Pipeline robot path planning and detecting method based on multi-data fusion

Abstract

The invention relates to the technical field of path planning, in particular to a pipeline robot path planning and detecting method based on multi-data fusion, which comprises the following steps: collecting data from vision, ultrasound, magnetic flux leakage and laser radar, performing timestamp alignment and space coordinate correction on the data, performing data fusion and checking the consistency of the data, an environmental data set is generated. According to the method, by constructing the three-dimensional pipeline model, not only is visual representation of the pipeline provided, but also the understanding of the internal structure of the pipeline is enhanced, so that a maintenance team can make a repair strategy more effectively, and the robot is allowed to check in the most effective sequence through the access priority determined through cost-benefit analysis and the optimized access path; the detection time is shortened, the operation cost is reduced, instant fault diagnosis and quick response are ensured, the detection accuracy and the operation efficiency of the robot are remarkably improved, the pipeline detection efficiency and accuracy are remarkably improved, manual intervention is reduced, and the maintenance cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of path planning, and in particular to a pipeline robot path planning and detection method based on multi-data fusion. Background Art

[0002] The field of path planning technology involves the intersection of computer science, robotics and automation engineering, and focuses on how to enable robots or automation systems to find the optimal path to fully detect the entire pipe network in a constrained environment. It integrates multiple technologies such as algorithm design, machine learning, and sensor data processing to handle and solve navigation problems in dynamic environments. Path planning has a wide range of applications, covering industrial automation, autonomous vehicles, drone navigation and other scenarios. The core goal is to improve the efficiency and safety of the path and reduce energy consumption and time costs.

[0003] Among them, the multi-data fusion pipeline robot path planning and detection method refers to the use of technology that integrates multiple sensors and data sources to optimize the pipeline robot's path selection and environmental detection efficiency. It is mainly used in pipeline maintenance and inspection, and the intelligent robot system automatically detects pipeline cracks, corrosion, sediment accumulation and other problems. By fusing data from different sensors, such as vision, acoustics, magnetism and pressure data, the robot can more accurately analyze the pipeline status and provide scientific data support and decision-making basis for operation and maintenance. This method can significantly improve the efficiency and accuracy of pipeline detection, reduce manual intervention, and reduce maintenance costs.

[0004] Existing path planning technologies rely on a single or small number of data sources when dealing with navigation problems in dynamic environments, resulting in limitations in environmental perception and increased errors. The lack of efficient data fusion processing logic and precise spatial correction technology limits the efficiency and accuracy of robot navigation in complex environments. Existing path planning technologies fail to make full use of real-time data and lack a dynamic adjustment mechanism, resulting in robots that are not responsive enough to sudden environmental changes. For example, when unforeseen pipeline blockages or damage occur, the robot cannot adjust the path in time, delaying the problem handling time, affecting the efficiency and timeliness of pipeline inspection and maintenance, and increasing the risks and costs of operation and maintenance. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a pipeline robot path planning and detection method based on multi-data fusion.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a pipeline robot path planning and detection method based on multi-data fusion, comprising the following steps:

[0007] S1: Collect data from vision, ultrasound, magnetic flux leakage and lidar, align the time stamp and correct the spatial coordinates of the data, perform data fusion and check the consistency of the data, and generate an environmental data set;

[0008] S2: Based on the environmental data set, extract structural information and identify abnormal signals, identify defects and abnormal areas of the pipeline, analyze the environment inside the pipeline, draw the structural conditions of the pipeline, and generate a three-dimensional pipeline model;

[0009] S3: According to the three-dimensional pipeline model, the detection requirements and cost data between pipeline nodes are compared, the access priority of the nodes is determined through cost-benefit analysis, and an optimized access path is generated;

[0010] S4: using the optimized access path to guide the robot's actions, adjusting the speed and steering parameters of the pipeline robot, detecting and verifying the predetermined path trajectory, and generating an optimized path planning map;

[0011] S5: When the robot moves along the optimized path planning map, continuously collect pipeline environment and robot status data, analyze abnormal data in real time, detect data points that deviate from the normal state, and generate real-time monitoring results;

[0012] S6: According to the feedback of the real-time monitoring results, the path and speed of the robot are adjusted, and the pipeline is inspected to evaluate the quality and completeness of the data set and generate a path evaluation result.

[0013] As a further solution of the present invention, the environmental data set includes sensor data with timestamp alignment, measurement points after spatial coordinate correction, and multi-sensor fusion data, the three-dimensional pipeline model includes the spatial layout of the pipeline, connection nodes and defect locations, the optimized access path includes economic evaluation, access priority and cost calculation of pipeline detection nodes, the optimized path planning diagram includes the robot's adjusted speed and steering parameters, the predetermined trajectory and key points in the path, the real-time monitoring results include records of environmental changes, real-time data of the robot status and data analysis of deviations from the normal state, and the path evaluation results include new defects detected, the effectiveness of the path and evaluation of the integrity of the data set.

