194 results about "Condition assessment" patented technology

The invention discloses a state evaluation method, system and device and a medium, and belongs to the field of machine learning, and the method comprises the steps: obtaining multi-modal sensor data, carrying out the preprocessing and fusion, and obtaining a fusion feature vector; detecting the fusion feature vector based on a mixed architecture model constructed by a spline enhanced multi-layer sensing network, a convolutional neural network and a Transform attention mechanism to obtain an anomaly type and confidence; when the detection result is abnormal, generating an abnormal event according to an event driving mechanism; responding and acquiring time sequence data from historical sensor data, and performing simulation prediction according to the physical information neural network model to obtain a prediction result; the prediction result is fed back to the detection model to update the confidence degree, the target detection result is obtained, the state evaluation result is obtained in combination with the prediction result, and therefore the state evaluation accuracy and reliability can be improved.

The invention relates to the technical field of ship hydraulic monitoring, and discloses a marine ship hydraulic oil cylinder state monitoring and sealing performance early warning system. A multi-source sensing data acquisition module of the system synchronously obtains multi-dimensional time sequence operation data of an oil cylinder through a vibration sensor, a pressure sensor and a temperature sensor; the data preprocessing fusion module is used for carrying out noise suppression, timestamp alignment and multi-modal fusion on the data to generate a standardized oil cylinder state feature sequence; the sealing characteristic depth extraction module is used for analyzing sealing cavity pressure fluctuation, piston rod displacement oscillation and sealing piece deformation trend characteristics from the characteristic sequence; the multi-scale state evaluation module is used for calculating a sealing performance degradation gradient and a dynamic operation stability coefficient according to the characteristics; and the early warning decision generation module is used for generating a sealing failure risk level and an early warning control instruction based on the calculation result. According to the system, stable operation of the oil cylinder is guaranteed, and the maintenance cost and the safety risk are reduced.

The invention relates to a defect detection method and system based on multi-modal feature fusion. The method comprises the following steps: acquiring multi-modal data of a transformer; predicting based on the multi-modal data through the fusion prediction model, and outputting an elastic modulus distribution diagram of the insulating material in the transformer; carrying out super-resolution enhancement on the elastic modulus distribution diagram based on a physical constraint diffusion model; and positioning the defects of the transformer through a time reversal algorithm on the basis of the internal Young modulus distribution and the super-resolution elastic modulus distribution diagram of the transformer. According to the invention, the problems of feature loss, large single modal error, fuzzy fractal defect reconstruction, insufficient defect positioning precision and the like caused by signal attenuation in traditional detection are solved. According to the method, the signal-to-noise ratio and modulus distribution prediction precision of the signal is remarkably improved, micron-order super-resolution reconstruction is achieved, winding deformation and insulation layering are accurately distinguished, harm is quantified, traditional imaging limitation is overcome, and a high-precision and physically consistent basis is provided for transformer state evaluation and maintenance.

The invention discloses a cable temperature detection method, which comprises the following steps: laying distributed optical fibers along a to-be-monitored cable, synchronously obtaining whole-line temperature, vibration sound wave signals and load current data, and carrying out dynamic compensation and correction on a temperature measurement value; on the basis of multi-source information fusion, abnormal points are accurately positioned, a cable multi-physical field digital twinborn model is constructed, a health index reflecting the insulation condition is calculated in real time, and quantitative evaluation of local and overall states is achieved; further simulating different load scenes to predict the time when the insulation performance is degraded to a failure threshold value, so as to output probability distribution of the remaining service life of the cable; according to the method, the whole-process intelligentization from accurate temperature measurement and abnormal positioning to state evaluation and life prediction is realized, and the perspectiveness and reliability of cable operation and maintenance are remarkably improved.

