20640 results about "Production line" patented technology

The invention provides a real-time transmission method and system based on internet-of-things sensing data in a digital twinborn scene, and the method comprises the steps: collecting and processing vibration spectrum data, pressure gradient change data and temperature distribution data of equipment in an industrial production line, generating a preprocessing data package, carrying out the analysis to obtain an equipment state fingerprint feature and an environment working condition label, and carrying out the real-time transmission of the environment working condition label. A target virtual control parameter and a target error compensation amount are generated by using a dynamic parameter correction loop of the digital twin, an actual material flow state is generated in combination with material flow rate data, an optimization strategy sequence of multi-device collaborative operation is generated by using a distributed computing architecture, and the optimization strategy sequence is converted into a track compensation parameter of an end effector in the device; in combination with the target error compensation amount, data closed-loop transmission is carried out; according to the invention, millisecond-level time domain matching of vibration, pressure and temperature data and material flow velocity is realized through a dynamic timestamp alignment mechanism of 5G edge computing nodes, and the problem of synchronization error accumulation of traditional internet-of-things sensing data is solved.

The invention belongs to the technical field of welding quality detection, and particularly relates to a visual image-based welding seam defect detection method, which comprises the steps of image acquisition, image preprocessing, data analysis, data output, defect classification and decision making, system closed-loop optimization and the like. According to the method, by synchronously collecting two-dimensional images, three-dimensional shapes and heat distribution data of metal welding seams and adopting a polarization filter and annular LED light source combination scheme, multi-dimensional conjoint analysis of physical defects and thermodynamic characteristics is achieved, basic characteristic data are extracted through primary processing, quantifiable defect coefficient indexes are generated through secondary processing, and the detection accuracy is improved. And finally, generating a comprehensive defect index through a multi-modal fusion algorithm, constructing a well-arranged intelligent analysis decision chain, and establishing a self-evolution mechanism of data acquisition-analysis decision-model iteration through real-time interaction of a detection result and an algorithm model. The system can continuously optimize the detection threshold value and the characteristic weight parameter according to the actual working condition of the production line, and the continuous improvement of the detection sensitivity is kept.

A prefabricated building production line and a prefabricated building structure. The prefabricated building production line comprises an assembly conveyor line, a functional module conveyor line, a transfer mechanical arm, an assembly mechanical arm, and a control platform; an assembly area, a testing area, and an inspection area are arranged in order along a transport direction of the assembly conveyor line; the assembly conveyor line is used for conveying a building unit main body to be assembled; a plurality of functional module conveyor branch lines are arranged at the side of the functional module conveyor line away from the assembly conveyor line; the functional module conveyor line and the functional module conveyor branch lines are used for transporting a functional module; the transfer mechanical arm is used for carrying a functional module and the building unit main body to be assembled; and the assembly mechanical arm is used for installing the functional module on the building unit main body to be assembled. The prefabricated building structure comprises: a lifting device for prefabricated building installation, an prefabricated building ventilation structure, a prefabricated building automated production fixture, etc.

The invention discloses a material grabbing offset real-time compensation method and device based on multi-sensor data fusion, relates to the technical field of automatic production, and discloses a material grabbing offset real-time compensation method and device based on multi-sensor data fusion. The three-dimensional offset is generated in real time in combination with the neural network model, mechanical arm compensation is controlled, the problems that a single sensor is large in positioning error and insufficient in compensation precision are solved, the material grabbing accuracy can be improved, and then the stability of an automatic production line is enhanced.

The invention provides a color steel plate coating flatness evaluation method and system based on artificial intelligence. According to the method, the three-dimensional point cloud data is generated by collecting the interference fringe image, and the surface fluctuation characteristics are quantified; capturing a multi-dimensional vibration spectrum of the transmission roller shaft, generating a servo motor compensation control signal, driving a multispectral scanning head to perform reverse displacement compensation, and generating real-time compensation data; inputting the surface topography features in the three-dimensional point cloud and the real-time compensation data into a lightweight convolutional neural network, and outputting fusion features; and dynamically classifying and identifying surface defects and uneven areas based on the fusion result, adjusting a classification threshold in combination with the speed of the production line, outputting a flatness evaluation result, and synchronizing the flatness evaluation result to a speed regulation system of the production line to realize closed-loop optimization. According to the method, laser interference, vibration compensation and lightweight AI technologies are fused, dynamic high-precision evaluation of the surface flatness of the color steel plate of the high-speed production line is achieved, and the problems of defect misjudgment and measurement distortion caused by vibration interference are solved.

