219 results about "DEVICE EVALUATION" patented technology

Wireless-enabled kiosk systems and associated methods for recycling and performing other processes with mobile phones and other electronic devices are described herein. In various embodiments, the present technology includes systems and methods for wirelessly connecting a consumer-operated kiosk with an electronic device to facilitate processing (e.g., purchasing) the device. In some embodiments, the present technology includes using a wireless link to identify a device, evaluate a device, resolve device issues to enable purchase of the device, locate a device, etc. Various other aspects of the present technology are described herein.

The invention relates to the field of equipment state monitoring in industrial Internet of Things, and discloses an equipment state monitoring, analysis and evaluation method and system based on big data, and the method comprises the steps: carrying out the adaptation of a multi-source heterogeneous data protocol, and carrying out the cleaning of a dynamic mask, and generating a standardized data stream; constructing a dynamic hypergraph of an embedded constraint equation based on physical topology; combining incremental tensor decomposition with manifold constraint to update a core tensor; abnormal association is positioned based on singular value distribution and a hyperedge propagation algorithm; cross-equipment model migration is realized through topological optimal transmission and knowledge distillation, and a target equipment evaluation model is generated; the system comprises a data preprocessing module, a hypergraph modeling module, a tensor analysis module, a state evaluation module, a transfer learning module and a dynamic tuning module. According to the method, through multi-source data dynamic cleaning, physical constraint hypergraph modeling, incremental tensor decomposition and manifold constraint, and in combination with an abnormal positioning closed loop and cross-equipment topology migration, equipment state monitoring and rapid model adaptation are realized.

The invention discloses a power equipment safety state assessment method and system, equipment and a readable storage medium, and relates to the technical field of power equipment assessment, and the method comprises the steps: collecting a power equipment communication interaction data flow, carrying out the time synchronization and outlier elimination, and constructing a multi-modal data matrix; evaluating the exception probability score and an exception probability threshold to obtain an exception state flag, and generating a security event data packet in combination with the preliminary exception type and the power equipment communication interaction data stream; adopting a LiNGAM causal discovery algorithm to perform non-Gaussian test and time sequence lag analysis on an abnormal event time sequence in the security event data packet, and constructing a directed acyclic causal graph; dynamically adjusting the causal intensity of the directed acyclic causal graph according to the real-time equipment load rate and the communication quality index, and outputting a dynamic causal graph; according to the method, the accuracy and adaptability of abnormal propagation analysis are improved, and finally the real-time performance and credibility of safety assessment of the power equipment are enhanced.

The invention relates to the technical field of power equipment state monitoring, in particular to a power equipment operation state evaluation and prediction method based on big data, which comprises a multi-source data acquisition module, a feature engineering processing module, an intelligent evaluation and prediction module and a decision support output module. By setting a multi-source sensing end, when the operation state of the power equipment is evaluated, the definiteness of state evaluation of different types of equipment is ensured by formulating multi-modal data fusion standard parameters and setting different state sensing weights for different types of equipment; and meanwhile, real-time state sensing is performed by fusing electrical parameters, mechanical vibration, thermal distribution and environmental stress data, so that whether a sensing blind area problem caused by a single data dimension occurs in an equipment evaluation process or not can be detected in real time, the comprehensiveness and accuracy of equipment operation state evaluation are ensured, and state sensing fragmentation errors are further reduced.

The invention discloses an intelligent operation risk analysis method and system based on deep learning, and relates to the technical field of power plant management, and the method comprises the steps: collecting the operation original data of power plant equipment in real time; carrying out data fusion on the collected data, and extracting equipment evaluation data; analyzing the equipment evaluation data through the long-short-term memory neural network model, and predicting an equipment fault based on an analysis result; based on the equipment fault prediction result, generating an equipment scheduling scheme by using a particle swarm optimization algorithm, and optimizing the scheduling scheme; and converting the optimized equipment scheduling scheme into a control instruction, and generating and executing equipment scheduling through a low-code development platform. According to the intelligent operation risk analysis method based on deep learning, a scheduling scheme can be optimized according to the health state and the fault risk of the equipment, the operation efficiency of the equipment is improved, a repair and maintenance plan is effectively arranged, and the problem of excessive maintenance or repair lag is avoided.

