9493results about How to "Reduce noise" patented technology

The invention provides a fine three-dimensional modeling method and system based on air-ground multi-source data fusion, and the method comprises the steps: collecting data from three data sources: unmanned plane oblique photography, scanning point cloud data and panoramic video data, and carrying out the preprocessing of the data; according to the method, the advantages of different data sources are comprehensively utilized, the refined and full-coverage three-dimensional reconstruction of the target environment is realized, the multi-source data fusion is realized by utilizing the improved ICP algorithm to carry out precise registration, and finally, the modeling is carried out by utilizing the improved Poisson surface reconstruction algorithm. The problems of detail missing and incomplete coverage caused by a single data source in a complex building modeling process are solved; according to the invention, the multi-source data fusion technology can comprehensively capture the geometric morphology and detail features of the building in a complex scene, is suitable for complex scenes such as villages in cities and old buildings, and achieves all-around and detailed three-dimensional modeling.

The invention discloses a method for realizing high-efficiency low-consumption micro-aerobic hydrolytic acidification of petrochemical wastewater by regulating and controlling the aeration rate in combination with a space-time diagram neural network and reinforced learning, and relates to a method for realizing high-efficiency low-consumption micro-aerobic hydrolytic acidification of petrochemical wastewater by regulating and controlling the aeration rate. The method comprises the following steps of: 1, collecting and cleaning data; 2, constructing a graph data set; 3, training the ST-GNN model; 4, constructing a deep Q reinforcement learning model; 5, the trained ST-GNN model is embedded into an automatic control system of a sewage treatment plant, and the model outputs the optimal aeration rate of the current time step and predicts the micro-aerobic hydrolytic acidification effect through interaction of the current working condition and historical data; and adjusting operation parameters of aeration equipment according to the output of the model, and carrying out hydrolytic acidification treatment. The method is high in regulation and control efficiency and low in operation error, and can be applied to the field of digital and intelligent management of sewage treatment plants.

The invention provides a space competition situation threat level evaluation method and system based on credibility weighting, and relates to the technical field of information, and the method comprises the steps: constructing a threat feature vector based on historical orbit data and an operation state, and obtaining an initial target threat probability vector through a deep learning model; then, a credibility weighting coefficient is calculated based on the multi-source situation awareness data, and an initial threat probability vector is corrected in combination with a feature weighting coefficient; and finally, classifying target threat levels by adopting an integrated learning method, determining a target priority in combination with the target orbit parameters and an ant colony algorithm, and outputting a threat target sequence and a situation threat assessment report. By fusing the multi-source situation awareness data and the credibility weighting mechanism, the threat level of the space target can be evaluated more accurately, and the reliability and efficiency of space situation awareness are effectively improved.

The invention discloses a real-time three-dimensional modeling method and system based on 5G flight control and a medium, and belongs to the field of three-dimensional modeling, and the method comprises the steps: obtaining a modeling demand and task environment parameters of a user, and planning a route; controlling the unmanned aerial vehicle to fly according to the planned route; judging whether the position data and the attitude data of the unmanned aerial vehicle at each waypoint are within a preset threshold range, and if so, controlling the unmanned aerial vehicle to collect image data of the target area; the collected image data is preprocessed; extracting feature point data of the target area by utilizing machine learning according to the preprocessed image data; converting the feature point data of the target area into sparse point clouds by using a triangulation algorithm; converting the sparse point clouds into dense point clouds through an MVS algorithm; and according to the pre-processed dense point cloud, a Poisson surface reconstruction algorithm is adopted to establish a three-dimensional grid model of the target area. Aiming at the low precision of unmanned aerial vehicle three-dimensional modeling in the prior art, the method improves the precision of unmanned aerial vehicle three-dimensional modeling.