The invention belongs to the technical field of image processing, and particularly relates to a mountain comprehensive ecological condition monitoring system based on remote sensing intelligent interpretation. The system comprises a remote sensing data processing unit, an object-oriented interpretation unit, an ecological patch diagram construction unit, a connectivity evaluation unit and an ecological output unit. The remote sensing data processing unit obtains a target mountain region remote sensing image and digital elevation data and generates a fusion base map; an object-oriented interpretation unit performs object-oriented segmentation on the fusion base map to generate an ecological patch object set; the ecological plaque graph construction unit constructs an ecological plaque graph through the ecological plaque object set; the connectivity evaluation unit generates an object state record and generates a connectivity anchor point object set; and the ecological output unit generates a comprehensive ecological condition score based on the connectivity enhanced ecological plaque object set and generates a comprehensive ecological condition assessment report. The spatial relation between the hydrological process and the ecological plaque can be stably expressed under the complex mountain terrain condition, and the influence of the initial interpretation error on the result is reduced.

The invention relates to the technical field of mechanical equipment intelligent operation and maintenance and state monitoring, and discloses a health monitoring method for bearing state evaluation and residual life and degradation trend prediction, which comprises the following steps of: firstly, extracting time domain, frequency domain and time-frequency domain characteristics from original vibration signals of a bearing under different working conditions; according to comprehensive evaluation indexes and known bearing degradation characteristics, features having good characterization capability and trend consistency for bearing degradation performance are screened out, a novel backbone network model is constructed, deep features are mined, effective features are enhanced, meanwhile, a time dependency relationship in a long sequence is captured, and then a multi-task learning mechanism is introduced, so that the bearing degradation performance is evaluated. Through parameter sharing and joint optimization, bearing state identification, residual life prediction and performance degradation trend prediction can be synchronously completed only by training and deploying a single model. According to the method, multi-task collaborative prediction under complex working conditions is realized, and the accuracy and robustness of bearing state recognition and service life prediction are improved.

A system and method are disclosed for advanced multimodal-sensor systems integrating various sensing characteristics for machine condition monitoring, including thermal imaging cameras, ultrasonic, vibration, temperature, magnetic sensors, high-band microphones, electro-magnetic emission detectors and others. The system's architecture comprises edge computing capabilities for real-time anomaly detection and operational feedback, utilizing edge machine learning (ML) models trained in the cloud. The system supports flexible configuration and calibration to optimize measurements based on machine requirements, while a cloud-based analysis framework enables robust data fusion, training and condition assessment. The method comprises performing automated diagnostic workflows enabled by models at the edge, reducing or eliminating human intervention, and significantly enhancing operational performance, maintenance and efficiency across multiple machines in an industrial setting.

The invention relates to a method for monitoring the condition of vehicle tires (10), comprising an electronic condition assessment device (12) comprising an electronic data processing device with a storage unit, comprising the method steps: a) detecting a surface topography for at least one profile section of the vehicle tire (10) with an optical detection unit (12), wherein the optical detection unit comprises an optical image acquisition device (14) and a projection device (16) configured to project a pattern onto the profile section, wherein the electronic data processing device is configured to calculate a three-dimensional representation of the profile section of the vehicle tire (10) from the image of the pattern detected by the optical detection unit on the profile section.b) Acquiring one or more tire parameters of the vehicle tire (10) with one or more electronic acquisition units (18) of the condition assessment device (12), and c) Outputting the surface topography of the vehicle tire (10) or profile condition information derived therefrom and the one or more tire parameters of the vehicle tire (10) via an output unit (20) of the condition assessment device (12).

The invention relates to the technical field of mold state monitoring, in particular to a mold state early warning method and system fused with big data analysis, and the method comprises the steps: building comprehensive operation data based on historical and real-time data, extracting key feature parameters to form a state evaluation system, and a mold state evaluation model is determined and constructed through multi-dimensional feature optimization, dynamic trend model calculation and weight distribution, and abnormal recognition and state early warning are realized in combination with intelligent sensing and distributed calculation. The comprehensive and intelligent level of mold state monitoring can be improved, the prediction accuracy is improved, the production loss caused by mold faults is reduced, and meanwhile the maintenance efficiency is improved.