The embodiment of the invention relates to the technical field of computers, in particular to an energy conservation and emission reduction scheme generation method and system based on a digital factory, and the method comprises the steps: obtaining real-time energy consumption data streams and emission monitoring signals of a plurality of pieces of production equipment in the digital factory; extracting dynamic energy consumption characteristics from the real-time energy consumption data stream, and identifying periodic emission parameters from the emission monitoring signal; a target energy consumption prediction algorithm is called to process the dynamic energy consumption characteristics and the periodic emission parameters, and energy consumption prediction results and emission intensity distribution of multiple time periods are generated; and generating an equipment-level optimization regulation and control scheme and a production line-level scheduling strategy according to the energy consumption prediction result and the emission intensity distribution in combination with preset energy conservation and emission reduction constraint conditions. Therefore, collaborative optimization of equipment-level real-time regulation and control and production line-level flexible scheduling can be realized.

The invention discloses a digital twinborn model real-time linkage method driven by information physics fusion, particularly relates to the technical field of digital twinborn cooperative control of an industrial automation production line, and is used for solving the problems of instruction conflict and control failure caused by mismatching of virtual model parameters and dynamic capability of physical equipment in the prior art. According to the method, equipment state and material flow data are collected in real time, equipment dynamic degradation parameters are extracted to generate capability attenuation feature vectors, hidden process conflict path detection and resource preemption probability simulation are combined, a collaborative optimization model of equipment health and process scheduling is constructed, and a control instruction set containing dynamic capability constraints is generated. After the screening instruction is verified through physical constraint matching, parameters are corrected in a closed loop mode based on an execution result, a model is iterated, and self-adaptive matching of the virtual instruction and the physical equipment capacity is achieved; the reliability of the digital twinning control instruction and the stability of a production line are remarkably improved, and the risk of abnormal shutdown caused by an overrun instruction is avoided.

The invention provides an automatic production line multi-parameter real-time monitoring and fault diagnosis early warning method and system, and relates to the technical field of automatic production line monitoring, and the method comprises the steps: obtaining the data of a multi-source sensor, carrying out the data quality evaluation and correction, carrying out the monitoring of the performance parameters of an equipment assembly, and calculating the degradation rate; fault types are analyzed and identified in combination with time-frequency domain features, and a fault propagation link is established based on a physical connection relation to determine a fault source. According to the invention, equipment abnormity can be found in time, the fault source component is accurately positioned, the accuracy and timeliness of fault diagnosis are improved, and the equipment maintenance cost is effectively reduced.

The invention discloses a multi-source process parameter mapping supervision system and method based on a big data model, and relates to the technical field of process parameter analysis. Initial process parameters in a process production line are collected, the initial process parameters are processed to obtain standardized process parameters, and the process production line is subjected to process stage division; analyzing a stage product deviation degree of the process stage, calculating correlation between the standardized process parameters and the product deviation degree of the process stage, analyzing the stage product deviation degree of the process stage, predicting the product deviation degree of the process stage in production, and predicting a product reject ratio in production based on the product deviation degree of the process stage. According to the method, the product deviation of each stage and the reject ratio of the whole product are continuously predicted, the production state is judged in real time, a dynamic quality control closed loop is constructed, and the pertinence and effectiveness of supervision are improved.

The invention belongs to the technical field of fault prediction and management, and discloses a production line abnormity real-time diagnosis system based on industrial Internet of Things, which comprises a data acquisition and processing module, a distributed sensor network covering key equipment of a production line is constructed, multi-dimensional production line data is acquired, and the data acquisition and processing module is used for acquiring data of the production line; a self-adaptive sampling technology is adopted to dynamically adjust the multi-dimensional production line data acquisition frequency according to the equipment state, and preliminary multi-dimensional production line data processing is executed at the edge end; and the multi-scale time sequence management module adopts a hot, warm and cold three-level hierarchical storage architecture, compulsively switches sampling frequencies of key equipment parameters in combination with a multi-level safety threshold mechanism, performs resource allocation through a hierarchical calculation architecture, and introduces an abnormal sensitive new mode detection and double-track system template updating mechanism to identify a novel abnormal mode. The state change of the equipment is continuously monitored; it is ensured that resources can be efficiently scheduled in normal, early warning and abnormal states, and the anti-risk capacity of the system is improved.

