787 results about "Device parameters" patented technology

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 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 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 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 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 provides a layout generation method, a constraint file editor, a layout generator and a terminal.The layout generation method comprises the steps that firstly, device parameters of a target fixed circuit structure are converted into device parameters of a corresponding layout structure based on a configuration file; and determining the arrangement type, array number, spacing and other structural layouts of the device according to the layout rule. And generating a constraint file of bottom design through preset process parameters, and finally generating a layout result of the target fixed circuit structure based on the structural layout of the device and the constraint file. According to the scheme, full-automatic layout and wiring of the analog circuit layout can be realized, the design efficiency is remarkably improved, and the dependence on a specific process and a professional threshold of a layout engineer is reduced. Meanwhile, through parameterization constraint and symmetrical layout control on a fixed circuit structure, high symmetry of the layout is ensured, and the stability and reliability of circuit performance are effectively improved.

The invention provides a global adaptive power supply adjustment method and system under the condition of local load increase, and relates to the technical field of power system dispatching. The method comprises the following steps: acquiring regional power grid topology, equipment parameters and real-time operation data; historical power consumption data of classified users in recent three years are collected and preprocessed; generating a reference load curve based on real-time data and a historical rule, calculating a deviation ratio, predicting a load in future 2 hours, and outputting a risk level; the regional energy storage equipment is called to dynamically adjust the discharge power when the risk is medium or low, and the adjacent regional resources are evaluated and cross-regional line switching and power supply support are executed when the risk is high or the energy storage is insufficient. Through layered adjustment and a dynamic response mechanism, rapid identification and accurate control of sudden load increase are realized, power supply stability and resource utilization efficiency are improved, and the method is suitable for local load fluctuation response of scenes such as residential areas and industrial parks.

The invention discloses a cigarette cut tobacco batch production circulation control system and method based on single-point condition judgment, and the system comprises a work order parameter verification module which is used for receiving a work order issued by a scheduling system, and carrying out the work order information integrity verification, process parameter compliance verification and equipment suitability verification; the regular expression configuration module is used for providing a visual interface and configuring a regular judgment rule set of equipment starting conditions, ending conditions and abnormal ending conditions; and the single-point judgment execution mechanism is embedded into the PLC control layer, acquires equipment parameters in real time, dynamically evaluates regular conditions based on a finite-state machine model, and triggers an equipment start-stop instruction. According to the system, the cigarette cut tobacco batch switching time is remarkably shortened, and the work order circulation efficiency is improved; the manual intervention frequency is further reduced, and the misoperation rate is further reduced; and the problem of production interruption caused by disconnection of the equipment state and the process parameters is solved.

The invention relates to the technical field of power systems, and discloses an automatic checking and correcting system and method for relay protection setting values of a booster station, and the system comprises a configuration analysis module which is used for analyzing an SCD configuration file of a transformer substation, extracting parameters of a protection device, and importing a setting value list to establish a mapping relation table; the data acquisition module is used for acquiring a constant value, a soft pressing plate state and a communication message of the IED equipment in real time through an MMS protocol; and the intelligent verification module is used for executing constant value comparison, pressing plate state verification and communication abnormity responsibility judgment. The invention develops a handheld intelligent inspection system, and solves the three major problems of low efficiency, high error rate and prominent safety risk in the checking operation of the relay protection constant value of the traditional transformer substation. According to the scheme, full-process automation of configuration analysis, data acquisition, intelligent verification, safety correction and report generation is realized, so that the routing inspection of the secondary equipment is upgraded from an extensive mode to a precise operation and maintenance normal form, and a guarantee is provided for safe and stable operation of a power grid.

According to the job task allocation method and device, the electronic equipment and the storage medium provided by the embodiment of the invention, the total task amount of job tasks to be allocated and the equipment parameters of all image forming equipment in an image forming equipment cluster are acquired, and the equipment parameters are used for indicating the health degrees of the image forming equipment; determining a preset task amount corresponding to each image forming device based on the total task amount and the device parameters of each image forming device; and allocating the job task based on the preset task load, so that the actual task load allocated to each image forming device does not exceed the corresponding preset task load. The health degree of the image forming equipment is considered when the job tasks are allocated, the image forming equipment with the low health degree allocates the printed manuscripts with fewer pages, the situation that the image forming equipment with the low health degree is damaged due to the fact that too much tasks are allocated is avoided, and the overall job task allocation strategy is optimized.

An electric hair cutting device including a housing having a first end and a second end opposite the first end, a bladeset mounted to the housing at the first end, the bladeset having a stationary blade and a reciprocating blade, an electric motor electrically connected to a power source within the housing, a drive shaft operatively connecting the electric motor to the reciprocating blade, a control module within the housing which controls a rotational speed of the electric motor; and a trigger which sends an actuation signal to the control module based on a partial actuation of the trigger between a minimum actuation and a maximum actuation, the control module adjusting a rotational speed of the electric motor based on the actuation signal. A related charging stand is provided that is compatible with the hair cutting device and features a digital display of device parameters.

