123 results about "Ice thickness" patented technology

The invention is suitable for the technical field of power transmission line maintenance, and provides a line early warning judgment method and system based on multi-source data fusion, and the method comprises the steps: constructing a multi-dimensional knowledge graph, and building a semantic association network; collecting meteorological and equipment state data in real time, fusing the data into a knowledge graph space through a mapping function, identifying an abnormal meteorological-equipment combination mode by using fuzzy reasoning, and calculating a thickness change rate in combination with an icing growth prediction model; the icing thickness and the galloping probability are calculated, and a composite early warning level is generated; and dynamically generating a risk thermodynamic diagram on the GIS map, and visually displaying a risk propagation path. Through semantic fusion of multi-source data, composite risk modeling and spatialization decision making, the system breaks through single parameter criterion limitation of traditional early warning, realizes intelligent identification and dynamic early warning of meteorological-equipment coupling risks, and significantly improves accuracy of power transmission line disaster early warning and operation and maintenance decision making efficiency.

The invention discloses an ice layer thickness prediction method and system based on multi-modal sensor data fusion. According to the invention, by integrating various types of sensors, such as a microwave sensor, an ultrasonic sensor and an infrared thermal imaging sensor, multi-dimensional and omnibearing monitoring of the thickness of the ice layer is realized. The multi-modal data fusion strategy effectively overcomes the limitation of a single sensor in a complex environment, and improves the comprehensiveness and accuracy of data acquisition. In particular, due to the application of a self-adaptive sensor calibration module, sensor data are calibrated in real time by using advanced technologies such as a Kalman filtering algorithm and the like, so that measurement errors and noise interference are remarkably reduced. And the fused data is intelligently analyzed and processed through a deep learning algorithm and a time sequence prediction model, so that the prediction model is further optimized, the accuracy and reliability of ice layer thickness prediction are improved, reliable data support is provided for applications such as ice region navigation and ocean engineering, and the safety risk and operation cost can be reduced.

The invention provides a polar region sea ice thickness inversion method based on multi-source remote sensing data fusion, and the method comprises the steps: carrying out the feature extraction of SAR data, MODIS data, AMSR2 data and MIRAS data in a target region, constructing an image and sequence feature data set, carrying out the fusion of the image and sequence feature data set through employing a designed multi-modal data fusion network, and obtaining a polar region sea ice thickness inversion result. A new sequence feature data set is obtained, multiple machine learning regression models are used for training the sequence feature data set, the machine learning regression model with the optimal index is selected for sea ice thickness inversion, optical data are deleted, and a sea ice thickness inversion model is obtained through re-training; and dividing the target area into a clouded area and a cloudless area by using the cloud mask data, and applying corresponding inversion models to the clouded area and the cloudless area respectively. According to the method, the advantages of various remote sensing data in sea ice thickness inversion are fully fused, continuous large-area ice thickness information extraction of sea ice can be realized, and the method has better universality.

The invention relates to the technical field of railway contact networks, and provides a railway contact network icing thickness prediction method and system. The method comprises the following steps: inputting meteorological environment parameters into a machine learning model, and carrying out dynamic calibration on icing medium parameters based on the meteorological environment parameters by utilizing the machine learning model to obtain calibrated icing medium parameters; obtaining an icing growth coefficient based on the meteorological environment parameters, the conductor physical characteristic parameters and the calibrated icing medium parameters; and inputting the calibrated icing medium parameters and the icing growth coefficient into a Makkonen model to obtain an icing thickness prediction value. According to the technical scheme provided by the invention, the key icing parameters are dynamically calibrated through machine learning, and the computational logic of the Makkonen model is optimized, so that the icing thickness prediction precision under complex meteorological conditions is improved, and the problem of prediction failure caused by insufficient environmental adaptability of a traditional static model is solved.

