20287 results about "Automation" patented technology

The invention discloses a logistics robot path planning method based on multi-modal perception, and relates to the technical field of robot path planning. Laser radar, visual camera and IMU data are fused, and environment state feature vectors are generated through multi-modal data synchronization and space-time alignment; the method comprises the following steps: analyzing environmental semantics by using models such as PointPill and YOLOv8, extracting dynamic characteristics, and identifying obstacles; constructing a space-time risk field, searching a path by a space-time algorithm, converting path points into a continuous trajectory, and optimizing the continuous trajectory; deviation is evaluated in real time, dynamic re-planning is triggered, and multi-robot cooperation and environment semantic understanding are included. Through multi-mode perception fusion, hierarchical planning, multi-target optimization and a cooperation mechanism, the obstacle detection accuracy and the obstacle avoidance success rate are improved, the path planning time is shortened, the energy consumption is reduced, the multi-robot conflict is reduced, the task efficiency is improved, the environment semantic understanding and task adaptive ability is enhanced, and the method is suitable for scenes such as intelligent storage and the like and has wide application prospects. The automation level is improved.

The invention discloses a multi-mechanical-arm space-time synchronization control method for coiled pipe welding, and belongs to the technical field of automation control, and the method comprises the steps: in a primary laser and TIG hybrid welding execution stage, carrying out real-time data acquisition on a welding area of a workpiece, carrying out real-time deviation detection, and once real-time welding deviation is detected, carrying out time-space synchronization on the welding area of the workpiece; a local correction mechanism is triggered immediately, and deviation is prevented from being accumulated in the single welding process; after one-time welding is completed, the workpiece enters a transition area, global contour reconstruction is conducted on a welded section of the workpiece through three-dimensional scanning equipment, global deviation recognition is conducted, and unprocessed hysteresis deformation and accumulative errors are covered and corrected in real time; on the basis of the analysis result of the global deviation recognition, double correction is conducted on secondary welding, and cooperative control over space track correction and technological parameter adaptation is achieved; and after secondary welding is completed, the correction effect is evaluated through a double-layer evaluation mechanism, and the stability of the correction process is ensured.

The invention discloses a process automation execution method based on a large language model, and belongs to the technical field of artificial intelligence and process automation. User intention is analyzed through multi-modal input, and a structured task definition is constructed; the semantic reasoning layer is used for performing task layering, complexity evaluation and sorting optimization; the task execution layer completes subtask scheduling and execution; and the feedback and optimization layer performs performance evaluation and model updating based on execution data to realize closed loop and continuous optimization of the process, so that the technical problems of dynamically analyzing unstructured instructions, automatically optimizing a complex task dependency relationship and adapting to business changes in real time by a process automation tool are solved; according to the method, end-to-end conversion from an unstructured instruction to a structured task is realized, a subtask execution path is dynamically optimized, cross-platform tool calling is supported, the existing system integration cost of an enterprise is reduced, visual display task decomposition logic and prediction and execution time consumption comparison are provided, and the system credibility is enhanced.

The invention discloses a modular reconfigurable production line control system integration method, which relates to the technical field of industrial automation, and comprises the following steps: establishing virtual mapping based on physical attribute parameters to form a production line digital twin basic model framework; collecting data in real time based on an on-site sensor, and establishing a digital twinborn dynamic mapping mechanism synchronous with a physical production line state; a reconstruction scheme is imported into a virtual environment, and key performance indexes are analyzed through a production line digital twin model rehearsal module combination process. A virtual production line model is constructed through a digital twin technology, a rehearsal and verification reconstruction scheme in a virtual environment is supported, trial and error time and cost required by traditional physical debugging are remarkably reduced, a reconstruction strategy is further optimized through a cloud AI algorithm, closed-loop optimization from virtual verification to physical execution is achieved in combination with edge end real-time control, and the real-time performance of the virtual production line is improved. The production line can quickly complete local or overall reconstruction according to production requirements, and the response speed and flexibility of the production line are greatly improved.