The invention, which relates to the technical field of power grid asset monitoring, discloses an intelligent monitoring and management system for a full life stage of power grid assets based on the Internet of Things, comprising a data acquisition module and a dynamic evaluation module. Receiving the parameter data of the data acquisition module by taking T as a period and a monitoring standard rolling calculation equipment state of the corresponding parameter data; the real-time monitoring module is used for monitoring the state of the power grid equipment in real time according to the evaluation value time sequence of the dynamic evaluation module and triggering an early warning signal; the management module is used for managing and adjusting operation and use of the power grid equipment; the full-life stage of the power grid equipment is divided into a newly-added stage, an operation stage and a decommissioning stage, and differentiated monitoring standards and management strategies are formulated according to the characteristics and the equipment state of each stage. In the newly-added stage, scientific grading is carried out according to equipment evaluation values, equipment loads and tasks are reasonably distributed, and it is ensured that the equipment can exert the maximum efficiency in the initial stage.

The invention discloses a high-speed rail vehicle-mounted equipment fault prediction and health management method, and relates to the technical field of high-speed rail vehicle-mounted equipment fault prediction, and the method comprises the steps: monitoring high-speed rail vehicle-mounted equipment through employing a high-precision sensor network, and obtaining an original monitoring data set; cleaning the original monitoring data set by adopting a multi-source data integration and preprocessing method to obtain a high-quality data set, and extracting key features based on the high-quality data set to obtain a feature data set; constructing a fault prediction model based on a deep learning method and the feature data set, inputting the high-quality data set to the fault prediction model, and outputting a fault early warning result; based on the fault early warning result, a decision support method is adopted to evaluate the equipment state, and a maintenance strategy suggestion is obtained; obtaining a comprehensive analysis report based on the maintenance strategy suggestion and the equipment evaluation state; environmental data and historical maintenance record data from a ground monitoring system are integrated, and the data are fused by using a weighted average method.

The invention belongs to the field of equipment evaluation, particularly relates to an intelligent evaluation method and system for the operation quality of a flight simulator, and aims to solve the problems of single evaluation dimension, poor real-time performance, low intelligent degree and lack of quantitative standards in the prior art. The method comprises the steps that operation data such as fault time, duration and available time are collected in real time through a sensor network; calculating core indexes such as fault frequency per unit time, accumulated duration and malignant fault rate; dynamically weighting and fusing the indexes to generate a single-machine health index; a correlation model is constructed based on fault space-time correlation between devices, and cluster health scores are output in combination with single machine indexes; when the malignant fault exceeds a threshold value, driving causal inference by utilizing a knowledge graph containing entity causal relationships of fault types, equipment parts and the like, and outputting a root cause diagnosis and maintenance scheme; and acquiring intervened data to update atlas parameters and models to form closed-loop optimization. Multi-dimensional real-time monitoring and diagnosis closed loop of the operation state are realized, and the maintenance efficiency is improved.

Security policies are made dependent on location of a device and the location of a device is determined and the appropriate security policy applied without providing the device's location to a server. A device determine its location and identifies a security policy identifier mapped to a zone including the location. The device requests the security policy corresponding to the identifier from a server and implements it. The device may also store a database of the security policies and implement them according to its location. Devices registered for a user evaluate whether locations detected for the devices correspond to impossible travel by the user. Objects encoding geolocation data of a device may be encrypted with a private key of the device and the public key of another to prevent access by an intermediary server.

The embodiment of the invention relates to the technical field of power system access safety control, and provides a power system access control method and device, electronic equipment and a medium, and the method comprises the steps: collecting equipment evaluation data of target access equipment, system evaluation data of a target power system and network risk evaluation data in real time, the target access equipment is equipment of a to-be-accessed target power system; performing equipment credibility evaluation on the target access equipment based on the equipment evaluation data, performing system resource environment evaluation on the target power system based on the system evaluation data, and performing network credibility evaluation on the security state of the current access network based on the network risk evaluation data; generating a target access control strategy based on the equipment trust evaluation result, the system resource environment evaluation result and the network trust evaluation result; and performing access control on the target access equipment based on the target access control policy. The flexibility, security and real-time response capability of access control are improved.

