314 results about "Condition assessment" patented technology

The invention relates to the technical field of skin disease diagnosis, and discloses an AI auxiliary decision support system for skin disease diagnosis, which integrates an image acquisition module, an image analysis module and a comprehensive analysis and diagnosis decision module. A high-definition image of the skin of a patient is obtained through high-precision skin photographing equipment, and a training data set is formed by combining basic information of the patient. A deep learning algorithm is utilized to train a skin disease recognition model and an illness state evaluation model, and accurate recognition of skin disease types and objective evaluation of severity are realized. And the comprehensive analysis module fuses output of the two models, constructs a fusion decision model and provides comprehensive diagnosis suggestions. And the diagnosis decision module is used for generating a disease type judgment and illness state evaluation report based on the trained model in combination with real-time image data so as to effectively assist doctors in making decisions. The accuracy, the efficiency and the popularization degree of skin disease diagnosis are improved, and complex and changeable diagnosis requirements in clinical practice are met.

The invention relates to the technical field of health assessment, in particular to a big data-based breathing health assessment system, which comprises a breathing parameter analysis module, an interaction relationship assessment module, a breathing anomaly detection module and a breathing condition assessment module. According to the method, accurate extraction of breathing parameters and evaluation of breathing periodic changes are achieved by analyzing thoracic pressure waveform data, the breathing mode of a user is accurately recognized, physiological parameters are collected, the mutual relation between the physiological parameters and the breathing mode is analyzed, the influence of various physiological factors on breathing health is revealed, the dynamic monitoring capacity on the breathing health is enhanced, and the user experience is improved. According to the method, by combining detection and identification of abnormal breathing parameters, identification and classification of abnormal breathing reasons are achieved, medical professionals are helped to make treatment plans and health management strategies, diagnosis efficiency is improved, the misdiagnosis rate is reduced, and powerful support is provided for management of various chronic diseases by combining continuous trend analysis.

The invention relates to the technical field of data processing, in particular to a shrimp group health condition assessment method based on deep clustering analysis, and the method comprises the steps: monitoring health parameters; determining a shrimp group dispersion grid; determining a temporary grid; identifying a non-shrimp group abnormal grid; calculating an actual health index; and adjusting the grid step length and evaluating health. According to the method, the health condition of the shrimp group can be accurately monitored and evaluated by combining physiological and behavior data such as real-time density, saturation and activeness, abnormal grids are eliminated step by step, more representative grids are screened out, evaluation deviation caused by environmental fluctuation or measurement errors is avoided, and the evaluation accuracy is improved. The dynamic monitoring of the health condition of the prawn group is realized, and the grid step length is automatically adjusted by comparing the ideal health index with the actual health index, so that the health assessment precision and reliability are improved, and the problem of inaccurate health assessment result caused by unstable environmental parameters is effectively solved.

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 provides an electrical stimulation acupoint physiotherapy massage device for cancer patients, which comprises a glove, a foot sleeve, an electrode plate 1 and an electrical stimulation system 2, the electrode plate 1 and the electrical stimulation system 2 are arranged on the glove and the foot sleeve, the electrical stimulation system 2 comprises a signal generator and a communication module, and the device further comprises a mobile phone end control calculation table connected with the communication module. The communication module receives a control instruction from the mobile phone end control computing table and transmits the control instruction to the signal generator; and the mobile phone end control calculation table is provided with an illness state evaluation and analysis module, a physiotherapy scheme making module and a user interface and operation module. The personalized physical therapy device can better adapt to individual differences and disease changes of different patients, and the pertinence and effectiveness of the physical therapy effect are improved.

The invention discloses a method, device and system for classifying and recognizing impurity particles in transformer oil and a medium, and the method comprises the steps: obtaining an image of the impurity particles in the transformer oil in real time, taking the image as an original image, and forming a data set based on the original image; preprocessing the original image in the data set to obtain a preprocessed image; a K-means + + algorithm is adopted to generate a new prior frame; according to the new prior frame, training a YOLO target detection algorithm to obtain a trained YOLO target detection model; and performing target detection on the preprocessed images based on the trained YOLO target detection model, automatically identifying different types of impurity particles, automatically counting the number of prediction frames on all the images, and outputting the number of different types of particles. According to the invention, the accuracy and efficiency of monitoring the particle impurities in the transformer oil can be effectively improved, and important reference is provided for state evaluation and fault detection of the transformer.