The invention discloses a dynamic path planning and self-adaptive control method and system for a coating robot, and relates to the technical field of coating automation. The method comprises the following steps: acquiring point cloud data through three-dimensional scanning equipment, constructing a dynamically updated workpiece curved surface model, and extracting curvature, edge and high-curvature mutation region features; a spraying path is generated based on a reinforcement learning algorithm, and path density, speed and coating supply are dynamically adjusted for a high-curvature area; distance, force feedback and environment parameters are fused, and mechanical arm and spray gun parameters are dynamically adjusted; dividing operation sub-areas and distributing tasks based on robot capability characteristics to realize multi-machine cooperation; and fault redundancy control and multispectral imaging are added to optimize the coating quality. The system comprises a sensing module, a decision-making module, an execution module, a redundancy control module and a communication module. According to the invention, the uniformity of the complex curved surface coating, the multi-machine cooperation efficiency and the system anti-interference capability are improved, and the method is suitable for spraying large workpieces such as aircraft fuselages and high-speed rail vehicle bodies.

The invention discloses a CAD automatic generation system and method based on intelligent model selection and application, and aims at achieving automatic modeling under the multi-modal design requirement. The system comprises a user interaction module for receiving multi-modal input such as natural language, sketch and voice; the intelligent demand analysis module is used for combining an industrial large language model and a product knowledge graph, combining semantic analysis and generating a structured demand; the intelligent model selection calculation module is used for matching the optimal parameter combination and the component list based on a multi-objective optimization algorithm; the CAD automatic generation module calls a parametric modeling engine to generate an editable three-dimensional model; the constraint solving module is used for processing hard constraints and soft constraints in real time and dynamically adjusting model parameters; and the model output and interaction module feeds back a design state and supports user iteration. The system realizes full-process automation from the design intention to the CAD model, improves the design efficiency and accuracy, and is suitable for the fields of mechanical design, intelligent manufacturing and the like.

The present disclosure relates to configured artificial intelligence methods and systems and related transportation systems and methods, including software-defined vehicles, for transportation systems using sensor and other data, and the integration of a transportation system with an AI convergence system of systems, providing a multi-layered system for intelligent automation and data-driven decision making across operational aspects of a transportation system.

The invention discloses a standardized centralized DTU multi-level fault diagnosis and self-healing method and system, and relates to the technical field of intelligent power grid fault diagnosis, and the method comprises the steps: building a dynamic topological graph according to a unified coding rule, extracting spatial-temporal characteristics through a graph convolution network, and obtaining a spatial-temporal characteristic power grid topological graph; building a power grid digital twin environment based on a multi-level standardized fault diagnosis result, generating a plurality of sets of self-healing strategy candidate sets by adopting a reinforcement learning algorithm, screening an optimal self-healing strategy, and converting the optimal self-healing strategy into a control instruction conforming to a substation automation standard protocol; the control instruction is sent to the DTU and analyzed, and the intelligent power switch cabinet is controlled to execute self-healing operation. By automatically correcting the abnormal diagnosis result, false alarm and missing alarm are reduced, and the accuracy of fault diagnosis is improved. The intelligent level of power grid management is obviously improved, the maintenance cost is reduced, and the operation efficiency and the service quality of a power system are optimized.

The invention relates to the technical field of industrial automation control, and discloses a change gear assembly processing technological parameter intelligent optimization system, which comprises a multi-dimensional data synchronous acquisition unit, an angular domain static map construction unit, a phase lead constraint control unit and an angle trigger execution unit, according to the method, a static angle-load topological graph with a main shaft angle as an index is established, a phase lead angle is calculated by combining inherent physical response lag time of a servo system, and a parameter adjustment instruction sequence is directly injected into a servo current loop at a preset angle window before physical load impact occurs; according to the feed-forward control mechanism based on angular domain reconstruction and phase lead constraint, the problem of physical lag of traditional time domain feedback control in response to high-frequency intermittent cutting is avoided, and zero time difference of periodic load impact and even pre-judgment torque compensation are achieved.

