22results about How to "Type fast" patented technology

The invention discloses a task regulation and control method and device of an electric power system and a storage medium, and is suitable for the electric power system.The method comprises the steps that a knowledge graph of the electric power system is constructed based on multi-source heterogeneous data of the electric power system; wherein the multi-source heterogeneous data comprises power operation data, renewable energy data, meteorological data and power grid event information; acquiring real-time monitoring data of the power system, and updating the knowledge graph by using the real-time monitoring data; acquiring initial input data at the current time, and performing enhancement processing on the initial input data according to the updated knowledge graph to obtain enhanced input data; and inputting the enhanced input data into a pre-trained large language model, and outputting a corresponding task regulation and control suggestion. According to the method, the operation mechanism and the interaction characteristics of the power system can be deeply understood in combination with the knowledge graph and the large language model, so that the corresponding task regulation and control suggestions can be accurately output based on enhanced input data, and the task regulation and control effect of the power system can be effectively improved.

The invention provides a spatial data processing method and system based on a large language model. The method comprises the steps of obtaining to-be-processed target spatial data and storing the to-be-processed target spatial data into a preset spatial database; inputting the target spatial data into a preset large language model to enable the large language model to perform data preprocessing on the target spatial data to generate corresponding processed data; performing data analysis on the target spatial data and the processed data by using a preset data analysis tool to obtain metadata information; storing the metadata information into a preset vector database by using a preset metadata information classification model; and obtaining text information input by a user, performing spatial data processing on the target spatial data by using the large language model according to the text information, the spatial database and the vector database, and outputting a corresponding spatial data processing result, thereby improving the processing efficiency of the spatial data and the use experience of the user.

The invention relates to the technical field of pathology detection, in particular to a pathology detection system and method based on image recognition, and the method comprises the steps: obtaining pathology image data; key image feature values in the image copies are extracted to form an image feature vector set; constructing a pathological detection model I; the image data is processed through the first pathological detection model, and an analysis result is output; obtaining electronic medical record information from an electronic health record server, and obtaining medical record key information; a second pathological detection model is constructed, and a prediction result is output; the system is used for executing the method. The illness state is output through the first pathology detection model, the output result of the first pathology detection model is quantized and fused with the feature value extracted from the electronic medical record information, and the fused result is input into the second pathology detection model, so that whether a subject suffers from illness in the future or not can be predicted; whether the result of the first pathological detection model is accurate or not can be further detected, and the detection accuracy is improved.

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 provides a transformer area energy storage multi-objective optimization configuration method for improving the bearing capacity of a source load, and belongs to the technical field of transformer area energy storage, and the method comprises the steps: firstly, continuously collecting the load, distribution network input, photovoltaic power generation, wind power generation, energy storage and other operation data of a transformer area, and building a transformer area operation response equation set; and obtaining a group of input and output data required by optimization through fitting and data augmentation and expansion. Then, the data are constructed into a multi-dimensional matrix, and main features are extracted by using a tensor decomposition technology; based on the characteristics, a multi-objective optimization model is constructed, a GPU accelerated multi-objective optimization algorithm is adopted for solving, and a non-inferior solution set is obtained. And finally, calculating the weighted score of each non-inferior solution, and selecting the energy storage configuration corresponding to the optimal solution as an output result. According to the process, the actual operation data of the transformer area can be fully utilized, the optimization model highly fitting the reality is constructed, the optimal configuration of the energy storage of the transformer area is found, and the problem that the configuration optimization of the energy storage of the transformer area is not comprehensively considered in the prior art is solved.

The invention provides a multispectral human body recognition control method, a recognition system, a computer readable storage medium and equipment. The multispectral human body recognition control method comprises the steps that multiple pieces of first multispectral image data are obtained, each piece of first multispectral image data comprises image data of human skin and image data of non-human skin at the same time, the multiple pieces of first multispectral image data are labeled so as to obtain a human skin sample and a non-human skin sample, and the human skin sample and the non-human skin sample are compared; training the labeled human body skin sample and the non-human body skin sample by adopting a machine learning method to obtain a pixel-level multispectral recognition model, inputting collected second multispectral image data of the target object into the pixel-level multispectral recognition model to obtain a human body skin binary image corresponding to the second multispectral image data, and outputting the human body skin binary image. And whether the human body exists in the target object is judged according to the human body skin binary image, so that the recognition precision is improved, and the time required for recognition is shortened.

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 present invention discloses a combination of caffeine and at least one of oleuropein or a metabolite thereof, which can be used for oral administration to a subject in an effective amount to achieve at least one of (i) improve mitochondrial calcium uptake in muscle cells, (ii) improve calcium utilization in muscle cells, (iii) increase energy of mitochondrial in muscle cells, in some embodiments, the methods may include (i) at least one of: (i) at least one of muscle function, muscle performance, or muscle strength, (v) reduce muscle fatigue, (vi) enhance mobility, and / or (vii) treat muscle diseases associated with calcium depletion or deficiency. The individual may be at least one of: an aged individual; elderly individuals; individuals with muscle fatigue or myasthenia; individuals of which mobility is impaired; weak individuals; weak early-stage individuals; a sarcopenia individual; individuals that recover from early stage of weakness, weakness, sarcopenia, or impaired mobility; individuals undergoing physical rehabilitation; an athlete; or a pet.

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 utility model belongs to the technical field of mold structures, and discloses an efficient and energy-saving mold structure of an EPS (Expandable Polystyrene) and EPP (Expandable Polypropylene) foam plastic molding machine. Comprising a male die steam chamber; the female die steam chamber comprises an inner wall and an outer wall, the inner wall is located on the side close to the male die steam chamber, a product is arranged between the inner wall and the male die steam chamber, the outline of the inner wall is the same as that of the product, the outer wall is located on the side away from the male die steam chamber, a steam chamber cavity is defined by the inner wall and the outer wall, and the outline of the outer wall is the same as that of the inner wall. And a steam inlet communicated with the steam chamber cavity is formed in the outer wall. The steam is input into the steam chamber cavity through the steam inlet, the steam moves in the steam chamber cavity along the outline of the inner wall, and due to the fact that the outline of the inner wall is the same as the outline of the product, heat in the steam can act on EPS and EPP raw materials to a greater extent along the outline of the inner wall, and reduction of heat loss is facilitated.

The invention discloses a method for increasing apatite phosphorus content in biochar, which comprises the following steps: mixing sludge, biomass, biochar and calcium oxide to prepare a mixed material, biologically drying the mixed material for less than or equal to 12 days, adding magnesium chloride after 25 hours in the biologically drying process, pyrolyzing the obtained biologically dried product to obtain the phosphorite phosphorus content in the biochar. The preparation of the biochar is completed. According to the method disclosed by the invention, under the combined action of calcium oxide and magnesium chloride, a biological drying product with low water content and high apatite phosphorus content can be formed through biological drying treatment; the content of phosphorite phosphorus in the biochar can be remarkably increased under the condition of low pyrolysis energy consumption through simple pyrolysis treatment, the biochar with the high phosphorite phosphorus content is obtained, and the method has the advantages of being simple in process, convenient to operate, low in cost, high in drying efficiency, small in pollution gas emission amount, low in energy consumption, environmentally friendly and the like; and the method has important promotion significance for realizing resource utilization of the sludge-based biochar.

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.