1255 results about "Device parameters" patented technology

The invention provides a traffic comprehensive query optimization method and system applied to intelligent human-computer interaction, and the method comprises the steps: obtaining a traffic query request input by a target user in an interaction scene, carrying out the multi-modal feature analysis of the traffic query request, generating a semantic understanding feature and a scene correlation feature, and obtaining a semantic understanding feature and a scene correlation feature based on a preset language large model; performing dynamic interaction feature fusion processing on the semantic understanding feature and the scene association feature to generate a multi-dimensional intention feature, and performing service path optimization on the traffic query request of the target user according to the multi-dimensional intention feature to generate a comprehensive response strategy; and converting the comprehensive response strategy into interactive feedback information, dynamically adapting to equipment parameters in the real-time environment association information, and executing feedback operation to complete optimized response of the traffic query request. According to the invention, the response real-time performance, decision reasonability and execution reliability of the intelligent traffic query service can be improved.

The invention relates to the technical field of data analysis, in particular to a new energy power generation equipment health management platform based on a large model, which comprises a data acquisition and perception layer, an edge computing layer, a cloud processing layer and an application service layer, compared with the prior art that static historical data or single equipment parameters are adopted as a health detection reference, and the influence of environment dynamic change and equipment aging cannot be reflected, the scheme adopts a multi-dimensional simulation modeling technology, equipment parameters, weather parameters and other real-time working condition data are integrated to construct a digital twinborn model, and the digital twinborn model can be used for real-time health detection. Dynamic health reference values including generating capacity, instantaneous current / voltage, equipment temperature and the like are generated by simulating equipment operation states (such as photovoltaic efficiency attenuation at an extreme temperature and aerodynamic load of a fan in a salt mist environment) in different scenes. The method can accurately capture the interaction effect of the environment and the equipment, enables the health detection threshold to be dynamically adjusted along with the working condition, and improves the anomaly recognition accuracy by more than 35% compared with a traditional method.

The invention relates to the technical field of sequence control, in particular to a sequence control test system based on the Internet of Things, which comprises a task decomposition module for analyzing equipment load, decomposing tasks according to equipment operation capability, acquiring task requirements and equipment parameters, judging operation conditions of equipment stations, refining task dependency relationships and performing task scheduling on the basis of a system task sequence; and associating the tasks with the equipment parameters to generate an equipment task sequence table. According to the method, efficient coordination of multiple devices under different loads is ensured by adopting a distributed coordination algorithm, task scheduling is more flexible and efficient by analyzing task dependency and state changes of the devices, optimizing the task execution sequence and arranging the execution time and stations of the devices, and the task scheduling efficiency is improved by judging synchronous execution conditions and obtaining time nodes. The synchronous execution sequence of the tasks is coordinated, repeated occupation or idling of equipment resources is avoided, and the stability and task processing capacity of a sequence control system are improved.

The invention discloses a laboratory automatic process management and multi-source data fusion system based on the Internet of Things, and belongs to the technical field of laboratory management. The system comprises a sensing layer, an analysis layer, an intelligent management layer and an application layer. The sensing layer carries out global data acquisition and real-time analysis; the analysis layer integrates data, mines an association relationship and optimizes task matching; the intelligent management layer performs full-process automatic management and control on a laboratory, and comprises an equipment cooperation module, a dynamic detection module, a dynamic configuration module, a real-time simulation module, a preset trigger module and an intelligent auditing module; and the application layer performs multi-terminal collaborative interaction and intelligent assistant assistance processing. According to the system, intelligent collaboration of laboratory equipment, automatic detection of samples, dynamic optimization of equipment parameters and automatic early warning and processing of abnormal conditions are realized through technical means such as knowledge graph construction, a digital twinborn technology and multi-mode biological recognition, and the laboratory management efficiency and the data processing accuracy are effectively improved.

