118results about How to "Improve migration ability" patented technology

The invention belongs to the technical field of power distribution network fault recovery, and provides a power distribution network fault recovery method and system based on multi-agent reinforcement learning, and the method comprises the steps: taking a recovered load as a target function based on the load priority of a power distribution network, and taking the operation limitation of each node line in the power distribution network as a constraint condition; constructing a power distribution network fault recovery problem model; based on the power distribution network fault recovery problem model, constructing a multi-agent reinforcement learning agent set, and converting the power distribution network fault recovery problem into a Markov decision process; and a multi-agent reinforcement learning framework is constructed based on the agent set of multi-agent reinforcement learning, multi-agent training is carried out, each agent generates a decision according to respective observation information, and a fault recovery strategy is obtained. The objective function and the constraint condition constructed based on the load priority of the power distribution network and the node line operation limitation can ensure the accuracy and the feasibility of the recovery strategy. And the strategy generation time is shortened through the multi-agent parallel decision-making process.

The invention discloses a zero sample image classification method and device, and belongs to the field of image recognition. According to the method, a category description document is generated through a large language model, global and local feature information of an image and the document is obtained through a visual encoder and a text encoder, then decoupling is performed through a visual semantic decomposition module and a text semantic decomposition module, and visual semantic embedding and text semantic embedding are generated; through joint training of the modules, optimizing total loss composed of local-to-semantic mapping variance loss, multi-semantic diversity loss, decomposition semantic alignment loss and local semantic alignment loss; and the training is used for image category prediction in the reasoning stage after completion of training. According to the invention, the difficulty of zero sample image classification can be reduced, and the classification accuracy and efficiency are improved.

The invention provides a mechanical arm control method, a server, equipment, a system and a medium, and relates to the technical field of industrial automation. According to the method, a cloud server responds to an input target task operation instruction, and a full-view observation image of a mechanical arm operation scene is obtained; the target task operation instruction and the full-view-angle observation image are input into a preset multi-mode large model, and a mechanical arm action planning instruction is obtained; the mechanical arm action planning instruction is sent to terminal equipment; the terminal equipment responds to a mechanical arm action planning instruction sent by the cloud server and obtains a current observation image of the mechanical arm; the mechanical arm action planning instruction and the current observation image of the mechanical arm are input into a preset action prediction model, and a corresponding action sequence is obtained; and controlling the mechanical arm to execute the target task according to the action sequence. According to the method, the generalization, migration ability and accuracy of mechanical arm control are improved through end-cloud cooperative control.

The invention discloses Burkholderia gladioli HM-72 and application thereof, the Burkholderia gladioli HM-72 is preserved in China Center for Type Culture Collection on July 13, 2023, the preservation address is Wuhan University, Wuhan, No. 299, eight road, Wuchang District, Wuhan, Hubei, and the preservation number is CCTCC NO: M20231291. The strain is high in Pb stress resistance, and has the capability of partially dissolving phosphorus and producing siderophores. After the strain is applied to the Pb-polluted soil, the migration rate of Pb can be improved, the removal of Pb in the soil is accelerated, the Pb is accumulated in the overground part of the enrichment plant, the problem of repairing the lead pollution of the soil is solved, meanwhile, the strain can inhibit the growth of pathogenic fungi, the growth promoting and bacteriostasis characteristics of the strain can provide favorable conditions for the growth of the enrichment plant in a high-lead environment, and the application prospect is wide. The biomass accumulation is promoted, and the method has an application prospect in heavy metal polluted environment restoration.

The invention relates to a polyanion solid electrolyte, a preparation method thereof and a solid-state battery, and belongs to the field of energy storage batteries. The polyanion solid electrolyte is AaM (3-a) / m (SO4) bE1-bGc; a is one of Li, Na and K; m is one or more of Li, Na, K, Rb, Cs, Be, Mg, Ca, Sr, Ba, Al, Ga, In, La and Y; e is one or more of SO4 < 2->, CO3 < 2->, NO3 <->, NO2 <->, PO4 < 3->, PO4 < 3->, CrO4 < 2->, MnO4 < 2->, SiO4 < 4->, SiO3 < 2->, BO3 < 3-> and TeO4 < 2->; g is a halogen element; a is equal to 0-3, b is equal to 0-1, c is equal to 0-1, and m is the valence state of M cations. The ion transmission capability of the polyanion solid electrolyte is enhanced, and the ionic conductivity is improved, so that the performance of a solid-state battery is improved.