According to the sewage discharge data management method based on the intelligent water affair, the sewage discharge data, the equipment state data and the environment data of an enterprise are subjected to edge calculation, the initial discharge data and the equipment evaluation data are generated, localized real-time processing of the data is achieved, and the transmission amount of invalid data is reduced. And performing data implicit anomaly identification and multi-modal data cleaning on the initial emission data and the equipment evaluation data based on the sewage component chemical association rule base, accurately capturing abnormal points which do not conform to chemical association rules in the data, and generating an abnormal mark list and intermediate emission data. Data association analysis is carried out based on the abnormal mark list, the intermediate emission data and enterprise operation data corresponding to the enterprise, a data logic verification map is constructed, errors in the intermediate emission data are automatically corrected according to a logic relation in the verification map, the processing efficiency is improved, and through cross verification of multiple data sources, the data processing efficiency is improved. And target emission data and a compliance report with high accuracy are generated.

In one embodiment of the present disclosure, a method for evaluating the quality of multiple electrocardiogram data and diagnosing diseases based only on good quality signals is disclosed. Specifically, according to the present disclosure, a computing device evaluates the quality of multiple electrocardiogram data obtained from multiple leads, and based on the evaluation of the quality of the multiple electrocardiogram data, excludes the electrocardiogram data obtained from at least one lead among the multiple leads, and performs electrocardiogram analysis based on the remaining multiple electrocardiogram data using a pre-trained electrocardiogram analysis model.

Systems and methods for monitoring structural integrity are provided. Acoustic sensors are disposed in relation to an area of interest of a structural member to detect an acoustic emission and output an acoustic emission signal. The signal is collected, and a computing device evaluates acoustic emission signal characteristics and predicts whether a crack is present. The acoustic emission signal characteristics are used to detect and predict aspects associated with formation and growth of cracks at various locations of the structural member.

The invention relates to a method for evaluating the state of an electronic component (12, 14) of a circuit arrangement (10) using an evaluation device (16), comprising the steps of: - specifying a target eye diagram (48) for the electronic component (12, 14) using an electronic computing unit (36) of the evaluation device (16); - acquiring at least one current signal (24) of the electronic component (12, 14) using a detection unit (26) of the evaluation device (16); - generating a current actual eye diagram (28) of the electronic component (12, 14) as a function of the acquired, current signal (24) using the electronic computing unit (36); - comparing the target eye diagram (48) with the actual eye diagram (28) using the electronic computing unit (36); and - evaluating the state as a function of the comparison using the electronic computing unit (36).Furthermore, the invention relates to a computer program product, a computer-readable storage medium and an evaluation device (36).

This application relates to the field of wireless communication, and in particular, to a radio frequency sensing method and related devices. The method includes: a first device sending a plurality of first frames including first indication information for indicating a second device to evaluate a change amount of the channel state information (CSI) from the first device to the second device; the first device quasi-omnidirectionally receiving a plurality of second frames including a first measurement result and second indication information, the first measurement result being used to feedback a transmission beam for which the change amount of the CSI from the first device to the second device is greater than a CSI change threshold, and the second indication information being used to indicate the first device to evaluate a change amount of the CSI from the second device to the first device; the first device sending a third frame including a second measurement result for feedbacking a transmission beam for which the change amount of the CSI from the second device to the first device is greater than the CSI change threshold. By adopting the embodiment of this application, a beamforming training mechanism can be combined with WLAN sensing to achieve sensing while performing communication beam training.

The invention provides an escalator operation monitoring method and device, electronic equipment and a storage medium, and the method comprises the steps: collecting various key working state parameters of a to-be-monitored escalator during working through a sensor array arranged on the to-be-monitored escalator, the working state parameters are uploaded to edge computing equipment beside the escalator to be monitored, and the edge computing equipment evaluates the comprehensive health index of the escalator to be monitored; the edge computing device uploads the monitored working state parameters of the escalator and the evaluated comprehensive health index to the cloud end, and the cloud end continues to monitor the operation condition of the escalator according to the indication condition of the comprehensive health index. By means of the method, comprehensive understanding of the state of the escalator is enhanced, and therefore the fault rate and the maintenance cost are reduced.

