1058results about How to "Strong targeting" patented technology

The invention discloses a dual-prevention intelligent interaction system based on a large language model, and belongs to the technical field of artificial intelligence. The system comprises a big language base model, a knowledge base management module, an agent module, a multi-modal big model and an evaluation module, and the big language base model understands user intentions and processes natural language tasks and provides token processing capacity for other modules; the knowledge base management module is used for classified storage and management of professional knowledge, establishing indexes according to semantics and finally performing localized storage; the intelligent agent module is used for analyzing input of a user, and different workflows are packaged in different intelligent agents so as to provide personalized services for the user; the multi-modal large model module receives voice input by a user, converts the voice into characters, transmits description of information to the Agent, generates answers by analyzing semantics, converts the characters into the voice and sends the voice to the user; the evaluation module establishes an evaluation index system from the four aspects of accuracy, answer correlation, context correlation and context recall rate.

The invention provides a three-phase inverter direct current side electric leakage fault type discrimination method and electronic equipment, and the method comprises the steps: carrying out the frequency spectrum time sequence characteristic analysis of the residual current of the alternating current side of a three-phase inverter, and analyzing the change of the amplitude of the residual current along with the time under different frequencies; the fault type of the direct current side electric leakage of the inverter can be known; by performing continuous energy monitoring on the residual current signal of the inverter, real-time current monitoring on the inverter is realized, and sudden change of the fault type of electric leakage or instantaneous electric leakage can be well monitored and distinguished. Aiming at the specific characteristics of the harmonic component of the leakage current of the inverter, different frequency spectrum characteristics of the leakage current under different fault types are determined, the electric leakage fault type discrimination method based on harmonic time-frequency characteristic extraction is provided on the basis, the specific fault type of the inverter can be discriminated under various working conditions, the position of the electric leakage fault is further positioned, and the reliability of the inverter is improved. The problem of inaccurate electric leakage fault type judgment and positioning in the current engineering application is solved.

The invention discloses a conflict detection processing method and system based on a BIM model, a medium and a product, and relates to the technical field of constructional engineering, and the method comprises the steps: obtaining a construction project design scheme; based on a preset model framework, determining a BIM model according to the construction project design scheme; predicting potential conflict points in the BIM model based on a preset conflict mode library; matching the potential conflict points with the building structure, the electromechanical pipeline and the equipment layout by using a space mapping algorithm, and determining possible conflict areas; on the basis of a natural language processing technology, operable solution suggestions are generated according to the possible conflict areas; after the operable solution suggestions are sent to the user, the solution suggestions determined by the user are received; and adjusting the BIM model based on the solution suggestion. By implementing the method, the accuracy and intelligence of conflict detection can be improved.

The preparation method comprises the following steps: mixing a tannic acid solution and a tetravalent cerium salt solution, adjusting the pH value to 9-11, and reacting to obtain the cerium tannate nano-enzyme, the molar ratio of the tannic acid to the tetravalent cerium salt is (1-4): 1; the hydration dynamic diameter of the cerium tannate nano-enzyme is 20 to 80 nm. The TA-Ce nano-enzyme prepared by the preparation method disclosed by the invention is smaller in particle size, can keep stable and good dispersibility in a physiological environment, and has good biocompatibility, low hemolysis rate and small systemic toxicity; meanwhile, the compound has good kidney targeting property, can be effectively enriched into the injured kidney, eliminates various ROS (reactive oxygen species) and relieves oxidative stress by utilizing unique redox cycle between Ce < 3 + > and Ce < 4 + > states, and has a good effect in treating acute kidney injury induced by rhabdomyolysis and / or cis-platinum. The research lays a foundation for the metal polyphenol nano material in the treatment of acute kidney injury.

The embodiment of the invention provides a multi-modal data fusion method and device based on a cross attention mechanism. The method comprises the following steps: acquiring biological information features and radiomics features of a target object; carrying out feature weighting and interactive processing on the biological information features and the radiomics features based on a cross attention mechanism, and generating the biological information features based on radiomics feature attention and the radiomics features based on the biological information feature attention; carrying out feature splicing on the four features to generate spliced features; and performing feature fusion on the spliced features based on a self-attention mechanism to generate fusion feature representation of the target object. The multi-modal data fusion method and device provided by the invention show higher flexibility and pertinence in the aspects of feature extraction and weighting.