The invention relates to a preparation method of CAR-T cells and a product thereof. A method for preparing a CAR-T cell according to the present invention comprises the step of introducing a cyclic mRNA into a T cell that does not contain a chimeric antigen receptor; and, the cyclic mRNA comprises a first cyclic mRNA encoding a chimeric antigen receptor; in addition, the first annular mRNA comprises the following sequence (a) or (b): (a) a translation starting sequence and a coding sequence; and (b) translating the starting sequence, the coding sequence and the insertion element sequence. In the method, the transfection efficiency of the annular mRNA is high, and the positive rate is as high as 90%; compared with preparation methods of viruses, transposon systems and the like, the process is simple, the production cost is greatly reduced, and the safety is also improved; compared with linear mRNA, the protein expression time is greatly prolonged.

The invention relates to the technical field of oil and gas safety, and provides a natural gas pipeline leakage concentration field reconstruction method and system based on a physical information neural network. According to the method, on the basis of acquired sparse concentration field data of natural gas pipeline leakage diffusion, physical constraint conditions of an initial network model based on a full-connection neural network architecture are set, and a loss function of the initial network model is defined; embedding the unsteady convective diffusion equation of natural gas pipeline leakage diffusion and the residual gradient thereof into a loss function of the initial network model to obtain a physical information neural network improved model of natural gas pipeline leakage; and based on the trained physical information neural network improved model, according to sparse concentration field data of natural gas pipeline leakage, reconstructing a natural gas concentration field during natural gas pipeline leakage diffusion. Concentration field reconstruction in the whole leakage area can be achieved through a small amount of discrete data, and the contradiction between calculation precision and calculation time of a traditional numerical model is effectively solved.

The invention discloses a robust feature-based physical world adversarial sample generation method, which comprises the following steps of: acquiring a pre-trained auto-encoder, three pre-trained image classification task models and a data set for an image classification task, setting attack parameters, and generating a robust feature-based physical world adversarial sample; an auto-encoder and three image classification task models are used to carry out iterative optimization processing on each image of which the category label number is t in the data set used for the image classification task, so as to obtain a robust feature code Ftar of the image of which the category label number is t; randomly selecting an image xc of which the category label number is c from a data set for an image classification task, initializing weight matrixes m and alpha, and respectively optimizing the weight matrixes m and alpha by utilizing robust feature codes Ftar of the selected image of which the category label number is v and the selected image of which the category label number is t; respectively obtaining optimized weight matrixes m'and alpha ', and obtaining a final adversarial sample according to the optimized weight matrixes m' and alpha '.

The invention discloses a voiceprint recognition system attack resisting method and device, equipment and a medium, by constructing a shadow voice sample set and a substitution model, dependence on target speaker data and query capability is reduced, and privacy risk and query overhead are reduced; meanwhile, two types of substitution models are trained based on an information theory method, so that the mobility and stability of the models are improved, and the generalization ability is enhanced; besides, the attacker model adopts a specific architecture and combines an alternate training strategy, so that attack loss and prediction probability difference are optimized, imperceptibility and attack effectiveness are balanced, and tone quality reduction or attack failure is avoided; and finally, through systematic training and optimization, a more uniform evaluation caliber is expected to be provided, and more reliable guidance is provided for engineering landing and risk evaluation.

The invention relates to the technical field of coal mine monitoring video alarm intelligent analysis, in particular to a coal mine monitoring video alarm intelligent analysis system and method based on Yolov5 and 3DCNN, and the system comprises a data processing module and a model reasoning service module which are electrically connected. The data processing module comprises interface service, evidence video request, video formatting processing and model data processing; the model reasoning service module comprises a dispatching room vacancy early warning and rechecking model and a belt operation abnormity early warning and rechecking model; through the coal mine monitoring video alarm intelligent analysis system based on YoloV5 and 3DCNN, model reasoning and data processing decoupling are independent, data processing processes of interface service data processing, video data requesting and downloading, video data unified processing and the like are integrated through a data processing module, the data processing process is abstracted, a unified data processing mode is provided, and the data processing efficiency is improved. And processing the data with different sources and different formats into a uniform data format under the same evaluation index.