The invention relates to the technical field of ultrasonic equipment management, and particularly discloses a fine management system and method for ultrasonic equipment, and the system comprises a data collection module which is used for collecting the operation data of the ultrasonic equipment in all directions; the cross-class combination module is used for carrying out cross-class combination mining on the operation data of the ultrasonic equipment and carrying out behavior classification and project analysis matching on the ultrasonic equipment to obtain a behavior classification and project analysis matching result; the model construction module is used for constructing an equipment end evaluation model by using a deep learning algorithm and a big data analysis method; the equipment evaluation module is used for performing deep mining analysis on the ultrasonic equipment operation data and the behavior classification and project analysis matching result by utilizing an equipment end evaluation model to predict an equipment end evaluation result; the report generation module is used for generating an equipment management data report in a customized manner based on the equipment end evaluation result; fine management of the ultrasonic equipment is realized, the efficiency and quality of equipment management are improved, the service life of the equipment is prolonged, and the maintenance cost is reduced.

The invention provides an intelligent warehouse logistics robot health degree assessment method and system, and relates to the technical field of equipment assessment, and the method comprises the steps: obtaining historical operation data of a logistics robot in different operation scenes, and carrying out the preprocessing and dividing; clustering is carried out through a DBSCAN algorithm, historical samples are screened, real-time operation data are obtained, the deviation degree between the real-time operation data and the historical samples is calculated, and the aging degree is obtained; calculating an external fault factor of the logistics robot according to the real-time environment data, and calculating an abnormal degree based on the external fault factor and the real-time performance data; according to the logistics robot health degree evaluation method and system, the association weight matrix of the logistics robots is constructed based on the cooperation relation between the logistics robots, the health degree of the logistics robots is comprehensively evaluated based on the association weight matrix, the aging degree and the abnormal degree, and the technical problems that in the prior art, the health degree is evaluated through a single operation parameter, and the evaluation result accuracy is low can be solved.

The invention discloses a prefabricated cabin type equipment maintenance method for a transformer substation based on a multi-cabin system, and relates to the technical field of transformer substation equipment maintenance, and the method comprises the following steps: analyzing each piece of prefabricated cabin type equipment according to the functions of the cabin type equipment of the transformer substation, and obtaining equipment monitoring data and operation load data; establishing a data analysis model based on machine learning according to the equipment monitoring data and the operation load data, and generating an equipment evaluation coefficient; comparing and analyzing the evaluation coefficient of each prefabricated cabin type equipment with a preset equipment maintenance threshold value, maintaining the prefabricated cabin type equipment according to a comparison and analysis result, and carrying out secondary evaluation on the maintained equipment; the problems that in an existing overhaul method, a preventive maintenance strategy is not accurate enough, and equipment cannot be detected and processed in time before a fault occurs are solved.

A device obtains modelling data associated with energy consumption of a model vehicle. The modelling data are generated by a digital model of the vehicle in operation. The device obtains operating data associated with energy consumption of the vehicle in operation. The device compares the operating data to the modelling data. Based on a result of the comparing, the device detects a discrepancy between the operating data and the modelling data and associated with the energy consumption. The device evaluates the detected discrepancy associated with the energy consumption. The device triggers an operation when the discrepancy has been detected.

The invention relates to a method for carrying out a container inspection task in a container handling system for handling a plurality of container parts for containers, preferably plastic containers and / or bottles, wherein a transport device transports the plurality of container parts as a container part stream from at least one treatment device of the container treatment system to at least another treatment device of the container treatment system along a predetermined transport path, at least one sensor device for carrying out the container inspection task captures, preferably optically, in particular spatially resolved sensor data, preferably camera images, relating to the container parts to be inspected, and a real-time evaluation device evaluates the in particular spatially resolved sensor data, preferably camera images. According to the invention, a set of container component features is predetermined for the real-time evaluation device, said set being a set of container component features extracted automatically, in particular from a neural network, within a framework of a machine learning method performed with respect to a training container inspection task different from the container component inspection task, and the real-time evaluation device performs the container inspection task based on the set of predetermined container component characteristics.

