15results about How to "Achieve early warning" patented technology

The invention relates to the technical field of data feature mining, in particular to a remote nursing management method based on data feature mining. The method comprises the following steps: acquiring specific conditions and demand data of a patient; performing data source selection and configuration according to the specific condition and demand data of the patient to obtain a data source configuration table; performing multi-dimensional data acquisition according to the data source configuration table, and performing multi-dimensional data preprocessing to obtain patient comprehensive data; carrying out individualized baseline modeling on the patient comprehensive data to obtain an individual baseline model; performing real-time data standardization on the patient comprehensive data to obtain standardized real-time data; performing deviation calculation according to the standardized real-time data and the individual baseline model to obtain a deviation value sequence; and performing index correlation analysis on the deviation value sequence to obtain an index correlation matrix. According to the invention, the utilization of data feature mining is improved, so that remote nursing management is more accurate and personalized, and finally the nursing quality and efficiency are improved.

The invention relates to the field of electrochemical energy storage, and discloses a testing method and a testing system of an all-vanadium redox flow battery, and the method comprises the following steps: describing diffusion, convection and electrochemical reaction processes of electrolyte in a battery flow channel through a partial differential equation, and establishing a dynamic distribution model of concentration gradient and current density; based on the model, an adaptive control algorithm is adopted to dynamically adjust the electrolyte flow velocity and the current density, and stable control of a test environment is realized; acquiring electrochemical impedance spectrum data by applying alternating-current voltage signals with different frequencies, extracting non-linear characteristics in an impedance spectrum by using a long-short-term memory network model, and analyzing and predicting the performance degradation trend of the battery; a multivariable optimization model is established, and parameters such as temperature, flow velocity and current density are dynamically adjusted, so that a test environment is always close to a target value. According to the method, the test precision and the environmental stability can be effectively improved, and reliable technical support is provided for performance evaluation and optimization of the all-vanadium redox flow battery.

The invention provides a transformer fire detection method based on infrared and visible light image fusion, and the system comprises an infrared camera, a visible light camera, an image fusion and analysis host, a large monitoring screen, and an alarm device. The use method of the detection system comprises the steps that infrared images and visible light images captured in real time are transferred into the image fusion and analysis host through the infrared camera and the visible light camera, and after the image fusion and analysis host receives the image flow, the infrared images and the visible light images are fused through an image fusion algorithm; a multi-modal image is formed, then a pre-trained deep learning model is utilized to analyze the fused image, an early occurrence sign of a fire disaster is identified, a fire hazard part is found in time, after identification, the fire hazard part is presented to a large monitoring screen, and meanwhile, a sound-light alarm device is triggered so as to timely remind related operators on duty to rapidly take measures. Early-stage fire disasters are extinguished, and operation safety of the transformer is guaranteed.

The application provides a kind of running state monitoring method, system, equipment and vehicle of vehicle-mounted SoC, it is related to vehicle-mounted chip technical field, this scheme is by deploying monitoring agent in target SoC application layer, and is combined with independent monitoring unit, realize all-around real-time monitoring to chip running state.System periodically acquires multidimensional state parameter transmission to independent unit and carries out comparison analysis: when parameter exceeds primary threshold value, generate early warning signal and save log to realize early risk early warning;When parameter exceeds secondary threshold value, trigger fault response and send forced reading instruction to target SoC.The mechanism ensures that when a serious fault occurs, the key data in the SoC memory can be forced to save to the storage unit powered by the vehicle constant power KL30.The present scheme effectively solves the defects of single monitoring dimension, log loss and early warning lag in the prior art, significantly improves the fault diagnosis capability and functional safety level of the vehicle system.

The application discloses a wind turbine gearbox cooling system fault prediction system and method in the technical field of wind power generation fault diagnosis, which comprises the following steps: collecting multiple source operation parameters of a wind turbine gearbox lubricating cooling system in real time; calculating key performance indicators reflecting the performance of the lubricating cooling system based on the multiple source operation parameters, and extracting features, wherein the key performance indicators at least include a cooling performance ratio; performing performance degradation identification and fault prediction based on the time sequence change trend of the key performance indicators; and outputting the performance degradation identification and fault prediction results. By introducing the comprehensive performance indicator of the cooling performance ratio, the application can sensitively identify the progressive degradation of the cooling system, overcome the defects of the traditional single parameter threshold alarm lag and fault positioning ambiguity, and provide an effective means for realizing predictive maintenance of the wind turbine.

