14results about How to "Type fast" patented technology

The invention discloses a cross-temperature health state assessment method and system for an underwater robot battery, and relates to the technical field of battery health state assessment, and the method comprises the steps: S1, collecting data in the operation process of an underwater robot; s2, an environment temperature profile is constructed, and working condition types are identified; s3, constructing a multi-dimensional health feature vector; s4, constructing an LSSVM and SVR regression model, and optimizing model hyper-parameters; s5, calculating characteristic stability and comprehensive complexity indexes, and adaptively selecting a model; s6, identifying the working condition category of the charge-discharge cycle, extracting and standardizing the features, calling the adaptive model, iteratively outputting SOH estimated values, and splicing to form an SOH evolution curve; according to the cross-temperature health state evaluation method and system for the underwater robot battery provided by the invention, the problems of unstable cross-temperature performance and insufficient generalization ability in the prior art are solved.

The invention relates to the technical field of data model analysis, in particular to an old people living behavior analysis method for intelligent old people care. Comprising the following steps: acquiring multi-modal data of living behaviors of old people to obtain synchronized multi-modal data; preprocessing the synchronized multi-modal data, and performing feature extraction and optimization on the preprocessed multi-modal data to obtain comprehensive feature data; acquiring historical living behavior data of old people to obtain historical comprehensive feature data; and based on the historical comprehensive feature data, constructing a hierarchical time sequence dynamic neural network model to obtain a trained and verified hierarchical time sequence dynamic neural network model, and processing the comprehensive feature data through the trained and verified hierarchical time sequence dynamic neural network model to obtain an old people living behavior analysis result. The technical problems that the multi-modal data feature extraction of the living behavior is not accurate enough and the sensitivity of an analysis model is low in the aged living behavior analysis are solved.

The invention relates to a device (1) for controlling the temperature of an article (2), comprising: a chamber (5) for accommodating the article (2), which chamber (5) is surrounded by a chamber circumference (7), a bottom element (4) and a cover element (8); a heating element; a running wheel (9) for circulating a first gas, said running wheel (9) being arranged in the chamber (5); and a running wheel drive (12), by means of which the running wheel (9) is rotated. The heating element is formed by the rotor (9) having the rotor drive (12).

The invention relates to the technical field of business state energy analysis, and provides an Internet of Things adaptive networking method and system for business state energy analysis, and the method comprises the steps: firstly obtaining business state feature data of the Internet of Things in different business states and energy consumption data in the corresponding business states, and building a prediction model according to the obtained data; establishing a networking strategy generation model for generating an energy optimization direction according to a prediction result of the prediction model; when the business state is changed, inputting the business state characteristic data into the prediction model to obtain energy consumption data of subsequent prediction in the corresponding business state, generating a corresponding networking strategy according to the prediction data, and performing adaptive networking according to the networking strategy and the Internet of Things; according to the method, the prediction model is established, the business state characteristic data is input into the model for prediction when business state demands are changed, the influence caused by system response lag is solved, meanwhile, the networking strategy is generated according to the prediction result, and the energy optimization demands of adaptive networking of the Internet of Things in various business states are met.

The present invention relates to the field of satellite Internet of Things technology, and in particular to a forest fire early warning method, system and equipment based on satellite Internet of Things, wherein the method specifically comprises: fusing the monitoring data of different monitoring devices in each target overlapping area according to a data fusion algorithm to generate a data fusion model; obtaining the monitoring data of the potential monitoring blind area by analyzing the monitoring data of the adjacent area of ​​the potential monitoring blind area, and predicting the long-term change trend of the monitoring data of the potential monitoring blind area by a time series prediction algorithm; transmitting the monitoring data of each monitoring device, the analysis results of the target overlapping area and the prediction results of the potential monitoring blind area to a data center via the satellite Internet of Things. The present invention significantly improves the coverage and data accuracy of the forest monitoring system, and effectively reduces the problems caused by monitoring blind areas and data overlap.