The invention provides an equipment quality state evaluation method fusing data driving and QMU, and belongs to the technical field of equipment evaluation and detection. The method comprises the steps of collecting test data and task environment data of equipment to construct a data set, and dividing the data set into a training set and a test set; based on the data set, importance calculation is carried out on the multi-dimensional features through a limit gradient boosting tree, the features meeting the equipment quality state contribution degree requirement are screened out, and an observation list of the data set is generated; based on the observation list of the training set, a Gaussian process regression model is established, and hyper-parameters of the Gaussian process regression model are optimized; predicting the equipment state by using a Gaussian process regression model, performing uncertainty quantization on a prediction result, and outputting a mean value and a variance of an equipment state prediction value; on the basis of the performance threshold PTL or PTU of the equipment, a predicted value of the margin is calculated in combination with the average value of equipment state prediction; and calculating a confidence coefficient CF based on the variance of the predicted value and the predicted value of the margin, and affirming the quality state of the equipment.

The invention discloses a multi-element networking charging pipeline cross validation treatment method and system and a storage medium, and belongs to the technical field of traffic control. The method comprises the following steps: acquiring ETC flow information, ETC equipment data, special situation type data and vehicle medium information, wherein the ETC flow information comprises vehicle driving data and vehicle abnormal data; the method comprises the steps of obtaining an ETC network evaluation value according to the special situation type data through comprehensive analysis, obtaining an ETC equipment evaluation value according to the ETC equipment data through comprehensive analysis, obtaining a flow abnormity evaluation threshold value according to the ETC equipment evaluation value, obtaining a flow abnormity evaluation value according to the ETC network evaluation value, the ETC equipment evaluation value and the vehicle abnormity data through comprehensive analysis, and obtaining a flow abnormity evaluation threshold value according to the flow abnormity evaluation threshold value. Screening is carried out according to the flow abnormality evaluation value to obtain an invalid flow; the ETC portal layout information is obtained, and the fee is calculated for the invalid flow by using the vehicle driving data and the ETC portal layout, so that the accurate evaluation and anomaly detection of the ETC fee collection flow are realized.

A computer-implemented method includes: receiving, by a computing device, an input prompt; routing, by the computing device, the input prompt to one or more restructuring routes; restructuring, by the computing device, the input prompt to provide a restructured input prompt to reduce tokens; evaluating, by the computing device, the input prompt to the restructured input prompt to determine that thresholds are met; and sending, by the computing device, the restructured input prompt that meets the thresholds to a large language model.

The invention discloses an elevator health condition assessment method based on a fusion network model, and belongs to the technical field of special equipment assessment. The method comprises the steps that multi-dimensional data of a to-be-tested elevator are collected and preprocessed; constructing a BP neural network model, taking a genetic algorithm GA as a strategy for optimizing a hyper-parameter, a weight and a threshold value of the BP neural network model to obtain a GA-BP neural network model, and obtaining a first evaluation result of the elevator health condition based on the GA-BP neural network model; a logic judgment model is constructed, and a second evaluation result of the elevator health condition is obtained; and fusing and grading evaluation results of the GA-BP neural network model and the logic judgment model, and taking a grading result as a final elevator health condition diagnosis result. According to the method, the learning and prediction capabilities of the neural network and the intuition and accuracy of logical judgment are combined, so that the accuracy and efficiency of elevator health condition evaluation are improved, and a powerful guarantee is provided for safe operation of the elevator.