The invention relates to the technical field of fan blade deicing, and discloses an intelligent temperature control type fan blade electric heating deicing control system and method, and the system comprises a meteorological sensing module which is used for collecting the environment temperature, humidity, wind speed and rainfall type; the blade state monitoring module is used for acquiring blade surface temperature distribution and ice layer thickness data; the dynamic decision-making module is used for generating a partition heating instruction based on the data of the weather sensing module and the blade state monitoring module; the temperature control execution module is used for converting the instruction into a gradient signal; the heating array module is used for executing electric heating deicing operation in the blade surface subareas; and the energy coordination module is used for dynamically constraining the total power of the system. The meteorological sensing module and the blade state monitoring module are integrated through the system, real-time collection and fusion of multi-source data of blade surrounding environment parameters and the thickness of an ice layer are achieved through the design, comprehensiveness and accuracy of icing monitoring are improved, a reliable basis is provided for dynamic decision making, and therefore the response capacity and early warning precision of the system to icing risks are improved.

The invention discloses an ice condition prediction method based on multi-feature fusion and physical constraint, and belongs to the technical field of hydrological forecasting. Firstly, various types of data of a target area are collected and preprocessed to serve as a data set, features of the various types of data are extracted, feature fusion is conducted on obtained image features, time sequence features and environment features through a multi-head attention mechanism, and a fusion feature vector is generated. Secondly, adopting a physical information neural network model, taking the fusion feature vector as input, taking ice thickness and ice stress as output layers, carrying out constraint by using a composite loss function, carrying out model optimization by using a verification set, carrying out processing through a full connection layer in the network, and carrying out end-to-end training and regularization of a prediction model; and finally, evaluating the final prediction model obtained by training through the test set, and verifying the effectiveness and generalization ability of the final prediction model. The method combines multi-source and multi-mode observation data, can effectively capture complex relations between ice surface changes and various factors, can improve prediction precision, stability and reliability, and can improve model training efficiency and generalization ability.

The invention discloses an icing prediction method and system based on terrain potential energy field and multi-scale meteorological coupling, relates to the technical field of power transmission line icing prediction, and solves the problems that a traditional icing thickness prediction model is split and traditional static terrain parameters cannot represent dynamic terrain potential energy. According to the technical scheme, the method comprises the following steps: acquiring meteorological data and high-resolution digital elevation data; calculating a topographic potential energy tensor; establishing a multi-meteorological coupling model, and correcting the multi-meteorological coupling model through the topographic potential energy tensor; carrying out meteorological element correction on the corrected multi-meteorological coupling model to obtain a corrected meteorological field; and obtaining a first icing thickness prediction value and a second icing thickness prediction value based on a pre-constructed icing growth physical model and a pre-trained machine learning model, and carrying out weighted calculation on the first icing thickness prediction value and the second icing thickness prediction value to obtain a final icing thickness prediction value. And the effect of improving the prediction precision under complex terrain and extreme meteorological conditions is achieved.

The invention relates to the technical field of power transmission lines, in particular to a power transmission line disaster toughness digital design method, which comprises the following steps: acquiring real-time environment parameters of a power transmission line, and calculating a comprehensive risk index based on the real-time environment parameters; calculating a ratio of an icing thickness change rate to a historical same-period average change rate based on the icing thickness sequence to obtain an icing risk index; obtaining a difference value sequence of galloping amplitudes of the power transmission line in adjacent monitoring time periods, and calculating a ratio of a variance of the difference value sequence to a historical galloping variance mean value to obtain a galloping risk index; and generating a comprehensive disaster early warning level based on the icing risk index and the galloping risk index, and adjusting a current load limit value of the power transmission line based on the comprehensive disaster early warning level. The unmanned aerial vehicle is used for monitoring the icing thickness, the vibration sensor is used for monitoring the galloping amplitude, different factors possibly influencing the safety of the power transmission line are comprehensively captured, and the comprehensiveness and accuracy of disaster early warning are improved through multi-dimensional monitoring.

The invention discloses a fan blade detecting and deicing system and method. The fan blade detecting and deicing system comprises a detecting unit, a deicing unit and a control unit. The detection unit comprises an icing detection assembly, an icing thickness detector and a temperature sensor are arranged in the icing detection assembly, the icing thickness detector detects icing thickness data of the blade surface in real time, and the temperature sensor detects temperature data of the fan blade surface in real time; the deicing unit comprises an electric heating film which is at least laid on the front edge of the blade in the length direction of the blade. And the control unit comprehensively analyzes the icing thickness and temperature change condition corresponding to the whole blade according to the icing thickness data and temperature data of the blade surface, and independently triggers and controls the heating state of each blade deicing unit based on the comprehensive analysis result. According to the fan blade detecting and deicing system and method, the precise monitoring function, the intelligent deicing strategy and stable and safe operation are integrated, and the wide application and popularization value is achieved.