The invention provides a live pig facility breeding hog house ammonia concentration detection system based on a neural network, and the system comprises a hog house environment parameter collection module and a hog house ammonia concentration detection module which are connected in sequence. Wherein the hog house environment parameter acquisition module is used for acquiring ammonia gas information, wind speed information, temperature information and humidity information in a hog house; and the pig house ammonia gas correction fusion sub-module is used for correcting the predicted ammonia gas concentration based on the predicted wind speed, the predicted humidity and the predicted temperature. The method analyzes the influence of pig house ammonia gas on pig health, considers the coupling effect of multiple environment parameters of the pig house, provides a temperature, humidity and wind speed correction fusion method of the pig house ammonia gas concentration, and aims to improve the detection precision of the pig house ammonia gas concentration of pig facility breeding.

The invention provides a transformer partial discharge monitoring method based on an ultrahigh frequency technology, and relates to the technical field of partial discharge monitoring, and the method comprises the steps: presetting a monitoring point array, laying an ultrahigh frequency sensor group for dynamic discharge monitoring, and obtaining an ultrahigh frequency signal time sequence array; a multi-dimensional environment signal time sequence array is obtained, filtering compensation is carried out, and a partial discharge signal time sequence array is obtained; carrying out distributed signal analysis to obtain a partial discharge characteristic parameter array; constructing a partial discharge feature tree, and performing correlation matching to obtain a partial discharge reason feature parameter array; calling a partial discharge operation and maintenance strategy library; and traversing and matching to obtain a target partial discharge operation and maintenance strategy set, and carrying out partial discharge early warning operation and maintenance processing. According to the invention, the technical problem of low accuracy and efficiency of partial discharge processing caused by the fact that transformer partial discharge monitoring mostly depends on empirical judgment and manual analysis and lacks systematic diagnosis and automatic processing decision support in the prior art is solved.

The invention discloses a Pick ball athlete action recognition and analysis system based on multi-modal data fusion, and the system comprises the steps: collecting the video image data and sensor data of a Pick ball athlete in the motion process, and carrying out the feature extraction of the body posture of the athlete in the video image data through a convolutional neural network; according to the method, a recurrent neural network is combined to perform action classification on a feature sequence, meanwhile, sensor data are fused, accurate recognition of actions of an athlete is achieved, meanwhile, detailed technical parameters of the athlete in the ball hitting action can be obtained by deeply analyzing the recognized actions, accurate guidance is provided for training of the athlete, and the training efficiency is improved. And the technical level and the competitive ability of athletes can be improved.

The invention discloses an inflammation-targeting bionic extracellular vesicle as well as a preparation method and application thereof, and particularly relates to the field of medicines. According to the invention, a new generation of aminoglycoside antibiotic teicoplanin (Teic), which is high in albumin binding rate and weak in intracellular antibacterial effect, is loaded on neutrophil-derived extracellular vesicles (NEV) with natural drug carrying capacity and inflammatory response chemotactic capacity to prepare the drug-loaded vesicles (Teic-NEV), wherein the new generation of aminoglycoside antibiotic teicoplanin (Teic) is used for loading the neutrophil-derived extracellular vesicles (NEV). The drug-loaded extracellular vesicle Teic-NEV can be used for targeted therapy of cells and tissues infected by MRSA (Methicillin-Resistant Staphylococcus Aureus). In vitro, the Teic (at) NEV can be used for destroying MRSA mature biological membranes and killing intracellular MRSA. According to the invention, the in-vivo and in-vitro biological safety of the TeicatNEV is evaluated. Therefore, the invention provides a certain theoretical basis for the application of NEV-based drug delivery in the field of intracellular bacteria treatment, and also provides a new treatment strategy for in-vivo targeted treatment.

The invention discloses a meta universe-based conference interaction method, and relates to the technical field of virtual conferences, and the method comprises the steps: extracting the facial features of participants, comparing the facial features with a pre-established facial feature library, obtaining the unique identity of each participant, and recognizing the identities of the participants; after the identities of the participants are determined, position changes of key body joint points of the participants are detected in real time, and specific actions of the participants are judged by analyzing relative positions and movement tracks between the joint points; the method comprises the following steps: receiving the acoustic characteristics of the participant language, converting the voice signal of the participant into a text, and analyzing the semantics and emotion of the text in combination with natural language processing to obtain the language expression intention of the participant. According to the method and the device, the optimal interaction mode can be dynamically selected from the preset library according to different interaction intentions, and the audio and video parameters and the interface layout are adjusted in real time, so that intelligent interaction in a multi-user scene is realized, the interaction experience of a conference is optimized, and the interaction effect can also be improved.