[0014] As a further solution of the present invention, the steps of collecting data from vision, ultrasound, magnetic flux leakage and lidar, aligning the data timestamps and correcting the spatial coordinates, fusing the data and checking the consistency of the data to generate the environmental data set are as follows:

[0015] S101: Collect visual, ultrasonic, magnetic flux leakage and lidar data, synchronously set the time frequency of the sensor during data collection, add a timestamp synchronized with the clock to each data point, and generate a time-synchronized data set;

[0016] S102: using the time-synchronized data set and a unified spatial reference frame, coordinate calibration is performed on the visual, ultrasonic, magnetic flux leakage and lidar data, and the consistency of the data in space is checked by adjusting the sensor position parameters and the calibration angle to generate a spatial correction data set;

[0017] S103: Based on the spatial correction data set, the corrected data is fused, the consistency between the visual, ultrasonic, magnetic flux leakage and lidar data is compared, abnormal areas in the data are marked through difference point analysis, and the environmental data set is constructed.

[0018] As a further solution of the present invention, based on the environmental data set, the structural information is extracted and abnormal signals are identified, defects and abnormal areas of the pipeline are marked, the environment inside the pipeline is analyzed, the structural conditions of the pipeline are drawn, and the steps of generating a three-dimensional pipeline model are specifically as follows:

[0019] S201: extracting structural information of the pipeline according to the environmental data set, identifying abnormal signals in the data, locating pipeline defects and abnormal areas by analyzing signal strength and continuity, generating defect identification results, and obtaining comprehensive confidence of defect sizes at various defect locations using a Bayesian method according to defect size confidence of a differentiated detection method;

[0020] S202: Utilizing the defect recognition result and combining it with the structural information of the pipeline, generating the internal state of the pipeline, drawing a structural diagram of the pipeline, analyzing the physical structure and internal characteristics of the pipeline, and generating a three-dimensional pipeline model.

[0021] As a further solution of the present invention, according to the three-dimensional pipeline model, the detection requirements and cost data between pipeline nodes are compared, and the access priority of the nodes is determined through cost-benefit analysis. The steps of generating an optimized access path are specifically as follows:

[0022] S301: Based on the three-dimensional pipeline model, by comparing data differences between differentiated nodes, determining the access priority of the node, and generating a node priority analysis result;

[0023] S302: Using the node priority analysis result, perform cost minimization path analysis, refer to the optimal configuration of time and resources, calculate the sequence of the lowest cost access path, and obtain the cost-based access sequence analysis result;

[0024] S303: According to the cost-based access sequence analysis result, with reference to real-time operating conditions and dynamic resource allocation, an access path for pipeline inspection and maintenance is determined, and an optimized access path is generated.

[0025] As a further solution of the present invention, the steps of using the optimized access path to guide the robot's actions, adjusting the speed and steering parameters of the pipeline robot, detecting and verifying the predetermined path trajectory, and generating the optimized path planning map are specifically as follows:

[0026] S401: Based on the optimized access path, adjust the speed and steering parameters of the pipeline robot, adjust according to the requirements and obstacle information of the target pipeline area, verify that the robot moves according to the predetermined trajectory, and generate the robot action parameter adjustment result;

[0027] S402: testing the robot's forward path through the robot action parameter adjustment result, verifying the adaptability of the speed and steering in the pipeline environment, verifying the consistency of the robot's behavior with the predetermined path, and generating a path verification result;

[0028] S403: Using the path verification result, integrating the robot's motion data and path planning requirements, drawing a real-time operation path map of the robot, and generating an optimized path planning map.

[0029] As a further solution of the present invention, when the robot moves along the optimized path planning map, the pipeline environment and robot status data are continuously collected, abnormal data are analyzed in real time, data points that deviate from the normal state are detected, and the steps of generating real-time monitoring results are specifically as follows:

[0030] S501: Based on the optimized path planning graph, estimate the real-time position of the robot and monitor environmental changes to generate environmental change recognition results;

[0031] S502: using the environmental change identification result, performing anomaly analysis on the collected information, identifying data points that deviate from preset parameters, determining potential risks and abnormal areas through comparative analysis, and generating anomaly analysis results;

[0032] S503: According to the abnormal analysis results, analyze the detected abnormal data points, adjust the monitoring strategy and response measures, verify the safety and efficiency of the robot operation, and generate real-time monitoring results.

[0033] As a further solution of the present invention, according to the feedback of the real-time monitoring results, the path and speed of the robot are adjusted, and the pipeline is detected to evaluate the quality and integrity of the data set. The steps of generating the path evaluation results are specifically as follows:

[0034] S601: Based on the real-time monitoring result, dynamically optimize the path and speed of the robot, avoid risks and optimize efficiency, and generate a path adjustment result;

[0035] S602: Using the path adjustment result, verify the data consistency and coverage, analyze the quality and completeness of the collected data set, check the reliability and validity of the data, and generate a data quality assessment result;

[0036] S603: Based on the data quality evaluation result, the path planning and operation effect of the robot are evaluated with reference to the real-time effect of the path adjustment and the integrity of the data set to generate a path evaluation result.