The invention belongs to the technical field of biomedical materials, and discloses an intervertebral disc repair material based on an intervertebral disc degeneration staged treatment strategy as well as a preparation method and application of the intervertebral disc repair material. The intervertebral disc repair material comprises a sandwich-structure dual-electroactive stent and composite hydrogel attached to the dual-electroactive stent, the double-electroactive stent with the sandwich structure comprises a conductive porous stent with a conductive through pore channel structure and piezoelectric fiber films positioned on the upper side and the lower side of the conductive porous stent; the composite hydrogel comprises hydrogel and antioxidant nano enzyme uniformly dispersed in the hydrogel; the antioxidant nano-enzyme is a core-shell structure nano-particle with Co-Ni bimetallic oxide as a core and polydopamine as a shell. The degeneration early intervertebral disc is repaired through the antioxidant and anti-inflammatory nano-enzyme, the defective intervertebral disc after resection is repaired through the double electroactive scaffolds, and the electroactive scaffold enhanced composite hydrogel can replace the degeneration later intervertebral disc, so that corresponding treatment is achieved for different stages of intervertebral disc degeneration.

The invention discloses a preparation method and application of a mesenchymal stem cell exosome-loaded absorbable suture line, noradrenaline is utilized to activate the surface of a collagen suture line, then human umbilical cord mesenchymal stem cell exosomes are assembled to the surface of the activated suture line through a soaking method, and the absorbable suture line with the surface functionalization is constructed. After the suture line is used, the wound healing effect is good, the blood vessel and cell recovery degree is good, proliferation and migration of fibroblasts can be remarkably promoted, proliferation of T lymphocytes and Th1 cells is effectively inhibited, and a powerful guarantee is provided for tissue repair.

The method comprises the following steps that the lithium slag is sequentially subjected to first magnetic separation treatment, ore grinding treatment and second magnetic separation treatment, and iron concentrate and magnetic separation treatment tailings are obtained; the magnetic separation treatment tailings are sequentially subjected to first-stage acid treatment and second-stage acid treatment, and iron-removed and desulfurized lithium slag is obtained; wherein the acids used in the first-stage acid treatment are citric acid and hydrochloric acid, and the acids used in the second-stage acid treatment are citric acid and nitric acid. According to the method, two times of high-strength magnetic separation and two times of acid treatment are combined, the removal process of iron and sulfur in the lithium slag is optimized, and the iron removal and sulfur removal efficiency is remarkably improved.

The invention provides a multi-feature fusion-based black box attack detection method (MFBA), device and system, different features are extracted by using a multi-model fusion strategy, the effectiveness and quality of opposite samples are improved, and the MFBA comprises the following three key steps: firstly, obtaining an initial data set from a target model; a decision boundary is approached by using a multi-feature fusion model; secondly, the stability of the model is enhanced through multiple rounds of training application data expansion and incremental learning technologies; and finally, generating opposite examples by using the optimized fusion model. By constructing the comprehensive attack framework, how to combine various model features to improve the performance against the resistance attack is demonstrated; compared with a single substitution model, the antagonism examples generated by the MFBA have higher mobility under different disturbance levels and have better attack effects; according to the method, an effective solution is provided for black box antagonism attacks, and the model in the generation of the advantage opposite paradigm of multi-feature fusion is verified.

The invention relates to a TCR-T cell preparation method and a TCR-T cell product. The preparation method of the TCR-T cell provided by the invention comprises a step of introducing annular mRNA (messenger Ribonucleic Acid) into a T cell modified by a non-T cell receptor gene; and, the cyclic mRNA comprises a first cyclic mRNA encoding a T cell receptor; in addition, the first annular mRNA comprises the following sequence (a) or (b): (a) a translation starting sequence and a coding sequence; and (b) translating the starting sequence, the coding sequence and the insertion element sequence. In the method, the transfection efficiency of the annular mRNA is high; compared with a virus system and the like, the process is extremely simple, and the production cost and the detection cost are greatly reduced; compared with linear mRNA, the protein expression time is greatly prolonged; compared with a virus system, a transposon system and a CRISPR system, the safety risk is greatly reduced.

