26 results about "Manufacturing intelligence" patented technology

According to the mechanical arm control method based on multi-scale dynamic state estimation and error compensation, high-frequency and high-precision estimation of the state of a mechanical arm system is achieved through multi-sensor data fusion and an extended Kalman filtering algorithm, and the accuracy and reliability of state estimation are remarkably improved; on the basis, a multi-scale error analysis method combining a time domain and a frequency domain is provided, and an autoregressive moving average model is utilized to dynamically predict system errors, so that the sensing and pre-judging capability of the system on the errors is enhanced; a comprehensive control strategy including multiple links of feedforward, self-adaption and feedback is designed, a dynamic compensation and adjustment mechanism of errors is introduced, meanwhile, a real-time execution framework based on three-layer task scheduling is constructed, cooperative operation of all functional modules is coordinated, and the real-time performance and high efficiency of a control system are ensured. The method is suitable for multiple fields of industrial manufacturing, intelligent robots and the like, and provides technical support for precise control of mechanical arms in complex scenes.

The invention discloses a multi-agent asynchronous collaboration method and system under a centralized architecture, and is suitable for task scheduling and resource coordination of heterogeneous agents in an intelligent Internet of Things environment. According to the method, a main scheduling node centralized control mechanism is adopted, a scheduling priority function is calculated in combination with a multi-agent game model according to real-time state information of a plurality of heterogeneous agents, and asynchronous allocation and feedback control of tasks are achieved. Meanwhile, an asynchronous and synchronous window mechanism is introduced, the scheduling continuity and efficiency can still be kept under the condition of incomplete information, and fairness and robustness of task distribution are achieved through agent feedback delay modeling and revenue function design. The system throughput and the response speed are improved, the isomerism and the expandability are considered, and the method is suitable for multi-agent cooperation scenes such as disaster emergency, industrial manufacturing, smart home and intelligent traffic.

A ship pipe fitting recognition system based on multi-source data enhancement and intelligent recommendation comprises a multi-source data enhancement module, a model training module, a recommendation decision module, a federated learning platform and a block chain evidence storage module, the system is of a'cloud + side 'double-layer architecture, the cloud is used for intelligent evolution, distributed knowledge is integrated through federated learning, and the block chain evidence storage module is used for storing the distributed knowledge. The global model is continuously updated and optimized, the edge end is used for edge real-time response, and efficient pipe fitting feature detection and pipe fitting number recommendation are carried out at the equipment end. According to the method, the detection precision and the non-standard pipe fitting recognition capability in complex illumination and shielding scenes can be remarkably improved, and the misjudgment rate is reduced; the decision-making problem of similar part hybrid scenes is solved, and the assembly efficiency and accuracy are improved; meanwhile, the data privacy security is ensured; the technical blank in the aspects of data diversity, algorithm robustness and industrial adaptability in the field of ship pipe fitting recognition is filled, the efficient requirement of a ship assembly scene is met, and technical support is provided for ship manufacturing intelligence.

The invention provides a discrete manufacturing intelligent scheduling method based on a graph theory. The discrete manufacturing intelligent scheduling method comprises the steps of 1, constructing a heterogeneous graph model of scheduling elements; step 2, static scheduling optimization based on a critical path method; step 3, resource allocation optimization based on a multi-resource bipartite graph matching method; and 4, dynamic response and rescheduling are carried out. Based on a graph theory method, a complex'process-resource-constraint 'relationship is converted into a visual heterogeneous graph model, and a systematic scheduling solution based on the graph theory is constructed, so that the scheduling solution is suitable for a discrete manufacturing scene with multi-process, multi-equipment, multi-constraint and dynamic disturbance characteristics; the method is used for realizing static planning of production plan scheduling, resource optimization distribution and dynamic adjustment full-process optimization.

The invention relates to the technical field of intelligent manufacturing of elevators, and particularly discloses an intelligent scheduling and collaborative operation platform for intelligent manufacturing of elevators. A structured event acquisition module overcomes the problems of data acquisition and transmission delay and information isolated island by receiving monitoring data streams in real time and performing anomaly detection and structured packaging; and a dispatching center can master accurate event details and influence ranges in real time. And the report generation module processes structured event information and real-time production data by utilizing an intelligent analysis engine, so that the problem of low efficiency of complex event chain reaction assessment is solved. The scheme generation module generates an alternative rescheduling scheme in combination with an event influence analysis report and a production global snapshot, and after the scheme approval module checks, the execution instruction pushing module decomposes a confirmation scheme and directionally pushes the confirmation scheme to a related execution level and a collaborative party, so that the problem of scheduling plan transmission and execution deviation is avoided, and the scheduling efficiency is improved. The real-time performance and the cooperation efficiency of production scheduling are improved, and elevator manufacturing enterprises can better adapt to market requirements.

