70 results about "Status assessment" patented technology

The invention provides a health state evaluation and product matching method and system based on a knowledge graph, and relates to the technical field of knowledge graphs, and the method comprises the steps: obtaining user health data, building a health knowledge graph, mapping the user data into a feature vector, and employing a multi-scale time convolution network to extract a time sequence change feature; and constructing a dynamic health feature association network, generating a health state evaluation result by using a graph convolutional neural network and a causal discovery algorithm, calculating a collaborative enhancement coefficient based on a product feature vector, and predicting an intervention effect to determine an optimal product combination. According to the method, personalized health assessment and precise product matching are realized, and the health management efficiency is improved.

The invention discloses a deicing system real-time state evaluation and fault prediction method and system, and the method comprises the steps: simulating the fault state of a deicing system through a ground state monitoring test of the deicing system, and obtaining sensor data in different fault states; constructing a data set by using sensor data in different fault states to train a state evaluation model and a fault prediction model; the method comprises the following steps: installing a sensor on an aircraft deployed with a deicing system to collect working data of the deicing system; and inputting data acquired by the sensor into the trained state evaluation model and the fault prediction model, evaluating the real-time state of the deicing system, and predicting possible faults. According to the invention, the current working state of the anti-icing and deicing system can be evaluated according to the current state monitoring data, and possible faults of the system can be predicted.

The invention discloses a giant magnetostrictive bar health state evaluation method considering subharmonic content, which comprises the following steps of: firstly, obtaining output displacement data of a giant magnetostrictive bar through a built giant magnetostrictive bar test platform; in order to select the filter length and the mode decomposition number of the characteristic mode decomposition method, combining with the Shannon entropy coefficient, taking the square envelope spectrum kurtosis as a weight, constructing a weighted square envelope spectrum kurtosis Shannon entropy coefficient objective function, and performing parameter optimization by using a Harris eagle optimization algorithm; according to the method, the principle of maximum square envelope spectrum kurtosis is taken as a principle to select a main mode component, finally, envelope spectrum analysis is carried out, the health state of the giant magnetostrictive bar is evaluated by counting the sub-harmonic content in the envelope spectrum, and the method plays a guiding role in development and application of a giant magnetostrictive transducer. And the service life of the subsequent transducer can be detected and evaluated.

The invention relates to the technical field of robots and network security, discloses a robot health consensus system and method based on ontology trust, and aims to solve the problems that an existing robot health declaration is easy in identity counterfeiting, one-sided in state evaluation, and easy in privacy leakage and replay attack in a certification process. The method comprises the following steps: generating an identity label strongly bound with robot hardware based on a physical unclonable function (PUF); fusing multi-source sensor data in a recursive updating mode to generate a state topology vector capable of reflecting a historical evolution trend; and obtaining a comprehensive health value in combination with the internal health value calculated by the vector and a network consensus factor. Through physical layer identity anchoring, dynamic and continuous state evaluation and an encryption certification mechanism, the authenticity and fraud resistance of the health declaration of the robot are remarkably improved, comprehensive evaluation of the health state of the robot is achieved, state privacy of the robot is effectively protected in interaction, and replay attacks are prevented.

The invention discloses an AI-based power equipment health state prediction system and method, and belongs to the field of intelligent operation and maintenance of power equipment, and the system comprises a data collection and preprocessing module, a multi-modal deep hierarchical attention fusion module, a health state evaluation and prediction module, and a decision support and linkage module. According to the invention, a multi-modal deep layered attention fusion architecture is innovatively provided, the sequential characteristics of the oil chromatography dissolved gas concentration are analyzed through a bottom-layer sequential characteristic extraction network, and a middle-layer spatial-temporal pattern recognition network is associated with spatial-temporal patterns of an infrared thermogram and an ultrasonic spectrum; the top equipment family relationship network constructs an equipment family defect propagation model to realize full-dimensional analysis and equipment group collaborative awareness of multi-modal data; and meanwhile, an information flow path between different levels of networks is established through a cross-level information fusion mechanism, so that the comprehensiveness and accuracy of feature extraction are improved.