The invention discloses an MES work order dynamic scheduling and resource self-adaption system based on real-time event triggering, and relates to the technical field of intelligent manufacturing and industrial automation, equipment states and material events are monitored in real time through interfaces such as a PLC and an OPCUA, event types and validity are analyzed through hardware filtering, feature code recognition and double verification, and the real-time event triggering is realized. Event data, MES batch information and equipment load data are integrated to generate standardized scheduling input, a preset strategy is matched, an optimal dispatching scheme is generated through a priority algorithm, material, equipment and logistics resource allocation and dispatching operation are completed, synchronization and MES interaction ensure that page logic is consistent, full-process automation of dynamic work order scheduling is achieved, and work order scheduling efficiency is improved. The equipment state and the material event are responded in real time, the production cooperation efficiency is improved by more than 30%, the resource adaptation accuracy rate reaches 95% or above, manual intervention is reduced, and the consistency of data and MES is guaranteed.

The invention relates to the field of heat energy engineering and automatic control, and discloses a multi-mode optimization system for combustion efficiency of a thermal power boiler. The system comprises a multi-modal data perception and space-time alignment module, a tensor manifold modeling and physical constraint feature extraction module, a space-time coupling dynamic prediction and uncertainty quantification module, a quantum optimization decision and DCS cooperative control module and a combustion state derivative early warning and optimization feedback module. Through multi-modal data space-time alignment, five-order tensor physical constraint modeling, PDE deep network prediction, quantum optimization decision and a closed-loop feedback mechanism, space-time unified fusion and physical feature extraction of combustion data are realized, the reliability of combustion state prediction is improved, an optimal control instruction is efficiently solved, system parameters are dynamically corrected, and the reliability of combustion state prediction is improved. The problems that in the prior art, data fusion is difficult, modeling physical constraints are lacked, optimization real-time performance is poor, and adaptivity is weak are solved, and the combustion efficiency and the intelligent control level of the thermal power boiler are remarkably improved.

The invention discloses an operation and maintenance manipulator intelligent control method and system based on visual identification, and relates to the technical field of intelligent manipulator control, and the method comprises the steps: collecting RGB image data and depth image data of an operation and maintenance operation area, and obtaining a standardized image matrix and a mapping relation matrix; inputting the standardized image matrix into an improved ResNet residual network model, generating a comprehensive feature descriptor, and calculating a spatial position coordinate and an attitude angle of the target equipment based on the mapping relation matrix; based on the current joint angle state of the manipulator, an improved Jacobian matrix inverse kinematics algorithm is used for solving a target angle sequence of each joint, a preset operation mode library is matched based on the comprehensive feature descriptor, and a grabbing force parameter and a motion speed parameter are determined; and converting the target angle sequence into a control instruction, and sending the control instruction to each joint driver of the manipulator to drive the manipulator to complete action planning. According to the invention, full-process automation from environment perception to task execution is realized.

The invention discloses an unmanned aerial vehicle highway intelligent patrol method and system based on patrol demands, and belongs to the field of unmanned aerial vehicle technologies, intelligent traffic systems and artificial intelligence, and the method comprises the steps: obtaining a natural language instruction, and carrying out the semantic analysis to generate a structured task vector; constructing and generating a self-adaptive inspection strategy based on the task vector and fusing real-time data; distributing the strategy to the unmanned aerial vehicle for autonomous inspection, and identifying and generating a classification event alarm; and finally responding to an alarm and triggering a processing mode to generate a closed-loop inspection task report. According to the method, a technical path of combining multi-level reasoning based on a large language model and dynamic behavior strategy synthesis is adopted, and a complete cognition and action framework from semantic intention understanding, behavior decision making to closed-loop response is constructed, so that autonomous generation and intelligent execution of a complex and fuzzy inspection task can be realized; and the automation level, the response speed and the decision-making intelligence of highway inspection are obviously improved.

