341 results about "Industrial yard" patented technology

The invention discloses an intelligent detection and dynamic supplement regulation and control system for electroplating bath solution components, and belongs to the technical field of electroplating. The system comprises an electroplating bath body, a detection module, a data processing and control module and a regulation and control module. The detection module is used for detecting the metal ion concentration, the pH value, the temperature and the additive concentration of the bath solution on line in real time through a spectrum sensor, an electrochemical sensor and a physical parameter sensor; the data processing and control module adopts a PID and machine learning fused intelligent regulation and control model, predicts the component change trend according to the detection data and the process parameters, and generates a dynamic regulation and control instruction; the regulation and control module quantitatively supplements target components through a high-precision metering pump, and the closed-loop feedback correction module is combined to ensure that the components of the bath solution are stabilized within a preset threshold range. Multi-parameter synchronous detection and accurate regulation and control are achieved, the quality stability of the plating layer is improved, raw material consumption and labor cost are reduced, and the method can be widely applied to industrial scenes such as metal surface treatment.

The invention discloses a multi-modal data fusion-based steel wire rope detection and real-time transmission method and system, and the method comprises the steps: starting a detection system, collecting a steel wire rope damage detection signal, collecting equipment position and operation state data, constructing an original data set, carrying out the preprocessing, building an incidence matrix, dividing a processing unit, and then extracting features, and forming an initial feature data set. And extracting damage features through a multi-branch network, strengthening the weight of a key region, fusing position and equipment state features through a multi-modal fusion module, generating fusion vectors, classifying and identifying, and outputting a structured detection result. And the acquisition equipment end performs grading processing on the data, distributes transmission channels according to priorities, dynamically adjusts parameters, adds integrity and time sequence identifiers, and the upper computer verifies the data integrity and triggers abnormal supplementary transmission. And the upper computer decodes the data, reconstructs the waveform, gives an alarm in real time in combination with preset parameters, and generates details of damage key information. Reliable technical support is provided for safe operation and maintenance of the steel wire rope in industrial scenes.

The invention provides a task processing method and device based on an industrial large model, a medium and a product, and relates to the technical field of industrial automation. The method comprises the following steps: constructing a multi-source data set based on an industrial scene, and training a plurality of adapters corresponding to the multi-source data set by adopting an adaptive low-rank adaptation algorithm; according to the learnable weight, fusing the plurality of adapters to obtain a fine-tuned industrial large model; and inputting an operation and maintenance task of the intelligent equipment in the industrial scene into the fine-tuned industrial large model, outputting an operation and maintenance strategy of the intelligent equipment, and controlling the intelligent equipment in the industrial scene to execute the operation and maintenance strategy. According to the method, efficient fine tuning and capability integration of an industrial large model in a multi-source heterogeneous data scene are achieved by introducing a self-adaptive low-rank adaptation algorithm and a multi-task adapter fusion framework, the fusion conflict problem of multi-source heterogeneous data is solved, efficient reasoning of full-process intelligent interaction of the air compressor is achieved, and the intelligent interaction of the air compressor is achieved. And the stability and the precision of the large industrial model in a complex industrial task are improved.

The invention relates to a self-adaptive vehicle washing system for an engineering vehicle and a control method. According to the self-adaptive vehicle washing system for the engineering vehicle and the control method, the existence, contour and dirt characteristics of the vehicle are automatically detected through the sensing recognition system, the cleaning logic is autonomously executed in combination with the intelligent control system, vehicle guiding, equipment starting and stopping and washing operation do not need to be manually intervened, the cleaning efficiency is improved, and the working efficiency is improved. And continuous operation requirements of industrial scenes such as metallurgy and mines are met. The spray washing system adopts a top spray head and a lateral spray head which can move longitudinally and transversely to be matched with a fixed chassis spray head, and contours of different vehicles can be accurately covered; according to the method, grating sensing data and an image recognition result are fused through a lightweight convolutional neural network, the vehicle type and the dirt adhesion level are accurately judged, the water pressure, the nozzle angle and the cleaning time are dynamically adjusted, optimal matching of cleaning parameters is achieved, the water consumption and energy consumption are reduced, and meanwhile a cleaning program is continuously optimized through machine learning.