The invention relates to the technical field of digital media art teaching, and discloses a digital media art teaching system constructed by fusing a virtual reality technology. A teaching state evaluation unit of the system comprises a plurality of student creation state acquisition subunits corresponding to student terminals, and can synchronously acquire multi-dimensional state information in a plurality of student creation processes, and a teaching balance degree judgment unit determines a teaching balance degree based on an acquisition result; the virtual creation environment construction unit judges whether to start three-dimensional scene acquisition or not according to the teaching balance degree, and acquires adaptive virtual creation scene data when judging that the three-dimensional scene acquisition is started; and the teaching strategy decision-making unit generates an optimal teaching path plan based on the virtual creation scene data, and adjusts virtual teaching scene parameters according to the optimal teaching path plan. The system can realize accurate monitoring of student creation states, targeted construction of a virtual environment and dynamic optimization of teaching strategies, and assists in improving digital media art teaching quality and student creation ability.

A substation equipment status assessment and early warning system and method based on a digital twin platform includes the following steps: parsing the digital twin model to obtain basic equipment information and creating an equipment information mapping table; matching equipment types to select assessment algorithms and rules, and generating an equipment status assessment model; parsing inspection system data to match equipment information points; forming an equipment inspection information point table; instantiating equipment assessment objects and matching information points, algorithm parameters, and rules; initiating status assessment using a sliding window and event triggering mechanism to generate an assessment result file; analyzing historical data to predict equipment status and issuing alarms when anomalies occur. This method establishes an assessment model for complex primary and secondary equipment in substations, abstracts and concretizes multi-source data such as primary equipment, secondary equipment, monitoring, inspection, and operation and maintenance systems, and adopts flexible combinations of algorithms and evaluation rules to shield the differences between equipment and the diversity of algorithms, thereby improving the accuracy of the assessment and early warning function and the standardization of assessment results.

This invention relates to a multi-dimensional dynamic assessment method and system for waterway operation status, belonging to the field of waterway transportation operation monitoring technology. This invention fuses processed data at the data layer, feature layer, and decision layer to form a deep coupling of multi-modal data; constructs a multi-dimensional waterway operation status assessment index system; utilizes this index system for real-time dynamic assessment of multi-dimensional waterway operation status; trains a deep learning model using historical multi-modal time-series data; and uses the deep learning model for time-series prediction and graded early warning of waterway operation status. Based on the assessment and early warning results, it outputs executable decision-making schemes. This invention constructs an integrated assessment system encompassing navigation environment, vessel flow, waterway siltation, structural health, hydrometeorology, and navigation safety dimensions, achieving a comprehensive depiction of waterway operation status. It employs a three-level fusion strategy to break down data silos from multiple sources, improving the accuracy and reliability of status assessment.

This invention provides a method for improving water conditions based on current status assessment and multi-objective synergy, relating to the field of water environment management technology. The invention comprehensively evaluates the current status of water areas to form an evaluation result table, identifies advantageous and disadvantageous indicators, determines the conflict relationships and optimization potential of engineering measures, constructs a mathematical response function between control indicators and engineering measures, establishes a multi-objective optimization model, and uses the NSGA-III algorithm to obtain the Pareto optimal solution set. Combined with multivariate criteria, it outputs optimized engineering combinations and expected results applicable to different management scenarios. This invention can transform static evaluation into dynamic decision support, improving the scientific rigor and accuracy of water management decisions.

This invention relates to the field of waste heat recovery technology in textile machinery, and more particularly to a multi-sensor fusion monitoring method and system for waste heat recovery systems in stenters. The method acquires data from multiple sensors and performs spatiotemporal alignment and fusion calculations to obtain a comprehensive system status assessment result. Based on this result, anomaly identification and multi-objective optimization are performed to generate a control execution scheme. After the scheme is executed, response data is collected to analyze control effect deviations, and relevant parameters are adaptively adjusted accordingly. This invention achieves precise monitoring and intelligent control of the waste heat recovery system status, improving system operational stability and energy efficiency.