[0037] Compared with the prior art, the advantages and positive effects of the present invention are:

[0038] In the present invention, the accuracy and consistency of environmental data are optimized by integrating visual, ultrasonic, magnetic flux leakage and lidar data, and the processing logic of timestamp alignment and spatial coordinate correction of the data. The environmental data set improves the recognition rate of pipeline defects and abnormal areas, which is crucial in pipeline detection. The three-dimensional pipeline model not only provides a visual representation of the pipeline, but also enhances the understanding of the internal structure of the pipeline, enabling the maintenance team to formulate repair strategies more effectively. The access priority and optimized access path determined by cost-benefit analysis allow the robot to inspect in the most efficient order, reducing inspection time and reducing operating costs. The generation and feedback mechanism of real-time monitoring results ensures immediate fault diagnosis and rapid response, significantly improves the accuracy of detection and the operating efficiency of the robot, significantly improves the efficiency and accuracy of pipeline detection, reduces manual intervention, and reduces maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of the main steps of the present invention;

[0040] Figure 2 It is a schematic diagram of the refinement of S1 of the present invention;

[0041] Figure 3 It is a schematic diagram of the refinement of S2 of the present invention;

[0042] Figure 4 It is a schematic diagram of the refinement of S3 of the present invention;

[0043] Figure 5 It is a schematic diagram of the refinement of S4 of the present invention;

[0044] Figure 6 It is a detailed schematic diagram of S5 of the present invention;

[0045] Figure 7 It is a detailed schematic diagram of S6 of the present invention. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0047] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0048] See also Figure 1 The present invention provides a technical solution, a pipeline robot path planning and detection method based on multi-data fusion, comprising the following steps:

[0049] S1: Collect data from vision, ultrasound, magnetic flux leakage and lidar, align the time stamp and correct the spatial coordinates of the data, perform data fusion and check the consistency of the data, and generate an environmental data set;

[0050] S2: Analyze the environmental data set, extract structural information and identify abnormal signals, identify defects and abnormal areas of the pipeline, analyze the environment inside the pipeline, draw the pipeline geometry and structure, and generate a three-dimensional pipeline model;

[0051] S3: According to the three-dimensional pipeline model, the detection requirements and cost data between pipeline nodes are compared, the access priority of the nodes is determined through cost-benefit analysis, and the optimized access path is generated according to the priority sorting;

[0052] S4: Use the optimized access path to guide the robot's actions, adjust the speed and steering parameters of the pipeline robot, detect and verify the predetermined path trajectory, monitor the proximity of key nodes to avoid collisions, and generate an optimized path planning map;

[0053] S5: When the robot moves along the optimized path planning map, it continuously collects pipeline environment and robot status data, performs real-time abnormal analysis on the collected data, detects data points that deviate from the normal state, and feeds back abnormal information to generate real-time monitoring results;

[0054] S6: Based on the feedback from the real-time monitoring results, the robot’s path and speed are adjusted, and the pipeline is inspected to evaluate the quality and completeness of the data set and generate path evaluation results.

[0055] The environmental data set includes sensor data with timestamp alignment, measurement points after spatial coordinate correction, and data from multi-sensor fusion. The three-dimensional pipeline model includes the spatial layout of the pipeline, connection nodes, and defect locations. The optimized access path includes economic evaluation of pipeline inspection nodes, access priority, and cost calculation. The optimized path planning diagram includes the robot's adjusted speed and steering parameters, the predetermined trajectory, and key points in the path. The real-time monitoring results include records of environmental changes, real-time data on the robot's status, and data analysis of deviations from the normal state. The path evaluation results include the detection of new defects, the effectiveness of the path, and the evaluation of the integrity of the data set.

[0056] See also Figure 2 , collect data from vision, ultrasound, magnetic flux leakage and lidar, align the data timestamps and correct the spatial coordinates, perform data fusion and check the consistency of the data. The specific steps to generate the environmental data set are:

[0057] S101: Collect visual, ultrasonic, magnetic flux leakage and lidar data, synchronously set the time frequency of the sensor during data collection, add a timestamp synchronized with the clock to each data point, and generate a time-synchronized data set. The execution process is as follows;

[0058] In the process of synchronously setting the time frequency of sensors, it is necessary to first ensure that the time frequency parameters of each sensor - vision, ultrasound, magnetic flux leakage and lidar - are accurately defined and configured, including the sampling frequency and response time of each sensor, and the synchronization mechanism with the system master clock. Ensuring the time consistency of data acquisition is the key. Through precise time marking, each data point is added with a timestamp synchronized with the system clock. The timestamp not only records the specific moment when the data point is collected, but also identifies the time sequence of the data points. It is the basis for constructing a time-synchronized data set. It is also necessary to consider the time offset and drift correction between sensors to ensure the continuous synchronization of each sensor data during long-term operation. Through strict control and adjustment of parameters, time errors can be reduced in analysis and subsequent processing, the accuracy and efficiency of data processing can be improved, and a time-synchronized data set can be generated.