The invention relates to a liver focal lesion image analysis method based on unbalanced optimal transmission. The analysis method comprises the following steps: extracting original image sample features, and embedding Gaussian distribution into each image to encode uncertainty; and then unbalanced optimal transmission loss calculation and classification loss calculation are carried out to realize accurate pairwise matching so as to reduce negative migration, and adaptive learning is carried out by using identification type perception information, so that an optimal transmission strategy is determined, and classification prediction is carried out. According to the analysis method, an unsupervised field adaptive network module, a feature uncertainty estimation mechanism module and a UOT strategy module are adopted. Compared with the prior art, the method has the advantages that the problem of pairwise wrong matching during feature distribution alignment in the existing optimal transmission strategy can be relieved, the uncertainty estimation of the noisy training sample is emphasized, and the migration performance, the robustness and the adaptability of the model can be remarkably improved by combining the feature uncertainty estimation and the UOT strategy.

The invention belongs to the technical field of aviation machinery state monitoring and fault diagnosis, and particularly relates to an aero-engine lubricating oil abrasive particle fault identification method based on a convolutional neural network, which comprises the following steps: S1, collecting color image data of aero-engine lubricating oil abrasive particles, and converting the color image data into gray level image data through a weighted average method; s2, bilateral filtering is carried out on the grayscale image data of the aero-engine lubricating oil abrasive particles; s3, segmenting the lubricating oil abrasive particle image and the image background of the aero-engine by using an Otsu method; and S4, classifying the processed black and white single-channel aero-engine lubricating oil abrasive particle image data, and performing fault identification and output by using a convolutional neural network model. According to the method, the abrasive particle image acquired by the lubricating oil sensor of the aero-engine is processed, the convolutional neural network model is constructed to train and recognize the image, the fault type of the engine is quickly and accurately recognized, and the efficiency and accuracy of aero-engine fault diagnosis are improved.

The invention relates to the technical field of clinical medical treatment, and discloses a disease deterioration risk identification method and system based on depth time sequence clustering enhancement, and the method comprises the steps: 1, obtaining multi-modal clinical sequence data of a patient, including physiological indexes of a time sequence, a laboratory detection result, historical diseases and medication data; 2, performing feature extraction and classification on the patient sequences by adopting a knowledge enhanced sequence clustering method, and grouping the patient sequences according to future outcome distribution of the patient sequences; 3, enabling the model to quickly adapt to a prediction task of a new patient subgroup through a meta-training process; and step 4, based on the trained meta-model, carrying out rapid adaptation on the new patient subtype, and predicting the possibility that the new patient subtype has a deterioration event in a certain time window in the future. The method and the system can effectively learn the disease change mode of the patient under the condition of limited clinical data, improve the prediction accuracy of the new patient subgroup, and are especially suitable for clinical prediction scenes under the condition of small samples.

The invention belongs to the technical field of organic light-emitting materials, and relates to a compound for an organic light-emitting material. The xanthene five-membered heterocycle in the compound provided by the invention is used as a mother nucleus, and different groups are connected to specific positions for modification, so that the obtained compound has an appropriate orbital energy level and relatively high triplet state energy, and can be used as a luminescent layer main body and a second hole transport material in an organic electroluminescent device; the luminous efficiency and the service life of the device can be obviously improved.

The invention discloses a chimeric antigen receptor double-negative T cell drug co-expressing CXCR5 and IL-7 and application thereof, the cell drug can co-express CXCR5 and IL-7, and the cell drug comprises EF1-alpha, a CD8 alpha signal peptide, an NKG2D extracellular domain, a CD8 alpha hinge region, a CD8 alpha transmembrane region, a costimulatory factor 4-1BB, a CD3 zeta intracellular signal region, T2A, a chemotactic factor receptor CXCR5, P2A and interleukin IL-7 which are connected in sequence; according to the chimeric antigen receptor double-negative T cell drug co-expressing the CXCR5 and the IL-7 and the application of the chimeric antigen receptor double-negative T cell drug, the CXCR5 and the IL-7 are co-expressed on CAR-DNT cells of targeted NKG2DL, so that the migration and infiltration capacity of the drug to a tumor site is enhanced, the survival and proliferation capacity of the CAR-T cells is improved, the killing effect of the CAR-DNT cells on tumors is improved, and the drug has a good application prospect. The purpose of treating tumors is achieved, and allogenic treatment can be achieved.