The invention discloses a flight space directional training auxiliary system and method, and the system comprises a flight task simulation module; the meteorological environment simulation module is used for simulating a meteorological scene corresponding to a flight task, and simulating the meteorological scene by establishing a multi-dimensional meteorological model and combining meteorological parameters; the meteorological environment parameter control module comprises a time step length control module which adopts a preset time step length to input meteorological parameter observation data collected in real time to the meteorological parameter setting module; the specific meteorological event control module is used for transmitting meteorological parameters of a specific meteorological event area to the meteorological parameter setting module when the airplane triggers a specific meteorological event; and the training scheme generation module is used for generating a proper training scheme according to the historical training information of the flight personnel. According to the system and the method, a real and personalized simulation training environment can be provided for the flight personnel, and the professional skills and the capability of coping with various flight conditions of the flight personnel are effectively improved.

The invention discloses a Sentinel-2 remote sensing image super-resolution reconstruction method based on an attention mechanism. The method comprises the following steps: 1, obtaining a Sentinel-2 remote sensing image data set; 2, carrying out the preprocessing of the Sentinel-2 remote sensing image data; 3, randomly dividing the preprocessed Sentinel-2 remote sensing image data into a training set and a test set; 4, constructing a deep learning model structure of the Sentinel-2 remote sensing image super-resolution method based on the attention mechanism; 5, training the built model by using the training set; and 6, testing the test set by using the trained model to obtain a Sentinel-2 remote sensing image super-division image. According to the method, the characteristics of the Sentinel-2 satellite image are fully utilized, and the remote sensing image fusion thought is combined, so that the quality of the super-resolution image is improved, and the accuracy and reliability of the earth surface information are enhanced.

The invention discloses a geological disaster monitoring and early warning method and system based on a digital twinborn technology, and relates to the technical field of data monitoring. The method comprises the following steps: acquiring satellite image information, meteorological information and geological detection information of a mountain area to be monitored in a preset sampling period; based on the satellite image information, the meteorological information and the geological detection information, constructing a three-dimensional digital model of the mountain area to be monitored; receiving current mountain data, geological data and weather conditions of the mountain area to be monitored, and determining a plurality of risk sources of the mountain area to be monitored under the weather conditions and positions corresponding to the risk sources in the three-dimensional digital model in combination with the mountain data and the geological data; and for each risk source, when the change rate of any monitoring index in the mountain data or geological data of the risk source exceeds a change rate threshold value, performing disaster early warning based on the position of the risk source. By implementing the technical scheme provided by the invention, the effect of improving the accuracy of geological disaster monitoring and early warning is achieved.

The invention provides a recombinant collagen ionic liquid composition as well as a preparation method and application thereof, and relates to the technical field of cosmetics. The recombinant collagen ionic liquid composition provided by the invention is prepared from the following components in parts by mass: 19.8 to 79.99 parts of lysine citric acid ionic liquid, 20 to 80 parts of glycerol and 0.01 to 0.2 part of recombinant collagen, the lysine citric acid ionic liquid is mainly composed of lysine and citric acid, and the molar ratio of the lysine to the citric acid is (4-1): (2-1). The recombinant collagen ionic liquid composition is good in solubility, high in transdermal efficiency, small in irritation, short in preparation period, simple in process and capable of being continuously produced.

The invention relates to the technical field of traffic management, and discloses an intelligent traffic dispersion method and system based on a smart city, and the method comprises the steps: determining a traffic facility operation and maintenance protocol corresponding to a traffic facility, and formulating a congestion evaluation factor of an urban traffic region; analyzing the traffic flow dimension state of the urban traffic area, calculating a congestion disturbance coefficient corresponding to the congestion evaluation element, and evaluating the congestion state intensity of the urban traffic area; analyzing vehicle behavior intention characteristics of vehicles in the urban traffic area, analyzing a traffic condition matrix of roads in the urban traffic area, and calculating traffic efficiency of the roads in the urban traffic area; extracting attraction element factors in the land utilization information and the real-time event information, and analyzing the traffic pressure conductivity of the surrounding area to the urban traffic area; and formulating a traffic intelligent dispersion strategy of the urban traffic area, and executing traffic dispersion work of the urban traffic area to obtain a traffic dispersion result. The intelligent traffic dispersion efficiency of the smart city can be improved.