According to the new energy Internet of Things multi-level analysis sharing platform based on digital twinning, the intelligent perception and edge access module collects data through a multi-mode sensor, and reliable data is provided for subsequent analysis through edge calculation preprocessing and hierarchical transmission. And the digital twinning construction and dynamic updating module constructs equipment-level, station-level and regional-level digital twinning models based on a Bayesian optimization algorithm to realize dynamic calibration of the models. And the multi-level analysis decision module realizes equipment fault diagnosis, generation power prediction, energy storage scheduling and power grid optimization through a four-level architecture. The digital twin mirror image regulation and control module establishes a bidirectional control channel and supports remote configuration of equipment parameters and automatic triggering of fault plans. The cross-domain data security sharing module adopts a three-layer security protection system and a federal learning mechanism to guarantee data security and sharing. According to the platform, full-process intelligent management of the new energy system is realized, and the operation efficiency and safety of the system are improved.

The invention relates to the technical field of communication, in particular to a twinborn scene two-way communication method. Collecting multi-source real-time operation data and state data of the physical entity of the equipment, and uploading the multi-source real-time operation data and the state data to the corresponding digital twin; calculating a relative deviation based on the predicted equipment parameter of the digital twin and the actual equipment parameter of the equipment physical entity; judging whether the relative deviation exceeds a preset error threshold value or not, if yes, starting difference analysis on the relative deviation, and determining a deviation root cause causing the prediction deviation; based on the deviation root cause, combining with the performance attenuation state data of the physical entity of the equipment, and generating a deviation correction coefficient through a quantitative deviation correction model; the predicted equipment parameters of the digital twin are corrected according to the correction coefficient, and corrected equipment parameters are generated; and issuing the corrected equipment parameters to a corresponding monitoring system to complete a two-way communication closed loop. According to the method, the prediction precision and accuracy of the digital twin are remarkably improved.

The invention relates to the technical field of electric valves, in particular to an electric valve abnormal working condition monitoring method and system. Comprising the steps of setting a plurality of monitoring points based on equipment parameters of the electric valve; operation deviation values of all the monitoring points are generated according to a preset state evaluation model, and a first-level diagnosis strategy is set according to all the operation deviation values; generating an abnormal risk value according to the primary diagnosis strategy, and judging whether an early warning instruction is generated or not according to the abnormal risk value; a plurality of monitoring points are established based on the equipment parameters and the historical fault parameters of the electrically operated valve, and the abnormal operation state of each monitoring point is analyzed and pre-warned in time by setting the diagnosis sub-model of each monitoring point, so that the pre-warning efficiency of the abnormal working condition of the electrically operated valve is improved, the fault hidden danger is eliminated in time, and the safe operation of the electrically operated valve is ensured.

The embodiment of the invention provides a form generation method, device and system based on AI enhancement and a storage medium, and relates to the technical field of form generation. The method comprises the steps of generating form structure metadata according to a natural language processing technology, a reinforcement learning algorithm and / or a type security verification rule; according to the form structure metadata, the equipment sensor data and / or the user visual behavior data, the rendering priority of each field is determined; and generating a target form according to the rendering priority and the equipment parameters. By generating the form structure metadata which is reasonable in structure and conforms to the type safety specification, the automation degree and reliability of form design are improved. By fusing form structure metadata, device sensor data and user visual behavior data, a rendering strategy can reflect device characteristics and user attention distribution. The target form is generated by combining the rendering priority with the equipment parameters, so that the form layout is adaptively matched with the terminal equipment, and the loading performance and the interaction experience of the form are improved.

The invention relates to a dynamic rule adaptive substation SDD file analysis method, device and system. The method comprises the following steps: acquiring a to-be-analyzed substation system description file; analyzing the file to generate a basic data model; extracting static parameters and dynamic operation parameters of the equipment based on the basic data model; matching the extracted equipment parameters with a predefined rule base to generate a logic interconnection rule and a physical connection rule between the equipment; according to the logic interconnection rule and the physical connection rule, constructing a topology network diagram representing the connection state of the equipment of the whole station; and based on the node and side information in the topological network diagram, generating a configuration file for describing the physical loop of the whole station, and storing the configuration file to a specified path. The full-station physical loop configuration file generated by the embodiment can provide accuracy for closed-loop testing, operation and maintenance debugging and digital twinborn application of the intelligent substation, and the intelligent level of substation design and operation management is remarkably improved.