The invention relates to the technical field of flexible circuit board production, in particular to a process optimization method and system for a flexible circuit board production line. The method comprises the following steps: acquiring characteristic data of a flexible circuit board material; executing a circuit board thermocuring action according to the material characteristic data of the flexible circuit board, performing thermal stress intensity evaluation, and generating stress hot spot distribution data; performing real-time curing temperature control feedback adjustment according to the stress hotspot distribution data to obtain circuit board curing effect evaluation data; detecting a real-time surface wettability index of the to-be-processed flexible circuit board in front of the laminating station; and fitting process parameter compensation is carried out based on the circuit board curing effect evaluation data and the real-time surface wettability index so as to realize curing-fitting process optimization of the flexible circuit board production line. According to the invention, intelligent optimization of the curing-laminating process of the flexible circuit board production line is realized, and the quality problems of uneven stress and overlarge deformation in the thermocuring-laminating process are effectively solved.

The invention discloses an improved YOLOv11-based high-reflection and high-transmittance material surface defect detection method, which comprises the following steps: S1, acquiring a mobile phone screen surface defect image, preprocessing and labeling, and generating a training data set containing three defects of scratches, edge breakage and cracks; s2, constructing a defect detection model; s3, averagely dividing an input image into four independent detection regions, adopting a bounding box overlapping area weight distribution strategy for cross-region defects, and performing partition counting on the image by using a defect detection model; s4, constructing a surface defect detection network meeting the real-time requirement of the industrial production line, and outputting a detection result and a processing frame rate; s5, pixel-level mask segmentation is carried out on the scratch defect, and contour coordinates and geometric features are extracted; and S6, fitting the actual damage size of the scratch defect on the surface of the high-reflection and high-transmittance material according to the segmentation result. According to the method, the detection speed is high under the condition that the accuracy is ensured, and a reliable solution is provided for surface flaw detection of the high-reflection and high-transmittance material.

The invention relates to the technical field of data analysis, provides an equipment state data management method and system applied to an automatic radix astragali seu hedysari refined production line, and is used for realizing equipment health precise prediction and active maintenance through whole-process data monitoring management. The method comprises the steps that a production line equipment state data set is acquired, a production line equipment state feature set is extracted from the production line equipment state data set, and the production line equipment state feature set comprises multi-dimensional time sequence features reflecting the equipment operation trend and topological features of the incidence relation between the equipment; training an astragalus membranaceus essence production line state prediction model based on the production line equipment state feature set, wherein the astragalus membranaceus essence production line state prediction model is used for predicting the equipment abnormal probability and the performance degradation trend in a selected time period according to the current equipment state feature; and generating an equipment management strategy set according to an output result of the radix astragali seu hedysari refined production line state prediction model, wherein the equipment management strategy set comprises production parameter adjustment instructions and maintenance node planning schemes for different equipment maintenance priorities.

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 material batch full-process traceability system based on a production process, particularly relates to the field of production and manufacturing traceability, is used for solving the problems of continuity and accuracy of material batch full-process traceability, and is characterized in that batch tracking coordinates are generated by constructing a process mapping matrix and an assembly topological index; detecting circulation holes in real time and inserting placeholder marks; judging broken chain credibility based on path integrity and structural complexity; compressing traceable chain segments; executing field dynamic alignment to generate batch circulation patches; a material batch whole-process continuous tracing view is established; and furthermore, the abnormity positioning time is remarkably shortened, the real-time performance is improved, the influenced batches are accurately locked, the recall range is reduced, the production line decision-making efficiency is optimized, and efficient and reliable technical support is provided for quality management and batch tracking in the production process.