The invention provides a multi-modal image analysis-based robot-assisted wound treatment method and system, and relates to the technical field of robots, and the method comprises the steps: collecting a multi-modal image of a to-be-treated wound, determining an image registration result, obtaining a wound region image through a multi-scale fusion algorithm and wound region segmentation, and carrying out the type classification of wound tissues. A wound tissue type classification result is obtained; performing surrounding shooting on a wound area to obtain a wound multi-view image, registering subsequent depth maps frame by frame by taking a first frame of depth map as a reference, accumulating point cloud data, determining a wound three-dimensional point cloud, generating a wound three-dimensional reconstruction result by combining a curved surface evolution algorithm, and determining a wound parameter measurement result; based on the wound tissue type classification result and the wound parameter measurement result, a robot path planning algorithm is adopted, the optimal movement track of the robot is generated, the movement parameters of the robot are adjusted in real time through a robot control algorithm of vision and force feedback, and wound adjuvant therapy is completed.

The invention relates to a power station screen cabinet equipment temperature early warning method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a dual-light image of power station screen cabinet equipment to be analyzed according to acquisition parameters corresponding to current acquisition; the dual-light image comprises an infrared thermal imaging image and a visible light image; performing preprocessing, feature extraction processing, spatial registration processing and information fusion processing on the dual-light image to generate a fused image; performing temperature field reconstruction processing and hot spot detection processing on the fused image to obtain temperature field distribution information and hot spot area information; and according to the temperature field distribution information and the hot spot area information, performing temperature analysis on the power station screen cabinet equipment to obtain temperature early warning information of the power station screen cabinet equipment. By adopting the method, the temperature early warning accuracy of the power station screen cabinet equipment can be improved.

The invention relates to the technical field of gas energy monitoring, and discloses a gas energy monitoring method based on Internet of Things, comprising the following steps: S1, multi-source data acquisition: using an intelligent sensor to acquire gas related data, and sending the data to an edge device or a cloud platform through a wireless network; s2, data preprocessing: carrying out data cleaning, denoising and feature extraction on edge equipment, executing multi-source data fusion, carrying out anomaly detection and processing, and filtering abnormal data; and S3, data transmission: performing data transmission by adopting an encrypted communication protocol, and using a data buffering and retransmission mechanism in the transmission process, the data buffering and retransmission mechanism being used for ensuring data integrity. By using a multi-source data fusion technology and an advanced data preprocessing algorithm, sensor noise and data errors are effectively reduced, the quality and consistency of gas data are remarkably improved, and the problem that the analysis accuracy is reduced due to data source diversity and complexity is solved.

The invention relates to the technical field of laser welding, in particular to a laser welding system control method for metal accessories. The method comprises the following steps that automobile industry metal fitting data are obtained, welding demand analysis is conducted according to the automobile industry metal fitting data, and therefore welding demand data are obtained; laser welding feature extraction and resistance spot welding feature extraction are carried out according to the welding demand data, so that laser welding data and resistance spot welding data are obtained; carrying out welding simulation according to the laser welding data and the resistance spot welding data so as to obtain welding simulation data; welding form feature extraction and welding strength feature extraction are carried out according to the welding simulation data, so that welding form data and welding strength data are obtained; and carrying out welding defect detection according to the welding form data and the welding strength data so as to obtain welding defect data. Automatic adjustment and accurate control of the laser welding process are achieved based on the laser welding technology.

The invention discloses a magnetic suspension high-speed motor with an energy-saving type air cooling structure, and relates to the technical field of motors, the magnetic suspension high-speed motor comprises an air cooling assembly, and the air cooling assembly comprises a machine shell, an air inlet, a filter shell, a filter element, a limiting ring, a heat dissipation disc, heat dissipation holes, heat dissipation fins and screws; a water cooling assembly is mounted on the upper surface of the mounting plate. The internal temperature of the motor is monitored in real time through the temperature sensor, the controller accurately adjusts the running states of the cooling fan and the circulating pump according to the preset temperature threshold value and the intelligent control strategy, the cooling effect is improved, the problem that energy consumption is greatly increased due to long-time high-speed running of the fan due to the fact that air cooling heat dissipation is insufficient is solved, and the service life of the motor is prolonged. Extra noise generated by high-speed operation of the fan is reduced; the air cooling assembly and the water cooling assembly are combined and cooperatively work to dissipate heat in the motor, so that the problem of insufficient heat dissipation efficiency of traditional single air cooling is solved, and the heat dissipation efficiency is improved.