[0059] S102: Using the time-synchronized data set and a unified spatial reference frame, coordinate calibration is performed on the visual, ultrasonic, magnetic flux leakage and lidar data. By adjusting the sensor position parameters and calibration angles, the consistency of the data in space is checked and the execution process of generating a spatial correction data set is as follows;

[0060] By adjusting the sensor position parameters and calibration angle, the consistency of the data in space is checked, according to the formula Calculate the spatial consistency between sensors. Where x1, y1, and z1 represent the coordinates of a sensor before calibration, x2, y2, and z2 represent the coordinates of the same sensor after calibration, and d sync Represents the spatial distance between the coordinates before and after calibration.

[0061] Detailed explanation of the formula and the process of formula calculation: A visual sensor and a lidar sensor are set up. The initial position of the visual sensor is (0, 0, 0), and it moves to (1, 1, 1) after calibration. The initial position of the lidar is (2, 2, 2), and the position after calibration is (3, 3, 3). Use the above formula to calculate the spatial distance between the two sensors before and after calibration, and you will get the distance of the visual sensor LiDAR distance The spatial consistency of the data can be verified by calculation.

[0062] S103: Based on the spatial correction data set, the corrected data is fused, the consistency between the visual, ultrasonic, magnetic flux leakage and lidar data is compared, and the abnormal areas in the data are marked through difference point analysis. The execution process of constructing the environmental data set is as follows;

[0063] In the process of fusing visual, ultrasonic, magnetic flux leakage and lidar data based on the spatial correction data set, it is necessary to conduct in-depth comparison of the data of each sensor to determine the correspondence in space and time. Through advanced data processing technology, such as multi-sensor data fusion algorithm, high-precision alignment of visual data with ultrasonic and lidar data can be achieved. The algorithm involves complex geometric transformation and time series analysis to ensure that the data obtained from different sensors can be correctly represented in a unified coordinate system. The abnormal areas in the data are marked by difference point analysis. The key is to use the algorithm to perform statistical analysis on the data set, identify data points that deviate from the normal pattern, mark the abnormal areas, provide accurate basic data for subsequent environmental modeling and analysis, and build an environmental data set.

[0064] See also Figure 3 , based on the environmental data set, extract structural information and identify abnormal signals, identify defects and abnormal areas of the pipeline, analyze the environment inside the pipeline, draw the structure of the pipeline, and generate a 3D pipeline model in the following steps:

[0065] S201: Extract the structural information of the pipeline according to the environmental data set, identify the abnormal signals in the data, locate the pipeline defects and abnormal areas by analyzing the signal strength and continuity, generate defect identification results, and use the Bayesian method to obtain the comprehensive confidence of the defect size of each defect position according to the defect size confidence of the differentiated detection method. The execution process is as follows;

[0066] By analyzing the environmental data set of the pipeline, extracting structural information and identifying abnormal signals, this process relies on precise data processing and analysis technology. Data is collected and organized from the environmental data set to ensure the integrity and accuracy of the data. Signal processing technology is used to filter the noise in the data to improve the accuracy of signal recognition. Data analysis methods such as Fourier transform or wavelet transform are used to analyze the frequency and intensity of the signal to determine the abnormal pattern of the signal. Through these analyses, defects and abnormal areas in the pipeline are identified. A series of precise data analysis and processing provide basic data and prerequisites for the subsequent defect size confidence calculation, and obtain the comprehensive confidence of the defect size at each defect location.

[0067] S202: Based on the defect recognition result, the internal state of the pipeline is generated in combination with the structural information of the pipeline, a structural diagram of the pipeline is drawn, the physical structure and internal characteristics of the pipeline are analyzed, and the execution process of generating a three-dimensional pipeline model is as follows;

[0068] The Bayesian method is used to calculate the comprehensive confidence of the defect size. Assuming that the uncertainty of the defect measured remains unchanged, the probability distribution of the defect size evaluated after the last inspection is a normal distribution N(1,1 2 ), the last inspection was 1 year after the pipeline was put into use. The corrosion rate is 1mm / year, which is the mean value of the defect size divided by the time. The inspection was carried out again in the second year, and the variance of the defect size was set to remain unchanged. The prior probability distribution of the defect size is π(θ)~N(2,1 2 ). The defect size obtained by one detection method is 1.8, and the variance of this detection method is 0.9 2 , the likelihood function of this detection method According to the Bayesian formula The posterior distribution probability of the defect size mean is π1(θ|x)~N(1.89,0.67 2 ). The probability distribution of the defect size obtained by the second detection method is 2.1 mm, and the variance of this detection method is 1.2 2 , then the posterior distribution of the defect size mean obtained by this method is π2(θ|x)~N(2.04,0.77 2 ). The Monte Carlo method is used to sample the distribution of corrosion defects from multiple detection methods, and the maximum likelihood estimation is used to fit the sampling distribution of the mean to a normal distribution. For the results obtained by the above two detection methods, the new defect size distribution obtained after sampling and fitting is N(1.96, 0.72 2 )mm, the updated corrosion growth rate is 0.98year / mm. The above are just two detection methods as examples, and other additional testing methods can also be processed according to the same steps.