The invention belongs to the field of information processing, discloses an industrial multi-modal data semantic alignment method based on vector space and topological constraints, and aims to solve the problem that multi-modal data semantic segmentation in an industrial scene is difficult to unify and associate. The method comprises the following steps: performing feature extraction and structured analysis on modal data, and mapping the modal data to a unified semantic vector space through a projection layer; on the basis of vector similarity matching, topological structure constraints derived from process drawings and the like are introduced, neighbor relation verification is carried out on candidate entities, context logic verification is carried out in combination with a large model, and therefore the accuracy of cross-source entity alignment is remarkably improved; mixed retrieval is supported by establishing an efficient vector database index, and incremental updating of the knowledge graph is achieved through large-model-assisted reasoning and rule base verification. Vector semantics and specific structural dependency of an industrial system are effectively fused, and high-precision and evolvable cross-modal semantic alignment and intelligent association analysis are achieved.

The invention discloses an industrial virtual power plant flexible resource capacity aggregation method based on artificial intelligence, and belongs to the field of power system scheduling and energy management, and the method comprises the following steps: S1, generating a dynamic model parameter containing a constraint boundary and an output prediction curve; s2, scoring the generated dynamic model parameters containing the constraint boundary and the output prediction curve result, and performing priority ranking according to the scoring result; s3, based on a priority ranking result, generating an optimal flexibility resource aggregation capacity allocation scheme; s4, determining a feasible declaration scheme of the industrial virtual power plant; and S5, dynamically updating the steps S2, S3 and S4 by using the real-time operation data. By the adoption of the industrial virtual power plant flexible resource capacity aggregation method based on artificial intelligence, accurate quantification and efficient aggregation of the industrial virtual power plant flexible resource capacity are achieved by fusing the AI algorithm of multi-dimensional feature extraction and industrial scene constraint modeling.

The invention discloses an industrial safety detection method combining time alignment and motion consistency, and belongs to the technical field of industrial safety monitoring. According to the method, through seven steps of multi-camera data acquisition and preprocessing, target feature extraction, coordinate projection, instantaneous speed estimation, multi-dimensional similarity fusion, soft matching optimization and model training and target trajectory generation, the problem of space-time inconsistency during multi-camera cooperative detection in an industrial scene is solved. A camera clock offset parameter is innovatively introduced to correct a cross-camera time difference, appearance, geometry and time prior multi-dimensional similarity are fused, a multi-loss function optimization model is constructed, and cross-camera target accurate matching and long-time trajectory tracking are realized. High-precision technical support is provided for personnel safety monitoring and equipment abnormal behavior detection in an industrial scene, and the industrial production safety protection capability is improved.

The invention relates to a desensitization method, system, equipment and medium for industrial data, and the method comprises the steps: building and dynamically maintaining an industrial field standard data variable rule table, and defining a desensitization strategy and a sensitivity level matched with specific industrial variables such as temperature, rotating speed and the like in physical meanings, time scenes and node levels of the specific industrial variables; the method comprises the following steps: adding a multi-dimensional identifier to original industrial data, establishing a six-dimensional binding relation model of data variable type-time dimension-node hierarchy-cluster role-sensitive level-rule entry, driving the data to execute a time-coordinated stepped desensitization process in four node hierarchies of a workshop, an enterprise, a park and a cluster, according to the method, refined access control and data decryption fusing time, space and role dimensions are realized according to the model and a full-link tracing log, and the problems that rules and industrial scenes are disjointed, dynamic time adjustment is lacked, cross-node collaboration is insufficient and authority control coarse granularity is caused in the prior art are effectively solved.

1. The name of the design product: a floor cleaning robot. 2. The use of the design product: automatic cleaning for industrial scenes. 3. The design points of the design product: in shape. 4. The picture or photo that best shows the design points: perspective view 1. 5. The bottom of the design product is not easily visible or not visible during use, and the top view is omitted.

The invention discloses an industrial equipment fault diagnosis method and system under supplementary mark constraint, and belongs to the technical field of industrial intelligent operation and maintenance and fault prediction health management. The method comprises the steps of collecting and preprocessing equipment vibration signals; constructing a one-dimensional residual error-based double-end deep network as a complementary mark learning model; a joint probabilistic loss function is generated by using a complementary mark based on maximum likelihood estimation, so that the model learns fault features from a negative tag; and high-precision fault classification is realized by using the trained model. According to the method, the complementary mark learning normal form in weak supervised learning is applied to industrial equipment fault diagnosis, more than 90% of diagnosis accuracy is obtained on a plurality of standard bearing data sets such as CWRU and MFPT, the data marking threshold is remarkably reduced, the problems of scarcity of fault samples and difficulty in marking in an industrial scene are effectively solved, and the fault diagnosis accuracy is improved. The method has important practical application value.