The invention provides an intelligent boxing method and system. The intelligent boxing method comprises the steps of obtaining boxing task information and generating a boxing scheme. The boxing task information comprises the size, the weight, the stacking limitation, the number and the priority of each cargo, and the size, the bearing capacity and the occupation state of each loading unit. And under the condition that the size, bearing and stacking constraint conditions are met, a plurality of candidate boxing combinations are generated, the loading utilization rate, the residual space distribution and the shape matching degree are evaluated based on a preset optimization target, and an optimal scheme is selected. The system comprises an input terminal, a task information receiving module, a data storage module, a task and scheme storage module, an operation processing module and a final boxing scheme generation module. According to the method, multi-dimensional constraint matching and weight balance are considered, and space waste and transportation risks are avoided; the maximum greedy algorithm and the adaptive large neighborhood search optimization are combined to realize the combination of automation and experience. The method can be expanded to batch task scheduling and grouping sequencing, so that the calculation complexity is reduced, and the overall boxing and transportation efficiency is improved.

The invention belongs to the technical field of glue filling control, and particularly relates to a glue filling amount control method and system. According to the method, the glue filling deviation can be controlled within an extremely small range through multi-time iteration control of a stage theoretical value and real-time feedback, the filling consistency and the bonding strength are remarkably improved, initial compensation is conducted in combination with various working condition parameters such as temperature, pressure and materials, the pressure and the diffusion dynamic state are monitored in real time, and the working efficiency is improved. The system can automatically adapt to environmental fluctuation and glue performance change, accurately control the filling amount, respond to overflow and viscosity abnormity in time, reduce glue waste, avoid product reworking caused by excess or insufficient glue, combine closed-loop feedback with maintenance strategies, automatically identify and process abnormal states, improve the automation degree and operation stability of a production line, and improve the production efficiency. Manual intervention is reduced, rapid configuration and expansion can be carried out according to different workpiece shapes, materials and production working conditions, and the device is suitable for various glue filling scenes.

The invention discloses a cable life dynamic evaluation system based on multi-physics field coupling, and particularly relates to the field of industrial automation and control systems, which comprises a multi-physics field sensing module, a coupling analysis engine module, a dynamic life evaluation module, a digital twin interaction module and an environmental interference suppression module, through a distributed optical fiber temperature sensor, a capacitive electric field sensor and a magnetostrictive stress sensor, temperature, electric field, magnetic field and mechanical stress data of a cable are collected in real time, multi-physical field characteristics and a cable defect database are matched in real time by using a cross-scale dynamic association algorithm, a damage state is evaluated, and the cable defect detection accuracy is improved. A time sequence neural network architecture is adopted to predict the remaining life, model self-correction is achieved through digital twin comparison, interference is suppressed in combination with an environment-physical field coupling compensation matrix, sensing and evaluation of the health state of the cable, life prediction and continuous optimization of the model are achieved, and the efficiency of cable life evaluation is improved.

The invention discloses a terminal product surface defect intelligent judgment quality control system and method, and relates to the technical field of industrial automatic detection and intelligent quality control, and the system comprises the following steps: a data acquisition module generates an original detection signal containing environmental interference compensation; the dynamic detection module receives an original detection signal, performs illumination invariance processing through a self-optimization feature extraction network, and outputs a defect feature vector with confidence rating; the quality association module receives the defect feature vector and constructs a three-dimensional association map with real-time equipment state data, and generates a tracing analysis signal containing root cause probability distribution; and the feedback control module analyzes the key process parameter offset in the tracing analysis signal, generates an equipment adjusting instruction and feeds back the equipment adjusting instruction to the production line. The terminal product surface defect intelligent judgment quality control system and method can solve the problems of insufficient surface defect detection precision, difficulty in quality tracing and lack of process closed-loop control in industrial production.

The invention discloses an industrial control data processing system and method based on an embedded real-time operating system, and relates to the technical field of industrial automation control, and the industrial control data processing system comprises a dynamic weight evaluation module, a resource elastic distribution module and a dual self-healing verification module; and the dynamic weight evaluation module is used for generating a dynamic weight value according to the task type, the data security level, the deadline margin and the historical execution abnormal rate. According to the industrial control data processing system and method based on the embedded real-time operating system, the task criticality is quantified in real time through the dynamic weight evaluation module, and the real-time response capability and the resource utilization efficiency of the industrial control system are improved in combination with elastic resource allocation driven by load prediction. A dynamic weight mechanism ensures that the emergency task can instantly preempt resources, and the problem of low-priority task blocking caused by traditional static priority scheduling is avoided.