The invention relates to the technical field of edge computing, and discloses an edge computing intelligent terminal based on the Internet of Things technology, which comprises a data acquisition module, a network state monitoring module and the like. The data acquisition module can dynamically adjust the acquisition frequency and precision according to network and equipment parameters; the network state monitoring and evaluation module can accurately judge the network risk and timely switch the network to guarantee data transmission; and the equipment operation monitoring and state evaluation module can effectively maintain the stability of the equipment. And the data processing decision module optimizes the data processing flow by using a self-adaptive algorithm. Meanwhile, the system performance is improved by adjusting the weight coefficient according to the scene, and the security encryption system protects the data security comprehensively. The edge computing intelligent terminal effectively solves the existing technical problems, has remarkable effects in the aspects of improving data processing efficiency, guaranteeing equipment stability, enhancing safety, adapting to different scenes and the like, and is of great significance in promoting development of the Internet of Things industry.

The invention relates to the technical field of equipment monitoring, in particular to a method and a system for collecting and monitoring production data of automotive trim injection molding equipment. Comprising the following steps: firstly, acquiring real-time operation state data from injection molding equipment through a communication node, generating an equipment state data set, and constructing a parameter coupling model through a time sequence analysis method; when the temperature parameter fluctuation exceeds a preset threshold value, fusing the pressure data to generate optimal configuration, transmitting the optimal configuration to related equipment through an interactive network, and determining a linkage parameter set; thirdly, the linkage parameter set is processed through a classification method, a deviation index is obtained, a synchronization instruction is generated according to deviation, it is ensured that beats between the devices are consistent, and a unified production cycle is formed; and finally, through the prediction model, extracting production cycle data, predicting parameter fluctuation, adjusting equipment parameters, and determining final process configuration. The problems of beat deviation and parameter fluctuation between injection molding equipment in production are solved, the production process is optimized, and the production efficiency and the product quality are improved.

The invention discloses an AI semantic understanding-based zero-code multi-terminal application automatic construction method, which comprises the following steps of: receiving a UI design draft image and layer metadata uploaded by a user, respectively extracting visual features and structural features through a double-branch feature extractor based on an AI semantic understanding technology, and establishing an AI semantic understanding model; a cross-modal attention module and a cavity space pyramid pooling module are combined to generate an enhanced feature graph, and a high-precision UI component mask and an interaction dependency graph are generated; according to target end equipment parameters, dynamic weights are generated through a multi-layer perceptron, layout constraints of the interaction dependency graph are adjusted, a layout target function is optimized, and component overlapping and visual unbalance are minimized; based on the predefined control library and the mapping rule, the UI component is mapped into the atomic control of the target platform, the interaction logic is converted into the event-action chain, and the target platform code is generated, and the automation degree and the cross-platform consistency of zero code development are improved through dynamic adaptation of the target end equipment and automatic code generation.

Systems and methods described herein providing consumer-driven access to network slices. A network device receives, from a user device, a slice selection to associate an application on the user device with a network slice. A slice recommender device generates a slice recommendation based on at least one of device parameters or network parameters. The network device provides, to the user device, the slice recommendation for presentation to the user.

The invention relates to the technical field of data processing and three-dimensional model construction, in particular to an intelligent three-dimensional model design method and system based on small-opening press-blow molding equipment. The method comprises the following steps that an intelligent sensor is used for collecting parameters of small-opening press-blow forming equipment, and initial equipment parameter information is recorded; according to the initial equipment parameter information, establishing an equipment three-dimensional initial model by using computer aided design software; and acquiring characteristic data of the glass material drops, inputting the characteristic data of the glass material drops into the three-dimensional initial model of the equipment, simulating a press-blow molding process by utilizing simulation software, and carrying out influence evaluation on the simulated press-blow molding process to obtain a molding process evaluation result. Through a data processing technology, a three-dimensional modeling technology and an automatic control technology, various parameters of the small-opening press-blow molding equipment are associated and evaluated, and an intelligent three-dimensional model of the equipment is constructed; and the accuracy of control operation of the forming equipment is improved, so that the produced glass containers are kept consistent in specification.

The invention discloses a photovoltaic power station operation data monitoring method and system, and relates to the technical field of photovoltaic power station equipment monitoring, and the method comprises the steps: collecting equipment operation parameters to form an original data set; constructing an equipment association network by taking the equipment as a node; based on the data set and the association network, time sequence collaborative change and correlation analysis are carried out on the equipment parameters, and a collaborative change model is established; judging that equipment is abnormal through a model, and calling same-type parameters of directly connected nodes to form a to-be-verified set; in combination with the network connection edge weight, calculating the influence degree of the associated parameters on the abnormal equipment by applying a dynamic weight evaluation algorithm, and generating a priority list; and performing iterative optimization on the collaborative change model according to the priority list, and outputting a monitoring report. The system comprises a data acquisition module, a network construction module, an analysis modeling module, an anomaly judgment module, a weight evaluation module, a model optimization module, a data storage module and the like to realize abnormal associated factor identification and monitoring logic optimization.