This invention discloses a method and system for assessing the remaining lifespan of vehicles based on multi-dimensional dynamic fusion, belonging to the field of intelligent connected vehicle health assessment technology. The assessment method includes: collecting multi-source data, calculating five-dimensional lifespan sub-factors, dynamically configuring weights based on vehicle type, life cycle, and usage scenario, and weighted fusion to output a comprehensive remaining lifespan index for the vehicle. The assessment system includes a data acquisition and processing layer, a multi-dimensional factor fusion and assessment layer, and an application interface layer. The multi-dimensional factor fusion and assessment layer includes a multi-dimensional health factor extraction module, a dynamic factor weight adjustment and fusion module, and a status assessment and remaining lifespan prediction module. This invention is based on a lifespan factor quantification system encompassing five dimensions: time, health, usage, environment, and value. It adaptively adjusts weights according to vehicle type, life cycle, and usage scenario to achieve a comprehensive and accurate assessment of the vehicle's remaining lifespan, improving the prediction accuracy of the vehicle's remaining lifespan.

This application provides a method and system for intelligent assessment of safety early warning for power distribution equipment, relating to the field of equipment safety early warning. It addresses the technical problem in existing technologies where the lack of systematic analysis of inter-equipment relationships in power distribution equipment safety assessments leads to weak forward-looking capabilities. The method includes: creating a digital twin for each power distribution device and constructing a dynamic causal graph based on the digital twin; each digital twin performing a status assessment based on operational status data and generating a status score; when the status score exceeds a first threshold, performing risk propagation deduction based on the dynamic causal graph and outputting a risk propagation path; and triggering a tiered early warning based on the risk propagation path. This application is used in the safety assessment and early warning process for power distribution equipment.

This invention discloses a health status prediction system and method for electric shovel equipment based on edge-cloud collaboration and digital twins, belonging to the field of electric shovel equipment status prediction technology. The system includes an electric shovel equipment health status management system based on edge-cloud collaboration and an electric shovel digital twin model. It collects multi-dimensional status data of the electric shovel through multiple sensors, and achieves layered transmission and edge-cloud collaborative processing via NB-IoT, edge devices, and 5G base stations. A high-fidelity electric shovel digital twin model is constructed to achieve synchronous mapping between the physical entity and the virtual model. The method utilizes grey relational analysis to calculate the correlation between real-time status data and offline fault samples, accurately assessing the equipment's health status, and outputs predictive maintenance processes through edge-cloud collaboration, synchronously updating the cloud database. This invention achieves closed-loop management of electric shovel equipment from data acquisition, twin modeling, fault diagnosis to predictive maintenance, significantly improving the accuracy of status assessment and the efficiency of operation and maintenance collaboration.

Embodiments of the present disclosure provide a health status assessment method for a bearing. The method includes receiving multi-modal data with respect to the bearing and prompt information related to the content of the health assessment of the bearing. The method includes performing feature extraction on the multi-modal data using a first group of agents to generate a set of features regarding the multi-modal data. The method includes performing a health assessment of the bearing based on the set of features regarding the multi-modal data and the prompt information using a second group of agents to generate a plurality of health assessment results regarding the bearing. The method includes processing the plurality of health assessments using a third group of agents to generate a health assessment report regarding the bearing.

This invention discloses a virtual power plant trading method based on a transportation-energy integration scenario, belonging to the field of virtual power plant trading technology. It includes the following steps: (1) transportation-energy data fusion collection and preprocessing; (2) hierarchical resource aggregation and status assessment; (3) dynamic trading demand prediction and matching; (4) blockchain trusted execution and feedback optimization; and (5) collaborative management of the entire battery lifecycle. This invention enables efficient collaborative trading of transportation and energy resources, significantly improving resource collaboration efficiency. Simultaneously, the trading response speed meets the grid's needs, and a trusted and secure trading system is constructed, maximizing battery value utilization and yielding significant economic and environmental benefits.