The invention relates to the technical field of transistor detection, in particular to a quick test method and system for an MOSFET device. The method comprises the following steps: acquiring and integrating multi-source parallel data of the MOSFET device to obtain real-time I-V characteristic data of the device; performing MOSFET device performance prediction based on the real-time device I-V characteristic data to obtain MOSFET device performance prediction data; screening according to the performance prediction data of the MOSFET device to obtain performance data of a primarily screened qualified device; performing adjustment based on the performance data of the preliminarily screened qualified device to obtain an MOSFET device test scheme; acquiring real-time test data of the MOSFET device, and screening qualified devices to obtain performance data of re-screened qualified devices; and performing device test summarization based on the performance data of the primarily screened qualified device and the performance data of the secondarily screened qualified device to obtain an MOSFET device test report. According to the invention, the test period can be shortened.

The invention provides a staple food grain crop disease grading method and system based on a prompt-driven large model, and the method comprises the following steps: collecting and preprocessing a main food crop image, and obtaining a main food crop data set; training a ResNet-18 network based on the main grain crop data set to perform initial disease grading, and obtaining disease characterization prompt information; utilizing disease characterization prompt information to drive a prompt drive large model, and segmenting a focus area and a leaf area of the to-be-graded main grain crop image; and calculating the proportion of the lesion area in the leaf area, obtaining a final fine-grained disease grading result of the to-be-graded main grain crop image, and completing staple food crop disease grading based on the prompt-driven large model. According to the method, automatic detection and grading of crop diseases are realized by integrating deep learning and an interactive segmentation technology, so that the accuracy of disease detection and classification is improved, possibility is provided for optimizing the use of pesticides, and environmental pollution and the risk of crop drug resistance are reduced.

The invention relates to the technical field of water treatment fluorine removal agent production, in particular to a water treatment fluorine removal agent and a preparation method and application thereof. Specific raw materials are added and reasonable treatment steps are adopted for the wastewater containing fluorine, copper and iron ions generated in the semiconductor chip manufacturing etching process, so that the fluorine ions can be effectively removed; the method can remove copper and iron ions or reduce the influence of the copper and iron ions on fluorine removal (especially wastewater simply treated by sodium sulfide which is specially used for removing copper and iron ions), and has strong pertinence and applicability to etching wastewater with specific water quality; the method is good in collaboration, has both operability and flexibility, and ensures the treatment effect.

The invention discloses a UI effect picture recognition method and system based on a multi-modal large model, and a medium. The method comprises the following steps: constructing a multi-modal pre-training model based on image coding, multi-modal projection and text coding; performing fine tuning training according to the historical image data and the multi-modal pre-training model to obtain a multi-modal large model; constructing a vector database based on the built-in component data structure; obtaining a to-be-recognized UI effect picture, and outputting first recognition data based on the multi-modal large model and the to-be-recognized UI effect picture; and searching a vector database for the first identification data by using a retrieval algorithm, and outputting generation information corresponding to the UI effect picture to be identified. The method has the beneficial effect that the generation consistency and efficiency of the UI effect picture are ensured.

The invention belongs to the technical field of biological medicine, and discloses an engineering bionic exosome with an anti-aging effect and a preparation method and application thereof, and the preparation method comprises the following steps: (S1) preparing a bionic cell membrane modified targeting ligand: adding collagen, mitochondrial targeting polypeptide, a condensing agent and 4-dimethylaminopyridine into an organic solvent, and stirring to obtain the bionic cell membrane modified targeting ligand; activating under a protective atmosphere, and then adding lysine into the system to react, so as to obtain a bionic cell membrane modified targeting ligand; and (S2) embedding the bionic cell membrane modified targeting ligand into the engineered exosome bionic membrane, and entrapping an active substance to form the engineered bionic exosome. The exosome provided by the invention has better skin permeability, mitochondrial targeting effect and cell endocytosis effect, has better protection effect on mitochondria, and can realize efficient delivery of active substances.

