5432results about How to "Reduce manual intervention" patented technology

The invention provides a cross-domain network security policy automatic generation and protection policy collaboration method and system, and relates to the technical field of network security, and the method comprises the steps: collecting multi-domain security event data; constructing a security threat map; generating a cross-domain security risk score by using a deep learning algorithm; determining an inter-domain access control rule set and an intra-domain protection strategy according to the score; forming a cross-domain collaborative protection strategy; deploying a strategy on the multi-domain security device; establishing an inter-domain information sharing channel; monitoring the strategy effect; optimizing the strategy by using a reinforcement learning algorithm; and updating to each domain security device. According to the method, the effectiveness of cross-domain network security protection is improved.

The invention discloses a data-driven cross-domain intelligent asset knowledge reasoning and value evaluation method and system, and relates to the technical field of intelligent assets, and the method comprises the steps: extracting multi-domain data features of intelligent assets based on a deep learning model, and constructing a cross-domain intelligent asset knowledge graph; analyzing a user evaluation target based on a pre-trained language model, retrieving related assets in a knowledge graph, and performing knowledge reasoning by using an attention mechanism and a graph reasoning algorithm; and on the basis of the value evaluation model of reinforcement learning training, a final evaluation value and an evaluation report containing a confidence interval are generated in combination with a time sequence prediction algorithm. According to the method, multi-source heterogeneous data can be effectively fused, knowledge reasoning and value evaluation of cross-domain intelligent assets are realized, and the evaluation efficiency and accuracy are improved.

The invention provides a three-dimensional geological modeling parameter optimization method and system based on deep learning, and relates to the field of geological prospecting and modeling.The method comprises the steps that drilling data and remote sensing data of a geological prospecting area are obtained, and multi-source geological data of three-dimensional geological modeling are formed; generating geological profile map data of the geological features according to the drilling data, generating landform profile map data corresponding to the topographic features according to the remote sensing data, and extracting boundary information of the geological exploration area; carrying out gridding processing on a three-dimensional space, dividing a geologic body of the geological exploration area into a plurality of small grid units, and defining geological modeling parameters; the preliminary three-dimensional geological grid model is constructed according to the geological profile map data, the landform contour map data and the boundary information, the size and the resolution of the grid are dynamically adjusted according to the geological change area, the three-dimensional geological model is obtained through optimization and adjustment, the modeling precision is improved, manual intervention is reduced, and computing resources are saved.

The invention belongs to the field of energy efficiency optimization in industrial production, and discloses a dynamic energy efficiency optimization method and system based on artificial intelligence and machine learning, and the method comprises the steps: collecting energy consumption data and environment parameters in real time through a sensor network and Internet of Things equipment; cleaning, screening and standardizing the collected data; constructing an energy efficiency prediction model and an optimization model based on a machine learning algorithm; training the prediction model and the optimization model by using historical data, and adjusting model parameters; and the trained model is utilized to analyze and predict data acquired in real time, and production parameters are dynamically adjusted to realize optimization of energy efficiency. According to the method, key parameter changes in the production process can be accurately predicted and optimized, dynamic adjustment of production parameters is achieved, an optimal production adjustment strategy can be automatically made, scientificity and efficiency of production decision making are improved, and the method has remarkable technical and economic effects in the field of industrial production.

The invention discloses an AI spraying system control method capable of achieving autonomous 3D scanning, and the method comprises the steps: obtaining three-dimensional surface data of a to-be-coated workpiece through high-precision 3D scanning equipment, constructing a surface model of the workpiece according to the data, and extracting surface feature parameters. Coating parameters set by a user are received, the coating parameters comprise the viscosity, the chemical property and the film thickness requirement of paint, the surface characteristic parameters and the coating parameters are input into the spraying optimization model, and an initial spraying track and technological parameters are generated through calculation in combination with historical data. Environment parameters are collected and associated with the initial process parameters, and the adjusted spraying track and the adjusted process parameters are generated through dynamic environment compensation. And through a robot control system, coating operation is executed according to the adjusted parameters, the spraying effect is optimized in real time, and it is ensured that the surface of the workpiece is evenly coated. Efficient automation of the spraying process is achieved, the coating quality and consistency are remarkably improved, and the automatic spraying device is particularly suitable for coating of workpieces with complex curved surfaces or changeable shapes.