This invention relates to a method for assessing the aging condition of transformers based on the analysis of sludge / trace main metal content in the bottom heavy oil. The method includes the following steps: collecting bottom heavy oil samples from transformers with different aging degrees; detecting the proportion of sludge in the bottom heavy oil samples to obtain the correlation between the proportion of sludge in the bottom heavy oil and the aging degree of the transformer; collecting bottom heavy oil samples from transformers whose aging degree is to be assessed; detecting the proportion of sludge in the bottom heavy oil samples; comparing the obtained detection results with the correlation between the proportion of sludge in the bottom heavy oil and the aging degree of the transformer to obtain the aging degree of the transformer to be assessed. The equipment and method involved in this invention are simple and facilitate efficient assessment of the aging condition of transformers.

This application relates to the field of AI data monitoring technology, and provides a health management status assessment method and AI server based on AI intelligent monitoring data. The method performs successive heartbeat waveform structure segmentation on the time series of photoplethysmography (PPG) signals to obtain a heartbeat waveform unit sequence, and performs successive respiratory waveform structure segmentation on the time series of chest impedance respiratory signals to obtain a respiratory waveform unit sequence. Based on the heartbeat waveform units, the method constructs the evolution trajectory of heartbeat waveform morphology features, and based on the respiratory waveform units, the method constructs the evolution trajectory of respiratory waveform morphology features. The method performs synchronous correlation and alignment processing on the time axis of the heartbeat and respiratory waveform morphology feature evolution trajectories to generate a joint state feature evolution path representing the coupling relationship of the cardiopulmonary system, and inputs it into a pre-trained health status assessment network for dynamic pattern encoding and mapping processing, and outputs a comprehensive assessment result of the cardiopulmonary function coupled health status.

This invention discloses a method and system for analyzing the status of aggregated equipment in a virtual power plant based on the Internet of Things (IoT). The method includes: prioritizing potential maintenance needs, integrating inter-equipment correlation data from synergy effect analysis, performing secondary calibration to assess the impact of multi-equipment collaborative operation, and determining the final scheme for predicting maintenance needs; generating a dynamic adjustment strategy based on the final scheme for predicting maintenance needs, combined with real-time feedback data from operational environment adaptation, to guide continuous monitoring of equipment status and obtain optimized operation scheduling instructions; and iteratively optimizing the health status assessment model using an automated update mechanism based on the optimized operation scheduling instructions and real-time changes in equipment operation data to determine the latest configuration of model parameters.

This invention discloses a method and system for load status assessment and early warning of a factory construction machine based on digital twins, belonging to the field of intelligent construction technology. The method includes the following steps: constructing a digital twin model of the factory construction machine; collecting multi-source sensor data of the physical entity and driving the twin to update synchronously; performing adaptive fusion of the data based on confidence level to generate a comprehensive load status vector; calculating a comprehensive load status assessment index based on the vector; generating real-time thresholds through a dynamic early warning threshold model to achieve graded early warning; predicting the remaining service life of key components by combining historical data; and finally displaying the early warning and prediction results through a visual interface. This invention achieves real-time comprehensive assessment of load status, adaptive early warning, and service life prediction, significantly improving the safety and intelligent operation and maintenance level of the factory construction machine.

The invention relates to the technical field of ecological system monitoring and evaluation, and discloses an ecological system health evaluation method based on machine learning. The method comprises the following steps: collecting multi-source continuous monitoring data of an ecological system, and carrying out integrity check and noise filtering to generate a standardized data set; performing multi-scale feature mining on the data set, and extracting ecological feature sequences of a time domain and a frequency domain; performing community discovery on the feature sequence by adopting a spectral clustering algorithm, and outputting a clustering result of the ecological health state; constructing a multi-class classification model according to the clustering result, and adjusting the weight of the model through iterative learning; and inputting the real-time ecological data flow into the trained multi-class classification model, performing probability prediction of the health state, and automatically generating a state evaluation chart. According to the method, the health state can be adaptively defined from the data, and objective and dynamic evaluation of ecological system health is realized.

