311results about How to "Improve fusion effect" patented technology

The invention discloses a medical diagnosis intelligent decision-making system based on multi-modal data fusion, and the system comprises the steps: obtaining a medical image, biological features and electronic medical record data of a patient through a patient information collection unit, and extracting feature vectors from different modal data through a feature processing unit; the core of the system is a modal fusion engine, and the module combines a deep neural network and a Bayesian inference engine to realize deep learning and probability-dependent modeling of an association relationship between features; carrying out weight optimization on different modal features by adopting an attention mechanism through a self-adaptive feature optimizer, and generating optimized feature representation; the intelligent diagnosis analysis unit calculates the diagnosis probability based on the optimized features, performs auxiliary diagnosis in combination with a case knowledge base when the confidence coefficient is insufficient, and outputs a diagnosis report containing diagnosis categories, basis and suggestions; according to the method, multi-source heterogeneous data are fused, deep learning and probabilistic reasoning are combined, intelligent medical auxiliary diagnosis is achieved, and diagnosis accuracy and interpretability are improved.

The invention provides an infrared and visible light image fusion method based on multi-scale feature interaction enhancement, belongs to the field of image processing, and aims to improve the image fusion quality in a complex environment. The method comprises the following steps: firstly, performing feature extraction on infrared and visible light images through a double-flow multi-scale encoder to generate multi-level features of different scales; and then, cross-modal information interaction is carried out by adopting a feature interaction enhancement module, fusion features are optimized through a space and channel attention mechanism, and clear expression of key areas such as target heat radiation and image edges is ensured. In the decoding stage, multi-scale jump connection and layer-by-layer up-sampling are utilized to reconstruct a high-resolution fusion image. And finally, training the fusion network through a content-gradient joint loss function to realize balance between detail reservation and global information. The method is suitable for tasks such as monitoring and target identification in a complex scene, and can effectively improve the image fusion effect especially under the conditions of low illumination, smoke shielding and the like.

The invention discloses a medical image segmentation method fusing multi-modal graphic and text information, and the method comprises the steps: carrying out the preprocessing and coding of a metadata text of a patient through a pre-trained BERT model, extracting the feature representation, and carrying out the attention-guided data enhancement operation of a pathological image. And finally, constructing a medical image segmentation network composed of an image encoder, a text encoder and a decoder, carrying out pathological image segmentation based on the feature representation extracted in the step 1 and the pathological image after data enhancement, and visualizing a segmentation result. In the segmentation network, a multi-modal feature fusion module is designed to fuse information of two modals, the module adopts a Transform architecture, a cross-modal attention mechanism and a self-attention mechanism are introduced, and the effectiveness of information fusion is ensured. According to the method, a more accurate segmentation result is obtained, the generalization ability on new data is improved, and the common problem of data imbalance in the medical image is effectively solved.

The invention relates to the technical field of medical systems, in particular to an intelligent gastrointestinal postoperative intestinal obstruction diagnosis and intervention decision-making system and method, and the system comprises a data preprocessing module, a multi-modal data fusion module, a gastrointestinal function recovery model construction module, a self-adaptive progressive reinforcement learning optimization module AHRL and a diagnosis and intervention recommendation module. The self-adaptive progressive reinforcement learning optimization module optimizes an intervention strategy layer by layer through a five-layer progressive algorithm from the steps of dynamic adjustment of feature weights, recursive state propagation, layered accumulation optimization, symptom similarity matrix generation, progressive weight reverse feedback and the like, deep fusion of key symptoms by multi-modal data is achieved, and the accuracy of the key symptoms is improved. And the intervention scheme is updated in real time according to the patient state change. In the intervention decision recommendation module, the system generates personalized diagnosis suggestions based on the optimized strategy, carries out real-time risk assessment, and provides continuous and dynamic intervention support for the patient.