The invention provides an AI multi-mode non-contact injury assessment system and device, and relates to the technical field of injury assessment, the system mainly comprises a data acquisition module, a data processing module and a result generation module; the data acquisition module is used for acquiring geographical location information, visible light image data, thermal imaging data and average distance information of the tested wounded; the visible light image data comprises a visible light video and a visible light photo; the thermal imaging data comprises a thermal imaging video, a thermal imaging picture and a radiation temperature; the average distance information comprises an average distance between the detected wounded person and a shooting lens in the same image; the data processing module comprises a posture analysis unit, a bleeding detection unit, a respiration monitoring unit, a body temperature estimation unit and a comprehensive evaluation unit; according to the scheme, injury assessment is carried out based on a standardized AI algorithm, inconsistent judgment caused by personal experience difference is avoided, and an objective basis is provided for rescue decision making.

The invention belongs to the technical field of teenager depression marker detection, and particularly relates to a teenager depression marker detection kit and a teenager depression marker detection method, the kit comprises an extraction liquid, an internal standard substance, a quality control substance and a mobile phase additive, the internal standard substance comprises a stable isotope labeled metabolic marker, the quality control substance comprises a standard metabolic marker, and the mobile phase additive comprises a mobile phase. The detection method comprises the following steps: S1, dissolving an internal standard substance by using a methanol-containing aqueous solution to prepare an internal standard stock solution; s2, methanol is added into the internal standard stock solution for dilution, uniform mixing is performed, and an extraction working solution is prepared; s3, taking a quality control product / sample, adding an extraction working solution, carrying out oscillation incubation, centrifuging, and taking supernate as a to-be-detected sample; s4, the sample to be detected is detected through a liquid chromatograph-mass spectrometer, various biomarkers related to depression in the body of the teenager can be rapidly and accurately detected, and an objective and reliable basis is provided for early diagnosis and illness state evaluation of the depression of the teenager.

The invention discloses an NPC converter closed-loop feedback data driven IGBT saturation voltage drop monitoring method, and relates to the field of electronic power device state monitoring, and the method comprises the steps: collecting the operation data of an NPC energy storage converter; performing analog-to-digital conversion on the acquired operation data; performing data cleaning on the operation data; deriving a mathematical model of the energy storage converter, and substituting the processed data into the mathematical model; performing curve fitting, calculating an objective function, searching an optimal solution to minimize the objective function, and obtaining an identification voltage drop parameter of the IGBT; according to an IGBT output characteristic curve, extracting parameters in a healthy state, and comparing the parameters with identification parameters to carry out state evaluation; a parameter self-correction or online correction mechanism is introduced; classifying the health states of the IGBTs through normal distribution; and online evolution evaluation and abnormity early warning of the device state are realized. By the adoption of the method, the actual IGBT state and behavior are simulated through the digital model, and therefore predictive maintenance is achieved.

The invention provides an interference area thermal environment simulation test device and method based on radiation and convection coupling. The interference area thermal environment simulation test device comprises a convection heating system, a radiation heating system, a test section, a model support and a vacuum system. The aerodynamic heat environment of a non-interference area is simulated through the convection heating system, and a local high heat flow interference area is formed by the radiation heating system through the water-cooling filter plate, so that independent control and coupling simulation of heat flows of the two areas are realized. According to the method, heat flow parameters of a non-interference area and an interference area are debugged step by step, and multiple test steps are connected in series to complete all-working-condition assessment. The method can accurately reproduce the high-heat-flow gradient characteristics of the local area of the surface of the aircraft, overcomes the limitation that a traditional test method cannot simulate the high-heat-flow gradient, remarkably improves the authenticity and refinement level of a ground test, provides reliable data support for the design of a heat-proof structure of the hypersonic aircraft, effectively reduces the design redundancy, and improves the design efficiency. And flight safety is guaranteed.