The invention relates to the technical field of green energy industry, and discloses an intelligent control system for green energy industry, which comprises an equipment information acquisition module, an equipment information analysis module, an equipment evaluation module and an early warning module, the equipment information acquisition module acquires battery operation parameters, equipment operation state data and battery capacity data of energy storage equipment, and the equipment information analysis module calculates a battery operation index, an equipment operation index, a charging rate, a discharging rate and an equipment comprehensive coefficient according to the acquired data. The equipment evaluation module compares the equipment comprehensive coefficient with an equipment operation coefficient threshold value to judge whether the energy storage equipment is abnormal or not, so that the operation condition of the energy storage equipment is comprehensively analyzed in multiple aspects, the performance and the service life of the energy storage equipment are more accurately evaluated, problems and defects existing in the operation process of the energy storage equipment can be found, and the energy storage equipment can be more accurately evaluated. Maintenance and optimization are carried out in time, and the fault occurrence frequency and the maintenance cost are reduced.

The invention provides an equipment evaluation and maintenance suggestion method and system based on multi-modal data fusion, and relates to the technical field of equipment evaluation.According to the method, fault feature retrieval and maintenance suggestion intelligent output are achieved by constructing a classification database containing multi-modal historical fault information, combining weight factors and utilizing a large language model; a vibration signal and a temperature field are effectively fused, a bearing, a strain gauge, a motor, a guide rail and a lead screw are analyzed one by one, feature vectors are extracted, correlation analysis is performed on the feature vectors, multi-source features are fused, an equipment fault evaluation index is generated, and a numerical control machine tool state and maintenance suggestion is output based on the equipment fault evaluation index. The system improves the accuracy and sensitivity of fault diagnosis, can dynamically reflect the health state of equipment, assists a worker in taking maintenance measures in time, and remarkably improves the operation safety and production efficiency of the equipment.

The present application relates to a kind of OPGW cable health condition evaluation method, comprising: S1: collection OPGW cable equipment evaluation index data;S2: according to the first weight of index data numerical distribution calculation;S3: according to the correlation between each index data calculation second weight;S4: according to the first weight and second weight, obtain fusion weight;S5: determine OPGW cable health state index value, assess cable health state.The present application according to the first weight of index data numerical distribution calculation;According to the correlation between each index data calculation second weight;According to the first weight and second weight, obtain fusion weight;Determine OPGW cable health state index value, assess cable health state.The algorithm of the present application can accurately evaluate the health state of OPGW cable, and provide reasonable and effective theoretical support for the equipment state management and routine operation of OPGW cable.

The invention relates to the technical field of disconnecting link maintenance and transformation, in particular to an isolation disconnecting link updating and transformation method. The method comprises the following steps: S1, performing inspection and quantitative analysis, performing equipment evaluation and problem diagnosis, and identifying operation problems and function deficiency; s2, performing model selection and material optimization, and setting a standard isolation switch and an electric operation assembly; s3, defining a transformation range, dividing transformation units based on the main wiring diagram, and covering the isolation disconnecting link and the grounding disconnecting link; s4, implementing a safety locking structure, adding an electrified locking and five-prevention system, and accessing whole plant management to prevent misoperation; and S5, accessing a monitoring system, and connecting an LCU screen and a computer monitoring system to realize remote centralized control. The contact resistance and temperature rise of the contact reach the standard, ablation is completely eradicated, the operating force and time are greatly reduced, jamming is returned to zero, the five-prevention locking success rate is extremely high, the misoperation drill can reach the zero penetration level, and the on-site actual effects of mechanical zero jamming, five-prevention zero penetration, remote control zero delay and old and waste utilization are achieved.

The invention provides a ship equipment rapid loading / reloading simulation method based on process node driving. The method comprises the following steps: S1) modeling process nodes; equipment needing to be added / replaced is confirmed, the condition of existing equipment is evaluated, and information of the equipment is analyzed; s2) constructing a simulation environment; creating a 3D model of the ship, establishing a detailed model for each loading / reloading device, and setting a process node of each operation step; s3) configuring and inputting parameters; determining the type of each device, the specific conditions of loading / reloading and the input data of an operator; s4) executing simulation, and analyzing a result; relevant data are collected in real time, key performance indexes are analyzed, and optimization and adjustment are carried out; according to the method, the whole process of equipment loading / reloading can be simulated in advance through simulation, potential bottlenecks and problems are identified, operation steps are optimized, and the overall efficiency is improved.