The invention relates to the technical field of industrial equipment monitoring, in particular to an industrial equipment remote monitoring and early warning method based on data fusion, which comprises a multi-dimensional parameter dynamic correlation analysis module, an edge side distributed data processing unit, a self-adaptive threshold generator and an abnormal state early warning mechanism. Through capturing a multi-parameter nonlinear relationship, distributed data processing, dynamic threshold adjustment and a hierarchical early warning strategy, the monitoring real-time performance and accuracy are significantly improved, and the false alarm rate and the missing report rate are reduced. The method can effectively solve the problems of long monitoring period, high abnormity identification delay and the like, is suitable for large-scale industrial scenes, and has a wide application prospect.

The invention relates to a laser cutting part sorting method and system based on a GNC file and point cloud vision, and the method comprises the steps: analyzing a numerical control machining file, and obtaining the nominal layout and prior information of a plate surface part; based on the nominal layout and the prior information, boundary extraction and initial pose coarse registration of a region of interest are carried out, and a candidate contour region is obtained; performing local point cloud fine registration on the candidate contour region to obtain six-degree-of-freedom pose estimation data of the part; and on the basis of the six-degree-of-freedom pose estimation data, grabbing pose data are solved under a world coordinate system, and a preset grabbing strategy and a preset placement strategy are adopted to sort the laser cutting parts. According to the method, collaborative optimization of positioning precision, recognition robustness and sorting rhythm is realized in a large-breadth, strong-reflection and multi-variety close-packed industrial scene, and the method has remarkable engineering implementation value and popularization potential.

The invention discloses a model fragment distribution method and device for industrial equipment, equipment and a medium. The method comprises the following steps: dividing a pre-trained machine learning model into a plurality of model fragments according to a preset function type; obtaining the computing power performance level of each device in the target industrial scene and the task risk level of the corresponding to-be-executed task, and according to the computing power performance level and the task risk level, generating an adaptation model fragment subset of each device, so that each device executes the reasoning task by using the respective adaptation model fragment subset; running state data and to-be-executed task data of each device are continuously collected, so that a real-time comprehensive performance index and a real-time comprehensive risk index are calculated respectively, and if there are indexes which do not meet preset requirements, new adaptive model fragment subsets are generated for the corresponding devices. According to the invention, model fragmentation distribution can be carried out according to computing power and task requirements of different devices, and the adaptation degree of each hardware device and the intelligent reasoning model in an industrial scene is improved.

The invention discloses a real-time AR data superposition and interaction system for an industrial inspection robot, and relates to the technical field of industrial man-machine interaction, and the system comprises the steps: collecting multi-dimensional inspection context data obtained by a sensor carried by the inspection robot, and building a unified space reference system; updating of the AR terminal in the industrial scene is completed; and the corresponding control command is issued to the inspection robot for execution. According to the invention, high-precision, low-delay and semantic-consistent closed-loop coordination between data sensed by the inspection robot and AR terminal display is realized, and the information presentation intuition, the man-machine coordination fluency and the field decision accuracy in the industrial inspection process are improved; the defects of interaction lag, virtual-real dislocation, operation interruption and the like existing in a traditional system are effectively overcome, and the urgent requirement for efficient and immersive man-machine interaction equipment in a high-risk complex industrial scene is met.

The present invention relates to an electric and non-electric combined dehumidifier in which, even though an electric dehumidifier that consumes an allowable margin electricity is used in an industrial site where it is previously installed and has difficulty in increasing the electric capacity, a non-electric dehumidifier that does not use electricity is used together when the dehumidification performance is insufficient, thereby capable of improving dehumidification performance.