The utility model provides a control cabinet of a platform household elevator. The control cabinet comprises a main control cabinet and a battery box, the main control cabinet is detachably assembled with the battery box through a connecting assembly; the main control cabinet and the battery box are mounted on a shaft or a wall surface of an elevator hall; the main control cabinet comprises a cabinet door and a cabinet body; the cabinet door and the cabinet body are integrally formed; a first buckle is arranged on the cabinet door; a first clamping groove is formed in the cabinet body; the first buckle is clamped into the first clamping groove; the battery box comprises a cover plate and a box body; a second buckle is arranged on the cover plate; a second clamping groove is formed in the box body; the second buckle is clamped into the second clamping groove; according to the utility model, the number of parts is small, the product cost is reduced, the product cost performance and the market competitiveness are improved, the structure is firm, the installation is flexible, the integration degree is high, the compatibility is good, the product intelligence degree is improved, and the user experience is improved.

The application relates to the field of spring manufacturing intelligent control, and discloses a spring horizontal processing and single heat treatment coordinated intelligent regulation and control system, which comprises the following steps: a disturbance characteristic online coding module, which is used for online acquisition and coding of physical disturbance generated by each spring in horizontal processing procedures such as coiling and welding, and generates a unique disturbance characteristic spectrum; an online control law dynamic calibration module, which receives the disturbance characteristic spectrum and inversely solves a compensatory individualized heat treatment instruction sequence for the spring; an individualized heat treatment execution module, which executes accurate single heat treatment on a single spring according to the instruction sequence; and a quality feedback and model self-optimization module, which optimizes a control model in a long period according to the final measured performance of the spring. Through construction of a direct mapping and feedback optimization closed loop from horizontal processing disturbance to single heat treatment control, the application realizes deep coordination between the two, effectively compensates for process fluctuation, and significantly improves the consistency of spring product quality.

The utility model relates to the technical field of data line production, in particular to full-automatic wire soldering production equipment. Comprising a machine table, a wire feeding device arranged on the machine table, an outer layer peeling device used for peeling the outer layer of a wire, a core wire peeling device used for peeling the core wire of the wire, and a tin soldering device used for welding the wire into a finished product. The detection device is used for detecting the finished products; and the feeding manipulator is used for transferring the finished products. According to the utility model, automatic production and manufacturing of data lines are realized, the intelligent degree is high, the working efficiency and the working quality are improved, and the rapid production requirements of modern data lines are met.

The application belongs to the field of precise manufacturing detection and industrial automation positioning technology in the new generation of information technology, and particularly relates to a three-degree-of-freedom optical measurement system and a use method thereof. In view of the technical problems of low integration and insufficient synchronous measurement accuracy in the three-degree-of-freedom measurement of objects in the existing precise manufacturing scene, the system comprises a point light source, a light splitting prism, a mirror with an included angle, an imaging optical system, a photoelectric detector and a data processing system, and through coupling calculation of the change of the reflected image information and the mirror included angle, integrated high-precision measurement of three-degree-of-freedom parameters is realized. The application relies on the fusion of optical imaging and data algorithm, breaks through the traditional measurement bottleneck, is suitable for high-precision positioning scenes such as semiconductor packaging detection, aerospace component assembly, industrial robot posture calibration and the like, and provides key technical support for the intelligent upgrading of the new generation of information technology supporting precise manufacturing.

The invention discloses a working data acquisition and processing platform for numerical control equipment. The platform is composed of a data acquisition module, a data storage module, a data calling module, an equipment modeling module and a data visualization module. The platform realizes efficient and credible acquisition of multi-source heterogeneous data through a multi-protocol plug-in, edge computing and an event-driven mechanism; data association, anomaly recognition and real-time early warning are completed based on semantic modeling and a graph neural network; ledger management, alarm linkage and a unified data service interface are integrated, and multi-system butt joint and business expansion are supported; and the visualization module supports dynamic display of the equipment state, the processing process and the early warning information. According to the method, the problems of limited data acquisition dimension, weak intelligent analysis capability, poor system expansibility and the like of an existing acquisition platform are solved, the method is suitable for complex state monitoring and predictive maintenance of production equipment in industrial scenes of high-end equipment manufacturing, intelligent workshop management and the like, and the method has good universality and popularization value.