The utility model provides a kind of polyvinyl fluoride resin slurry monomer recovery and washing device, including polymerizer, the bottom end of the polymerizer is connected with infusion tube, the infusion tube is connected with washing tank, infusion valve is equipped on infusion tube, the top of the polymerizer is connected with first gas pipe, the first gas pipe is connected with gas storage tank, first cut-off valve and first vacuum pump are equipped on first gas pipe, the top of the washing tank is connected with second gas pipe, the second gas pipe is connected with gas storage tank, second cut-off valve and cyclone separator are equipped on second gas pipe, the bottom end of the washing tank is connected with liquid outlet pipe, liquid outlet valve is equipped on liquid outlet pipe.The utility model sets up gas pipe with vacuum pump in the top of polymerizer and washing tank, the gas phase remaining in polymerizer and washing tank is extracted by vacuum pump, effectively improve the recovery effect of gas, ensure that there is no VDF gas residue.

The invention provides a power grid data restoration method and system based on Huber norm tensor decomposition, and belongs to the technical field of intelligent power grids and high-dimensional data processing. The method comprises the following steps: constructing multi-source and multi-dimensional power grid load data into a high-dimensional tensor structure; a Huber norm-based tensor decomposition model is adopted as a core reconstruction engine, through differential punishment of different residual errors, random noise is effectively suppressed, key abnormal modes such as electricity stealing are reserved, and excessive smoothing of abnormal features by a traditional method is avoided; efficiently solving the low-rank factor matrix of the tensor by using a stochastic optimization algorithm; and finally, realizing accurate filling of massive missing values in the original data through the reconstructed factor matrix. According to the method, the restoration precision and robustness of the power grid data containing noise and abnormal values can be remarkably improved, a high-quality data basis is provided for subsequent advanced analysis tasks such as electricity larceny detection and load prediction, and the method has important practical application value.

The application relates to the technical field of wind tunnel equipment health monitoring and intelligent fault diagnosis, and provides a fault sample generation method based on a frequency domain guided diffusion model under variable working conditions. The method realizes adaptive generation of missing samples under continuous variable working conditions by constructing a working condition guided diffusion generation mechanism, converts a traditional sample completion problem into a working condition condition-oriented distribution learning process, can infer dense continuous missing working condition samples from sparse known working condition samples, and thus provides high authenticity and high fidelity enhanced sample input for a subsequent fault diagnosis model; meanwhile, by combining a frequency domain sliding average denoising strategy, the application can effectively accelerate diffusion model sampling convergence, inhibit generation process fluctuations, and significantly improve the quality and stability of the generated samples.

The invention relates to the technical field of information identification, in particular to a medicine information identification method and system based on AI vision, and the method comprises the following steps: importing a medicine outer package image, adjusting the contrast and brightness of the image, removing background noise, and obtaining a processed image. According to the method, the image of the external package of the medicine is processed, so that the definition of the image is improved, and high-quality input is provided for feature extraction. And after conversion to the gray mode, through analysis and morphological transformation of the convolutional neural network, accurate capture of drug shapes and textures is improved, and more accurate drug category classification is realized. According to the method, the accuracy of medicine identification is effectively improved, particularly the diversity and complexity of medicine processing are excellent, the error rate in medicine distribution is reduced, the problems of wrong distribution, missed distribution and frequent distribution in the medicine distribution process are reduced, and the medicine circulation efficiency is optimized.

The utility model discloses a kind of mine gushing water multi-medium filtering mechanism with high adaptability, it is related to mine gushing water filtering technical field, including base and the fixed connection of its upside middle part supporting plate, the upside of base is fixedly installed with two-stage filter assembly and primary filter assembly side by side, one side of supporting plate is fixedly connected with pressurizing assembly, pressurizing assembly fixedly communicates in two-stage filter assembly and primary filter assembly intermediate position, mine gushing water in the utility model first enters primary filter assembly, quartz sand or anthracite is used as filter medium in primary filter assembly, suspended solids, colloid and organic matter etc. are filtered, the water after first filtration is pressurized and transported to two-stage filter assembly by pressurizing assembly, activated carbon or manganese sand is used as filter medium in two-stage filter assembly, heavy metal ions etc. in it are adsorbed or removed, in practical application, the kind and combination of each stage filter medium can be flexibly adjusted according to specific mine gushing water quality condition, improve the application range of filtration, improve filtration quality.