[0069] See also Figure 4 ,According to the three-dimensional pipeline model, the detection requirements and cost data between pipeline nodes are compared, and the access priority of the nodes is determined through cost-benefit analysis. The specific steps for generating the optimized access path are:

[0070] S301: Based on the three-dimensional pipeline model, by comparing the data differences between differentiated nodes, the access priority of the node is determined, and the execution process of generating the node priority analysis result is as follows;

[0071] Based on the three-dimensional pipeline model, by comparing the data differences between differentiated nodes, using three-dimensional coordinates to represent the position of each node, evaluating the observation information between each pair of nodes, and calculating the difference between the two, including using the data difference method to determine the observation position between nodes, by calculating the sum of the differences of all node pairs, sorting each node, and determining the access priority of each node, it not only helps to clarify that the node is a key node in the network, but also provides basic data for subsequent path optimization analysis. This method makes it possible to screen out high-priority nodes from batch data to optimize resource allocation and time management, and generate node priority analysis results.

[0072] S302: Using the node priority analysis results, perform cost minimization path analysis, refer to the optimal configuration of time and resources, calculate the sequence of the lowest cost access path, and obtain the cost-based access sequence analysis results. The execution process is as follows;

[0073] According to the detection requirements between pipeline nodes, the path with the lowest cost is selected, referring to the optimal configuration of time and resources, according to the formula Calculate the total cost C of the minimum cost path min In the formula, w(v b ,v b+1 ) represents node v b to v b The weight of the edge with +1, n b Represents the number of nodes in the path. Formula explanation and formula calculation derivation process: Consider a specific network topology with four nodes, forming a path: v1→v2→v3→v4, and the weights of the edges are 3, 2, and 5 respectively. According to the minimum cost path analysis, the total cost from v1 to v4 needs to be calculated. Using the formula, the calculation process is as follows: The cost from v1 to v2 is 3. The cost from v2 to v3 is 2. The cost from v3 to v4 is 5. Substitute the values ​​into the formula: C min =w(v1,v2)+w(v2,v3)+w(v3,v4)=3+2+5=10. The total cost of the lowest cost path is 10, indicating that the path provides the most cost-effective access sequence under the optimal configuration of time and resources.

[0074] S303: According to the cost-based access sequence analysis results, with reference to real-time operating conditions and dynamic resource allocation, the access path for pipeline inspection and maintenance is determined, and the execution flow of generating the optimized access path is as follows;

[0075] Based on the results of the cost-based access sequence analysis, as well as the actual needs of real-time operating conditions and dynamic resource allocation, the optimized access path for pipeline inspection and maintenance is determined. The process involves comprehensive consideration of the access cost, urgency, and operational obstacles of each node, and calculating the most economical and efficient path through an algorithm model. This involves a path optimization algorithm, such as the shortest path algorithm in graph theory or a variant of the traveling salesman problem algorithm. The algorithm takes into account cost minimization while also considering the feasibility of the path, ensuring that the optimal inspection and maintenance operations are achieved within limited resources and time, providing the maintenance team with the most reasonable inspection path dynamically generated based on the latest data, and obtaining an optimized access path.

[0076] See also Figure 5 , use the optimized access path to guide the robot's actions, adjust the speed and steering parameters of the pipeline robot, detect and verify the predetermined path trajectory, and generate the optimized path planning map in the following steps:

[0077] S401: Based on the optimized access path, the speed and steering parameters of the pipeline robot are adjusted according to the requirements and obstacle information of the target pipeline area, and the robot is verified to move according to the predetermined trajectory. The execution process of generating the robot action parameter adjustment result is as follows;

[0078] Based on the optimized access path, the robot's speed and steering parameters are adjusted to respond to changes in the pipeline environment and obstacle information. Operations performed include obtaining specific requirements and obstacle type data of the pipeline area, analyzing and processing these data to adjust the robot's speed and steering to ensure that the robot can effectively avoid obstacles and improve movement efficiency. This adjustment process involves multiple parameters, such as the robot's current speed, target speed, current steering angle, target steering angle, and obstacle distance and type. Through real-time data analysis, parameters are adjusted to adapt to environmental changes to ensure that the robot moves along the predetermined trajectory and generate robot action parameter adjustment results.