The invention discloses a bridge concrete apparent damage identification method and system based on texture analysis, and belongs to the crossing field of bridge engineering detection and computer vision technology, and the method comprises the steps: employing an unmanned plane to collect a bridge member surface image, and generating an orthoimage; extracting contrast, correlation and entropy features based on the gray-level co-occurrence matrix; extracting histogram features based on a local binary pattern; detecting a corrosion dialysis area based on an HSV color space; fusing the multi-dimensional features, inputting the fused multi-dimensional features into a multi-label classifier, and outputting pixel-level segmentation masks of various damages such as cracks, spalling, exposed reinforcement corrosion and dialysis at the same time; according to the method, the complementary texture features are fused to realize simultaneous identification of multiple types of damages, a pixel-level segmentation result is output, and an evaluation report conforming to engineering specifications is automatically generated.

The invention relates to the technical field of intelligent monitoring of industrial valves, in particular to an intelligent monitoring and state evaluation system for sealing wear of a semi-ball valve, which comprises the following steps of: dividing action stages in the opening and closing process of the semi-ball valve, and organizing sealing monitoring in an operation stage of forming stable sealing contact between a valve core and a sealing surface; corresponding data acquisition modes are adopted according to different operation stages, and operation parameters directly related to the sealing contact behavior are obtained; staged processing is carried out on collected data, a sealing response characteristic section with clear physical significance is constructed, and a sealing contact dominant state and a change relation thereof are identified in multiple operation cycles; further comprehensively evaluating the sealing wear state of the semi-ball valve based on the persistence characteristic and the space migration condition of the dominant sealing response characteristic section in the continuous operation cycle; according to the invention, the operation evaluation and maintenance decision-making capability of the sealing state of the semi-ball valve is greatly improved.

This application discloses a method and system for online condition assessment of permanent magnet motors based on multi-scale fuzzy entropy, relating to the technical field of permanent magnet motor fault diagnosis and reliability index analysis. It solves the technical problems of existing technologies that often rely on macroscopic parameters such as failure rate, making it difficult to quantify the degree of motor performance degradation and lacking consideration for real-time online condition assessment of permanent magnet motors, resulting in low accuracy and real-time performance of permanent magnet motor condition assessment. The method generates a radial air gap magnetic flux density difference signal based on the radial air gap magnetic flux density signal, and uses this to generate a multi-scale fuzzy entropy and a permanent magnet demagnetization assessment model. It generates the permanent magnet condition result based on the multi-scale fuzzy entropy corresponding to the online radial air gap magnetic flux density signal, and eliminates magnetic flux density background interference through the radial air gap magnetic flux density difference signal. By characterizing the subtle differences in signal complexity under different demagnetization states through multi-scale fuzzy entropy, it achieves a quantitative description of the degree of permanent magnet degradation, improving the real-time performance and accuracy of condition assessment.

An authorization device (100) includes a valid condition assessment unit (180) that assesses whether a valid condition is met so as to judge whether an authorization rule is valid or not. The valid condition is a condition which is generated on a basis of a countermeasure scenario to address a vulnerability related to access to a target system storing an electronic file, and which concerns an authorization target requiring authorization for access to the target system. The authorization rule is a rule for the valid condition and authorizes access to the target system.

This invention discloses a method and system for cable line condition assessment and maintenance decision-making, relating to the field of power system technology. The method includes: acquiring cable condition data and determining missing data information; the missing data information includes the location and type of the missing data; based on the missing data information, employing a corresponding data completion strategy to complete the cable condition data, obtaining completed values ​​and corresponding uncertainty indicators; inputting the completed values ​​and corresponding uncertainty indicators into a pre-constructed fault prediction model to obtain the cable's fault probability and confidence interval within a specified future time window; and determining the optimal maintenance strategy based on the fault probability. This invention enables accurate assessment and scientific decision-making regarding cable line conditions even with incomplete information.

The invention relates to the technical field of environmental protection monitoring, in particular to an Internet of Things environmental protection monitoring system, which comprises a data acquisition module for dividing a monitoring area into a plurality of sub-areas according to environmental conditions, and monitoring air quality of key areas in the sub-areas by using sensors; the environmental condition evaluation module is used for constructing a pollutant concentration model according to the pollutant concentration data collected by the data collection module and evaluating the environmental condition of each sub-region; the risk assessment module is used for receiving the data transmitted by the cloud platform, constructing a factor state set and generating a risk coefficient set of each factor according to the factor state set; and the priority treatment module is used for evaluating the degree of each pollutant exceeding the safety standard according to the environmental quality evaluation result of the sub-region in combination with the risk coefficient, and providing a treatment priority for treating the environment of the sub-region. And through calculation of the risk coefficient and evaluation of the priority, resources can be reasonably allocated and treated, and the most concerned problems are preferentially solved.

