning technology, using historical successful layout data to train a classification model to assist in determining which areas are more suitable for sensor placement, improving the practicality of the division results.

[0075] The system focuses on the balance between computational efficiency and data accuracy during implementation. The offset calculation uses an incremental update algorithm to avoid recalculating all historical data each time. The safety area division uses a spatial index structure to accelerate geometric calculations, using R-tree to organize defect areas and boundary nodes to improve spatial query efficiency. Error control mechanisms are used in all calculation processes to ensure the numerical stability of the final results. Data recording and tracking are important parts of the implementation process. The system records the basic data, intermediate results, and final values of each offset calculation, forming a complete data chain. Each step of the safety area division also leaves process data, including initial boundary nodes, intermediate boundary lines, and Boolean operation results. These data are not only used for real-time decision-making but also provide support for subsequent analysis and algorithm improvement.

[0076] Table 1: Offset calculation table for magnetic field calibration delay time and parameter reconstruction frequency

[0077]

[0078] Referring to Table 1, an offset calculation example in five detection tasks is shown, including delay time offset, parameter reconstruction frequency offset, and the final calculated comprehensive interference value. The historical average delay column shows the reference delay time calculated from historical data, and the current delay column records the actual measurement value. The delay offset is expressed as a percentage of the change in the current value relative to the historical reference. The reference reconstruction frequency column shows the reference level of parameter reconstruction, and the current reconstruction frequency column records the actual number of parameters that need to be reconstructed. The frequency offset uses standardized values, and the comprehensive interference value is the final result obtained by weighted combination of the two offsets. These data show the changes in interference levels in different detection tasks, providing quantitative basis for layout decisions. During implementation, the system uses real-time monitoring mechanisms to track the trend of offset changes. When the offset of multiple consecutive detection tasks exceeds the normal range, the system will trigger a warning mechanism to indicate possible device performance degradation or environmental abnormalities. The safety area division results are visualized to help operators intuitively understand the spatial distribution characteristics of the layoutable areas. All calculation processes are repeatable, and the same input data always produces the same output results, which ensures the consistency and predictability of the system behavior.

[0079] Embodiment 5: The implementation of the magnetic sensor array optimized layout's magnetic flux leakage detection system focuses on the determination method of the comprehensive interference impact value in the optimal layout decision update module. This module makes decision analysis by associating multiple key parameters, including array reconstruction interference value, material thickness characteristics, and path length ratio between the current detection path and the optimal layout scheme. The array reconstruction interference value comes from the previous evaluation process, reflecting the degree of system interference caused by state switching. Material thickness data is extracted from environmental feature information, and different thickness areas have different effects on magnetic field propagation, requiring differentiated evaluation standards. The path length ratio is obtained by calculating the ratio of the required path length of the new scheme to the current path length, which directly affects the execution efficiency and time cost of the detection task.

[0080] The determination of the comprehensive interference impact value uses a multi-parameter fusion algorithm, which integrates the above three core parameters into a comprehensive index through weighted combination. The algorithm first normalizes each parameter to eliminate the influence of dimensional differences. The array reconstruction interference value is normalized using the maximum and minimum value method, mapping it to a value range of zero to one. The material thickness parameter is segmented according to material type and detection standards, and different thickness segments are assigned different normalization coefficients. The path length ratio directly participates in the calculation as a proportional factor, reflecting the relative influence degree of path changes. These processed parameters are fused through a linear weighting model, and the weight coefficients are dynamically configured according to the specific requirements of the detection task.

[0081] The determination process of the weight coefficient considers multiple factors, including detection accuracy requirements, task urgency, and device state. For high-precision detection tasks, the weight of the array reconstruction interference value will be appropriately increased, as system stability has a greater impact on detection quality. In time-critical tasks, the weight of the path length ratio is correspondingly increased, giving priority to detection efficiency. Device state monitoring data is used to fine-tune weight distribution, and when sensor performance fluctuates, the system will reduce the weight requirement for path efficiency and increase the attention to system stability. This dynamic weight mechanism enables the system to adapt to different working scenarios and changes in demand.