The invention belongs to the technical field of image processing, and discloses a transformer fault detection method based on an improved YOLOv8 model. Aiming at the problems of inaccuracy in feature extraction, low precision, poor real-time detection capability and the like existing in fault detection of a traditional transformer fault detection method, the invention establishes a transformer fault detection model based on an improved YOLOV8 model architecture, a self-calibration convolution defogging module is introduced, a generative adversarial network (GAN) and a multi-source data fusion network structure are added, a BiFPN structure is used, an ASPP module is replaced by an ASPFCSPC module, and a YOLOV8 model architecture is improved, so that the feature extraction capability of a transformer fault detection model is enhanced, the calculation complexity of the model is reduced, and the fault detection efficiency of the transformer is improved. The transformer fault detection precision, accuracy and real-time detection capability are improved, and the stability and adaptability of the model are enhanced.

The invention discloses a multi-modal data fusion student mental health early warning method and system. The method relates to the technical field of property detection, and comprises the following steps: collecting student psychological health data, analyzing the student psychological health data, and comprehensively analyzing, optimizing and adjusting the student psychological health early warning method. The student psychological health data is acquired and processed, the student psychological health data is analyzed, the student psychological health data image quality evaluation index and the student psychological health data fusion network volatility evaluation index are obtained, and the student psychological health early warning method is comprehensively analyzed, optimized and adjusted. The accuracy of the student psychological health early warning data quality is improved, and the problem that the accuracy of the student psychological health early warning data quality is insufficient in the prior art is solved.

The intelligent factory data acquisition platform comprises a data sensing layer, a multi-protocol transmission layer, a data fusion layer and an application service layer, the data sensing layer is used for collecting equipment state and environment parameter multi-source heterogeneous data through various sensors, preprocessing the collected data and transmitting the preprocessed data to the multi-protocol transmission layer; the multi-protocol transmission layer is used for receiving the preprocessed data transmitted by the data sensing layer, packaging the data through a multi-protocol data transmission unit, then carrying out multi-protocol transmission, and outputting the transmitted data to the data fusion layer; the data fusion layer is used for receiving the multi-source heterogeneous data transmitted by the multi-protocol transmission layer, performing feature extraction and fusion on the heterogeneous data by adopting a self-correcting convolutional recurrent neural network, and outputting fused feature representation to the application service layer; and the application service layer is used for receiving the fusion features output by the data fusion layer, and performing equipment health diagnosis, process parameter optimization and energy consumption statistical analysis intelligent application services based on the fusion features.

The invention discloses a monocular depth estimation method based on hybrid CNN-Transformer, belongs to the aviation field and the computer vision field, and is applied to the field of environment depth perception in low-altitude flight of an unmanned aerial vehicle. Aiming at the problem that local-global information cannot be effectively extracted by a current depth estimation method, the design content mainly comprises the following steps: improving a visual Transform architecture based on an additive attention mechanism, optimizing feature interaction operation between image blocks, and improving the capability of capturing global semantic information; the method comprises the following steps: designing a deep network encoder mixed with a CNN-Transform module, and effectively extracting local-global feature information; a channel attention mechanism is introduced, and the feature fusion capability of the decoder is enhanced. The method is simple in process, an excellent test result is obtained through training on the flight data set Mid-Air of the low-altitude unmanned aerial vehicle, the extraction capability of local-global feature information is effectively enhanced, and the depth perception accuracy of the monocular depth estimation method in the flight scene of the low-altitude unmanned aerial vehicle is improved.

The invention provides an unmanned aerial vehicle aerial photography small target detection method based on improved YOLOv8, belongs to the field of image detection, and mainly comprises four steps of data preparation, model building, model training and model testing. The method comprises the following steps: firstly, collecting small target image data aerial-photographed by an unmanned aerial vehicle, and marking the data; then, an unmanned aerial vehicle aerial photography small target detection model based on the improved YOLOv8 is built, the model comprises a new aggregation and distribution mechanism and a Dynamic Head detection head, and the performance of the model is improved; thirdly, training set data are input into the model for training, and parameters are optimized; and finally, inputting test data of the verification set into the model to realize small target detection. The method combines an aggregation-distribution mechanism and a dynamic detection head, improves the precision of unmanned aerial vehicle small target aerial photography detection, and can be widely applied to the fields of environmental protection, agriculture, electric power and the like.