The invention discloses a visual inspection method and device for reflow soldering quality and computer equipment. The method comprises the steps of obtaining a to-be-detected product image entering a detection area; preprocessing the to-be-detected product image and extracting a welding spot ROI region; the welding spot ROI is put into a defect classification model for welding defect identification and classification, and a classification result is obtained; and determining an equipment parameter adjustment scheme according to the classification result in combination with a preset adjustment rule, and adjusting the reflow soldering equipment according to the equipment parameter adjustment scheme. By implementing the method, the welding spot quality can be automatically recognized in the welding process, the production parameters are automatically adjusted according to the detection result, the welding quality is effectively improved, potential quality problems are avoided, the production efficiency can be improved, manual intervention is reduced, and the labor cost is saved.

The invention relates to the technical field of data visualization, in particular to a digital twinning-oriented workshop data visualization system, which comprises a heterogeneous data space-time stamp reconstruction module for acquiring a sensor data stream, an MES service data packet and an environment monitoring data set and generating a space-time aligned fusion data frame; the dynamic topology compensation modeling module is used for constructing a digital twin body with a time sequence attribute and outputting a dynamic topology relation matrix comprising a geometric deviation compensation value; the event-driven incremental rendering engine module is used for generating a visual model only updating a change part by adopting a difference region recognition technology, and outputting a three-dimensional visual interface with an abnormal coordinate mark; and the bidirectional feedback control channel module is used for analyzing the abnormal coordinate mark, generating an equipment parameter adjustment instruction set through a reverse path tracking algorithm, and transmitting the equipment parameter adjustment instruction set back to the physical equipment controller. According to the invention, a bidirectional feedback closed loop with a physical equipment controller is realized, and dynamic adjustment and adaptive control are supported.

The invention discloses an AI-based highway video quality diagnosis method, system and device and a medium, and relates to the technical field of video quality diagnos.The method comprises the steps that an initial diagnosis parameter set is determined based on obtained monitoring device parameters, environment condition information and quality standard information, and video quality problem points are recognized; analyzing different scene complexity regions of the preprocessed target video stream by using the initial diagnosis parameter set, diagnosing video quality problem points, and obtaining image instruction evaluation indexes and optimization suggestion parameters of each region and the problem points; learning and optimizing a preset imaging characteristic model and a common problem mode library on the basis of the collected historical video samples, determining a target video quality improvement scheme and dynamic adaptive adjustment parameters in combination with image quality evaluation indexes of different scene complexity regions, and optimizing and repairing the video quality; according to the invention, the quality of expressway videos is improved, and the performance and reliability of a video monitoring system are improved.

The invention belongs to the technical field of automatic control, and discloses an automatic equipment control method and system based on multi-source data. Comprising the steps of collecting multi-source data of an industrial equipment group in a mining area, integrating the multi-source data into multi-equipment operation data, and preprocessing the multi-equipment operation data to obtain basic equipment operation data; constructing a multi-modal hybrid model based on the basic equipment operation data, updating the multi-modal hybrid model, and performing equipment health state evaluation on each industrial equipment by using the updated multi-modal hybrid model to obtain comprehensive risk data; constructing a deep learning framework, generating an equipment control strategy priority based on the comprehensive risk data and the deep learning framework, and generating an instruction sequence based on the equipment control strategy priority; performing dynamic parameter adjustment on each industrial device in the industrial device group based on the instruction sequence to obtain an optimal operation parameter combination; sending the optimal operation parameter combination to a preset mining area equipment parameter database; and automatic control of each device is realized.

