254 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.

Generating and providing a condition assessment for a habitat is described. A geographic area having one or more geographic sub-areas is received. For each geographic sub-area, image data is received, a set of features is generated using the image data, a habitat for the geographic sub-area is received, and one or more condition assessment criteria are identified based on the habitat. For each condition assessment criterion, a subset of features is determined, and a trained model is applied to the subset of features to obtain a response to the condition assessment criterion. For each geographic sub-area, a condition assessment for the habitat is generated based on one or more responses to the one or more condition assessment criteria, and the condition assessment is provided.

Generating and providing a condition assessment for a habitat is described. A geographic area having one or more geographic sub-areas is received. For each geographic sub-area, image data is received, a set of features is generated using the image data, a habitat for the geographic sub-area is received, and one or more condition assessment criteria are identified based on the habitat. For each condition assessment criterion, a subset of features is determined, and a trained model is applied to the subset of features to obtain a response to the condition assessment criterion. For each geographic sub-area, a condition assessment for the habitat is generated based on one or more responses to the one or more condition assessment criteria, and the condition assessment is provided.

The invention belongs to the technical field of gear box wear state evaluation. The invention provides a vibration and oil fused wind power gear box wear state evaluation method and system, and the method comprises the steps: converting a vibration signal of a wind power gear box into a time-frequency domain image through employing a Markov transfer field, carrying out the resampling of the time-frequency domain image, and obtaining a vibration two-dimensional time-frequency graph with the same pixel as a two-dimensional image of an abrasive particle ring; performing down-sampling on the two-dimensional image of the abrasive particle ring and the two-dimensional time-frequency image of the vibration, fusing the two-dimensional image of the abrasive particle ring after down-sampling and the two-dimensional time-frequency image after down-sampling, and performing up-sampling on a fusion result; and obtaining a wear state evaluation result according to an up-sampling result. According to the method, feature selection deviation caused by subjective factors is avoided, and the accuracy and robustness of wear state recognition are improved.

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 relates to a cable aging section state evaluation method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring impedance spectrum data of a to-be-detected cable; dividing the to-be-tested cable into a plurality of initial aging sections according to the impedance spectrum data, and determining a complex dielectric constant of each initial aging section; constructing a target function according to the impedance spectrum data and the complex dielectric constants, and performing fitting calculation on the complex dielectric constants according to the target function to obtain a target complex dielectric constant; and correcting the section length of each initial aging section according to the target complex dielectric constant to obtain a target aging section. By adopting the method, the specific aging condition of the cable can be accurately evaluated.

The invention discloses an animal husbandry environment monitoring and early warning system based on a big data platform, and relates to the technical field of intelligent animal husbandry environment monitoring and early warning systems, and the system comprises an acquisition module which acquires farm environment data and animal behavior image data and uploads the data to an edge computing node, a preprocessing module, a data processing module and a data processing module, the edge computing node is used for preprocessing the farm environment data and the animal behavior image data, the fusion module is used for fusing the preprocessed farm environment data and animal behavior image data to form a multi-modal data set, and the prediction module is used for establishing a health and risk prediction model. Obtaining an animal health condition assessment score and a potential risk level score according to the multi-modal data set; according to the method, data from different sources are integrated, so that health and risk assessment is more comprehensive and accurate, the spatial relevance of the data is adjusted by using a geographic weighting function, and the representativeness of the data is further improved.

The invention discloses a vacuum degree condition evaluation method and system of a vacuum arc extinguish chamber, and belongs to the technical field of vacuum circuit breakers. The evaluation method combines RF and an XGBoost algorithm to realize heterogeneous integration, adds a GAN discriminator to perform physical constraint enhancement, and constructs a universal relation model of X-ray intensity and vacuum degree; the feature importance of RF can guide the feature selection or weighting of XGBoost and enhance feature interaction, the confrontation mechanism of GAN can coordinate data fitting and physical constraints, the physical rationality and data authenticity of model prediction are enhanced by means of confrontation training, and a discriminator is used for dual verification of a generated result. The model is ensured to conform to experimental data distribution and theoretical physical law constraints at the same time, and the result credibility is remarkably improved. According to the evaluation method, accurate fitting of the stable attenuation trend in the high vacuum stage and robustness prediction of fluctuation data in the critical vacuum stage can be achieved, and a new thought is provided for vacuum degree condition evaluation.