The embodiment of the invention relates to a patient physical condition assessment method, device and equipment and a storage medium. The method comprises the following steps: according to patient case data, carrying out experience dependence evaluation on the physical condition of a patient to obtain an experience evaluation result; performing rule dependence evaluation on the physical condition of the patient according to the patient medical record data to obtain a rule evaluation result; and under the condition that at least one of the experience assessment result and the rule assessment result comprises a severe assessment result, carrying out risk assessment feedback on the physical condition of the patient. According to the technical scheme, high-efficiency and high-reliability automatic patient physical power condition evaluation independent of manual work can be achieved.

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.

The present application relates to a kind of individual future health state prediction method, device and medium based on proteomics, wherein the method comprises the following steps: obtaining proteomics data and preprocessing;Shared network construction: construct individual past and future comorbidity condition prediction neural network based on twin network framework;Trunk network construction: construct health-specific outcome prediction neural network based on multilayer perceptron method;Health assessment network integrates the shared network and trunk network architecture, and extracts features thereof to fuse and fine-tune in latent space, updates fine-tuning network parameters by further training, and outputs the risk assessment probability of multiple health-specific outcomes in the future.Compared with prior art, the present application focuses on proteomics data processing and modeling method, uses past and future health estimates as prior information, and realizes individual health condition assessment with multiple diseases and death as outcome.

This invention relates to the field of maintenance management decision-making technology and discloses an intelligent pavement condition assessment and resource allocation system integrated into a mobile platform. The system includes sequentially connected modules for mobile data acquisition, data preprocessing, pavement condition assessment, resource optimization allocation, and decision support and feedback. The mobile data acquisition module collects multi-source data through a sensor array and dynamic sampling strategy; the preprocessing module generates a high-quality dataset through cleaning, spatiotemporal calibration, and standardization; the assessment module calculates the Pavement Health Index (PHI) based on a three-level model to accurately classify pavement condition levels; the resource optimization allocation module aims to maximize overall maintenance benefits by solving for the optimal resource allocation using a multi-constraint linear programming model; and the decision support and feedback module provides visualized decision display and corrects model parameters based on post-maintenance re-inspection data. The system improves the accuracy of pavement assessment and the scientific nature of resource allocation, adapting to the needs of large-scale road network maintenance management.

This invention discloses an ultra-high frequency partial discharge and temperature monitoring system for use in power distribution network systems, belonging to the field of smart grid facility condition monitoring technology. The system includes: a signal acquisition module for acquiring mixed signals via a main ultra-high frequency channel and interference reference signals via a mirror channel; an inversion compensation module for establishing interference propagation relationships and progressively reducing interference to obtain candidate pulses; a thermal response construction module for constructing a partial discharge thermal excitation coding sequence and generating an event-triggered thermal response matrix; a thermal memory extraction module for constructing an exponentially decaying thermal memory kernel and extracting thermal memory spectral features; a mapping solution module for inverting heat source parameters and thermal resistance distribution based on a reversible Fourier neural operator model; and an assessment and early warning module for generating the spatial distribution of heat sources, analyzing the defect evolution sequence, and outputting condition assessment and early warning information. This invention combines mirror interference compensation and a reversible Fourier neural operator model to achieve internal defect location and early warning in power distribution facilities.