This invention relates to an air electrode material and its preparation method. The preparation method includes: using metal salts of Ce, Pr, La, Ba, and Sr, metal salts of Co, and a fluorine source as raw materials, and preparing a fluorine-containing perovskite precursor according to a stoichiometric ratio; pretreating the precursor and then sintering it to obtain a composite powder; the composite powder includes a fluorine-containing perovskite main phase and a fluorine-containing CeO2 nanophase; pulverizing and sieving the composite powder to obtain air electrode powder; the fluorine-containing perovskite main phase is an A-site high-entropy perovskite oxide; the fluorine-containing CeO2 nanophase is precipitated through the self-assembly of some Ce elements during sintering; the fluorine-containing CeO2 nanophase is dispersed inside the main phase grains or at grain boundaries, and forms a strongly coupled heterogeneous interface with the main phase. This invention solves the technical problems of poor surface exchange activity, bulk diffusion transport, and long-term stability of existing air electrodes.

The invention discloses a vehicle confrontation camouflage generation method and system for DE-YOLO cross-modal detection, and the method comprises the steps: employing a three-dimensional neural network renderer to initialize a full-coverage texture as an attack patch, covering the surface of a target object, and preliminarily simulating a basic attack scene; simulating a multi-factor interference scene in combination with a diffusion generation model, and generating target vehicle pictures in different scenes; forming an input batch by using the generated target vehicle pictures in different scenes, and inputting the input batch into a model DE-YOLO cross-modal detector to obtain three different-scale infrared feature maps and visible light feature maps in different scenes; and fusing the feature patterns of different scales into a cross-modal loss item, and performing back propagation optimization to generate infrared and visible light cross-modal full-coverage confrontation textures which are attached to the surface of the vehicle. According to the method, the neural network renderer is adopted to generate the full-coverage texture to serve as the attack patch to cover the surface of the target object, the target detection system is attacked, and the attack failure problem under multi-factor interference is solved.

The invention relates to a photoelectrochemical urea degradation photoanode based on a bismuth vanadate semiconductor film and an electrode system thereof. The photoanode comprises a BiVO4 light absorption layer arranged on a transparent conductive substrate, a hole transport layer arranged on the BiVO4 light absorption layer and a Co-Ci cocatalyst layer arranged on the hole transport layer, the BiVO4 light absorption layer is excited by light to generate electron hole pairs, holes enter electrolyte under the combined action of extraction of the NiOBix hole transport layer and external bias voltage to carry out urea degradation half reaction, and meanwhile, the Co-Ci cocatalyst plays a role in improving surface reaction kinetics and reducing overpotential of urea oxidation reaction, so that the degradation efficiency of urea is improved. The photoanode has stronger carrier separation and migration capability and higher working stability, and the efficiency and stability of urea degradation are improved.

The invention discloses an amino acid organic fertilizer and a preparation method thereof. The method adopts compounded fungi and fermentation aids to reduce the peculiar smell and fatty acid content of fish viscera after fermentation; a specially prepared viscous additive is used to prevent the fertilizer from being washed away by rainwater and reducing the fertilizer efficiency, and the amino acids in the fertilizer from being oxidized by light; and a chelating synergistic coating agent containing trace elements and having a slow-release function is used to coat the organic fertilizer particles. The amino acid organic fertilizer prepared by the invention has the characteristics of short fermentation time, low fatty acid content, easy absorption by crops, long-lasting fertilizer efficiency, and comprehensive nutrition.

The invention provides IncRNA and application thereof in improving the regeneration capacity of mesenchymal stem cells, and belongs to the technical field of biological medicine. The lncRNA provided by the invention has the effect of improving the regeneration capacity of endometrial-derived mesenchymal stem cells of old people, and solves the problem of limited performance of cell materials in the prior art. The invention proves that the regeneration capacity can be improved by changing the expression of the lncRNA in the endometrium-derived mesenchymal stem cells of the old people, and a theoretical basis is provided for clinical application of the lncRNA in multiple aspects. CCK8 shows that the lncRNA has the effects of promoting the proliferation of Old-hEMSCs and resisting oxidative damage; scratch experiments show that the overexpressed lncRNA has the capability of promoting the migration of the Old-hEMSCs.