The present invention relates to the field of meteorological forecasting and artificial intelligence technology, and in particular to a grey correlation-based ice thickness prediction method, system, device and medium, comprising collecting multi-dimensional ice cover data, dynamically screening key meteorological features through grey correlation; constructing and dividing a cleaned and normalized data set; integrating a bidirectional gated recurrent unit with a multi-head self-attention mechanism to design a hybrid model; performing parameter optimization and testing verification based on a training set; eliminating redundant interference through dynamic feature screening, bidirectionally analyzing the long-term and short-term ice cover evolution laws, strengthening the focus on extreme meteorological event features with an attention mechanism, and realizing cross-climate zone migration deployment in combination with a parameter adaptive compensation module, thereby significantly improving prediction accuracy and early warning capabilities for sudden ice and snow conditions, and providing intelligent decision-making support for power grid disaster prevention.

The invention relates to the technical field of power system risk assessment, in particular to a deep neural network-based distribution line icing pole falling and breaking probability prediction method and a deep neural network-based distribution line icing pole falling and breaking probability prediction system. Acquiring historical meteorological data and pole and tower back-off information of the icing disaster area of the distribution line; constructing a deep neural network prediction model and optimizing the deep neural network prediction model; and training the deep neural network prediction model by adopting a simulated annealing whale algorithm to obtain a probability prediction result of rod falling and breaking under an icing condition. According to the deep neural network prediction model, an input layer is provided with four input neurons which are respectively corresponding to a wind direction, a wind speed, an icing thickness and a temperature; the hidden layers comprise 16 nodes in the first hidden layer, 8 nodes in the second hidden layer and 4 nodes in the third hidden layer; and the output layer adopts a Sigmoid activation function. By combining the whale optimization algorithm with the simulated annealing algorithm, effective combination of global search and local optimization is realized, and the training efficiency and prediction accuracy of the prediction model are remarkably improved.

The invention provides an icing thickness detection method, a laser ranging device and a system, and relates to the technical field of high-voltage line monitoring, and the method comprises the steps: mounting the laser ranging device on an unmanned aerial vehicle, and enabling the unmanned aerial vehicle to hover above a power transmission conductor based on an RTK differential positioning technology; adjusting flight parameters of the unmanned aerial vehicle based on a visual identification algorithm, so that the laser ranging device is mounted on the power transmission conductor and is aligned with the target measurement position; starting a laser ranging device, and collecting surface icing thickness data and ice layer surface temperature data of the power transmission conductor; and performing fusion processing on the measurement data based on a Kalman filtering algorithm, and outputting processed icing thickness data. The method can accurately position the unmanned aerial vehicle, ensures that the laser ranging device accurately aligns to the target measurement point, and improves the autonomous operation capability of the system; the laser ranging device is used for obtaining multiple types of data and performing fusion analysis, the measurement precision is improved, it can be ensured that high-precision measurement is achieved in the live-line state of the transmission conductor, and the safety risk is reduced.

The invention discloses a simplified test natural river ice section flow calculation method, which belongs to the technical field of hydrological test, and comprises the following steps: dividing a section into a plurality of strip units; calculating the effective water depth of each measuring point according to the water depth and the water immersion ice thickness of the measuring point corresponding to each strip unit; calculating the area of each strip unit according to the effective water depth at each measuring point; according to the area of each strip unit, the accumulated section area and the accumulated hydraulic radius from the left bank and the right bank of the section to the strip units are calculated respectively; according to the accumulated section area and the accumulated hydraulic radius from the left bank and the right bank of the section to the strip unit, the relative unit width flow of the sections on the left side and the right side of the strip unit is calculated, and then the comprehensive relative unit width flow of the strip unit is calculated; and calculating the total flow of the section according to the proportional relation between the comprehensive relative unit width flow and the actual unit width flow of the strip unit. According to the method, the full-section flow can be reversely deduced through a small amount of observation, so that the ice period flow testing process is simplified.