The invention discloses an intelligent film-drawing and unwinding control method and system based on real-time tension, and relates to the field of automatic control and intelligent manufacturing, and the method comprises the following steps: ensuring normal starting of equipment and system states through equipment self-inspection and technological parameter input; tension and speed data are collected in real time and preprocessed, and support is provided for fuzzy PID control; the parameters of the fuzzy PID controller are dynamically adjusted by calculating the tension error and the change rate of the tension error so as to adapt to different membrane material characteristics and unwinding working conditions; an improved genetic algorithm is used for optimizing parameters of a PID controller, overshoot is minimized, the adjusting time is shortened, and the robustness and the anti-interference capability of the system are improved; a closed-loop control system is constructed, and the stability of unwinding tension is ensured through real-time feedback signals; and the system state is monitored in real time in combination with an anomaly detection algorithm, and a control strategy is automatically adjusted or fault protection is performed. The control method disclosed by the invention can be widely applied to the fields of film unwinding and automatic production lines, and has a relatively high intelligent level.

The invention provides a control method of tension adjusting equipment on a composite copper foil continuous production line, which comprises the following steps: acquiring a tension set value and a tension actual value of adjacent stations on the composite copper foil continuous production line, and calculating a difference value between the tension set value and the tension actual value to obtain tension deviation data; in the process of adjusting the tension between the adjacent stations, judging whether the copper foil snakes or not, and if yes, feeding back snaking offset data of the copper foil to a feed-forward compensation function and a feedback controller so as to cooperatively adjust the control of the tension; on the basis of tension balance between adjacent stations, operation parameters of all stations of the composite copper foil continuous production line are collected in real time, and a production line operation mathematical description function is established; and optimizing control parameters of production line operation in combination with a preset rule in the preset knowledge base.

The invention relates to the technical field of manufacturing process control, and discloses a multi-dimensional process data co-simulation control method and system and a storage medium. The method comprises the following steps: firstly, collecting a multi-dimensional process parameter set of a plurality of process equipment in a manufacturing production line, carrying out cross-dimensional feature extraction, and generating a comprehensive feature matrix containing a time sequence feature, a spatial distribution feature and an energy consumption feature; according to feature relevance of different dimensions in the comprehensive feature matrix, a process parameter dynamic coupling model is constructed, co-simulation is carried out on interaction between process equipment, and a process state evolution sequence is output; extracting abnormal fluctuation characteristics in the sequence, and generating a process parameter adjustment instruction set; and finally, according to the adjustment instruction set and real-time process feedback data, dynamically correcting model simulation parameters, generating an optimized process control strategy, and executing the optimized process control strategy. According to the method, collaborative analysis and dynamic control of multi-dimensional process parameters are realized, and the accuracy and adaptability of process control are improved.

The invention discloses an automatic identification method and system for a wafer internal defect image of a 3D stacked chip, and belongs to the technical field of semiconductor manufacturing and detection. According to the method, optical, X-ray and ultrasonic image data are synchronously acquired based on a multi-modal imaging technology, imaging parameters are dynamically adjusted to adapt to different wafer levels and material characteristics, multi-modal features are extracted in combination with layered filtering and denoising, multi-resolution registration and a self-supervised deep learning method, and micron-sized defects are positioned by using an attention mechanism. And further constructing a defect-process parameter correlation model through reinforcement learning, generating a closed-loop process optimization instruction, and transmitting the closed-loop process optimization instruction to an execution system. The system comprises a multi-modal imaging module, a noise suppression module, a deep learning analysis module and a process optimization module, and supports edge computing deployment. According to the invention, the internal defect detection efficiency and precision of the multi-layer stacked chip are significantly improved, real-time closed-loop control of detection-analysis is realized, the wafer manufacturing quality risk is reduced, and the method is suitable for intelligent defect detection of an advanced packaging production line.

The invention provides a management method and system for coal preparation quality intelligent control, and the method comprises the steps: firstly obtaining raw material quality, equipment operation and environment state index sets of a target production line, then carrying out the dynamic association processing of the index sets, and generating a technological process feature set; then calling a preset optimization decision model to carry out parameter prediction on the technological process feature set to obtain a process regulation parameter set, generating a quality optimization instruction set based on the process regulation parameter set, transmitting the quality optimization instruction set to a production control unit to execute dynamic regulation operation, and finally, according to a quality feedback index set after regulation execution, carrying out dynamic regulation operation. And the parameter weight distribution of the optimization decision model is updated, intelligent control and continuous optimization of the coal dressing quality are realized, and the stability of coal dressing production and the product quality are improved.