[0079] S402: Through the robot action parameter adjustment result, the robot's forward path is tested, the adaptability of speed and steering is verified in the pipeline environment, and the consistency of the robot's behavior with the predetermined path is verified. The execution process of generating the path verification result is as follows;

[0080] The robot's forward path is tested through the results of the robot's action parameter adjustment, mainly to verify the robot's adaptability in speed and steering in the pipeline environment, including adjusting the robot's speed and steering parameters under different types of pipeline conditions, such as diameter changes, bending degree, and obstacles inside the pipeline, to test its responsiveness to complex environments. During the experiment, the robot's real-time motion data is collected through sensors and monitoring systems. By comparing the differences between the predetermined path and the actual operating trajectory, the consistency of the robot's behavior is evaluated to ensure that the robot can accurately navigate in a complex pipeline system. The test results will be used to further optimize the robot's action strategy, improve efficiency and safety in actual operations, and generate path verification results.

[0081] S403: Using the path verification result, integrating the robot's motion data and path planning requirements, drawing the robot's real-time operation path map, and generating the optimized path planning map. The execution process is as follows;

[0082] Using the path verification results, the robot's motion data and path planning requirements are integrated to draw a real-time operation path diagram for the robot. The process involves advanced data processing and visualization technology. By analyzing the robot's speed, steering, position and other data during the test, combined with the specific layout and operation requirements of the pipeline, it not only provides detailed guidance on robot operation, but also displays the robot's planned and actual paths through graphics, which helps engineers understand the robot's efficiency and accuracy in the pipeline, as well as the adjustments needed in future operations, ensuring that the robot's operation is more in line with actual application scenarios and requirements, and generating optimized path planning diagrams.

[0083] See also Figure 6 When the robot moves along the optimized path planning map, it continuously collects pipeline environment and robot status data, analyzes abnormal data in real time, detects data points that deviate from the normal state, and generates real-time monitoring results in the following steps:

[0084] S501: Based on the optimized path planning graph, the real-time position of the robot is estimated and the environmental change is monitored, and the execution process of generating the environmental change recognition result is as follows;

[0085] Based on the optimized path planning map, the execution operation includes monitoring the real-time position of the robot and continuously monitoring the changes in the environment. The execution process involves the precise measurement of the robot's position and the real-time update of environmental parameters, including changes in environmental factors such as temperature, humidity, and light intensity. These monitoring data are collected through sensors and compared with the preset environmental model to determine any significant deviations or anomalies. The accuracy of environmental monitoring depends on the sensitivity of the sensor and the accuracy of the data processing algorithm. By combining real-time position data with environmental parameters, the changing trend of environmental conditions can be displayed in real time, providing a basis for subsequent robot navigation and path adjustment, and generating environmental change identification results.

[0086] S502: Using the environmental change identification results, perform anomaly analysis on the collected information, identify data points that deviate from preset parameters, determine potential risks and abnormal areas through comparative analysis, and generate the execution process of abnormal analysis results as follows;

[0087] The results of environmental change identification are used to perform abnormal analysis on the collected information. The execution process includes the collection and analysis of data points, especially those data points that deviate from the preset parameters. These data points indicate potential risks and abnormal areas. The analysis method involves comparative analysis and risk assessment. Statistical methods, such as standard deviation and variance analysis, are used in the analysis process to identify the degree of deviation of the data. The determination of abnormal areas is based on set thresholds, which are preset according to historical data and risk management strategies. Through these analyses, the risk factors near the robot's operating path are determined, and all identified abnormal areas and potential risks are listed to provide reference and early warning for the safe operation of the robot and generate abnormal analysis results.

[0088] S503: According to the abnormal analysis results, analyze the detected abnormal data points, adjust the monitoring strategy and response measures, verify the safety and efficiency of the robot operation, and generate the execution process of the real-time monitoring results as follows;

[0089] According to the results of the abnormal analysis, the detected abnormal data points are analyzed. In the process of adjusting the monitoring strategy and response measures, each data point identified as abnormal is analyzed in detail to determine the specific impact on the robot operation, and the corresponding monitoring strategy is adjusted for each type of abnormality, including more frequent data collection, adding additional sensor monitoring or modifying the robot's behavior pattern to respond to the identified risks. For example, if the battery power is detected to be lower than the safety threshold, the robot's route will be adjusted to shorten the task time or arrange temporary charging to ensure the continuity of operation and the safety of the robot. After the adjustment is implemented, field testing is carried out again to verify the effectiveness of the adjustment measures and the safety of the robot operation. Through real-time monitoring and feedback, the reliability of the robot system and the efficiency of task execution can be significantly improved, and real-time monitoring results can be generated.