The invention discloses a power distribution overhead line icing thickness detection method, system and device and a storage medium, and relates to the field of icing thickness detection.The method comprises the steps that multi-source detection data of a power distribution overhead line is collected, self-adaptive interpolation completion is conducted based on data distribution characteristics, for example, bilinear interpolation is conducted on a two-dimensional image, linear interpolation is conducted on sequence data, and the detection result is obtained; complementing point cloud data in combination with information such as wind direction; performing multi-source icing thickness detection on the detection data set, and calculating a detection sequence based on image pixel gradient, point cloud intensity and the like; performing multi-source fusion by adopting Bayesian inference to obtain an accurate detection value and a confidence interval; predicting an icing trend in combination with historical data, and performing real-time monitoring and early warning; according to the invention, image edges and ice layer boundaries can be protected, so that interpolation better conforms to physical phenomena, and the detection precision is improved; icing thickness features are described from different aspects, posterior probability distribution can be updated by combining prior and real-time information, the icing thickness is accurately estimated, uncertainty is quantified, and powerful support is provided for decision making.

The invention discloses an ice layer thickness measuring method and device and relates to the field of radar technology and electromagnetism. According to an existing ice thickness detection technology, additional instruments need to be arranged on an ice cover, the complexity and cost of equipment are increased, the requirement for the environment is high, and in order to solve the technical defects existing in the prior art, the technical scheme provided by the invention is that the ice layer thickness measurement method comprises the steps of collecting a preset algorithm and algorithm parameters; a step of preprocessing the collected high-frequency electromagnetic wave echo signal according to the preset algorithm; carrying out peak detection on the preprocessed high-frequency electromagnetic wave echo signal; a step of obtaining a horizon detection result of the preprocessed high-frequency electromagnetic wave echo signal according to a peak value detection result; and obtaining the thickness of the ice layer according to the result of the layer position detection. The method can be applied to ice layer thickness measurement and ice cover bearing capacity calculation.

The invention discloses a power transmission line icing analysis method and system influenced by microtopography and micrometeorology, and relates to the technical field of power transmission line risk prediction.The method comprises the steps that original meteorological data of an area where a power transmission line is located is collected and fused, and micrometeorological field variables are generated based on topographic parameters extracted by a DEM; on the basis of the fusion data and the micrometeorological field variables, three-dimensional micrometeorological field data are obtained through a fluid mechanics model, the three-dimensional micrometeorological field data are corrected through terrain parameters obtained through a DEM model, an exponential weighted moving average method is used for adjustment, and accurate three-dimensional micrometeorological field data are obtained; based on the accurate micrometeorological field data, the icing thickness is obtained by adopting a physical-empirical hybrid icing growth model, the output icing thickness is adjusted in real time through a dynamic correction algorithm, and a final icing simulation result is obtained. The accuracy of power transmission line icing disaster prediction and the flexibility of emergency response are improved.

The invention relates to the technical field of ice layer thickness measurement, in particular to a portable laser ice layer thickness measuring device and a method for measuring the ice layer thickness, and the method comprises the following steps: S1, arranging equipment; s2, measuring the thickness of an ice layer; s3, an ice layer thickness calibration room; s4, optimizing the computing network; a light reflecting plate is arranged on the ice surface, laser is emitted through a laser sensor to measure the descending speed in real time, and data are uploaded to a single chip microcomputer through an A / D conversion module; after the electric drill drills through the ice layer, the laser sensor detects sudden change of the descending speed, measurement can be stopped immediately, and the thickness of the ice layer is obtained; according to the invention, the difficulty of ice layer thickness measurement caused by factors such as more impurities and large sand content in the Yellow River water can be overcome, and the measurement efficiency is high; according to the invention, the accuracy of ice layer thickness measurement is further improved through artificial neural network calibration and correction.

The invention discloses a measuring device and method for detecting the thickness of an ice layer through a temperature sensor. The device comprises a temperature probe, a data processing module, a communication module and a power supply module, the temperature probe comprises a pipeline and a plurality of temperature sensors arranged in the pipeline, the output ends of the temperature sensors are connected with the input end of the data processing module, and the communication module is connected with the data processing module; the method comprises the following steps: acquiring temperature data; calculating the temperature gradient of the ice layer and the temperature gradient of the water layer; determining the position of 0 DEG C; and calculating interfaces between air and ice / water and between ice and water so as to determine the thickness of the ice layer. The ice layer thickness is detected through networking of the temperature sensors, large-area monitoring can be achieved, and the ice layer thickness monitoring device has the advantages of being low in cost, high in precision and capable of achieving real-time monitoring, can be used for monitoring the ice layer thickness and has important application value for meteorological research, on-ice activities and production operation.