The invention relates to the technical field of modular assembly production lines, in particular to a modular flexible digital twin layout optimization method and system. Comprising the following steps: constructing multi-dimensional constraint modeling, constructing a multi-objective optimization model containing route length, equipment cost and human resource quantity, and generating an initial layout scheme based on three-dimensional geometric attributes and logistics paths of production line equipment; dynamic weight distribution is carried out, according to priority parameters input by a user, a route optimization weight # imgabs0 #, a cost optimization weight # imgabs1 # and a human resource weight # imgabs2 # are adjusted in real time, and a dynamic weight combination matrix is generated; digital twinborn simulation is carried out, the initial layout scheme is mapped to a digital twinborn platform, and the actual equipment utilization rate, the material carrying efficiency and the worker operation path are calculated through logistics path simulation; according to the scheme, dynamic balance multiple targets can be provided based on a simulation result, simulation and optimization real-time linkage can be realized, and a layout optimization technology of a multi-dimensional verifiable scheme is provided.

The invention discloses a welded pipe conveying abnormity prediction method and system based on large model reasoning, and aims to solve the problems that multi-source data is difficult to align, cross-station false correlation is caused, prediction lacks executable positioning and time sequence, and linkage control reliability is insufficient. Event alignment is carried out by taking a controller edge signal and an encoder zero position as time anchor points, a production line topology semantic graph containing time delay, capacity and interlocking attributes is constructed, and topology reachability and physical time delay constraints are applied in a self-attention long sequence model to carry out multi-step rolling prediction. And outputting a risk probability, refining the risk probability to spatial positioning of a roller way section or a shaft and the minimum executable intervention time, and generating a risk interval in combination with uncertainty estimation and calibration so as to drive an upstream beat self-adaptive speed reduction, shunting or stopping strategy. The technical effects of improving accuracy and interpretability, reducing false alarm and missing alarm, ensuring that linkage can be executed in advance and meeting edge time delay budget are achieved.

The invention discloses a self-optimized injection molding prediction and monitoring method. The method comprises a data acquisition module, a data analysis module, a remote monitoring module, an appearance detection module, a fault processing module and a process adjustment module. And the data analysis module trains a process parameter prediction model and a defect prediction model by using historical production data to realize real-time prediction of key process parameters and early warning of defects. And the process adjustment module adaptively optimizes the process parameter setting of the injection molding machine according to the prediction result, so that the stability of the product quality is ensured, and human intervention is reduced. And the remote monitoring module is connected with a smart phone and a PC terminal of a user through the Internet, so that a technician can check the production state at any time and intervene in time when the system cannot be automatically adjusted, long-time shutdown of equipment and damage to the product quality are avoided, and the stability and efficiency of a production line are further improved.

The invention discloses a multi-sensor data fusion control method for an automatic assembly line, and relates to the technical field of data fusion, and the method comprises the following steps: when multi-source data fusion is carried out, firstly, data information is obtained in real time through a multi-source sensor, the obtained data information is preprocessed, and the accuracy and integrity of data are ensured; by analyzing data quality and fusion performance indexes and system robustness and environmental adaptability indexes in real time, the system can accurately detect the influence of electromagnetic interference and environmental fluctuation on data and dynamically evaluate the state. And an early warning and adjustment strategy is automatically triggered in case of abnormality, so that data fusion continuity is guaranteed, and downtime and human intervention are reduced. And when the abnormity exceeds the self-repairing capability of the system, the system can give an early warning, so that the efficient and safe operation of the production line is ensured, and the capability of dealing with sudden problems is improved.

The invention discloses a package defect elimination method based on visual inspection, and relates to the technical field of image processing and computer vision, and the method comprises the following steps: setting a polarized light source with a fixed angle on a package production line, and uniformly illuminating a target package area to enable the incident light polarization direction and the package surface reflection direction to form a deviation angle; an industrial camera provided with a polarization filter is used for carrying out image collection on a packaging target area, and the polarization direction of the filter is perpendicular to the polarization direction of a light source so as to restrain reflection interference to the maximum extent. Reflection interference is eliminated through polarized light, and the image quality is improved; by combining a neural network and feature fusion scoring, the defect recognition accuracy and adaptability are improved; a closed-loop elimination control and data tracing mechanism is introduced, the elimination precision is guaranteed, quality responsibility investigation is supported, and the intelligence and reliability of the packaging defect detection and elimination system are integrally improved.