The invention belongs to the technical field of data security, and discloses a data management method based on a data resource security identification level, and the method achieves the automatic and intelligent decision making: achieves the data classification and the automatic identification of the security level through a machine learning and deep learning model, remarkably reduces the manual intervention, and improves the efficiency and accuracy. And dynamic real-time performance: in combination with a risk assessment model, the system can detect abnormal behaviors in real time and dynamically adjust a security policy in a data flow process, so as to ensure that the sensitive data is properly protected in different scenes. And security and compliance: a log auditing module ensures the traceability of all data operations, improves the response capability to potential risks through an anomaly detection algorithm, and provides effective compliance guarantee for data security governance. And the system can be compatible with various data sources, supports configuration and execution of various security policies, and is suitable for data security management requirements of different industries (such as finance and medical treatment).

The invention provides a multi-modal large model construction and operation method and system for reservoir dam safety. The method comprises the following steps: constructing a training set, wherein the training set comprises a single sample; multi-modal data as model input: sensor data arranged on the dam, historical hidden danger records, engineering technology documents formed in various stages of planning, design, construction and operation of the dam, information resources related to dam safety, geometric and material information of the BIM model, timestamps and spatial position information; the probability value of occurrence of the hidden danger event serving as the training label is obtained; the training set is adopted to train a multi-mode pre-training model based on a Transform framework; performing real-time prediction and early warning on potential safety hazards of the dam; and calculating the actual influence degree, the comprehensive risk score, the dynamically set early warning threshold and the emergency measure suggestion of the hidden danger event according to the probability value of whether the hidden danger event occurs or not output by the model. The scientificity and effectiveness of dam safety monitoring are comprehensively improved.

The invention provides a tumor area double-view detection method and system based on image modalities and views, and relates to the field of medical image analys.The method comprises the steps that multi-modal image data are collected from the body of a patient, and image data of different view angles at the same position are obtained according to time frames; extracting multi-modal features from the image data of different modalities and different view angles, and performing weighted fusion on the extracted multi-modal features to form a fused feature map; inputting the fused feature map to train and predict the 3D-CNN network so as to identify and position a tumor region, and marking the tumor region by using a mask output by the segmentation model; calculating the pixel number of the tumor area in each layer of image according to the marked mask, and multiplying the pixel number by the volume of each pixel to obtain the volume of the tumor area; and the segmentation results of each layer of image are superposed, and a three-dimensional model of a tumor region is generated by using 3D reconstruction, so that the limitation of single-mode and single-view image data in tumor detection is overcome.

The invention provides an intelligent post matching and employee growth path planning system and method and a storage medium. The system comprises a data acquisition and processing module, an employee portrait construction module, an intelligent post matching module and an occupational path planning module. The data acquisition and processing module collects employee performance data, education background, work experience and other information from internal and external data sources. The employee portrait construction module constructs a comprehensive portrait of each employee by using a clustering analysis and factor analysis technology; the intelligent post matching module performs intelligent matching by applying a recommendation system algorithm according to post demands and employee portraits, and finds the posts most suitable for the current capabilities and development desires of the employees; the occupational path planning module analyzes the industry development trend and future skill requirements based on the prediction model, and provides personalized occupational development paths and training suggestions for employees. According to the invention, historical performance evaluation and industry trend analysis of employees can be integrated, and comprehensive post matching and occupational planning solutions are provided.

The invention discloses a method and a system for realizing data synchronization between heterogeneous databases based on logs. The method comprises the following steps: acquiring connection information of a source database and a target database; querying a table structure of the source end database and generating an SQL statement to establish a data mapping rule; matching a corresponding rule model from a mapping rule model library; if the matching is not successful, the system abnormally quits and prompts the user to create a new mapping model and add the new mapping model to the mapping rule model base; converting a table structure of the source end database into a JSON character string based on a standard field type; an SQL statement required by the target database is output through the data mapping engine module; the message monitoring program reads the data in the temporary storage area and converts the data into data meeting the requirements of the target database; formatting the converted data into SQL statements supported by a target database; and sending the generated SQL statement to a target database to complete data migration. Accurate and controllable synchronization of the data and the model in a heterogeneous environment is realized.