The embodiment of the invention provides a device parameter optimization method, optical network equipment and a medium. The method comprises the following steps: firstly, acquiring first data sent by opposite-end equipment; then, obtaining second data obtained after the first data is processed by each physical layer device; then, a signal quality reference index related to each device is determined according to the second data. Then, acquiring historical configuration parameters of each device from a historical parameter configuration library, and determining a signal quality prediction index related to each device based on the historical configuration parameters; and determining a first target configuration parameter related to each device according to the signal quality reference index and the signal quality prediction index, and performing parameter optimization on each device according to the first target configuration parameter. According to the embodiment of the invention, the physical layer device parameters are dynamically optimized, the limitation of static configuration is broken through, and the stability and transmission efficiency of the PON system are improved.

The invention provides a power communication resource dynamic allocation method, and relates to the technical field of power communication resource dynamic allocation, and the method comprises the steps: recognizing a power business data type, and calculating a business weight W; monitoring the quality state of each communication channel in real time and calculating the channel quality Q; monitoring service requirements or channel quality in real time, and triggering dynamic optimization when the channel quality cannot meet the service requirements; excluding businesses needing fixed resource allocation and businesses whose business weights are higher than those of the businesses triggered to be optimized this time, and enabling the rest businesses to participate in dynamic optimization; configuring network equipment parameters according to the result to execute resource allocation optimization; according to the method, the state change of the communication network can be responded in real time to adjust the resource allocation strategy, the risk of communication interruption or flash of key services due to resource optimization is effectively reduced, and finally, the utilization rate of power communication resources is guaranteed; and the reliability, the stability and the real-time performance of core service transmission such as power grid production control and operation monitoring are greatly improved.

The invention relates to the technical field of fault prediction and health management, and discloses a pump station equipment risk intelligent prediction method based on big data analysis, and the method comprises the steps: collecting equipment operation and environment data; a hybrid intelligent causal analysis model for automatic screening is constructed to obtain correlation characteristics among equipment parameters; constructing a risk analysis model which combines graph topology analysis and time sequence characteristics and is calibrated by environmental data, and outputting a systematic operation risk index and a dynamic critical value; and constructing a long-time-sequence prediction model, carrying out long-term trend pre-judgment on the risk index, calling a causal model to carry out root cause tracing when the predicted risk reaches a critical value, and generating a prevention management scheme containing a clear early warning level and a specific resource demand. According to the method, the whole-process intelligentization from unified risk assessment and accurate prediction to closed-loop decision support is realized.

The invention discloses a PDF engineering drawing cross-end synchronization method and system based on operation instruction serialization and a storage medium, and belongs to the technical field of cross-end engineering drawing cooperative process.The method comprises the steps that operation instructions of multiple ends on a PDF engineering drawing are captured, and operation types, target object IDs and parameters are extracted to serve as semantic metadata; constructing a dependency graph of the operation instruction based on the target object ID, and identifying a key path instruction; encoding an operation instruction and a dependency relationship into a serialized format, and only transmitting an instruction of a changed part; the receiving end analyzes the serialized instruction, detects conflicts based on semantic metadata, and executes the instruction according to the dependency relationship; and dynamically adapting the rendering result according to the equipment parameters, including coordinate system conversion and symbol mapping. Therefore, the problems of synchronization efficiency and accuracy in the prior art are solved, and the effects of multi-terminal real-time, efficient and synchronous consistency of the engineering drawing are achieved.

The invention discloses a capacitance parameter determination method and device of an iron core sensing device for transformer partial discharge detection, electronic equipment and a storage medium, and belongs to the technical field of sensor design, and the method comprises the steps: obtaining an iron core three-dimensional model of a transformer; performing electromagnetic simulation on the iron core three-dimensional model to generate antenna characteristic parameters of the transformer iron core under each frequency in a preset frequency band; determining a target detection frequency band of the transformer core according to the antenna characteristic parameters; repeatedly executing the parameter adjustment operation of the iron core sensing device until the current test frequency band covers the target detection frequency band, and determining the capacitance parameter of the iron core sensing device; therefore, by implementing the invention, the problem that the capacitance parameters of the iron core sensing device in the prior art are not accurately determined can be solved.

Aspects presented herein relate to methods and devices for wireless communication including an apparatus, e.g., a device or a server. The apparatus may obtain a set of device parameters for one or more route segments from a server, wherein the set of device parameters for the one or more route segments includes an indication of an operation of the device along each of the one or more route segments. Additionally, the apparatus may set one or more device parameters of the set of device parameters for the one or more route segments based on a proximity of the device to a corresponding route segment of the one or more route segments.