The invention discloses an eRNA molecular marker for auxiliary diagnosis of intestinal cancer and application of the eRNA molecular marker, and is characterized in that the eRNA molecular marker is eRNA58704, and the nucleotide sequence of the eRNA molecular marker is shown as SEQ ID NO.1. The invention further provides application of the eRNA molecular marker in preparation of a kit for auxiliary diagnosis of intestinal cancer. The kit comprises an eRNA58704 fluorescent quantitative PCR specific amplification primer, the nucleotide sequence of an upstream primer is 5 '-AAGTCAGGTTCATTCGTGC-3', and the nucleotide sequence of a downstream primer is 5 '-AAGTCAGGTTCATTCGTGC-3'. The nucleotide sequence of the downstream primer is 5 '-AGCAACCAACGCTCTTGTCA-3', and the kit further comprises a carbohydrate antigen CA19-9, and the kit has the advantages that early diagnosis of intestinal cancer patients on the molecular level can be conveniently, rapidly and efficiently achieved, pertinence, specificity and sensitivity are high, and early discovery and timely treatment of intestinal cancer are facilitated.

The invention provides a concrete pouring method for reducing generation of cracks, which belongs to the technical field of concrete pouring construction, and comprises the following steps: collecting parameters such as construction environment, concrete structure, concrete parameters and mix proportion, establishing and fitting a shrinkage stress equation set of concrete, calculating stress distribution at different time points and positions, and calculating a shrinkage stress equation set; the method comprises the steps of determining the maximum stress value and the occurrence time and position of the maximum stress value, adopting a pre-trained shrinkage joint arrangement model, determining the optimal shrinkage joint distance and position, adopting a pre-trained stress expanding agent model, and determining the type and the adding amount of a needed expanding agent and the optimal distribution of the expanding agent in concrete. And the thickness of each layer, the pouring sequence and the interval time of layered pouring are determined, the calculated expanding agent is added in the mixing process, and uniform distribution of the expanding agent is ensured by adopting an efficient mixing technology. Pouring is conducted according to a layered pouring plan, an optimized shrinkage joint is arranged, and it is ensured that concrete is compact through intelligent vibrating equipment. And curing the constructed concrete.

The invention relates to the technical field of ecological environment monitoring and evaluation, in particular to a deep-sea mining disturbance ecological damage evaluation method based on data analysis, which comprises the following steps of: S1, simultaneously acquiring data of various disturbance factors in a direct influence area and an indirect influence area of mining disturbance; s2, extracting characteristic parameters of each disturbance factor to obtain a characteristic set of each factor; s3, identifying factor combinations with interaction; s4, constructing a multi-factor fused ecological damage assessment model, and generating an ecological damage value; s5, dividing ecological damage grades; when the damage value reaches or exceeds the threshold value of the corresponding grade, a corresponding early warning mechanism is triggered, and a corresponding ecological protection suggestion is generated. According to the method, through multi-factor fusion analysis and a grading early warning mechanism, accurate evaluation and timely response to deep-sea mining disturbance ecological damage are realized, and the effectiveness of ecological protection measures is improved.

The invention discloses an industrial robot control method and system based on digital twinning, and belongs to the technical field of robot control, and the method specifically comprises the steps: collecting the data of an entity industrial robot, building a digital twinning model of a virtual robot and related corollary equipment, and carrying out the digital twinning model of the virtual robot and related corollary equipment in a virtual simulation engine Unity, the method comprises the following steps: configuring a digital twinborn model of a virtual robot and a related environment, performing visual rendering on a scene where the virtual robot is located, displaying a rendered picture on terminal display equipment in real time, obtaining a robot operation instruction, analyzing, synchronously transmitting the operation instruction, and controlling the posture of the robot, according to the method, the physical robot and the virtual robot can be dynamically synchronized by adjusting the physical robot, or the virtual robot and the physical robot can be dynamically synchronized by adjusting the virtual robot, the effect is good, the pertinence is high, the actual operation of each student can be implemented, the safety coefficient is high, and the personal safety cannot be influenced in the real operation.

The invention discloses a charger communication test method and system, a storage medium and a program product, and the method comprises the steps: obtaining a state parameter of a to-be-tested charger; collecting a communication message of the charger to be tested, and constructing a message time sequence queue; generating a message interval sequence reflecting the time sequence characteristics of the message; calculating a discrete degree coefficient of a message time sequence according to the message interval sequence; recording record data at an abnormal moment when the communication abnormality occurs, and inputting the record data into a preset state transition matrix to obtain a corresponding relationship; calculating probability distribution of communication abnormity under different state parameters to obtain a correlation model; generating a message sequence for testing based on the association model, wherein the time sequence characteristic of the message sequence for testing is the same as the state parameter and the dispersion degree coefficient corresponding to the high-probability abnormal scene of which the probability is greater than a preset probability threshold in the association model; and sending the test message sequence to the to-be-tested charger to detect response data of the to-be-tested charger. And the accuracy and efficiency of the communication test of the charger are improved.