[0082] The calculation process of the comprehensive interference impact value uses an iterative optimization method, and the system generates multiple weight combination schemes, calculates the corresponding comprehensive values, and determines the optimal value through comparison and analysis. Each weight combination scheme is generated based on decision preferences, covering different evaluation perspectives with different emphases. Constraints are introduced in the calculation process to ensure that the comprehensive value truly reflects the overall interference condition of the system. The finally determined comprehensive interference impact value serves as a dimensionless index for scheme comparison and selection.

[0083] In the scheme comparison phase, the system ranks the comprehensive interference impact values of each candidate layout scheme and selects the scheme with the smallest value as the optimal solution. The comparison process not only considers the numerical size, but also analyzes the contribution of each parameter to ensure that the selected scheme achieves a good balance in all aspects. For schemes with similar numerical values, the system performs sensitivity analysis to evaluate the impact of parameter changes on the comprehensive value and selects the scheme with better stability. The final selected optimal scheme needs to meet all physical constraint conditions, including sensor movement range limitations, energy consumption constraints, and real-time requirements. After the scheme is determined, the implementation process uses a gradual adjustment strategy to avoid sudden state switching that may cause excessive interference. The system generates a transition plan to adjust the layout parameters of the sensor array in stages, and after each stage is completed, the performance is evaluated to ensure smooth transition of the system state. During the transition process, the system continuously monitors the changes in the comprehensive interference impact value, and if abnormal fluctuations are found, it will pause the adjustment and re-evaluate the scheme. This cautious implementation approach minimizes the impact of layout optimization on the detection task.

[0084] Data recording and analysis are carried out throughout the entire implementation process, and the system records the comprehensive interference impact value of each candidate scheme and the value of its constituent parameters in detail. These data are used for subsequent analysis and improvement, and the correlation between parameters is mined through machine learning methods to optimize the weight allocation strategy. The accumulation of historical data helps the system establish a more accurate evaluation model, improving the accuracy and reliability of decision-making. All decision-making processes are accompanied by complete audit trails, including parameter values, calculation processes, and final results, ensuring traceability and transparency of decision-making.

[0085] The system places particular emphasis on real-time performance during implementation. The calculation of the comprehensive interference impact value uses an efficient algorithm that can be completed within milliseconds. The parameter standardization process uses pre-calculated conversion coefficients to avoid delays caused by real-time calculations. The weight allocation strategy uses a lookup table method, pre-configuring weight combinations according to common scenarios, and selecting the most suitable configuration according to task characteristics in real time. These optimization measures ensure that the system can make layout decisions quickly during real-time detection. Collaboration with other modules is also an important aspect of implementation. The comprehensive interference impact value calculation module maintains real-time data synchronization with the state monitoring module, using the latest device state information. Interaction with the path planning module ensures accurate calculation of path length ratios, using actual executable path data rather than theoretical values. Cooperation with the environment perception module ensures real-time updating of material thickness data, reflecting environmental changes during detection. This cross-module collaboration mechanism ensures that decisions are based on comprehensive and accurate information. Error handling and exception management mechanisms ensure the reliability of implementation. When input data is abnormal or errors occur during calculation, the system can automatically switch to a backup decision mode, using conservative weight configurations for calculation. All abnormal situations are recorded and analyzed to improve the robustness of the system. The implementation process also includes self-checking functions that periodically verify the reasonableness and consistency of calculation results, ensuring long-term stable operation of the system.