[0090] See also Figure 7 ,According to the feedback of real-time monitoring results, the path and speed of the robot are adjusted, and the pipeline is tested to evaluate the quality and completeness of the data set. The specific steps for generating path evaluation results are:

[0091] S601: Based on the real-time monitoring results, the path and speed of the robot are dynamically optimized to avoid risks and optimize efficiency. The execution process of generating the path adjustment result is as follows;

[0092] In the process of dynamically optimizing the robot's path and speed based on real-time monitoring results, it is necessary to collect real-time status data of the environment and the robot itself, including the robot's current position, the position and distance of the obstacle in front, the current travel speed and the expected target path. The path planning algorithm is used to calculate the optimal travel route, and the speed is adjusted in time to adapt to environmental changes. In this way, potential risks can be effectively avoided and travel efficiency can be optimized. If the sensor detects an obstacle in front, the path will be recalculated to bypass the obstacle. At the same time, the speed is adjusted according to the size and distance of the obstacle to ensure that the robot minimizes the risk of collision while maintaining high efficiency. The adjustment of the path and speed will also take into account the robot's energy consumption and the time requirements of the scheduled task to ensure that while completing the task, the task will not fail due to excessive energy consumption. Through this dynamic optimization, the robot can maintain optimal performance in a complex and changing environment.

[0093] S602: Using the path adjustment results, verifying data consistency and coverage, analyzing the quality and integrity of the collected data set, checking the reliability and validity of the data, and generating the data quality assessment results. The execution process is as follows;

[0094] The results of path adjustment are used to verify data consistency and coverage. Data is collected according to the adjusted path, and statistical analysis is performed on the data set, including data integrity, consistency, and coverage. This involves comparing and verifying information collected from multiple data sources. Statistical tests are used to evaluate the quality of the data set, such as calculating indicators such as data missing rate, outlier detection, and data duplication rate, removing invalid or duplicate data, and ensuring the reliability and validity of the data set. This helps to ensure the efficiency and accuracy of the data set in practical applications, reflects the application potential and limitations of the data set in real-world scenarios, and generates data quality assessment results.

[0095] S603: Based on the data quality evaluation result, the path planning and operation effect of the robot are evaluated with reference to the real-time effect of the path adjustment and the integrity of the data set. The execution process of generating the path evaluation result is as follows;

[0096] In the process of evaluating the robot's path planning and operation effects through data quality assessment results, the real-time effect of path adjustment and the integrity of the data set are considered, and the actual execution results of the optimized path planning map are analyzed in detail, including the breadth of path coverage, the uniformity of data collection, and the timeliness of task completion. Through dimensional evaluation, it can be determined whether the optimized path planning map effectively improves operational efficiency and data quality. For example, if the new optimized path planning map can reduce the robot's moving distance while increasing the detection frequency of key areas, it is regarded as improving efficiency and effectiveness. Through repeated testing and adjustment, the path planning map continues to be optimized to ensure that it can better adapt to changes in the pipeline environment and the needs of robot operations in future tasks, and generate path evaluation results.

[0097] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A pipeline robot path planning and detection method based on multi-data fusion, characterized in that: The following steps are involved: Collect data from vision, ultrasound, magnetic flux leakage and lidar, align the data timestamps and correct the spatial coordinates, perform data fusion and check the consistency of the data to generate an environmental data set; Based on the environmental data set, extract structural information and identify abnormal signals, mark defects and abnormal areas of the pipeline, analyze the environment inside the pipeline, draw the structural conditions of the pipeline, and generate a three-dimensional pipeline model; According to the three-dimensional pipeline model, the detection requirements and cost data between pipeline nodes are compared, the access priority of the nodes is determined through cost-benefit analysis, and an optimized access path is generated; Using the optimized access path to guide the robot's actions, adjusting the speed and steering parameters of the pipeline robot, detecting and verifying the predetermined path trajectory, and generating an optimized path planning map; When the robot moves along the optimized path planning map, it continuously collects pipeline environment and robot status data, analyzes abnormal data in real time, detects data points that deviate from the normal state, and generates real-time monitoring results; Based on the feedback of the real-time monitoring results, the path and speed of the robot are adjusted, and the pipeline is inspected to evaluate the quality and completeness of the data set and generate a path evaluation result.

2. The pipeline robot path planning and detection method based on multi-data fusion according to claim 1 is characterized in that: The environmental data set includes sensor data aligned with timestamps, measurement points after spatial coordinate correction, and multi-sensor fusion data; the three-dimensional pipeline model includes the spatial layout of the pipeline, connection nodes, and defect locations; the optimized access path includes economic evaluation, access priority, and cost calculation of pipeline detection nodes; the optimized path planning diagram includes the robot's adjusted speed and steering parameters, the predetermined trajectory, and key points in the path; the real-time monitoring results include records of environmental changes, real-time data on the robot's status, and data analysis of deviations from the normal state; and the path evaluation results include new defects detected, the effectiveness of the path, and evaluation of the integrity of the data set.