The utility model relates to the technical field of industrial power utilization, in particular to a dustproof industrial switch socket, which comprises a mounting shell, a protective cover and a switch socket, the switch socket is fixed in the mounting shell, the protective cover is arranged on the mounting shell and comprises an upper cover and a lower cover, the lower cover is fixedly arranged at the bottom of the mounting shell, and the switch socket is arranged in the mounting shell. The two sides of the top of the lower cover are fixedly connected to the two sides of the mounting shell, the upper cover covers the lower cover, and the top of the upper cover is rotationally connected with the protective cover through a damping rotating shaft. Through an assembly composed of the mounting shell, the protective cover and the switch socket, a set of dustproof switch socket structure with a good heat dissipation effect is provided for an industrial site, dust in an industrial operation environment can be effectively prevented from floating and making contact with the plugging position of the socket, the heat exchange efficiency can be better improved, and the service life of the socket is prolonged. And the electrical equipment is better in use stability and higher in safety after being plugged with the switch socket.

A computer-implemented method for monitoring productivity, health and safety risks posed by activities and objects, and other signals present at industrial sites comprises: receiving data inputs from input devices at an industrial site; selecting a data model that is programmed to detect activities or objects associated with workers or equipment present at the industrial sites; applying the data inputs to the data model to receive output data specifying whether the activities or objects associated with workers or equipment are present at the industrial site; and if they are present: based the output data, determining characteristics of the activities or objects; based on the characteristics, determining whether that the activities or objects cause any productivity, health or safety risks at the industrial site; and if so, generating notifications indicating the health or safety risks at the industrial site.

1. Name of the product in this design: Industrial Endoscope (High-end). 2. Purpose of this design: For internal inspection in industrial settings. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

The utility model discloses a miniature travelling crane track device, which belongs to the field of travelling crane devices and comprises two track travelling cranes slidably arranged on an external track. The two supporting pieces are respectively arranged on the two rail travelling cranes; the transverse moving type lifting appliance is arranged at the tops of the two supporting pieces; the supporting piece comprises two driving pieces, a telescopic piece, a transmission piece and a servo motor, and the lower driving piece is arranged at the top of the rail crane; the upper driving piece is arranged at the bottom of the transverse moving type lifting appliance; the telescopic piece is arranged between the two driving pieces; the transmission parts are arranged on the outer walls of the two driving parts; and the servo motor is arranged on the outer wall of one driving piece. According to the miniature traveling track device, through cooperative operation of the structures, the miniature traveling track device can be driven by the servo motor, through linkage of the driving piece, the telescopic piece and the transmission piece, accurate adjustment of the height and space occupation of the transverse moving type lifting appliance is achieved, and the application requirements of diversified industrial scenes are met.

The application relates to the technical field of industrial network identification flow scheduling, and discloses an intelligent industrial identification flow scheduling method and system.The method comprises the following steps: S1, receiving an identification analysis request sent by an industrial network terminal, and extracting a service urgency feature of an application layer and a transmission reliability feature of a transmission layer in the identification analysis request; S2, analyzing a dynamic coupling influence relationship between the service urgency feature and the transmission reliability feature, and generating a priority feature label corresponding to the request based on the dynamic coupling influence relationship and a preset balancing strategy; and S3, distributing the identification analysis request to a preset high-throughput processing path or a low-latency processing path based on the priority feature label.The application can solve the problems that the prior art is difficult to balance the conflict between service timeliness and transmission stability and difficult to meet the dynamic scheduling demand of diversified services in an industrial scene.

The utility model relates to the technical field of gear speed reducers, and discloses a gear speed reducer beneficial for filling lubricating oil, which comprises a gear speed reducer, and an oil filling port is arranged at the top of an upper shell of the gear speed reducer; a top plate is mounted above an oil injection port of the gear reducer upper shell through bolts, a through port communicated with the oil injection port is formed in the top plate, a sealing plug is inserted into the through port, a connecting rod is fixedly connected to the bottom of the sealing plug, and a movable plate is fixedly connected to the bottom of the connecting rod. The outer side of the connecting rod is sleeved with a first spring, the top of the first spring is fixedly connected with the top plate, and the two ends of the sealing plug are fixedly connected with limiting blocks. According to the gear reducer beneficial to lubricating oil filling, lubricating oil enters the gear reducer through the movable plate through the oil penetrating holes, so that lubricating oil filling is facilitated, the upper shell of the gear reducer does not need to be detached, and the gear reducer is suitable for industrial scenes where lubricating oil needs to be frequently filled.