The invention relates to the field of hard disk data protection, in particular to a hard disk data protection method and device based on artificial intelligence. The method comprises the following steps: acquiring a hard disk history monitoring log; performing multi-period hard disk load analysis on the historical hard disk monitoring log, and performing load peak fluctuation fitting so as to construct a hard disk load peak fluctuation curve; carrying out peak value trend evolution analysis on the hard disk load peak value fluctuation curve, and carrying out abnormal load peak value detection to obtain a time period where an abnormal peak value point is located; carrying out current time period redundant data calculation on the hard disk according to the time period of the abnormal peak point, and carrying out dynamic global data backup decision, so as to generate an abnormal peak global backup strategy; and performing abnormal write behavior detection and attack node tracing positioning on the hard disk historical monitoring log, and marking a hostile attack tracing positioning point. According to the invention, safe and reliable hard disk data protection is realized.

The invention relates to the technical field of electric line fault detection, and discloses a power transmission line fault early warning system and method based on big data driving, and the system comprises a state prediction module, a risk assessment module, an early warning decision module and a management execution module. The state prediction module constructs a prediction model by using historical operation data, environmental conditions and line parameters, and outputs future state indexes; the risk assessment module comprehensively considers multi-source information such as real-time operation data and historical maintenance records, and assesses a current fault risk index; the early warning decision-making module models the management process into a Markov decision-making process, and carries out early warning decision-making based on the output of the two modules; and the management execution module is responsible for managing and executing the decision result in real time. By comprehensively utilizing the big data technology, the accuracy and the real-time performance of power transmission line fault early warning are improved, the defects of a traditional fault management mode are overcome, and a powerful guarantee is provided for safe and stable operation of a power system.

The invention relates to the technical field of computer processing, in particular to a software architecture code generation method and system based on a large language model, and provides the following scheme: obtaining demand information input by a user, analyzing demands by using a natural language processing technology, and extracting functional elements; performing classification processing on the functional elements through a clustering algorithm to generate a functional module group; mapping the function module group to a system architecture layer, and generating an architecture diagram represented by UML (Unified Modeling Language); and generating a corresponding source code according to the UML graph, and optimizing the source code. The optimization strategy comprises static analysis of format problems and potential vulnerabilities in identification codes and performance optimization of complex functions. Through combination of the large language model and the automatic code generation and optimization technology, the software development efficiency is improved, manual intervention is reduced, and the correctness and maintainability of the generated code are ensured.

The invention provides a crane balance beam crack defect detection method and system, and relates to the technical field of defect detection.The crane balance beam crack defect detection method comprises the steps that a plurality of ultrasonic guided wave sensor arrays are arranged on the surface of a crane balance beam to be detected, ultrasonic guided wave signals in the balance beam are collected, and ultrasonic guided wave time-frequency domain feature vectors of the balance beam are constructed; constructing a three-dimensional finite element simulation model of the crane balance beam to obtain multiple groups of balance beam crack defect expansion strain field data, and constructing a crack defect deformation ultrasonic-strain field fusion feature vector; training a crack defect deformation detection intelligent discrimination model by using the crack defect deformation ultrasonic-strain field fusion feature vector, obtaining a time-frequency domain feature vector and a strain distribution feature vector of the crane balance beam to be detected, and inputting the time-frequency domain feature vector and the strain distribution feature vector into the trained crack defect deformation detection intelligent discrimination model for intelligent discrimination screening. And outputting a crack defect detection result of the balance beam to be detected, wherein the crack defect detection result comprises the type, size, form and specific position of the crack defect.

The invention discloses an AIGC multi-mode audio-visual content creation method and system, and belongs to the technical field of multi-mode audio-visual content generation and automatic creation, the AIGC multi-mode audio-visual content creation method comprises the following steps: an input module for inputting text description and materials required by creation by a user; the AIGC generation module is used for converting the text description and the material input by the user into a video sequence and a music clip; the multi-modal fusion and alignment module fuses and aligns the generated video sequence and the music clip to obtain an audio / video; the video intelligent editing module is used for splicing the segments of the audios and the videos and keeping visual coherence and smooth connection between the two audios and the videos; and the output module is used for outputting the generated audio and video as the media format required by the user. The method solves the problems that in the prior art, multi-modal audio-visual content creation lacks the ability of cross-modal cooperative work, the content generated during multi-modal information fusion and alignment is inconsistent in rhythm, semantics and emotion, the support of a later editing process is lacked, the content connection is not natural enough, and the creation speed is low.