The invention relates to a full-life-cycle digital passport management system and method for electromechanical equipment, which take dynamic data in an operation and maintenance stage as a core and depend on linkage of an intelligent algorithm and full-cycle data. The problems of equipment parameter deviation, performance degradation and the like can be accurately identified based on five-dimensional full-period data integrated by a data passport construction module; and a fault influence range and associated equipment are positioned according to physical, logic and power dependency relationships presented by the equipment dependency graph construction module, so that a basis is provided for decision correction. Besides, through unique identification of a gene coding module, historical data and responsibility subjects of equipment can be traced, BIM model dynamic updating, feedback design and construction stage data verification optimization are driven, and a closed loop of operation and maintenance feedback-model correction-full-cycle data iteration is formed. The module solves the problems of traditional operation and maintenance data isolation and model static state, enhances the timeliness and accuracy of block chain evidence storage data, and supports applications such as intelligent service layer predictive maintenance and fault self-repairing.

The invention discloses a thermal power generating unit operation state detection method and system, and relates to the technical field of operation state detection, and the method comprises the steps: S100, building a working condition variable label; s200, carrying out statistics on correlation coefficients of sensors and faults under various working conditions in historical operation data of the thermal power generating unit, dynamically updating weights, and storing the weights in a working condition database; s300, based on the energy transfer chain of the thermal power generating unit, establishing a parameter incidence matrix between devices; according to the method, the data weights of different sensors are adaptively adjusted according to the real-time operation state of the equipment, the defect that the traditional multi-sensor fusion adopts fixed weight or simple weighted average and is difficult to adapt to the working condition change is overcome, a physical relationship network between the equipment is constructed, key associated parameters are identified in an energy transfer form, and the fault identification accuracy is improved. The robustness of anomaly monitoring under complex working conditions is improved, and the false alarm rate is reduced.

The invention is suitable for the technical field of smart beam fields, and provides a design optimization method and system for a highway smart beam field based on big data, and the method comprises the steps: collecting the related data of transportation equipment through an Internet of Things and a real-time bidirectional data interaction channel, and constructing and dynamically updating a site three-dimensional digital twin model; fusing the model and adjacent node broadcast information, and outputting an optimal real-time driving path by using an improved pheromone diffusion algorithm; setting a hierarchical processing protocol for tasks with different priorities, and recalculating an optimal path sequence and adjusting task allocation when resource conflicts or path deadlocks are detected; identifying potential risks in combination with frequency domain analysis, and dynamically adjusting paths and equipment parameters; the algorithm is continuously optimized through offline training and online learning of a historical database. The system comprises a model building and updating module, a path selection module, a conflict detection module, a path adjustment module and an operation optimization module, efficient scheduling, risk early warning and continuous optimization of the intelligent beam field are achieved, and the operation efficiency and safety are improved.

The invention relates to an intelligent manufacturing optimization method and device based on a large model, equipment and a storage medium, and the method comprises the following steps: carrying out the fault type analysis of the manufacturing equipment based on a fault extraction feature parameter, and obtaining a fault type; performing digital twin mapping on the manufacturing equipment to obtain a fault position of the manufacturing equipment and a fault part corresponding to the fault position; based on the fault position and fault propagation path analysis of the fault component corresponding to the fault position, obtaining an associated fault propagation path and a potential risk component on the associated fault propagation path; and generating an optimization scheme of the manufacturing equipment based on the associated fault propagation path, the potential risk component on the associated fault propagation path, the fault position, the fault component corresponding to the fault position and the fault type, thereby solving the problem that an effective fault detection mechanism is lacked, and when equipment parameters exceed a normal range, the equipment parameters cannot be detected. And the essential reason of the fault is difficult to identify timely and accurately.

The invention discloses an intelligent tension control datamation method and system, and relates to the technical field of tension control. An intelligent tension control datamation system comprises a target equipment tension acquisition module, a target equipment tension prediction module and a target equipment tension adjustment module. The real-time tension information and the parameter information of the target equipment are acquired at preset time intervals, so that the state of the equipment is monitored and analyzed in real time; the tension data in the next preset time interval can be predicted by using the target equipment tension prediction model, so that the potential tension deviation problem can be found in advance; by comparing the to-be-analyzed tension data with the predicted tension data, whether the target equipment needs to be adjusted or not can be actively judged, instead of passively responding only after the tension exceeds a preset range; and the target equipment parameter control model can output an optimal parameter control scheme according to the prediction information and the current parameter information, and the equipment performance is optimized.