The application belongs to the technical field of industrial artificial intelligence, and discloses an AI-based industrial edge intelligent task cooperation method and system, which comprises the following steps: based on the probability distribution prediction of the future resource state, a main execution DAG and multiple or DAGs are automatically generated, instant switching is realized when a preset failure mode is detected, and the system robustness is greatly improved; a deep diagnosis is performed by using a causal inference engine, and the agent is guided to perform skill specialization and skill pruning, so that an intelligent ecology capable of self-purification and evolution is constructed; and a cross-topology meta-learning framework is adopted, so that the system can master the ability of quickly adapting to new and unseen tasks with few samples. Through the three innovations of multi-path or planning, skill life cycle management driven by causality and cross-topology meta-learning, the application brings the effects of nearly zero delay of task recovery, efficient evolution of collective intelligence and rapid generalization of new tasks, and enhances the application value of the system in complex and changeable industrial scenes such as flexible manufacturing.

The invention belongs to the technical field of large-size industrial measurement positioning, and particularly relates to a visual measurement positioning system and method based on environment structured information. The system comprises an intelligent sensing hardware layer, an environment structured feature library and a core processing and control unit. Through an intelligent perception hardware layer, agreement positioning of laser coarse positioning-visual fine positioning is realized, and in combination with natural feature points and auxiliary calibration objects included in an environment structured feature library, image processing, deep learning identification, feature extraction matching and multi-view geometric calculation are carried out. Obtaining a three-dimensional coordinate of a target feature point of the to-be-detected target in the global coordinate system; and dynamic calibration and intelligent scheduling of the camera are carried out through the intelligent scheduling and calibration module, so that continuous measurement of the to-be-measured target is realized. According to the invention, the environment structured information of the measurement field is converted from background to available perception assets, and high-precision, high-efficiency and high-robustness visual measurement positioning in a large-scale dynamic industrial scene is realized.

The invention discloses a method for designing a porous throttling sleeve of a steam regulating valve with a multi-stage pressure drop structure. The method comprises the steps that the type, working condition parameters and geometric parameters of a valve needing to be designed are obtained; the blocking pressure difference of the regulating valve is calculated, and the minimum pressure reduction stage number where choking flow does not occur is calculated according to the pressure distribution of the indexes; the throttling area needed by each stage when the pressure drop requirement is met is determined through a computational fluid mechanics method; the noise level of the regulating valve under different apertures is determined by adopting a computational fluid mechanics method, and the aperture corresponding to the minimum noise is found through a curve equation fitting method. The method is applied to the design process of the porous throttling sleeve of the steam regulating valve, the optimal stage number is determined by calculating the minimum pressure reduction stage number, the flow area of each stage and the pore diameter with the lowest noise level, the structural design of the porous sleeve of the steam regulating valve is achieved, and the method is particularly suitable for industrial scenes with high steam parameter control precision and strict noise requirements; and the method has remarkable technical value and market application prospect.

The embodiment of the invention provides an industrial large model task collaboration method and device and a storage medium, and relates to the technical field of industrial intelligence, and the method comprises the steps: carrying out the multi-dimensional feature extraction of an obtained abnormal event; inputting the multi-dimensional features into a pre-trained classification model for quantitative analysis to obtain an event type and a discrimination confidence coefficient of the abnormal event; determining an event type of the abnormal event according to a comparison result of the judgment confidence and a preset confidence threshold; calling the first system and / or the second system to process the abnormal event according to the event type of the abnormal event; the first system is used for performing quick reasoning on events through the industrial large model, and the second system is used for performing deep reasoning through the industrial large model events. According to different event types, independent execution or cooperative processing of the first system and the second system is selected, so that efficiency and precision balance of task hybrid processing of an industrial large model of a single system architecture in an industrial scene is effectively solved in the face of different abnormal events.