The invention discloses a hydraulic engineering operation and maintenance monitoring system based on digital twinning, and the system comprises the steps: S1, a data collection module which is used for obtaining multi-modal actual detection data from a plurality of monitoring points of a hydraulic engineering; s2, a data generation module which simulates and generates multi-dimensional virtual monitoring data and an abnormal sample library related to hydraulic engineering operation and maintenance; s3, a data fusion module which fuses the multi-dimensional virtual monitoring data and the multi-modal actual detection data; s4, a digital twinborn model construction module which dynamically constructs a multi-dimensional digital twinborn model of the water conservancy project based on the fused multi-modal data set; s5, an anomaly detection module which detects potential anomaly and sudden faults through an anomaly sample library generated by the generative adversarial network in combination with a multi-dimensional spatial-temporal trajectory clustering analysis method; and S6, an operation and maintenance monitoring module which is used for providing real-time operation and maintenance monitoring service for the multi-dimensional digital twin model. The method has the advantages of being high in dynamic adaptive capacity, high in monitoring accuracy and high in abnormal response speed.

The invention discloses a screening efficiency online monitoring method, device and system based on a vibrating screen, and relates to the technical field of vibrating screen efficiency monitoring, and the screening efficiency online monitoring method comprises the following steps: collecting working parameter data of the vibrating screen in real time through a sensor arranged on the vibrating screen; the accuracy and stability of screening efficiency are improved by monitoring key parameters such as humidity, particle size distribution and vibration frequency in real time and combining a screening effect model and a dynamic correction algorithm. The system automatically adjusts equipment parameters, equipment overload or screen damage is avoided, the material passing rate is optimized, and production losses are reduced. Meanwhile, manual intervention is reduced through an automatic control mechanism, the automation level of a production line is improved, human errors are reduced, the equipment fault and shutdown time is shortened, the production efficiency is improved, and the service life of the equipment is prolonged.

The invention provides a heat input regulation and control method and system for electric arc additive continuous forming, and relates to the technical field of additive manufacturing, and the method comprises the following steps: collecting process parameters in real time, inputting pretreatment process data into a self-adaptive heat input quantity calculation model, and constructing and calculating to obtain a real-time heat input quantity based on a thermodynamic principle and a heat transfer theory; starting the multispectral imaging equipment, performing real-time temperature field scanning, recording temperature field scanning data, extracting temperature gradient and cooling rate information, performing correlation calculation with real-time heat input quantity, and determining an influence relation evaluation result; generating a heat input sequence according to the influence relation evaluation result; and inputting the heat input sequence into a self-adaptive control system constructed based on a reinforcement learning algorithm, calculating an optimal parameter scheme according to a preset target sedimentary layer performance index and a current temperature field state, adjusting corresponding spatial-temporal distribution of control energy input according to the optimal parameter scheme, and performing control heat input on different positions and different layers of the sedimentary layer.

The invention relates to the field of transformer parameter regulation and control, in particular to a parameter optimization method and device for a coupling type transformer. The method comprises the following steps: acquiring multi-dimensional real-time working monitoring parameters and real-time working environment state parameters of the coupling type transformer; performing nonlinear dynamic analysis and global state sensing modeling on the multi-dimensional real-time working monitoring parameters so as to construct a transformer global state sensing model; performing multi-parameter combination definition on the multi-dimensional real-time working monitoring parameters, performing dynamic operation simulation based on a transformer global state sensing model, and collecting operation simulation response data of a plurality of schemes; performing multi-target dynamic balance analysis on the operation simulation response data of the plurality of schemes to obtain an optimal transformer working parameter combination; and carrying out environment dynamic parameter change analysis on the real-time working environment state parameters, and constructing a real-time environment condition distribution field. According to the invention, efficient and self-adaptive adjustment transformer parameter optimization is realized.

The invention discloses a device for measuring flowability of explosive modeling powder, which comprises a step-press-loading analogue unit, a timing and weighing unit and a computer control unit, wherein the step-press-loading analogue unit directly and indirectly drives a screw rod which extends into a blanking bin to rotate and perform reciprocating movements along an axial line and drives a pusher dog in the blanking bin to perform the reciprocating movements along an arc section through the combined action of a cylinder and a pneumatic reversal valve so as to ensure that the explosive modeling powder in the blanking bin falls into the timing and weighing unit, and the process more truly imitates an explosive conveying process of an explosive step-press process. The timing and weighing unit transmits a weighing value of the explosive modeling powder which falls in the timing and weighing unit to the computer control unit in real time, and the computer control unit calculates the instantaneous flow speed of the explosive modeling powder of a north side and generates a flow speed-time curve according to sampling data. The device can measure the flowability of the explosive modeling powder so as to provide an objective basis for choosing a proper explosive step-press process for the explosive modeling powder.