The embodiment of the invention provides a method and device for determining the icing thickness of a power transmission line and a storage medium. The method comprises the following steps: acquiring a current microwave transmitting signal and a current microwave receiving signal acquired by a microwave sensor; determining the current icing thickness of the power transmission line according to the first phase of the current microwave transmitting signal and the second phase of the current microwave receiving signal; acquiring the historical icing thickness of the power transmission line at the previous moment, and determining the current icing change rate of the power transmission line; when the current icing thickness is greater than a preset value and the current icing change rate is not stabilized in a preset range, acquiring meteorological data monitored by a meteorological sensor; when the icing state of the power transmission line is determined to be an ice-water mixed state according to the meteorological data, acquiring a to-be-detected image for the microwave sensor acquired by the image acquisition equipment; the final icing thickness of the power transmission line is determined according to the to-be-detected image, the influence of the environment and the icing forming process is reduced, and the icing thickness of the power transmission line can be accurately judged.

The embodiment of the invention provides an icing thickness prediction method, device and equipment based on multi-source data. The method comprises the following steps: acquiring meteorological data, state data and an icing image of a target power transmission line at a target moment; based on a preset prediction model, according to the meteorological data, determining an icing potential factor at the target moment; based on a preset prediction model, according to the icing potential factor, the meteorological data, the state data and the icing image, obtaining fusion features of icing; wherein the fusion feature is used for representing a nonlinear relationship between the icing influence factors; and on the basis of the prediction model, according to the fusion features, predicting the icing thickness of the target power transmission line. The method is used for achieving the effect of improving the icing thickness prediction accuracy.

Mobile platform for measuring flow velocity, ice thickness and water depth across ice, comprising a carriage (6) equipped with an angle controller (5), wherein an ice thickness and water depth transmitter (1) and a flow velocity transmitter (2) are each mounted on a lifting device (3), wherein a heating device (4) is provided at the bottom of the lifting device (3); wherein the lifting device (3) on which the ice thickness and water depth transmitter (1) are arranged is mounted directly on the carriage (6), and the lifting device (3) on which the flow velocity transmitter (2) is arranged is mounted on the angle controller (5).

The present application relates to the technical field of railway contact networks, and provides a method and system for predicting ice thickness of railway contact networks. The method comprises: inputting meteorological environment parameters into a machine learning model, using the machine learning model to dynamically calibrate the ice medium parameters based on the meteorological environment parameters, and obtaining calibrated ice medium parameters; obtaining an ice growth coefficient based on the meteorological environment parameters, the physical characteristic parameters of the conductor, and the calibrated ice medium parameters; and inputting the calibrated ice medium parameters and the ice growth coefficient into a Makkonen model to obtain an ice thickness prediction value. The technical solution provided by the present application improves the accuracy of ice thickness prediction under complex meteorological conditions by dynamically calibrating key ice parameters through machine learning and optimizing the calculation logic of the Makkonen model, thereby solving the problem of prediction failure caused by insufficient environmental adaptability of traditional static models.

This invention discloses a method and system for identifying ice thickness using a power line de-icing robot, relating to the field of power transmission line maintenance technology. The method includes: determining whether to adjust the perception mode based on ambient lighting conditions; initiating an active enhanced perception process based on the adjustment requirement to acquire image data containing feature differences of the target object; acquiring the three-dimensional geometric information of the target object; determining the geometric contour of the target object by combining the three-dimensional geometric information with the image data; calculating the thickness information of the target object based on the geometric contour; and obtaining the distribution of the target object by combining the position information of the power line de-icing robot. This application can obtain the continuous distribution and morphology of the overall ice on the power line, rather than single-point local information, thereby ensuring the accuracy and reliability of the final thickness calculation and improving the accuracy and timeliness of de-icing operations.