[0086] It should be noted that the relational terms herein such as first and second and the like are used solely to distinguish one entity or action from another, without necessarily requiring or implying any such actual relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0087] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A magnetic sensor array optimized layout magnetic flux leakage detection system, characterized in that, The system comprises: a magnetic sensor array layout feature extraction module for acquiring spatial magnetic field distribution data of a ferromagnetic material surface in real time through a magnetic sensor array, constructing a defect feature information set and detecting environmental feature information; a region detection completeness analysis module for obtaining region detection completeness corresponding to the defect feature information set based on the detection environmental feature information, combining initial layout parameters of the magnetic sensor array, a current detection path and current time magnetic sensor array working state information; a multi-level optimization layout generation module for generating a multi-level optimization layout scheme set based on the defect feature information set when the region detection completeness is lower than a preset abnormal threshold; an array reconstruction interference analysis module for calculating an array reconstruction interference value corresponding to a working state switch of the current time magnetic sensor array to the initial layout parameters in combination with the detection environmental feature information; an optimal layout decision update module for filtering an optimal layout scheme of the magnetic sensor array based on the array reconstruction interference value and updating a current detection path of the magnetic sensor array.

2. The magnetic sensor array optimally laid-out magnetic flux leakage detection system of claim 1, wherein, In the magnetic sensor array layout feature extraction module, the defect feature information set includes defect positions, sizes and magnetic field strength gradients extracted based on spatial magnetic field distribution data; The detection environmental feature information includes material thicknesses of different regions of the ferromagnetic material surface and environmental magnetic noise intensities.

3. The magnetic sensor array optimally laid-out magnetic flux leakage detection system of claim 2, wherein, The region detection completeness analysis module determines the region detection completeness by a minimum coverage distance of the current detection path and the defect positions, a magnetic field strength gradient variation of the defect size and a deviation degree of the environmental magnetic noise intensity from a preset reference noise.

4. The magnetic sensor array optimally laid-out magnetic flux leakage detection system of claim 3, wherein, When the region detection completeness is lower than the preset abnormal threshold, the multi-level optimization layout generation module acquires boundary topological nodes of each defect in the defect feature information set; Based on the boundary topological nodes, a safe layout region without magnetic field interference is divided, and a multi-level optimization layout scheme set of the magnetic sensor array is generated through a multi-level optimization algorithm.

5. The magnetic sensor array optimally laid-out magnetic flux leakage detection system of claim 4, wherein, The array reconstruction interference analysis module comprises: a state switching cost calculation unit for acquiring an instruction execution sequence of switching the magnetic sensor array from a current working state to initial layout parameters; a dynamic interference quantization unit for calculating an array reconstruction interference value according to a parameter reconstruction frequency and a magnetic field calibration delay time in the instruction execution sequence.

6. The magnetic sensor array optimally laid-out magnetic flux leakage detection system of claim 5, wherein, The optimal layout decision update module performs the following operations on each scheme in the multi-level optimization layout scheme set: According to the array reconstruction interference value and the material thickness in the detection environmental feature information, a comprehensive interference influence value of each layout scheme is calculated; A gradient optimization algorithm is used to select a layout scheme with the minimum comprehensive interference influence value as the optimal layout scheme.

7. The magnetic sensor array optimally laid-out magnetic flux leakage detection system of claim 6, wherein, After updating the current detection path of the magnetic sensor array, the optimal layout decision update module generates a defect analysis report containing the defect feature information set, the detection environmental feature information and the optimal layout scheme.

8. The magnetic sensor array optimally laid-out magnetic flux leakage detection system of claim 7, wherein, The array reconstruction interference analysis module calculates the array reconstruction interference value based on a deviation of the magnetic field calibration delay time and the parameter reconstruction frequency.

9. The magnetic sensor array optimally laid-out magnetic flux leakage detection system of claim 8, wherein, The safe layout region division process of the multi-level optimization layout generation module comprises: generating a magnetic field attenuation safe boundary according to the defect boundary topological nodes; The current position of the magnetic sensor array is connected with each magnetic field attenuation safety boundary to form a continuous area of intersection of defect-free magnetic fields.

10. The magnetic sensor array optimally laid-out magnetic flux leakage detection system of claim 9, wherein, The optimal layout decision updating module determines the comprehensive interference influence value by correlating the reconstruction interference value, the material thickness and the path length ratio of the current detection path to the optimal layout scheme.

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