The invention relates to the technical field of bolt mounting equipment, in particular to an angle steel tower bolt perforation mounting method. By means of autonomous navigation of the installation robot and flexible operation of the mechanical arm, rapid positioning and taking-out of bolts and nuts are achieved, and the risk of manual climbing and operation is reduced. Accurate insertion and preliminary positioning of the bolt are ensured through accurate control of the mechanical arm, then the fastening effect is enhanced by combining impact tightening with the intermittent hammering technology, the fit clearance between the angle steel is effectively eliminated, and the bolt is prevented from loosening. The whole process is highly automatic, personal errors are reduced, and the mounting quality and the working safety are improved.

The invention discloses a long time sequence information prediction method based on a multi-scale double-source fusion network model, time domain and frequency domain features are modeled at the same time, the ability to understand a complex time sequence is enhanced, an efficient multi-scale attention module of cross-space learning can capture short-term and long-term dependency relationships at the same time, and the prediction accuracy is improved. The modeling capability of different time scale information is improved, dynamic weighted fusion of time domain and frequency domain features is realized by a gating mechanism, and the flexibility and robustness of the model are improved. And through standardization and size projection, feature deviation and memory occupation are reduced, model convergence is accelerated, and the overall memory demand is reduced. The efficient multi-scale attention module accurately captures features, weakens noise and abnormal value influences, and reduces unnecessary calculation and memory consumption. The gating mechanism dynamically adjusts the feature weight, preferentially processes key information, and reduces the memory demand.

A buffering hardware hinge outside a cup comprises a hinge cup and a hinge arm, the hinge cup and the hinge arm are connected in a hinged mode through an upper connecting rod and a lower connecting rod, a guide groove penetrating through a body is formed in the bottom of the hinge cup, and a driving plate is installed at the outer bottom of the hinge cup in a covering mode; a sliding block is arranged on the top of the driving plate and installed in the guide groove in a sliding mode. The end, facing the hinge cup, of the upper connecting rod extends outwards to form a driving arm, and when the hinge is closed, the driving arm acts on the sliding block to push the sliding block to slide in the guide groove; a buffer is fixedly mounted at the outer bottom of the hinge arm, a piston rod or a cylinder body part of the buffer is connected with the driving plate, and the buffer has a damping effect on the movement of the driving plate, so that the damping effect when the hinge is closed is realized. The device has the beneficial effects that the buffer is installed outside the hinge arm, through the design of the guide groove and the driving plate, the external space is effectively utilized, and the problem that the internal space is limited is avoided.

The invention relates to the technical field of image data processing, in particular to a wood composite board furniture surface defect identification method and system. And wood board images are collected from the surface of the wood composite board furniture. Adaptive grid division is carried out on the wood board image according to different scales, texture direction information and high and low frequency texture components are extracted, and a texture feature map is obtained; and extracting structure edge information of the wood board image to obtain a structure information graph. Matching the texture feature map with the structure information map, and calculating local mutual information of the texture feature map and the structure information map in each region; the method comprises the following steps: acquiring resolution images of a wood board image under different scales, and calculating a mutual information matrix of the resolution images; distributing an abnormal score for each matrix element based on the mutual information matrix; and dividing grids according to the abnormal score, calculating a correlation value of adjacent grids, and obtaining a defect area based on the correlation value. And carrying out defect classification on the defect area. According to the invention, interference of natural textures can be reduced, and defect identification of small defects and tiny changes can be improved.