The invention discloses an IMU (Inertial Measurement Unit) data and navigation signal fusion processing method and device, navigation equipment and a medium. The method comprises the following steps: acquiring data, and constructing an original sampling sequence corresponding to a timestamp; performing data inspection, and calculating a data difference in a current sampling period to obtain a sampling supplementary value; carrying out comprehensive fitting processing on the sampling supplementary value; establishing a corresponding relationship of the data to obtain an initial fusion parameter; performing incremental calculation, and establishing a corresponding relationship between data increment and fusion precision to obtain an increment fusion sequence; calculating the actual difference between the IMU and the navigation data to obtain a data deviation value; performing data calibration on the fitting sequence to obtain a calibration fusion sequence; performing secondary data fitting operation to obtain a final fitting parameter; and applying the final fitting parameter to the calibration fusion sequence, and obtaining a final fusion signal through data point-by-point matching. According to the technical scheme, the fusion precision is improved, and the method can adapt to complex scenes and has dynamic evaluation and exception handling capabilities.

The invention belongs to the technical field of marine surveying and mapping, and discloses an unmanned aerial vehicle laser / shipborne multi-beam data land and water topographic mapping method, which comprises the following steps of: coarsely extracting a data overlapping region through a virtual grid, finely extracting the overlapping region by using an alpha-shape algorithm, and obtaining boundary points; removing edge triangles in an irregular triangulation network constructed by the unmanned aerial vehicle laser sounding data and the shipborne multi-beam sounding data; calculating a vertex normal vector, taking a vertex tangent plane of the multi-beam triangular irregular network as a registration element, carrying out weight distribution according to the distance from a point to the vertex, and carrying out coarse registration through a point-to-plane ICP registration algorithm; and constructing a precise registration model based on a point-surface distance, and eliminating the elevation difference of the overlapping region by minimizing the normal distance from the unmanned aerial vehicle laser sounding point to the multi-beam sounding triangular surface element. By adopting the point cloud fusion model for processing, the phenomenon that different source point cloud elevations in the overlapping area are inconsistent is effectively improved, and underwater topographic data fusion is realized.

The invention discloses a defect identification method and system based on an efficient multi-path feature fusion network, relates to the technical field of cloth defect detection, and solves the problems that deep-layer, shallow-layer, large-scale and small-scale feature information cannot be effectively fused in the prior art, and an existing model is high in calculation complexity and large in model parameter quantity. The method comprises the steps that a cloth flaw data set is constructed, an improved YOLOv5s model is constructed, and model training is carried out, and the improvement content comprises the steps that GFPN is fused into a Neck network of YOLOv5s, cross-scale connection layers between interval levels and cross-layer jump connection layers between the same scales are introduced into a backbone network, in the Neck network, C3Ghost lightweight feature fusion nodes are adopted, and the cross-scale jump connection layers between the interval levels and the cross-scale jump connection layers between the interval levels are fused into the backbone network. According to the method, the deep feature map is subjected to up-sampling by adopting a Dysample method, deep and shallow feature information and large-scale and small-scale feature information are effectively fused, the performance of a YOLOv5s model in multi-scale and multi-proportion target detection is improved, and meanwhile, calculation parameters and complexity of the model are reduced.

The invention discloses a liver tumor segmentation method based on SCAF-TransUNet, and the method comprises the steps: firstly, segmenting a liver image into semantically related superpixel regions through SLIC superpixel preprocessing, enabling a model to be more accurately focused on a tumor and other key regions, reducing the calculation redundancy, and improving the efficiency and precision of feature extraction; secondly, a CECA module introduced in jump connection replaces a traditional full connection layer through one-dimensional convolution, the attention mechanism between channels is simplified, capture of the channel dependency relation is enhanced, and meanwhile the feature extraction precision is kept; finally, the FPN is added in the decoder, and the fusion capability of the multi-scale features is further enhanced. According to the FPN, feature maps with different resolutions are fused layer by layer, so that the model can effectively combine global semantic information and local detail features before up-sampling, and the processing capacity for complex liver boundaries and small tumors is improved. Finally, the improvements significantly improve the accuracy and robustness of the liver tumor segmentation task.