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.

An example embodiment of the present invention provides a method of assessing the condition of a pavement site, comprising: (a) acquiring aerial images of the site from above, for example by an unmanned aerial system (UAS); (b) using photogrammetry tools to generate an orthomosaic that represents the airport pavement surface; (c) using image analysis tools and machine learning methods to determine the location and extent of defects in the pavement; (c) producing an image representation of the site and the defects, where the location and extent of defects are discernible from the image; (d) using software application techniques to store and present defect data and other related information for client-side user access.

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 provides a first-aid transfer monitoring system and method, and the system comprises a data communication module which is used for the communication management between a terminal device in an ambulance and each module, and is used for storing communication data; managing authentication between the ambulance and the system; the detection control module is used for carrying out monitoring and early warning on the first-aid environment in the ambulance, the operation condition of the monitoring equipment, the patient information and the monitoring data; receiving the control instruction to adjust equipment in the ambulance; the evaluation strategy module is used for receiving an instruction of the medical staff to the system, generating etiological analysis, first-aid measure suggestions and patient condition evaluation for the medical staff, and generating a first-aid transfer report; and the interface interaction module is used for displaying the output of each module of the system, including interface interaction in an ambulance and interface interaction in a hospital. The method has the advantages that it is ensured that the medical staff can master the vital signs of the patient and the state of the monitoring equipment in real time, and scientific decision support is provided for the medical staff.

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 provides a fan bearing state evaluation method and system based on a sound energy flow deviation index. The method comprises the following steps: acquiring a plurality of sound signals under the stable operation of a normal fan bearing as reference samples; acquiring a sound signal under the stable operation of the current fan bearing as an evaluation sample; calculating the wavelet packet energy flow of each reference sample and the wavelet packet energy flow of each evaluation sample; calculating a wavelet packet energy flow deviation degree reference value of the wavelet packet energy flow of each reference sample in pairs, and selecting a maximum reference value of the wavelet packet energy flow deviation degree; calculating the wavelet packet energy flow deviation degree between the wavelet packet energy flow of the evaluation sample and the wavelet packet energy flow of any reference sample; if the wavelet packet energy flow deviation degree is larger than the maximum reference value of the wavelet packet energy flow deviation degree, the fan bearing is abnormal, and otherwise, the fan bearing is normal. The method has the characteristics of high evaluation speed and high accuracy.

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 relates to the technical field of engineering construction, and provides an AI identification-based worker health condition assessment method, which comprises the steps of obtaining worker health data in engineering construction, and establishing a health file; importing the health archive into a pre-configured exposure factor identification network, identifying exposure factors in engineering construction, and establishing attribution risk degree functions of different exposure factors; according to the attribution risk degree function, attribution risk degree values of different exposure factors relative to the workers are determined, and a worker health condition assessment report is generated. The method not only provides powerful support for engineering safety management, but also shows a remarkable effect in solving practical problems. According to the method, the source, the influence degree and the occurrence probability of various potential risk factors in the engineering construction process are deeply analyzed, so that the risk degree is accurately and quantitatively evaluated. And corresponding risk prevention and control measures are formulated according to the evaluation result, so that the possibility of accidents is effectively reduced, and safe and stable engineering construction is ensured.

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.

A controller may monitor sensor data collected by one or more sensors of a machine while the machine is operating at a worksite. The controller may determine whether the sensor data is indicative of the machine operating in an unsafe condition at a location of the machine at the worksite. The controller may cause, based on a determination that the sensor data is indicative of the machine operating in the unsafe condition, at least one of: storing of the sensor data relating to the machine operating in the unsafe condition at least until the sensor data relating to the machine operating in the unsafe condition is offboarded from the machine, or transmission of the sensor data relating to the machine operating in the unsafe condition offboard the machine.

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.