The invention relates to the field of image enhancement, in particular to an image enhancement method and system of a vehicle-mounted image sensor. The method comprises the following steps: acquiring driving record images of a vehicle at multiple angles; dynamic scene self-adaptive exposure adjustment is carried out on the driving record images of the vehicle at the multiple angles, so that multiple self-adaptive exposure driving images are obtained; carrying out deep network noise identification on the plurality of adaptive exposure driving images, and carrying out dynamic filtering and noise reduction on sub-images one by one so as to generate a plurality of filtering and noise reduction optimized images; carrying out image texture analysis on the plurality of exposure sub-images, and carrying out texture distortion reconstruction to construct a plurality of texture reconstruction images; and road object visual identification is carried out on the plurality of texture reconstruction images, and adaptive contrast enhancement processing is carried out, so that a plurality of detail contrast enhancement images are constructed. According to the invention, a high-definition and stable panoramic vehicle-mounted image is realized.

The invention discloses a power prediction method considering photovoltaic power clustering, which comprises the following steps of: firstly, acquiring historical data of a photovoltaic power station, decomposing a photovoltaic power time sequence by using an STL time sequence decomposition method, performing DBSCAN clustering on the decomposed photovoltaic power data, identifying a low-power cluster and a high-power cluster, and predicting the power of the photovoltaic power station according to the low-power cluster and the high-power cluster; establishing a photovoltaic power prediction model based on a time convolutional network and Times Net for low power and high power respectively; secondly, a badger algorithm is improved, Chebyshev chaotic mapping is adopted to improve initialization of a badger population, and a Gaussian mutation strategy and a lens imaging reverse learning strategy are introduced to obtain MHBA; and optimizing hyper-parameters of the TCN photovoltaic low-power prediction model and the TimesNet photovoltaic high-power prediction model by using an improved badger algorithm, and predicting photovoltaic power by using the optimized models to obtain a final prediction value. Compared with the prior art, the precision and effectiveness of the photovoltaic power prediction model can be improved.

The invention relates to the technical field of fault detection for a wireless communication network, and discloses a fault detection method for the wireless communication network. By collecting data of base station signal strength, network delay, data packet loss rate and user equipment signal quality, the problem of insufficient fault detection caused by a single index is solved. Through multi-dimensional data acquisition, the system can comprehensively evaluate network performance, the fault detection accuracy is improved, and different fault types can be effectively identified especially in a complex fault mode. By storing the data according to the time sequence, the problem of time sequence chaos is avoided, and accurate detection of time-varying faults is ensured. The comprehensive data vector generated by each time of sampling comprises a plurality of network performance indexes, so that the fault detection and positioning capability is improved. Abnormal values are eliminated through data preprocessing, the data quality is ensured, an accurate basis is provided for subsequent analysis, and the reliability of a detection result is ensured.

The invention provides a multi-platform internet marketing activity effect evaluation and prediction model construction method, and relates to the technical field of data analysis. The method comprises the following steps: collecting marketing activity data from a plurality of internet platforms; performing cleaning, normalization and feature engineering processing on the data; constructing a multi-level evaluation and prediction model based on a machine learning algorithm, wherein the multi-level evaluation and prediction model comprises a data fusion model, an effect evaluation model and an effect prediction model; calculating a click rate, a conversion rate, a user retention rate and a user life cycle value by using an effect evaluation model so as to comprehensively evaluate a marketing activity effect; and performing effect prediction by using the effect prediction model, and evaluating the model through a root-mean-square error, a mean absolute error and a mean absolute percentage error. According to the invention, the accuracy and comprehensiveness of marketing activity effect evaluation and prediction can be improved, and an important basis is provided for optimizing a marketing strategy.