The invention relates to the technical field of industrial production management, and discloses an industrial manufacturing operation management method based on hybrid enhanced intelligence, and the method comprises the following steps: S1, constructing a multi-source knowledge base, and building a task matching initial model based on multiple dimensions; s2, performing autonomous task planning, task decomposition, task allocation and task release based on the task matching initial model, and updating a work allocation method knowledge base according to a real-time task execution condition; and S3, according to the updated work distribution method knowledge base and real-time feedback, cooperatively adjusting a management strategy so as to ensure that the knowledge base is synchronously updated with an actual demand, and optimizing a task distribution strategy. By constructing a knowledge-driven intelligent decision closed loop, efficient and collaborative operation, dynamic resource optimization, management cost reduction and continuous self-adaption of the system are realized, and the overall manufacturing intelligence level is remarkably improved.

The invention provides an intelligent scheduling method and device for product manufacturing, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: obtaining an original workpiece set and a new arrival workpiece set, enabling the original workpiece set to comprise original workpieces, enabling the new arrival workpiece set to comprise new arrival workpieces, and enabling the new arrival workpiece set to comprise new arrival workpieces; an initial optimal scheduling scheme is correspondingly provided; setting a selectable processing strategy for each newly arrived workpiece; the absolute value of the difference between the completion time of the original workpiece in the new scheduling scheme and the completion time of the original workpiece in the initial optimal scheduling scheme is limited not to exceed a preset threshold value, and the maximum completion time deviation constraint is obtained; constructing an objective function according to the weighted total completion time, the maximum completion time deviation cost, the compression cost and the rejection cost; and establishing a mixed integer programming model based on the target function and the maximum completion time deviation constraint, and realizing global optimization or approximate optimization of the total cost on the premise of guaranteeing the stability of the original plan while accepting a new order.

The intelligent sensor self-diagnosis optimization system comprises a laser interference unit and a temperature detection unit which are integrated in the sensor, the displacement phase difference of the sensor is obtained through laser interference measurement, and internal temperature data are combined to obtain the self-diagnosis optimization of the intelligent sensor. And the physical displacement is calculated in real time by a built-in artificial intelligence processor of the edge calculation unit. And the dynamic reference calibration unit is used for identifying reference workpiece characteristics in the production line based on the target detection neural network and generating a space error compensation matrix in combination with the physical displacement. And a compensation result is dynamically loaded by an FPGA register in the sensor compensation circuit unit, so that fine compensation of output signals of the sensor is realized. The method has high-precision dynamic error compensation, autonomous diagnosis optimization capability and good system integration, and is suitable for sensor adaptive control in scenes of high-precision manufacturing, intelligent factories and the like.

The present application relates to the technical field of intelligent management of ship manufacturing, and particularly relates to a method and system for ship pipeline design drawing stage management, which acquires and structurally processes production schedule and process specification data, constructs a standardized input set, uses a multi-objective optimization engine to resolve conflicts, optimize progress and cost, and generates a preliminary pipeline scheme in combination with a four-dimensional model, semantic verification and dynamic scheduling, and outputs construction drawings and animations after three-dimensional simulation verification, collects feedback in real time during construction, triggers secondary scheduling and optimization, realizes continuous iteration and accurate execution of design, and through intelligent decision-making of the multi-objective optimization engine, the design drawing efficiency can be improved by more than 40%, the design error rate is reduced by 90%, and the rework in the later stage is greatly reduced.

The invention discloses a modular plug-in connection structure and method for a transformer winding lead and a sleeve, and relates to the technical field of power transformer manufacturing. Comprising a plug module, a socket module and a winding lead, and the plug module is fixedly connected to the tail end of the winding lead; the plug module comprises a plug base and a male conductive contact arranged on the plug base, and the socket module comprises a socket base and a female conductive contact arranged on the socket base; a first guide part and a first locking part are arranged on the plug base; the socket base is provided with a second guide part matched with the first guide part and a second locking part matched with the first locking part; an inner cavity of the female conductive contact is internally provided with an elastic contact member which is in crimping cooperation with the male conductive contact. According to the invention, the assembly and maintenance time is greatly shortened, the connection consistency and safety are improved, and the manufacturing intelligence and operation and maintenance efficiency of the transformer are supported.

