8results about How to "Alleviate needs" patented technology

The utility model discloses a composite board made of bagasse, and relates to the technical field of composite board preparation, the composite board comprises a foundation board, a plurality of mounting grooves are formed in the foundation board, reinforcing strips are mounted in the mounting grooves in a matched mode, the mounting grooves are machined through a grooving mechanism, and the grooving mechanism comprises a rack, a plurality of clamping mechanisms and a plurality of clamping mechanisms; the pressing wheels are used for machining the mounting grooves; the driving mechanism is mounted on the rack, and a plurality of pressing wheels are arranged at the lower end of the driving mechanism at intervals; the conveying mechanism is arranged at the lower end of the rack, the conveying mechanism is used for conveying the foundation plate, and a protective outer plate or a waterproof plate layer is arranged on the outer side of the foundation plate. Bagasse is used as a composite plate raw material, so that the problem of subsequent treatment of the bagasse is solved, wood can be replaced to meet the use requirement, and the service life of the bagasse is prolonged. Therefore, the production cost is reduced, the economic benefit is improved, and in addition, the prepared composite board is good in compressive strength.

The invention belongs to the technical field of fracturing fluids, and particularly relates to a seawater-based nano synergistic pressure flooding fluid system as well as a preparation method and application thereof. The seawater-based nano synergistic pressure flooding liquid system comprises modified nano SiO2 particles, a drag reducer, a bactericide, an anti-swelling agent, a scale inhibitor and water. According to the seawater-based nano synergistic pressure driving liquid system, seawater resources are reasonably utilized during offshore pressure driving, the demand of an offshore platform for fresh water resources is relieved, and the construction cost of the pressure driving liquid is greatly reduced.

The application discloses a face soft tissue defect point cloud completion method based on a PF-Net, and belongs to the technical field of face soft tissue defect completion. The method comprises the following steps: S1, a data preparation stage: a data enhancement algorithm is designed to obtain a paired data set of incomplete input and defect labels; S2, a model construction stage: a dynamic graph convolution and a boundary attention mechanism are introduced into an encoder to optimize a point cloud completion network model PF-Net; S3, a model training stage: a training set is input into the PF-Net model for training; S4, a model testing stage: a test set is input into a generator for testing; and S5, a model inference stage: for clinical face soft defect point cloud data, multi-scale input data are acquired and completion is performed. The application realizes autonomous simulation design of face soft tissue defects, solves technical barriers in existing digital prosthetic technology, such as dependence on healthy side mirror image data or standard organ models, and solves the problem of unstable repair effect caused by dependence on doctor experience and aesthetic literacy.

This invention discloses a process for producing converter slagging agent from lithium extraction residue of electrolytic aluminum ash. Addressing the characteristic that lithium extraction residue of aluminum ash contains multiple fluxing components for converter steelmaking, this invention employs an innovative process method. Utilizing the principles of calcination dehydration and reduction desulfurization, the harmful sulfur component in the lithium extraction residue is removed to a level that meets the standards for steelmaking flux, making it a steelmaking flux raw material that can be directly utilized in converter steelmaking. This invention provides a highly competitive process for the large-scale treatment of lithium extraction residue from hazardous waste of electrolytic aluminum in the steel industry, demonstrating the technological advantages of cross-disciplinary innovation.

The present invention discloses a method and device for identifying transformer faults by fine-tuning fitting based on text mining, and belongs to the technical field of transformer fault identification. The method for identifying transformer faults by fine-tuning fitting based on text mining of the present invention, during data preprocessing, segments the transformer fault text data, and performs data enhancement processing at the same time, which can effectively avoid semantic information loss on the one hand, and provide as many features as possible to the main model in a limited sample volume on the other hand, so that the fault description text with a small number of samples in the transformer fault type accounts for a larger proportion when the model calculates the fitting loss value, avoiding overfitting and enhancing the model recognition effect; linear dimensionality reduction processing is performed through the fully connected layer to obtain a global semantic vector; the Canopy+K-Means model is used to cluster all calculation results, identify the specific fault location and fault type of the transformer fault text data, and improve the overall semantic understanding ability, and the scheme is scientific, reasonable and feasible.

The invention relates to a battery replacement recommendation method and system based on multi-target deep learning, an electronic device and a storage medium, and the method comprises the steps: employing the basic attributes of a battery, the related data of a battery replacement cabinet, the personal information of a rider, and the interaction record between the rider and the battery, and carrying out the multi-dimensional basic information through a multi-target recommendation system based on deep learning, potential variables in the data are deeply mined, the basic information and the mined hidden variables are integrated into a recommendation system model, and battery replacement cabinet and battery suggestions which most meet the requirements of a rider are output in real time. The problems of low user matching degree and low resource utilization rate of a battery replacement recommendation method in related technologies are solved, the cost and the production pressure are reduced, and the balance of a supply-demand relationship is enhanced.

The present invention proposes a portable variable swept wing device for a hybrid underwater vehicle, comprising a guide body, a connection assembly for connecting to the underwater vehicle is provided at the bottom of the guide body, and horizontal wings are symmetrically provided on both sides of the guide body; a driving assembly is also provided on the guide body, and the driving assembly is used to drive the two horizontal wings to synchronously and symmetrically unfold or retract. The present invention takes into account the requirements for power and resistance in two modes, and alleviates the contradiction between the two power modes on hydrodynamics and vehicle structure: in the power mode of the glider, the horizontal wings provide the vehicle with the lift required for gliding; in the power mode of propeller propulsion, the horizontal wings are retracted into the guide body, eliminating the resistance brought by the horizontal wings and protecting the horizontal wings.

The invention relates to the technical field of lithium ion batteries, in particular to an energy storage lithium ion battery health state prediction method, which comprises the following steps that sparse test point data are acquired through a battery aging experiment, and each test point comprises a cycle number, internal resistance, an increment health state characteristic and a health state label; systematic slicing and resampling are carried out on a time axis by utilizing an aging mining method, training samples containing different circulation history paths are generated, and a mapping relation between circulation history and health state attenuation is learned by adopting a deep learning model, so that high-precision health state prediction is realized; the method has the beneficial effects that the problem of scarcity of battery aging data is solved, and the health state prediction precision is remarkably improved; the method has good generalization ability and has flexibility and expandability.