The invention belongs to the technical field of display driving, and particularly relates to a display panel, a display driving method and a display device.The display panel comprises N rows of scanning lines, M columns of data lines and 2N rows * 2M columns of pixel circuits; each pixel circuit comprises a pixel unit and a control unit, and when the nth row of scanning line outputs a first scanning signal, a data signal on the mth row of data line controls any one of the control units in the (2m-1) th row and the (2n-1) th row and the control units in the (2m) th row and the (2n-1) th row to be switched on or switched on alternately in a time-sharing manner; when the nth scanning line outputs a second scanning signal, the data signal on the mth data line controls any one of the (2m-1) th and 2nth control units and the 2m and 2nth control units to be switched on or switched on alternately in a time-sharing manner; according to the display panel, the number of the scanning lines and the number of the data lines are reduced by at least half under the display condition of the same resolution, the power consumption and the cost of the panel are reduced, and the refresh rate and the aperture opening rate are improved.

The invention discloses a high-integration-level and efficient-cooling external rotor motor ducted fan system, which belongs to the technical field of ducted fans and comprises a motor stator winding and a fan external rotor integrated part integrally formed by fan blades and a motor external rotor turntable, the integrated shell is integrally formed by a motor inner stator sleeve, a ducted nacelle, a motor controller main shell and a plurality of groups of ducted stationary blades; a cooling structure is mounted on the integrated shell; a motor stator winding is fixedly connected to the outer wall of one end of the integrated shell, the front flow guide cover is fixedly connected to the fan outer rotor integrated part, and the rear flow guide cover is installed at the other end of the integrated shell. And the integrated shell is rotationally connected with the fan outer rotor integrated piece. The system is low in noise, high in integration level, high in system reliability, high in system cooling efficiency, high in power level compatibility and high in expandability.

The invention relates to a transformer voiceprint anomaly detection method based on a generative adversarial network, and the method comprises the steps: obtaining voiceprint data, and carrying out the preprocessing of the voiceprint data, and obtaining the preprocessed spectrum data; constructing an abnormal voiceprint detection model; inputting the preprocessed spectrum data into an abnormal voiceprint detection model for training; and obtaining voiceprint data to be detected and preprocessing the voiceprint data to be detected, inputting the preprocessed voiceprint data to be detected into the trained abnormal voiceprint detection model, and outputting a detection result. According to the method, unbalanced distribution of normal voiceprint data and abnormal voiceprint data can be balanced, the generalization ability of the model is enhanced, and the model can keep high detection performance when processing a small amount of abnormal data; according to the method, the distinguishing capability of the abnormal voiceprint is enhanced, the abnormal voiceprint can be effectively recognized in a complex noise environment, the influence of noise and interference factors is reduced, and the detection accuracy is improved; fine-grained features in the voiceprint signals can be captured, extraction of the features is further optimized, and therefore abnormal voiceprints can be recognized more accurately.

The invention discloses an entity relationship extraction method fusing semantic information and syntactic information, and belongs to the technical field of language processing. According to the method, the information in the sentences is learned, so that the fusion of semantic and syntactic information is realized, and the accuracy of relationship classification between entities is improved. According to the method, an external syntax device and word vector information are combined, the influence of noise in a text is reduced by using a graph convolutional neural network of a self-attention pooling layer and a residual shrinkage network, and long-distance dependence between entities is improved. Meanwhile, loss is calculated in an interpolation prediction mode, and interaction between the BERT and the graph neural network is perfected. Experiments show that semantic vectors and syntactic structure information are fully fused, so that the relation extraction effect is remarkably improved compared with other reference models, and the problems that the recognition rate of the long-distance relation between entities in sentences is low and noise exists are solved.

The present invention discloses a pipe gear rolling machine and a gear rolling method, which belong to the technical field of pipe processing. The pipe gear rolling machine includes a pipe conveying mechanism, a gear rolling main machine and a through-rod conveying mechanism. The pipe conveying mechanism includes a pipe conveying frame, a pusher assembly arranged on the pipe conveying frame and a plurality of pipe roller assemblies arranged along the length direction of the pipe conveying frame. The pusher assembly includes a push rod and a push rod driving mechanism. A follower pipe head is rotatably arranged on the push rod, and a ring table is arranged on the follower pipe head. The push rod driving mechanism includes a driving wheel, a driven wheel, a synchronous belt and a push rod driving motor, and the push rod driving motor is connected to the driving wheel through a clutch. The push rod of the present invention can push the pipe to be loaded, the through-rod can support the pipe, and the pipe can maintain the insertion fit with the push rod when the pipe is discharged. The push rod is passively pushed back from the pipe, which reduces or even avoids the jumping, noise and the throwing out of the cutting fluid in the pipe during the processing and discharge of the pipe, thereby ensuring the tooth rolling quality of the pipe.