This invention primarily relates to the field of neural network technology. It discloses a method, device, and medium for recognizing traditional Chinese medicine (TCM) decoction pieces based on dynamic serpentine convolution. The method includes: acquiring an image of TCM decoction pieces and inputting it into a preset feature extraction network, which includes multiple convolutional kernels; adjusting a target convolutional kernel among the multiple convolutional kernels to adaptively align the sampling points of the target convolutional kernel along the meandering geometric contour in the TCM decoction piece image; extracting features from the adaptively aligned meandering geometric contour to obtain target features; classifying and recognizing the target features using a preset classifier, and outputting the recognition result of the TCM decoction piece type. This application effectively solves the problem of mismatch between the fixed geometric structure of standard convolutional kernels and the features of the decoction piece image, thereby significantly improving the recognition accuracy and efficiency of TCM decoction pieces.

This invention relates to an input receiving system and a control method for such an system. After the engine is started, the user can quickly input operating instructions corresponding to the vehicle's interior environment. The input receiving system includes: first and second input receiving devices capable of receiving input of operating instructions for operating an object of the vehicle; and a temperature detection unit for detecting the interior temperature of the vehicle. The first input receiving device includes: a first input receiving unit capable of receiving input based on a pressed or touched operating instruction and including a first display unit capable of displaying information; and a first control unit controlling the action of the operating object based on the input of the operating instruction via the first input receiving unit. The second input receiving device requires a longer start-up time than the first input receiving device. The first control unit displays recommended operating information on the first display unit based on the temperature detected by the temperature detection unit, and performs an operation corresponding to the recommended operating information based on pressing or touching the first display unit.

The invention discloses a double-stroke switch button, and relates to the field of switch buttons, and the switch button comprises a housing, a shaft body which is downwards and vertically inserted into the housing from the top end, a stroke adjustment part which is inserted from the bottom end and is cooperatively connected with the housing, and a trigger mechanism which is installed on the stroke adjustment part of the housing, a reset spring used for resetting the shaft body after the shaft body moves downwards is installed in the shell. A guide mechanism for linearly guiding the shaft body is arranged in the shell; the shaft body comprises a handle part, a rod part extending downwards from the top end of the handle part and a working part installed on the side edge of the handle part and used for working with a trigger mechanism, and the bottom end of the rod part is oppositely matched with the stroke adjusting part. According to the switch button, the stroke length of the button can be flexibly adjusted so as to adapt to different use requirements. In a quiet environment, a user can select a short key stroke, so that the noise of the key can be reduced, the pressing force can be reduced, and the fatigue feeling during long-time typing can be reduced.

The invention discloses an automobile charging station energy management method based on the Internet of Things, and relates to the technical field of charging station energy management, and the method comprises the steps: collecting the operation data of a charging pile in real time through an Internet of Things sensor, carrying out the preprocessing and fitting of the data, and generating a systematized operation data set YW; compared with a traditional inspection mode, real-time data acquisition and analysis can be realized, the change of the running state of the equipment can be quickly captured, and high-quality basic data can be provided for subsequent anomaly detection and maintenance decision. By means of the improvement, the timeliness and accuracy of data collection are remarkably improved, and the operation condition of equipment can be comprehensively mastered. Through deep analysis of an operation data set YW and an extracted abnormal feature set YF, two key indexes of a state abnormal score yS and a cumulative abnormal growth rate yG are introduced, and the current abnormal degree and the long-term health state of equipment are accurately evaluated.