The invention discloses the identification of nucleotide point mutations in the cytochrome b protein-related gene PlCyt b of litchi downy mildew pathogen and its application in monitoring resistance to the fungicide oxadiazine, and specifically discloses a molecular detection method and special primers for identifying the resistance of litchi downy mildew pathogen PlCyt b gene 370 or 372 nucleotide mutations to oxadiazine. The molecular detection method provided by the invention has high sensitivity, good stability and short detection cycle, and can be used for high-throughput monitoring of the frequency of resistance genes and the occurrence and development of resistance of litchi downy mildew pathogen to the fungicide oxadiazine in the field.

The application discloses a kind of based on multi-source association analysis's power battery thermal runaway detection method and system, belong to power battery field.The method includes: according to vehicle state, charging state, single number judges whether vehicle triggers high charging low discharging or low charging high discharging early warning;Under charging condition, according to the signal collected by vehicle judges whether vehicle triggers power battery water inlet or liquid leakage insulation risk early warning;According to vehicle state and collected historical data judges whether vehicle triggers battery overuse early warning;Monitoring the voltage difference of each single battery, judges whether triggers pressure difference early warning;After vehicle triggers high charging low discharging or low charging low discharging risk early warning, according to the single number of early warning output judges whether triggers temperature anomaly early warning;Based on preset early warning strategy judges whether vehicle triggers battery thermal runaway risk early warning.The application realizes multiple power battery risk early warning by integrating multi-source data, and realizes early warning of thermal runaway based on preset comprehensive early warning strategy.

The application is suitable for the technical field of agricultural monitoring, and particularly relates to a smart agricultural monitoring method and system.The method comprises the following steps: delimiting a target area requiring disease and pest monitoring, and dividing the target area into a plurality of sections, selecting monitoring points, irradiating the sections by using light source devices previously arranged at the monitoring points, and clustering the light source devices into a fixed group and an adjustable group; starting the light source devices of the fixed group, collecting spectral reflectance data at each monitoring point, wherein each wave band corresponds to a reflectance, drawing a plurality of spectral curves, and identifying the change trend of each spectral curve.The application can adjust the irradiation angle, optimize the light coverage, reduce the monitoring dead angle, meet the monitoring requirements of different leaf orientations and plant structures, greatly improve the monitoring accuracy, and improve the disease and pest control efficiency and intelligent level of crops.

The utility model discloses a kind of monitoring devices of broken resin, including being arranged on the fine treatment mixed bed fine treatment mixed bed import sampling point and fine treatment mixed bed export sampling point, fine treatment mixed bed import sampling point and fine treatment mixed bed export sampling point are connected with cooling device entrance, cooling device outlet is divided into two routes, one road is connected with fine treatment mixed bed import on-line TOCi analyzer and DCS host computer display system, another road is connected with DCS host computer display system through fine treatment mixed bed export on-line TOCi analyzer.The utility model can monitor mixed bed inlet and outlet water TOCi value and its change trend in real time.Once detection value is abnormally increased (indicating resin broken leakage), system can send alarm in time, it is convenient for fast response and processing, avoid water vapor system pollution.

The invention discloses a rock fracture seepage-corrosion experiment opening distribution in-situ monitoring device based on resistivity tomography and a test method thereof. The rock fracture seepage-corrosion experiment opening distribution in-situ monitoring device comprises a transparent interface model unit, a circulating fluid supply unit, an automatic switching valve group and an effluent collecting and monitoring unit. The transparent interface model unit forms an interlayer space by upper and lower transparent plates; the circulating fluid supply unit comprises an acid corrosion liquid storage tank and a clear water storage tank; the automatic switching valve group periodically switches the flow path; the effluent monitoring unit collects ion concentration, particle content and seepage parameters in real time. The permeability evolution is inversed by monitoring the pressure difference change, the corrosion rate and the scouring falling amount are calculated in combination with waste liquid analysis, and an accumulated degradation index and a failure threshold value are introduced to realize intelligent judgment. According to the method, the chemical-hydraulic coupling effect in the underground environment can be truly reproduced, the interface degradation mechanism is visually revealed, the long-term durability of the grouting interface is quantitatively evaluated, and a scientific basis is provided for grouting material optimization, mix proportion optimization and engineering life prediction.