The invention discloses a cascaded discrete wavelet multiband decomposition attention image denoising method and device, and the method comprises the following steps: building a CDW-MDA image denoising network which employs an encoder-sub-network-decoder structure; inputting a noise image and extracting high and low frequency band characteristic patterns under multiple scales; global and local key feature information is enhanced; evaluating the importance of texture detail components and weighting noise features of different frequency bands; enhancing selective features of the processed high and low frequency band feature maps, and fusing the high and low frequency band feature maps; and converting the fused high-low frequency band feature map to obtain a residual image, and finally obtaining a target image. According to the image denoising method, clearer texture details can be generated, blurring is avoided, and more texture detail information is reserved in the restored image; noise can be reduced while image feature textures are kept, edge information is reserved, and a better visual effect is achieved.

The invention discloses a fault root cause analysis method based on multi-source equipment data fusion. According to the method, multi-source heterogeneous data such as equipment operation data, maintenance records and environment data are collected and efficiently fused, a fault mode is automatically recognized from fused data, and time sequence evolution characteristics related to faults are mined. And based on the established knowledge base of the fault modes and reasons, and in combination with interactive Bayesian network reasoning and manual feedback, the root cause of the fault is deduced. According to the method, innovative technologies such as active learning and transfer learning are introduced, and the intelligent level, accuracy and efficiency of fault diagnosis are improved. And a visual interface visually presents an analysis process, supports man-machine interaction correction, and enhances interpretability. The method is suitable for the fields of industrial equipment, transportation tools, medical equipment and the like, and the system stability and usability are improved.

The invention discloses a method for converting a two-dimensional image of a building component into a three-dimensional model, which relates to the technical field of image processing, and comprises the following steps of: comparing the size of the three-dimensional model of the component with the size of an actual building component to obtain error data between the two, and generating corresponding accuracy through a model error data set; if the accuracy of the component three-dimensional model does not exceed the expectation, carrying out coincidence analysis on the component three-dimensional model and the building component and obtaining the coincidence degree, generating a complete coefficient according to the coincidence degree, screening out defect areas in the component three-dimensional model according to the complete coefficient, and associating to obtain the optimization priority of each defect area; and after a model optimization scheme is given for the defect areas by the building component model optimization knowledge graph, optimizing the defect areas in sequence according to optimization priorities. A corresponding model optimization scheme is provided for the defect area through the building component model optimization knowledge graph, and the optimization pertinence is higher when all the defect areas are optimized.

The invention relates to the field of constructional engineering, and discloses an assembly type reinforced concrete-steel structure foundation pit supporting system convenient to disassemble and a construction technology, and the assembly type reinforced concrete-steel structure foundation pit supporting system comprises a top beam, a waist beam, a supporting pile and an inner supporting system; the inner supporting system is formed by assembling a standard steel-concrete beam section main support and a steel member auxiliary support in cooperation with a jack adjusting system. The invention further provides a new system on-site assembly construction process, namely detailed steps of assembly construction from the uppermost layer to the lower layer step by step. The fabricated structure integrates the advantages of two fabricated structures, is suitable for foundation pit supporting with large stress and strict deformation control, and has the advantages of being high in construction speed, short in construction period, low in construction cost, convenient to dismantle and capable of completely recycling most of inner supporting components compared with a traditional cast-in-place steel-concrete foundation pit supporting system.