The invention discloses an integrated verification and test system and method for complex rules of a substation monitoring system, and the system comprises a rule editing module which provides a visual interface to support a user to edit the complex rules containing various types of variables and time sequence constraints; the analysis and verification module is used for performing grammar, variable conflict and business compliance multi-level verification on the rules; the intelligent test case generation module is used for automatically generating test cases covering normal, boundary and abnormal conditions based on the rule tree structure; the simulation test interface module converts the use case into a standard test script, submits the standard test script to a simulation tool for execution, and outputs the standard test script to a monitoring system through protocol conversion; the result analysis module is used for comparing an execution result with an expected result and generating a structured test report; according to the invention, full-process automation of rule verification and testing is realized, the verification efficiency is remarkably improved, and the test requirements of various transformer substation business scenes are met.

The invention discloses a gradient welding strength control method and system for a welded galvanized steel pipe for fire fighting, particularly relates to the technical field of welding automation control, and aims to solve the problem of abnormal welding seam strength gradient caused by instantaneous unbalance of an electric field and a thermal field during dynamic parameter switching in the prior art. Electromagnetic distortion characteristic quantity is extracted through frequency domain energy analysis, and thermodynamic offset state quantity is deduced in combination with a thermal diffusion trend; on the basis of frequency band correlation mapping of electromagnetic and thermodynamic parameters, thermoelectric cooperative imbalance levels are divided through mutual information entropy evolution, and a self-adaptive harmonic attenuation weight tuning instruction is generated; analyzing a mismatching relation between transient disturbance of an environment magnetic field and arc voltage modulation distortion in real time, dynamically generating a magnetic field compensation factor and reversely superposing the magnetic field compensation factor to a control instruction; and in combination with the matching degree of molten pool oscillation energy and a harmonic frequency spectrum, correcting wire feeding rate calibration, reconstructing a multi-band harmonic energy proportion spectrum, outputting an anti-interference welding control signal, and realizing dynamic balance between arc stability and molten pool heat input.

The invention provides a pipe thread positioning and measuring method and system based on three-dimensional vision, and belongs to the technical field of industrial automation and three-dimensional vision. According to the method, a measuring system composed of a robot measuring system and a three-dimensional visual locating system is built, and hand-eye calibration is completed; the method comprises the following steps: acquiring a three-dimensional point cloud model of the end part of a pipe thread workpiece, and extracting an end surface normal vector, a tangent vector and a circle center coordinate through preprocessing, cylinder fitting, near-end surface point cloud extraction and end surface circle fitting so as to establish a measurement reference coordinate system; and converting the reference coordinate system to a robot base coordinate system based on a hand-eye calibration result and the tail end pose of the robot, generating a robot moving trajectory, guiding the robot to be positioned to a measurement position, and completing preparation before measurement. According to the invention, automatic high-precision extraction of the pipe thread measurement reference is realized, the problems of non-uniform reference, large error and low efficiency in traditional measurement are solved, the method is suitable for various pipe thread workpieces, and a reliable basis is provided for subsequent high-precision measurement.

The invention relates to the technical field of automatic control, in particular to an intelligent equipment management method and system, and the method comprises the following steps: obtaining a control cabinet log, extracting an equipment temperature load pressure sequence, comparing a set offset to generate a deviation label, collecting an operation time length and load change mapping early warning table, and generating an abnormal record; and extracting task configuration and equipment state re-marking alternating points to generate load configuration, resetting an instruction sequence to generate a scheduling sequence, extracting an instruction time-consuming marking interference section, and switching a standby link to generate an evasion list. According to the invention, by collecting the temperature, the load rate and the pressure sequence in the operation cycle of the equipment, the dynamic state identification capability is improved, the scheduling path is optimized, the resource contention is relieved, the delay path is marked in combination with the original response data, and the standby link is switched to avoid interference, so that the flexibility and stability of linkage scheduling are improved, and the task execution beat accuracy is enhanced; the instruction blocking and interruption risks are effectively reduced, and the operation toughness and response efficiency of a production line are improved.