The invention discloses a steel rail surface defect detection method based on LAH-YOLO. The detection precision of the YOLOv5-s algorithm on a steel rail surface defect data set still needs to be improved. The method comprises the following steps: acquiring a steel rail surface image, and inputting the steel rail surface image into a constructed LAH-YOLO defect detection network; the LAH-YOLO defect detection network comprises a backbone network, a feature fusion network and a detection head; the backbone network comprises a GRF-C3 module, and the feature fusion network is an AFPN feature fusion network. The method is suitable for small target and small sample data sets, the detection precision is remarkably improved while the parameter quantity is reduced, and the method has better detection performance on the steel rail surface defect data set.

The invention discloses an intelligent drought risk management method and system based on disaster attribution analysis. The method comprises the steps of data acquisition, data enhancement, disaster attribution analysis, disaster dynamic risk assessment and intelligent drought risk management. The invention relates to the field of drought risk management, and adopts an intelligent drought risk management integrated method combining disaster attribution analysis and disaster dynamic risk assessment to perform drought disaster dynamic analysis from the angle of attribution, and perform multi-factor feature mining in combination with a plurality of feature optimization models. The complexity processing capability and the real-time precision of intelligent drought risk management are improved, and the fusion performance of multi-field data is also improved; performing disaster attribution analysis by adopting an improved support vector machine model combining an expert model and graph neural network feature optimization; and carrying out disaster dynamic risk assessment by adopting a gating circulation unit which combines the generative adversarial network and the graph convolution network and is deepened in feature.

The invention discloses a lightweight rice disease prediction method based on SCL-YOLOv8n, and belongs to the technical field of rice disease prediction. The method comprises the following steps: constructing a rice disease data set; performing enhancement, labeling and division processing on the rice disease data set; in a GSConv module of a Slim-Neck network structure, before Shuffle operation, lightweight convolution is introduced to be connected with an original path in parallel to form a GSLConv module; the method comprises the following steps: based on a CSP-RFA structure, dividing data, and distributing the data to two branches to form an RFCA-CSP module; a lightweight sharing enhanced detection head LSCSBD is adopted; an SCL-YOLOv8n model is constructed according to the model; taking the processed data set as the input of an improved model SCL-YOLOv8n, and training the improved model SCL-YOLOv8n by using the training set; in the aspect of rice disease prediction under the complex background, the parameter quantity and the calculation complexity are reduced, the precision is improved, and an effective reference is provided for rice disease prediction under the complex background.

The utility model discloses a chemical sewage pretreatment device for ozonation, which particularly relates to the technical field of sewage pretreatment and comprises a shell, an air pump is arranged at the top of the shell, an exhaust pipe is arranged at the top of the air pump, a ladder is arranged on the outer side of the shell, a first water inlet pipe is arranged on the outer side of the shell, and a second water inlet pipe is arranged on the outer side of the shell. A motor is arranged on one side of the shell, a second water inlet pipe is arranged on one side of the shell, and a first water drainage pipe is arranged on the other side of the shell. The connecting bent rod is arranged in the shell, the motor drives the connecting bent rod to rotate, so that the connecting bent rod drives the fixed disc and the fixed plate on the outer side of the fixed disc to rotate, sewage in the shell is stirred, and meanwhile, the middle section of the connecting bent rod drives the rectangular plate to rotate; through different rotating speeds of the middle section and the two ends, sufficient fusion of the sewage and the liquid medicine is improved, and the practicability of the device is improved.