The invention discloses a tensor program generation method and system based on a large language model, and the method comprises the following steps: (1) generating a random ONNX computational graph through an AI compiler test tool nnsmith; (2) importing an ONNX calculation graph generated by nnsmith into a TVM, and converting the ONNX calculation graph into a RelayIR intermediate representation; (3) based on a model intermediate representation RelayIR in the TVM, adjusting and optimizing the model through an automatic adjusting and optimizing device Ansor in the TVM, generating an optimal tensor program, and outputting a JSON file measured during adjusting and optimizing; (4) continuously carrying out model tuning and tensor program measurement, generating a large number of measurement records during tuning, and forming a tensor program data set which comprises features of a tensor program and a corresponding optimization pass list; and (5) training a large language model according to the generated tensor program data set to realize understanding and generation of the tensor program. Through combination of an automatic tool and a large language model, efficient tensor program generation and optimization can be realized.

The invention relates to the technical field of sleep data processing, in particular to a sleep improvement environment sound control auxiliary method and system. The method comprises the following steps: acquiring sleep state data; performing sleep state feature extraction according to the sleep state data to obtain sleep state feature data; performing environmental sound type selection according to the sleep state feature data to obtain environmental sound type data; according to the environment sound type data and the sleep state feature data, environment audio rate adjustment and environment volume adjustment are carried out, environment audio rate adjustment data and environment volume adjustment data are obtained respectively, and sleep improvement environment sound control auxiliary work is carried out. According to the invention, the system can better adapt to the long-term sleep mode change of the user, and can help the user gradually establish a healthier sleep habit through long-term adjustment and optimization, thereby realizing long-term improvement of the sleep mode.

The invention discloses a brain function excitation inhibition state evaluation method, which comprises the steps of detection tool selection, signal acquisition, preprocessing, characteristic parameter extraction and result evaluation, the detection tools comprise electroencephalogram, functional near infrared spectrum imaging and functional magnetic resonance imaging, the detection tools are selected according to the adaptability of a patient, and a treatment strategy is customized in a personalized manner. The method has high pertinence, and the brain function state of the patient can be rapidly evaluated; by extracting a plurality of characteristic parameters as an evaluation basis, the dynamic attributes of the brain are comprehensively considered, and the accuracy and comprehensiveness of a detection result are ensured; and meanwhile, based on assignment of characteristic parameters, dynamic changes of the brain regions can be evaluated in real time during interaction between the brain regions, and the accuracy and reliability of a detection result are ensured by deeply considering and integrating dynamic characteristics of the brain.

The invention discloses an emotional support dialogue method based on user belief, which comprises the following steps of: forming a feature vector of a user by using past dialogue information, user characters, emotions and scenes, and then generating a divergent expansion model based on a large language model agent and knowledge graph mode to obtain a user scene vector. On the basis of a consistency algorithm of a mental theory, on the premise of information flow consistency and coherence, a consistency check model is generated, scene vectors are mapped into belief vectors of users, and the belief vectors are added into a belief library. According to the method, from the perspective of generative artificial intelligence, the cognitive process between a person and a system is simulated, and the method is generally divided into three steps of perceiving, guessing and verifying. And after the basic information of the dialogue is dynamically obtained, a divergent expansion model is utilized to generate a detailed description text and a knowledge chain, so that the cognition on the user is enhanced. And the high correlation between the dynamic updating condition of the belief information and the real condition of the user is ensured through a consistency model.

The invention belongs to the technical field of electromagnetic interference elimination, and discloses a method and system for processing electromagnetic leakage interference of an electromagnetic vibration table, and the method comprises the steps: starting a signal collection and analysis module, monitoring an electromagnetic signal generated in the operation process of the electromagnetic vibration table in real time, and recognizing an interference frequency range; a proper filter is selected or designed based on the identified frequency range through a filter selection and design module; a filter processing unit is utilized to install filters on necessary power lines and signal lines, and interference suppression measures are executed; the test and optimization module is operated, the filtering effect is evaluated, the configuration of the filter is adjusted according to needs, and the electromagnetic interference suppression effect is optimized; and the monitoring and adjusting module is continuously executed to adapt to the change of an experimental environment, so that long-term effective electromagnetic interference control is ensured. The invention provides a solution which is more accurate, effective and flexible compared with the traditional physical shielding, and is suitable for the occasion of carrying out high-precision vibration test on the magnetic sensitive sample.