An accessory device includes: an antenna; a processing unit; and a memory storing a first accessory device parameter; wherein the processing unit is configured to: obtain the first accessory device parameter, obtain a first hearing device parameter received via the antenna of the accessory device, determine a first criterion value based on the first hearing device parameter and the first accessory device parameter, and perform a comparison between a received signal strength indicator and the first criterion value, the received signal strength indicator indicating or being associated with a radiated power transmitted from a hearing device and received at the accessory device; and wherein the processing unit is configured to perform a pairing action if a result of the comparison satisfies a criterion.

The invention belongs to the technical field of industrial Internet of Things, and particularly discloses a data classification method, system and device based on the industrial Internet of Things and a medium, and the method comprises the steps: obtaining the use authority after the system of the industrial Internet of Things is accessed, and then connecting an industrial robot through an Internet protocol; acquiring equipment parameter data and product detection data based on a data interface of the industrial robot; according to the key features of the product detection data, obtaining a product classification result through a MinHash algorithm; and based on the equipment parameter data and the differential privacy federated learning framework, classifying the data of the industrial robot through the classification model. The invention aims to solve the problem that the data classification method of the industrial Internet of Things in the prior art cannot realize the classification of the data of the industrial robot on the premise of protecting the data privacy and security. The problems that robot management devices in the industrial Internet of Things cannot be efficiently cooperated for joint training, and precise classification of industrial robots cannot be achieved are solved.

The invention discloses an intelligent process control method and system for 3D printing of metal parts, and relates to the technical field of intelligent control. The method comprises the steps that after a CAD model is imported into a 3D printing robot, equipment parameters are intelligently configured, process simulation is carried out based on the equipment parameters, and key process identifiers are established; when the 3D printing robot is started, the collaborative sensing unit is activated to execute multi-mode sensing recognition, and a sensing database is established; establishing a calibration response data set and a control deviation, configuring 3D printing demand data according to the CAD model, and establishing a deviation following target; synchronizing the control deviation and the deviation following target to a collaborative optimization channel to execute control optimization, and establishing a control optimization result; and intelligent process control management of the 3D printing robot is carried out according to a control optimization result. The technical problem that in the prior art, 3D printing is difficult to adjust in time according to real-time working conditions, and consequently the printing quality is unstable is solved, and the technical effect of improving the printing quality is achieved.

The invention provides a material carrying equipment control instruction generation method and device, equipment and a storage medium, and relates to the technical field of information. The method comprises the steps that material storage device parameters and material carrying equipment parameters are obtained, wherein the material storage device parameters are used for indicating specification and position information of a material storage device and material storage real-time state information; according to the parameters of the material storage device and the parameters of the material carrying equipment, a space state mapping model is constructed, and the space state mapping model is used for synchronously mapping the real-time states of the material storage device and the material carrying equipment. A material processing request initiated by a user is received, the material processing request is used for indicating the current position information, the target position information and the processing type of the to-be-processed material, and the processing type comprises storing and / or taking out. And in response to the material processing request, generating a control instruction for controlling the material handling equipment based on the spatial state mapping model.

The invention relates to the technical field of simulation, in particular to an intelligent multi-target parameter optimization method and system for analog machine equipment. The method comprises the following steps of: constructing a decoupling architecture of a component model algorithm and a debugging algorithm of a simulation platform, and respectively compiling the component model algorithm and the debugging algorithm into a dynamic link library; a standardized debugging parameter input framework is established, and flexible input of multi-working-condition debugging parameters is achieved; automatically loading a corresponding dynamic link library file, and converting multiple groups of working condition data configured by a user into a memory structure body; the structural body transmits to the currently loaded debugging algorithm for execution; the debugging algorithm is operated, and monitoring and feedback are implemented in the debugging algorithm execution process; when the debugging algorithm is terminated, the simulation platform receives a state code returned by the debugging algorithm, and converts the state code into error information readable by a user through a predefined error code mapping table; and the simulation platform extracts the optimized parameter result from the debugging algorithm output, and the result data is verified and automatically updated to the corresponding equipment parameters. According to the invention, multi-working-condition automatic debugging of the simulator equipment is realized, and the debugging efficiency is improved.

The invention discloses a multi-device flexible parameter self-tuning method based on transfer learning, and belongs to the technical field of multi-device parameter tuning, and the method comprises the following steps: S1, data preprocessing and feature screening; s2, aligning the feature space of the target equipment; s3, carrying out transfer learning model initialization and knowledge transfer; s4, performing online adaptive parameter optimization; and S5, multi-device parameter self-tuning circulation and model updating. According to the multi-device flexible parameter self-tuning method based on transfer learning, source device knowledge is reused through transfer learning, data dependence of target devices is reduced, and the cross-device transfer effect is improved; and convergence is accelerated and initial parameter sensitivity is reduced through an optimization strategy, so that efficient self-tuning of multi-device parameters is realized.