The invention relates to the field of sea ice thickness measurement, in particular to a sea ice thickness measuring device based on an icebreaker, which comprises a mounting part, an outward extending part is arranged in the mounting part, a micro moving part is arranged in the outward extending part, a lifting part is arranged in the micro moving part, a measuring part is arranged in the lifting part, and the measuring part is provided with a snow cleaning chassis. The bottom of the probe rod is fixedly connected with six scrapers which are circumferentially distributed, the scrapers are used for rapidly cleaning a snow accumulation layer and floating ice impurities in a to-be-measured area, the surface of the ice layer is completely exposed, when the probe rod makes contact with the ice layer, a signal is immediately fed back to an external control assembly, the downward moving cylinder body is controlled to stop descending, and it is ensured that the ice thickness measurer only conducts thickness measurement from the surface of the ice layer. And the problems that the ice layer and the accumulated snow layer are difficult to effectively distinguish and the accumulated snow thickness is easy to be included into the sea ice thickness measurement result by utilizing an intelligent sensor and a sound wave type measurement technology are effectively avoided.

This invention proposes an icebreaking device, vessel, and method combining jet-resonance technology for ice-trapped vessels, relating to the field of polar icebreaking technology. It addresses the problem of conventionally powered vessels or icebreakers with low ice levels struggling to break through ice in areas with ice thickness greater than 1.2 meters. It includes a hoisting platform, a jet icebreaking module, a resonant icebreaking module, and an attachment stabilization module. The hoisting platform is used for assembly with an external crane. The jet icebreaking module outputs water jets to break the ice. The resonant icebreaking module induces resonance in the ice layer. The attachment stabilization module includes a helical anchor bolt and a power mechanism. The power mechanism drives the helical anchor bolt to rotate relative to the hoisting platform, and the helical anchor bolt drills into the ice surface to fix the hoisting platform. It is primarily used on polar vessels to break through ice and escape when a vessel is trapped.

The invention relates to the field of sea ice remote sensing, and discloses a sea ice thickness measuring method based on solar microwave radiation enhancement, which comprises the following specific steps of: calculating a solar altitude angle and an azimuth angle at a current position and moment; setting a microwave radiometer overlook sea ice area based on the solar altitude angle and the azimuth angle, and receiving a reflection signal of solar microwave heat radiation; extracting the relative propagation delay inequality of the reflected signal; and calculating the thickness of the sea ice according to the relative propagation delay inequality. The ice thickness is measured by using the characteristics of strong solar microwave radiation and related reception, the problem that the sun is regarded as a harmful factor in the existing remote sensing technology is solved, and the method has the characteristic of being capable of reducing data acquisition and processing pressure.

This utility model discloses a novel ice condition monitoring skateboard, relating to the field of emergency disaster prevention technology. Specifically, it is a novel ice condition monitoring skateboard comprising a skateboard body, a track assembly mounted on the lower front end of the skateboard body, a pivot connecting the track assembly and the skateboard body, and a high-definition infrared camera mounted on the upper front end of the skateboard body. Inside the skateboard body, from front to rear, are a controller, a battery, and an ice and snow radar. Through the track assembly and ice and snow radar, this novel ice condition monitoring skateboard can automatically and quickly detect ice thickness. The coordinated arrangement of the track assembly, high-definition infrared camera, ice and snow radar, temperature sensor, and humidity sensor enables the skateboard to obtain dynamic video of ice surface changes and their relationship with ambient temperature and humidity at close range, providing accurate predictions for the development trend of subsequent ice jams.

The invention belongs to the field of acoustic measurement, and relates to an ice layer thickness measurement method, in particular to a method for monitoring the ice layer thickness by using air coupling bending waves. A sensor arranged on the ice surface is used for collecting air coupling bending waves propagated in the ice layer; calculating the characteristic frequency of the air coupling bending wave; performing frequency spectrum solving or time-frequency spectrum solving on the air-coupled bending waves collected in the step 1, and determining the characteristic frequency f0 of the air-coupled bending waves. And calculating characteristic frequencies of different ice thicknesses. When different ice layer thicknesses d are set, solving the characteristic equation of the air coupling bending wave to obtain a change function f (d) of the characteristic frequency f of the air coupling bending wave along with the ice layer thicknesses d; according to f0 and f (d), f (d) is made to be equal to f0, and finally the thickness d of the ice layer is obtained. Collection can be performed by using a single sensor, and the method has the characteristics of economy and high efficiency; the activity safety of people on ice can be effectively ensured; finally, the effectiveness of the method is verified through an external field test.