The invention discloses a metal grinding machine tool grinding wheel wear compensation type intelligent control system, and relates to the technical field of mechanical manufacturing automation, and the system comprises a contact type reference detection module which is used for obtaining the initial contour and reference wear loss of a grinding wheel through discrete point measurement in a non-machining state; the multi-parameter non-contact real-time detection module is used for collecting acoustic emission, vibration and spindle current signals in the grinding process through three types of sensors; the grinding environment sensing module collects the temperature of a grinding area and the cutting fluid concentration; the intelligent control unit receives reference data and an environment signal, performs deep learning on a wear loss prediction model after multi-source data fusion, performs periodic reference calibration, analyzes the dynamic wear loss, and synchronously transmits the data to the data storage and tracing module; the executing mechanism adjusts grinding wheel feed amount compensation according to the dynamic abrasion loss; and the data storage and tracing module stores full-process data in real time. According to the system, high-precision and real-time wear compensation is achieved, the machining precision and consistency are improved, and high-end manufacturing requirements are met.

The invention discloses an intelligent management and control system based on a large model and integrated multi-dimensional spatio-temporal data fusion, and belongs to the field of artificial intelligence and engineering application. The system comprises a space-time large model base, an engineering object three-dimensional or four-dimensional integrated model, an intelligent agent cooperation module and an autonomous analysis decision and control module. The method comprises the following steps: embedding engineering field knowledge type public domain data based on a large model, forming a space-time large model base, fusing three-dimensional or four-dimensional geometry and attribute data of an engineering object and dynamic business data such as monitoring and equipment automation, constructing a private domain data source which is integrally expressed, stored and managed, and autonomously analyzing and deciding by utilizing the space-time large model reasoning capability. And the scheduling agent collaboratively completes task planning and arrangement, safety production analysis and decision support and control, and automatically generates various engineering image-text reports. The intelligent management and control problem in the engineering fields of mines, factories, traffic, water conservancy, buildings, machinery and the like can be solved, and the practicability of an artificial intelligence system in the engineering field is remarkably improved.

The invention discloses a valve actuator self-adaptive control method and system based on artificial intelligence, and relates to the technical field of industrial automation control, the valve actuator self-adaptive control system comprises multiple modules, a data acquisition module uses a multi-modal sensor fusion technology to acquire multi-dimensional data, the acquisition frequency is dynamically adjusted according to the operation state, and the data acquisition module is used for acquiring the multi-dimensional data; the data preprocessing module cleans data by means of a deep convolution auto-encoder and normalizes by using an improved method, the model training module constructs a space-time diagram convolution network model fused with an attention mechanism, adversarial training optimization is adopted, the control strategy generation module is combined with reinforcement learning and model prediction control technologies, stability and energy consumption generation strategies are considered, and the time-space diagram convolution network model is optimized. The instruction execution module executes instructions in parallel through multiple threads, and stable action is guaranteed. The system has remarkable advantages, can accurately collect and process data, can cope with various working condition changes due to the strong self-adaptive capability, has efficient fault diagnosis and processing capability and dynamic evaluation and optimization performance, improves the production efficiency, and brings much convenience to industrial production.

The invention discloses an automobile production line gluing system based on visual inspection, and relates to the technical field of industrial automation and intelligent control, and the automobile production line gluing system comprises a gluing data acquisition module, a gluing feature extraction module, a compensation correction module, a gluing decision module and a closed-loop management and control module; the gluing data acquisition module preprocesses assembly line gluing track images and glue gun nozzle three-dimensional coordinate data, the gluing feature extraction module constructs a gluing feature sequence table, the compensation correction module outputs glue outlet opening degree compensation amount and nozzle track correction amount, and the gluing decision-making module outputs glue gun pressure adjusting amount and nozzle movement speed. And the closed-loop management and control module detects pressure abnormity and speed stability in real time, optimizes control parameters and generates a process quality map. Through real-time visual detection and dynamic closed-loop control, the glue line width precision is improved, the track compensation response is shortened, the glue gun speed fluctuation coefficient and glue waste are reduced, the gluing qualification rate is improved, the production cost is reduced, and the automobile manufacturing and production requirements are met.