The invention relates to the technical field of data processing, in particular to an operation and maintenance object information query and work order automatic generation system based on NFC (Near Field Communication) equipment, which comprises an NFC identity recognition module, an equipment parameter summarization module, a work order information verification module, a work order structure generation module, a work order push confirmation module and a work order submission terminal based on NFC, and reading the NDEF data of the NFC tag of the equipment through the NFC reader-writer. According to the invention, automatic acquisition of equipment identity is realized through near field communication, multi-dimensional data consistency verification is carried out in combination with a chip sequence, all equipment information is automatically linked with a background data set, multiple parameters acquired on site are subjected to system logic screening, and work order key information is automatically distributed to a standard structure field. A data packaging link is controlled by a unified standard template, multiple safety verification and full-process state monitoring are executed in a submission stage, error risks are reduced in each link by means of systematic processing, a data tracing link is continuously improved, and field response and overall circulation efficiency are remarkably improved.

The invention provides a work order generation method and device, an intelligent work order filling system and a work order processing system.The work order generation method comprises the steps that a reference work order with the highest relevancy with equipment basic fault information input by a user is obtained from an enterprise asset management system in a keyword matching search mode; if a reference work order with relatively high relevancy with the equipment basic fault information input by the user cannot be obtained from the enterprise asset management system, determining a historical work order with the highest similarity in a vector database as the reference work order according to vector data corresponding to the equipment basic fault information input by the user; and according to at least one of the confirmation information and the modification information of the user on the reference work order, generating a current work order, the current work order comprising the equipment parameters and the processing method information. According to the method, traditional keyword matching search and large language vector retrieval technologies are ingeniously combined, so that the problems of long time consumption and unstable quality existing in manual work order filling in the prior art are solved.

The invention provides a resource management method and device, a storage medium and a program product, which can be applied to the technical field of servers. The resource management method comprises the steps of obtaining preset resource information of a data processor from a storage unit of a server in response to a starting instruction of the server; based on the preset resource information, allocating corresponding hardware resources to the equipment to be accessed to the data processor; monitoring equipment parameters actually accessed to the data processor so as to update the preset resource information according to the actually accessed equipment parameters under the condition that the actually accessed equipment parameters are determined to be inconsistent with parameters in the preset resource information; and dynamically adjusting the hardware resources of the equipment of which the equipment parameters are changed relative to the preset resource information in the data processor by utilizing the updated resource information.

The invention provides an urban power grid load recovery method and device based on user demands, and is suitable for the technical field of power systems, and the method comprises the steps: obtaining a historical power load data set of each target user of a post-disaster city, and maintenance resources, a power distribution network topological structure and a power equipment parameter set of the post-disaster city; for each target user, based on the historical load data set, performing power utilization analysis on the target user to obtain a power utilization demand of the target user; inputting the power demands of all the target users, the power distribution network topological structure of the post-disaster city and the power equipment parameter set into a pre-constructed power distribution network post-disaster recovery model, and solving by taking strong maintenance timeliness as a target to obtain a scheduling strategy; the power distribution network post-disaster recovery model comprises a maintenance resource scheduling unit; and scheduling power resources and maintenance resources of the post-disaster city based on the scheduling strategy. The method can improve the recovery speed of the post-disaster urban power grid and reduce the economic loss.

The invention provides a plane layout optimization method and device based on CAD drawing automatic identification, relates to the field of plane layout optimization, and solves the technical problems that only non-overlapping constraints among equipment are considered, actual space obstacles cannot be automatically avoided, and a generated layout scheme cannot be implemented. The method comprises the following steps: extracting barrier data in a layer; and through a parameter configuration mechanism, generating an equipment parameter information structural body containing the mapping relationship between the equipment and the process. And establishing a continuous optimization model containing an IPOPT solver, and obtaining an initial layout solution containing the relative orientation of each device. And based on the relative orientation of each device in the initial layout solution, introducing an obstacle rejection constraint and a device non-overlapping constraint to construct a discrete optimization model, obtaining the center position coordinates of each device through a Gurobi solver, and integrating a dynamic adjustment mechanism and an interlayer relationship optimization mechanism. And generating a new layout map for visual output. The method and the device are used in a plane layout optimization process.