The invention discloses a terrain-level real-scene three-dimensional entity and element dynamic updating method, which comprises the following steps: S1, data acquisition and preprocessing: acquiring data of a target area by an acquisition mechanism based on entity and element type priorities, and preprocessing the acquired data to obtain model data of the target area; s2, change detection: comparing newly collected model data with historical model data, and identifying a change region and type; s3, entities and elements are extracted and updated, wherein the entities and the elements of the change area are processed to obtain updated models; s4, model fusion and optimization: fusing the updated entity and element model with the original model, and realizing efficient acquisition and preprocessing of target area data by using the multi-source surveying and mapping equipment and an acquisition mechanism based on entity and element type priorities; by dividing the small regions according to the region characteristics and correcting the acquisition frequency, the resource allocation is optimized, and the pertinence and efficiency of data acquisition are improved.

The invention belongs to the field of artificial intelligence, and particularly relates to a bill information extraction method and system and a storage medium, and the method comprises the following steps: obtaining an electronic file containing a bill, and preprocessing the electronic file into an image file facilitating information extraction; extracting bill information in the image file by using a multi-modal large model and an OCR model, and outputting text information in a structured format; and analyzing, correcting and comparing text information respectively output by the multi-modal large model and the OCR model through the large language model, and performing fine tuning or cue word engineering guidance on the large language model for different tasks, so that an output result conforms to expectation. Compared with the prior art, the problems that in the prior art, OCR recognition accuracy is not high, and extracted information is incomplete are solved, the bill information processing speed is increased, the precision of the automatic processing process is greatly improved, and in financial, auditing and other related business scenes, the working efficiency and the data reliability are remarkably improved.

The invention relates to the field of unmanned aerial vehicle route acquisition, in particular to an unmanned aerial vehicle dynamic inspection route acquisition method and system based on laser point cloud. The method comprises the steps of comprehensive processing of three-dimensional point cloud data, marking of equipment inspection point positions to generate waypoints, safe distance collision inspection of the waypoints, position adjustment of the waypoints, planning of inspection paths and the like, through comprehensive processing of the three-dimensional point cloud data, including denoising, classification and thinning, the quality of the point cloud data can be remarkably improved, and by means of the laser point cloud technology, the quality of the three-dimensional point cloud data is improved. The terrain and equipment characteristics of the to-be-inspected area can be accurately captured, so that the accuracy and reliability of inspection are improved. The steps of waypoint safe distance collision inspection and waypoint position adjustment further enhance the autonomous flight ability of the unmanned aerial vehicle, and ensure the safety and stability of the inspection process. When the routing inspection path is planned, an optimal safe path algorithm is adopted, the path between two waypoints is searched, a local route is generated, and finally the local route is connected to form a complete route, so that the high efficiency and integrity of routing inspection are ensured.

The invention belongs to the technical field of data detection, and particularly relates to an efficient data anomaly automatic detection and alarm system. The detection and alarm system comprises a sensor and data acquisition device, a data processing and analysis module, an alarm module, a monitoring center and visual interface, a communication and network module, a storage and backup module, an intelligent decision support module and a user feedback and adjustment module. According to the system, data can be monitored and analyzed in real time, abnormity can be quickly found, an alarm is given, manual monitoring requirements are reduced, data misjudgment is avoided, data accuracy is ensured through data cleaning and processing, an intelligent decision support module provides optimization suggestions based on AI, and a user can adjust rules to adapt to requirements. The system has the functions of data processing, anomaly recognition and trend prediction, the monitoring center visually displays the state, the storage and backup module ensures long-term storage of data, the automatic mechanism improves the response speed and the operation benefits, and the adaptability is high.