The invention relates to the technical field of artificial intelligence multi-modal large models, and discloses a multi-modal human body large model training method and system considering multi-granularity semantic alignment, and the method comprises the steps: carrying out the prefix construction of a large-scale training set through improving a network structure, adding prefixes related to description to the data in the training set based on human body features; and training the multi-modal human body large model in batches, pairing each image and each text in each batch of training data, improving comparison loss through similarity calculation and semantic relaxation, and optimizing visual and text modal encoder parameters, so as to improve the accuracy of the multi-modal human body large model. According to the multi-modal human body large model training system considering multi-granularity semantic alignment, aiming at the multi-granularity semantic alignment problem encountered by a current multi-modal large model in human body cross-modal retrieval application, a general multi-modal large model is improved from two aspects of a network structure and a loss function; and the performance of cross-modal human body retrieval under multi-granularity semantics is enhanced.

The invention discloses a multi-sensor fusion vehicle rapid positioning method and device, a storage medium and electronic equipment, and belongs to the technical field of vehicle positioning. The positioning method comprises the following steps: firstly, acquiring standard data of a vehicle in a driving process on a preset planned path through multiple sensors, and constructing an offline three-dimensional map containing a key frame image database; secondly, collecting fact pose data in a vehicle driving process as initial pose estimation, collecting online current frame environment image data in real time through a camera, extracting a global feature vector in the online current frame environment image, matching K pictures similar to the image from a key frame image database, and carrying out initial pose estimation on the K pictures; and extracting local feature points and descriptors in the K pictures, screening out the most approximate picture from the K pictures as a target picture, and calculating to obtain real-time pose estimation of the vehicle. According to the method provided by the invention, more efficient, more accurate and more reliable vehicle positioning is realized through the cooperation of online processing and offline processing.

The invention relates to an infrared and visible light image fusion method and system based on CRITIC weight. The method comprises the following steps: decomposing an input visible light image and an infrared image into a feature image and an original image based on a Gaussian filter; respectively calculating corresponding variable weight coefficients based on the visible light original image and the visible light characteristic image; fusing the visible light original image and the infrared original image based on the variable weight coefficient, and obtaining a visible light fusion image; fusing the visible feature map and the infrared feature map based on a variable weight coefficient, and obtaining an infrared fusion map; and fusing the visible light fusion image and the infrared fusion image to obtain a final fusion image. According to the method, when the light is too bright or too dark, the variable weight coefficient is matched with the weight which can best reflect the features of the fused image at present for fusion, so that the fusion effect of the fused image is improved.

Belonging to the technical field of thermal insulation material preparation, the invention relates to a corrosion resistant thermal insulation material for marine thermal insulation, which is prepared by the following steps: carrying out acidolysis treatment on plants, then filtering, and pulping the filtered product to obtain a fiber acidolysis solution and a fiber acidolysis product. And then modifying the fiber acidolysis solution and the fiber acidolysis product by using dopamine hydrochloride and a silane coupling agent respectively. The modified cellulose has a good fusion effect in a polyurethane system due to the fact that the modified cellulose contains an amide group, and can be stably adhered to the surface of a metal pipeline for energy transportation in the ocean. Si-O-Si bonds formed by the modified plant fibers can improve the structural stability of the fibers, so that the weather resistance, tensile strength, impact strength and other mechanical properties are improved. The prepared thermal insulation material has the characteristics of good mechanical property and high stability.

The invention relates to the technical field of remote sensing image processing, in particular to a hyperspectral image and laser radar image fusion classification method and device. According to the method, a deep network model is constructed and comprises a multi-level modal assimilation encoder and a multi-level assimilation modal reconstruction decoder; and for each hyperspectral pixel in the normalized hyperspectral image, inputting a neighborhood block of the current hyperspectral pixel and a neighborhood block of a laser radar pixel with the same position as the hyperspectral pixel into a multi-level modal assimilation encoder, and performing multi-stage modal information fusion and feature extraction to obtain the hyperspectral image. Obtaining a remote sensing assimilation mode of the current hyperspectral pixel and the corresponding laser radar pixel; and inputting each hyperspectral pixel and the remote sensing assimilation mode of the corresponding laser radar pixel into a multi-level assimilation mode reconstruction decoder to obtain a pixel classification result after each hyperspectral pixel and the corresponding laser radar pixel are fused. According to the method, the hyperspectral image and the laser radar image can be effectively fused and classified.