The application provides a product manufacturing intelligent scheduling method and device, and relates to the technical field of intelligent manufacturing. The method comprises the following steps: acquiring an original workpiece set and a newly-arrived workpiece set, wherein the original workpiece set comprises original workpieces, the newly-arrived workpiece set comprises newly-arrived workpieces, and there is an initial optimal scheduling scheme corresponding to the original workpiece set; setting an optional processing strategy for each newly-arrived workpiece; limiting the absolute value of the difference between the completion time of the original workpiece in a new scheduling scheme and the completion time of the original workpiece in the initial optimal scheduling scheme to be not more than a preset threshold value, and obtaining a maximum completion time deviation constraint; constructing a target function according to a weighted total completion time, a maximum completion time deviation cost, a compression cost and a rejection cost; and based on the target function and the maximum completion time deviation constraint, a mixed integer programming model is established, so that the global optimal or approximate optimal total cost is realized while the new order is accepted and the stability of the original plan is ensured.

The system is applied to the field of intelligent and digital operation management of industrial manufacturing. According to the method, digital simulation modeling is visually and accurately carried out on product manufacturing capabilities of productivity units such as working centers and intelligent production lines of an intelligent factory in a software graphical mode, and a unique manufacturing digital control execution system of each product is generated according to an established product productivity model. The graphical configuration model highly abstracts production elements such as human, machine, material, method, measurement and the like in the manufacturing capability and a combination mechanism, and provides the modeling capability of quickly constructing a product capacity model in a graph dragging mode. The problem that intelligent manufacturing core software MESMOM is difficult to adapt to products in different industries, different manufacturing processes and different management and control events is solved, manufacturing capacity digital models of all the products of the enterprises are changed from nothing to existence, the demand difference of intelligent manufacturing of all the enterprises is undertaken through model difference, the difficulty of digital manufacturing is reduced, and the production efficiency is improved. And the method has a wide market popularization value.

The application relates to the technical field of intelligent equipment manufacturing, and specifically discloses an AI-based equipment manufacturing process energy consumption optimization and carbon footprint tracking system, which comprises a central collaborative scheduler, and the central collaborative scheduler is communicatively connected with the following modules: a data self-checking reconstruction module, a wavelet neural network optimized by using an improved particle swarm algorithm is used to construct a data self-checking model, and time sequence analysis and correlation comparison are carried out on multi-source heterogeneous sensing data of an equipment manufacturing process; the application can dynamically identify and correct inherent errors of sensing data by deploying a multi-source heterogeneous sensor network, combining a precise time synchronization protocol, constructing a time sequence alignment sequence, using a wavelet neural network optimized by using an improved particle swarm algorithm to construct a data self-checking model, outputting a high-fidelity data stream, and solving the problem of insufficient reliability of carbon footprint accounting data in a traditional manufacturing process from the source, thereby providing a data basis for energy consumption optimization and carbon emission tracking, and ensuring the authenticity and effectiveness of accounting results.

The utility model relates to the field of mobile robots, and provides a base station which is used for being connected with a mobile robot and comprises a body and a signal reflection device, and at least a charging assembly is arranged in the body and used for being connected with the mobile robot for charging; the signal reflection device is used for reflecting signals emitted by the mobile robot, the signal reflection device is installed on the body, and the signal reflection device is located on the side, away from the mobile robot, of the body in the signal transmission direction. Thus, the distance between the mobile robot and the signal reflection device is increased, the transmission path of the signal sent by the mobile robot is lengthened, even if the mobile robot is in a small movable space, the mobile robot can have a larger distance to adjust the position in the process of approaching the base station so as to align with the base station, and the positioning accuracy of the mobile robot is improved. Therefore, docking and homing with the base station are smoothly completed, functions such as charging and water injection are automatically realized, the reliability of product intelligence is improved, and the use experience of a user is optimized.

The invention discloses an equipment parameter error simulation and optimization method, device, equipment and medium, and relates to the technical field of intelligent manufacturing and industrial Internet of Things, and the method realizes high-precision simulation and real-time optimization of manufacturing equipment errors by combining DDQN and LSTM. The LSTM layer effectively captures the dynamic characteristics of errors changing along with time, and the defect of time-dependent processing is overcome; the DDQN realizes online optimization of error prediction network parameters through cooperative work of an evaluation network and a target network, solves the problem of insufficient adaptability of a pre-training model to specific manufacturing equipment, and improves the error prediction accuracy; the integration of the digital twinning technology supports online learning and parallel model retraining, solves the problem of calculation power limitation of edge equipment, dynamically adapts to manufacturing environment changes, remarkably improves the consistency of manufacturing quality, can be widely applied to the fields of high-precision manufacturing, intelligent factories, aerospace and the like, and provides a brand new solution for error prediction and process optimization.