This invention belongs to the field of RFID robot technology and discloses a real-time status assessment method and related system for UHF RFID robots based on dynamic scenarios. This invention utilizes MAC layer and physical layer performance indicators to construct a MAC layer system status assessment model and a physical layer link quality assessment model. By integrating the MAC layer system status and physical layer link quality assessments, a real-time status assessment model for the UHF RFID robot system is constructed to obtain the real-time status level of the RFID system. This invention, by acquiring multiple feature values ​​and time slot information per unit time, can monitor the status of the UHF RFID robot in real time, promptly identify problems, and make adjustments. By simultaneously evaluating the performance indicators of the MAC layer and physical layer, a comprehensive system status assessment is provided, which can more accurately reflect the working status of the entire RFID system.

This invention relates to a large language model-based automatic battery health status assessment system and method. The system includes: a program knowledge base for storing State of Health (SOH) estimation algorithms; a data processing module for acquiring battery data, preprocessing it, and extracting features highly sensitive to SOH changes to form a feature set; a large language model module for receiving dynamic prompts, retrieving corresponding code segments from the program knowledge base based on the prompts, and generating a new SOH estimation algorithm using the large language model or modifying a parent SOH estimation algorithm in the program knowledge base; an evaluation sandbox for receiving the feature set and calling the new or modified parent SOH estimation algorithm for SOH estimation and evaluation; and an evolutionary controller module for controlling the evolutionary cycle of the SOH estimation algorithm and generating dynamic prompts. Compared with existing technologies, this invention has advantages such as automatic algorithm iteration.

This invention relates to the field of status assessment technology for winding and packaging equipment, and particularly to a method, device, electronic device, and storage medium for intelligent decision-making regarding the status of winding and splicing equipment. The method includes the following steps: real-time data collection from multiple winding and splicing machines in a winding and packaging workshop; preprocessing of the collected real-time data based on specific data processing rules; constructing a scoring calculation model for the winding and splicing equipment, inputting the preprocessed production data into the model, and outputting a score for each machine; determining the lowest-scoring machine based on its score, analyzing the reasons for its low score, and generating a diagnostic analysis report and visualization charts corresponding to the score; and identifying and adjusting the weaknesses in the lowest-scoring machine based on the diagnostic analysis report and visualization charts. This addresses the problem that existing evaluation methods for winding and splicing equipment, due to the reliance on a single evaluation indicator, cannot accurately assess the overall status of the equipment and fail to identify potential problems by not conducting deeper analysis of the equipment's indicator data.

This invention relates to the field of anatomical specimen preservation technology, and more particularly to a method for the long-term preservation of anatomical visceral specimens. The method includes: collecting environmental parameters, visual images, and attribute data of the specimens; generating a monitoring data set after standardization processing; constructing and updating an individual characteristic profile of the specimen based on this data set; performing a fusion analysis on the data set by combining pre-set weights corresponding to organ type and preservation method to generate a comprehensive status assessment result; inputting the data set and assessment result into a pre-constructed hybrid prediction model to output a future status prediction report with confidence scores; inputting the assessment result and prediction report into a pre-constructed hierarchical decision engine to generate personalized preservation and maintenance instructions; and finally, based on these instructions, performing adaptive regulation or triggering early warnings for the specimen preservation environment. This invention achieves individualized and precise assessment and proactive maintenance of specimen status, improving the stability and intelligence level of long-term preservation.

The invention relates to the technical field of digital data processing, and particularly discloses an urban and rural digital fusion co-rich support system based on circulation network reconstruction, which comprises a digital twin mapping module, a network toughness evaluation module, a dynamic reconstruction decision module and a collaborative execution engine. Real-time mapping and state evaluation are performed by constructing a circulation network digital twinborn body, a network reconstruction scheme is generated based on multi-objective optimization, cross-system resource cooperative execution and closed-loop feedback are driven, self-adaptive and flexible intelligent reconstruction of an urban and rural circulation network along with environment change is realized, and the network reconstruction efficiency is improved. And the overall network toughness, the circulation efficiency and the balance of the co-richness target are improved.