The present invention belongs to the technical field of underground coal mine fire monitoring, and specifically is a coal mine underground fire monitoring and analysis method based on image recognition. The method adopts a guide rail mobile deployment form for the monitoring equipment in the target coal mine underground area, and plans the deployment strategy of the monitoring equipment in the target coal mine underground area on the day based on the detection results of the operating health status of each monitoring equipment. The real-time all-round images collected by each available monitoring equipment for its covered monitoring sub-area are used to perceive whether there is a potential fire risk manifestation, and the fire risk concrete area block corresponding to each risk sub-area is locked and the corresponding available monitoring equipment is moved to capture the detailed image. Through dynamic texture tracking processing and multi-spectral recognition and detection processing, the fire occurrence probability of the fire risk concrete area block corresponding to each risk sub-area is evaluated, and the fire alarm sub-area is identified to trigger the target coal mine underground area fire warning, so as to realize the accurate evaluation and timely warning of the coal mine underground fire risk.

This invention discloses a method and system for precise positioning of cattle in a pen. The method involves deploying a Bluetooth beacon network within the pen, collecting RSSI signal strength data and RTT ranging data between positioning tags and each Bluetooth beacon node, and fusing the data using a secondary improved algorithm for edge estimation based on independent set queries to generate an initial set of cattle position coordinates. A grid map is then established based on the pen's structural features, and optimized using a low-diameter wiring decomposition technique and a fully dynamic algorithm. A static structure database of pen locations is established, and a Bayesian probability model is constructed using the initial position coordinate set to generate a mapping relationship between cattle and pen locations. The positioning results are optimized using an adaptive robust resettable flow algorithm to generate the final positioning result. A virtual geofence is then established based on the final positioning result to monitor and generate real-time status warning information for the cattle.

The invention relates to an assembly type building wall reinforcing device which comprises connecting blocks, fastening blocks, strain pipes, strain rings, outer pressing plates, inner pressing plates, bidirectional screw pipes and fastening bolts which are symmetrically arranged, the connecting blocks are fixed between walls, the fastening blocks are arranged between the connecting blocks, the strain pipes and the strain rings are made of electro-telescopic materials and piezoelectric materials, and the outer pressing plates and the inner pressing plates are connected through the fastening bolts. The outer pressing plate and the inner pressing plate are installed in the fastening grooves of the fastening blocks respectively, the outer pressing plate and the inner pressing plate are connected with the two-way screw pipe through the outer thread sleeve and the inner thread sleeve to press a wall, the fastening bolts are arranged in the two-way screw pipe, the fixing effect is further enhanced, the device further comprises a foam sealing plate, a tooth-shaped pressing strip and a wedge-shaped boss structure, the sealing performance and the impact resistance are improved, and the service life of the device is prolonged. The strain pipe and the strain ring are electrically connected with an external monitoring system, real-time monitoring and dynamic response are achieved, and the problems that a traditional wall reinforcing device is poor in fixing effect and lacks an intelligent monitoring function are solved.

This application relates to a data processing method, system, electronic device, and storage medium for wind turbine generator sets. The method includes: real-time acquisition and uploading of multi-dimensional operational data from the wind turbine generator set; preprocessing and normalizing the multi-dimensional operational data, and extracting key features from the processed data using a convolutional neural network to obtain key feature data; acquiring historical datasets and a Logistic regression model, training the Logistic regression model based on the historical dataset to obtain the fault early warning model; calculating the probability of fault occurrence using the fault early warning model on the key feature data, and issuing an early warning signal when the probability of fault occurrence exceeds a preset threshold. Through precise data processing and model training, higher model fit and smaller prediction errors are ensured, enabling more accurate prediction of faults in the wind turbine generator set's drivetrain, achieving early warning, and improving the accuracy and reliability of fault early warning.

The application discloses a surface cover change detection method based on multi-temporal remote sensing images and relates to the technical field of natural resource law enforcement and ecological protection supervision; the method comprises the following steps: obtaining monthly multi-temporal remote sensing images covering 12-24 months, basic geographic information data and ecological function regionalization data and performing pretreatment; performing per-pixel time series decomposition on the standardized images, extracting change starting time, duration, change rate and stage spectral characteristics, and constructing change behavior fingerprints of each pixel; generating to-be-detected change map spots through spatial clustering; performing similarity matching on the change behavior fingerprints of the map spots and a pre-trained standard behavior fingerprint library, identifying surface cover change types; and finally, calculating the severity of illegal activities and evaluating ecological hazards, and generating a dynamic law enforcement report. The application breaks through the static thinking of traditional double-temporal change detection, can automatically identify multiple illegal activity types and evaluate ecological hazards.