The invention provides an industrial visual anomaly recognition method, system and device based on multi-modal feedback and a medium, and belongs to the technical field of industrial visual detection.The method comprises the steps that a production line video stream is collected in real time, an ROI is automatically segmented, a picture sequence is generated, features of a set dimension are extracted, and the features of the set dimension are extracted; comparing a preset dynamic distance threshold with a feature library, and preliminarily identifying an abnormal image; a cue word of a detection requirement is constructed, the cue word and abnormal related information are input into the multi-modal large model, and a determined abnormal image is output; using the determined abnormal image to construct a data set to train a target detection model, and adjusting the learning rate in the training process; and inputting pictures captured in an industrial scene into the deep learning model, the multi-modal large model and the target detection model in sequence to obtain a recognition result, feeding back the recognition result to a user for confirmation, adding the recognition result to the data set, and optimizing training of the target detection model. According to the invention, accurate identification of abnormal images in industrial production is realized, and the identification efficiency is high.

The invention provides a flexible production line adaptive scheduling control method fusing multi-agent reinforcement learning, and relates to the technical field of production line scheduling, and the method comprises the steps: obtaining the state of processing equipment and workpiece demand information in real time, constructing a state and action space, mapping the equipment into agents, achieving information sharing, and then selecting a scheduling action and executing the scheduling action. And the state-action value function is updated through the experience playback pool, and the multi-agent model is optimized. The system monitors the equipment state during operation, dynamically updates the state and action space to deal with faults or material shortage, and generates a compensation scheduling scheme. Finally, in combination with a real-time scheduling instruction, adaptive scheduling control of the flexible production line is realized, and the production efficiency and flexibility are improved.

The present invention relates to the field of semiconductor carrier technology, and in particular to a wafer box with a protective limiting device, which includes a bottom plate, a wafer storage box, a box cover, and a limiting assembly. The limiting assembly includes a limiting seat, a top assembly, a rotating plate, a limiting frame, and a rotating wheel. The limiting seat includes a connecting cross bar, a connecting arm, and a top blocking plate. The top assembly includes a connecting portion, a top protrusion, a connecting rod, a tensile sheet, and a reinforcing rib. Compared with the prior art, the present invention is provided with the wafer storage box for inserting wafers, and the limiting assembly is provided to top the wafers, thereby effectively preventing the wafers from being damaged. At the same time, by providing the top assembly, the top action has a protective limit, thereby preventing the rigid top from damaging the wafers. By providing the wafer storage box for loading 6-inch wafers, the cooperation of the fixing portion and the fixing assembly enables 6-inch wafers to be transported on an 8-inch wafer production line.

The invention relates to an intelligent analysis method for energy consumption of industrial solid waste treatment, which comprises the following steps of: constructing a layered digital twinborn model for mapping a physical structure, a process flow and an energy consumption relationship through multi-point multi-dimensional data fusion acquisition and data cleaning, synchronization and standardized preprocessing; the model is used for feature coupling extraction and anomaly detection, spatial positioning and analysis of energy consumption anomaly are realized, multi-scene deduction and parameter optimization are performed through a digital twin sandbox, influence paths and anomaly factors are accurately identified, and optimal intervention suggestions giving consideration to energy conservation, productivity and process stability are automatically given. According to the scheme, the real-time performance and accuracy of energy consumption abnormity diagnosis of the production line are improved.

The invention relates to an equipment fault intelligent question-answering method based on large model enhancement, and the method comprises the following steps: obtaining steel production line data, and constructing a fault knowledge graph; user questions are obtained and classified through semantic analysis, and classification results comprise single-hop questions, short-hop questions and multi-hop questions; calling a corresponding reasoning mechanism according to the category of the user question, generating an answer according to the fault knowledge graph, and outputting equipment fault analysis in a natural language form through a retrieval enhancement generation technology; for a single-hop problem, core entities and relationships are matched based on an Aho-Corasick automaton, and answers are directly retrieved from the fault knowledge graph; for a short jump problem, deriving an answer from the fault knowledge graph by adopting a single-agent inference model; for a multi-hop problem, a multi-agent collaborative reasoning model is adopted to generate a multi-step reasoning path based on the fault knowledge graph. Compared with the prior art, intelligent question answering with high reliability and high readability can be efficiently realized in a complex industrial scene.