A test method determines at least one status parameter of an individual undergoing peritoneal dialysis. The status parameter(s) may include a transport property of the peritoneum, a tonicity, or a residual volume. The test method includes receiving first data indicative of a flow rate as a function of time of a treatment fluid into and out of a peritoneal cavity during a test procedure, and second data comprising measured data samples representing a concentration of one or more solutes in the treatment fluid at time points during the test procedure. The test method also includes computing, based on the first data and by use of a transport model for a peritoneal membrane, estimated data samples representing the concentration of the one or more solutes in the treatment fluid at the time points and determining the status parameter(s) as a function of the measured data samples and the estimated data samples.

This invention relates to the field of construction status assessment technology, specifically a method and system for fusion of compaction data and analysis of compaction quality. This invention accurately captures the soil impact response at the moment of maximum excitation energy by monitoring the rotation phase of the vibrating wheel. It utilizes a time-adaptive weighted information entropy calculation method to deeply analyze the micro-fluctuation patterns of the acceleration sequence, quantitatively assessing the orderliness and stability of soil particle arrangement. This allows for the precise elimination of unstable areas in a falsely solidified or loose state. Simultaneously, it combines historical rebound data with second-order difference operations to construct a compaction growth acceleration sequence, keenly capturing polarity reversals or abrupt changes in soil strength growth trends. It provides real-time warnings of the risk of shear failure induced by excessive compaction, achieving a leap from a single strength index to a multi-dimensional coupled analysis of structural stability and evolution trends. This ensures accurate identification of the true and false compaction quality under various geological conditions, effectively avoiding potential structural hazards and improving the level of refined management in dam filling operations.

This invention belongs to the field of intelligent status assessment technology for generator sets, and discloses a method and system for intelligent status assessment of natural gas generator sets. The method includes: acquiring multi-physics field operation data; reconstructing the phase space of the multi-physics field operation data based on different time scales to obtain a set of physically perceived state point clouds; performing complex filtering on the point clouds at each time scale, and conducting multi-scale topological evolution analysis of the filtered complex sequences; inputting the topological feature vectors into a topological random forest model for training, and performing joint inference to output the posterior distribution of fault modes; calculating a generator set health index, and combining the generator set health index with a dynamic early warning threshold to achieve the assessment of the operating status of the natural gas generator set. This invention can deeply explore the dynamic evolution laws of different physical processes such as combustion, mechanical, thermal, and degradation, and effectively capture early weak faults and multi-field coupling anomalies that are difficult to identify using traditional methods.

The invention relates to the technical field of computer communication, and discloses a communication system, a communication setting method and a program, and the system comprises a network congestion state evaluation module which is used for evaluating a network congestion state; the transmission rate regulation and control module is used for regulating and controlling the data packet transmission rate based on the network congestion state evaluation result so as to reduce delay fluctuation; the data packet processing module is used for performing segment processing on the data packet according to the regulated transmission rate and generating a transmission sequence; the channel error probability evaluation module is used for evaluating the error probability of the current channel; the redundant coding control module is used for regulating and controlling a redundant coding proportion based on a channel error probability evaluation result so as to enhance data integrity; and the data sending module sends the data packet added with the redundant codes to target equipment to complete a communication process, a computer communication method is optimized through an intelligent means, the transmission rate is dynamically adjusted to suppress delay during congestion, the redundant codes are adjusted according to the channel error probability to ensure completeness, and the communication efficiency and reliability are improved.

The embodiment of the application provides a wheelchair adaptation and use state evaluation and detection method, device and system, relates to the wheelchair detection technical field, and the method comprises the following steps: acquiring various sensing data; wherein the sensing data comprises pressure sensing data, humidity sensing data and temperature sensing data; acquiring detection angle data of a gyroscope; performing timestamp alignment and standardization processing on the sensing data; based on a set detection rule, obtaining analysis results according to the sensing data and the detection angle data, judging whether an abnormal situation exists, and issuing a reminder; by detecting various sensing data and judging whether an abnormal use situation exists according to data analysis, comprehensive user state information can be provided, and the comfort and safety of a wheelchair user are significantly improved.