8results about How to "Accurate decision basis" patented technology

The embodiment of the invention provides a wind tower structure health monitoring system and method, electronic equipment and a medium, and the system comprises an end layer which is used for sensing the physical quantity change of a specific part of a wind tower in real time, generating a sensor analog signal, converting the sensor analog signal into a sensor digital signal, obtaining preprocessed sensor data, and transmitting the preprocessed sensor data to an edge layer; the edge layer is used for analyzing the sensor data, providing an analysis result for operation and maintenance personnel, sending local early warning information when abnormality is detected, and sending the corresponding analysis result and related data as operation and maintenance data to the cloud layer; the cloud layer is used for storing the operation and maintenance data of each wind tower structure health monitoring system, and comprehensively evaluating and predicting the health condition of the wind tower structure by adopting a structure health evaluation and damage prediction model based on big data analysis and an artificial intelligence algorithm according to a large amount of historical operation and maintenance data; and remote management and work coordination are carried out on the equipment of the end layer and the edge layer.

The utility model relates to a soil sampling and rapid detection device, belongs to the technical field of soil pollution monitoring, and is used for solving the problems that in the prior art, a mode of regularly collecting a soil sample to be detected in a laboratory is adopted for monitoring the soil pollution condition, manpower and time are consumed, and the monitoring result is lagged. Comprising a vehicle body, a soil sample collection mechanism and a soil sample analysis mechanism, the soil sample collecting mechanism is fixedly mounted at the tail part of the vehicle body and comprises a mounting frame, a first telescopic part, a lifting assembly, a rotary driving part and a sampling assembly; the mounting frame is fixedly mounted on the vehicle body, one end of the first telescopic piece is hinged to the mounting frame, the other end of the first telescopic piece is hinged to the lifting assembly, and the lifting assembly is controlled to rotate on the mounting frame through the first telescopic piece; the rotating assembly is fixedly mounted at the lifting end of the lifting assembly; the sampling assembly is fixed at the output end of the rotating driving part; the soil sample analysis mechanism is fixedly mounted in the middle of the vehicle body.

The invention belongs to the technical field of data transmission, and provides a multi-domain cooperative routing reconstruction method and system for a backbone transmission network and a data network, and the method comprises the steps: collecting power service data, and calculating the transmission time delay of the power service data between routing nodes and the transmission risk of the power service data in a link; constructing a route selection reconstruction Markov decision process MDP model by taking minimization of transmission delay and transmission risk as a target; initializing a matching price and a state action value function of the routing selection reconstruction MDP model, and constructing a routing selection reconstruction preference list based on the state action value function; and sending a transmission request to the routing node with the highest ranking in the preference list, carrying out matching application, and outputting a routing reconstruction optimal decision. According to the method, the continuity and stability of data network services can be improved, the accuracy of risk modeling is enhanced, routing selection is optimized, time delay and risks are reduced, routing selection conflicts are effectively solved, the utilization rate of network resources is improved, and the adaptivity and expandability of the network are enhanced.

This invention discloses an AI-based intelligent safety monitoring method and system for coal bunkers in thermal power plants, belonging to the field of thermal power plant monitoring technology. The method includes: dividing the thermal power plant into coal bunker operating areas and non-operating areas based on a 3D visualization model; collecting video data from the coal bunker operating areas and preprocessing the video data; using anomaly recognition technology to analyze the preprocessed video data frame by frame to identify potential safety hazards in the coal bunker operating areas; these hazards include improper wearing of safety helmets, improper wearing of work clothes, and improper deployment of fire extinguishing equipment; constructing a risk level assessment model and using this model to assess the risks of potential safety hazards; and generating a multi-level early warning mechanism for the thermal power plant's coal bunker based on the risk assessment results. This invention achieves comprehensive and refined intelligent monitoring of the safety status of personnel operating in the thermal power plant's coal bunker and the deployment of fire extinguishing equipment.

The application discloses a kind of aviation flight state monitoring and fault early warning method based on multi-source data fusion, including multi-source data fusion, the data of same time period in flight state is collected, the influence of removing noise is adopted Kalman filtering algorithm, and according to 3 guidelines removes outlier, and the main characteristic parameters are extracted using grey correlation analysis method and are fused with multi-source data;Flight state monitoring and fault early warning, select the main characteristic parameter monitoring data of flight state related fault as the target parameter and model output of prediction model, and the state input of high correlation degree is used as input, and a support vector machine prediction model is established to predict the main characteristic parameters, compare the predicted value with the actual value, and realize its fault early warning through trend analysis.

The utility model relates to photovoltaic power plant monitoring technical field, specifically disclose the monitoring device for photovoltaic power plant cleaning data collection, including the main rod and install the auxiliary rod of main rod one side, the auxiliary rod top installs the crosspiece, the crosspiece top installs the environmental monitoring mechanism, the main rod top installs the fixed link, and the fixed link far from the bottom of auxiliary rod one end installs thermal imaging appearance, main rod one side is equipped with the engagement frame, and the engagement frame is equipped with the butt joint groove with photovoltaic board butt joint in, and the engagement frame top connects the tilt angle sensor, and the tilt angle sensor engagement frame one side is connected with the rotation axis of rotation cooperation with main rod, the utility model has the real -time monitoring function, and the device is installed in photovoltaic power plant, and is installed on photovoltaic support one side, and the surrounding environmental data collection of photovoltaic board is provided with more scientific, accurate decision basis for the cleaning time through real -time acquisition and analysis these data, and the subsequent cleaning frequency setting is convenient, can realize the comprehensive monitoring and management to photovoltaic power plant cleaning work.

The invention relates to the field of power dispatching, in particular to a power resource dispatching method and system based on power demands. The power resource scheduling system based on the power demand comprises an influence degree analysis module, a power consumption prediction module, a generating capacity prediction module and a power resource scheduling module. According to the method, the influence degrees of various factors such as air temperature, humidity, rainfall, date types and time periods on the electricity consumption of the commercial district load center are comprehensively considered, and the electricity consumption demand prediction model is constructed based on the factors, so that the electricity consumption of the load center in the next preset time period can be predicted more accurately; the multi-factor comprehensive prediction method breaks through the limitation of traditional single-factor or simple historical data prediction, is helpful for improving the operation efficiency and reliability of the power system, and provides a more accurate decision basis for power market participants.

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.