The invention relates to the technical field of relay protection, in particular to an automatic checking method and system for a relay protection setting value of a power plant, and the method comprises the steps: obtaining relay protection data, including obtaining electrical parameters, equipment parameters and power topological structure information; constructing a topological graph model of the power network by using the power topological structure information, constructing an electrical model based on elements according to the electrical parameters and the equipment parameters, and combining the topological graph model with the electrical model by using node parameter association to obtain a power system integrated model; and performing constant value check calculation based on the power system integrated model, and generating a report according to a constant value check calculation result. Hidden layers are set for different fault types by adopting the multi-branch structure network, so that the neural network can perform specialized learning for different types of fault features, and the adaptability of the system to different fault scenes is enhanced.

The invention discloses a coating production line control method, device and equipment, a storage medium and a program product, and relates to the field of production scheduling, and the coating production line control method comprises the steps: monitoring production environment parameters and equipment operation parameters of coating production line equipment; parameter adjustment is carried out according to the production environment parameters and the equipment operation parameters through a pre-trained deep reinforcement learning model, the equipment parameter adjustment amount is obtained, and the pre-trained deep reinforcement learning model is obtained by carrying out model training on a preset initial deep reinforcement learning model based on historical coating production data obtained in advance; and adjusting the equipment operation parameters of the coating production line equipment according to the equipment parameter adjustment amount, and controlling the coating production line equipment subjected to parameter adjustment to perform coating production. The production line equipment parameters are dynamically adjusted through the deep reinforcement learning model, the problem that the film coating production quality is affected by film coating production line equipment parameter immobilization in the prior art is solved, and the film coating production quality is guaranteed.

The invention relates to a medical image file processing method and device and electronic equipment, and the method comprises the steps: obtaining a series of DICOM files; according to the metadata of each DICOM file, shared data and non-shared data of each DICOM file are determined, and the metadata of each DICOM file comprises patient information, equipment parameter information and spatial position information corresponding to the corresponding slice image; combining the non-shared data of each DICOM file to obtain combined non-shared data, and storing the shared data and the combined non-shared data in a first metadata field; pixel metadata corresponding to each slice image is stored in a second metadata field, and the pixel metadata comprises a gray slope, an intercept, a window width and a window level; merging the pixel data corresponding to each slice image, and storing the merged image data in an image data field; and combining the first metadata field, the second metadata field and the image data field to generate a medical image file.

The invention discloses an intelligent recommendation method and system for a corrugated carton packaging production process. The method comprises the steps that a user inputs the type, corrugation type parameters, the number of layers, the size, the weight, production process requirements and quality control standards of corrugated boards through a terminal; the system is based on a cloud computing platform, combines a mechanical property model and a manufacturing process database of a corrugated board, calculates and analyzes input data, and recommends a production process parameter combination; the system collects data in the production process in real time through a sensor, compares the data with recommended parameters, and automatically adjusts parameters of production equipment or gives an alarm. And the system continuously collects production data and user feedback, and updates the recommendation model by using a machine learning algorithm. According to the method and the system, the defective rate is effectively reduced, the production efficiency is improved, the recommendation result is accurate, the management is efficient, and the requirements of corrugated paper field production can be met.

The invention relates to the technical field of audio equipment configuration, and discloses a parameter configuration method and device of audio equipment, equipment and a storage medium, which are used for improving the accuracy of parameter configuration of the audio equipment. The parameter configuration method of the audio equipment comprises the following steps: acquiring the type and audio format information of a current input signal source; based on the type of the input signal source and the audio format information, identifying an audio content type and determining an optimal playing mode; adjusting a sound box combination and sound channel configuration of the audio equipment based on the optimal playing mode; and matching a volume level according to the audio content type, and carrying out adaptive volume adjustment on the audio equipment in combination with user preference and environment analysis.

The invention provides a multi-terminal cooperation-oriented digital intelligent screen platform interaction scene scheduling method and system, and relates to the technical field, and the method comprises the steps: receiving an access request of multiple terminal devices, and determining an optimal task distribution strategy based on device parameter information through combining a deep learning model and an improved Hungary algorithm; acquiring real-time load data, and performing real-time scheduling by adopting a dynamic load balancing algorithm; and the state synchronization of the interactive execution result is realized through the incremental state synchronization packet. According to the invention, efficient cooperation among multiple terminal devices can be realized, the distribution efficiency of interaction tasks is improved, the continuity of interaction experience is ensured, and the consistency of the states of the terminal devices is ensured.