The invention provides a gas quick switching-off valve emergency switching-off system and method based on triple cooperative redundancy, relates to the field of safety protection of gas pipeline equipment, and aims to solve the problems of operation refusal, response delay, single-point failure risk and the like of an existing quick switching-off valve system under the power-losing working condition. According to the system, through a triple collaborative architecture of a hydraulic system transformation module, an electrical system transformation module and a control program optimization module, a power-on quick-closing type electromagnetic valve is replaced by a power-off quick-closing type electromagnetic valve, an emergency power supply loop of a UPS power supply series sudden stop button is constructed, a DCS direct tripping mechanism is optimized, and a triple redundant link of hydraulic automatic control, electrical emergency and control direct tripping is formed. The corresponding emergency cut-off method is executed according to the processes of fault detection, control response, electrical execution, hydraulic action and valve closing, ultra-fast emergency cut-off smaller than or equal to 0.4 second is achieved, the accident rate is reduced by 90% or above, and the method is suitable for high-risk fluid industrial scenes such as steel, chemical engineering and natural gas conveying and has remarkable safety benefits and popularization value.

This invention discloses a robotic arm grasping method and system based on calibration constraint point cloud reconstruction. The method includes: setting a calibration board at a target location in an industrial scene; performing hand-eye calibration on the robotic arm based on the calibration board to obtain a corresponding hand-eye transformation matrix; capturing images of the industrial scene using an industrial RGB camera mounted on the robotic arm to obtain a target image containing the calibration board; obtaining the calibration camera pose of the industrial RGB camera based on the target image; reconstructing a sparse point cloud based on the target image and the calibration camera pose to obtain a scene sparse point cloud corresponding to the industrial scene; performing path planning on the robotic arm based on the calibration camera pose, the scene sparse point cloud, and the hand-eye transformation matrix to obtain a planned grasping path for the robotic arm; and controlling the robotic arm to grasp the target according to the planned grasping path. This significantly improves the accuracy of sparse point cloud reconstruction, enhances the accuracy and stability of robotic arm grasping, and reduces the cost of image acquisition equipment.

Cogeneration installation with a nuclear reactor site and an industrial site with a chemical production unit, the industrial and nuclear sites being separated and connected by a closed-loop thermal energy storage system. The invention essentially consists of physically relocating and functionally separating a nuclear site from an industrial site comprising at least one chemical production unit that utilizes the heat produced by a reactor block at the nuclear site. This physical relocation is achieved by a thermal storage loop with hot and cold tanks located both at the nuclear site and at the industrial site. Figure for the abstract: Fig. 1

The utility model discloses a wire cutting machine tool control cabinet convenient to overhaul, belongs to the technical field of wire cutting machines, and aims to solve the problem that a traditional control cabinet is generally inconvenient to overhaul: a large number of connecting pieces need to be disassembled during overhaul when internal electrical components are installed and fixed; the cable distribution cabinet comprises a cabinet body, the cabinet body is divided into an installation space and a cable thinning space by a partition plate fixedly connected in the middle, groove pieces are arranged in the middles of the front ends of the two sides of the installation space, each groove piece is provided with a plurality of sets of rotating mechanisms, and the front side of the installation space is provided with a double-opening-closing door capable of being completely opened. A maintenance groove is formed in the rear side of the cabinet body, a rear cover plate is fixedly connected to the rear side of the cabinet body through a plurality of bolts, a drawable cable storage box is arranged in the cable thinning space, and the rotating mechanism can enable the mounting plate to rotate outwards by 90-180 degrees; according to the utility model, through rotation exposure type maintenance and modular cable management, the maintenance efficiency is significantly improved, and the device is suitable for industrial scenes of high-frequency maintenance.

The invention discloses a dust removal and air distribution control system and method based on a mathematical model, relates to the field of power failure prediction methods of power big data, and provides the following scheme that the dust removal and air distribution control system comprises a data acquisition unit, a mathematical model operation unit, a control decision unit, an execution unit and a feedback correction unit; the data acquisition unit is used for acquiring real-time environment parameters and equipment operation parameters in an industrial scene, and transmitting the acquired data to the mathematical model arithmetic unit through wireless transmission or an industrial Ethernet, and the transmission delay is less than or equal to 100ms. According to the invention, traditional control hysteresis is broken through, 'predictive 'accurate dust removal is realized, and the problem of scene adaptation is solved; meanwhile, self-adaptive regulation and control of multiple industrial scenes are achieved, the energy consumption optimization target can be fused, the win-win situation of efficient dust removal and low-consumption operation is achieved, long-term stability and reliability of the system are guaranteed by constructing a closed-loop optimization system, and the technology is high in expandability and adapts to future industrial intelligent upgrading.