The present invention relates to the technical field of conveying mechanisms, and specifically provides an intelligent storage device. When storing documents is required, the conveying mechanism is controlled by the main control unit, driving the grasping device to shift to the feeding area, grasping the document to be stored from the feeding area. At the same time, the server matches a storage location for the document, that is, sending the address code corresponding to the storage location to the main control unit. The main control unit controls the conveying mechanism to convey the grasping device to the specified storage location according to the address code and align the document with it, and store the document. When retrieving a specified document is required, the server sends the specified address code to the main control unit. The main control unit controls the conveying mechanism to convey the grasping device to the specified storage location, retrieve the document, and convey it to the feeding area, completing the document retrieval operation, realizing fully intelligent and automated access operations, saving time and effort, capable of expanding the storage capacity of the target area, greatly reducing the cost investment, and achieving the effects of product intelligence and automation.

This invention discloses an intelligent control system for electrode foil formation process parameters that integrates diffusion models and ensemble learning. The system comprises five modules: multi-source data preprocessing and feature extraction, anomaly identification, time-series data generation, multi-objective performance prediction, and a front-end interface. Feature extraction employs a multi-scale comparative learning mechanism to extract time-series and batch features. Anomaly identification utilizes principal component analysis and weighted voting for real-time detection. Time-series data generation is based on a conditional diffusion model, learning the process and performance distribution mapping to generate controllable data. Performance prediction integrates multiple models and wavelet transform decomposition to jointly predict indicators such as specific capacitance and bending. The front-end is built on a responsive platform based on Grado, supporting asynchronous high concurrency. The system uses a diffusion model as its core, reconstructing full-cycle time-series data to reveal the control direction, and heatmaps enhance interpretability. Experimental results show that the system combines real-time performance with accuracy, identifying anomalies, predicting performance, and providing process suggestions, thereby improving the level of manufacturing intelligence.

The invention discloses a satellite manufacturing intelligent detection method, system and device and a medium, and the method comprises the steps: determining a to-be-detected region of a to-be-detected target element based on a detection database, and obtaining an initial mapping image of the target element through an augmented reality AR space registration technology; obtaining sensing data of a to-be-detected area of the to-be-detected target element through a preset sensing device; inputting the feature data of the target element after the perception data preprocessing into a pre-trained deep neural network model to obtain a detection and recognition result of the target element output by the deep neural network model; and overlapping and displaying the detection and recognition result of the target element on the surface of the initial mapping image of the target element in a visual layer form through augmented reality (AR) equipment to obtain a detection result image, and completing the detection of the target element. According to the method, the detection efficiency and the detection accuracy of the satellite manufacturing element are effectively improved, and the quality guarantee of the satellite manufacturing element is improved.

The invention relates to the technical field of transformers, in particular to a transformer design and process adaptive optimization method and system based on deep learning. Through a multi-modal data fusion technology, multi-source heterogeneous data in design, material and manufacturing processes are represented in a unified manner. A physical information neural network is utilized to construct an efficient multi-physical field agent model, and an optimization design scheme is quickly generated in combination with a material knowledge graph. And the physical manufacturing process is mapped in real time and the product performance is predicted through the digital twin. And finally, on the basis of a deep reinforcement learning model, according to the state prediction and target deviation of the digital twin, adaptively adjusting subsequent process parameters or feeding back a fine tuning design scheme, and forming a'design-process-detection-optimization 'closed loop. According to the method, the problems that the design and manufacturing links of the transformer are separated, the transformer depends on experience, and online optimization is difficult are solved, data driving and self-adaptive collaboration of the whole process are achieved, and the product performance consistency, the design efficiency and the manufacturing intelligence level are remarkably improved.

This invention relates to the field of intelligent manufacturing and industrial automation technology, specifically to a manufacturing intelligent scheduling and dispatching method based on AI edge computing terminals. The method includes: generating a theoretical scheduling baseline timeline based on work order BOMs and standard process routes (SOPs) issued by ERP / MES, as well as real-time data collected by the edge computing terminal on equipment OEE time-series slices, workstation cycle times, and edge buffer pool inventory levels; performing parametric disturbance simulation on this data to generate a theoretical residual time-series set; generating a real residual time-series based on actual workstation completion times; determining the disturbance type using dynamic time warping distance calculation and similarity calculation based on process route topology; and generating local work order translation scheduling instructions accordingly, followed by dynamic calibration to optimize the scheduling accuracy of subsequent production plans.