The invention discloses an active polypeptide for promoting healing of oral ulcer and application thereof, and belongs to the technical field of biological medicines. The amino acid sequence of the active polypeptide NPTX3 capable of promoting healing of the oral ulcer is DLKPIAEDIPSLEK. An in-vitro experiment result shows that the polypeptide NPTX3 can remarkably promote the scratch closure of the human immortalized keratinocytes at the concentration as low as 500 pM. Transwell migration experiments further prove that the polypeptide NPTX3 can obviously enhance the migration ability of HaCaT cells at the concentrations of 1nM and 10nM, which prompts that the polypeptide NPTX3 may accelerate wound repair by promoting epithelial cell migration. In an SD (Sprague Dawley) rat oral ulcer model, the ulcer healing rate can be obviously improved by locally applying the polypeptide NPTX3 (1 nM, 10 nM). The polypeptide NPTX3 provided by the invention shows excellent activity of promoting cell migration and tissue repair at an extremely low dose, has the advantages of low effective dose, strong biological activity, high safety and the like, and provides an efficient and mild new choice for drug treatment of oral ulcer.

The application discloses a kind of low-carbon cementing material by waste incineration fly ash excited ore powder and preparation method thereof, by weight percentage, including the following components: modified waste incineration fly ash 8~12%, ion complex component 1~3%, composite shrinkage component 2~6%, ore powder 55~65%, water 23~28%.Preparation method includes the following steps: S1, preparation modified waste incineration fly ash, ion complex component, composite shrinkage component;S2, modified waste incineration fly ash, ion complex component, composite shrinkage component, ore powder, mixing water, fully mixed and stirred evenly, can obtain the low-carbon cementing material by waste incineration fly ash excited ore powder described in the application.In the aspect of component design, the application is environmentally friendly, low cost, in the performance aspect, the flow rate can be adjusted, the mechanical properties are excellent, the volume shrinkage is small.

The invention discloses a task-independent industrial defect visual detection model training and detection method and device, and the method comprises the steps: initializing a first vector and a meta parameter of a classifier head, and obtaining support set data and query set data of a training task; sequentially performing internal loop training on each training task according to the meta-parameters, the classifier head first vector and the support set data, updating the basic parameters of the basic learning device after each training to obtain each task parameter corresponding to each training task, and before each training, obtaining each task parameter corresponding to each training task according to the classifier head first vector and the meta-parameters; updating a classifier head and basic parameters of the basic learner; according to the query set data and the task parameters of each training task, performing outer loop training in a meta learning device, and updating meta parameters and a classifier head first vector; and according to the updated meta parameters and the classifier head first vector, initializing an original detection model to obtain an industrial defect visual detection model. By improving the meta-learning framework, the training effect of the detection model is improved.

The application discloses a SnTe-based thermoelectric material and a preparation method thereof, and belongs to the technical field of thermoelectricity. The application adopts high-purity metal elements as raw materials, and the raw materials are mixed according to a stoichiometric ratio, and then a cast ingot is obtained through a melting reaction after vacuum packaging. Subsequently, the cast ingot is subjected to high-temperature annealing treatment. After the cast ingot subjected to the high-temperature annealing treatment is ground into powder, a dense block-shaped SnTe-based thermoelectric material is formed through discharge plasma sintering. The chemical formula of the SnTe-based thermoelectric material is Sn 2.9‑X W X Te 2.9 -(In2Te3) 0.03 -(Cu2Te) 0.18 The application significantly improves the thermoelectric figure of merit of the SnTe-based thermoelectric material and improves the thermoelectric performance of the SnTe-based thermoelectric material by doping tungsten elements in the SnTe matrix and compounding the two metal compounds of In2Te3 and Cu2Te.

The invention discloses a wind turbine generator gearbox state monitoring and fault early warning method and system. The method comprises the following steps: defining a key part of a gearbox of a wind turbine generator as a flexible body, and constructing a multi-body dynamic model based on the flexible body; the multi-body dynamics model receives input multi-source time sequence data, then performs feature extraction and multi-index fault early warning algorithm processing, and outputs signal abnormal information for the current multi-source time sequence data; the signal abnormal information is gearbox fault data; constructing a mixed data set guided by a physical mechanism and performing data enhancement; and inputting a spatial-temporal feature coding fault diagnosis model based on the enhanced mixed data set, and then outputting a health index. According to the intelligent early warning system for the wind turbine generator gearbox, through the technical innovation of making up for data missing through modeling simulation, improving diagnosis precision through an intelligent algorithm and guaranteeing real-time early warning through edge calculation, a set of intelligent early warning system for the wind turbine generator gearbox which is high in precision, low in time delay and strong in generalization is successfully constructed.