The invention discloses a hyperspectral and panchromatic image fusion method based on feature enhancement channel shuffling, and the method comprises the steps: obtaining a hyperspectral image and a panchromatic image, carrying out the preprocessing of the hyperspectral image and the panchromatic image, and correspondingly obtaining a preprocessed hyperspectral image and a preprocessed panchromatic image; a hyperspectral image feature enhancement module based on matrix rank constraint performs feature extraction on the preprocessed hyperspectral image to obtain hyperspectral image features; a filtering-based panchromatic image feature extraction module performs feature extraction on the preprocessed panchromatic image to obtain panchromatic image features; splicing the hyperspectral image features and the panchromatic image features to obtain input features of an image feature fusion part; and carrying out feature fusion on the input features based on multi-scale convolution, extracting features of different scales, and carrying out deep fusion on the features of different scales based on a deep and shallow fusion module of channel shuffling to obtain a final fusion result. According to the invention, the image fusion effect is further improved.

The invention discloses a continuous fiber reinforced co-extrusion die, a forming line body and a fiber reinforced foaming floor, and relates to the technical field of foaming floors, the continuous fiber reinforced co-extrusion die comprises a die body, the front end face and the rear end face of the die body are a mixed extrusion face and a core material feeding face respectively, and one side face of the die body is provided with a shell material feeding port; a core material supply channel penetrating through the mixed extrusion face and the core material supply face is formed in the mold body, a shell material supply cavity surrounding the core material supply channel is formed in the mold body, two fiber channels are formed in the mold body, the two fiber channels are symmetrically arranged up and down, and the fiber channels are arc-shaped bent channels. The forming line body comprises a continuous fiber reinforced co-extrusion die, a core material extruder and a shell material extruder; a shaping module, a vacuum water cooling group, an open type water cooling group and a traction machine are sequentially arranged on the mixed extrusion surface of the mold body. According to the invention, the overall strength and performance of the composite material are optimized, and uniform distribution and orientation of reinforced fibers are ensured.

The invention discloses a UDA SAR ATR method and device based on multi-modal feature fusion and global local domain alignment. The method comprises the steps that a to-be-recognized actually-measured full-aperture SAR image is acquired; dividing the image into a group of sub-aperture images; constructing a scattering topological graph of the image; processing a group of sub-aperture images and a scattering topological graph of the image by adopting a trained deep recognition network to obtain the category of a target contained in the image; the trained deep recognition network is obtained by training based on a preset data set; the preset data set comprises a plurality of categories of simulation full-aperture SAR images and actual measurement full-aperture SAR images, each simulation full-aperture SAR image has a real domain label and a category label, each actual measurement full-aperture SAR image only has a real domain label, the domain label represents whether the image is a simulation image or an actual measurement image, and the category label represents whether the image is an actual measurement image. The category tag represents a category of a target in the image. According to the method, the cross-domain SAR target recognition performance can be improved.

The invention relates to the field of waste gas treatment, in particular to a waste gas treatment device and method for adhesive tape production, the waste gas treatment device comprises a treatment tank, one side of the treatment tank is communicated with a gas inlet pipe, one side below the treatment tank is connected with a gas outlet pipe, and a liquid discharge pipe is mounted below the treatment tank. According to the waste gas treatment device, the connecting holes are annularly distributed and are also obliquely formed, so that waste gas is annularly conveyed into the treatment tank when entering the treatment tank, and the waste gas and conveyed air can be spirally downwards conveyed while the air conveying efficiency is improved through the rotation of the fan I, so that the waste gas and the conveyed air are uniformly sprayed when a spraying nozzle sprays; according to the device, the mixing effect with waste gas can be improved, particles obtained after spraying and filtering can fall on the connecting disc and are scraped through rotation of the connecting disc, and the spraying liquid falls and finally remains at the bottom of the treatment tank, so that waste gas particles left in the used spraying liquid are reduced, and the recycling and filtering cost of the waste spraying liquid is reduced; meanwhile, the waste gas filtering efficiency and quality can be improved.