The invention relates to an active splitting and isolated island operation method based on load importance under a disaster condition, and belongs to the technical field of power systems and automation thereof. On the basis of disaster types and power supply area characteristics, constructing a load importance dynamic grading system, optimizing the weight through an analytic hierarchy process-index weight optimization collaborative algorithm, and combining with real-time updating to generate a grading result; the method comprises the following steps: deploying wide-area and local monitoring devices to collect power grid data, setting a safety threshold, triggering a splitting decision when the safety threshold exceeds the limit and the trend deteriorates, calculating an instability rate by using a multi-parameter collaborative instability pre-judgment algorithm, and starting pre-splitting preparation; and the main control center calls a target splitting section optimization algorithm to determine a section in combination with the grading result and the power grid data, generates a splitting instruction after verifying the stability of the island through load flow calculation, and performs classified stability control on the split island. The method can realize accurate load grading, early instability pre-judgment, guarantee of important load power supply such as medical treatment and the like, reduces catastrophe loss, and is suitable for power grid management and control under various disasters.

The invention discloses a welding robot path planning system cooperating with industrial vision and collision detection, and relates to the technical field of robot automatic welding, the welding robot path planning system comprises an upper computer, the upper computer is in communication connection with the following modules: an environment sensing module used for collecting welding environment information in real time, comprising three-dimensional point cloud data of a workpiece and obstacle information; and the data processing and fusion module is used for fusing the visual identification result and the obstacle detection data. By integrating industrial visual recognition and collision detection technologies, the dynamic change of the welding environment can be sensed in real time, an accurate environment model is constructed based on three-dimensional point cloud data, compared with a traditional welding robot with a preset path, the welding track can be dynamically adjusted, welding defects caused by environment interference are avoided, and meanwhile the welding efficiency is improved. In combination with an assembly error compensation mechanism, the deviation between a theoretical model and an actual workpiece can be corrected, the welding position precision is ensured, and the high-precision ship manufacturing requirement is met.

The invention provides a marine equipment drawing recognition method and system based on a large model, and is applied to the field of intelligent analysis and knowledge management of ship engineering. The method comprises the following steps: receiving a marine equipment engineering drawing image, extracting global features through character and line detection after preprocessing so as to identify a drawing type, and combining symbol classification and topological graph construction to determine an equipment connection relationship, performing multi-source information reasoning on the target component by fusing a rule engine and a large language model, and outputting a high-confidence identification result; according to the scheme, the accuracy and the automation level of recognition of the equipment parts in the complex marine drawing can be remarkably improved, and the technical bottlenecks of a traditional method in the aspects of insufficient cross-modal information fusion, limited semantic understanding depth, poor heterogeneous drawing adaptability and the like are effectively solved.

The invention relates to the technical field of data analysis, provides an intelligent factory automatic monitoring method and system based on knowledge base enhancement, and realizes more accurate anomaly analysis and more effective process adjustment of an intelligent factory. The method comprises the steps of performing knowledge enhancement fusion processing on an obtained real-time monitoring data set of an intelligent factory through a pre-constructed process knowledge base and a pre-constructed monitoring rule base, and generating a process knowledge graph; performing abnormal mode recognition processing on the process knowledge graph based on a semantic matching strategy, extracting feature description of an abnormal event and a semantic association path with a historical monitoring text, and generating an abnormal mode analysis result containing abnormal root cause inference; according to the abnormal mode analysis result and the dynamic incidence relation in the process knowledge graph, an automatic monitoring report containing root cause priority ranking and optimization operation guidance is generated, and the automatic monitoring report is fed back to the intelligent factory control terminal to trigger process adjustment operation.