3. The pipeline robot path planning and detection method based on multi-data fusion according to claim 1 is characterized in that: Collect data from vision, ultrasound, magnetic flux leakage and lidar, align the data timestamps and correct the spatial coordinates, perform data fusion and check the consistency of the data. The specific steps to generate the environmental data set are as follows: Collect visual, ultrasonic, magnetic flux leakage and lidar data, synchronize the sensor's time frequency during data collection, add a clock-synchronized timestamp to each data point, and generate a time-synchronized data set; Adopting the time-synchronized data set, using a unified spatial reference frame, coordinate calibration is performed on the visual, ultrasonic, magnetic flux leakage and lidar data, and by adjusting the sensor position parameters and calibration angles, the spatial consistency of the data is checked to generate a spatial correction data set; Based on the spatial correction data set, the corrected data is fused, the consistency between the visual, ultrasonic, magnetic flux leakage and lidar data is compared, the abnormal areas in the data are marked through difference point analysis, and the environmental data set is constructed.

4. The pipeline robot path planning and detection method based on multi-data fusion according to claim 1 is characterized in that: Based on the environmental data set, the steps of extracting structural information and identifying abnormal signals, marking defects and abnormal areas of the pipeline, analyzing the environment inside the pipeline, drawing the structural conditions of the pipeline, and generating a three-dimensional pipeline model are as follows: According to the environmental data set, the structural information of the pipeline is extracted, the abnormal signals in the data are identified, the pipeline defects and abnormal areas are located by analyzing the signal strength and continuity, and the defect identification results are generated. According to the defect size confidence of the differentiated detection method, the Bayesian method is used to obtain the comprehensive confidence of the defect size of each defect position; According to the defect identification results, the internal state of the pipeline is generated in combination with the structural information of the pipeline, a structural diagram of the pipeline is drawn, the physical structure and internal characteristics of the pipeline are analyzed, and a three-dimensional pipeline model is generated.

5. The pipeline robot path planning and detection method based on multi-data fusion according to claim 1 is characterized in that: According to the three-dimensional pipeline model, the detection requirements and cost data between pipeline nodes are compared, and the access priority of the nodes is determined through cost-benefit analysis. The steps of generating the optimized access path are as follows: Based on the three-dimensional pipeline model, by comparing the data differences between differentiated nodes, the access priority of the node is determined, and a node priority analysis result is generated; Using the node priority analysis results, perform cost minimization path analysis, refer to the optimal configuration of time and resources, calculate the sequence of the lowest cost access path, and obtain the cost-based access sequence analysis results; According to the cost-based access sequence analysis result, with reference to real-time operating conditions and dynamic resource allocation, an access path for pipeline inspection and maintenance is determined, and an optimized access path is generated.

6. The pipeline robot path planning and detection method based on multi-data fusion according to claim 1 is characterized in that: The steps of using the optimized access path to guide the robot's actions, adjusting the speed and steering parameters of the pipeline robot, detecting and verifying the predetermined path trajectory, and generating an optimized path planning map are specifically as follows: Based on the optimized access path, the speed and steering parameters of the pipeline robot are adjusted according to the requirements and obstacle information of the target pipeline area, and the robot is verified to move according to the predetermined trajectory, and the robot action parameter adjustment result is generated; Through the robot action parameter adjustment results, the robot's forward path is tested, the adaptability of speed and steering is verified in the pipeline environment, the consistency of the robot's behavior with the predetermined path is verified, and a path verification result is generated; The path verification result is used to integrate the robot's motion data and path planning requirements, draw a real-time operation path map of the robot, and generate an optimized path planning map.

7. The pipeline robot path planning and detection method based on multi-data fusion according to claim 1 is characterized in that: When the robot moves along the optimized path planning map, the pipeline environment and robot status data are continuously collected, abnormal data are analyzed in real time, data points that deviate from the normal state are detected, and the steps of generating real-time monitoring results are specifically as follows: Based on the optimized path planning graph, the real-time position of the robot is estimated and the environmental change is monitored to generate an environmental change recognition result; Using the environmental change identification results, perform anomaly analysis on the collected information, identify data points that deviate from preset parameters, determine potential risks and abnormal areas through comparative analysis, and generate anomaly analysis results; According to the abnormal analysis results, the detected abnormal data points are analyzed, the monitoring strategy and response measures are adjusted, the safety and efficiency of the robot operation are verified, and real-time monitoring results are generated.

8. The pipeline robot path planning and detection method based on multi-data fusion according to claim 1 is characterized in that: According to the feedback of the real-time monitoring results, the path and speed of the robot are adjusted, and the pipeline is detected to evaluate the quality and integrity of the data set. The specific steps of generating the path evaluation results are: Based on the real-time monitoring results, the path and speed of the robot are dynamically optimized, and risks are avoided and efficiency is optimized to generate a path adjustment result; Using the path adjustment results, verify data consistency and coverage, analyze the quality and completeness of the collected data set, check the reliability and validity of the data, and generate data quality assessment results; Through the data quality assessment results, the path planning and operation effects of the robot are evaluated with reference to the real-time effect of the path adjustment and the integrity of the data set to generate a path assessment result.

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