The invention relates to the technical field of state monitoring, and discloses an actuator mechanical state evaluation method and system based on time sequence characteristics, and the method comprises the steps: carrying out the normalization processing of an original time sequence signal of an execution machine, and obtaining a time sequence signal of the execution machine; identifying a local extreme point in the time sequence signal, fitting an upper envelope line and a lower envelope line of the time sequence signal, and separating the time sequence signal into intrinsic mode components according to the upper envelope line, the lower envelope line and the local extreme point; extracting a mean value, a variance and an energy value of the intrinsic mode component to obtain an initial feature set of the execution machine; removing the redundant features of which the feature correlation is higher than a preset threshold value to obtain an optimized feature set of the execution machine; performing forward propagation on the optimized feature set to obtain a generation state probability vector of a mechanical state category; and generating the working state of the execution machine according to the generated state probability vector. According to the invention, the efficiency and reliability of actuator mechanical state evaluation can be improved.

A platform for automatically detecting and analyzing defects in infrastructure assets uses aerial imagery, machine learning models and an interactive geographic information system (GIS) interface. Drone or aircraft images are processed by deep neural networks to identify and quantify surface and subsurface defects. Defects are mapped onto 3D models with precise geospatial coordinates. A web platform allows engineers to visualize defects from individual or multiple time periods, filter and query based on attributes, analyze density heatmaps, highlight changes, and perform side-by-side comparative analysis. The unique interface enables rapid condition assessments to support proactive maintenance.

The present disclosure provides a wind turbine unit state evaluation method, a controller and a wind turbine unit. The state evaluation method comprises: obtaining a blade root bending moment of the wind turbine unit; obtaining an aerodynamic torque according to the blade root bending moment; obtaining an impeller absorbed power based on a product of the aerodynamic torque, an impeller rotating speed of the wind turbine unit and a predetermined blade absorbed efficiency; obtaining a first power according to a product of the impeller absorbed power and a predetermined unit efficiency; and evaluating a state of the wind turbine unit according to a product of the first power and an actual output power or a grid-connected power of the wind turbine unit. The state evaluation method according to the embodiment of the present disclosure can determine whether the unit has an abnormal state of too much energy accumulation.

The invention relates to a method for the automated determination of the status of parking spaces (1), comprising the steps of: providing (S1) position information of parking spaces (1) in a central unit (3); transmitting (S2) the position information from the central unit (3) to a plurality of vehicles (5); continuously comparing (S3) each of the vehicles (5) its own current position with the position information; checking (S4) each of the vehicles (5) whether a parking space (1) is occupied; and if not: capturing (S5) an image of this parking space (1) with an environmental sensing unit of the vehicle (5) and transmitting the image to the central unit (3); evaluating (S6) the image by an analysis algorithm executed in the central unit (3) to determine the status of the parking space (1);Updating (S7) the position information by the central unit (3) and transmitting the updated position information to the multitude of vehicles (5).;

This invention relates to the field of straddle-type monorail vehicle operation simulation testing technology, and discloses a method and apparatus for simulating the operation of straddle-type monorail vehicles. The method involves adjusting the test bench to match the vehicle's wheel system, applying a vertical load to simulate vehicle weight using an axle load-simulating actuator, and achieving longitudinal and lateral fixation through a traction rod and pulley system. An onboard detection system acquires track irregularity displacement signals and track spectra from the actual track, which are then imported into a vibration simulation system to drive a vibration simulation device to reproduce the vehicle's operating state. Stability parameters are tested using sensors to complete the overall vehicle vibration reliability test. The corresponding apparatus includes a test bench foundation, a reaction frame, a loading gantry, and a vibration simulation system. This invention can accurately simulate actual operating conditions, achieve fault reproduction and extreme condition assessment, and effectively improve the realism and efficiency of the test.

A method for determining road surface information in a vehicle (100), comprising: Receiving control data sampled by sensors (130); Determining at least one condition assessment value based on the received control data, wherein the at least one condition assessment value is a SAT mode associated with a linearity of the restoring torque, SAT; Determining a set of features that includes at least one of the SAT, slip angle, SAT variance, steering rate, and lateral acceleration, based on the state appraisal value; Processing the tax data obtained during a tax maneuver and associated with the feature set, using a pattern classification technique; Determining a surface type based on processing; Determining a tax maneuver type, and wherein determining the feature set and processing the tax data is based on the tax maneuver type; and Controlling one or more vehicle systems (100) based on the specific surface type.