The invention discloses an intelligent bidding and tendering information identification method, which relates to the technical field of engineering management, and comprises the following steps: standardizing and preprocessing a bidding and tendering information document format; constructing a natural language processing model special for the bidding and tendering field; training a multi-level information extraction model based on deep learning; carrying out cross-document information integration and correlation analysis; performing anomaly detection and verification based on rules; semantic understanding and sentiment analysis; automatic subject matter description and technical specification extraction; a real-time dynamic monitoring and information updating mechanism; the invention discloses a graph-based decision support and recommendation system. According to the intelligent bidding and tendering information identification method, efficient and intelligent identification of bidding and tendering information is realized by constructing a natural language processing model specially aiming at the field of bidding and tendering. The bidirectional coding capability of BERT and the sequence modeling capability of LTM are combined, and a hierarchical attention mechanism is introduced, so that the model can accurately identify various key information in bidding and tendering documents.

The invention discloses a multi-underwater-robot cooperative control system and method based on predictive control, and relates to the technical field of cooperative control. During operation of the system, underwater environment data are collected through sensor equipment, an underwater three-dimensional dynamic environment model is generated by using a data fusion technology, and the underwater three-dimensional dynamic environment model is established based on task requirements; tasks are decomposed and distributed to all the robots, through an optimization algorithm, optimal paths of the robots and the mechanical arms are planned, the motion trails of the underwater robot mechanical arms are dynamically optimized based on model predictive control MPC, a real-time control strategy is provided for task division among the multiple mechanical arms, and the real-time control strategy of the underwater robot mechanical arms is provided based on environment modeling and the real-time state. Future motion states of the robot and the mechanical arm of the robot are predicted, an optimization objective function including task completion time, energy consumption and mechanical arm operation precision is constructed, a rolling optimization method is utilized, a control instruction is updated in real time, and real-time data sharing among multiple underwater robots is supported based on an acoustic communication technology.

The invention discloses a virtual power plant system design method based on multilevel federated learning, and relates to the technical field of power system planning, and the design method comprises the steps: designing and constructing a virtual power plant system architecture comprising an energy resource module, a local controller, a regional coordinator, a central coordinator and a regional block chain network. Intelligent prediction and optimal control of distributed energy resources are realized by using a deep learning framework and a model training algorithm, the intelligent level of the system is improved, automatic management of energy transaction and execution of an intelligent contract are realized by using a block chain technology, transparency and security of the transaction are ensured, and multi-level federated learning is adopted, so that the efficiency of the system is improved. According to the invention, distributed cooperation of data distributed processing and model training is realized, risks of data leakage and privacy invasion are effectively avoided, cooperative work of a local controller and a regional coordinator is cooperated, intelligent management and optimal scheduling of distributed energy resources are realized, and manual intervention and management cost are reduced.

The invention provides a database adaptive migration method and system based on a credential environment, and relates to the technical field of data processing.The method comprises the steps that an intelligent migration model is used for analyzing the structure, including a table structure, index configuration, constraint conditions and data types, of a source database, data size distribution is calculated, and a source database structure feature result is generated; according to a source database structure feature result, automatically selecting and configuring a target database type, adjusting environment parameters of a target database, and generating target database configuration information; according to the structure of a source database and configuration information of a target database, a data mapping rule is automatically established by utilizing an intelligent migration model, and the data mapping rule comprises automatic correspondence of table names and field names, automatic conversion of data types, and an automatic migration scheme of indexes and constraints. According to the method, automatic and intelligent migration from the source database to the target database is realized through the intelligent migration model, the performance is automatically optimized, the migration report is generated, and the migration efficiency and quality are improved.

The invention provides a material simulation test method and platform for mold flow analysis, and relates to the technical field of material simulation testing, and the method comprises the steps: carrying out the product demand analysis of a target test material, and obtaining the product demand information; performing injection molding analysis to obtain an injection molding control parameter threshold; obtaining an injection molding simulation test model; carrying out an injection molding simulation test, and carrying out image pre-acquisition to obtain a first pre-acquired image; defect feature analysis is carried out, attention area distribution is carried out, and a target attention area is obtained; an injection molding defect area image is obtained, and defect feature information is obtained; and feedback testing and parameter optimization are carried out, optimized injection molding control parameters are obtained, and injection molding control is carried out. The technical problems that in the prior art, injection molding analysis generally depends on a fixed mathematical model and limited technological parameters, and problems in the molding process cannot be recognized and adjusted in real time, so that the production efficiency and the product quality are low are solved.

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.