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 a mental stress quantitative evaluation method and device for physiological and biochemical fusion monitoring, a skin interface microfluidic module of the device is used for realizing fresh sweat sampling and rapid refreshing, and accurate sweat analysis is carried out with high time resolution; the multiple layers of sensor patches are used for collecting physiological and biochemical signals related to mental stress; and the evaluation module is used for realizing continuous, real-time and accurate mental pressure monitoring according to the physiological and biochemical signals. According to the invention, long-term, real-time and accurate mental stress condition assessment can be carried out by using the wearable device without intervention of professionals; physiological and biochemical signals of a human body can be monitored at the same time, and specific recognition of a pressure source and a pressure level is achieved by combining the two signals; and sweat can be automatically collected for non-invasive monitoring.

The invention provides a to-be-assessed object investment risk level determination method and device, a medium and a program, and the method comprises the steps: determining an industry to which a to-be-assessed object belongs as a target industry, and determining a financial model corresponding to the target industry; inputting the financial index data of the to-be-evaluated object into the Bootstrap model, the fixed effect model and the K-nearest neighbor model, and operating to respectively obtain first result data, second result data and third result data; and according to the first result data, the second result data and the third result data, determining a financial index model value, and according to the financial index model value, calculating scores of the to-be-evaluated object under financial indexes corresponding to the financial index data so as to obtain a final score of the to-be-evaluated object. And evaluating the financial condition of the to-be-evaluated object by adopting the final score. The problem that the investment risk of the to-be-assessed object is increased due to the fact that the model adopted for financial condition assessment is single and industry differences are not considered in the prior art is solved.

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 partial discharge mode identification method, system and device based on a convolutional neural network, and a medium, and the method comprises the steps: employing a multi-layer convolutional structure, and generating a feature map; filling the boundary of the feature map to retain information features at the boundary of the input sample; dynamically distributing the weight of the channel of the feature map and the weight of the spatial region through an attention mechanism to obtain a weight feature map; performing maximum pooling operation on the weight feature map to obtain key features of the partial discharge mode; establishing a random inactivation layer after the pooling layer, and performing dynamic sparsification on the key features; and integrating the weight feature map and the key features, and outputting a classification result of the partial discharge mode. According to the method, the extraction and classification performance of partial discharge features is effectively improved, the robustness and generalization ability of the model under complex working conditions are enhanced, and reliable technical support is provided for power cable insulation state evaluation.

The invention discloses an existing concrete beam bridge crack diagnosis method based on an influence line sparse coefficient, which is characterized in that force method graph multiplication and a crack nonlinear damage function are substituted into an analytical solution for solving, and finite element simulation relates to respiration crack simulation. A nonlinear spring is adopted to simulate crack opening and perform grid division and encryption processing on the tip of the crack, bridge section rigidity change caused by the breathing crack is accurately simulated by constructing a crack rigidity damage function, and the model is introduced into an influence line analytical solution to obtain a nonlinear influence line of the cracked bridge. In combination with a sparse coefficient representation method, damage position and damage degree information is extracted from influence line data, and accurate positioning and quantitative evaluation of beam bridge section cracking are achieved; the method not only improves the accuracy and efficiency of damage identification, but also provides a reliable technical means for rapid detection and state evaluation of the existing beam bridge.

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.

The present invention relates to the technical field of bridge performance evaluation, and specifically to a method and system for evaluating the overall state of a railway bridge. The method comprises: first determining the influencing parameters that have a major impact on the key components of the bridge and the first distribution characteristics of the influencing parameters, and further constructing a multi-factor degradation analysis model; determining the load parameters of different loads and the second distribution characteristics of the load parameters, and calculating the effects on the key components in combination with the finite element model of the bridge; obtaining the structural function function of the key components based on the effects and the multi-factor degradation analysis model; calculating the time-varying reliability of the key components based on the structural function function, the first distribution characteristics, and the second distribution characteristics; deconstructing the bridge, and based on the results of the deconstruction, using the time-varying reliability of the key components to evaluate the overall state of the bridge. This solves the problem of low evaluation efficiency in the prior art.

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.