The invention relates to the technical field of data processing, in particular to a multi-modal recommendation method based on an information bottleneck principle. The method comprises the following steps: constructing a data set # imgabs0 # according to a user-article historical interaction record in an Amazon data set; constructing a user-article graph for each mode according to the data set, and performing multi-layer graph convolution calculation on the embedding of the user and the article to obtain the final expression of the user and the fusion embedding of the article; an article-article similarity graph is constructed for each mode of the articles, neighbors of the first k articles with the highest similarity are reserved, article semantic signals are grouped according to the contribution degree of the article modes to downstream recommendation tasks, and final expressions of the articles are obtained; and performing optimization through denoising, loss calculation and an Adam optimizer. According to the method, key information can be extracted from multi-modal data, redundancy and noise can be removed, and the contribution degree of article characterization to downstream recommendation tasks in different modals can be calculated.

The invention belongs to the technical field of agricultural mechanical equipment, and discloses a well cellar type zero-speed punching transplanter and a well cellar hole forming method.The well cellar type zero-speed punching transplanter comprises a rack and two zero-speed transplanting devices, the zero-speed transplanting devices are driven by a transmission device, and each zero-speed transplanting device comprises a double-crank floating type punching mechanism and a seedling throwing mechanism; the double-crank floating type punching mechanism comprises a horizontal frame, the horizontal frame extends in the advancing direction of the advancing wheels, a double-crank assembly is arranged between the horizontal frame and the rack, a floating type hole head assembly is installed on the horizontal frame, and a hole head limiting assembly is jointly arranged between the floating type hole head assembly and the horizontal frame; upright zero-speed efficient punching is achieved through the double-crank floating type punching device, the problem that a well cellar hole is oval due to relative movement between a well cellar hole head and a soil ridge is solved, the well cellar hole punching quality and efficiency of the transplanter are improved, and the effects of no impact and low noise in the punching process are achieved.

A range hood includes a box assembly and a fan frame installed on the top of the box assembly, a fan is installed inside the fan frame, a smoke inlet is provided at the bottom of the box assembly, the lower part of the fan extends into the box assembly, the fan is a centrifugal fan having a front air inlet and a rear air inlet, and correspondingly, a front air inlet channel and a rear air inlet channel are formed inside the box assembly, the smoke inlet is fluidly connected with the front air inlet through the front air inlet channel, and the smoke inlet is fluidly connected with the rear air inlet through the rear air inlet channel. The lower part of the fan of the range hood extends into the box assembly, and the fan adopts a front and rear double air inlet structure. After the fan suction port is placed downward, the suction port is closer to the smoke inlet, which is conducive to improving the oil fume extraction effect. Therefore, the oil fume extraction effect can be guaranteed by adopting a thin fan system, thereby reducing the thickness of the entire box, making the product a thin range hood in appearance, which can reduce the risk of users hitting their heads and reduce the sense of aggression during use.

The invention relates to a compact three-phase superconductive air-core reactor which comprises a vacuum heat insulation container, a cooling medium is contained in the vacuum heat insulation container, the vacuum heat insulation container comprises a vacuum heat insulation shell and a vacuum heat insulation cover, and a plurality of hanging rods extending into the vacuum heat insulation shell are arranged on the vacuum heat insulation cover; the three-phase superconducting magnet is immersed in the cooling medium, the three-phase superconducting magnet is formed by vertically stacking three single-phase superconducting magnets which sleeve the plurality of suspenders at intervals, and each single-phase superconducting magnet is of an iron-core-free hollow structure; the three low-temperature sleeve assemblies correspond to the single-phase superconducting magnets one to one, each low-temperature sleeve assembly comprises two low-temperature sleeves, the low-temperature sleeves partially extend into the vacuum heat insulation shell from the vacuum heat insulation cover, and the ends, located outside the vacuum heat insulation shell, of the low-temperature sleeves are connected into a power grid; and one end in the vacuum heat insulation shell is connected with the corresponding single-phase superconducting magnet through a current lead. The application scene of the reactor is expanded, and the problem that reactive compensation of a power grid is insufficient is effectively solved.