The utility model relates to the field of extraction equipment, in particular to a composite plant anti-allergy preparation device capable of fully stirring, which comprises an extraction tank, the top of the extraction tank is provided with a feeding port and a steam output port, the position of the extraction tank close to the bottom is provided with a steam outlet, and the steam outlet is communicated with a condensing device; the condensing device comprises a shell and tube condenser and a coil condenser; an output port of the coil type condenser is connected with an oil-water separator through a pipeline, an oil collector is arranged at the top of the oil-water separator, a liquid discharge port is formed in the bottom of the oil-water separator and connected with a return tank through a pipeline, and a discharge port of the return tank is communicated to input ports of the tubular condenser and the coil type condenser through pipelines; a stirring paddle is arranged in the extraction tank, the stirring paddle is rotatably mounted in the extraction tank, and the stirring paddle ascends and descends in the vertical direction while rotating. The purpose of the utility model is to effectively guarantee the quality of subsequent extraction of an extracting solution.

The invention discloses a data fusion method and system based on deep learning, and relates to the technical field of deep learning, and the method comprises the steps: carrying out the classification of various types of sensor data in an unmanned system according to a set classification rule, and obtaining the classification data; performing preprocessing based on the basic attributes of the classified data to obtain preprocessed classified data; performing data-level fusion on the preprocessed classification data to obtain a plurality of classification fusion data; based on a deep recurrent neural network DRNN, in combination with a bidirectional long-short term memory unit Bi-LSTM and a sparse attention mechanism, constructing a data fusion model, and optimizing the data fusion model through model pruning to obtain an optimized data fusion model; and inputting the plurality of classified fusion data into the optimized data fusion model for secondary fusion to obtain a final fusion feature. The potential of multi-source information is fully mined, and the accuracy and effectiveness of decision making in a complex environment are improved.

The invention discloses a preparation method of a lithium ion battery electrolyte, and relates to the technical field of lithium ion battery electrolytes.The preparation method comprises the following steps that S1, single organic solvents, namely diethyl fluoromalonate, fluoroethylene carbonate and dimethyl carbonate are taken and mixed in proportion, and a mixed organic solvent is prepared; s2, preparing a composite lithium salt; and S3, the mixed organic solvent, the composite lithium salt and an additive are taken in proportion and mixed, the lithium ion battery electrolyte is prepared, and the additive is a sulfonate compound and a fluorine-containing sulfonamide compound. According to the invention, by adding the sulfonate compound and the fluorine-containing sulfonamide compound additive, a stable, uniform and thin interfacial film can be formed, the stability of CEI and SEI films can be improved, and the positive and negative electrode materials of the battery can be protected; and the freezing point of the organic solvent is reduced while the ionic conductivity under the high-temperature condition is improved, so that the low-temperature effect of the electrolyte is improved, and the lithium ion battery electrolyte has a wider temperature range.

The utility model discloses a highway subgrade soft soil treatment device, which comprises a treatment device main body, a filling cylinder, a feed port and a frame body, universal wheels are arranged at the four corners of the bottom of the treatment device main body, the frame body is fixed at one end of the top of the treatment device main body, a screw rod is arranged in the frame body, a driving block is sleeved on the screw rod, and the driving block is connected with the filling cylinder. A movable plate is arranged on one side of the driving block, a first rotary driving mechanism is fixed to the center of one side of the movable plate through a fixing piece, the output end of the first rotary driving mechanism is connected with a filling cylinder, a piling head is arranged at the bottom end of the filling cylinder, and discharging holes are evenly formed in the outer side of the filling cylinder. According to the treatment device for the soft soil roadbed, the treatment device body, the filling cylinder, the feeding port, the frame body, the lead screw, the driving block, the movable plate, the first rotary driving mechanism, the piling head and the discharging hole are installed, curing materials are introduced in the drilling and grooving process, and the treatment efficiency of the soft soil roadbed is improved.